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114 Artificial Intelligence Essay Topic Ideas & Examples

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Artificial Intelligence (AI) is a rapidly growing field that has the potential to revolutionize various aspects of our lives. From autonomous vehicles to virtual assistants, AI technologies are becoming increasingly prevalent. If you have been assigned an essay on artificial intelligence and are struggling to come up with a topic, look no further. Here are 114 AI essay topic ideas and examples to inspire your writing:

• The impact of AI on job automation: How will AI technologies reshape the workforce?
• The ethical implications of AI: Should there be limits on how AI can be used?
• The future of AI in healthcare: How can AI enhance medical diagnosis and treatment?
• The role of AI in education: How can AI technologies improve the learning experience?
• AI and privacy concerns: What are the risks associated with AI technologies and personal data?
• The use of AI in criminal justice: Can AI systems make fair and unbiased decisions?
• The potential dangers of superintelligent AI: Should we be concerned about AI surpassing human intelligence?
• AI and creativity: Can AI systems be creative in the same way humans are?
• The impact of AI on mental health: How can AI technologies assist in diagnosing and treating mental illnesses?
• The role of AI in climate change mitigation: How can AI help reduce carbon emissions?
• The future of transportation with AI: How will autonomous vehicles change the way we travel?
• AI and cybersecurity: Can AI technologies enhance our ability to detect and prevent cyber attacks?
• The impact of AI on social interactions: How will AI-powered virtual assistants affect human relationships?
• Bias in AI algorithms: How can we ensure fairness and impartiality in AI decision-making?
• The ethical implications of using AI in warfare: Should autonomous weapons be allowed?
• AI and the arts: How can AI technologies be used in creative fields such as music and painting?
• The role of AI in disaster response: How can AI help in predicting and managing natural disasters?
• The impact of AI on journalism: How will AI technologies influence news reporting and media?
• The use of AI in agriculture: How can AI optimize farming practices?
• AI and financial markets: How can AI algorithms be used for better investment decisions?
• The challenges of regulating AI: How can governments ensure safe and responsible development of AI technologies?
• AI and human rights: What are the potential threats to privacy and freedom posed by AI?
• The role of AI in space exploration: How can AI assist in exploring the universe?
• AI and language translation: How can AI technologies improve communication across different languages?
• The impact of AI on creativity: Will AI systems replace human creativity or enhance it?
• The use of AI in customer service: How can AI-powered chatbots improve customer experiences?
• AI and the future of work: How will AI technologies affect employment opportunities?
• The role of AI in personalized medicine: How can AI help tailor treatments for individual patients?
• AI and education inequality: How can AI technologies bridge the gap between privileged and underprivileged students?
• The impact of AI on the economy: Will AI lead to job creation or job displacement?
• AI and augmented reality: How can AI enhance the AR experience?
• The use of AI in sports: How can AI technologies optimize performance and training?
• AI and natural language processing: How can AI understand and generate human language?
• The impact of AI on the legal profession: Will AI replace lawyers in the future?
• The role of AI in combating fake news: How can AI technologies detect and prevent misinformation?
• AI and emotional intelligence: Can AI systems develop emotional intelligence?
• The use of AI in wildlife conservation: How can AI technologies help protect endangered species?
• AI and transportation infrastructure: How can AI improve traffic management and reduce congestion?
• The impact of AI on the entertainment industry: How will AI technologies shape the future of movies and gaming?
• AI and personalized advertising: How can AI algorithms target ads to individual preferences?
• The role of AI in disaster recovery: How can AI assist in rebuilding after natural disasters?
• AI and mental well-being: Can AI technologies provide therapy and support for mental health?
• The use of AI in social media: How can AI detect and prevent harmful content?
• AI and the future of energy: How can AI optimize energy consumption and production?
• The impact of AI on democracy: What are the implications of AI for political systems?
• AI and robotics: How can AI enhance the capabilities and interactions of robots?
• AI and the aging population: How can AI technologies improve the quality of life for elderly individuals?
• The use of AI in retail: How can AI technologies personalize the shopping experience?
• AI and virtual reality: How can AI enhance the VR experience?
• The impact of AI on creativity in the workplace: Will AI systems replace or empower human creativity?
• AI and autonomous drones: What are the potential applications and risks?
• The role of AI in social justice: How can AI technologies address systemic biases and discrimination?
• AI and disaster prediction: How can AI assist in predicting natural disasters?
• The use of AI in architecture and design: How can AI technologies optimize building design?
• AI and sustainable development: How can AI help achieve environmental and social sustainability?
• The impact of AI on the music industry: How will AI technologies shape music production and consumption?
• AI and the future of democracy: Can AI improve citizen engagement and participation?
• The role of AI in personalized learning: How can AI technologies adapt educational content to individual students?
• AI and autonomous robots in healthcare: What are the benefits and risks?
• The use of AI in supply chain management: How can AI optimize logistics and inventory management?
• AI and emotional recognition: How can AI systems understand and respond to human emotions?
• The impact of AI on urban planning: How can AI technologies create smarter and more sustainable cities?
• AI and cybersecurity threats: How can AI be used to detect and prevent cyber attacks?
• The role of AI in personalized news curation: How can AI algorithms tailor news articles to individual interests?
• AI and personalized fashion: How can AI technologies help consumers find their unique style?
• The impact of AI on social inequality: Will AI exacerbate or alleviate existing inequalities?
• AI and decision-making: Can AI systems make better decisions than humans?
• The use of AI in cultural preservation: How can AI technologies help protect and restore cultural heritage?
• AI and the future of transportation infrastructure: How can AI technologies improve roads, bridges, and public transportation?
• The role of AI in early detection of diseases: How can AI assist in diagnosing illnesses at an early stage?
• AI and personalized entertainment: How can AI technologies tailor movies, music, and games to individual preferences?
• The impact of AI on customer behavior analysis: How can AI algorithms predict and influence consumer choices?
• AI and the future of democracy: How can AI technologies promote transparency and accountability in governance?
• The use of AI in disaster relief: How can AI assist in coordinating rescue and aid efforts?
• AI and sustainable agriculture: How can AI technologies optimize farming practices while minimizing environmental impact?
• The role of AI in personalized marketing: How can AI algorithms target advertisements to individual preferences?
• AI and the future of privacy: How can AI technologies protect personal data in an increasingly connected world?
• The impact of AI on creative industries: Will AI systems replace or collaborate with human artists?
• AI and autonomous ships: What are the potential benefits and challenges?
• The use of AI in wildlife monitoring: How can AI technologies help track and protect endangered species?
• AI and the future of cybersecurity: How can AI technologies stay ahead of evolving cyber threats?
• The role of AI in personalized fitness: How can AI technologies optimize exercise routines and nutrition plans?
• AI and personalized travel recommendations: How can AI algorithms suggest tailored itineraries to individual travelers?
• The impact of AI on income inequality: Will AI exacerbate or reduce economic disparities?
• AI and the future of journalism: How can AI technologies assist in news reporting and fact-checking?
• The use of AI in waste management: How can AI technologies optimize recycling and waste disposal?
• AI and autonomous farming: How can AI technologies improve crop yield and reduce resource consumption?
• The role of AI in personalized financial advice: How can AI algorithms help individuals make better financial decisions?
• AI and the future of privacy: How can AI technologies protect personal information in the age of big data?
• The impact of AI on the film industry: How will AI technologies influence movie production and special effects?
• AI and autonomous construction: What are the potential applications and challenges?
• The use of AI in marine conservation: How can AI technologies help protect marine ecosystems?
• AI and the future of transportation logistics: How can AI optimize the movement of goods and reduce carbon emissions?
• The role of AI in personalized healthcare: How can AI technologies tailor treatments to individual patients?
• AI and personalized gaming: How can AI algorithms create unique gaming experiences for individual players?
• The impact of AI on voting systems: Can AI technologies improve the accuracy and security of elections?
• AI and sustainable urban planning: How can AI technologies create greener and more livable cities?
• The use of AI in personalized nutrition: How can AI algorithms optimize diets for individual health goals?
• AI and the future of privacy: How can AI technologies balance the benefits of data analysis with privacy concerns?
• The role of AI in personalized advertising: How can AI algorithms target ads to individual preferences without invading privacy?
• AI and autonomous underwater vehicles: What are the potential applications and challenges?
• The impact of AI on wildlife conservation: How will AI technologies enhance conservation efforts?
• AI and the future of transportation safety: How can AI technologies prevent accidents and improve road conditions?
• The use of AI in personalized fashion design: How can AI algorithms create customized clothing?
• AI and sustainable energy management: How can AI technologies optimize energy usage in homes and buildings?
• The role of AI in personalized learning platforms: How can AI technologies adapt educational content to individual students' needs?
• AI and the future of privacy: How can AI technologies protect personal information from unauthorized access?
• The impact of AI on the gaming industry: How will AI technologies enhance gameplay and virtual worlds?
• AI and autonomous construction robots: What are the potential benefits and risks?
• The use of AI in personalized travel planning: How can AI algorithms create tailored itineraries based on individual preferences?
• AI and sustainable transportation: How can AI technologies optimize public transportation and reduce carbon emissions?
• The role of AI in personalized mental health support: How can AI technologies provide therapy and counseling?
• AI and personalized music creation: How can AI algorithms compose music based on individual preferences?
• The impact of AI on social media manipulation: Can AI technologies detect and prevent the spread of fake news and misinformation?

