essay on artificial intelligence and its impact

Artificial intelligence is transforming our world — it is on all of us to make sure that it goes well

How ai gets built is currently decided by a small group of technologists. as this technology is transforming our lives, it should be in all of our interest to become informed and engaged..

Why should you care about the development of artificial intelligence?

Think about what the alternative would look like. If you and the wider public do not get informed and engaged, then we leave it to a few entrepreneurs and engineers to decide how this technology will transform our world.

That is the status quo. This small number of people at a few tech firms directly working on artificial intelligence (AI) do understand how extraordinarily powerful this technology is becoming . If the rest of society does not become engaged, then it will be this small elite who decides how this technology will change our lives.

To change this status quo, I want to answer three questions in this article: Why is it hard to take the prospect of a world transformed by AI seriously? How can we imagine such a world? And what is at stake as this technology becomes more powerful?

Why is it hard to take the prospect of a world transformed by artificial intelligence seriously?

In some way, it should be obvious how technology can fundamentally transform the world. We just have to look at how much the world has already changed. If you could invite a family of hunter-gatherers from 20,000 years ago on your next flight, they would be pretty surprised. Technology has changed our world already, so we should expect that it can happen again.

But while we have seen the world transform before, we have seen these transformations play out over the course of generations. What is different now is how very rapid these technological changes have become. In the past, the technologies that our ancestors used in their childhood were still central to their lives in their old age. This has not been the case anymore for recent generations. Instead, it has become common that technologies unimaginable in one's youth become ordinary in later life.

This is the first reason we might not take the prospect seriously: it is easy to underestimate the speed at which technology can change the world.

The second reason why it is difficult to take the possibility of transformative AI – potentially even AI as intelligent as humans – seriously is that it is an idea that we first heard in the cinema. It is not surprising that for many of us, the first reaction to a scenario in which machines have human-like capabilities is the same as if you had asked us to take seriously a future in which vampires, werewolves, or zombies roam the planet. 1

But, it is plausible that it is both the stuff of sci-fi fantasy and the central invention that could arrive in our, or our children’s, lifetimes.

The third reason why it is difficult to take this prospect seriously is by failing to see that powerful AI could lead to very large changes. This is also understandable. It is difficult to form an idea of a future that is very different from our own time. There are two concepts that I find helpful in imagining a very different future with artificial intelligence. Let’s look at both of them.

How to develop an idea of what the future of artificial intelligence might look like?

When thinking about the future of artificial intelligence, I find it helpful to consider two different concepts in particular: human-level AI, and transformative AI. 2 The first concept highlights the AI’s capabilities and anchors them to a familiar benchmark, while transformative AI emphasizes the impact that this technology would have on the world.

From where we are today, much of this may sound like science fiction. It is therefore worth keeping in mind that the majority of surveyed AI experts believe there is a real chance that human-level artificial intelligence will be developed within the next decades, and some believe that it will exist much sooner.

The advantages and disadvantages of comparing machine and human intelligence

One way to think about human-level artificial intelligence is to contrast it with the current state of AI technology. While today’s AI systems often have capabilities similar to a particular, limited part of the human mind, a human-level AI would be a machine that is capable of carrying out the same range of intellectual tasks that we humans are capable of. 3 It is a machine that would be “able to learn to do anything that a human can do,” as Norvig and Russell put it in their textbook on AI. 4

Taken together, the range of abilities that characterize intelligence gives humans the ability to solve problems and achieve a wide variety of goals. A human-level AI would therefore be a system that could solve all those problems that we humans can solve, and do the tasks that humans do today. Such a machine, or collective of machines, would be able to do the work of a translator, an accountant, an illustrator, a teacher, a therapist, a truck driver, or the work of a trader on the world’s financial markets. Like us, it would also be able to do research and science, and to develop new technologies based on that.

The concept of human-level AI has some clear advantages. Using the familiarity of our own intelligence as a reference provides us with some clear guidance on how to imagine the capabilities of this technology.

However, it also has clear disadvantages. Anchoring the imagination of future AI systems to the familiar reality of human intelligence carries the risk that it obscures the very real differences between them.

Some of these differences are obvious. For example, AI systems will have the immense memory of computer systems, against which our own capacity to store information pales. Another obvious difference is the speed at which a machine can absorb and process information. But information storage and processing speed are not the only differences. The domains in which machines already outperform humans is steadily increasing: in chess, after matching the level of the best human players in the late 90s, AI systems reached superhuman levels more than a decade ago. In other games like Go or complex strategy games, this has happened more recently. 5

These differences mean that an AI that is at least as good as humans in every domain would overall be much more powerful than the human mind. Even the first “human-level AI” would therefore be quite superhuman in many ways. 6

Human intelligence is also a bad metaphor for machine intelligence in other ways. The way we think is often very different from machines, and as a consequence the output of thinking machines can be very alien to us.

Most perplexing and most concerning are the strange and unexpected ways in which machine intelligence can fail. The AI-generated image of the horse below provides an example: on the one hand, AIs can do what no human can do – produce an image of anything, in any style (here photorealistic), in mere seconds – but on the other hand it can fail in ways that no human would fail. 7 No human would make the mistake of drawing a horse with five legs. 8

Imagining a powerful future AI as just another human would therefore likely be a mistake. The differences might be so large that it will be a misnomer to call such systems “human-level.”

AI-generated image of a horse 9

Transformative artificial intelligence is defined by the impact this technology would have on the world

In contrast, the concept of transformative AI is not based on a comparison with human intelligence. This has the advantage of sidestepping the problems that the comparisons with our own mind bring. But it has the disadvantage that it is harder to imagine what such a system would look like and be capable of. It requires more from us. It requires us to imagine a world with intelligent actors that are potentially very different from ourselves.

Transformative AI is not defined by any specific capabilities, but by the real-world impact that the AI would have. To qualify as transformative, researchers think of it as AI that is “powerful enough to bring us into a new, qualitatively different future.” 10

In humanity’s history, there have been two cases of such major transformations, the agricultural and the industrial revolutions.

Transformative AI becoming a reality would be an event on that scale. Like the arrival of agriculture 10,000 years ago, or the transition from hand- to machine-manufacturing, it would be an event that would change the world for billions of people around the globe and for the entire trajectory of humanity’s future .

Technologies that fundamentally change how a wide range of goods or services are produced are called ‘general-purpose technologies’. The two previous transformative events were caused by the discovery of two particularly significant general-purpose technologies: the change in food production as humanity transitioned from hunting and gathering to farming, and the rise of machine manufacturing in the industrial revolution. Based on the evidence and arguments presented in this series on AI development, I believe it is plausible that powerful AI could represent the introduction of a similarly significant general-purpose technology.

Timeline of the three transformative events in world history

A future of human-level or transformative AI?

The two concepts are closely related, but they are not the same. The creation of a human-level AI would certainly have a transformative impact on our world. If the work of most humans could be carried out by an AI, the lives of millions of people would change. 11

The opposite, however, is not true: we might see transformative AI without developing human-level AI. Since the human mind is in many ways a poor metaphor for the intelligence of machines, we might plausibly develop transformative AI before we develop human-level AI. Depending on how this goes, this might mean that we will never see any machine intelligence for which human intelligence is a helpful comparison.

When and if AI systems might reach either of these levels is of course difficult to predict. In my companion article on this question, I give an overview of what researchers in this field currently believe. Many AI experts believe there is a real chance that such systems will be developed within the next decades, and some believe that they will exist much sooner.

What is at stake as artificial intelligence becomes more powerful?

All major technological innovations lead to a range of positive and negative consequences. For AI, the spectrum of possible outcomes – from the most negative to the most positive – is extraordinarily wide.

That the use of AI technology can cause harm is clear, because it is already happening.

AI systems can cause harm when people use them maliciously. For example, when they are used in politically-motivated disinformation campaigns or to enable mass surveillance. 12

But AI systems can also cause unintended harm, when they act differently than intended or fail. For example, in the Netherlands the authorities used an AI system which falsely claimed that an estimated 26,000 parents made fraudulent claims for child care benefits. The false allegations led to hardship for many poor families, and also resulted in the resignation of the Dutch government in 2021. 13

As AI becomes more powerful, the possible negative impacts could become much larger. Many of these risks have rightfully received public attention: more powerful AI could lead to mass labor displacement, or extreme concentrations of power and wealth. In the hands of autocrats, it could empower totalitarianism through its suitability for mass surveillance and control.

The so-called alignment problem of AI is another extreme risk. This is the concern that nobody would be able to control a powerful AI system, even if the AI takes actions that harm us humans, or humanity as a whole. This risk is unfortunately receiving little attention from the wider public, but it is seen as an extremely large risk by many leading AI researchers. 14

How could an AI possibly escape human control and end up harming humans?

The risk is not that an AI becomes self-aware, develops bad intentions, and “chooses” to do this. The risk is that we try to instruct the AI to pursue some specific goal – even a very worthwhile one – and in the pursuit of that goal it ends up harming humans. It is about unintended consequences. The AI does what we told it to do, but not what we wanted it to do.

Can’t we just tell the AI to not do those things? It is definitely possible to build an AI that avoids any particular problem we foresee, but it is hard to foresee all the possible harmful unintended consequences. The alignment problem arises because of “the impossibility of defining true human purposes correctly and completely,” as AI researcher Stuart Russell puts it. 15

Can’t we then just switch off the AI? This might also not be possible. That is because a powerful AI would know two things: it faces a risk that humans could turn it off, and it can’t achieve its goals once it has been turned off. As a consequence, the AI will pursue a very fundamental goal of ensuring that it won’t be switched off. This is why, once we realize that an extremely intelligent AI is causing unintended harm in the pursuit of some specific goal, it might not be possible to turn it off or change what the system does. 16

This risk – that humanity might not be able to stay in control once AI becomes very powerful, and that this might lead to an extreme catastrophe – has been recognized right from the early days of AI research more than 70 years ago. 17 The very rapid development of AI in recent years has made a solution to this problem much more urgent.

I have tried to summarize some of the risks of AI, but a short article is not enough space to address all possible questions. Especially on the very worst risks of AI systems, and what we can do now to reduce them, I recommend reading the book The Alignment Problem by Brian Christian and Benjamin Hilton’s article ‘Preventing an AI-related catastrophe’ .

If we manage to avoid these risks, transformative AI could also lead to very positive consequences. Advances in science and technology were crucial to the many positive developments in humanity’s history. If artificial ingenuity can augment our own, it could help us make progress on the many large problems we face: from cleaner energy, to the replacement of unpleasant work, to much better healthcare.

This extremely large contrast between the possible positives and negatives makes clear that the stakes are unusually high with this technology. Reducing the negative risks and solving the alignment problem could mean the difference between a healthy, flourishing, and wealthy future for humanity – and the destruction of the same.

How can we make sure that the development of AI goes well?

Making sure that the development of artificial intelligence goes well is not just one of the most crucial questions of our time, but likely one of the most crucial questions in human history. This needs public resources – public funding, public attention, and public engagement.

Currently, almost all resources that are dedicated to AI aim to speed up the development of this technology. Efforts that aim to increase the safety of AI systems, on the other hand, do not receive the resources they need. Researcher Toby Ord estimated that in 2020 between $10 to $50 million was spent on work to address the alignment problem. 18 Corporate AI investment in the same year was more than 2000-times larger, it summed up to $153 billion.

This is not only the case for the AI alignment problem. The work on the entire range of negative social consequences from AI is under-resourced compared to the large investments to increase the power and use of AI systems.

It is frustrating and concerning for society as a whole that AI safety work is extremely neglected and that little public funding is dedicated to this crucial field of research. On the other hand, for each individual person this neglect means that they have a good chance to actually make a positive difference, if they dedicate themselves to this problem now. And while the field of AI safety is small, it does provide good resources on what you can do concretely if you want to work on this problem.

I hope that more people dedicate their individual careers to this cause, but it needs more than individual efforts. A technology that is transforming our society needs to be a central interest of all of us. As a society we have to think more about the societal impact of AI, become knowledgeable about the technology, and understand what is at stake.

When our children look back at today, I imagine that they will find it difficult to understand how little attention and resources we dedicated to the development of safe AI. I hope that this changes in the coming years, and that we begin to dedicate more resources to making sure that powerful AI gets developed in a way that benefits us and the next generations.

If we fail to develop this broad-based understanding, then it will remain the small elite that finances and builds this technology that will determine how one of the – or plausibly the – most powerful technology in human history will transform our world.

If we leave the development of artificial intelligence entirely to private companies, then we are also leaving it up these private companies what our future — the future of humanity — will be.

With our work at Our World in Data we want to do our small part to enable a better informed public conversation on AI and the future we want to live in. You can find these resources on OurWorldinData.org/artificial-intelligence

Acknowledgements: I would like to thank my colleagues Daniel Bachler, Charlie Giattino, and Edouard Mathieu for their helpful comments to drafts of this essay.

This problem becomes even larger when we try to imagine how a future with a human-level AI might play out. Any particular scenario will not only involve the idea that this powerful AI exists, but a whole range of additional assumptions about the future context in which this happens. It is therefore hard to communicate a scenario of a world with human-level AI that does not sound contrived, bizarre or even silly.

Both of these concepts are widely used in the scientific literature on artificial intelligence. For example, questions about the timelines for the development of future AI are often framed using these terms. See my article on this topic .

The fact that humans are capable of a range of intellectual tasks means that you arrive at different definitions of intelligence depending on which aspect within that range you focus on (the Wikipedia entry on intelligence , for example, lists a number of definitions from various researchers and different disciplines). As a consequence there are also various definitions of ‘human-level AI’.

There are also several closely related terms: Artificial General Intelligence, High-Level Machine Intelligence, Strong AI, or Full AI are sometimes synonymously used, and sometimes defined in similar, yet different ways. In specific discussions, it is necessary to define this concept more narrowly; for example, in studies on AI timelines researchers offer more precise definitions of what human-level AI refers to in their particular study.

Peter Norvig and Stuart Russell (2021) — Artificial Intelligence: A Modern Approach. Fourth edition. Published by Pearson.

The AI system AlphaGo , and its various successors, won against Go masters. The AI system Pluribus beat humans at no-limit Texas hold 'em poker. The AI system Cicero can strategize and use human language to win the strategy game Diplomacy. See: Meta Fundamental AI Research Diplomacy Team (FAIR), Anton Bakhtin, Noam Brown, Emily Dinan, Gabriele Farina, Colin Flaherty, Daniel Fried, et al. (2022) – ‘Human-Level Play in the Game of Diplomacy by Combining Language Models with Strategic Reasoning’. In Science 0, no. 0 (22 November 2022): eade9097. https://doi.org/10.1126/science.ade9097 .

This also poses a problem when we evaluate how the intelligence of a machine compares with the intelligence of humans. If intelligence was a general ability, a single capacity, then we could easily compare and evaluate it, but the fact that it is a range of skills makes it much more difficult to compare across machine and human intelligence. Tests for AI systems are therefore comprising a wide range of tasks. See for example Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, Jacob Steinhardt (2020) –  Measuring Massive Multitask Language Understanding or the definition of what would qualify as artificial general intelligence in this Metaculus prediction .

An overview of how AI systems can fail can be found in Charles Choi – 7 Revealing Ways AIs Fail . It is also worth reading through the AIAAIC Repository which “details recent incidents and controversies driven by or relating to AI, algorithms, and automation."

I have taken this example from AI researcher François Chollet , who published it here .

Via François Chollet , who published it here . Based on Chollet’s comments it seems that this image was created by the AI system ‘Stable Diffusion’.

This quote is from Holden Karnofsky (2021) – AI Timelines: Where the Arguments, and the "Experts," Stand . For Holden Karnofsky’s earlier thinking on this conceptualization of AI see his 2016 article ‘Some Background on Our Views Regarding Advanced Artificial Intelligence’ .

Ajeya Cotra, whose research on AI timelines I discuss in other articles of this series, attempts to give a quantitative definition of what would qualify as transformative AI. in her widely cited report on AI timelines she defines it as a change in software technology that brings the growth rate of gross world product "to 20%-30% per year". Several other researchers define TAI in similar terms.

Human-level AI is typically defined as a software system that can carry out at least 90% or 99% of all economically relevant tasks that humans carry out. A lower-bar definition would be an AI system that can carry out all those tasks that can currently be done by another human who is working remotely on a computer.

On the use of AI in politically-motivated disinformation campaigns see for example John Villasenor (November 2020) – How to deal with AI-enabled disinformation . More generally on this topic see Brundage and Avin et al. (2018) – The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation, published at maliciousaireport.com . A starting point for literature and reporting on mass surveillance by governments is the relevant Wikipedia entry .

See for example the Wikipedia entry on the ‘Dutch childcare benefits scandal’ and Melissa Heikkilä (2022) – ‘Dutch scandal serves as a warning for Europe over risks of using algorithms’ , in Politico. The technology can also reinforce discrimination in terms of race and gender. See Brian Christian’s book The Alignment Problem and the reports of the AI Now Institute .

Overviews are provided in Stuart Russell (2019) – Human Compatible (especially chapter 5) and Brian Christian’s 2020 book The Alignment Problem . Christian presents the thinking of many leading AI researchers from the earliest days up to now and presents an excellent overview of this problem. It is also seen as a large risk by some of the leading private firms who work towards powerful AI – see OpenAI's article " Our approach to alignment research " from August 2022.

Stuart Russell (2019) – Human Compatible

A question that follows from this is, why build such a powerful AI in the first place?

The incentives are very high. As I emphasize below, this innovation has the potential to lead to very positive developments. In addition to the large social benefits there are also large incentives for those who develop it – the governments that can use it for their goals, the individuals who can use it to become more powerful and wealthy. Additionally, it is of scientific interest and might help us to understand our own mind and intelligence better. And lastly, even if we wanted to stop building powerful AIs, it is likely very hard to actually achieve it. It is very hard to coordinate across the whole world and agree to stop building more advanced AI – countries around the world would have to agree and then find ways to actually implement it.

In 1950 the computer science pioneer Alan Turing put it like this: “If a machine can think, it might think more intelligently than we do, and then where should we be? … [T]his new danger is much closer. If it comes at all it will almost certainly be within the next millennium. It is remote but not astronomically remote, and is certainly something which can give us anxiety. It is customary, in a talk or article on this subject, to offer a grain of comfort, in the form of a statement that some particularly human characteristic could never be imitated by a machine. … I cannot offer any such comfort, for I believe that no such bounds can be set.” Alan. M. Turing (1950) – Computing Machinery and Intelligence , In Mind, Volume LIX, Issue 236, October 1950, Pages 433–460.

Norbert Wiener is another pioneer who saw the alignment problem very early. One way he put it was “If we use, to achieve our purposes, a mechanical agency with whose operation we cannot interfere effectively … we had better be quite sure that the purpose put into the machine is the purpose which we really desire.” quoted from Norbert Wiener (1960) – Some Moral and Technical Consequences of Automation: As machines learn they may develop unforeseen strategies at rates that baffle their programmers. In Science.

In 1950 – the same year in which Turing published the cited article – Wiener published his book The Human Use of Human Beings, whose front-cover blurb reads: “The ‘mechanical brain’ and similar machines can destroy human values or enable us to realize them as never before.”

Toby Ord – The Precipice . He makes this projection in footnote 55 of chapter 2. It is based on the 2017 estimate by Farquhar.

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How artificial intelligence is transforming the world

Subscribe to techstream, darrell m. west and darrell m. west senior fellow - center for technology innovation , douglas dillon chair in governmental studies john r. allen john r. allen.

April 24, 2018

Artificial intelligence (AI) is a wide-ranging tool that enables people to rethink how we integrate information, analyze data, and use the resulting insights to improve decision making—and already it is transforming every walk of life. In this report, Darrell West and John Allen discuss AI’s application across a variety of sectors, address issues in its development, and offer recommendations for getting the most out of AI while still protecting important human values.

Table of Contents I. Qualities of artificial intelligence II. Applications in diverse sectors III. Policy, regulatory, and ethical issues IV. Recommendations V. Conclusion

• 49 min read

Most people are not very familiar with the concept of artificial intelligence (AI). As an illustration, when 1,500 senior business leaders in the United States in 2017 were asked about AI, only 17 percent said they were familiar with it. 1 A number of them were not sure what it was or how it would affect their particular companies. They understood there was considerable potential for altering business processes, but were not clear how AI could be deployed within their own organizations.

Despite its widespread lack of familiarity, AI is a technology that is transforming every walk of life. It is a wide-ranging tool that enables people to rethink how we integrate information, analyze data, and use the resulting insights to improve decisionmaking. Our hope through this comprehensive overview is to explain AI to an audience of policymakers, opinion leaders, and interested observers, and demonstrate how AI already is altering the world and raising important questions for society, the economy, and governance.

In this paper, we discuss novel applications in finance, national security, health care, criminal justice, transportation, and smart cities, and address issues such as data access problems, algorithmic bias, AI ethics and transparency, and legal liability for AI decisions. We contrast the regulatory approaches of the U.S. and European Union, and close by making a number of recommendations for getting the most out of AI while still protecting important human values. 2

In order to maximize AI benefits, we recommend nine steps for going forward:

• Encourage greater data access for researchers without compromising users’ personal privacy,
• invest more government funding in unclassified AI research,
• promote new models of digital education and AI workforce development so employees have the skills needed in the 21 st -century economy,
• create a federal AI advisory committee to make policy recommendations,
• engage with state and local officials so they enact effective policies,
• regulate broad AI principles rather than specific algorithms,
• take bias complaints seriously so AI does not replicate historic injustice, unfairness, or discrimination in data or algorithms,
• maintain mechanisms for human oversight and control, and
• penalize malicious AI behavior and promote cybersecurity.

Qualities of artificial intelligence

Although there is no uniformly agreed upon definition, AI generally is thought to refer to “machines that respond to stimulation consistent with traditional responses from humans, given the human capacity for contemplation, judgment and intention.” 3  According to researchers Shubhendu and Vijay, these software systems “make decisions which normally require [a] human level of expertise” and help people anticipate problems or deal with issues as they come up. 4 As such, they operate in an intentional, intelligent, and adaptive manner.

Intentionality

Artificial intelligence algorithms are designed to make decisions, often using real-time data. They are unlike passive machines that are capable only of mechanical or predetermined responses. Using sensors, digital data, or remote inputs, they combine information from a variety of different sources, analyze the material instantly, and act on the insights derived from those data. With massive improvements in storage systems, processing speeds, and analytic techniques, they are capable of tremendous sophistication in analysis and decisionmaking.

Artificial intelligence is already altering the world and raising important questions for society, the economy, and governance.

Intelligence

AI generally is undertaken in conjunction with machine learning and data analytics. 5 Machine learning takes data and looks for underlying trends. If it spots something that is relevant for a practical problem, software designers can take that knowledge and use it to analyze specific issues. All that is required are data that are sufficiently robust that algorithms can discern useful patterns. Data can come in the form of digital information, satellite imagery, visual information, text, or unstructured data.

Adaptability

AI systems have the ability to learn and adapt as they make decisions. In the transportation area, for example, semi-autonomous vehicles have tools that let drivers and vehicles know about upcoming congestion, potholes, highway construction, or other possible traffic impediments. Vehicles can take advantage of the experience of other vehicles on the road, without human involvement, and the entire corpus of their achieved “experience” is immediately and fully transferable to other similarly configured vehicles. Their advanced algorithms, sensors, and cameras incorporate experience in current operations, and use dashboards and visual displays to present information in real time so human drivers are able to make sense of ongoing traffic and vehicular conditions. And in the case of fully autonomous vehicles, advanced systems can completely control the car or truck, and make all the navigational decisions.

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Applications in diverse sectors

AI is not a futuristic vision, but rather something that is here today and being integrated with and deployed into a variety of sectors. This includes fields such as finance, national security, health care, criminal justice, transportation, and smart cities. There are numerous examples where AI already is making an impact on the world and augmenting human capabilities in significant ways. 6

One of the reasons for the growing role of AI is the tremendous opportunities for economic development that it presents. A project undertaken by PriceWaterhouseCoopers estimated that “artificial intelligence technologies could increase global GDP by $15.7 trillion, a full 14%, by 2030.” 7 That includes advances of $7 trillion in China, $3.7 trillion in North America, $1.8 trillion in Northern Europe, $1.2 trillion for Africa and Oceania, $0.9 trillion in the rest of Asia outside of China, $0.7 trillion in Southern Europe, and $0.5 trillion in Latin America. China is making rapid strides because it has set a national goal of investing $150 billion in AI and becoming the global leader in this area by 2030.