These 114 artificial intelligence essay topic ideas and examples cover a wide range of areas where AI technologies can make a significant impact. Whether you're interested in the ethical implications of AI or its potential applications in various industries, there is a topic here for you. Choose one that sparks your curiosity and start writing an insightful and engaging essay on artificial intelligence.

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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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Home — Essay Samples — Information Science and Technology — Modern Technology — Artificial Intelligence

Essays on Artificial Intelligence

Writing an essay on artificial intelligence is not just an academic exercise; it's a chance to explore the cutting-edge innovations and the profound impact AI has on our lives. For students looking to delve deeper into this topic, utilizing the best AI tools for students can provide a significant edge in crafting a well-researched and analytical essay. 🚀 So, get ready to unlock the potential of AI with your words!

Artificial Intelligence Essay Topics for "Artificial Intelligence" 📝

Choosing the right topic is key to writing a compelling essay. Here's how to pick the perfect one:

Artificial Intelligence Argumentative Essay 🤨

Argumentative AI essays require you to take a stance on AI-related issues. Here are ten thought-provoking topics:

• 1. The ethical implications of AI in autonomous weaponry.
• 2. Should AI be granted legal personhood and rights?
• 3. Analyze the impact of AI on the job market and employment prospects.
• 4. The role of AI in addressing climate change and environmental challenges.
• 5. Discuss the risks and benefits of AI in healthcare and medical diagnostics.
• 6. AI's impact on privacy and surveillance in modern society.
• 7. Evaluate the use of AI in education and personalized learning.
• 8. The role of AI in improving cybersecurity and data protection.
• 9. Discuss the potential biases and discrimination in AI algorithms.
• 10. AI and its implications for creativity and the arts.
• 11. The Ethical Implications of Programming Bias into Artificial Intelligence

Artificial Intelligence Cause and Effect Essay 🤯

Dive into cause and effect relationships in the AI realm with these topics:

• 1. Explore how AI-powered virtual assistants have changed communication habits.
• 2. Analyze the effects of AI-driven predictive policing on crime rates.
• 3. Discuss how AI-driven healthcare advancements have extended human lifespans.
• 4. The consequences of AI-powered autonomous vehicles on transportation and traffic safety.
• 5. Investigate the impact of AI algorithms on social media echo chambers and polarization.
• 6. The influence of AI-driven personalized marketing on consumer behavior.
• 7. Explore how AI has revolutionized the entertainment industry and storytelling.
• 8. Analyze the cause and effect of AI's role in financial markets and investment strategies.
• 9. Discuss the effects of AI on reducing energy consumption and sustainable living.
• 10. The consequences of AI in aiding scientific research and discovery.

Artificial Intelligence Opinion Essay 😌

Express your personal views and interpretations on AI through these essay topics:

• 1. Share your opinion on the potential dangers of superintelligent AI.
• 2. Discuss your perspective on AI's role in enhancing human capabilities.
• 3. Express your thoughts on the future of work in an AI-dominated world.
• 4. Debate the significance of AI in addressing global challenges like pandemics.
• 5. Share your views on the ethical responsibilities of AI developers and researchers.
• 6. Discuss the impact of AI on human creativity and innovation.
• 7. Express your opinion on AI's influence on education and personalized learning.
• 8. Debate the ethics of AI in decision-making, such as self-driving car dilemmas.
• 9. Share your perspective on AI's potential to bridge the digital divide and promote equity.
• 10. Discuss your favorite AI-related invention or innovation and its implications.

Artificial Intelligence Informative Essay 🧐

Inform and educate your readers with these informative AI essay topics:

• 1. Explore the history and evolution of artificial intelligence.
• 2. Provide an in-depth analysis of popular AI technologies like deep learning and neural networks.
• 3. Investigate the significance of AI in autonomous robotics and space exploration.
• 4. Analyze the role of AI in natural language processing and language translation.
• 5. Examine the applications of AI in climate modeling and environmental conservation.
• 6. Explore the cultural and societal impacts of AI in science fiction literature and films.
• 7. Provide insights into the ethics of AI in medical decision-making and diagnosis.
• 8. Analyze the potential for AI in disaster response and emergency management.
• 9. Discuss the role of AI in enhancing cybersecurity and threat detection.
• 10. Examine the future trends and possibilities of AI in various industries.
• 11. Ethical Implications of AI in Healthcare: Patient Privacy
• 12. Impact of AI on Government Services: Study of Role in UPSC Exam Process

Artificial Intelligence Essay Example 📄

Artificial intelligence thesis statement examples 📜.

Here are five examples of strong thesis statements for your AI essay:

• 1. "The rapid advancements in artificial intelligence present both unprecedented opportunities and ethical dilemmas, as we navigate the journey toward an AI-driven future."
• 2. "In analyzing the impact of AI on healthcare, we unveil a transformative force that promises to revolutionize medical diagnosis and treatment, but also raises concerns about data privacy and security."
• 3. "The development of superintelligent AI systems demands careful consideration of ethical frameworks to ensure their responsible and beneficial integration into society."
• 4. "Artificial intelligence is not a replacement for human creativity but a powerful tool that amplifies our capabilities, ushering in an era of unprecedented innovation and discovery."
• 5. "AI-driven autonomous vehicles represent a technological leap that holds the potential to reshape transportation, reduce accidents, and increase accessibility, but also raises questions about liability and safety."

Artificial Intelligence Essay Introduction Examples 🚀

Here are three captivating introduction paragraphs to begin your essay:

• 1. "In a world driven by data and algorithms, artificial intelligence has emerged as both a beacon of innovation and a source of profound ethical contemplation. As we embark on this essay journey into the realm of AI, we peel back the layers of silicon and software to explore the implications, promises, and challenges of our AI-driven future."
• 2. "Imagine a world where machines not only assist us but also think, learn, and adapt. The rise of artificial intelligence has ignited a conversation that transcends technology—it delves into the very essence of human potential and the responsibilities we bear as creators. Join us as we navigate the AI landscape, one algorithm at a time."
• 3. "In an era marked by digital transformations and the ubiquity of smart devices, artificial intelligence stands as the sentinel of change. As we step into the world of AI analysis, we are confronted with a paradox: the immense power of machines and the ethical dilemmas they pose. Together, let's dissect the AI phenomenon, from its inception to its potential to shape the destiny of humanity."

Artificial Intelligence Conclusion Examples 🌟

Conclude your essay with impact using these examples:

• 1. "As we draw the curtains on this AI exploration, we stand at the intersection of innovation and ethics. Artificial intelligence, with all its wonders and complexities, challenges us to not only harness its power for progress but also to ensure its responsible and ethical use. The journey continues, and the conversation evolves as we navigate the evolving landscape of AI."
• 2. "In the closing frame of our AI analysis, we reflect on the ever-expanding possibilities and responsibilities that AI brings to our doorstep. The pages of this essay mark a beginning—a call to action. Together, we have explored the AI landscape, and the future is now in our hands, waiting for our choices to shape it."
• 3. "As the AI narrative reaches its conclusion, we find ourselves at the crossroads of human ingenuity and artificial intelligence. The journey has been both enlightening and thought-provoking, reminding us that the future of AI is a collaborative endeavor, guided by ethics, curiosity, and a shared vision of a better world."

Ai's Prospects and Its Impact on Humanity

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Artificial Intelligence in Security and Warfare

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Ethical Issues in Using Ai Technology Today

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Artificial intelligence (AI) refers to the intellectual capabilities exhibited by machines, contrasting with the innate intelligence observed in living beings, such as animals and humans.

The inception of artificial intelligence research as an academic field can be traced back to its establishment in 1956. It was during the renowned Dartmouth conference of the same year that artificial intelligence acquired its distinctive name, definitive purpose, initial accomplishments, and notable pioneers, thereby earning its reputation as the birthplace of AI. The esteemed figures of Marvin Minsky and John McCarthy are widely recognized as the founding fathers of this discipline.

Early pioneers such as John McCarthy, Marvin Minsky, and Allen Newell played instrumental roles in shaping the foundations of AI research. In the following years after its original inception, AI witnessed both periods of optimism and periods of skepticism, as researchers explored different approaches and techniques. Notable breakthroughs include the development of expert systems in the 1970s, which aimed to replicate human knowledge and reasoning, and the emergence of machine learning algorithms in the 1980s and 1990s. The turn of the 21st century witnessed significant advancements in AI, with the rise of big data, powerful computing technologies, and deep learning algorithms. This led to remarkable achievements in areas such as natural language processing, computer vision, and autonomous systems.

There are four types of artificial intelligence: reactive machines, limited memory, theory of mind and self-awareness.

Healthcare: AI assists in medical diagnosis, drug discovery, personalized treatment plans, and analyzing medical images. Finance: AI is used for automated trading, fraud detection, risk assessment, and customer service through chatbots. Transportation: AI powers autonomous vehicles, traffic optimization, logistics, and supply chain management. Entertainment: AI contributes to recommendation systems, AI-generated music and art, virtual reality experiences, and content creation. Cybersecurity: AI helps in detecting and preventing cyber threats and enhancing network security. Agriculture: AI optimizes farming practices, crop management, and precision agriculture. Education: AI enables personalized learning, adaptive assessments, and intelligent tutoring systems. Natural Language Processing: AI facilitates language translation, voice assistants, chatbots, and sentiment analysis. Robotics: AI powers robots in various applications, such as manufacturing, healthcare, and exploration. Environmental Conservation: AI aids in environmental monitoring, wildlife protection, and climate modeling.

John McCarthy: Coined the term "artificial intelligence" and organized the Dartmouth Conference in 1956, which is considered the birth of AI as an academic discipline. Marvin Minsky: A cognitive scientist and AI pioneer, Minsky co-founded the Massachusetts Institute of Technology's AI Laboratory and made notable contributions to robotics and cognitive psychology. Geoffrey Hinton: Renowned for his work on neural networks and deep learning, Hinton's research has greatly advanced the field of AI and revolutionized areas such as image and speech recognition. Andrew Ng: An influential figure in the field of AI, Ng co-founded Google Brain, led the development of the deep learning framework TensorFlow, and has made significant contributions to machine learning algorithms. Fei-Fei Li: A prominent researcher in computer vision and AI, Li has made groundbreaking contributions to image recognition and has been a strong advocate for responsible and ethical AI development.. Demis Hassabis: Co-founder of DeepMind, a leading AI research company, Hassabis has made notable contributions to areas such as deep reinforcement learning and has led the development of groundbreaking AI systems. Elon Musk: Although primarily known for his role in space exploration and electric vehicles, Musk has also made notable contributions to AI through his involvement in companies like OpenAI and Neuralink, advocating for AI safety and ethics.

1. According to a report by IDC, global spending on AI systems is expected to reach $98.4 billion in 2023, indicating a significant increase from the $37.5 billion spent in 2019. 2. The job market for AI professionals is thriving. LinkedIn's 2021 Emerging Jobs Report listed AI specialist as one of the top emerging jobs, with a 74% annual growth rate over the past four years. 3. AI-powered chatbots are revolutionizing customer service. A study by Oracle found that 80% of businesses plan to use chatbots by 2022. Furthermore, 58% of consumers have already interacted with chatbots for customer support, indicating the growing acceptance and adoption of AI in enhancing customer experiences. 4. McKinsey Global Institute estimates that by 2030, automation and AI technologies could contribute to a global economic impact of $13 trillion. 5. The healthcare industry is leveraging AI for improved patient care. A study published in the journal Nature Medicine reported that an AI model was able to detect breast cancer with an accuracy of 94.5%, outperforming human radiologists.

The topic of artificial intelligence (AI) holds immense importance in today's world, making it an intriguing subject to explore in an essay. AI has revolutionized multiple facets of human life, ranging from technology and business to healthcare and transportation. Understanding its significance is crucial for comprehending the potential and impact of this rapidly evolving field. Firstly, AI has the power to reshape industries and transform economies. It enables automation, streamlines processes, and enhances efficiency, leading to increased productivity and economic growth. Moreover, AI advancements have the potential to address complex societal challenges, such as healthcare accessibility, environmental sustainability, and resource management. Secondly, AI raises ethical considerations and socio-economic implications. Discussions on privacy, bias, job displacement, and AI's role in decision-making become essential for navigating its responsible implementation. Examining the ethical dimensions of AI fosters critical thinking and encourages the development of guidelines and regulations to ensure its ethical use. Lastly, exploring AI allows us to envision the future possibilities and risks associated with this technology. It sparks discussions on the boundaries of machine intelligence, the potential for sentient AI, and the impact on human existence. By studying AI, we gain insights into technological progress, its limitations, and the responsibilities associated with harnessing its potential.

1. Russell, S. J., & Norvig, P. (2016). Artificial Intelligence: A Modern Approach (3rd ed.). Prentice Hall. 2. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. 3. Kurzweil, R. (2005). The Singularity Is Near: When Humans Transcend Biology. Viking. 4. Bostrom, N. (2014). Superintelligence: Paths, Dangers, Strategies. Oxford University Press. 5. Chollet, F. (2017). Deep Learning with Python. Manning Publications. 6. Domingos, P. (2018). The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World. Basic Books. 7. Ng, A. (2017). Machine Learning Yearning. deeplearning.ai. 8. Marcus, G. (2018). Rebooting AI: Building Artificial Intelligence We Can Trust. Vintage. 9. Winfield, A. (2018). Robotics: A Very Short Introduction. Oxford University Press. 10. Shalev-Shwartz, S., & Ben-David, S. (2014). Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press.

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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.

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Artificial Intelligence - Essay Samples And Topic Ideas For Free

A college essay on AI allows one to delve into the intriguing world where machines and algorithms shape the future. It demonstrates how exciting the field of advanced technology and its impact on human society can be. When preparing a persuasive and argumentative essay on artificial intelligence, it is essential to explore the potential danger and benefits associated with AI.

To begin, select from a range of compelling essay topics related to this. This could include exploring the ethical implications of AI, the role of it in healthcare, or its impact on the job market. Conduct careful analysis using reputable sources. For example, it can be an interesting research paper on artificial intelligence or free samples to support your arguments.

The next step is to formulate a clear and concise thesis statement that will convey your position on the topic. Creating an outline will help you with this. Each paragraph in the body should focus on a specific aspect of AI, such as cybernetic systems or the ethical considerations surrounding AI development.

In the introduction of your paper, you can highlight the rapid advancements in AI and its pervasive presence in various industries. It will be useful to mention such an organization as OpenAI. Do not forget to craft a captivating hook to capture the reader’s attention. It could be a thought-provoking question, a startling statistic, or a compelling anecdote. No matter what you choose, it should emphasize the significance of AI technology in today’s world. At the conclusion of the essay, all you have to do is summarize the key points discussed in your paper.

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In the late 90s and early 21st century, AI technology became widely used as elements of larger systems, but the field is rarely credited for these successes. For example, music, toys and games, transportation, finance, hospitals and medicine, news, publishing & writing, aviation, and heavy industries. Not only that, but "it has increased the level of performance of physicians at hospital facilities. The situation acts in the interest of patients who are regarded as customers" (Nadimpalli, 1). According to the […]

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Technology is in our lives every day. Smartphones, computers, tablets, and laptops have all become extensions of ourselves. Now, a new type of technology has appeared: artificial intelligence. Unlike previous technology, artificial intelligence is just that. A machine that simulates intelligence. It does this so well that nobody can tell the difference. Artificial intelligence, or AI, is divided into three subsections: artificial narrow intelligence, artificial general intelligence, and artificial superintelligence (Pasichnyk and Strelkova). Artificial narrow intelligence (ANI) is AI specifically […]

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In this research paper, it will explain what artificial intelligence is and how it has affected the accounting industry. Whenever people think of artificial intelligence they contemplate of new technology that has now evolved and has taken over human and animal intelligence. So basically, a machine doing human tasks, for example a self-driving car which doesn't need a human body to drive it because the device (car) will drive on its own. This can both be a good and a […]

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Over one hundred thirty million people worldwide use the Netflix streaming service; however, most may not know how the recommendation system works. The brilliant mind behind this program is actually an algorithm produced under the influence of Artificial Intelligence (AI). AI is a developing technology with a "learning" capacity that seemingly imitates human capabilities. The field of AI originated in 1950s thanks to John McCarthy, a professor of computer science at Stanford, whose goal was to "[mimic] the logic-based reasoning […]

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Since the dawn of novels and television, the notion of artificial intelligence in the form of robots has been a reoccurring theme in the science fiction genre. The over dramatization of inimical artificial intelligence in these fictional narratives has led the general population to form a slight aversion to the idea of further developing artificial intelligence. The inherent fear of the unknown has also contributed to this problem; people are afraid of developing a race that could potentially replace us […]

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How To Write An Essay On Artificial Intelligence

Introduction to the concept of artificial intelligence.

When writing an essay on artificial intelligence (AI), it's important to start by defining what AI is and its significance in the modern world. Artificial intelligence refers to the simulation of human intelligence in machines that are programmed to think and learn like humans. The introduction should provide a brief overview of the development of AI, from its inception to its current state. This will set the stage for a deeper exploration of various aspects of AI, such as its applications, ethical considerations, and potential future developments. Your introduction should also clearly state your thesis or main argument, which will guide the direction of your essay.

Exploring the Applications and Benefits of AI

The body of your essay should delve into the various applications and benefits of AI in different sectors. Discuss how AI is transforming industries such as healthcare, finance, transportation, and more. For instance, in healthcare, AI can assist in diagnosing diseases and personalizing treatment plans. In finance, AI algorithms are used for risk assessment and fraud detection. Highlight the efficiency, accuracy, and cost-effectiveness AI brings to these fields. This part of the essay should provide concrete examples of AI applications, demonstrating the significant impact of AI on improving various aspects of society and business.

Addressing Ethical and Societal Implications

An essential aspect of writing about AI is addressing the ethical and societal implications. Discuss the ethical dilemmas posed by AI, such as privacy concerns, job displacement due to automation, and the potential misuse of AI technologies. Explore how AI could affect social dynamics, including the digital divide and biases in AI algorithms. This section should also consider how regulations and policies are being developed to guide the ethical development and deployment of AI. The objective here is to present a balanced view that not only highlights the advancements AI brings but also critically examines the challenges and concerns it poses.

Concluding with Future Perspectives on AI

Conclude your essay by summarizing the main points discussed and offering a perspective on the future of AI. Reflect on the potential advancements in AI technology and what they could mean for society. Consider the role of AI in shaping future job markets, its integration in everyday life, and how it might evolve in the coming years. Discuss the importance of responsible innovation and the role of governments, industries, and academia in shaping the future of AI. A well-crafted conclusion will not only bring closure to your essay but also encourage further thought and discussion about the role of AI in shaping our future.

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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 .

Get the huge list of more than 500 Essay Topics and Ideas

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 Essay

Who would have imagined that we would live in a world with self-driven cars, robots that could clean our homes, or virtual assistants that could respond to any question we had without having to type it into our mobile devices? Yet, here we are, with all of these amenities that are enhancing and simplifying our lives. Here are some sample essays on artificial intelligence.