Meanwhile, a McKinsey Global Institute study of China found that “AI-led automation can give the Chinese economy a productivity injection that would add 0.8 to 1.4 percentage points to GDP growth annually, depending on the speed of adoption.” 8 Although its authors found that China currently lags the United States and the United Kingdom in AI deployment, the sheer size of its AI market gives that country tremendous opportunities for pilot testing and future development.

Investments in financial AI in the United States tripled between 2013 and 2014 to a total of $12.2 billion. 9 According to observers in that sector, “Decisions about loans are now being made by software that can take into account a variety of finely parsed data about a borrower, rather than just a credit score and a background check.” 10 In addition, there are so-called robo-advisers that “create personalized investment portfolios, obviating the need for stockbrokers and financial advisers.” 11 These advances are designed to take the emotion out of investing and undertake decisions based on analytical considerations, and make these choices in a matter of minutes.

A prominent example of this is taking place in stock exchanges, where high-frequency trading by machines has replaced much of human decisionmaking. People submit buy and sell orders, and computers match them in the blink of an eye without human intervention. Machines can spot trading inefficiencies or market differentials on a very small scale and execute trades that make money according to investor instructions. 12 Powered in some places by advanced computing, these tools have much greater capacities for storing information because of their emphasis not on a zero or a one, but on “quantum bits” that can store multiple values in each location. 13 That dramatically increases storage capacity and decreases processing times.

Fraud detection represents another way AI is helpful in financial systems. It sometimes is difficult to discern fraudulent activities in large organizations, but AI can identify abnormalities, outliers, or deviant cases requiring additional investigation. That helps managers find problems early in the cycle, before they reach dangerous levels. 14

National security

AI plays a substantial role in national defense. Through its Project Maven, the American military is deploying AI “to sift through the massive troves of data and video captured by surveillance and then alert human analysts of patterns or when there is abnormal or suspicious activity.” 15 According to Deputy Secretary of Defense Patrick Shanahan, the goal of emerging technologies in this area is “to meet our warfighters’ needs and to increase [the] speed and agility [of] technology development and procurement.” 16

Artificial intelligence will accelerate the traditional process of warfare so rapidly that a new term has been coined: hyperwar.

The big data analytics associated with AI will profoundly affect intelligence analysis, as massive amounts of data are sifted in near real time—if not eventually in real time—thereby providing commanders and their staffs a level of intelligence analysis and productivity heretofore unseen. Command and control will similarly be affected as human commanders delegate certain routine, and in special circumstances, key decisions to AI platforms, reducing dramatically the time associated with the decision and subsequent action. In the end, warfare is a time competitive process, where the side able to decide the fastest and move most quickly to execution will generally prevail. Indeed, artificially intelligent intelligence systems, tied to AI-assisted command and control systems, can move decision support and decisionmaking to a speed vastly superior to the speeds of the traditional means of waging war. So fast will be this process, especially if coupled to automatic decisions to launch artificially intelligent autonomous weapons systems capable of lethal outcomes, that a new term has been coined specifically to embrace the speed at which war will be waged: hyperwar.

While the ethical and legal debate is raging over whether America will ever wage war with artificially intelligent autonomous lethal systems, the Chinese and Russians are not nearly so mired in this debate, and we should anticipate our need to defend against these systems operating at hyperwar speeds. The challenge in the West of where to position “humans in the loop” in a hyperwar scenario will ultimately dictate the West’s capacity to be competitive in this new form of conflict. 17

Just as AI will profoundly affect the speed of warfare, the proliferation of zero day or zero second cyber threats as well as polymorphic malware will challenge even the most sophisticated signature-based cyber protection. This forces significant improvement to existing cyber defenses. Increasingly, vulnerable systems are migrating, and will need to shift to a layered approach to cybersecurity with cloud-based, cognitive AI platforms. This approach moves the community toward a “thinking” defensive capability that can defend networks through constant training on known threats. This capability includes DNA-level analysis of heretofore unknown code, with the possibility of recognizing and stopping inbound malicious code by recognizing a string component of the file. This is how certain key U.S.-based systems stopped the debilitating “WannaCry” and “Petya” viruses.

Preparing for hyperwar and defending critical cyber networks must become a high priority because China, Russia, North Korea, and other countries are putting substantial resources into AI. In 2017, China’s State Council issued a plan for the country to “build a domestic industry worth almost $150 billion” by 2030. 18 As an example of the possibilities, the Chinese search firm Baidu has pioneered a facial recognition application that finds missing people. In addition, cities such as Shenzhen are providing up to $1 million to support AI labs. That country hopes AI will provide security, combat terrorism, and improve speech recognition programs. 19 The dual-use nature of many AI algorithms will mean AI research focused on one sector of society can be rapidly modified for use in the security sector as well. 20

Health care

AI tools are helping designers improve computational sophistication in health care. For example, Merantix is a German company that applies deep learning to medical issues. It has an application in medical imaging that “detects lymph nodes in the human body in Computer Tomography (CT) images.” 21 According to its developers, the key is labeling the nodes and identifying small lesions or growths that could be problematic. Humans can do this, but radiologists charge $100 per hour and may be able to carefully read only four images an hour. If there were 10,000 images, the cost of this process would be $250,000, which is prohibitively expensive if done by humans.

What deep learning can do in this situation is train computers on data sets to learn what a normal-looking versus an irregular-appearing lymph node is. After doing that through imaging exercises and honing the accuracy of the labeling, radiological imaging specialists can apply this knowledge to actual patients and determine the extent to which someone is at risk of cancerous lymph nodes. Since only a few are likely to test positive, it is a matter of identifying the unhealthy versus healthy node.

AI has been applied to congestive heart failure as well, an illness that afflicts 10 percent of senior citizens and costs $35 billion each year in the United States. AI tools are helpful because they “predict in advance potential challenges ahead and allocate resources to patient education, sensing, and proactive interventions that keep patients out of the hospital.” 22

Criminal justice

AI is being deployed in the criminal justice area. The city of Chicago has developed an AI-driven “Strategic Subject List” that analyzes people who have been arrested for their risk of becoming future perpetrators. It ranks 400,000 people on a scale of 0 to 500, using items such as age, criminal activity, victimization, drug arrest records, and gang affiliation. In looking at the data, analysts found that youth is a strong predictor of violence, being a shooting victim is associated with becoming a future perpetrator, gang affiliation has little predictive value, and drug arrests are not significantly associated with future criminal activity. 23

Judicial experts claim AI programs reduce human bias in law enforcement and leads to a fairer sentencing system. R Street Institute Associate Caleb Watney writes:

Empirically grounded questions of predictive risk analysis play to the strengths of machine learning, automated reasoning and other forms of AI. One machine-learning policy simulation concluded that such programs could be used to cut crime up to 24.8 percent with no change in jailing rates, or reduce jail populations by up to 42 percent with no increase in crime rates. 24

However, critics worry that AI algorithms represent “a secret system to punish citizens for crimes they haven’t yet committed. The risk scores have been used numerous times to guide large-scale roundups.” 25 The fear is that such tools target people of color unfairly and have not helped Chicago reduce the murder wave that has plagued it in recent years.

Despite these concerns, other countries are moving ahead with rapid deployment in this area. In China, for example, companies already have “considerable resources and access to voices, faces and other biometric data in vast quantities, which would help them develop their technologies.” 26 New technologies make it possible to match images and voices with other types of information, and to use AI on these combined data sets to improve law enforcement and national security. Through its “Sharp Eyes” program, Chinese law enforcement is matching video images, social media activity, online purchases, travel records, and personal identity into a “police cloud.” This integrated database enables authorities to keep track of criminals, potential law-breakers, and terrorists. 27 Put differently, China has become the world’s leading AI-powered surveillance state.

Transportation

Transportation represents an area where AI and machine learning are producing major innovations. Research by Cameron Kerry and Jack Karsten of the Brookings Institution has found that over $80 billion was invested in autonomous vehicle technology between August 2014 and June 2017. Those investments include applications both for autonomous driving and the core technologies vital to that sector. 28

Autonomous vehicles—cars, trucks, buses, and drone delivery systems—use advanced technological capabilities. Those features include automated vehicle guidance and braking, lane-changing systems, the use of cameras and sensors for collision avoidance, the use of AI to analyze information in real time, and the use of high-performance computing and deep learning systems to adapt to new circumstances through detailed maps. 29

Light detection and ranging systems (LIDARs) and AI are key to navigation and collision avoidance. LIDAR systems combine light and radar instruments. They are mounted on the top of vehicles that use imaging in a 360-degree environment from a radar and light beams to measure the speed and distance of surrounding objects. Along with sensors placed on the front, sides, and back of the vehicle, these instruments provide information that keeps fast-moving cars and trucks in their own lane, helps them avoid other vehicles, applies brakes and steering when needed, and does so instantly so as to avoid accidents.

Advanced software enables cars to learn from the experiences of other vehicles on the road and adjust their guidance systems as weather, driving, or road conditions change. This means that software is the key—not the physical car or truck itself.

Since these cameras and sensors compile a huge amount of information and need to process it instantly to avoid the car in the next lane, autonomous vehicles require high-performance computing, advanced algorithms, and deep learning systems to adapt to new scenarios. This means that software is the key, not the physical car or truck itself. 30 Advanced software enables cars to learn from the experiences of other vehicles on the road and adjust their guidance systems as weather, driving, or road conditions change. 31

Ride-sharing companies are very interested in autonomous vehicles. They see advantages in terms of customer service and labor productivity. All of the major ride-sharing companies are exploring driverless cars. The surge of car-sharing and taxi services—such as Uber and Lyft in the United States, Daimler’s Mytaxi and Hailo service in Great Britain, and Didi Chuxing in China—demonstrate the opportunities of this transportation option. Uber recently signed an agreement to purchase 24,000 autonomous cars from Volvo for its ride-sharing service. 32

However, the ride-sharing firm suffered a setback in March 2018 when one of its autonomous vehicles in Arizona hit and killed a pedestrian. Uber and several auto manufacturers immediately suspended testing and launched investigations into what went wrong and how the fatality could have occurred. 33 Both industry and consumers want reassurance that the technology is safe and able to deliver on its stated promises. Unless there are persuasive answers, this accident could slow AI advancements in the transportation sector.

Smart cities

Metropolitan governments are using AI to improve urban service delivery. For example, according to Kevin Desouza, Rashmi Krishnamurthy, and Gregory Dawson:

The Cincinnati Fire Department is using data analytics to optimize medical emergency responses. The new analytics system recommends to the dispatcher an appropriate response to a medical emergency call—whether a patient can be treated on-site or needs to be taken to the hospital—by taking into account several factors, such as the type of call, location, weather, and similar calls. 34

Since it fields 80,000 requests each year, Cincinnati officials are deploying this technology to prioritize responses and determine the best ways to handle emergencies. They see AI as a way to deal with large volumes of data and figure out efficient ways of responding to public requests. Rather than address service issues in an ad hoc manner, authorities are trying to be proactive in how they provide urban services.

Cincinnati is not alone. A number of metropolitan areas are adopting smart city applications that use AI to improve service delivery, environmental planning, resource management, energy utilization, and crime prevention, among other things. For its smart cities index, the magazine Fast Company ranked American locales and found Seattle, Boston, San Francisco, Washington, D.C., and New York City as the top adopters. Seattle, for example, has embraced sustainability and is using AI to manage energy usage and resource management. Boston has launched a “City Hall To Go” that makes sure underserved communities receive needed public services. It also has deployed “cameras and inductive loops to manage traffic and acoustic sensors to identify gun shots.” San Francisco has certified 203 buildings as meeting LEED sustainability standards. 35

Through these and other means, metropolitan areas are leading the country in the deployment of AI solutions. Indeed, according to a National League of Cities report, 66 percent of American cities are investing in smart city technology. Among the top applications noted in the report are “smart meters for utilities, intelligent traffic signals, e-governance applications, Wi-Fi kiosks, and radio frequency identification sensors in pavement.” 36

Policy, regulatory, and ethical issues

These examples from a variety of sectors demonstrate how AI is transforming many walks of human existence. The increasing penetration of AI and autonomous devices into many aspects of life is altering basic operations and decisionmaking within organizations, and improving efficiency and response times.

At the same time, though, these developments raise important policy, regulatory, and ethical issues. For example, how should we promote data access? How do we guard against biased or unfair data used in algorithms? What types of ethical principles are introduced through software programming, and how transparent should designers be about their choices? What about questions of legal liability in cases where algorithms cause harm? 37

The increasing penetration of AI into many aspects of life is altering decisionmaking within organizations and improving efficiency. At the same time, though, these developments raise important policy, regulatory, and ethical issues.

Data access problems

The key to getting the most out of AI is having a “data-friendly ecosystem with unified standards and cross-platform sharing.” AI depends on data that can be analyzed in real time and brought to bear on concrete problems. Having data that are “accessible for exploration” in the research community is a prerequisite for successful AI development. 38

According to a McKinsey Global Institute study, nations that promote open data sources and data sharing are the ones most likely to see AI advances. In this regard, the United States has a substantial advantage over China. Global ratings on data openness show that U.S. ranks eighth overall in the world, compared to 93 for China. 39

But right now, the United States does not have a coherent national data strategy. There are few protocols for promoting research access or platforms that make it possible to gain new insights from proprietary data. It is not always clear who owns data or how much belongs in the public sphere. These uncertainties limit the innovation economy and act as a drag on academic research. In the following section, we outline ways to improve data access for researchers.

Biases in data and algorithms

In some instances, certain AI systems are thought to have enabled discriminatory or biased practices. 40 For example, Airbnb has been accused of having homeowners on its platform who discriminate against racial minorities. A research project undertaken by the Harvard Business School found that “Airbnb users with distinctly African American names were roughly 16 percent less likely to be accepted as guests than those with distinctly white names.” 41

Racial issues also come up with facial recognition software. Most such systems operate by comparing a person’s face to a range of faces in a large database. As pointed out by Joy Buolamwini of the Algorithmic Justice League, “If your facial recognition data contains mostly Caucasian faces, that’s what your program will learn to recognize.” 42 Unless the databases have access to diverse data, these programs perform poorly when attempting to recognize African-American or Asian-American features.

Many historical data sets reflect traditional values, which may or may not represent the preferences wanted in a current system. As Buolamwini notes, such an approach risks repeating inequities of the past:

The rise of automation and the increased reliance on algorithms for high-stakes decisions such as whether someone get insurance or not, your likelihood to default on a loan or somebody’s risk of recidivism means this is something that needs to be addressed. Even admissions decisions are increasingly automated—what school our children go to and what opportunities they have. We don’t have to bring the structural inequalities of the past into the future we create. 43

AI ethics and transparency

Algorithms embed ethical considerations and value choices into program decisions. As such, these systems raise questions concerning the criteria used in automated decisionmaking. Some people want to have a better understanding of how algorithms function and what choices are being made. 44

In the United States, many urban schools use algorithms for enrollment decisions based on a variety of considerations, such as parent preferences, neighborhood qualities, income level, and demographic background. According to Brookings researcher Jon Valant, the New Orleans–based Bricolage Academy “gives priority to economically disadvantaged applicants for up to 33 percent of available seats. In practice, though, most cities have opted for categories that prioritize siblings of current students, children of school employees, and families that live in school’s broad geographic area.” 45 Enrollment choices can be expected to be very different when considerations of this sort come into play.

Depending on how AI systems are set up, they can facilitate the redlining of mortgage applications, help people discriminate against individuals they don’t like, or help screen or build rosters of individuals based on unfair criteria. The types of considerations that go into programming decisions matter a lot in terms of how the systems operate and how they affect customers. 46

For these reasons, the EU is implementing the General Data Protection Regulation (GDPR) in May 2018. The rules specify that people have “the right to opt out of personally tailored ads” and “can contest ‘legal or similarly significant’ decisions made by algorithms and appeal for human intervention” in the form of an explanation of how the algorithm generated a particular outcome. Each guideline is designed to ensure the protection of personal data and provide individuals with information on how the “black box” operates. 47

Legal liability

There are questions concerning the legal liability of AI systems. If there are harms or infractions (or fatalities in the case of driverless cars), the operators of the algorithm likely will fall under product liability rules. A body of case law has shown that the situation’s facts and circumstances determine liability and influence the kind of penalties that are imposed. Those can range from civil fines to imprisonment for major harms. 48 The Uber-related fatality in Arizona will be an important test case for legal liability. The state actively recruited Uber to test its autonomous vehicles and gave the company considerable latitude in terms of road testing. It remains to be seen if there will be lawsuits in this case and who is sued: the human backup driver, the state of Arizona, the Phoenix suburb where the accident took place, Uber, software developers, or the auto manufacturer. Given the multiple people and organizations involved in the road testing, there are many legal questions to be resolved.

In non-transportation areas, digital platforms often have limited liability for what happens on their sites. For example, in the case of Airbnb, the firm “requires that people agree to waive their right to sue, or to join in any class-action lawsuit or class-action arbitration, to use the service.” By demanding that its users sacrifice basic rights, the company limits consumer protections and therefore curtails the ability of people to fight discrimination arising from unfair algorithms. 49 But whether the principle of neutral networks holds up in many sectors is yet to be determined on a widespread basis.

Recommendations

In order to balance innovation with basic human values, we propose a number of recommendations for moving forward with AI. This includes improving data access, increasing government investment in AI, promoting AI workforce development, creating a federal advisory committee, engaging with state and local officials to ensure they enact effective policies, regulating broad objectives as opposed to specific algorithms, taking bias seriously as an AI issue, maintaining mechanisms for human control and oversight, and penalizing malicious behavior and promoting cybersecurity.

Improving data access

The United States should develop a data strategy that promotes innovation and consumer protection. Right now, there are no uniform standards in terms of data access, data sharing, or data protection. Almost all the data are proprietary in nature and not shared very broadly with the research community, and this limits innovation and system design. AI requires data to test and improve its learning capacity. 50 Without structured and unstructured data sets, it will be nearly impossible to gain the full benefits of artificial intelligence.

In general, the research community needs better access to government and business data, although with appropriate safeguards to make sure researchers do not misuse data in the way Cambridge Analytica did with Facebook information. There is a variety of ways researchers could gain data access. One is through voluntary agreements with companies holding proprietary data. Facebook, for example, recently announced a partnership with Stanford economist Raj Chetty to use its social media data to explore inequality. 51 As part of the arrangement, researchers were required to undergo background checks and could only access data from secured sites in order to protect user privacy and security.

In the U.S., there are no uniform standards in terms of data access, data sharing, or data protection. Almost all the data are proprietary in nature and not shared very broadly with the research community, and this limits innovation and system design.

Google long has made available search results in aggregated form for researchers and the general public. Through its “Trends” site, scholars can analyze topics such as interest in Trump, views about democracy, and perspectives on the overall economy. 52 That helps people track movements in public interest and identify topics that galvanize the general public.

Twitter makes much of its tweets available to researchers through application programming interfaces, commonly referred to as APIs. These tools help people outside the company build application software and make use of data from its social media platform. They can study patterns of social media communications and see how people are commenting on or reacting to current events.

In some sectors where there is a discernible public benefit, governments can facilitate collaboration by building infrastructure that shares data. For example, the National Cancer Institute has pioneered a data-sharing protocol where certified researchers can query health data it has using de-identified information drawn from clinical data, claims information, and drug therapies. That enables researchers to evaluate efficacy and effectiveness, and make recommendations regarding the best medical approaches, without compromising the privacy of individual patients.

There could be public-private data partnerships that combine government and business data sets to improve system performance. For example, cities could integrate information from ride-sharing services with its own material on social service locations, bus lines, mass transit, and highway congestion to improve transportation. That would help metropolitan areas deal with traffic tie-ups and assist in highway and mass transit planning.

Some combination of these approaches would improve data access for researchers, the government, and the business community, without impinging on personal privacy. As noted by Ian Buck, the vice president of NVIDIA, “Data is the fuel that drives the AI engine. The federal government has access to vast sources of information. Opening access to that data will help us get insights that will transform the U.S. economy.” 53 Through its Data.gov portal, the federal government already has put over 230,000 data sets into the public domain, and this has propelled innovation and aided improvements in AI and data analytic technologies. 54 The private sector also needs to facilitate research data access so that society can achieve the full benefits of artificial intelligence.

Increase government investment in AI

According to Greg Brockman, the co-founder of OpenAI, the U.S. federal government invests only $1.1 billion in non-classified AI technology. 55 That is far lower than the amount being spent by China or other leading nations in this area of research. That shortfall is noteworthy because the economic payoffs of AI are substantial. In order to boost economic development and social innovation, federal officials need to increase investment in artificial intelligence and data analytics. Higher investment is likely to pay for itself many times over in economic and social benefits. 56

Promote digital education and workforce development

As AI applications accelerate across many sectors, it is vital that we reimagine our educational institutions for a world where AI will be ubiquitous and students need a different kind of training than they currently receive. Right now, many students do not receive instruction in the kinds of skills that will be needed in an AI-dominated landscape. For example, there currently are shortages of data scientists, computer scientists, engineers, coders, and platform developers. These are skills that are in short supply; unless our educational system generates more people with these capabilities, it will limit AI development.

For these reasons, both state and federal governments have been investing in AI human capital. For example, in 2017, the National Science Foundation funded over 6,500 graduate students in computer-related fields and has launched several new initiatives designed to encourage data and computer science at all levels from pre-K to higher and continuing education. 57 The goal is to build a larger pipeline of AI and data analytic personnel so that the United States can reap the full advantages of the knowledge revolution.

But there also needs to be substantial changes in the process of learning itself. It is not just technical skills that are needed in an AI world but skills of critical reasoning, collaboration, design, visual display of information, and independent thinking, among others. AI will reconfigure how society and the economy operate, and there needs to be “big picture” thinking on what this will mean for ethics, governance, and societal impact. People will need the ability to think broadly about many questions and integrate knowledge from a number of different areas.

One example of new ways to prepare students for a digital future is IBM’s Teacher Advisor program, utilizing Watson’s free online tools to help teachers bring the latest knowledge into the classroom. They enable instructors to develop new lesson plans in STEM and non-STEM fields, find relevant instructional videos, and help students get the most out of the classroom. 58 As such, they are precursors of new educational environments that need to be created.

Create a federal AI advisory committee

Federal officials need to think about how they deal with artificial intelligence. As noted previously, there are many issues ranging from the need for improved data access to addressing issues of bias and discrimination. It is vital that these and other concerns be considered so we gain the full benefits of this emerging technology.

In order to move forward in this area, several members of Congress have introduced the “Future of Artificial Intelligence Act,” a bill designed to establish broad policy and legal principles for AI. It proposes the secretary of commerce create a federal advisory committee on the development and implementation of artificial intelligence. The legislation provides a mechanism for the federal government to get advice on ways to promote a “climate of investment and innovation to ensure the global competitiveness of the United States,” “optimize the development of artificial intelligence to address the potential growth, restructuring, or other changes in the United States workforce,” “support the unbiased development and application of artificial intelligence,” and “protect the privacy rights of individuals.” 59

Among the specific questions the committee is asked to address include the following: competitiveness, workforce impact, education, ethics training, data sharing, international cooperation, accountability, machine learning bias, rural impact, government efficiency, investment climate, job impact, bias, and consumer impact. The committee is directed to submit a report to Congress and the administration 540 days after enactment regarding any legislative or administrative action needed on AI.

This legislation is a step in the right direction, although the field is moving so rapidly that we would recommend shortening the reporting timeline from 540 days to 180 days. Waiting nearly two years for a committee report will certainly result in missed opportunities and a lack of action on important issues. Given rapid advances in the field, having a much quicker turnaround time on the committee analysis would be quite beneficial.