100 Words Essay On Artificial Intelligence

200 words essay on artificial intelligence, 500 words essay on artificial intelligence, 4 types of artificial intelligence.

Artificial intelligence is referred to as machine intelligence. It basically refers to the capability of machines to emulate human intelligence. Machines are now capable of learning, planning, thinking, and problem-solving, thanks to artificial intelligence. The most notable aspect of artificial intelligence is how robots simulate human intellect. It is probably the fastest-growing development in the world of technology and innovation. AI is a technology that is transforming every walk of life. Today, artificial intelligence is being used more and more in practically every industry. While technology has improved and eased our lives, it has also put many people's employment at risk.

Artificial intelligence was developed in 1950. John McCarthy is considered as the “Father of artificial intelligence” as he first coined the term. The development of gadgets that can mimic human intelligence, including abilities such as voice recognition, decision-making, and language translation, is known as artificial intelligence. Making computers understand like humans, think like humans, and act like humans is done by instilling data as instructions and inputs.

Taking Over Industry | The need for human help is dwindling as AI has taken over many parts of the industry, which may result in concerns with unemployment in many occupations. However, the degree of control the human species chooses to give technology will always be up to them. As is correctly said, technology works best when it unites people.

AI In Everyday Life | Advancement in technology has a direct influence on our lives, businesses and social life. Whether or not we consciously realise it, AI has begun to penetrate the various aspects of our everyday lives. If we observe, many aspects of our everyday lives now involve the use of artificial intelligence, for instance, face ID and image recognition features in mobile phones, emails, various apps, digital voice assistants like Apple’s Siri and Amazon’s Alexa, Google search, route mapping, traffic updates, weather updates, Netflix and Amazon for entertainment, etc.

The field of computer science and engineering has attempted to simulate the features of human intelligence for a very long time with the help of machines, which is called artificial intelligence. The acronym for it is AI. The things that machines are designed to do through the implementation of AI are to learn, understand, reason, adapt, etc.

British mathematician and computer pioneer, Alan Turing, initiated developments in the then new field of artificial intelligence. In 1950, Turing predicted that a machine will one day be able to completely replicate human intellect.

Artificial Intelligence is the science and engineering of making intelligent machines that would make human life easier. It is concerned with getting computers to do tasks that would normally require human intelligence. AI started showing promising solutions for industry and businesses as well as our daily lives. The developments in artificial intelligence were initially slow and eventually gained pace. But recently, due to advancement of the technological era, the popularity of artificial intelligence got a boost with the evolutionary discoveries being made in the field.

Artificial Intelligence has been categorised into four categories by Arend Hintze:-

The first types of AI are those machines which can react to certain situations but don’t have any sort of memory and hence cannot learn or use past experience. For example – computer chess games.

The second types of AI are those machines which are capable of using past memory to form future ones. An excellent example of this second type of AI is self-driven cars.

The third type of AI at present only exists in theory, and as per the imagination, it will be able to have human emotions like beliefs, desires, opinions, intentions, etc.

The fourth form of AI if ever comes to exist would be the type of AI machine that will be able to have a sense of self-awareness, emotions and consciousness. If this type of AI ever comes to exist, then it would be a real revolution.

Advantages And Disadvantages Of Artificial Intelligence

Just like two sides to a coin, artificial intelligence is subject to be beneficial and have harmful impacts too.

Saves Manual Effort | One of the emerging technologies, artificial intelligence, is proven to be beneficial. It lessens the burden of work that is to be done manually and so a person may save a lot of time with the help of technology. AI can work endlessly without getting tired, unlike humans, and hence bring more work productivity.

Reduces Human Interaction | The negative effects of artificial intelligence are numerous. Any new technology is only intended to make our work simpler; it does not imply that we should stop working. AI requires less human interaction, making students more engaged with the online world than the world in front of them.

Technology is always evolving. It is demonstrating to be a benefit to humanity. Every type of technology has both advantages and disadvantages, and artificial intelligence is no exception. It is important in several fields of business and study, but if misused, it might become a danger to humanity.

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Artificial Intelligence and Its Impact on Education Essay

Introduction, ai’s impact on education, the impact of ai on teachers, the impact of ai on students, reference list.

Rooted in computer science, Artificial Intelligence (AI) is defined by the development of digital systems that can perform tasks, which are dependent on human intelligence (Rexford, 2018). Interest in the adoption of AI in the education sector started in the 1980s when researchers were exploring the possibilities of adopting robotic technologies in learning (Mikropoulos, 2018). Their mission was to help learners to study conveniently and efficiently. Today, some of the events and impact of AI on the education sector are concentrated in the fields of online learning, task automation, and personalization learning (Chen, Chen and Lin, 2020). The COVID-19 pandemic is a recent news event that has drawn attention to AI and its role in facilitating online learning among other virtual educational programs. This paper seeks to find out the possible impact of artificial intelligence on the education sector from the perspectives of teachers and learners.

Technology has transformed the education sector in unique ways and AI is no exception. As highlighted above, AI is a relatively new area of technological development, which has attracted global interest in academic and teaching circles. Increased awareness of the benefits of AI in the education sector and the integration of high-performance computing systems in administrative work have accelerated the pace of transformation in the field (Fengchun et al. , 2021). This change has affected different facets of learning to the extent that government agencies and companies are looking to replicate the same success in their respective fields (IBM, 2020). However, while the advantages of AI are widely reported in the corporate scene, few people understand its impact on the interactions between students and teachers. This research gap can be filled by understanding the impact of AI on the education sector, as a holistic ecosystem of learning.

As these gaps in education are minimized, AI is contributing to the growth of the education sector. Particularly, it has increased the number of online learning platforms using big data intelligence systems (Chen, Chen and Lin, 2020). This outcome has been achieved by exploiting opportunities in big data analysis to enhance educational outcomes (IBM, 2020). Overall, the positive contributions that AI has had to the education sector mean that it has expanded opportunities for growth and development in the education sector (Rexford, 2018). Therefore, teachers are likely to benefit from increased opportunities for learning and growth that would emerge from the adoption of AI in the education system.

The impact of AI on teachers can be estimated by examining its effects on the learning environment. Some of the positive outcomes that teachers have associated with AI adoption include increased work efficiency, expanded opportunities for career growth, and an improved rate of innovation adoption (Chen, Chen and Lin, 2020). These benefits are achievable because AI makes it possible to automate learning activities. This process gives teachers the freedom to complete supplementary tasks that support their core activities. At the same time, the freedom they enjoy may be used to enhance creativity and innovation in their teaching practice. Despite the positive outcomes of AI adoption in learning, it undermines the relevance of teachers as educators (Fengchun et al., 2021). This concern is shared among educators because the increased reliance on robotics and automation through AI adoption has created conditions for learning to occur without human input. Therefore, there is a risk that teacher participation may be replaced by machine input.

Performance Evaluation emerges as a critical area where teachers can benefit from AI adoption. This outcome is feasible because AI empowers teachers to monitor the behaviors of their learners and the differences in their scores over a specific time (Mikropoulos, 2018). This comparative analysis is achievable using advanced data management techniques in AI-backed performance appraisal systems (Fengchun et al., 2021). Researchers have used these systems to enhance adaptive group formation programs where groups of students are formed based on a balance of the strengths and weaknesses of the members (Live Tiles, 2021). The information collected using AI-backed data analysis techniques can be recalibrated to capture different types of data. For example, teachers have used AI to understand students’ learning patterns and the correlation between these configurations with the individual understanding of learning concepts (Rexford, 2018). Furthermore, advanced biometric techniques in AI have made it possible for teachers to assess their student’s learning attentiveness.

Overall, the contributions of AI to the teaching practice empower teachers to redesign their learning programs to fill the gaps identified in the performance assessments. Employing the capabilities of AI in their teaching programs has also made it possible to personalize their curriculums to empower students to learn more effectively (Live Tiles, 2021). Nonetheless, the benefits of AI to teachers could be undermined by the possibility of job losses due to the replacement of human labor with machines and robots (Gulson et al. , 2018). These fears are yet to materialize but indications suggest that AI adoption may elevate the importance of machines above those of human beings in learning.

The benefits of AI to teachers can be replicated in student learning because learners are recipients of the teaching strategies adopted by teachers. In this regard, AI has created unique benefits for different groups of learners based on the supportive role it plays in the education sector (Fengchun et al., 2021). For example, it has created conditions necessary for the use of virtual reality in learning. This development has created an opportunity for students to learn at their pace (Live Tiles, 2021). Allowing students to learn at their pace has enhanced their learning experiences because of varied learning speeds. The creation of virtual reality using AI learning has played a significant role in promoting equality in learning by adapting to different learning needs (Live Tiles, 2021). For example, it has helped students to better track their performances at home and identify areas of improvement in the process. In this regard, the adoption of AI in learning has allowed for the customization of learning styles to improve students’ attention and involvement in learning.

AI also benefits students by personalizing education activities to suit different learning styles and competencies. In this analysis, AI holds the promise to develop personalized learning at scale by customizing tools and features of learning in contemporary education systems (du Boulay, 2016). Personalized learning offers several benefits to students, including a reduction in learning time, increased levels of engagement with teachers, improved knowledge retention, and increased motivation to study (Fengchun et al., 2021). The presence of these benefits means that AI enriches students’ learning experiences. Furthermore, AI shares the promise of expanding educational opportunities for people who would have otherwise been unable to access learning opportunities. For example, disabled people are unable to access the same quality of education as ordinary students do. Today, technology has made it possible for these underserved learners to access education services.