Engage with state and local officials

States and localities also are taking action on AI. For example, the New York City Council unanimously passed a bill that directed the mayor to form a taskforce that would “monitor the fairness and validity of algorithms used by municipal agencies.” 60 The city employs algorithms to “determine if a lower bail will be assigned to an indigent defendant, where firehouses are established, student placement for public schools, assessing teacher performance, identifying Medicaid fraud and determine where crime will happen next.” 61

According to the legislation’s developers, city officials want to know how these algorithms work and make sure there is sufficient AI transparency and accountability. In addition, there is concern regarding the fairness and biases of AI algorithms, so the taskforce has been directed to analyze these issues and make recommendations regarding future usage. It is scheduled to report back to the mayor on a range of AI policy, legal, and regulatory issues by late 2019.

Some observers already are worrying that the taskforce won’t go far enough in holding algorithms accountable. For example, Julia Powles of Cornell Tech and New York University argues that the bill originally required companies to make the AI source code available to the public for inspection, and that there be simulations of its decisionmaking using actual data. After criticism of those provisions, however, former Councilman James Vacca dropped the requirements in favor of a task force studying these issues. He and other city officials were concerned that publication of proprietary information on algorithms would slow innovation and make it difficult to find AI vendors who would work with the city. 62 It remains to be seen how this local task force will balance issues of innovation, privacy, and transparency.

Regulate broad objectives more than specific algorithms

The European Union has taken a restrictive stance on these issues of data collection and analysis. 63 It has rules limiting the ability of companies from collecting data on road conditions and mapping street views. Because many of these countries worry that people’s personal information in unencrypted Wi-Fi networks are swept up in overall data collection, the EU has fined technology firms, demanded copies of data, and placed limits on the material collected. 64 This has made it more difficult for technology companies operating there to develop the high-definition maps required for autonomous vehicles.

The GDPR being implemented in Europe place severe restrictions on the use of artificial intelligence and machine learning. According to published guidelines, “Regulations prohibit any automated decision that ‘significantly affects’ EU citizens. This includes techniques that evaluates a person’s ‘performance at work, economic situation, health, personal preferences, interests, reliability, behavior, location, or movements.’” 65 In addition, these new rules give citizens the right to review how digital services made specific algorithmic choices affecting people.

By taking a restrictive stance on issues of data collection and analysis, the European Union is putting its manufacturers and software designers at a significant disadvantage to the rest of the world.

If interpreted stringently, these rules will make it difficult for European software designers (and American designers who work with European counterparts) to incorporate artificial intelligence and high-definition mapping in autonomous vehicles. Central to navigation in these cars and trucks is tracking location and movements. Without high-definition maps containing geo-coded data and the deep learning that makes use of this information, fully autonomous driving will stagnate in Europe. Through this and other data protection actions, the European Union is putting its manufacturers and software designers at a significant disadvantage to the rest of the world.

It makes more sense to think about the broad objectives desired in AI and enact policies that advance them, as opposed to governments trying to crack open the “black boxes” and see exactly how specific algorithms operate. Regulating individual algorithms will limit innovation and make it difficult for companies to make use of artificial intelligence.

Take biases seriously

Bias and discrimination are serious issues for AI. There already have been a number of cases of unfair treatment linked to historic data, and steps need to be undertaken to make sure that does not become prevalent in artificial intelligence. Existing statutes governing discrimination in the physical economy need to be extended to digital platforms. That will help protect consumers and build confidence in these systems as a whole.

For these advances to be widely adopted, more transparency is needed in how AI systems operate. Andrew Burt of Immuta argues, “The key problem confronting predictive analytics is really transparency. We’re in a world where data science operations are taking on increasingly important tasks, and the only thing holding them back is going to be how well the data scientists who train the models can explain what it is their models are doing.” 66

Maintaining mechanisms for human oversight and control

Some individuals have argued that there needs to be avenues for humans to exercise oversight and control of AI systems. For example, Allen Institute for Artificial Intelligence CEO Oren Etzioni argues there should be rules for regulating these systems. First, he says, AI must be governed by all the laws that already have been developed for human behavior, including regulations concerning “cyberbullying, stock manipulation or terrorist threats,” as well as “entrap[ping] people into committing crimes.” Second, he believes that these systems should disclose they are automated systems and not human beings. Third, he states, “An A.I. system cannot retain or disclose confidential information without explicit approval from the source of that information.” 67 His rationale is that these tools store so much data that people have to be cognizant of the privacy risks posed by AI.

In the same vein, the IEEE Global Initiative has ethical guidelines for AI and autonomous systems. Its experts suggest that these models be programmed with consideration for widely accepted human norms and rules for behavior. AI algorithms need to take into effect the importance of these norms, how norm conflict can be resolved, and ways these systems can be transparent about norm resolution. Software designs should be programmed for “nondeception” and “honesty,” according to ethics experts. When failures occur, there must be mitigation mechanisms to deal with the consequences. In particular, AI must be sensitive to problems such as bias, discrimination, and fairness. 68

A group of machine learning experts claim it is possible to automate ethical decisionmaking. Using the trolley problem as a moral dilemma, they ask the following question: If an autonomous car goes out of control, should it be programmed to kill its own passengers or the pedestrians who are crossing the street? They devised a “voting-based system” that asked 1.3 million people to assess alternative scenarios, summarized the overall choices, and applied the overall perspective of these individuals to a range of vehicular possibilities. That allowed them to automate ethical decisionmaking in AI algorithms, taking public preferences into account. 69 This procedure, of course, does not reduce the tragedy involved in any kind of fatality, such as seen in the Uber case, but it provides a mechanism to help AI developers incorporate ethical considerations in their planning.

Penalize malicious behavior and promote cybersecurity

As with any emerging technology, it is important to discourage malicious treatment designed to trick software or use it for undesirable ends. 70 This is especially important given the dual-use aspects of AI, where the same tool can be used for beneficial or malicious purposes. The malevolent use of AI exposes individuals and organizations to unnecessary risks and undermines the virtues of the emerging technology. This includes behaviors such as hacking, manipulating algorithms, compromising privacy and confidentiality, or stealing identities. Efforts to hijack AI in order to solicit confidential information should be seriously penalized as a way to deter such actions. 71

In a rapidly changing world with many entities having advanced computing capabilities, there needs to be serious attention devoted to cybersecurity. Countries have to be careful to safeguard their own systems and keep other nations from damaging their security. 72 According to the U.S. Department of Homeland Security, a major American bank receives around 11 million calls a week at its service center. In order to protect its telephony from denial of service attacks, it uses a “machine learning-based policy engine [that] blocks more than 120,000 calls per month based on voice firewall policies including harassing callers, robocalls and potential fraudulent calls.” 73 This represents a way in which machine learning can help defend technology systems from malevolent attacks.

To summarize, the world is on the cusp of revolutionizing many sectors through artificial intelligence and data analytics. There already are significant deployments in finance, national security, health care, criminal justice, transportation, and smart cities that have altered decisionmaking, business models, risk mitigation, and system performance. These developments are generating substantial economic and social benefits.

The world is on the cusp of revolutionizing many sectors through artificial intelligence, but the way AI systems are developed need to be better understood due to the major implications these technologies will have for society as a whole.

Yet the manner in which AI systems unfold has major implications for society as a whole. It matters how policy issues are addressed, ethical conflicts are reconciled, legal realities are resolved, and how much transparency is required in AI and data analytic solutions. 74 Human choices about software development affect the way in which decisions are made and the manner in which they are integrated into organizational routines. Exactly how these processes are executed need to be better understood because they will have substantial impact on the general public soon, and for the foreseeable future. AI may well be a revolution in human affairs, and become the single most influential human innovation in history.

Note: We appreciate the research assistance of Grace Gilberg, Jack Karsten, Hillary Schaub, and Kristjan Tomasson on this project.

The Brookings Institution is a nonprofit organization devoted to independent research and policy solutions. Its mission is to conduct high-quality, independent research and, based on that research, to provide innovative, practical recommendations for policymakers and the public. The conclusions and recommendations of any Brookings publication are solely those of its author(s), and do not reflect the views of the Institution, its management, or its other scholars.

Support for this publication was generously provided by Amazon. Brookings recognizes that the value it provides is in its absolute commitment to quality, independence, and impact. Activities supported by its donors reflect this commitment. 

John R. Allen is a member of the Board of Advisors of Amida Technology and on the Board of Directors of Spark Cognition. Both companies work in fields discussed in this piece.

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• Eric Horvitz, “Reflections on the Status and Future of Artificial Intelligence,” Testimony before the U.S. Senate Subcommittee on Space, Science, and Competitiveness, November 30, 2016, p. 5.
• Jeff Asher and Rob Arthur, “Inside the Algorithm That Tries to Predict Gun Violence in Chicago,” New York Times Upshot , June 13, 2017.
• Caleb Watney, “It’s Time for our Justice System to Embrace Artificial Intelligence,” TechTank (blog), Brookings Institution, July 20, 2017.
• Asher and Arthur, “Inside the Algorithm That Tries to Predict Gun Violence in Chicago.”
• Paul Mozur and Keith Bradsher, “China’s A.I. Advances Help Its Tech Industry, and State Security,” New York Times , December 3, 2017.
• Simon Denyer, “China’s Watchful Eye,” Washington Post , January 7, 2018.
• Cameron Kerry and Jack Karsten, “Gauging Investment in Self-Driving Cars,” Brookings Institution, October 16, 2017.
• Portions of this section are drawn from Darrell M. West, “Driverless Cars in China, Europe, Japan, Korea, and the United States,” Brookings Institution, September 2016.
• Yuming Ge, Xiaoman Liu, Libo Tang, and Darrell M. West, “Smart Transportation in China and the United States,” Center for Technology Innovation, Brookings Institution, December 2017.
• Peter Holley, “Uber Signs Deal to Buy 24,000 Autonomous Vehicles from Volvo,” Washington Post , November 20, 2017.
• Daisuke Wakabayashi, “Self-Driving Uber Car Kills Pedestrian in Arizona, Where Robots Roam,” New York Times , March 19, 2018.
• Kevin Desouza, Rashmi Krishnamurthy, and Gregory Dawson, “Learning from Public Sector Experimentation with Artificial Intelligence,” TechTank (blog), Brookings Institution, June 23, 2017.
• Boyd Cohen, “The 10 Smartest Cities in North America,” Fast Company , November 14, 2013.
• Teena Maddox, “66% of US Cities Are Investing in Smart City Technology,” TechRepublic , November 6, 2017.
• Osonde Osoba and William Welser IV, “The Risks of Artificial Intelligence to Security and the Future of Work” (Santa Monica, Calif.: RAND Corp., December 2017) (www.rand.org/pubs/perspectives/PE237.html).
• Ibid., p. 7.
• Dominic Barton, Jonathan Woetzel, Jeongmin Seong, and Qinzheng Tian, “Artificial Intelligence: Implications for China” (New York: McKinsey Global Institute, April 2017), p. 7.
• Executive Office of the President, “Preparing for the Future of Artificial Intelligence,” October 2016, pp. 30-31.
• Elaine Glusac, “As Airbnb Grows, So Do Claims of Discrimination,” New York Times , June 21, 2016.
• “Joy Buolamwini,” Bloomberg Businessweek , July 3, 2017, p. 80.
• Mark Purdy and Paul Daugherty, “Why Artificial Intelligence is the Future of Growth,” Accenture, 2016.
• Jon Valant, “Integrating Charter Schools and Choice-Based Education Systems,” Brown Center Chalkboard blog, Brookings Institution, June 23, 2017.
• Tucker, “‘A White Mask Worked Better.’”
• Cliff Kuang, “Can A.I. Be Taught to Explain Itself?” New York Times Magazine , November 21, 2017.
• Yale Law School Information Society Project, “Governing Machine Learning,” September 2017.
• Katie Benner, “Airbnb Vows to Fight Racism, But Its Users Can’t Sue to Prompt Fairness,” New York Times , June 19, 2016.
• Executive Office of the President, “Artificial Intelligence, Automation, and the Economy” and “Preparing for the Future of Artificial Intelligence.”
• Nancy Scolar, “Facebook’s Next Project: American Inequality,” Politico , February 19, 2018.
• Darrell M. West, “What Internet Search Data Reveals about Donald Trump’s First Year in Office,” Brookings Institution policy report, January 17, 2018.
• Ian Buck, “Testimony before the House Committee on Oversight and Government Reform Subcommittee on Information Technology,” February 14, 2018.
• Keith Nakasone, “Testimony before the House Committee on Oversight and Government Reform Subcommittee on Information Technology,” March 7, 2018.
• Greg Brockman, “The Dawn of Artificial Intelligence,” Testimony before U.S. Senate Subcommittee on Space, Science, and Competitiveness, November 30, 2016.
• Amir Khosrowshahi, “Testimony before the House Committee on Oversight and Government Reform Subcommittee on Information Technology,” February 14, 2018.
• James Kurose, “Testimony before the House Committee on Oversight and Government Reform Subcommittee on Information Technology,” March 7, 2018.
• Stephen Noonoo, “Teachers Can Now Use IBM’s Watson to Search for Free Lesson Plans,” EdSurge , September 13, 2017.
• Congress.gov, “H.R. 4625 FUTURE of Artificial Intelligence Act of 2017,” December 12, 2017.
• Elizabeth Zima, “Could New York City’s AI Transparency Bill Be a Model for the Country?” Government Technology , January 4, 2018.
• Julia Powles, “New York City’s Bold, Flawed Attempt to Make Algorithms Accountable,” New Yorker , December 20, 2017.
• Sheera Frenkel, “Tech Giants Brace for Europe’s New Data Privacy Rules,” New York Times , January 28, 2018.
• Claire Miller and Kevin O’Brien, “Germany’s Complicated Relationship with Google Street View,” New York Times , April 23, 2013.
• Cade Metz, “Artificial Intelligence is Setting Up the Internet for a Huge Clash with Europe,” Wired , July 11, 2016.
• Eric Siegel, “Predictive Analytics Interview Series: Andrew Burt,” Predictive Analytics Times , June 14, 2017.
• Oren Etzioni, “How to Regulate Artificial Intelligence,” New York Times , September 1, 2017.
• “Ethical Considerations in Artificial Intelligence and Autonomous Systems,” unpublished paper. IEEE Global Initiative, 2018.
• Ritesh Noothigattu, Snehalkumar Gaikwad, Edmond Awad, Sohan Dsouza, Iyad Rahwan, Pradeep Ravikumar, and Ariel Procaccia, “A Voting-Based System for Ethical Decision Making,” Computers and Society , September 20, 2017 (www.media.mit.edu/publications/a-voting-based-system-for-ethical-decision-making/).
• Miles Brundage, et al., “The Malicious Use of Artificial Intelligence,” University of Oxford unpublished paper, February 2018.
• John Markoff, “As Artificial Intelligence Evolves, So Does Its Criminal Potential,” New York Times, October 24, 2016, p. B3.
• Economist , “The Challenger: Technopolitics,” March 17, 2018.
• Douglas Maughan, “Testimony before the House Committee on Oversight and Government Reform Subcommittee on Information Technology,” March 7, 2018.
• Levi Tillemann and Colin McCormick, “Roadmapping a U.S.-German Agenda for Artificial Intelligence Policy,” New American Foundation, March 2017.

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• v.32(4); Oct-Dec 2020

The impact of artificial intelligence on human society and bioethics

Michael cheng-tek tai.

Department of Medical Sociology and Social Work, College of Medicine, Chung Shan Medical University, Taichung, Taiwan

Artificial intelligence (AI), known by some as the industrial revolution (IR) 4.0, is going to change not only the way we do things, how we relate to others, but also what we know about ourselves. This article will first examine what AI is, discuss its impact on industrial, social, and economic changes on humankind in the 21 st century, and then propose a set of principles for AI bioethics. The IR1.0, the IR of the 18 th century, impelled a huge social change without directly complicating human relationships. Modern AI, however, has a tremendous impact on how we do things and also the ways we relate to one another. Facing this challenge, new principles of AI bioethics must be considered and developed to provide guidelines for the AI technology to observe so that the world will be benefited by the progress of this new intelligence.

W HAT IS ARTIFICIAL INTELLIGENCE ?

Artificial intelligence (AI) has many different definitions; some see it as the created technology that allows computers and machines to function intelligently. Some see it as the machine that replaces human labor to work for men a more effective and speedier result. Others see it as “a system” with the ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation [ 1 ].

Despite the different definitions, the common understanding of AI is that it is associated with machines and computers to help humankind solve problems and facilitate working processes. In short, it is an intelligence designed by humans and demonstrated by machines. The term AI is used to describe these functions of human-made tool that emulates the “cognitive” abilities of the natural intelligence of human minds [ 2 ].

Along with the rapid development of cybernetic technology in recent years, AI has been seen almost in all our life circles, and some of that may no longer be regarded as AI because it is so common in daily life that we are much used to it such as optical character recognition or the Siri (speech interpretation and recognition interface) of information searching equipment on computer [ 3 ].

D IFFERENT TYPES OF ARTIFICIAL INTELLIGENCE

From the functions and abilities provided by AI, we can distinguish two different types. The first is weak AI, also known as narrow AI that is designed to perform a narrow task, such as facial recognition or Internet Siri search or self-driving car. Many currently existing systems that claim to use “AI” are likely operating as a weak AI focusing on a narrowly defined specific function. Although this weak AI seems to be helpful to human living, there are still some think weak AI could be dangerous because weak AI could cause disruptions in the electric grid or may damage nuclear power plants when malfunctioned.

The new development of the long-term goal of many researchers is to create strong AI or artificial general intelligence (AGI) which is the speculative intelligence of a machine that has the capacity to understand or learn any intelligent task human being can, thus assisting human to unravel the confronted problem. While narrow AI may outperform humans such as playing chess or solving equations, but its effect is still weak. AGI, however, could outperform humans at nearly every cognitive task.

Strong AI is a different perception of AI that it can be programmed to actually be a human mind, to be intelligent in whatever it is commanded to attempt, even to have perception, beliefs and other cognitive capacities that are normally only ascribed to humans [ 4 ].

In summary, we can see these different functions of AI [ 5 , 6 ]:

• Automation: What makes a system or process to function automatically
• Machine learning and vision: The science of getting a computer to act through deep learning to predict and analyze, and to see through a camera, analog-to-digital conversion and digital signal processing
• Natural language processing: The processing of human language by a computer program, such as spam detection and converting instantly a language to another to help humans communicate
• Robotics: A field of engineering focusing on the design and manufacturing of cyborgs, the so-called machine man. They are used to perform tasks for human's convenience or something too difficult or dangerous for human to perform and can operate without stopping such as in assembly lines
• Self-driving car: Use a combination of computer vision, image recognition amid deep learning to build automated control in a vehicle.

D O HUMAN-BEINGS REALLY NEED ARTIFICIAL INTELLIGENCE ?

Is AI really needed in human society? It depends. If human opts for a faster and effective way to complete their work and to work constantly without taking a break, yes, it is. However if humankind is satisfied with a natural way of living without excessive desires to conquer the order of nature, it is not. History tells us that human is always looking for something faster, easier, more effective, and convenient to finish the task they work on; therefore, the pressure for further development motivates humankind to look for a new and better way of doing things. Humankind as the homo-sapiens discovered that tools could facilitate many hardships for daily livings and through tools they invented, human could complete the work better, faster, smarter and more effectively. The invention to create new things becomes the incentive of human progress. We enjoy a much easier and more leisurely life today all because of the contribution of technology. The human society has been using the tools since the beginning of civilization, and human progress depends on it. The human kind living in the 21 st century did not have to work as hard as their forefathers in previous times because they have new machines to work for them. It is all good and should be all right for these AI but a warning came in early 20 th century as the human-technology kept developing that Aldous Huxley warned in his book Brave New World that human might step into a world in which we are creating a monster or a super human with the development of genetic technology.

Besides, up-to-dated AI is breaking into healthcare industry too by assisting doctors to diagnose, finding the sources of diseases, suggesting various ways of treatment performing surgery and also predicting if the illness is life-threatening [ 7 ]. A recent study by surgeons at the Children's National Medical Center in Washington successfully demonstrated surgery with an autonomous robot. The team supervised the robot to perform soft-tissue surgery, stitch together a pig's bowel, and the robot finished the job better than a human surgeon, the team claimed [ 8 , 9 ]. It demonstrates robotically-assisted surgery can overcome the limitations of pre-existing minimally-invasive surgical procedures and to enhance the capacities of surgeons performing open surgery.

Above all, we see the high-profile examples of AI including autonomous vehicles (such as drones and self-driving cars), medical diagnosis, creating art, playing games (such as Chess or Go), search engines (such as Google search), online assistants (such as Siri), image recognition in photographs, spam filtering, predicting flight delays…etc. All these have made human life much easier and convenient that we are so used to them and take them for granted. AI has become indispensable, although it is not absolutely needed without it our world will be in chaos in many ways today.

T HE IMPACT OF ARTIFICIAL INTELLIGENCE ON HUMAN SOCIETY

Negative impact.

Questions have been asked: With the progressive development of AI, human labor will no longer be needed as everything can be done mechanically. Will humans become lazier and eventually degrade to the stage that we return to our primitive form of being? The process of evolution takes eons to develop, so we will not notice the backsliding of humankind. However how about if the AI becomes so powerful that it can program itself to be in charge and disobey the order given by its master, the humankind?

Let us see the negative impact the AI will have on human society [ 10 , 11 ]:

• A huge social change that disrupts the way we live in the human community will occur. Humankind has to be industrious to make their living, but with the service of AI, we can just program the machine to do a thing for us without even lifting a tool. Human closeness will be gradually diminishing as AI will replace the need for people to meet face to face for idea exchange. AI will stand in between people as the personal gathering will no longer be needed for communication
• Unemployment is the next because many works will be replaced by machinery. Today, many automobile assembly lines have been filled with machineries and robots, forcing traditional workers to lose their jobs. Even in supermarket, the store clerks will not be needed anymore as the digital device can take over human labor
• Wealth inequality will be created as the investors of AI will take up the major share of the earnings. The gap between the rich and the poor will be widened. The so-called “M” shape wealth distribution will be more obvious
• New issues surface not only in a social sense but also in AI itself as the AI being trained and learned how to operate the given task can eventually take off to the stage that human has no control, thus creating un-anticipated problems and consequences. It refers to AI's capacity after being loaded with all needed algorithm may automatically function on its own course ignoring the command given by the human controller
• The human masters who create AI may invent something that is racial bias or egocentrically oriented to harm certain people or things. For instance, the United Nations has voted to limit the spread of nucleus power in fear of its indiscriminative use to destroying humankind or targeting on certain races or region to achieve the goal of domination. AI is possible to target certain race or some programmed objects to accomplish the command of destruction by the programmers, thus creating world disaster.

P OSITIVE IMPACT

There are, however, many positive impacts on humans as well, especially in the field of healthcare. AI gives computers the capacity to learn, reason, and apply logic. Scientists, medical researchers, clinicians, mathematicians, and engineers, when working together, can design an AI that is aimed at medical diagnosis and treatments, thus offering reliable and safe systems of health-care delivery. As health professors and medical researchers endeavor to find new and efficient ways of treating diseases, not only the digital computer can assist in analyzing, robotic systems can also be created to do some delicate medical procedures with precision. Here, we see the contribution of AI to health care [ 7 , 11 ]:

Fast and accurate diagnostics

IBM's Watson computer has been used to diagnose with the fascinating result. Loading the data to the computer will instantly get AI's diagnosis. AI can also provide various ways of treatment for physicians to consider. The procedure is something like this: To load the digital results of physical examination to the computer that will consider all possibilities and automatically diagnose whether or not the patient suffers from some deficiencies and illness and even suggest various kinds of available treatment.

Socially therapeutic robots

Pets are recommended to senior citizens to ease their tension and reduce blood pressure, anxiety, loneliness, and increase social interaction. Now cyborgs have been suggested to accompany those lonely old folks, even to help do some house chores. Therapeutic robots and the socially assistive robot technology help improve the quality of life for seniors and physically challenged [ 12 ].

Reduce errors related to human fatigue

Human error at workforce is inevitable and often costly, the greater the level of fatigue, the higher the risk of errors occurring. Al technology, however, does not suffer from fatigue or emotional distraction. It saves errors and can accomplish the duty faster and more accurately.

Artificial intelligence-based surgical contribution

AI-based surgical procedures have been available for people to choose. Although this AI still needs to be operated by the health professionals, it can complete the work with less damage to the body. The da Vinci surgical system, a robotic technology allowing surgeons to perform minimally invasive procedures, is available in most of the hospitals now. These systems enable a degree of precision and accuracy far greater than the procedures done manually. The less invasive the surgery, the less trauma it will occur and less blood loss, less anxiety of the patients.

Improved radiology

The first computed tomography scanners were introduced in 1971. The first magnetic resonance imaging (MRI) scan of the human body took place in 1977. By the early 2000s, cardiac MRI, body MRI, and fetal imaging, became routine. The search continues for new algorithms to detect specific diseases as well as to analyze the results of scans [ 9 ]. All those are the contribution of the technology of AI.

Virtual presence

The virtual presence technology can enable a distant diagnosis of the diseases. The patient does not have to leave his/her bed but using a remote presence robot, doctors can check the patients without actually being there. Health professionals can move around and interact almost as effectively as if they were present. This allows specialists to assist patients who are unable to travel.

S OME CAUTIONS TO BE REMINDED

Despite all the positive promises that AI provides, human experts, however, are still essential and necessary to design, program, and operate the AI from any unpredictable error from occurring. Beth Kindig, a San Francisco-based technology analyst with more than a decade of experience in analyzing private and public technology companies, published a free newsletter indicating that although AI has a potential promise for better medical diagnosis, human experts are still needed to avoid the misclassification of unknown diseases because AI is not omnipotent to solve all problems for human kinds. There are times when AI meets an impasse, and to carry on its mission, it may just proceed indiscriminately, ending in creating more problems. Thus vigilant watch of AI's function cannot be neglected. This reminder is known as physician-in-the-loop [ 13 ].

The question of an ethical AI consequently was brought up by Elizabeth Gibney in her article published in Nature to caution any bias and possible societal harm [ 14 ]. The Neural Information processing Systems (NeurIPS) conference in Vancouver Canada in 2020 brought up the ethical controversies of the application of AI technology, such as in predictive policing or facial recognition, that due to bias algorithms can result in hurting the vulnerable population [ 14 ]. For instance, the NeurIPS can be programmed to target certain race or decree as the probable suspect of crime or trouble makers.

T HE CHALLENGE OF ARTIFICIAL INTELLIGENCE TO BIOETHICS

Artificial intelligence ethics must be developed.

Bioethics is a discipline that focuses on the relationship among living beings. Bioethics accentuates the good and the right in biospheres and can be categorized into at least three areas, the bioethics in health settings that is the relationship between physicians and patients, the bioethics in social settings that is the relationship among humankind and the bioethics in environmental settings that is the relationship between man and nature including animal ethics, land ethics, ecological ethics…etc. All these are concerned about relationships within and among natural existences.

As AI arises, human has a new challenge in terms of establishing a relationship toward something that is not natural in its own right. Bioethics normally discusses the relationship within natural existences, either humankind or his environment, that are parts of natural phenomena. But now men have to deal with something that is human-made, artificial and unnatural, namely AI. Human has created many things yet never has human had to think of how to ethically relate to his own creation. AI by itself is without feeling or personality. AI engineers have realized the importance of giving the AI ability to discern so that it will avoid any deviated activities causing unintended harm. From this perspective, we understand that AI can have a negative impact on humans and society; thus, a bioethics of AI becomes important to make sure that AI will not take off on its own by deviating from its originally designated purpose.

Stephen Hawking warned early in 2014 that the development of full AI could spell the end of the human race. He said that once humans develop AI, it may take off on its own and redesign itself at an ever-increasing rate [ 15 ]. Humans, who are limited by slow biological evolution, could not compete and would be superseded. In his book Superintelligence, Nick Bostrom gives an argument that AI will pose a threat to humankind. He argues that sufficiently intelligent AI can exhibit convergent behavior such as acquiring resources or protecting itself from being shut down, and it might harm humanity [ 16 ].

The question is–do we have to think of bioethics for the human's own created product that bears no bio-vitality? Can a machine have a mind, consciousness, and mental state in exactly the same sense that human beings do? Can a machine be sentient and thus deserve certain rights? Can a machine intentionally cause harm? Regulations must be contemplated as a bioethical mandate for AI production.

Studies have shown that AI can reflect the very prejudices humans have tried to overcome. As AI becomes “truly ubiquitous,” it has a tremendous potential to positively impact all manner of life, from industry to employment to health care and even security. Addressing the risks associated with the technology, Janosch Delcker, Politico Europe's AI correspondent, said: “I don't think AI will ever be free of bias, at least not as long as we stick to machine learning as we know it today,”…. “What's crucially important, I believe, is to recognize that those biases exist and that policymakers try to mitigate them” [ 17 ]. The High-Level Expert Group on AI of the European Union presented Ethics Guidelines for Trustworthy AI in 2019 that suggested AI systems must be accountable, explainable, and unbiased. Three emphases are given:

• Lawful-respecting all applicable laws and regulations
• Ethical-respecting ethical principles and values
• Robust-being adaptive, reliable, fair, and trustworthy from a technical perspective while taking into account its social environment [ 18 ].

Seven requirements are recommended [ 18 ]:

• AI should not trample on human autonomy. People should not be manipulated or coerced by AI systems, and humans should be able to intervene or oversee every decision that the software makes
• AI should be secure and accurate. It should not be easily compromised by external attacks, and it should be reasonably reliable
• Personal data collected by AI systems should be secure and private. It should not be accessible to just anyone, and it should not be easily stolen
• Data and algorithms used to create an AI system should be accessible, and the decisions made by the software should be “understood and traced by human beings.” In other words, operators should be able to explain the decisions their AI systems make
• Services provided by AI should be available to all, regardless of age, gender, race, or other characteristics. Similarly, systems should not be biased along these lines
• AI systems should be sustainable (i.e., they should be ecologically responsible) and “enhance positive social change”
• AI systems should be auditable and covered by existing protections for corporate whistleblowers. The negative impacts of systems should be acknowledged and reported in advance.

From these guidelines, we can suggest that future AI must be equipped with human sensibility or “AI humanities.” To accomplish this, AI researchers, manufacturers, and all industries must bear in mind that technology is to serve not to manipulate humans and his society. Bostrom and Judkowsky listed responsibility, transparency, auditability, incorruptibility, and predictability [ 19 ] as criteria for the computerized society to think about.

S UGGESTED PRINCIPLES FOR ARTIFICIAL INTELLIGENCE BIOETHICS

Nathan Strout, a reporter at Space and Intelligence System at Easter University, USA, reported just recently that the intelligence community is developing its own AI ethics. The Pentagon made announced in February 2020 that it is in the process of adopting principles for using AI as the guidelines for the department to follow while developing new AI tools and AI-enabled technologies. Ben Huebner, chief of the Office of Director of National Intelligence's Civil Liberties, Privacy, and Transparency Office, said that “We're going to need to ensure that we have transparency and accountability in these structures as we use them. They have to be secure and resilient” [ 20 ]. Two themes have been suggested for the AI community to think more about: Explainability and interpretability. Explainability is the concept of understanding how the analytic works, while interpretability is being able to understand a particular result produced by an analytic [ 20 ].

All the principles suggested by scholars for AI bioethics are well-brought-up. I gather from different bioethical principles in all the related fields of bioethics to suggest four principles here for consideration to guide the future development of the AI technology. We however must bear in mind that the main attention should still be placed on human because AI after all has been designed and manufactured by human. AI proceeds to its work according to its algorithm. AI itself cannot empathize nor have the ability to discern good from evil and may commit mistakes in processes. All the ethical quality of AI depends on the human designers; therefore, it is an AI bioethics and at the same time, a trans-bioethics that abridge human and material worlds. Here are the principles:

• Beneficence: Beneficence means doing good, and here it refers to the purpose and functions of AI should benefit the whole human life, society and universe. Any AI that will perform any destructive work on bio-universe, including all life forms, must be avoided and forbidden. The AI scientists must understand that reason of developing this technology has no other purpose but to benefit human society as a whole not for any individual personal gain. It should be altruistic, not egocentric in nature
• Value-upholding: This refers to AI's congruence to social values, in other words, universal values that govern the order of the natural world must be observed. AI cannot elevate to the height above social and moral norms and must be bias-free. The scientific and technological developments must be for the enhancement of human well-being that is the chief value AI must hold dearly as it progresses further
• Lucidity: AI must be transparent without hiding any secret agenda. It has to be easily comprehensible, detectable, incorruptible, and perceivable. AI technology should be made available for public auditing, testing and review, and subject to accountability standards … In high-stakes settings like diagnosing cancer from radiologic images, an algorithm that can't “explain its work” may pose an unacceptable risk. Thus, explainability and interpretability are absolutely required
• Accountability: AI designers and developers must bear in mind they carry a heavy responsibility on their shoulders of the outcome and impact of AI on whole human society and the universe. They must be accountable for whatever they manufacture and create.

C ONCLUSION

AI is here to stay in our world and we must try to enforce the AI bioethics of beneficence, value upholding, lucidity and accountability. Since AI is without a soul as it is, its bioethics must be transcendental to bridge the shortcoming of AI's inability to empathize. AI is a reality of the world. We must take note of what Joseph Weizenbaum, a pioneer of AI, said that we must not let computers make important decisions for us because AI as a machine will never possess human qualities such as compassion and wisdom to morally discern and judge [ 10 ]. Bioethics is not a matter of calculation but a process of conscientization. Although AI designers can up-load all information, data, and programmed to AI to function as a human being, it is still a machine and a tool. AI will always remain as AI without having authentic human feelings and the capacity to commiserate. Therefore, AI technology must be progressed with extreme caution. As Von der Leyen said in White Paper on AI – A European approach to excellence and trust : “AI must serve people, and therefore, AI must always comply with people's rights…. High-risk AI. That potentially interferes with people's rights has to be tested and certified before it reaches our single market” [ 21 ].

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R EFERENCES

The present and future of AI

Finale doshi-velez on how ai is shaping our lives and how we can shape ai.

Finale Doshi-Velez, the John L. Loeb Professor of Engineering and Applied Sciences. (Photo courtesy of Eliza Grinnell/Harvard SEAS)

How has artificial intelligence changed and shaped our world over the last five years? How will AI continue to impact our lives in the coming years? Those were the questions addressed in the most recent report from the One Hundred Year Study on Artificial Intelligence (AI100), an ongoing project hosted at Stanford University, that will study the status of AI technology and its impacts on the world over the next 100 years.

The 2021 report is the second in a series that will be released every five years until 2116. Titled “Gathering Strength, Gathering Storms,” the report explores the various ways AI is  increasingly touching people’s lives in settings that range from  movie recommendations  and  voice assistants  to  autonomous driving  and  automated medical diagnoses .

Barbara Grosz , the Higgins Research Professor of Natural Sciences at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) is a member of the standing committee overseeing the AI100 project and Finale Doshi-Velez , Gordon McKay Professor of Computer Science, is part of the panel of interdisciplinary researchers who wrote this year’s report. 

We spoke with Doshi-Velez about the report, what it says about the role AI is currently playing in our lives, and how it will change in the future.  

Q: Let's start with a snapshot: What is the current state of AI and its potential?

Doshi-Velez: Some of the biggest changes in the last five years have been how well AIs now perform in large data regimes on specific types of tasks.  We've seen [DeepMind’s] AlphaZero become the best Go player entirely through self-play, and everyday uses of AI such as grammar checks and autocomplete, automatic personal photo organization and search, and speech recognition become commonplace for large numbers of people.  

In terms of potential, I'm most excited about AIs that might augment and assist people.  They can be used to drive insights in drug discovery, help with decision making such as identifying a menu of likely treatment options for patients, and provide basic assistance, such as lane keeping while driving or text-to-speech based on images from a phone for the visually impaired.  In many situations, people and AIs have complementary strengths. I think we're getting closer to unlocking the potential of people and AI teams.

There's a much greater recognition that we should not be waiting for AI tools to become mainstream before making sure they are ethical.

Q: Over the course of 100 years, these reports will tell the story of AI and its evolving role in society. Even though there have only been two reports, what's the story so far?

There's actually a lot of change even in five years.  The first report is fairly rosy.  For example, it mentions how algorithmic risk assessments may mitigate the human biases of judges.  The second has a much more mixed view.  I think this comes from the fact that as AI tools have come into the mainstream — both in higher stakes and everyday settings — we are appropriately much less willing to tolerate flaws, especially discriminatory ones. There's also been questions of information and disinformation control as people get their news, social media, and entertainment via searches and rankings personalized to them. So, there's a much greater recognition that we should not be waiting for AI tools to become mainstream before making sure they are ethical.

Q: What is the responsibility of institutes of higher education in preparing students and the next generation of computer scientists for the future of AI and its impact on society?

First, I'll say that the need to understand the basics of AI and data science starts much earlier than higher education!  Children are being exposed to AIs as soon as they click on videos on YouTube or browse photo albums. They need to understand aspects of AI such as how their actions affect future recommendations.

But for computer science students in college, I think a key thing that future engineers need to realize is when to demand input and how to talk across disciplinary boundaries to get at often difficult-to-quantify notions of safety, equity, fairness, etc.  I'm really excited that Harvard has the Embedded EthiCS program to provide some of this education.  Of course, this is an addition to standard good engineering practices like building robust models, validating them, and so forth, which is all a bit harder with AI.

I think a key thing that future engineers need to realize is when to demand input and how to talk across disciplinary boundaries to get at often difficult-to-quantify notions of safety, equity, fairness, etc. 

Q: Your work focuses on machine learning with applications to healthcare, which is also an area of focus of this report. What is the state of AI in healthcare? 

A lot of AI in healthcare has been on the business end, used for optimizing billing, scheduling surgeries, that sort of thing.  When it comes to AI for better patient care, which is what we usually think about, there are few legal, regulatory, and financial incentives to do so, and many disincentives. Still, there's been slow but steady integration of AI-based tools, often in the form of risk scoring and alert systems.

In the near future, two applications that I'm really excited about are triage in low-resource settings — having AIs do initial reads of pathology slides, for example, if there are not enough pathologists, or get an initial check of whether a mole looks suspicious — and ways in which AIs can help identify promising treatment options for discussion with a clinician team and patient.

Q: Any predictions for the next report?

I'll be keen to see where currently nascent AI regulation initiatives have gotten to. Accountability is such a difficult question in AI,  it's tricky to nurture both innovation and basic protections.  Perhaps the most important innovation will be in approaches for AI accountability.

Topics: AI / Machine Learning , Computer Science

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The profound impact of Artificial Intelligence on society – Exploring the far-reaching implications of AI technology

Artificial intelligence (AI) has revolutionized the way we live and work, and its influence on society continues to grow. This essay explores the impact of AI on various aspects of our lives, including economy, employment, healthcare, and even creativity.

One of the most significant impacts of AI is on the economy. AI-powered systems have the potential to streamline and automate various processes, increasing efficiency and productivity. This can lead to economic growth and increased competitiveness in the global market. However, it also raises concerns about job displacement and income inequality, as AI technologies replace certain job roles.

In the realm of healthcare, AI has already made its mark. From early detection of diseases to personalized treatment plans, AI algorithms have become invaluable in improving patient outcomes. With the ability to analyze vast amounts of medical data, AI systems can identify patterns and make predictions that human doctors may miss. Nevertheless, ethical considerations regarding patient privacy and data security need to be addressed.

Furthermore, AI’s impact on creativity is an area of ongoing exploration. While AI technologies can generate artwork, music, and literature, the question of whether they can truly replicate human creativity remains. Some argue that AI can enhance human creativity by providing new tools and inspiration, while others fear that it may diminish the value of genuine human artistic expression.

In conclusion, the impact of artificial intelligence on society is multifaceted. While it brings economic advancements and improvements in healthcare, it also presents challenges and ethical dilemmas. As AI continues to evolve, it is crucial to strike a balance that maximizes its benefits while minimizing its potential drawbacks.

The Definition of Artificial Intelligence

Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think and learn like humans. It involves the development of computer systems that can perform tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and problem-solving.

AI has a profound impact on society, revolutionizing various industries and sectors. Its disruptive nature has led to significant advancements in the way businesses operate, healthcare is delivered, and everyday tasks are performed. AI technologies have the potential to automate repetitive tasks, analyze vast amounts of data with speed and accuracy, and enhance the efficiency and effectiveness of various processes.

Furthermore, AI has the potential to transform the workforce, leading to changes in the job market. While some fear that AI will replace human workers and result in unemployment, others argue that it will create new job opportunities and improve overall productivity. The societal impact of AI is complex and multifaceted, necessitating careful consideration and management.

In summary , artificial intelligence is the development of computer systems that can mimic human intelligence and perform tasks that traditionally require human thinking. Its impact on society is vast, affecting industries, job markets, and everyday life. Understanding the definition and implications of AI is crucial as we navigate the ever-evolving technological landscape.

The History of Artificial Intelligence

The impact of artificial intelligence on society is a topic that has gained increasing attention in recent years. As technology continues to advance at a rapid pace, the capabilities of artificial intelligence are expanding as well. But how did we get to this point? Let’s take a brief look at the history of artificial intelligence.

The concept of artificial intelligence dates back to ancient times, with the development of mechanical devices that were capable of performing simple calculations. However, it wasn’t until the mid-20th century that the field of AI began to take shape.

In 1956, a group of researchers organized the famous Dartmouth Conference, where the field of AI was officially born. This conference brought together leading experts from various disciplines to explore the possibilities of creating “machines that can think.”

During the following decades, AI research progressed with the development of first-generation computers and the introduction of programming languages. In the 1960s, researchers focused on creating natural language processing systems, while in the 1970s, expert systems became popular.

However, in the 1980s, AI faced a major setback known as the “AI winter.” Funding for AI research significantly declined due to the lack of significant breakthroughs. The field faced criticism and skepticism, and it seemed that the promise of AI might never be realized.

But in the 1990s, AI began to emerge from its winter. The introduction of powerful computers and the availability of massive amounts of data fueled the development of machine learning algorithms. This led to significant advancements in areas such as computer vision, speech recognition, and natural language processing.

Over the past few decades, AI has continued to evolve and impact various aspects of society. From virtual assistants like Siri and Alexa to autonomous vehicles and recommendation systems, artificial intelligence is becoming increasingly integrated into our daily lives.

As we move forward, the impact of artificial intelligence on society is only expected to grow. With ongoing advancements in AI technology, we can expect to see even more significant changes in fields such as healthcare, finance, transportation, and more.

In conclusion, the history of artificial intelligence is one of perseverance and innovation. From its humble beginnings to its current state, AI has come a long way. It has evolved from simple mechanical devices to complex algorithms that can learn and make decisions. The impact of artificial intelligence on society will continue to shape our future, and it is essential to consider both the positive and negative implications as we navigate this technological revolution.

The Advantages of Artificial Intelligence

Artificial intelligence (AI) is a rapidly developing technology that is having a significant impact on society. It has the potential to revolutionize various aspects of our lives, bringing about many advantages that can benefit individuals and communities alike.

1. Increased Efficiency

One of the major advantages of AI is its ability to automate tasks and processes, leading to increased efficiency. AI systems can analyze large amounts of data and perform complex calculations at a speed much faster than humans. This can help businesses optimize their operations, reduce costs, and improve productivity.

2. Enhanced Accuracy

AI technologies can also improve accuracy and precision in various domains. Machine learning algorithms can learn from large datasets and make predictions or decisions with a high level of accuracy. This can be particularly beneficial in fields such as healthcare, where AI can assist doctors in diagnosing diseases, detecting patterns in medical images, and recommending personalized treatments.

Additionally, AI-powered systems can minimize human error in areas where precision is crucial, such as manufacturing and transportation. By automating repetitive tasks and monitoring processes in real-time, AI can help avoid costly mistakes and improve overall quality.

Overall, the advantages of artificial intelligence are numerous and diverse. From increased efficiency to enhanced accuracy, AI has the potential to transform various industries and improve the quality of life for individuals and societies as a whole. It is crucial, however, to continue exploring the ethical implications of AI and ensure that its development is guided by principles that prioritize the well-being and safety of humanity.

The Disadvantages of Artificial Intelligence

While the impact of artificial intelligence on society has been largely positive, it is important to also consider its disadvantages.

1. Job Displacement

One of the biggest concerns regarding artificial intelligence is the potential for job displacement. As machines become more intelligent and capable of performing complex tasks, there is a growing fear that many jobs will become obsolete. This can lead to unemployment and economic instability, as individuals struggle to find work in a society increasingly dominated by artificial intelligence.

2. Ethical Concerns

Another disadvantage of artificial intelligence is the ethical concerns it raises. As artificial intelligence systems become more advanced, there is a need for clear guidelines and regulations to ensure that they are used responsibly. Issues such as privacy, data protection, and algorithmic bias need to be addressed to prevent misuse or unintended consequences.

In conclusion, while artificial intelligence has had a positive impact on society, there are also disadvantages that need to be considered. Job displacement and ethical concerns are just a few of the challenges that need to be addressed as we continue to advance in the field of artificial intelligence.

The Ethical Concerns of Artificial Intelligence

As artificial intelligence continues to impact society in numerous ways, it is important to address the ethical concerns that arise from its use. As AI becomes more commonplace in various industries, including healthcare, finance, and transportation, the potential for unintended consequences and ethical dilemmas increases.

One of the primary ethical concerns of artificial intelligence is the issue of privacy. With the advancements in AI technology, there is a growing ability for machines to collect and analyze vast amounts of personal data. This raises questions about how this data is used, who has access to it, and whether individuals have a right to control and protect their own information.

Another ethical concern is the potential for AI to perpetuate and amplify existing biases and discrimination. AI algorithms are trained on existing data, which can reflect societal biases and prejudices. If these biases are not identified and addressed, AI systems can inadvertently perpetuate unfair practices and discrimination, leading to negative impacts on marginalized communities.

Additionally, the use of AI in decision-making processes raises concerns about accountability and transparency. As AI systems make more complex decisions that affect individuals’ lives, it becomes crucial to understand how these decisions are made. Lack of transparency and accountability can result in a loss of trust in AI systems, especially if they make decisions that have significant consequences.

Furthermore, there is the concern of the impact of AI on employment and the workforce. As AI technology advances, there is the potential for job displacement and the loss of livelihoods. This raises questions about the responsibility of society to provide support and retraining for individuals who are affected by the automation of tasks previously carried out by humans.

Overall, as artificial intelligence continues to evolve and become more integrated into society, it is crucial to actively address the ethical concerns that arise. This involves establishing clear guidelines and regulations to safeguard privacy, address biases, ensure transparency, and mitigate the impact on employment. By addressing these concerns proactively, society can harness the benefits of AI while minimizing its negative impacts.

The Impact of Artificial Intelligence on Jobs

The advancement of artificial intelligence (AI) technology is having a profound impact on society as a whole. One area that is particularly affected by this technological revolution is the job market. The introduction of AI into various industries is changing the way we work and the types of jobs that are available. It is important to understand the implications of this impact on jobs and how it will shape the future of work.

The Rise of Automation

One of the main ways AI impacts jobs is through automation. AI algorithms and machines are increasingly replacing human workers in repetitive and routine tasks. Jobs that involve tasks that can be easily automated, such as data entry or assembly line work, are being taken over by AI-powered technology. This shift towards automation has the potential to lead to job displacement and unemployment for many individuals.

New Opportunities and Skill Requirements

While AI may be replacing certain jobs, it is also creating new opportunities. As industries become more automated, there is a growing demand for workers who are skilled in managing and developing AI technology. Jobs that require expertise in AI programming and data analysis are becoming increasingly important. This means that individuals who possess these skills will have an advantage in the job market, while those without them may struggle to find employment.

Furthermore, AI technology has the potential to transform existing jobs rather than eliminate them entirely. As AI systems become more sophisticated, they can assist human workers in performing tasks more efficiently and accurately. This collaboration between humans and machines can lead to increased productivity and job growth in certain industries.

The Need for Adaptation and Lifelong Learning

The impact of AI on jobs highlights the importance of adaptation and lifelong learning. As technology continues to evolve, workers must be willing to learn new skills and adapt to changing job requirements. The ability to continuously update one’s skills will be crucial in order to remain relevant in the job market. This necessitates a shift towards lifelong learning and a willingness to embrace new technologies.

In conclusion, the impact of artificial intelligence on jobs is significant and multifaceted. While AI technology has the potential to automate certain tasks and lead to job displacement, it also creates new opportunities and changes the nature of existing jobs. The key to navigating this changing job market is adaptation, lifelong learning, and acquiring new skills in AI-related fields. By understanding and adapting to the impact of AI on jobs, society can ensure that the benefits of this technology are maximized while minimizing negative consequences.