Based on the findings highlighted above, AI has made it possible to customize education services to suit the needs of unique groups of learners. By extension, AI has made it possible for teachers to select the most appropriate teaching methods to use for these student groups (du Boulay, 2016). Teachers have reported positive outcomes of using AI to meet the needs of these underserved learners (Fengchun et al., 2021). For example, through online learning, some of them have learned to be more patient and tolerant when interacting with disabled students (Fengchun et al., 2021). AI has also made it possible to integrate the educational and curriculum development plans of disabled and mainstream students, thereby standardizing the education outcomes across the divide. Broadly, these statements indicate that the expansion of opportunities via AI adoption has increased access to education services for underserved groups of learners.

Overall, AI holds the promise to solve most educational challenges that affect the world today. UNESCO (2021) affirms this statement by saying that AI can address most problems in learning through innovation. Therefore, there is hope that the adoption of new technology would accelerate the process of streamlining the education sector. This outcome could be achieved by improving the design of AI learning programs to make them more effective in meeting student and teachers’ needs. This contribution to learning will help to maximize the positive impact and minimize the negative effects of AI on both parties.

The findings of this study demonstrate that the application of AI in education has a largely positive impact on students and teachers. The positive effects are summarized as follows: improved access to education for underserved populations improved teaching practices/instructional learning, and enhanced enthusiasm for students to stay in school. Despite the existence of these positive views, negative outcomes have also been highlighted in this paper. They include the potential for job losses, an increase in education inequalities, and the high cost of installing AI systems. These concerns are relevant to the adoption of AI in the education sector but the benefits of integration outweigh them. Therefore, there should be more support given to educational institutions that intend to adopt AI. Overall, this study demonstrates that AI is beneficial to the education sector. It will improve the quality of teaching, help students to understand knowledge quickly, and spread knowledge via the expansion of educational opportunities.

Chen, L., Chen, P. and Lin, Z. (2020) ‘Artificial intelligence in education: a review’, Institute of Electrical and Electronics Engineers Access , 8(1), pp. 75264-75278.

du Boulay, B. (2016) Artificial intelligence as an effective classroom assistant. Institute of Electrical and Electronics Engineers Intelligent Systems , 31(6), pp.76–81.

Fengchun, M. et al. (2021) AI and education: a guide for policymakers . Paris: UNESCO Publishing.

Gulson, K . et al. (2018) Education, work and Australian society in an AI world . Web.

IBM. (2020) Artificial intelligence . Web.

Live Tiles. (2021) 15 pros and 6 cons of artificial intelligence in the classroom . Web.

Mikropoulos, T. A. (2018) Research on e-Learning and ICT in education: technological, pedagogical and instructional perspectives . New York, NY: Springer.

Rexford, J. (2018) The role of education in AI (and vice versa). Web.

Seo, K. et al. (2021) The impact of artificial intelligence on learner–instructor interaction in online learning. International Journal of Educational Technology in Higher Education , 18(54), pp. 1-12.

UNESCO. (2021) Artificial intelligence in education . Web.

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IvyPanda. (2023, October 1). Artificial Intelligence and Its Impact on Education. https://ivypanda.com/essays/artificial-intelligence-and-its-impact-on-education/

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How artificial intelligence is transforming the world

Subscribe to the center for technology innovation newsletter, 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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• 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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This repository is an up-to-date list of significant AI papers organized by publication date. It covers five fields : computer vision, natural language processing, audio processing, multimodal learning and reinforcement learning. Feel free to give this repository a star if you enjoy the work.

Maintainer: Aimerou Ndiaye

Table of Contents

Computer vision.

• Natural Language Processing

Audio Processing

Multimodal learning, reinforcement learning, other papers, historical papers.

To select the most relevant papers, we chose subjective limits in terms of number of citations. Each icon here designates a paper type that meets one of these criteria.

🏆 Historical Paper : more than 10k citations and a decisive impact in the evolution of AI.

⭐ Important Paper : more than 50 citations and state of the art results.

⏫ Trend : 1 to 50 citations, recent and innovative paper with growing adoption.

📰 Important Article : decisive work that was not accompanied by a research paper.

2023 Papers

• ⭐ 01/2023: Muse: Text-To-Image Generation via Masked Generative Transformers (Muse)
• ⭐ 02/2023: Structure and Content-Guided Video Synthesis with Diffusion Models (Gen-1)
• ⭐ 02/2023: Scaling Vision Transformers to 22 Billion Parameters (ViT 22B)
• ⭐ 02/2023: Adding Conditional Control to Text-to-Image Diffusion Models (ControlNet)
• ⭐ 03/2023: Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models (Visual ChatGPT)
• ⭐ 03/2023: Scaling up GANs for Text-to-Image Synthesis (GigaGAN)
• ⭐ 04/2023: Segment Anything (SAM)
• ⭐ 04/2023: DINOv2: Learning Robust Visual Features without Supervision (DINOv2)
• ⭐ 04/2023: Visual Instruction Tuning
• ⭐ 04/2023: Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models (VideoLDM)
• ⭐ 04/2023: Synthetic Data from Diffusion Models Improves ImageNet Classification
• ⭐ 04/2023: Segment Anything in Medical Images (MedSAM)
• ⭐ 05/2023: Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold (DragGAN)
• ⭐ 06/2023: Neuralangelo: High-Fidelity Neural Surface Reconstruction (Neuralangelo)
• ⭐ 07/2023: SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis (SDXL)
• ⭐ 08/2023: 3D Gaussian Splatting for Real-Time Radiance Field Rendering
• ⭐ 08/2023: Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization... (Qwen-VL)
• ⏫ 08/2023: MVDream: Multi-view Diffusion for 3D Generation (MVDream)
• ⏫ 11/2023: Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks (Florence-2)
• ⏫ 12/2023: VideoPoet: A Large Language Model for Zero-Shot Video Generation (VideoPoet)
• ⭐ 01/2023: DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature (DetectGPT)
• ⭐ 02/2023: Toolformer: Language Models Can Teach Themselves to Use Tools (Toolformer)
• ⭐ 02/2023: LLaMA: Open and Efficient Foundation Language Models (LLaMA)
• 📰 03/2023: GPT-4
• ⭐ 03/2023: Sparks of Artificial General Intelligence: Early experiments with GPT-4 (GPT-4 Eval)
• ⭐ 03/2023: HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in HuggingFace (HuggingGPT)
• ⭐ 03/2023: BloombergGPT: A Large Language Model for Finance (BloombergGPT)
• ⭐ 04/2023: Instruction Tuning with GPT-4
• ⭐ 04/2023: Generative Agents: Interactive Simulacra of Human (Gen Agents)
• ⭐ 05/2023: PaLM 2 Technical Report (PaLM-2)
• ⭐ 05/2023: Tree of Thoughts: Deliberate Problem Solving with Large Language Models (ToT)
• ⭐ 05/2023: LIMA: Less Is More for Alignment (LIMA)
• ⭐ 05/2023: QLoRA: Efficient Finetuning of Quantized LLMs (QLoRA)
• ⭐ 05/2023: Voyager: An Open-Ended Embodied Agent with Large Language Models (Voyager)
• ⭐ 07/2023: ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs (ToolLLM)
• ⭐ 08/2023: MetaGPT: Meta Programming for Multi-Agent Collaborative Framework (MetaGPT)
• ⭐ 08/2023: Code Llama: Open Foundation Models for Code (Code Llama)
• ⏫ 09/2023: RLAIF: Scaling Reinforcement Learning from Human Feedback with AI Feedback (RLAIF)
• ⭐ 09/2023: Large Language Models as Optimizers (OPRO)
• ⏫ 10/2023: Eureka: Human-Level Reward Design via Coding Large Language Models (Eureka)
• ⏫ 12/2023: Mathematical discoveries from program search with large language models (FunSearch)
• ⭐ 01/2023: Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers (VALL-E)
• ⭐ 01/2023: MusicLM: Generating Music From Text (MusicLM)
• ⭐ 01/2023: AudioLDM: Text-to-Audio Generation with Latent Diffusion Models (AudioLDM)
• ⭐ 03/2023: Google USM: Scaling Automatic Speech Recognition Beyond 100 Languages (USM)
• ⭐ 05/2023: Scaling Speech Technology to 1,000+ Languages (MMS)
• ⏫ 06/2023: Simple and Controllable Music Generation (MusicGen)
• ⏫ 06/2023: AudioPaLM: A Large Language Model That Can Speak and Listen (AudioPaLM)
• ⏫ 06/2023: Voicebox: Text-Guided Multilingual Universal Speech Generation at Scale (Voicebox)
• ⭐ 02/2023: Language Is Not All You Need: Aligning Perception with Language Models (Kosmos-1)
• ⭐ 03/2023: PaLM-E: An Embodied Multimodal Language Model (PaLM-E)
• ⭐ 04/2023: AudioGPT: Understanding and Generating Speech, Music, Sound, and Talking Head (AudioGPT)
• ⭐ 05/2023: ImageBind: One Embedding Space To Bind Them All (ImageBind)
• ⏫ 07/2023: Scaling Autoregressive Multi-Modal Models: Pretraining and Instruction Tuning (CM3Leon)
• ⏫ 07/2023: Meta-Transformer: A Unified Framework for Multimodal Learning (Meta-Transformer)
• ⏫ 08/2023: SeamlessM4T: Massively Multilingual & Multimodal Machine Translation (SeamlessM4T)
• ⭐ 01/2023: Mastering Diverse Domains through World Models (DreamerV3)
• ⏫ 02/2023: Grounding Large Language Models in Interactive Environments with Online RL (GLAM)
• ⏫ 02/2023: Efficient Online Reinforcement Learning with Offline Data (RLPD)
• ⏫ 03/2023: Reward Design with Language Models
• ⭐ 05/2023: Direct Preference Optimization: Your Language Model is Secretly a Reward Model (DPO)
• ⏫ 06/2023: Faster sorting algorithms discovered using deep reinforcement learning (AlphaDev)
• ⏫ 08/2023: Retroformer: Retrospective Large Language Agents with Policy Gradient Optimization (Retroformer)
• ⭐ 02/2023: Symbolic Discovery of Optimization Algorithms (Lion)
• ⭐ 07/2023: RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control (RT-2)
• ⏫ 11/2023: Scaling deep learning for materials discovery (GNoME)
• ⏫ 12/2023: Discovery of a structural class of antibiotics with explainable deep learning