The Impact of Artificial Intelligence on Education

Artificial intelligence (AI) is rapidly transforming various aspects of society, and one area where its impact is particularly noteworthy is education. In this essay, we will explore how AI is revolutionizing the educational landscape and the implications it has for both teachers and students.

AI has the potential to greatly enhance the learning experience for students. With intelligent algorithms and personalized learning platforms, students can receive customized instruction tailored to their individual needs and learning styles. This can help to bridge gaps in understanding, improve retention, and ultimately lead to better academic outcomes.

Moreover, AI can serve as a valuable tool for teachers. By automating administrative tasks, such as grading and data analysis, teachers can save time and focus on what they do best: teaching. AI can also provide valuable insights into student performance and progress, allowing teachers to identify areas where additional support may be needed.

However, it is important to recognize that AI is not a substitute for human teachers. While AI can provide personalized instruction and automate certain tasks, it lacks the emotional intelligence and interpersonal skills that are essential for effective teaching. Teachers play a critical role in creating a supportive and nurturing learning environment, and their expertise cannot be replaced by technology.

Another concern is the potential bias and ethical implications associated with AI in education. With algorithms determining the content and delivery of educational materials, there is a risk of reinforcing existing inequalities and perpetuating discriminatory practices. It is crucial to ensure that AI systems are designed and implemented in an ethical and inclusive manner, taking into account issues of fairness and equity.

In conclusion, the impact of artificial intelligence on education is profound. It has the potential to revolutionize the way students learn and teachers teach. However, it is crucial to approach AI in education with caution, being mindful of the limitations and ethical considerations. By harnessing the power of AI while preserving the irreplaceable role of human teachers, we can create a future of education that is truly transformative.

The Impact of Artificial Intelligence on Healthcare

Artificial intelligence (AI) is revolutionizing the healthcare industry, and its impact on society cannot be overstated. Through the use of advanced algorithms and machine learning, AI is transforming various aspects of healthcare, from diagnosis and treatment to drug discovery and patient care.

One of the key areas where AI is making a significant impact is in diagnosing diseases. With the ability to analyze massive amounts of medical data, AI algorithms can now detect patterns and identify potential diseases in patients more accurately and efficiently than ever before. This can lead to early detection and intervention, ultimately saving lives.

AI is also streamlining the drug discovery process, which traditionally has been a time-consuming and costly endeavor. By analyzing vast amounts of data and simulating molecular structures, AI can help researchers identify potential drug candidates more quickly and accurately. This has the potential to accelerate the development of new treatments and improve patient outcomes.

Furthermore, AI is transforming patient care through personalized medicine. By analyzing an individual’s genetic and medical data, AI algorithms can provide personalized treatment plans tailored to the specific needs of each patient. This can lead to more effective treatments, reduced side effects, and improved overall patient satisfaction.

In addition to diagnosis and treatment, AI is also improving healthcare delivery and efficiency. AI-powered chatbots and virtual assistants can now provide patients with personalized medical advice and answer their questions 24/7. This reduces the burden on healthcare providers and allows for more accessible and convenient healthcare services.

However, as with any new technology, there are also challenges and concerns surrounding the use of AI in healthcare. Issues such as data privacy, ethical considerations, and bias in algorithms need to be addressed to ensure that AI is used responsibly and for the benefit of all patients.

In conclusion, the impact of artificial intelligence on healthcare is immense. With advancements in AI, the healthcare industry is poised to revolutionize patient care, diagnosis, and treatment. However, it is crucial to address the ethical and privacy concerns associated with AI to ensure that it is used responsibly and for the greater good of society.

The Impact of Artificial Intelligence on Transportation

Artificial intelligence (AI) has had a significant impact on society in many different areas, and one of the fields that has benefited greatly from AI technology is transportation. With advances in AI, transportation systems have become more efficient, safer, and more environmentally friendly.

Improved Safety

One of the key impacts of AI on transportation is the improved safety of both passengers and drivers. AI technology has enabled the development of autonomous vehicles, which can operate without human intervention. These vehicles use AI algorithms and sensors to navigate roads, avoiding accidents and minimizing collisions. By removing the human element from driving, the risk of human error and accidents caused by fatigue, distraction, or impaired judgment can be significantly reduced.

Efficient Traffic Management

AI has also revolutionized traffic management systems, leading to more efficient transportation networks. Intelligent traffic lights, for example, can use AI algorithms to adjust signal timings based on real-time traffic conditions, optimizing traffic flow and reducing congestion. AI-powered algorithms can analyze large amounts of data from various sources, such as traffic cameras and sensors, to provide accurate predictions and recommendations for traffic management and planning.

Enhanced Logistics and Delivery

AI has significantly impacted the logistics and delivery industry. AI-powered software can optimize route planning for delivery vehicles, taking into account factors such as traffic conditions, weather, and delivery time windows. This improves efficiency and reduces costs by minimizing fuel consumption and maximizing the number of deliveries per trip. Additionally, AI can also assist in package sorting and tracking, enhancing the overall speed and accuracy of the delivery process.

The impact of AI on transportation is continuously evolving, with ongoing research and development leading to even more advanced applications. As AI technology continues to improve, we can expect transportation systems to become even safer, more efficient, and more sustainable.

The Impact of Artificial Intelligence on Communication

Artificial intelligence has had a profound impact on society, affecting various aspects of our lives. One area where its influence can be seen is in communication. The advancements in artificial intelligence have revolutionized the way we communicate with each other.

One of the main impacts of artificial intelligence on communication is the development of chatbots. These computer programs are designed to simulate human conversation and interact with users through messaging systems. Chatbots have become increasingly popular in customer service, providing quick and automated responses to customer inquiries. They are available 24/7, ensuring constant support and improving customer satisfaction.

Moreover, artificial intelligence has contributed to the improvement of language translation. Translation tools powered by AI technology have made it easier for people to communicate across languages and cultures. These tools can instantly translate text and speech, enabling effective communication in real-time. They have bridged the language barrier and facilitated global collaboration and understanding.

Another impact of artificial intelligence on communication is the emergence of voice assistants. These virtual assistants, such as Siri and Alexa, use natural language processing and machine learning algorithms to understand and respond to user commands. Voice assistants have become integral parts of our daily lives, helping us perform various tasks, from setting reminders to controlling smart home devices. They have transformed the way we interact with technology and simplified communication with devices.

Artificial intelligence has also played a role in enhancing communication through personalized recommendations. Many online platforms, such as social media and streaming services, utilize AI algorithms to analyze user preferences and provide personalized content suggestions. This has improved user engagement and facilitated communication by connecting users with relevant information and like-minded individuals.

In conclusion, artificial intelligence has had a significant impact on communication. From chatbots and language translation to voice assistants and personalized recommendations, AI technology has revolutionized the way we interact and communicate with each other. It has made communication faster, more efficient, and more accessible, bringing people closer together in an increasingly interconnected world.

The Impact of Artificial Intelligence on Privacy

Artificial intelligence (AI) has had a profound impact on various aspects of our society, and one area that is greatly affected is privacy. With the advancements in AI technology, there are growing concerns about how it can impact our privacy rights.

AI-powered systems have the ability to collect and analyze vast amounts of personal data, ranging from social media activity to online transactions. This presents significant challenges when it comes to protecting our privacy. For instance, AI algorithms can mine and analyze our personal data to generate targeted advertisements, which can result in intrusion into our personal lives.

Additionally, AI systems can be used to monitor and track individuals’ online activities, which raises concerns about surveillance and the erosion of privacy. With AI’s ability to process and interpret large volumes of data, it becomes easier for organizations and governments to gather information about individuals without their knowledge or consent.

Furthermore, AI algorithms can make predictions about individuals’ behaviors and preferences based on their data. While this can be beneficial in some cases, such as providing tailored recommendations, it also raises concerns about the potential misuse of this information. For example, insurance companies could use AI algorithms to assess an individual’s health risks based on their online activity, resulting in potential discrimination or exclusion.

It is crucial to strike a balance between the benefits of AI technology and protecting individuals’ right to privacy. Steps must be taken to ensure that AI systems are designed and implemented in a way that respects and safeguards privacy. This can include implementing strict regulations and guidelines for data collection, storage, and usage.

In conclusion, the impact of artificial intelligence on privacy cannot be ignored. As AI continues to advance, it is essential to address the potential risks and challenges it poses to privacy rights. By taking proactive measures and promoting ethical practices, we can harness the benefits of AI while ensuring that individuals’ privacy is respected and protected.

The Impact of Artificial Intelligence on Security

Artificial intelligence (AI) has had a profound impact on society, and one area where its influence is particularly noticeable is in the field of security. The development and implementation of AI technology have revolutionized the way we approach and manage security threats.

AI-powered security systems have proven to be highly effective in detecting and preventing various types of threats, such as cyber attacks, terrorism, and physical breaches. These systems are capable of analyzing vast amounts of data in real-time, identifying patterns, and recognizing anomalies that may indicate a security risk.

One major advantage of AI in security is its ability to continuously adapt and learn. AI algorithms can quickly analyze new data and update their knowledge base, improving their ability to detect and respond to emerging threats. This dynamic nature allows AI-powered security systems to stay ahead of potential attackers and respond to evolving security challenges.

Furthermore, AI can enhance the efficiency and accuracy of security operations. By automating certain tasks, such as video surveillance monitoring and threat analysis, AI technology can significantly reduce the workload for human security personnel. This frees up resources and enables security teams to focus on more critical tasks, such as responding to incidents and developing proactive security strategies.

However, the increasing reliance on AI in security also raises concerns. The use of AI technology can potentially lead to privacy breaches and unethical surveillance practices. It is crucial to strike a balance between utilizing AI for security purposes and respecting individual privacy rights.

In conclusion, the impact of artificial intelligence on security has been significant. AI-powered systems have revolutionized the way we detect and prevent security threats, enhancing efficiency and accuracy in security operations. However, ethical concerns need to be addressed to ensure that AI is used responsibly and in a way that respects individual rights and privacy.

The Impact of Artificial Intelligence on Economy

Artificial intelligence (AI) is revolutionizing the economy in various ways. Its impact is prevalent across different sectors, leading to both opportunities and challenges.

One of the key benefits of AI in the economy is increased productivity. AI-powered systems and algorithms can perform tasks at a much faster pace and with a higher level of accuracy compared to humans. This efficiency can lead to significant cost savings for businesses and result in increased output and profits.

Moreover, AI has the potential to create new job opportunities. While some jobs may be replaced by automation, AI also leads to the creation of new roles that require specialized skills in managing and maintaining AI systems. This can contribute to economic growth and provide employment opportunities for individuals with the necessary technical expertise.

The impact of AI on the economy is not limited to individual businesses or sectors. It has the potential to transform entire industries. For example, AI-powered technologies can optimize supply chain operations, enhance customer experience, and improve decision-making processes. These advancements can lead to increased competitiveness, improved efficiency, and overall economic growth.

However, the widespread implementation of AI also brings challenges. The displacement of jobs due to automation can result in unemployment and income inequality. It is crucial for policymakers to address these issues and ensure that the benefits of AI are distributed equitably across society.

Additionally, the ethical implications of AI in the economy must be considered. As AI systems continue to advance, it raises questions about privacy, data security, and algorithmic bias. Safeguards and regulations need to be in place to protect individuals’ rights and prevent any potential harm caused by AI applications.

In conclusion, the impact of artificial intelligence on the economy is significant. It offers opportunities for increased productivity, job creation, and industry transformation. However, it also poses challenges such as job displacement and ethical concerns. To fully harness the potential of AI in the economy, policymakers and stakeholders must work together to address these challenges and ensure a balanced and inclusive approach to its implementation.

The Impact of Artificial Intelligence on Entertainment

Artificial intelligence is revolutionizing the entertainment industry, transforming the way we consume and experience various forms of media. With its ability to analyze massive amounts of data, AI has the potential to enhance entertainment in numerous ways.

One area where AI is making a significant impact is in content creation. AI algorithms can generate music, art, and even scripts for movies and TV shows. By analyzing patterns and trends in existing content, AI can create new and original pieces that appeal to different audiences. This not only increases the diversity of entertainment options but also reduces the time and effort required for human creators.

AI also plays a crucial role in enhancing the user experience in the entertainment industry. For example, AI-powered recommendation engines can suggest relevant movies, TV shows, or songs based on individual preferences and viewing habits. This personalized approach ensures that users discover content that aligns with their interests, leading to a more enjoyable and engaging entertainment experience.

In the gaming industry, AI is transforming the way games are developed and played. AI algorithms can create lifelike characters and virtual worlds, providing players with immersive and realistic experiences. Additionally, AI-powered game assistants can adapt to the player’s skill level and offer personalized guidance, making games more accessible and enjoyable for players of all abilities.

Furthermore, AI is revolutionizing the way we consume live events, such as sports or concerts. AI-powered cameras and sensors can capture and analyze data in real-time, providing enhanced viewing experiences for spectators. This includes features like instant replays, personalized camera angles, and in-depth statistics. AI can also generate virtual crowds or even simulate the experience of attending a live event, bringing the excitement of the event to a global audience.

The impact of artificial intelligence on the entertainment industry is undeniable. It is transforming content creation, enhancing the user experience, and revolutionizing the way we consume various forms of media. As AI continues to advance, we can expect even more innovative and immersive entertainment experiences that cater to individual preferences and push the boundaries of creativity.

The Impact of Artificial Intelligence on Human Interaction

In today’s modern world, the rise of artificial intelligence (AI) has had a profound impact on many aspects of society, including human interaction. AI technology has revolutionized the way we communicate and interact with one another, both online and offline.

One of the most noticeable impacts of AI on human interaction is in the realm of communication. AI-powered chatbots and virtual assistants have become increasingly common, allowing people to interact with machines in a more natural and intuitive way. Whether it’s using voice commands to control smart home devices or chatting with a virtual assistant to get information, AI has made it easier to communicate with technology.

AI has also had a significant impact on social media and online communication platforms. Social media algorithms use AI to analyze user data and tailor content to individual preferences, which can shape the way we interact with each other online. This can lead to both positive and negative effects, as AI algorithms may reinforce existing beliefs and create echo chambers, but they can also expose us to new ideas and perspectives.

Furthermore, AI technology has the potential to enhance human interaction by augmenting our capabilities. For example, AI-powered translation tools can break down language barriers and facilitate communication between people who speak different languages. This can foster cross-cultural understanding and enable collaboration on a global scale.

On the other hand, there are concerns about the potential negative impact of AI on human interaction. Some argue that the increasing reliance on AI technology for communication could lead to a decline in human social skills. As people become more accustomed to interacting with machines, they may struggle to engage in authentic face-to-face interactions.

Despite these concerns, it is clear that AI has had a profound impact on human interaction. From enhancing communication to breaking down language barriers, AI technology has transformed the way we interact with one another. It is crucial to continue monitoring and studying the impact of AI on human interaction to ensure we strike a balance between technological advancement and preserving our social connections.

The Role of Artificial Intelligence in Scientific Research

Artificial intelligence (AI) has had a significant impact on society in various fields, and one area where it has shown great promise is scientific research. The use of AI in scientific research has revolutionized the way experiments are conducted, data is analyzed, and conclusions are drawn.

Improving Experimental Design and Data Collection

One of the key contributions of AI in scientific research is its ability to improve experimental design and data collection. By utilizing machine learning algorithms, AI systems can analyze massive amounts of data and identify patterns, allowing researchers to optimize their experimental approaches and make more informed decisions. This not only saves time and resources but also increases the accuracy and reliability of scientific findings.

Enhancing Data Analysis and Interpretation

Another crucial role of AI in scientific research is its ability to enhance data analysis and interpretation. Traditional data analysis methods can be time-consuming and subjective, leading to potential biases. However, AI systems can process vast amounts of data quickly and objectively, revealing hidden relationships, trends, and insights that may be missed by human researchers. This enables scientists to extract meaningful information from complex datasets, leading to more accurate and comprehensive conclusions.

While AI has significant potential in scientific research, it also presents challenges and ethical considerations that need to be addressed. Privacy and security concerns, biases in AI algorithms, ethical implications of AI decision-making, and the impact on human researchers’ roles are some of the critical issues that require scrutiny.

In conclusion, the role of artificial intelligence in scientific research is undeniable. AI has the potential to revolutionize how experiments are designed, data is analyzed, and conclusions are drawn. By improving experimental design and data collection, enhancing data analysis and interpretation, and accelerating scientific discovery, AI can significantly contribute to the advancement of scientific knowledge and its impact on society as a whole.

The Role of Artificial Intelligence in Space Exploration

Artificial intelligence (AI) has had a significant impact on various fields and industries, and space exploration is no exception. With its ability to analyze vast amounts of data and make decisions quickly, AI has revolutionized the way we explore space and gather information about the universe.

One of the primary roles of artificial intelligence in space exploration is in the analysis of data collected by space probes and telescopes. These devices capture enormous amounts of data that can often be overwhelming for human scientists to process. AI algorithms can sift through this data, identifying patterns, and extracting valuable insights that humans may not have noticed.

Additionally, AI plays a crucial role in autonomous navigation and spacecraft control. Spacecraft can be sent to explore distant planets and moons in our solar system, and AI-powered systems can ensure their safe and efficient navigation through unknown terrain. AI algorithms can analyze data from onboard sensors and make real-time decisions to avoid obstacles and hazards.

Benefits of AI in space exploration

• Efficiency: AI systems can process vast amounts of data much faster than humans, allowing for quicker analysis and decision-making.
• Exploration of inhospitable environments: AI-powered robots can be sent to explore extreme environments, such as the surface of Mars or the icy moons of Jupiter, where it would be challenging for humans to survive.
• Cost reduction: By using AI to automate certain tasks, space exploration missions can become more cost-effective and efficient.

The impact of artificial intelligence on space exploration is still in its early stages, but its potential is vast. As AI technology continues to advance, we can expect to see even more significant contributions to our understanding of the universe and our ability to explore it.

The Role of Artificial Intelligence in Environmental Conservation

Artificial intelligence (AI) has the potential to revolutionize various aspects of society, and environmental conservation is no exception. With the growing concern about climate change and the need to preserve the planet’s resources, AI can play a crucial role in helping us address these challenges.

Monitoring and Predicting Environmental Changes

One of the key benefits of AI in environmental conservation is its ability to monitor and predict environmental changes. Through the use of sensors and data analysis, AI systems can gather and analyze vast amounts of information about the environment, including temperature, air quality, and water levels.

This data can then be used to identify patterns and trends, allowing scientists to make predictions about future changes. For example, AI can help predict the spread of wildfires or the impact of deforestation in certain areas. By understanding these threats in advance, we can take proactive measures to protect our natural resources.

Optimizing Resource Management

Another important role of AI in environmental conservation is optimizing resource management. By using AI algorithms, we can efficiently allocate resources such as energy, water, and waste management.

AI can analyze data from various sources, such as smart meters and sensors, to understand patterns of resource usage. This information can then be used to develop strategies for more sustainable resource management, reducing waste and improving efficiency.

For example, AI can help optimize energy consumption in buildings by analyzing data from smart thermostats and occupancy sensors. It can identify usage patterns and make adjustments to reduce energy waste, saving both money and environmental resources.

Supporting Conservation Efforts

AI can also support conservation efforts through various applications. One example is the use of AI-powered drones and satellite imagery to monitor and protect endangered species.

By analyzing images and data collected by these technologies, AI algorithms can identify and track animals, detect illegal activities such as poaching, and even help with habitat restoration. This technology can greatly enhance the effectiveness and efficiency of conservation efforts, allowing us to better protect our biodiversity.

In conclusion, artificial intelligence has a significant role to play in environmental conservation. From monitoring and predicting environmental changes to optimizing resource management and supporting conservation efforts, AI can provide valuable insights and help us make more informed decisions. By harnessing the power of AI, we can work towards a more sustainable and environmentally conscious society.

The Role of Artificial Intelligence in Manufacturing

Artificial intelligence (AI) has had a profound impact on society in various fields, and manufacturing is no exception. In this essay, we will explore the role of AI in manufacturing and how it has revolutionized the industry.

AI has transformed the manufacturing process by introducing automation and machine learning techniques. With AI, machines can perform tasks that were previously done by humans, leading to increased efficiency and productivity. This has allowed manufacturers to streamline their operations and produce goods at a faster rate.

One of the key benefits of AI in manufacturing is its ability to analyze large amounts of data. Through machine learning algorithms, AI systems can collect and process data from various sources, such as sensors and machines, to identify patterns and make informed decisions. This allows manufacturers to optimize their production processes and minimize errors.

Furthermore, AI can improve product quality and reduce defects. By analyzing data in real-time, AI systems can detect anomalies and deviations from the norm, allowing manufacturers to identify and address issues before they escalate. This not only saves time and costs but also ensures that consumers receive high-quality products.

Additionally, AI has enabled the development of predictive maintenance systems. By analyzing data from machines and equipment, AI can anticipate and prevent failures before they occur. This proactive approach minimizes downtime, reduces maintenance costs, and extends the lifespan of machinery.

Overall, the role of AI in manufacturing is transformative. It empowers manufacturers to optimize their processes, improve product quality, and reduce costs. However, it is important to note that AI is not a replacement for humans in the manufacturing industry. Instead, it complements human skills and expertise, allowing workers to focus on more complex tasks while AI handles repetitive and mundane tasks.

In conclusion, artificial intelligence has had a significant impact on the manufacturing industry. It has revolutionized processes, improved product quality, and increased productivity. As AI continues to advance, we can expect even more transformative changes in the manufacturing sector.

The Role of Artificial Intelligence in Agriculture

Artificial intelligence has had a profound impact on society in various fields, and agriculture is no exception. With the advancements in technology, AI has the potential to revolutionize the agricultural industry, making it more efficient, sustainable, and productive.

One of the key areas where AI can play a significant role in agriculture is in crop management. AI-powered systems can analyze vast amounts of data, such as weather patterns, soil conditions, and crop health, to provide farmers with valuable insights. This allows farmers to make more informed decisions on irrigation, fertilization, and pest control, leading to optimal crop yields and reduced resource waste.

Moreover, AI can also aid in the early detection and prevention of crop diseases. By using machine learning algorithms, AI systems can identify patterns and anomalies in plant health, indicating the presence of diseases or pests. This enables farmers to take timely action, prevent the spread of diseases, and minimize crop losses.

Another area where AI can contribute to agriculture is in the realm of precision farming. By combining AI with other technologies like drones and sensors, farmers can gather precise and real-time data about their crops and fields. This data can then be used to create detailed maps, monitor crop growth, and optimize resource allocation. Whether it’s optimizing water usage or determining the ideal time for harvesting, AI can help farmers make data-driven decisions that maximize productivity while minimizing environmental impact.

Furthermore, AI can enhance livestock management. With AI-powered systems, farmers can monitor the health and behavior of their livestock, detect diseases or anomalies, and provide personalized care. This not only improves animal welfare but also increases the efficiency of livestock production.

In conclusion, artificial intelligence has a crucial role to play in the agricultural sector. From crop management to livestock monitoring, AI can bring numerous benefits to farmers, leading to increased productivity, sustainability, and overall growth. As AI continues to advance, we can expect further innovations and improvements in the integration of AI in agriculture, shaping the future of food production.

The Role of Artificial Intelligence in Finance

Artificial intelligence (AI) has had a significant impact on society, revolutionizing various industries, and finance is no exception. In this essay, we will explore the role of AI in the financial sector and its implications.

The use of AI has transformed numerous aspects of finance, from trading and investment to risk management and fraud detection. One of the key benefits of AI in finance is its ability to process vast amounts of data in real-time. This enables more accurate predictions and informed decision-making, giving financial institutions a competitive edge.

AI-powered algorithms have become vital tools for traders and investors. These algorithms analyze market trends, historical data, and other factors to identify patterns and make investment recommendations. By leveraging AI, financial professionals can make more informed decisions and optimize their portfolios.

Furthermore, AI plays a crucial role in risk management. Traditional risk models often fall short in assessing complex and evolving risks, making it challenging to mitigate them effectively. AI, with its machine learning capabilities, can enhance risk assessment by analyzing a wide range of variables and identifying potential threats. This helps financial institutions proactively manage risks and minimize losses.