2022 Papers

• ⭐ 01/2022: A ConvNet for the 2020s (ConvNeXt)
• ⭐ 01/2022: Patches Are All You Need (ConvMixer)
• ⭐ 02/2022: Block-NeRF: Scalable Large Scene Neural View Synthesis (Block-NeRF)
• ⭐ 03/2022: DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection (DINO)
• ⭐ 03/2022: Scaling Up Your Kernels to 31×31: Revisiting Large Kernel Design in CNNs (Large Kernel CNN)
• ⭐ 03/2022: TensoRF: Tensorial Radiance Fields (TensoRF)
• ⭐ 04/2022: MaxViT: Multi-Axis Vision Transformer (MaxViT)
• ⭐ 04/2022: Hierarchical Text-Conditional Image Generation with CLIP Latents (DALL-E 2)
• ⭐ 05/2022: Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding (Imagen)
• ⭐ 05/2022: GIT: A Generative Image-to-text Transformer for Vision and Language (GIT)
• ⭐ 06/2022: CMT: Convolutional Neural Network Meet Vision Transformers (CMT)
• ⭐ 07/2022: Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors... (Swin UNETR)
• ⭐ 07/2022: Classifier-Free Diffusion Guidance
• ⭐ 08/2022: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation (DreamBooth)
• ⭐ 09/2022: DreamFusion: Text-to-3D using 2D Diffusion (DreamFusion)
• ⭐ 09/2022: Make-A-Video: Text-to-Video Generation without Text-Video Data (Make-A-Video)
• ⭐ 10/2022: On Distillation of Guided Diffusion Models
• ⭐ 10/2022: LAION-5B: An open large-scale dataset for training next generation image-text models (LAION-5B)
• ⭐ 10/2022: Imagic: Text-Based Real Image Editing with Diffusion Models (Imagic)
• ⭐ 11/2022: Visual Prompt Tuning
• ⭐ 11/2022: Magic3D: High-Resolution Text-to-3D Content Creation (Magic3D)
• ⭐ 11/2022: DiffusionDet: Diffusion Model for Object Detection (DiffusionDet)
• ⭐ 11/2022: InstructPix2Pix: Learning to Follow Image Editing Instructions (InstructPix2Pix)
• ⭐ 12/2022: Multi-Concept Customization of Text-to-Image Diffusion (Custom Diffusion)
• ⭐ 12/2022: Scalable Diffusion Models with Transformers (DiT)
• ⭐ 01/2022: LaMBDA: Language Models for Dialog Applications (LaMBDA)
• ⭐ 01/2022: Chain-of-Thought Prompting Elicits Reasoning in Large Language Models (CoT)
• ⭐ 02/2022: Competition-Level Code Generation with AlphaCode (AlphaCode)
• ⭐ 02/2022: Finetuned Language Models Are Zero-Shot Learners (FLAN)
• ⭐ 03/2022: Training language models to follow human instructions with human feedback (InstructGPT)
• ⭐ 03/2022: Multitask Prompted Training Enables Zero-Shot Task Generalization (T0)
• ⭐ 03/2022: Training Compute-Optimal Large Language Models (Chinchilla)
• ⭐ 04/2022: Do As I Can, Not As I Say: Grounding Language in Robotic Affordances (SayCan)
• ⭐ 04/2022: GPT-NeoX-20B: An Open-Source Autoregressive Language Model (GPT-NeoX)
• ⭐ 04/2022: PaLM: Scaling Language Modeling with Pathways (PaLM)
• ⭐ 06/2022: Beyond the Imitation Game: Quantifying and extrapolating the capabilities of lang... (BIG-bench)
• ⭐ 06/2022: Solving Quantitative Reasoning Problems with Language Models (Minerva)
• ⭐ 10/2022: ReAct: Synergizing Reasoning and Acting in Language Models (ReAct)
• ⭐ 11/2022: BLOOM: A 176B-Parameter Open-Access Multilingual Language Model (BLOOM)
• 📰 11/2022: Optimizing Language Models for Dialogue (ChatGPT)
• ⭐ 12/2022: Large Language Models Encode Clinical Knowledge (Med-PaLM)
• ⭐ 02/2022: mSLAM: Massively multilingual joint pre-training for speech and text (mSLAM)
• ⭐ 02/2022: ADD 2022: the First Audio Deep Synthesis Detection Challenge (ADD)
• ⭐ 03/2022: Efficient Training of Audio Transformers with Patchout (PaSST)
• ⭐ 04/2022: MAESTRO: Matched Speech Text Representations through Modality Matching (Maestro)
• ⭐ 05/2022: SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language... (SpeechT5)
• ⭐ 06/2022: WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing (WavLM)
• ⭐ 07/2022: BigSSL: Exploring the Frontier of Large-Scale Semi-Supervised Learning for ASR (BigSSL)
• ⭐ 08/2022: MuLan: A Joint Embedding of Music Audio and Natural Language (MuLan)
• ⭐ 09/2022: AudioLM: a Language Modeling Approach to Audio Generation (AudioLM)
• ⭐ 09/2022: AudioGen: Textually Guided Audio Generation (AudioGen)
• ⭐ 10/2022: High Fidelity Neural Audio Compression (EnCodec)
• ⭐ 12/2022: Robust Speech Recognition via Large-Scale Weak Supervision (Whisper)
• ⭐ 01/2022: BLIP: Boostrapping Language-Image Pre-training for Unified Vision-Language... (BLIP)
• ⭐ 02/2022: data2vec: A General Framework for Self-supervised Learning in Speech, Vision and... (Data2vec)
• ⭐ 03/2022: VL-Adapter: Parameter-Efficient Transfer Learning for Vision-and-Language Tasks (VL-Adapter)
• ⭐ 04/2022: Winoground: Probing Vision and Language Models for Visio-Linguistic... (Winoground)
• ⭐ 04/2022: Flamingo: a Visual Language Model for Few-Shot Learning (Flamingo)
• ⭐ 05/2022: A Generalist Agent (Gato)
• ⭐ 05/2022: CoCa: Contrastive Captioners are Image-Text Foundation Models (CoCa)
• ⭐ 05/2022: VLMo: Unified Vision-Language Pre-Training with Mixture-of-Modality-Experts (VLMo)
• ⭐ 08/2022: Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language Tasks (BEiT)
• ⭐ 09/2022: PaLI: A Jointly-Scaled Multilingual Language-Image Model (PaLI)
• ⭐ 01/2022: Learning robust perceptive locomotion for quadrupedal robots in the wild
• ⭐ 02/2022: BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learning
• ⭐ 02/2022: Outracing champion Gran Turismo drivers with deep reinforcement learning (Sophy)
• ⭐ 02/2022: Magnetic control of tokamak plasmas through deep reinforcement learning
• ⭐ 08/2022: Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning (ANYmal)
• ⭐ 10/2022: Discovering faster matrix multiplication algorithms with reinforcement learning (AlphaTensor)
• ⭐ 02/2022: FourCastNet: A Global Data-driven High-resolution Weather Model... (FourCastNet)
• ⭐ 05/2022: ColabFold: making protein folding accessible to all (ColabFold)
• ⭐ 06/2022: Measuring and Improving the Use of Graph Information in GNN
• ⭐ 10/2022: TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis (TimesNet)
• ⭐ 12/2022: RT-1: Robotics Transformer for Real-World Control at Scale (RT-1)
• 🏆 1958: Perceptron: A probabilistic model for information storage and organization in the brain (Perceptron)
• 🏆 1986: Learning representations by back-propagating errors (Backpropagation)
• 🏆 1986: Induction of decision trees (CART)
• 🏆 1989: A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition (HMM)
• 🏆 1989: Multilayer feedforward networks are universal approximators
• 🏆 1992: A training algorithm for optimal margin classifiers (SVM)
• 🏆 1996: Bagging predictors
• 🏆 1998: Gradient-based learning applied to document recognition (CNN/GTN)
• 🏆 2001: Random Forests
• 🏆 2001: A fast and elitist multiobjective genetic algorithm (NSGA-II)
• 🏆 2003: Latent Dirichlet Allocation (LDA)
• 🏆 2006: Reducing the Dimensionality of Data with Neural Networks (Autoencoder)
• 🏆 2008: Visualizing Data using t-SNE (t-SNE)
• 🏆 2009: ImageNet: A large-scale hierarchical image database (ImageNet)
• 🏆 2012: ImageNet Classification with Deep Convolutional Neural Networks (AlexNet)
• 🏆 2013: Efficient Estimation of Word Representations in Vector Space (Word2vec)
• 🏆 2013: Auto-Encoding Variational Bayes (VAE)
• 🏆 2014: Generative Adversarial Networks (GAN)
• 🏆 2014: Dropout: A Simple Way to Prevent Neural Networks from Overfitting (Dropout)
• 🏆 2014: Sequence to Sequence Learning with Neural Networks
• 🏆 2014: Neural Machine Translation by Jointly Learning to Align and Translate (RNNSearch-50)
• 🏆 2014: Adam: A Method for Stochastic Optimization (Adam)
• 🏆 2015: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Cov... (BatchNorm)
• 🏆 2015: Going Deeper With Convolutions (Inception)
• 🏆 2015: Human-level control through deep reinforcement learning (Deep Q Network)
• 🏆 2015: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (Faster R-CNN)
• 🏆 2015: U-Net: Convolutional Networks for Biomedical Image Segmentation (U-Net)
• 🏆 2015: Deep Residual Learning for Image Recognition (ResNet)
• 🏆 2016: You Only Look Once: Unified, Real-Time Object Detection (YOLO)
• 🏆 2017: Attention is All you Need (Transformer)
• 🏆 2018: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (BERT)
• 🏆 2020: Language Models are Few-Shot Learners (GPT-3)
• 🏆 2020: An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale (ViT)
• 🏆 2021: Highly accurate protein structure prediction with AlphaFold (Alphafold)
• 📰 2022: ChatGPT: Optimizing Language Models For Dialogue (ChatGPT)