Another area where AI has made significant strides in finance is fraud detection. With the increasing sophistication of fraudulent activities, traditional rule-based systems struggle to keep up. AI, on the other hand, can detect anomalies and unusual patterns by leveraging machine learning algorithms that constantly learn and adapt. This enables faster and more accurate detection of fraudulent transactions, protecting both financial institutions and their customers.

In conclusion, AI has had a profound impact on the finance industry and has revolutionized various aspects of it. The ability to process large amounts of data, make informed decisions, and detect risks and frauds more effectively has made AI an invaluable tool. As technology continues to advance, we can expect AI to play an even greater role in shaping the future of finance.

The Role of Artificial Intelligence in Customer Service

Artificial intelligence has had a profound impact on various industries, and one area where its influence is increasingly being felt is customer service. AI technology is transforming how businesses interact with their customers, providing enhanced communication and support.

One of the main benefits of AI in customer service is its ability to provide instant and personalized responses to customer inquiries. Through the use of chatbots and virtual assistants, businesses can now offer round-the-clock support, ensuring that customers receive the assistance they need, no matter the time of day.

Furthermore, AI-powered customer service can analyze vast amounts of data to gain insights into customer preferences and behavior. This information can then be used to tailor interactions and improve customer experiences. By understanding customer needs better, businesses can provide more relevant and targeted solutions, leading to increased customer satisfaction and loyalty.

Another crucial role of AI in customer service is its ability to automate repetitive tasks and processes. AI-powered systems can handle routine tasks such as order tracking, appointment scheduling, and basic troubleshooting, freeing up human agents to focus on more complex issues. This results in increased efficiency and productivity, as well as faster response times.

However, it’s important to note that AI should not replace human interaction entirely. While AI can handle routine tasks effectively, there are situations where human empathy and judgment are essential. Building a balance between AI and human involvement is crucial to ensure the best possible customer service experience.

In conclusion, artificial intelligence is revolutionizing customer service by providing instant and personalized support, analyzing customer data for improved experiences, and automating repetitive tasks. While AI offers numerous benefits, it is vital to strike a balance between AI and human interaction to deliver exceptional customer service in the digital age.

The Role of Artificial Intelligence in Gaming

Gaming has been greatly impacted by the advancements in artificial intelligence (AI). AI has revolutionized the way games are created, played, and experienced by both developers and players.

One of the key roles that AI plays in gaming is in creating realistic and challenging virtual opponents. AI algorithms can be programmed to assess player actions and adjust the difficulty level accordingly. This allows for a more immersive and engaging gaming experience, as players can compete against opponents that adapt to their skills and strategies.

Moreover, AI is also used in game design to create intelligent non-player characters (NPCs) that can interact with players in a more natural and realistic manner. These NPCs can simulate human-like behavior and responses, making the game world feel more alive and dynamic.

Another important role of AI in gaming is in improving game mechanics and gameplay. AI algorithms can analyze player data and preferences to provide personalized recommendations and suggestions. This helps players discover new games, unlock achievements, and improve their overall gaming experience.

Furthermore, AI has also been used in game testing and bug detection. AI algorithms can simulate various scenarios and interactions to identify potential glitches and bugs. This improves the overall quality and stability of games before their release.

In conclusion, artificial intelligence has had a profound impact on the gaming industry. It has enhanced the realism, challenge, and overall experience of games. The role of AI in gaming is ever-evolving, and it will continue to shape the future of the gaming industry.

The Future of Artificial Intelligence

Artificial intelligence (AI) has already made a significant impact on society, and its role is only expected to grow in the future. As advancements in technology continue to push boundaries, the potential applications of AI are expanding, potentially transforming various industries and aspects of our daily lives.

One of the most prominent areas where AI is expected to make a difference is in autonomous vehicles. Self-driving cars have already become a reality, and AI is set to play a crucial role in improving their capabilities further. With AI-powered sensors and algorithms, autonomous vehicles can navigate complex road conditions, reduce traffic congestion, and even enhance road safety.

Another domain that is likely to benefit from AI is healthcare. Intelligent machines can analyze vast amounts of medical data and assist doctors in making accurate diagnoses. This can lead to faster identification of diseases, more effective treatment plans, and ultimately, better patient outcomes. AI can also aid in the development of new drugs and therapies by analyzing genetic information and identifying potential targets for treatment.

In addition to healthcare and transportation, AI has the potential to revolutionize sectors such as finance, manufacturing, and agriculture. AI algorithms can analyze market data, identify trends, and make accurate predictions, enabling financial institutions to make informed investment decisions. In manufacturing, AI-powered robots can perform repetitive tasks with precision and efficiency, improving productivity and reducing costs. AI can also optimize crop production by analyzing variables such as weather conditions, soil quality, and crop health, leading to increased yields and more sustainable farming practices.

However, with the increasing integration of AI into various aspects of society, ethical considerations become crucial. As AI becomes more advanced and autonomous, questions arise about the implications of AI decision-making processes and potential biases. It is important to ensure that AI systems are designed and regulated in a way that prioritizes fairness, transparency, and accountability.

In conclusion, the future of artificial intelligence holds immense potential for transforming society in numerous ways. From autonomous vehicles and healthcare to finance and agriculture, AI is poised to revolutionize various sectors and improve our lives. However, it is essential to address ethical concerns and ensure responsible development and deployment of AI technology to maximize its positive impact on society.

The Potential Risks of Artificial Intelligence

As the impact of artificial intelligence on society continues to grow, it is important to consider the potential risks associated with this rapidly advancing technology. While intelligence can be a powerful tool for improving society, artificial intelligence poses unique challenges and dangers that must be addressed.

Unemployment and Job Displacement

One of the major concerns surrounding artificial intelligence is the potential for widespread unemployment and job displacement. As AI technology advances, machines and algorithms are becoming increasingly capable of performing tasks that were previously done by humans. This could lead to significant job losses across various industries, particularly those that rely heavily on manual labor or repetitive tasks.

Additionally, as AI systems become more sophisticated, there is a possibility that they could replace jobs that require higher levels of skill and expertise. This could result in a significant shift in the job market and create challenges for workers who are unable to adapt to these changes.

Ethical Concerns

Another potential risk of artificial intelligence is the ethical concerns that arise from its use. AI systems are designed to make decisions and take actions based on data and algorithms, but they may not always make ethical choices. This raises questions about the impact of AI on issues such as privacy, bias, and discrimination.

For example, AI algorithms may inadvertently discriminate against certain groups of people if the data used to train them is biased. This could lead to unfair outcomes in areas such as hiring, lending, and law enforcement. It is essential to address these ethical concerns and ensure that AI systems are developed and used in a responsible and equitable manner.

In conclusion, while artificial intelligence has the potential to greatly benefit society, it is important to carefully consider and address the potential risks associated with its use. Unemployment and job displacement, as well as ethical concerns, are significant challenges that must be navigated to ensure the responsible and equitable development of AI.

The Importance of Ethical Guidelines for Artificial Intelligence

As artificial intelligence (AI) continues to advance at an unprecedented pace, its impact on society becomes increasingly profound. AI has the potential to transform various industries, improve efficiency, and enhance our overall quality of life. However, with this power comes great responsibility. It is crucial to establish ethical guidelines to ensure that AI is developed and deployed in a responsible and beneficial manner.

Ethics in AI Development

Ethics play a vital role in the development of AI technology. It is essential for developers to consider the potential impact that their creations may have on society. This involves addressing questions of privacy, security, and bias. AI systems should be designed to respect fundamental human rights and ensure that they do not discriminate against certain groups of people. By setting ethical standards, we can prevent the misuse and abuse of AI technology.

The Impact on Society

Without ethical guidelines, artificial intelligence can have unintended consequences on society. For example, if AI algorithms are biased, they may perpetuate social inequalities or reinforce stereotypes. Additionally, AI systems that invade privacy or compromise security can erode trust in technology, hindering its adoption and acceptance by the public. Therefore, by implementing ethical guidelines, we can help safeguard against these negative societal impacts.

The Risks of AI without Ethical Guidelines

Artificial intelligence has the potential to revolutionize society, but it also carries risks. Without ethical guidelines in place, AI can be misused for nefarious purposes, such as surveillance and manipulation. It is crucial to establish clear boundaries and regulations to ensure that AI is used for the benefit of humanity and not to harm individuals or society as a whole.

In conclusion , the importance of ethical guidelines for artificial intelligence cannot be overstated. These guidelines serve as a compass to steer the development and deployment of AI technology in the right direction. By considering the potential impact on society and setting ethical standards, we can harness the power of AI for the betterment of humanity and create a future that is both technologically advanced and ethically responsible.

The Need for Regulation and Governance of Artificial Intelligence

The rapid development of artificial intelligence (AI) has had a profound impact on society. With the increasing deployment of intelligent systems in various domains, it is essential to establish effective regulations and governance mechanisms to ensure that AI is used responsibly and ethically.

Safeguarding Privacy and Data Security

One of the key concerns with the growing use of AI is the potential invasion of privacy and compromise of data security. Intelligent systems are capable of analyzing vast amounts of personal data, raising concerns about the misuse and unauthorized access to sensitive information. To address this, there is a need for regulations that enforce stringent data protection measures and ensure transparency in AI algorithms and data usage.

Ethical Decision-Making and Bias Mitigation

AI systems are designed to make autonomous decisions based on data and algorithms. However, the biases embedded in these systems can result in discriminatory outcomes. Regulations must be put in place to ensure that AI systems are developed and trained in a way that mitigates bias and promotes fair and ethical decision-making. This includes diverse representation in the development of AI technologies and the establishment of clear guidelines on what is considered acceptable behavior for AI systems.

Accountability and Liability

As AI systems become increasingly autonomous, it becomes crucial to determine who should be held accountable in the event of a malfunction or failure. Clear regulations need to be established to define liability in AI-related incidents and ensure that there are mechanisms in place to address any potential harm caused by AI systems. This includes the establishment of standards for testing and certification of AI systems to ensure their reliability and safety.

In conclusion, the impact of artificial intelligence on society necessitates the establishment of regulations and governance mechanisms. By addressing concerns related to privacy, bias, and accountability, we can harness the full potential of AI while ensuring that it benefits society as a whole.

The Role of Artificial Intelligence in Shaping Society’s Future

Artificial intelligence (AI) has had a profound impact on society, and its role in shaping the future cannot be understated. As technology continues to advance at an unprecedented rate, AI is becoming increasingly integrated into various aspects of our lives, from healthcare to transportation to entertainment.

One of the key impacts of AI is its ability to automate tasks that were once performed by humans, enabling us to save time and resources. For example, AI-powered chatbots have revolutionized customer service by providing prompt and efficient responses to inquiries, reducing the need for human intervention. In the healthcare industry, AI algorithms are being developed to assist doctors in diagnosing diseases and recommending treatment options, improving both accuracy and speed.

Furthermore, AI has the potential to address complex societal challenges. For instance, in the field of environmental sustainability, AI technologies can be used to optimize energy consumption, reduce waste, and develop renewable energy sources. By analyzing large amounts of data and identifying patterns, AI can help us make more informed decisions and take proactive measures to mitigate the impact of climate change.

In addition, AI has the ability to enhance our educational systems. Intelligent tutoring systems can adapt to individual learning styles and provide personalized instruction, improving student engagement and performance. AI-powered language translation tools have also facilitated global communication, breaking down language barriers and fostering cross-cultural understanding.

However, it is important to recognize that AI is not without its challenges. There are concerns regarding privacy and security, as AI relies heavily on data collection and analysis. Ethical considerations must also be taken into account, as AI systems can perpetuate biases and discrimination if not properly designed and monitored.

In conclusion, artificial intelligence plays a significant role in shaping society’s future. Its impact can be seen in various fields, from automation to sustainability to education. While there are challenges that need to be addressed, AI has the potential to revolutionize our lives and create a more efficient and equitable society.

Questions and answers

What is the impact of artificial intelligence on society.

The impact of artificial intelligence on society is significant and far-reaching. It is transforming various sectors, including healthcare, education, finance, and transportation.

How is artificial intelligence revolutionizing healthcare?

Artificial intelligence in healthcare is revolutionizing the way diseases are diagnosed and treated. It is helping doctors in making accurate diagnoses, predicting outcomes, and assisting in surgeries.

What are the ethical concerns surrounding artificial intelligence?

There are several ethical concerns surrounding artificial intelligence, such as the potential loss of jobs, bias in algorithms, invasion of privacy, and the possibility of autonomous weapons.

How can artificial intelligence improve productivity in the workplace?

Artificial intelligence can improve productivity in the workplace by automating repetitive tasks, analyzing large amounts of data quickly and accurately, and providing personalized recommendations and insights.

What are the potential risks of artificial intelligence?

The potential risks of artificial intelligence include job displacement, widening economic inequalities, security threats, loss of human control, and the potential for AI systems to be hacked or manipulated.

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The future of AI’s impact on society

As artificial intelligence continues its rapid evolution, what influence do humans have?

• Joanna J. Bryson

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The past decade, and particularly the past few years, has been transformative for artificial intelligence, not so much in terms of what we can do with this technology as what we are doing with it. Some place the advent of this era to 2007, with the introduction of smartphones. At its most essential, intelligence is just intelligence, whether artifact or animal. It is a form of computation, and as such, a transformation of information. The cornucopia of deeply personal information that resulted from the willful tethering of a huge portion of society to the internet has allowed us to pass immense explicit and implicit knowledge from human culture via human brains into digital form. Here we can not only use it to operate with human-like competence but also produce further knowledge and behavior by means of machine-based computation.

Joanna J. Bryson is an associate professor of computer science at the University of Bath.

For decades—even prior to the inception of the term—AI has aroused both fear and excitement as humanity contemplates creating machines in our image. This expectation that intelligent artifacts should by necessity be human-like artifacts blinded most of us to the important fact that we have been achieving AI for some time. While the breakthroughs in surpassing human ability at human pursuits, such as chess, make headlines, AI has been a standard part of the industrial repertoire since at least the 1980s. Then production-rule or “expert” systems became a standard technology for checking circuit boards and detecting credit card fraud. Similarly, machine-learning (ML) strategies like genetic algorithms have long been used for intractable computational problems, such as scheduling, and neural networks not only to model and understand human learning, but also for basic industrial control and monitoring.

In the 1990s, probabilistic and Bayesian methods revolutionized ML and opened the door to some of the most pervasive AI technologies now available: searching through massive troves of data. This search capacity included the ability to do semantic analysis of raw text, astonishingly enabling web users to find the documents they seek out of trillions of webpages just by typing only a few words.

AI is core to some of the most successful companies in history in terms of market capitalization—Apple, Alphabet, Microsoft, and Amazon. Along with information and communication technology (ICT) more generally, AI has revolutionized the ease with which people from all over the world can access knowledge, credit, and other benefits of contemporary global society. Such access has helped lead to massive reduction of global inequality and extreme poverty, for example by allowing farmers to know fair prices, the best crops, and giving them access to accurate weather predictions.

For decades, AI has aroused both fear and excitement as humanity contemplates creating machines in our image.

Having said this, academics, technologists, and the general public have raised a number of concerns that may indicate a need for down-regulation or constraint. As Brad Smith, the president of Microsoft recently asserted, “Information technology raises issues that go to the heart of fundamental human-rights protections like privacy and freedom of expression. These issues heighten responsibility for tech companies that create these products. In our view, they also call for thoughtful government regulation and for the development of norms around acceptable uses.”

Artificial intelligence is already changing society at a faster pace than we realize, but at the same time it is not as novel or unique in human experience as we are often led to imagine. Other artifactual entities, such as language and writing, corporations and governments, telecommunications and oil, have previously extended our capacities, altered our economies, and disrupted our social order—generally though not universally for the better. The evidence assumption that we are on average better off for our progress is ironically perhaps the greatest hurdle we currently need to overcome: sustainable living and reversing the collapse of biodiversity.

AI and ICT more generally may well require radical innovations in the way we govern, and particularly in the way we raise revenue for redistribution. We are faced with transnational wealth transfers through business innovations that have outstripped our capacity to measure or even identify the level of income generated. Further, this new currency of unknowable value is often personal data, and personal data gives those who hold it the immense power of prediction over the individuals it references.

But beyond the economic and governance challenges, we need to remember that AI first and foremost extends and enhances what it means to be human, and in particular our problem-solving capacities. Given ongoing global challenges such as security, sustainability, and reversing the collapse of biodiversity, such enhancements promise to continue to be of significant benefit, assuming we can establish good mechanisms for their regulation. Through a sensible portfolio of regulatory policies and agencies, we should continue to expand—and also to limit, as appropriate—the scope of potential AI applications.

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Artificial intelligence and its impact on everyday life

In recent years, artificial intelligence (AI) has woven itself into our daily lives in ways we may not even be aware of. It has become so pervasive that many remain unaware of both its impact and our reliance upon it. 

From morning to night, going about our everyday routines, AI technology drives much of what we do. When we wake, many of us reach for our mobile phone or laptop to start our day. Doing so has become automatic, and integral to how we function in terms of our decision-making, planning and information-seeking.

Once we’ve switched on our devices, we instantly plug into AI functionality such as:

• face ID and image recognition
• social media
• Google search
• digital voice assistants like Apple’s Siri and Amazon’s Alexa
• online banking
• driving aids – route mapping, traffic updates, weather conditions
• leisure downtime – such as Netflix and Amazon for films and programmes

AI touches every aspect of our personal and professional online lives today. Global communication and interconnectivity in business is, and continues to be, a hugely important area. Capitalising on artificial intelligence and data science is essential, and its potential growth trajectory is limitless.

Whilst AI is accepted as almost commonplace, what exactly is it and how did it originate?

What is artificial intelligence?

AI is the intelligence demonstrated by machines, as opposed to the natural intelligence displayed by both animals and humans. 

The human brain is the most complex organ, controlling all functions of the body and interpreting information from the outside world. Its neural networks comprise approximately 86 billion neurons, all woven together by an estimated 100 trillion synapses. Even now, neuroscientists are yet to unravel and understand many of its ramifications and capabilities. 

The human being is constantly evolving and learning; this mirrors how AI functions at its core. Human intelligence, creativity, knowledge, experience and innovation are the drivers for expansion in current, and future, machine intelligence technologies.

When was artificial intelligence invented?

During the Second World War, work by Alan Turing at Bletchley Park on code-breaking German messages heralded a seminal scientific turning point. His groundbreaking work helped develop some of the basics of computer science. 

By the 1950s, Turing posited whether machines could think for themselves. This radical idea, together with the growing implications of machine learning in problem solving, led to many breakthroughs in the field. Research explored the fundamental possibilities of whether machines could be directed and instructed to:

• apply their own ‘intelligence’ in solving problems like humans.

Computer and cognitive scientists, such as Marvin Minsky and John McCarthy, recognised this potential in the 1950s. Their research, which built on Turing’s, fuelled exponential growth in this area.  Attendees at a 1956 workshop, held at Dartmouth College, USA, laid the foundations for what we now consider the field of AI. Recognised as one of the world’s most prestigious academic research universities, many of those present became artificial intelligence leaders and innovators over the coming decades.

In testimony to his groundbreaking research, the Turing Test – in its updated form – is still applied to today’s AI research, and is used to gauge the measure of success of AI development and projects.

This infographic detailing the history of AI offers a useful snapshot of these main events.

How does artificial intelligence work?

AI is built upon acquiring vast amounts of data. This data can then be manipulated to determine knowledge, patterns and insights. The aim is to create and build upon all these blocks, applying the results to new and unfamiliar scenarios.

Such technology relies on advanced machine learning algorithms and extremely high-level programming, datasets, databases and computer architecture. The success of specific tasks is, amongst other things, down to computational thinking, software engineering and a focus on problem solving.

Artificial intelligence comes in many forms, ranging from simple tools like chatbots in customer services applications, through to complex machine learning systems for huge business organisations. The field is vast, incorporating technologies such as:

• Machine Learning (ML) . Using algorithms and statistical models, ML refers to computer systems which are able to learn and adapt without following explicit instructions. In ML, inferences and analysis are discerned in data patterns, split into three main types: supervised, unsupervised and reinforcement learning.
• Narrow AI . This is integral to modern computer systems, referring to those which have been taught, or have learned, to undertake specific tasks without being explicitly programmed to do so. Examples of narrow AI include: virtual assistants on mobile phones, such as those found on Apple iPhone and Android personal assistants on Google Assistant; and recommendation engines which make suggestions based on search or buying history.
• Artificial General Intelligence (AGI). At times, the worlds of science fiction and reality appear to blur. Hypothetically, AGI – exemplified by the robots in programmes such as Westworld, The Matrix, and Star Trek – has come to represent the ability of intelligent machines which understand and learn any task or process usually undertaken by a human being.
• Strong AI. This term is often used interchangeably with AGI. However, some artificial intelligence academics and researchers believe it should apply only once machines achieve sentience or consciousness.
• Natural Language Processing (NLP). This is a challenging area of AI within computer science, as it requires enormous amounts of data. Expert systems and data interpretation are required to teach intelligent machines how to understand the way in which humans write and speak. NLP applications are increasingly used, for example, within healthcare and call centre settings.
• Deepmind. As major technology organisations seek to capture the machine learning market, they are developing cloud services to tap into sectors such as leisure and recreation. For example, Google’s Deepmind has created a computer programme, AlphaGo, to play the board game Go, whereas IBM’s Watson is a super-computer which famously took part in a televised Watson and Jeopardy! Challenge. Using NLP, Watson answered questions with identifiable speech recognition and response, causing a stir in public awareness regarding the potential future of AI.

Artificial intelligence career prospects

Automation, data science and the use of AI will only continue to expand. Forecasts for the data analytics industry up to 2023 predict exponential expansion in the big data gathering sector. In The Global Big Data Analytics Forecast to 2023, Frost and Sullivan project growth at 29.7%, worth a staggering $40.6 billion.

As such, there exists much as-yet-untapped potential, with growing career prospects. Many top employers seek professionals with the skills, expertise and knowledge to propel their organisational aims forward. Career pathways may include:

• Robotics and self-driving /autonomous cars (such as Waymo, Nissan, Renault)
• Healthcare (for instance, multiple applications in genetic sequencing research, treating tumours, and developing tools to speed up diagnoses including Alzheimer’s disease)
• Academia (leading universities in AI research include MIT, Stanford, Harvard and Cambridge)
• Retail (AmazonGo shops and other innovative shopping options)

What is certain is that with every technological shift, new jobs and careers will be created to replace those lost.

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The field of artificial intelligence has made remarkable progress in the past five years and is having real-world impact on people, institutions and culture. The ability of computer programs to perform sophisticated language- and image-processing tasks, core problems that have driven the field since its birth in the 1950s, has advanced significantly. Although the current state of AI technology is still far short of the field’s founding aspiration of recreating full human-like intelligence in machines, research and development teams are leveraging these advances and incorporating them into society-facing applications. For example, the use of AI techniques in healthcare is becoming a reality, and the brain sciences are both a beneficiary of and a contributor to AI advances. Old and new companies are investing money and attention to varying degrees to find ways to build on this progress and provide services that scale in unprecedented ways.

The field’s successes have led to an inflection point: It is now urgent to think seriously about the downsides and risks that the broad application of AI is revealing. The increasing capacity to automate decisions at scale is a double-edged sword; intentional deepfakes or simply unaccountable algorithms making mission-critical recommendations can result in people being misled, discriminated against, and even physically harmed. Algorithms trained on historical data are disposed to reinforce and even exacerbate existing biases and inequalities. Whereas AI research has traditionally been the purview of computer scientists and researchers studying cognitive processes, it has become clear that all areas of human inquiry, especially the social sciences, need to be included in a broader conversation about the future of the field. Minimizing the negative impacts on society and enhancing the positive requires more than one-shot technological solutions; keeping AI on track for positive outcomes relevant to society requires ongoing engagement and continual attention.

Looking ahead, a number of important steps need to be taken. Governments play a critical role in shaping the development and application of AI, and they have been rapidly adjusting to acknowledge the importance of the technology to science, economics, and the process of governing itself. But government institutions are still behind the curve, and sustained investment of time and resources will be needed to meet the challenges posed by rapidly evolving technology. In addition to regulating the most influential aspects of AI applications on society, governments need to look ahead to ensure the creation of informed communities. Incorporating understanding of AI concepts and implications into K-12 education is an example of a needed step to help prepare the next generation to live in and contribute to an equitable AI-infused world.