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• Artificial Intelligence Essay

Essay on Artificial Intelligence

Artificial Intelligence is the intelligence possessed by the machines under which they can perform various functions with human help. With the help of A.I, machines will be able to learn, solve problems, plan things, think, etc. Artificial Intelligence, for example, is the simulation of human intelligence by machines. In the field of technology, Artificial Intelligence is evolving rapidly day by day and it is believed that in the near future, artificial intelligence is going to change human life very drastically and will most probably end all the crises of the world by sorting out the major problems. 

Our life in this modern age depends largely on computers. It is almost impossible to think about life without computers. We need computers in everything that we use in our daily lives. So it becomes very important to make computers intelligent so that our lives become easy. Artificial Intelligence is the theory and development of computers, which imitates the human intelligence and senses, such as visual perception, speech recognition, decision-making, and translation between languages. Artificial Intelligence has brought a revolution in the world of technology. 

Artificial Intelligence Applications

AI is widely used in the field of healthcare. Companies are attempting to develop technologies that will allow for rapid diagnosis. Artificial Intelligence would be able to operate on patients without the need for human oversight. Surgical procedures based on technology are already being performed.

Artificial Intelligence would save a lot of our time. The use of robots would decrease human labour. For example, in industries robots are used which have saved a lot of human effort and time. 

In the field of education, AI has the potential to be very effective. It can bring innovative ways of teaching students with the help of which students will be able to learn the concepts better. 

Artificial intelligence is the future of innovative technology as we can use it in many fields. For example, it can be used in the Military sector, Industrial sector, Automobiles, etc. In the coming years, we will be able to see more applications of AI as this technology is evolving day by day. 

Marketing: Artificial Intelligence provides a deep knowledge of consumers and potential clients to the marketers by enabling them to deliver information at the right time. Through AI solutions, the marketers can refine their campaigns and strategies.

Agriculture: AI technology can be used to detect diseases in plants, pests, and poor plant nutrition. With the help of AI, farmers can analyze the weather conditions, temperature, water usage, and condition of the soil.

Banking: Fraudulent activities can be detected through AI solutions. AI bots, digital payment advisers can create a high quality of service.

Health Care: Artificial Intelligence can surpass human cognition in the analysis, diagnosis, and complication of complicated medical data.

History of Artificial Intelligence

Artificial Intelligence may seem to be a new technology but if we do a bit of research, we will find that it has roots deep in the past. In Greek Mythology, it is said that the concepts of AI were used. 

The model of Artificial neurons was first brought forward in 1943 by Warren McCulloch and Walter Pits. After seven years, in 1950, a research paper related to AI was published by Alan Turing which was titled 'Computer Machinery and Intelligence. The term Artificial Intelligence was first coined in 1956 by John McCarthy, who is known as the father of Artificial Intelligence. 

To conclude, we can say that Artificial Intelligence will be the future of the world. As per the experts, we won't be able to separate ourselves from this technology as it would become an integral part of our lives shortly. AI would change the way we live in this world. This technology would prove to be revolutionary because it will change our lives for good. 

Branches of Artificial Intelligence:

Knowledge Engineering

Machines Learning

Natural Language Processing

Types of Artificial Intelligence

Artificial Intelligence is categorized in two types based on capabilities and functionalities. 

Artificial Intelligence Type-1

Artificial intelligence type-2.

Narrow AI (weak AI): This is designed to perform a specific task with intelligence. It is termed as weak AI because it cannot perform beyond its limitations. It is trained to do a specific task. Some examples of Narrow AI are facial recognition (Siri in Apple phones), speech, and image recognition. IBM’s Watson supercomputer, self-driving cars, playing chess, and solving equations are also some of the examples of weak AI.

General AI (AGI or strong AI): This system can perform nearly every cognitive task as efficiently as humans can do. The main characteristic of general AI is to make a system that can think like a human on its own. This is a long-term goal of many researchers to create such machines.

Super AI: Super AI is a type of intelligence of systems in which machines can surpass human intelligence and can perform any cognitive task better than humans. The main features of strong AI would be the ability to think, reason, solve puzzles, make judgments, plan and communicate on its own. The creation of strong AI might be the biggest revolution in human history.

Reactive Machines: These machines are the basic types of AI. Such AI systems focus only on current situations and react as per the best possible action. They do not store memories for future actions. IBM’s deep blue system and Google’s Alpha go are the examples of reactive machines.

Limited Memory: These machines can store data or past memories for a short period of time. Examples are self-driving cars. They can store information to navigate the road, speed, and distance of nearby cars.

Theory of Mind: These systems understand emotions, beliefs, and requirements like humans. These kinds of machines are still not invented and it’s a long-term goal for the researchers to create one. 

Self-Awareness: Self-awareness AI is the future of artificial intelligence. These machines can outsmart the humans. If these machines are invented then it can bring a revolution in human society. 

Artificial Intelligence will bring a huge revolution in the history of mankind. Human civilization will flourish by amplifying human intelligence with artificial intelligence, as long as we manage to keep the technology beneficial.

FAQs on Artificial Intelligence Essay

1. What is Artificial Intelligence?

Artificial Intelligence is a branch of computer science that emphasizes the development of intelligent machines that would think and work like humans.

2. How is Artificial Intelligence Categorised?

Artificial Intelligence is categorized in two types based on capabilities and functionalities. Based on capabilities, AI includes Narrow AI (weak AI), General AI, and super AI. Based on functionalities, AI includes Relative Machines, limited memory, theory of mind, self-awareness.

3. How Does AI Help in Marketing?

AI helps marketers to strategize their marketing campaigns and keep data of their prospective clients and consumers.

4. Give an Example of a Relative Machine?

IBM’s deep blue system and Google’s Alpha go are examples of reactive machines.

5. How can Artificial Intelligence help us?

Artificial Intelligence can help us in many ways. It is already helping us in some cases. For example, if we think about the robots used in a factory, they all run on the principle of Artificial Intelligence. In the automobile sector, some vehicles have been invented that don't need any humans to drive them, they are self-driving. The search engines these days are also AI-powered. There are many other uses of Artificial Intelligence as well.

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Press Pause on the Silicon Valley Hype Machine

By Julia Angwin

Ms. Angwin is a contributing Opinion writer and an investigative journalist.

It’s a little hard to believe that just over a year ago, a group of leading researchers asked for a six-month pause in the development of larger systems of artificial intelligence, fearing that the systems would become too powerful. “Should we risk loss of control of our civilization?” they asked.

There was no pause. But now, a year later, the question isn’t really whether A.I. is too smart and will take over the world. It’s whether A.I. is too stupid and unreliable to be useful. Consider this week’s announcement from OpenAI’s chief executive, Sam Altman, who promised he would unveil “new stuff” that “ feels like magic to me.” But it was just a rather routine update that makes ChatGPT cheaper and faster .

It feels like another sign that A.I. is not even close to living up to its hype. In my eyes, it’s looking less like an all-powerful being and more like a bad intern whose work is so unreliable that it’s often easier to do the task yourself. That realization has real implications for the way we, our employers and our government should deal with Silicon Valley’s latest dazzling new, new thing. Acknowledging A.I.’s flaws could help us invest our resources more efficiently and also allow us to turn our attention toward more realistic solutions.

Others voice similar concerns. “I find my feelings about A.I. are actually pretty similar to my feelings about blockchains: They do a poor job of much of what people try to do with them, they can’t do the things their creators claim they one day might, and many of the things they are well suited to do may not be altogether that beneficial,” wrote Molly White, a cryptocurrency researcher and critic , in her newsletter last month.

Let’s look at the research.

In the past 10 years, A.I. has conquered many tasks that were previously unimaginable, such as successfully identifying images, writing complete coherent sentences and transcribing audio. A.I. enabled a singer who had lost his voice to release a new song using A.I. trained with clips from his old songs.