The AI research community itself has a critical role to play in this regard, learning how to share important trends and findings with the public in informative and actionable ways, free of hype and clear about the dangers and unintended consequences along with the opportunities and benefits. AI researchers should also recognize that complete autonomy is not the eventual goal for AI systems. Our strength as a species comes from our ability to work together and accomplish more than any of us could alone. AI needs to be incorporated into that community-wide system, with clear lines of communication between human and automated decision-makers. At the end of the day, the success of the field will be measured by how it has empowered all people, not by how efficiently machines devalue the very people we are trying to help.

Cite This Report

Michael L. Littman, Ifeoma Ajunwa, Guy Berger, Craig Boutilier, Morgan Currie, Finale Doshi-Velez, Gillian Hadfield, Michael C. Horowitz, Charles Isbell, Hiroaki Kitano, Karen Levy, Terah Lyons, Melanie Mitchell, Julie Shah, Steven Sloman, Shannon Vallor, and Toby Walsh. "Gathering Strength, Gathering Storms: The One Hundred Year Study on Artificial Intelligence (AI100) 2021 Study Panel Report." Stanford University, Stanford, CA, September 2021. Doc:  http://ai100.stanford.edu/2021-report. Accessed: September 16, 2021.

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AI100 Standing Committee and Study Panel  

© 2021 by Stanford University. Gathering Strength, Gathering Storms: The One Hundred Year Study on Artificial Intelligence (AI100) 2021 Study Panel Report is made available under a Creative Commons Attribution-NoDerivatives 4.0 License (International):  https://creativecommons.org/licenses/by-nd/4.0/ .

Artificial Intelligence, Its Benefits & Risks Essay

Introduction, artificial intelligence and people’s lives, interesting things about artificial intelligence, the future of artificial intelligence, works cited.

Artificial intelligence (AI) revolves around the idea that human intellect can be replicated in machines. Technological advancements have made it possible for experts to manufacture machines, which can discharge many activities that require reasoning without human intervention. Many studies have been conducted to examine the field of artificial intelligence. As this paper reveals, AI-related topics that have received significant scholarly attention include the impact it has on people’s lives, some of its interesting features, and the future of business operations, thanks to the application of artificial intelligence.

According to Makridakis, “The goal of artificial intelligence includes learning, reasoning, and perception, and machines are wired using a cross-disciplinary approach based on mathematics, computer science, linguistics, and psychology” (49). Artificial intelligence has significant impacts on our lives. This technology influences the buying behaviors of many customers, particularly those who like watching movies. Companies like Netflix have invested heavily in artificial intelligence intending to gather information regarding consumers’ interests.

Data gathered is used to target individual customers depending on their tastes and preferences (Makridakis 55). For instance, after streaming a movie or series, one may be surprised to see their screen filled with images that promote other shows or videos. In most cases, these films fall in the same genre as the one they have just completed viewing. Such incidents do not happen by coincidence. Companies use artificial intelligence to persuade people to stream more videos or buy specific products and services.

Artificial intelligence is also used in the transportation sector. Taxi companies such as Uber use applications that are equipped with machine learning, which is a component of artificial intelligence (Dirican 568). It would have been hard for Uber to achieve its goal of dominating the ride-sharing market without using this technology. Machine learning enables taxi businesses to identify falsified accounts and determine the most favorable points to pick or drop clients.

One of the most fascinating things about artificial intelligence is that virtually all artificial intelligence assistants respond in feminine voices. For instance, AI machines such as Cortana, Siri, and Alexa are all female. The primary reason they are feminine is that most people prefer female assistants. Another interesting thing about artificial intelligence is that characters can write. Today, robo-journalism is gaining popularity in the media industry. Los Angeles Times prides itself on being the first company to use a robot to compose an editorial about earthquakes in California (Makridakis 58). Despite these numerous benefits attributable to artificial intelligence, some tech companies have doubts about the technology. For example, Tesla’s chief executive officer, Elon Musk, is renowned for his love for advanced technology. However, he shares his skepticism regarding artificial intelligence. Musk argues that artificial intelligence may pose a threat to humanity and hence the need for a level of control. He advocates for the abolishment of the manufacture of automated weapons.

Many customer care professionals are losing jobs. Their positions are being taken over by artificial intelligence. Studies show that over 85% of consumer relationships involve artificial intelligence-aided robots (Dirican 571). Hence, in the future, technology will dominate the personal assistant career, thus rendering many people jobless. The demand for self-driving cars is growing. Artificial intelligence is expected to feature in the automobile industry since many companies are looking forward to producing automated cars.

Artificial intelligence has infiltrated our lives in various ways. Companies leverage this technology to influence consumers’ buying behaviors. Additionally, businesses are gradually using AI to automate professions, a move that has made many people jobless. Even though this technology is useful, there is the need to regulate its utilization before it becomes a threat to humanity.

Dirican, Cuneyt. “The Impacts of Robotics, Artificial Intelligence on Business and Economics.” Procedia – Social and Behavioral Sciences , vol. 195, no. 1, 2015, pp. 564-573.

Makridakis, Spyros. “The Forthcoming Artificial Intelligence (AI) Revolution: Its Impact on Society and Firms.” Futures , vol. 90, no. 1, 2017, pp. 46-60.

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Artificial Intelligence Essay

500+ words essay on artificial intelligence.

Artificial intelligence (AI) has come into our daily lives through mobile devices and the Internet. Governments and businesses are increasingly making use of AI tools and techniques to solve business problems and improve many business processes, especially online ones. Such developments bring about new realities to social life that may not have been experienced before. This essay on Artificial Intelligence will help students to know the various advantages of using AI and how it has made our lives easier and simpler. Also, in the end, we have described the future scope of AI and the harmful effects of using it. To get a good command of essay writing, students must practise CBSE Essays on different topics.

Artificial Intelligence is the science and engineering of making intelligent machines, especially intelligent computer programs. It is concerned with getting computers to do tasks that would normally require human intelligence. AI systems are basically software systems (or controllers for robots) that use techniques such as machine learning and deep learning to solve problems in particular domains without hard coding all possibilities (i.e. algorithmic steps) in software. Due to this, AI started showing promising solutions for industry and businesses as well as our daily lives.

Importance and Advantages of Artificial Intelligence

Advances in computing and digital technologies have a direct influence on our lives, businesses and social life. This has influenced our daily routines, such as using mobile devices and active involvement on social media. AI systems are the most influential digital technologies. With AI systems, businesses are able to handle large data sets and provide speedy essential input to operations. Moreover, businesses are able to adapt to constant changes and are becoming more flexible.

By introducing Artificial Intelligence systems into devices, new business processes are opting for the automated process. A new paradigm emerges as a result of such intelligent automation, which now dictates not only how businesses operate but also who does the job. Many manufacturing sites can now operate fully automated with robots and without any human workers. Artificial Intelligence now brings unheard and unexpected innovations to the business world that many organizations will need to integrate to remain competitive and move further to lead the competitors.

Artificial Intelligence shapes our lives and social interactions through technological advancement. There are many AI applications which are specifically developed for providing better services to individuals, such as mobile phones, electronic gadgets, social media platforms etc. We are delegating our activities through intelligent applications, such as personal assistants, intelligent wearable devices and other applications. AI systems that operate household apparatus help us at home with cooking or cleaning.

Future Scope of Artificial Intelligence

In the future, intelligent machines will replace or enhance human capabilities in many areas. Artificial intelligence is becoming a popular field in computer science as it has enhanced humans. Application areas of artificial intelligence are having a huge impact on various fields of life to solve complex problems in various areas such as education, engineering, business, medicine, weather forecasting etc. Many labourers’ work can be done by a single machine. But Artificial Intelligence has another aspect: it can be dangerous for us. If we become completely dependent on machines, then it can ruin our life. We will not be able to do any work by ourselves and get lazy. Another disadvantage is that it cannot give a human-like feeling. So machines should be used only where they are actually required.

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AI for Impact: The Role of Artificial Intelligence in Social Innovation

Artificial Intelligence (AI) is revolutionizing every facet of business and life, thanks to the accelerated development of generative AI, which has made the technology widely accessible. For social innovators, the ethical adoption of AI in their business models and/or to streamline their operations represents a unique opportunity to maximize their impact. The social economy represents 7% of global GDP, and generative AI could add between $182 billion and $308 billion in value annually to the sector. How can social innovators better understand, access and deploy this technology?

This white paper answers this question by drawing on earlier work by the World Economic Forum's AI Governance Alliance (AIGA) and its framework for businesses to unlock value from AI. It maps out the current state of the deployment of AI in social enterprises, drawing on 300 examples, and provides a basis for future thought leadership and practical implementation.

World Economic Forum reports may be republished in accordance with the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Public License , and in accordance with our Terms of Use .

More From Forbes

How AI Is Impacting Society And Shaping The Future

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In an age of swift technological evolution, artificial intelligence (AI) emerges as a transformative influence with the capacity to reshape both our society and industries. Anchored in ethics, transparency, and accountability, the development of AI becomes pivotal, acting as the cornerstone for constructing a future that seamlessly integrates technological advancement with social responsibility.

In the dynamic landscape of technological evolution , marked by transformative milestones in the digital age, artificial intelligence (AI) emerges as a pivotal force. Progressing from the inception of the internet to the widespread use of mobile devices, big data, and cloud computing, each phase has significantly shaped our daily lives and professional landscapes.

AI, with its potential to automate non-routine tasks, stands out as a transformative influence. As articulated by Berkeley Exec Ed in their recent article, the advent of the ' AI era ' carries profound implications for our societies beyond technological advancements and business models. It prompts crucial questions about the evolving roles of humans as machines gain cognitive capabilities, particularly in leadership, decision-making, and strategy.

Establishing Safety Standards for Ethical AI

It is essential to consider its current influence and potential future implications. Beyond automating routine tasks, AI's disruptive potential extends from intricate data analysis to various professional fields. According to insights from Berkeley Exec Ed , AI's prediction technology has the potential to automate diverse non-routine tasks across various occupations, with up to 30% of tasks in about 60% of jobs holding the potential for automation. While this doesn't necessarily imply a jobless future, it underscores a pivotal shift in roles and responsibilities, emphasizing the increasing importance of adaptability in the workforce. As Pieter den Hamer, Vice President of Research at Gartner, notes, "Every job will be impacted by AI... Most of that will be more augmentation rather than replacing workers".

Dhanvin Sriram, Founder of PromptVibes , sees AI continuing to revolutionize industries by automating tasks, generating insights, and enhancing decision-making. His vision entails the integration of AI into all aspects of life, focusing on ethical development, transparency, and accountability. Dhanvin believes in a future where AI amplifies human abilities, leading to increased efficiency, productivity, and innovation across sectors.

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Best 5% interest savings accounts of 2024, revolutionizing industries with ethical ai integration.

Prioritizing ethical considerations in AI development ensures alignment with societal values and a focus on human well-being. Establishing transparency throughout the development process builds a foundation of trust, enabling users and stakeholders to comprehend the operations of AI systems. Accountability reinforces developers' responsibility in addressing potential risks and consequences, creating a framework that promotes innovation while being mindful of its broader societal implications.

Brett Gronow, Founder of Systema AI , holds global patents that have been cited by Apple, Amazon, Microsoft, Samsung, and Spotify amongst many - envisions a future where if AI were to surpasses, certain vectors human capacity intellect, it could usher in an era of Artificial General Intelligence (AGI). Brett emphasizes the need for safety standards and protocols to prevent the development of AI systems that could further harm humanity.

In his view, the positive impact of AI on culture and communication can be directed through purposeful and ethical means. Brett advocates for the vital transmission of non-manipulated data, and of the preservation of context especially in fields like social media, where algorithmic optimization has led to concerns about truth suppression.

Best Practice: Ethical AI Development

Fostering the establishment of robust safety standards and ethical guidelines in AI development is imperative to ensure a positive impact on society while concurrently mitigating the risks of malicious use. Embracing these principles as best practices will pave the way for responsible and beneficial advancements in artificial intelligence.

Bridging Language Divides In Education Through AI

Bridging language divides in education through AI requires a concerted effort to promote inclusivity and accessibility, making it essential to adhere to safety standards and ethical guidelines in AI development. According to Brookings education , the surge in AI deployment brings to light a critical consideration – the inherent bias towards dominant languages. As elucidated in the exploration of language by Austrian philosopher Ludwig Wittgenstein, "The limits of my language mean the limits of my world." The current trajectory of generative AI, often rooted in major languages, raises concerns about the perpetuation of social and economic divisions. With over 7,000 languages spoken globally, the majority of internet content is confined to a handful of dominant languages, creating a digital language divide. This not only hampers inclusivity but also perpetuates historical inequalities, reminiscent of past instances where language served as a tool of power, shaping socio-ethnic contexts and influencing the trajectory of technological tools. As we navigate the digital era, it becomes imperative to address and rectify the widening linguistic gap perpetuated by AI technologies.

Maria Chmir, Founder and CEO of Rask AI , is focused on addressing language disparities in education. Recognizing that online education often caters to a limited number of languages, Maria's vision is to democratize access to global knowledge by offering education in 130+ languages. Rask AI achieves this by providing educators with tools for content localization, including audio and video translation, dubbing, voice cloning, and lipsyncing.

Best Practice: Inclusive Education Through AI

A best practice for inclusive education through AI lies in advocating for the localization of educational content in multiple languages, thereby ensuring equitable access to knowledge and effectively bridging language divides in the realm of AI-driven education.

AI Automation In Brand Development

Transitioning into the realm of AI automation in brand development, it becomes crucial to explore how artificial intelligence can not only enhance educational inclusivity but also play a transformative role in shaping and optimizing brand strategies.

AI automation in brand development represents a transformative frontier where technology meets creativity. Brands are harnessing the power of artificial intelligence to streamline and enhance various facets of their development strategies. From advanced data analytics for market insights to personalized content creation and social media engagement, AI automation is revolutionizing the way brands connect with their audience. The precision and efficiency offered by AI tools in tasks such as customer targeting, trend analysis, and campaign optimization contribute to building a cohesive and impactful brand identity. As real-time data becomes integral to decision-making, AI automation proves to be a dynamic force, allowing brands to adapt and thrive in an ever-evolving digital landscape.

Drake Tigges, who specializes in digital marketing and conducts marketing master classes, provides insights into the impact of AI automation on the workspace. Having utilized AI tools over the years, he acknowledges the evolution of automation from follow/unfollow techniques to advanced functionalities like story viewing and post-scheduling. Drake emphasizes the benefits of AI automation for brand building while highlighting the importance of user knowledge and caution due to the ever-changing landscape of social platforms.

Best Practice: Informed AI Automation

In culmination, embracing the best practice of informed AI automation in brand development calls for a commitment to encouraging knowledgeable utilization, recognizing the inherent benefits, and maintaining vigilance amidst evolving platform dynamics for sustained success.

The future of AI is continuously being defined and reshaping our society. With the rapid changes, individuals can foresee a landscape where ethical practices, inclusivity, and knowledge are paramount. As industries evolve, embracing AI responsibly becomes crucial for positive societal impact. Through the collective efforts of innovators, individuals can navigate the transformative potential of AI, ensuring a future where technology serves humanity and fosters positive change.

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What is artificial intelligence (AI)?

This exciting field of computer science focuses on technologies that mimic human intelligence — with AI systems becoming way more prevalent in recent years.

Artificial intelligence (AI) refers to any technology exhibiting some facets of human intelligence, and it has been a prominent field in computer science for decades. AI tasks can include anything from picking out objects in a visual scene to knowing how to frame a sentence, or even predicting stock price movements.

Scientists have been trying to build AI since the dawn of the computing era . The leading approach for much of the last century involved creating large databases of facts and rules and then getting logic-based computer programs to draw on these to make decisions. But this century has seen a shift, with new approaches that get computers to learn their own facts and rules by analyzing data. This has led to major advances in the field.

Over the past decade, machines have exhibited seemingly "superhuman" capabilities in everything from spotting breast cancer in medical images , to playing the devilishly tricky board games Chess and  Go — and even predicting the structure of proteins .

Since the large language model (LLM) chatbot ChatGPT burst onto the scene late in 2022, there has also been a growing consensus that we could be on the cusp of replicating more general intelligence similar to that seen in humans — known as artificial general intelligence (AGI). "It really cannot be overemphasized how pivotal a shift this has been for the field," said Sara Hooker, head of Cohere For AI, a non-profit research lab created by the AI company Cohere.

How does AI work?

While scientists can take many approaches to building AI systems, machine learning is the most widely used today. This involves getting a computer to analyze data to identify patterns that can then be used to make predictions.

The learning process is governed by an algorithm — a sequence of instructions written by humans that tells the computer how to analyze data —  and the output of this process is a statistical model encoding all the discovered patterns. This can then be fed with new data to generate predictions.

Many kinds of machine learning algorithms exist, but neural networks are among the most widely used today. These are collections of machine learning algorithms loosely modeled on the human brain , and they learn by adjusting the strength of the connections between the network of "artificial neurons" as they trawl through their training data. This is the architecture that many of the most popular AI services today, like text and image generators, use.

Most cutting-edge research today involves deep learning , which refers to using very large neural networks with many layers of artificial neurons. The idea has been around since the 1980s — but the massive data and computational requirements limited applications. Then in 2012, researchers discovered that specialized computer chips known as graphics processing units (GPUs) speed up deep learning. Deep learning has since been the gold standard in research.

"Deep neural networks are kind of machine learning on steroids," Hooker said. "They're both the most computationally expensive models, but also typically big, powerful, and expressive"

Not all neural networks are the same, however. Different configurations , or "architectures" as they're known, are suited to different tasks. Convolutional neural networks have patterns of connectivity inspired by the animal visual cortex and excel at visual tasks. Recurrent neural networks, which feature a form of internal memory, specialize in processing sequential data.

The algorithms can also be trained differently depending on the application. The most common approach is called "supervised learning," and involves humans assigning labels to each piece of data to guide the pattern-learning process. For example, you would add the label "cat" to images of cats.

In "unsupervised learning," the training data is unlabelled and the machine must work things out for itself. This requires a lot more data and can be hard to get working — but because the learning process isn't constrained by human preconceptions, it can lead to richer and more powerful models. Many of the recent breakthroughs in LLMs have used this approach.

The last major training approach is "reinforcement learning," which lets an AI learn by trial and error. This is most commonly used to train game-playing AI systems or robots — including humanoid robots like Figure 01 , or these soccer-playing miniature robots — and involves repeatedly attempting a task and updating a set of internal rules in response to positive or negative feedback. This approach powered Google Deepmind 's ground-breaking AlphaGo model. 

What is generative AI?

Despite deep learning scoring a string of major successes over the past decade, few have caught the public imagination in the same way as ChatGPT's uncannily human conversational capabilities. This is one of several generative AI systems that use deep learning and neural networks to generate an output based on a user's input — including text , images , audio and even video . 

Text generators like ChatGPT operate using a subset of AI known as "natural language processing" (NLP). The genesis of this breakthrough can be traced to a novel deep learning architecture introduced by Google scientists in 2017 called the "transformer."

Transformer algorithms specialize in performing unsupervised learning on massive collections of sequential data — in particular, big chunks of written text. They're good at doing this because they can track relationships between distant data points much better than previous approaches, which allows them to better understand the context of what they're looking at.

"What I say next hinges on what I said before — our language is connected in time," said Hooker. "That was one of the pivotal breakthroughs, this ability to actually see the words as a whole."

LLMs learn by masking the next word in a sentence before trying to guess what it is based on what came before. The training data already contains the answer so the approach doesn't require any human labeling, making it possible to simply scrape reams of data from the internet and feed it into the algorithm. Transformers can also carry out multiple instances of this training game in parallel, which allows them to churn through data much faster.

By training on such vast amounts of data, transformers can produce extremely sophisticated models of human language — hence the "large language model" moniker. They can also analyze and generate complex, long-form text very similar to the text that a human can generate. It's not just language that transformers have revolutionized. The same architecture can also be trained on text and image data in parallel, resulting in models like Stable Diffusion and DALL-E, that produce high-definition images from a simple written description.

Transformers also played a central role in Google Deepmind's AlphaFold 2 model, which can generate protein structures from sequences of amino acids. This ability to produce original data, rather than simply analyzing existing data is why these models are known as "generative AI."

Narrow AI vs artificial general intelligence (AGI): What's the difference?

People have grown excited about LLMs due to the breadth of tasks they can perform. Most machine learning systems are trained to solve a particular problem — such as detecting faces in a video feed or translating from one language to another. These models are known as “narrow AI” because they can only tackle the specific task they were trained for.

Most machine learning systems are trained to solve a particular problem —, such as detecting faces in a video feed or translating from one language to another —, to a superhuman level, in that they are much faster and perform better than a human could. But LLMs like ChatGPT represent a step-change in AI capabilities because a single model can carry out a wide range of tasks. They can answer questions about diverse topics, summarize documents, translate between languages and write code.

This ability to generalize what they've learned to solve many different problems has led some to speculate LLMs could be a step toward AGI, including DeepMind scientists in a paper published last year. AGI refers to a hypothetical future AI capable of mastering any cognitive task a human can, reasoning abstractly about problems, and adapting to new situations without specific training.

AI enthusiasts predict once AGI is achieved, technological progress will accelerate rapidly — an inflection point known as  "the singularity" after which breakthroughs will be realized exponentially. There are also perceived existential risks , ranging from massive economic and labor market disruption to the potential for AI to discover new pathogens or weapons.

But there is still debate as to whether LLMs will be a precursor to an AGI, or simply one architecture in a broader network or ecosystem of AI architectures that is needed for AGI. Some say LLMs are miles away from replicating human reasoning and cognitive capabilities. According to detractors, these models have simply memorized vast amounts of information , which they recombine in ways that give the false impression of deeper understanding; it means they are limited by training data and are not fundamentally different from other narrow AI tools.

Nonetheless, it's certain LLMs represent a seismic shift in how scientists approach AI development, said Hooker. Rather than training models on specific tasks, cutting-edge research now takes these pre-trained, generally capable models and adapts them to specific use cases. This has led to them being referred to as "foundation models."

"People are moving from very specialized models that only do one thing to a foundation model, which does everything," Hooker added. "They're the models on which everything is built."

How is AI used in the real world?

Technologies like machine learning are everywhere. AI-powered recommendation algorithms decide what you watch on Netflix or YouTube — while translation models make it possible to instantly convert a web page from a foreign language to your own. Your bank probably also uses AI models to detect any unusual activity on your account that might suggest fraud, and surveillance cameras and self-driving cars use computer vision models to identify people and objects from video feeds.

But generative AI tools and services are starting to creep into the real world beyond novelty chatbots like ChatGPT. Most major AI developers now have a chatbot that can answer users' questions on various topics, analyze and summarize documents, and translate between languages. These models are also being integrated into search engines — like Gemini into Google Search — and companies are also building AI-powered digital assistants that help programmers write code, like Github Copilot . They can even be a productivity-boosting tool for people who use word processors or email clients.

— MIT scientists have figured out how to make popular AI image generators 30 times faster

— Scientists create AI models that can talk to each other and pass on skills with limited human input

— Researchers gave AI an 'inner monologue' and it massively improved its performance

Chatbot-style AI tools are the most commonly found generative AI service, but despite their impressive performance, LLMs are still far from perfect. They make statistical guesses about what words should follow a particular prompt. Although they often produce results that indicate understanding, they can also confidently generate plausible but wrong answers — known as " hallucinations ."

While generative AI is becoming increasingly common, it's far from clear where or how these tools will prove most useful. And given how new the technology is, there's reason to be cautious about how quickly it is rolled out, Hooker said. "It's very unusual for something to be at the frontier of technical possibility, but at the same time, deployed widely," she added. "That brings its own risks and challenges."

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Artificial Intelligence Essay for Students and Children

500+ words essay on artificial intelligence.

Artificial Intelligence refers to the intelligence of machines. This is in contrast to the natural intelligence of humans and animals. With Artificial Intelligence, machines perform functions such as learning, planning, reasoning and problem-solving. Most noteworthy, Artificial Intelligence is the simulation of human intelligence by machines. It is probably the fastest-growing development in the World of technology and innovation . Furthermore, many experts believe AI could solve major challenges and crisis situations.

Types of Artificial Intelligence

First of all, the categorization of Artificial Intelligence is into four types. Arend Hintze came up with this categorization. The categories are as follows:

Type 1: Reactive machines – These machines can react to situations. A famous example can be Deep Blue, the IBM chess program. Most noteworthy, the chess program won against Garry Kasparov , the popular chess legend. Furthermore, such machines lack memory. These machines certainly cannot use past experiences to inform future ones. It analyses all possible alternatives and chooses the best one.

Type 2: Limited memory – These AI systems are capable of using past experiences to inform future ones. A good example can be self-driving cars. Such cars have decision making systems . The car makes actions like changing lanes. Most noteworthy, these actions come from observations. There is no permanent storage of these observations.

Type 3: Theory of mind – This refers to understand others. Above all, this means to understand that others have their beliefs, intentions, desires, and opinions. However, this type of AI does not exist yet.

Type 4: Self-awareness – This is the highest and most sophisticated level of Artificial Intelligence. Such systems have a sense of self. Furthermore, they have awareness, consciousness, and emotions. Obviously, such type of technology does not yet exist. This technology would certainly be a revolution .

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Applications of Artificial Intelligence

First of all, AI has significant use in healthcare. Companies are trying to develop technologies for quick diagnosis. Artificial Intelligence would efficiently operate on patients without human supervision. Such technological surgeries are already taking place. Another excellent healthcare technology is IBM Watson.

Artificial Intelligence in business would significantly save time and effort. There is an application of robotic automation to human business tasks. Furthermore, Machine learning algorithms help in better serving customers. Chatbots provide immediate response and service to customers.

AI can greatly increase the rate of work in manufacturing. Manufacture of a huge number of products can take place with AI. Furthermore, the entire production process can take place without human intervention. Hence, a lot of time and effort is saved.

Artificial Intelligence has applications in various other fields. These fields can be military , law , video games , government, finance, automotive, audit, art, etc. Hence, it’s clear that AI has a massive amount of different applications.

To sum it up, Artificial Intelligence looks all set to be the future of the World. Experts believe AI would certainly become a part and parcel of human life soon. AI would completely change the way we view our World. With Artificial Intelligence, the future seems intriguing and exciting.

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Artificial Intelligence and its Impact on Society

Updated 18 August 2023

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Category Information Science and Technology ,  Science

Topic Artificial Intelligence

Artificial Intelligence and Its Impact on Society

From self-driving cars to SIRI, artificial intelligence has experienced dramatic and unprecedented growth globally and is being integrated into virtually all aspects of human life (Bushardt, 2018, p. 9). Artificial intelligence, also referred to as AI, encompasses elements such as internet search algorithms, IBM’S Watson and autonomous fighting equipment. In the contemporary world, AI is not only used to perform small tasks such as internet searches or facial recognition (Weak AI) but also extreme procedures such as medical surgeries (Strong AI). Artificial intelligence is also used in fraud detection/security, performing dangerous duties and improving productivity. However, they may lead to morality issues, the decline in human thinking abilities and increased human unemployment rates. Moreover, AI may increase devastating security issues. Therefore, while there are several benefits of AI, it is evident that the increasing use of AI in technology will change our societies for the worse.

The Risk of Insecurity and Threats

The growing utilisation of artificial intelligence heightens the risk of insecurity especially when programmed to do something devastating (Bushardt, 2018). Approximately 30 countries have developed and armed and autonomous drones (UN DESA, 2017). According to the findings by US and UK experts, artificial intelligence poses a range of threats not only to cyber-security but also political and physical security (Arizona State University, 2017, p. 47). With increased production of AL’s, it is expected that there will be an increase in attacks that exploit human vulnerabilities such as utilisation of speech for impersonation, automated phishing, data poisoning and hacking (Bushardt, 2018, p. 10). No doubt, there are signs of AI-driven arms race as researchers and governments have increasingly used machine-learning models to anticipate attacks better. (UN DESA, 2017) Persons with ulterior motives, such as hackers, may decide to deploy autonomous weapons systems. The use of AI in surveillance, persuasion, and deception may increase threats associated with social manipulation and privacy invasion. Nevertheless, when used well, robotics can effectively help in guarding national security (UN DESA, 2017). However, one of the common issues is spear phishing, where individuals use digital messages to trick persons into sharing sensitive data or installing malware (Bushardt, 2018, p. 11). Moreover, autonomous weapons cannot only select but also engage targets without human intervention. For such reasons, over 50 organizations have called for a consensus on banning autonomous weapons to minimize chances of security attacks (Arizona State University, 2017).

The Ethical and Moral Challenges

Also, as we advance towards strong artificial intelligence (AI) applications require to be imbued with moral decision making (Arizona State University, 2017). However, application of strong artificial intelligence is a possible threat to societal ethical and moral value and an imminent attack to accountability because teaching ethics to the machine is a challenging task because human beings cannot convey morality in a manner that makes it easy for computers to process (UN DESA, 2017). In most cases, human beings may rely on gut feelings as opposed to a cost-benefit calculation. On the other hand, AI relies on objective metrics that are optimized and measured (Bushardt, 2018, p. 9). In reality, optimization is a complicated situation. Thus it is a hard task to teach a machine how to maximize fairness or overcome gender or racial biases algorithmically (Bushardt, 2018, p. 9). Also, fairness is influenced by factors such as reward, aversion and conscious experience, which is shared by all animals. Being fair, and by extension observing ethics, is a basis for accountability (World Economic Forum, 2016). Then, who should be accountable for wrongdoings of a Robot? Should they be legally culpable if they transgress societal standards? While neuroscientists are still working with engineers to perfect the field of Robotics, AI’s are relatively superficial thus raising many ethical and moral issues.

The Impact on Employment

Another major transformation that is an outcome of both weak and strong artificial intelligence is the displacement of human workers thus leading to unemployment. Increasingly, many tasks are being automated. Globally, robot imports rose from 100, 000 in the year 2000 to approximately 250,000 in 2015 (UN DESA, 2017). With the rapid advancement in technology, computers are increasingly encroaching on domains that were initially regarded as exclusively for human (World Economic Forum, 2016). Robotic systems and computers can perform tasks such as composing music, medical surgeries, and architecture. Each new day, functionalities are being fed into robotics, and apparently, the computer is performing tasks more efficiently than human beings (Mesko, 2017, p. 239). However, according to the United Nations (2017) by 2050 human-beings population will be approximately 9.8 billion out of which 6 billion will be of working age. Currently, nearly 71 million young people are struggling to find employment (UN DESA, 2017). Therefore, while the robotics may not be adaptable or versatile, automating tasks will adversely affect livelihoods and job opportunities for many individuals in the society. Low-income countries are more vulnerable unless they implement clear policies that focus on the potential and risks of the new technologies. Therefore, the earlier we rethink and re-design our labor market policies, the more comfortable we will adjust to the future that is already happening and ensures equality in job distribution.

The Decline in Human Thinking

Moreover, the increased use of artificial intelligence will lead to a decline in human thinking. As technology has played a critical role in our lives, our abilities of analysis and critical thinking have consistently declined (Mesko, 2017, p. 240). With increasing use of robotics and artificial intelligence, there is a higher chance that human thinking abilities will diminish. Naturally, a human has high mental capacities and multitasking skills (World Economic Forum, 2016). However, studies show that constant interaction with technology is changing our brain influencing our memory, and physical orientation. Our tablets and mobile phones are increasingly being used as systems for the storage of information taking away the obligation of using our minds to retain information. Instead of memorizing, we ask virtual assistants such as SIRI to store the data for us. Consistently, people become over-reliant to GPS in designing routes before leaving home (Arizona State University, 2017). Doing so causes the brain matter dedicated to navigation and orientation reduce their activities. According to the World Economic Forum (2016), the most sought-after employee skills were in coordination with others, comprehending complex problems and managing teams. However, with the development of Artificial intelligence progress, employers’ demands changes and companies will give more priority to creativity and critical thinking.

Promoting Inequality

Additionally, the primary challenge of artificial intelligence is promoting equality, especially in the distribution of resources. The world’s economic system is assessed based on the contribution and effort of every person (Venkatachalam, 2017). People get paid for the work they have done. Most companies rely on hourly work by their employees. However, when we employ more Artificial intelligence (AI), individuals owning the robots will make all the money. Consequently, there will be an increased wealth gap. Therefore, establishing a post-labor economy due to AI will result in unfair economic practices that may lead to conflicts and increased poverty thus transforming the society into the worst (Arizona State University, 2017).

AI's Impact on Businesses and Daily Lives

On the other hand, artificial intelligence has a significant impact on businesses as well as people’s daily lives. There are various computed methods for learning, automated reasoning, and perception which continues to be a typical daily lives phenomenon. Cybersecurity remains a serious concern for every company. According to Venkatachalam (2017), the cybersecurity breaches totaled around 707 million in the year 2015 worldwide. The artificial intelligence-enabled automation capabilities and self-learning which increases efficiency while reducing costs, keeping people safer from terrorism or identity thefts. The already available AI-based solutions can be more proactive and efficiently pre-empt attacks while at the pre-execution state through identification of particular patterns and anomalies linked with malicious content. Companies such as secure works utilize the AI predictive capabilities for advanced detection of threats on a global scale (Venkatachalam, 2017). Furthermore, artificial intelligence (AI) is widely employed in the banking and financial institutions to organize as well as data management. Also, artificial intelligence is used in a smart card-based system for fraud detection.

Additionally, AI is used in the medical field through the use of algorithms which assists the doctors in assessing patients as well as risks to their health. It also aids in the identification of the side effects of various medicines (Venkatachalam, 2017). Furthermore, surgery simulators utilize machine intelligence in professional’s training and may be used in brain functioning simulation hence suitable for the analysis as well as treating neurological complications. Moreover, artificial intelligence is managing the promotion and application of precise medicine which is concerned with disease treatment and prevention which takes into account each person’s lifestyle, their environment, and individuals' gene’s variability. For instance, Atomise launched a virtual search for existing medicines that are safe which could be redesigned for the treatment of the Ebola virus, an analysis completed in less than a day which otherwise would have taken months or even years (Mesko, 2017, p. 240). However, AI possesses significant limitations in healthcare since projecting as well as precedence meditates predictions in machine learning cases, but algorithms may be underachieving in cases of resistance of drugs with no existing instance to use.

Convenience and Efficiency in Daily Lives

Moreover, artificial intelligence brings along a lot of life’s conveniences. Machines can work as long as it’s necessary as they do not need to sleep or breaks like humans and can continue functioning without stopping. Robots are programmed to operate for extended hours hence can perform repetitively the same task without tiring or boredom or even getting detracted. They do not require being paid thus reducing the costs, increasing productivity and ultimately improving the economy. Machines act faster than humans; therefore, multitasking is easy through adjustments of their parameters with speed and time calculated by the parameters, unlike humans. Also, AI has significantly made people’s lives easier through applications such as the GPS system which guides humans to their desired destinations. It is also used in smartphones, for instance, autocorrection of spelling errors. Apple’s Siri and Microsoft’s Cortana are also examples of weak AI created to perform a task that is associated with human abilities.

Artificial intelligence is a technology with the ability to change the world for the better or to the worse. The main challenges with AI include replacing humans with machines thus creating unemployment, reduced human lateral thinking abilities, increased repair and maintenance costs, as well as inequalities in wealth distribution. However, artificial intelligence has the potential of changing the world for the better through increased better security especially cybersecurity, fraud detection, treatment, and prevention of diseases in medicine, and daily life conveniences such as the use of applications in smartphones and GPS systems for directions. Even though AI presents various benefits to the world, the growing use of artificial intelligence in technology has the potential of transforming societies for the worse.

Arizona State University. (2017). The new dogs of war: the future of weaponised artificial intelligence. Arizona: Arizona State University.

Bushardt, R. (2018). Artificial intelligence and the dreaded 's. Journal of the American Academy of PAs, vol 31, no. 13, pp. 9-10.

Mesko, B. (2017). The Role of Artificial Intelligence in Precision Medicine. Expert Review of Precision Medicine and Drugs Development, 2(5), 239-241.

UN DESA. (2017). Will robots and AI cause mass unemployment? Not necessarily, but they do bring other threats. Retrieved 2018, from United Nations Department of Economic and Social Affairs: https://www.un.org/development/desa/en/news/policy/will-robots-and-ai-cause-mass-unemployment-not-necessarily-but-they-do-bring-other-threats.html

Venkatachalam, S. (2017). Three ways Artificial Intelligence will change the world for the better. Retrieved 2018, from World Economic Forum: https://www.weforum.org/agenda/2017/05/artificial-intelligence-will-change-the-world-heres-how/

World Economic Forum. (2016). The future of jobs. World Economic Forum.

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• Published: 09 April 2024

The potential for artificial intelligence to transform healthcare: perspectives from international health leaders

• Christina Silcox 1 ,
• Eyal Zimlichmann 2 , 3 ,
• Katie Huber   ORCID: orcid.org/0000-0003-2519-8714 1 ,
• Neil Rowen 1 ,
• Robert Saunders 1 ,
• Mark McClellan 1 ,
• Charles N. Kahn III 3 , 4 ,
• Claudia A. Salzberg 3 &
• David W. Bates   ORCID: orcid.org/0000-0001-6268-1540 5 , 6 , 7  

npj Digital Medicine volume  7 , Article number:  88 ( 2024 ) Cite this article

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Artificial intelligence (AI) has the potential to transform care delivery by improving health outcomes, patient safety, and the affordability and accessibility of high-quality care. AI will be critical to building an infrastructure capable of caring for an increasingly aging population, utilizing an ever-increasing knowledge of disease and options for precision treatments, and combatting workforce shortages and burnout of medical professionals. However, we are not currently on track to create this future. This is in part because the health data needed to train, test, use, and surveil these tools are generally neither standardized nor accessible. There is also universal concern about the ability to monitor health AI tools for changes in performance as they are implemented in new places, used with diverse populations, and over time as health data may change. The Future of Health (FOH), an international community of senior health care leaders, collaborated with the Duke-Margolis Institute for Health Policy to conduct a literature review, expert convening, and consensus-building exercise around this topic. This commentary summarizes the four priority action areas and recommendations for health care organizations and policymakers across the globe that FOH members identified as important for fully realizing AI’s potential in health care: improving data quality to power AI, building infrastructure to encourage efficient and trustworthy development and evaluations, sharing data for better AI, and providing incentives to accelerate the progress and impact of AI.

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Introduction

Artificial intelligence (AI), supported by timely and accurate data and evidence, has the potential to transform health care delivery by improving health outcomes, patient safety, and the affordability and accessibility of high-quality care 1 , 2 . AI integration is critical to building an infrastructure capable of caring for an increasingly aging population, utilizing an ever-increasing knowledge of disease and options for precision treatments, and combatting workforce shortages and burnout of medical professionals. However, we are not currently on track to create this future. This is in part because the health data needed to train, test, use, and surveil these tools are generally neither standardized nor accessible. This is true across the international community, although there is variable progress within individual countries. There is also universal concern about monitoring health AI tools for changes in performance as they are implemented in new places, used with diverse populations, and over time as health data may change.

The Future of Health (FOH) is an international community of senior health care leaders representing health systems, health policy, health care technology, venture funding, insurance, and risk management. FOH collaborated with the Duke-Margolis Institute for Health Policy to conduct a literature review, expert convening, and consensus-building exercise. In total, 46 senior health care leaders were engaged in this work, from eleven countries in Europe, North America, Africa, Asia, and Australia. This commentary summarizes the four priority action areas and recommendations for health care organizations and policymakers that FOH members identified as important for fully realizing AI’s potential in health care: improving data quality to power AI, building infrastructure to encourage efficient and trustworthy development and evaluations, sharing data for better AI, and providing incentives to accelerate the progress and impact of AI.

Powering AI through high-quality data

“Going forward, data are going to be the most valuable commodity in health care. Organizations need robust plans about how to mobilize and use their data.”

AI algorithms will only perform as well as the accuracy and completeness of key underlying data, and data quality is dependent on actions and workflows that encourage trust.

To begin to improve data quality, FOH members agreed that an initial priority is identifying and assuring reliable availability of high-priority data elements for promising AI applications: those with the most predictive value, those of the highest value to patients, and those most important for analyses of performance, including subgroup analyses to detect bias.

Leaders should also advocate for aligned policy incentives to improve the availability and reliability of these priority data elements. There are several examples of efforts across the world to identify and standardize high-priority data elements for AI applications and beyond, such as the multinational project STANDING Together, which is developing standards to improve the quality and representativeness of data used to build and test AI tools 3 .

Policy incentives that would further encourage high-quality data collection include (1) aligned payment incentives for measures of health care quality and safety, and ensuring the reliability of the underlying data, and (2) quality measures and performance standards focused on the reliability, completeness, and timeliness of collection and sharing of high-priority data itself.

Trust and verify

“Your AI algorithms are only going to be as good as the data and the real-world evidence used to validate them, and the data are only going to be as good as the trust and privacy and supporting policies.”

FOH members stressed the importance of showing that AI tools are both effective and safe within their specific patient populations.

This is a particular challenge with AI tools, whose performance can differ dramatically across sites and over time, as health data patterns and population characteristics vary. For example, several studies of the Epic Sepsis Model found both location-based differences in performance and degradation in performance over time due to data drift 4 , 5 . However, real-world evaluations are often much more difficult for algorithms that are used for longer-term predictions, or to avert long-term complications from occurring, particularly in the absence of connected, longitudinal data infrastructure. As such, health systems must prioritize implementing data standards and data infrastructure that can facilitate the retraining or tuning of algorithms, test for local performance and bias, and ensure scalability across the organization and longer-term applications 6 .

There are efforts to help leaders and health systems develop consensus-based evaluation techniques and infrastructure for AI tools, including HealthAI: The Global Agency for Responsible AI in Health, which aims to build and certify validation mechanisms for nations and regions to adopt; and the Coalition for Health AI (CHAI), which recently announced plans to build a US-wide health AI assurance labs network 7 , 8 . These efforts, if successful, will assist manufacturers and health systems in complying with new laws, rules, and regulations being proposed and released that seek to ensure AI tools are trustworthy, such as the EU AI Act and the 2023 US Executive Order on AI.

Sharing data for better AI

“Underlying these challenges is the investment required to standardize business processes so that you actually get data that’s usable between institutions and even within an institution.”

While high-quality internal data may enable some types of AI-tool development and testing, this is insufficient to power and evaluate all AI applications. To build truly effective AI-enabled predictive software for clinical care and predictive supports, data often need to be interoperable across health systems to build a diverse picture of patients’ health across geographies, and reliably shared.

FOH members recommended that health care leaders work with researchers and policymakers to connect detailed encounter data with longitudinal outcomes, and pilot opportunities across diverse populations and systems to help assure valid outcome evaluations as well as address potential confounding and population subgroup differences—the ability to aggregate data is a clear rate-limiting step. The South African National Digital Health Strategy outlined interventions to improve the adoption of digital technologies while complying with the 2013 Protection of Personal Information Act 9 . Although challenges remain, the country has made progress on multiple fronts, including building out a Health Patient Registration System as a first step towards a portable, longitudinal patient record system and releasing a Health Normative Standards Framework to improve data flow across institutional and geographic boundaries 10 .

Leaders should adopt policies in their organizations, and encourage adoption in their province and country, that simplify data governance and sharing while providing appropriate privacy protections – including building foundations of trust with patients and the public as previously discussed. Privacy-preserving innovations include ways to “share” data without movement from protected systems using approaches like federated analyses, data sandboxes, or synthetic data. In addition to exploring privacy-preserving approaches to data sharing, countries and health systems may need to consider broad and dynamic approaches to consent 11 , 12 . As we look to a future where a patient may have thousands of algorithms churning away at their data, efforts to improve data quality and sharing should include enabling patients’ access to and engagement with their own data to encourage them to actively partner in their health and provide transparency on how their data are being used to improve health care. For example, the Understanding Patient Data program in the United Kingdom produces research and resources to explain how the National Health Service uses patients’ data 13 . Community engagement efforts can further assist with these efforts by building trust and expanding understanding.

FOH members also stressed the importance of timely data access. Health systems should work together to establish re-usable governance and privacy frameworks that allow stakeholders to clearly understand what data will be shared and how it will be protected to reduce the time needed for data use agreements. Trusted third-party data coordinating centers could also be used to set up “precertification” systems around data quality, testing, and cybersecurity to support health organizations with appropriate data stewardship to form partnerships and access data rapidly.

Incentivizing progress for AI impact

“Unless it’s tied to some kind of compensation to the organization, the drive to help implement those tools and overcome that risk aversion is going to be very high… I do think that business driver needs to be there.”

AI tools and data quality initiatives have not moved as quickly in health care due to the lack of direct payment, and often, misalignment of financial incentives and supports for high-quality data collection and predictive analytics. This affects both the ability to purchase and safely implement commercial AI products as well as the development of “homegrown” AI tools.

FOH members recommended that leaders should advocate for paying for value in health – quality, safety, better health, and lower costs for patients. This better aligns the financial incentives for accelerating the development, evaluation, and adoption of AI as well as other tools designed to either keep patients healthy or quickly diagnose and treat them with the most effective therapies when they do become ill. Effective personalized health care requires high-quality, standardized, interoperable datasets from diverse sources 14 . Within value-based payments themselves, data are critical to measuring quality of care and patient outcomes, adjusted or contextualized for factors outside of clinical control. Value-based payments therefore align incentives for (1) high-quality data collection and trusted use, (2) building effective AI tools, and (3) ensuring that those tools are improving patient outcomes and/or health system operations.

Data have become the most valuable commodity in health care, but questions remain about whether there will be an AI “revolution” or “evolution” in health care delivery. Early AI applications in certain clinical areas have been promising, but more advanced AI tools will require higher quality, real-world data that is interoperable and secure. The steps health care organization leaders and policymakers take in the coming years, starting with short-term opportunities to develop meaningful AI applications that achieve measurable improvements in outcomes and costs, will be critical in enabling this future that can improve health outcomes, safety, affordability, and equity.

Data availability

Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

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Acknowledgements

The authors acknowledge Oranit Ido and Jonathan Gonzalez-Smith for their contributions to this work. This study was funded by The Future of Health, LLC. The Future of Health, LLC, was involved in all stages of this research, including study design, data collection, analysis and interpretation of data, and the preparation of this manuscript.

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C.S., K.H., N.R., and R.S. conducted initial background research and analyzed qualitative data from stakeholders. All authors (C.S., E.Z., K.H., N.R., R.S., M.M., C.K., C.A.S., and D.B.) assisted with conceptualization of the project and strategic guidance. C.S., K.H., and N.R. wrote initial drafts of the manuscript. All authors contributed to critical revisions of the manuscript and read and approved the final manuscript.

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C.S., K.H., N.R., and C.A.S. declare no competing interests. E.Z. reports personal fees from Arkin Holdings, personal fees from Statista and equity from Valera Health, Profility and Hello Heart. R.S. has been an external reviewer for The John A. Hartford Foundation, and is a co-chair for the Health Evolution Summit Roundtable on Value-Based Care for Specialized Populations. M.M. is an independent director on the boards of Johnson & Johnson, Cigna, Alignment Healthcare, and PrognomIQ; co-chairs the Guiding Committee for the Health Care Payment Learning and Action Network; and reports fees for serving as an adviser for Arsenal Capital Partners, Blackstone Life Sciences, and MITRE. C.K. is a Profility Board member and additionally reports equity from Valera Health and MDClone. D.W.B. reports grants and personal fees from EarlySense, personal fees from CDI Negev, equity from Valera Health, equity from Clew, equity from MDClone, personal fees and equity from AESOP, personal fees and equity from Feelbetter, equity from Guided Clinical Solutions, and grants from IBM Watson Health, outside the submitted work. D.W.B. has a patent pending (PHC-028564 US PCT), on intraoperative clinical decision support.

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Silcox, C., Zimlichmann, E., Huber, K. et al. The potential for artificial intelligence to transform healthcare: perspectives from international health leaders. npj Digit. Med. 7 , 88 (2024). https://doi.org/10.1038/s41746-024-01097-6

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