But some of A.I.’s greatest accomplishments seem inflated. Some of you may remember that the A.I. model ChatGPT-4 aced the uniform bar exam a year ago. Turns out that it scored in the 48th percentile, not the 90th, as claimed by OpenAI , according to a re-examination by the M.I.T. researcher Eric Martínez . Or what about Google’s claim that it used A.I. to discover more than two million new chemical compounds ? A re-examination by experimental materials chemists at the University of California, Santa Barbara, found “ scant evidence for compounds that fulfill the trifecta of novelty, credibility and utility .”

Meanwhile, researchers in many fields have found that A.I. often struggles to answer even simple questions, whether about the law , medicine or voter information . Researchers have even found that A.I. does not always improve the quality of computer programming , the task it is supposed to excel at.

I don’t think we’re in cryptocurrency territory, where the hype turned out to be a cover story for a number of illegal schemes that landed a few big names in prison . But it’s also pretty clear that we’re a long way from Mr. Altman’s promise that A.I. will become “ the most powerful technology humanity has yet invented .”

Take Devin, a recently released “ A.I. software engineer ” that was breathlessly touted by the tech press. A flesh-and-bones software developer named Carl Brown decided to take on Devin . A task that took the generative A.I.-powered agent over six hours took Mr. Brown just 36 minutes. Devin also executed poorly, running a slower, outdated programming language through a complicated process. “Right now the state of the art of generative A.I. is it just does a bad, complicated, convoluted job that just makes more work for everyone else,” Mr. Brown concluded in his YouTube video .

Cognition, Devin’s maker, responded by acknowledging that Devin did not complete the output requested and added that it was eager for more feedback so it can keep improving its product. Of course, A.I. companies are always promising that an actually useful version of their technology is just around the corner. “ GPT-4 is the dumbest model any of you will ever have to use again by a lot ,” Mr. Altman said recently while talking up GPT-5 at a recent event at Stanford University.

The reality is that A.I. models can often prepare a decent first draft. But I find that when I use A.I., I have to spend almost as much time correcting and revising its output as it would have taken me to do the work myself.

And consider for a moment the possibility that perhaps A.I. isn’t going to get that much better anytime soon. After all, the A.I. companies are running out of new data on which to train their models, and they are running out of energy to fuel their power-hungry A.I. machines . Meanwhile, authors and news organizations (including The New York Times ) are contesting the legality of having their data ingested into the A.I. models without their consent, which could end up forcing quality data to be withdrawn from the models.

Given these constraints, it seems just as likely to me that generative A.I. could end up like the Roomba, the mediocre vacuum robot that does a passable job when you are home alone but not if you are expecting guests.

Companies that can get by with Roomba-quality work will, of course, still try to replace workers. But in workplaces where quality matters — and where workforces such as screenwriters and nurses are unionized — A.I. may not make significant inroads.

And if the A.I. models are relegated to producing mediocre work, they may have to compete on price rather than quality, which is never good for profit margins. In that scenario, skeptics such as Jeremy Grantham, an investor known for correctly predicting market crashes, could be right that the A.I. investment bubble is very likely to deflate soon .

The biggest question raised by a future populated by unexceptional A.I., however, is existential. Should we as a society be investing tens of billions of dollars, our precious electricity that could be used toward moving away from fossil fuels, and a generation of the brightest math and science minds on incremental improvements in mediocre email writing?

We can’t abandon work on improving A.I. The technology, however middling, is here to stay, and people are going to use it. But we should reckon with the possibility that we are investing in an ideal future that may not materialize.

The Times is committed to publishing a diversity of letters to the editor. We’d like to hear what you think about this or any of our articles. Here are some tips . And here’s our email: [email protected] .

Follow the New York Times Opinion section on Facebook , Instagram , TikTok , WhatsApp , X and Threads .

Julia Angwin, a contributing Opinion writer and the founder of Proof News , writes about tech policy. You can follow her on Twitter or Mastodon or her personal newsletter .

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How to Remove Section Breaks in Word? [For Students]

As a student navigating the intricacies of academic formatting, I understand the frustration of encountering stubborn section breaks that disrupt the flow of your document. In this guide, I share my insights and experiences to help you tackle this common issue effectively. Let's dive in and unravel the mystery of removing section breaks in Word!

Part 1: Why Can’t I Remove Section Breaks in Word?

Have you ever struggled to remove a section break in your Word document? You're not alone. Section breaks, while useful for formatting, can sometimes be a bit tricky to delete. This part will explore two common scenarios where removing section breaks might cause frustration:

1. Failing to Remove Section Breaks in Word on Mac

Unlike Windows, deleting section breaks on Mac using Backspace or Delete might not work as intended. Here's why:

Hidden Section Break: Section breaks are hidden by default in Word. To see them, you need to enable the "Show/Hide" formatting marks.

Unselectable Break: Even with formatting marks displayed, the section break might still appear unselectable. This can happen if it's followed by a page break or other formatting element.

2. Failing to Remove the Last Section Break Without Losing Formatting in Your Essay

Especially for essays, the last section break might be causing an unwanted page break. However, deleting it can remove your formatting throughout the document. Here's why this occurs:

Linked Sections: Sections in your document can inherit formatting from the previous section. Deleting the last section break can merge its formatting with the preceding section, potentially causing inconsistencies.

Scenario: Imagine your essay has two sections. The first section has single line spacing, while the second (shorter) section has double line spacing for the references. Deleting the last section break might apply the double spacing to the entire essay.

Solution to these problems will be covered in Part 2!

Part 2: Easy Steps to Remove Section Breaks in Word for Your Essay

In Part 2 of this guide, I'll walk you through the simple steps to remove section breaks in Word for your essays. Whether you're using WPS Office or Microsoft Word, these easy-to-follow instructions will help you maintain smooth formatting in your documents. Say goodbye to pesky section breaks and hello to seamless editing with these straightforward techniques.

Remove a section break in Word

There are two main ways to remove section breaks in Word:

Using the Show/Hide button:

Step 1: Click the Show/Hide button in the Paragraph group on the Home tab. This will reveal all formatting marks in your document, including section breaks.

Step 2: Place your cursor at the beginning of the line after the section break you want to remove.

Step 3: Press the Backspace key.

Using the Navigation Pane :

Step 1: Open your Word document: Launch WPS Office and open the document where you want to remove the section break.

Step 2: Access the "View" tab: Look for the toolbar at the top of the Word window. Click on the "View" tab located in the menu options.

Step 3: Enable the Navigation Pane: Within the "View" tab, locate the "Show" section. Make sure that the "Navigation Pane" option is checked or enabled.

Step 4: Navigate to the "Section" tab: Once the Navigation Pane is activated, you'll see it appear on the left side of your document. Click on the " Section" tab within the Navigation Pane.

Step 5: Identify the section break: In the Navigation Pane, you'll now see a list of headings or sections in your document. Scroll through this list to find the section break you want to remove.

Step 6: Select the section break: Click on the section break you wish to delete. It will be highlighted in the Navigation Pane.

Step 7: Remove the section break: Once the section break is selected, simply press the "Delete" key on your keyboard. The section break will be deleted from your document.

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Removing Multiple or All Section Breaks

There is no built-in way to remove all section breaks at once in Word. However, you can use the Find and Replace feature to find all section breaks and replace them with paragraph marks.

Here's how to do it:

Step 1: Press Ctrl+H to open the Find and Replace dialog box.

Step 2: In the Find what field, type ^b. This is the code for a section break in Word .

Step 3: Leave the Replace with field empty.

Step 4: Click Replace All.

WPS AI: Your Best Assistant with Essay Paper Work

Feeling overwhelmed by the blank page when starting your essay? Don't worry, WPS AI is here to be your secret weapon!

WPS AI is a powerful built-in feature within WPS Office that utilizes artificial intelligence to assist you in various writing tasks. One of its valuable tools is the ability to generate outlines and brainstorm ideas, giving your essay a strong foundation.

Here's how to leverage WPS AI to create an outline for your essay:

Step 1: Access WPS AI

Open your WPS Writer document and locate the AI Assistant icon on the top right corner. It might be labelled as "AI Writer" depending on your version. Click on the icon to activate the AI features.

Step 2: Describe Your Essay Topic

In the AI Assistant panel, you'll see a text box prompting you to "Enter instructions or questions." Here, clearly state your essay topic. For example, if your essay is about the environmental impact of fast fashion, you could type: "Outline for essay: Environmental impact of fast fashion"

Step 3: Generate the Outline

Once you've described your topic, click the "Generate" button. WPS AI will analyze your input and utilize its knowledge base to create a draft outline for your essay.

Step 4: Review and Refine

WPS AI will present a structured outline for your essay, including main points, sub-points, and even potential supporting arguments. Review the outline carefully and make any necessary adjustments to suit your specific essay needs. You can add, remove, or rearrange sections to ensure the outline accurately reflects your approach.

WPS AI: Streamlining Your Essay Writing Process

By using WPS AI to generate outlines, you can save valuable time and overcome writer's block. This AI assistant provides a solid framework to structure your essay, allowing you to focus on developing strong arguments and crafting compelling content.

Remember, WPS AI is a tool to empower you, not replace your own critical thinking. Use the generated outline as a starting point and personalize it to create a unique and well-structured essay.

How can I insert a section break in Word?

To insert a section break in Word, follow these steps:

Step 1. Place your cursor where you want to insert the section break.

Step 2. Go to the "Layout" or "Page Layout" tab in the toolbar.

Step 3. Click on the "Breaks" option.

Step 4. Select "Next Page" under the "Section Breaks" section.

How can I insert a page break in Word?

To insert a page break in Word , follow these steps:

Step 1. Place your cursor where you want to insert the page break.

Step 2. Go to the "Insert" tab in the toolbar.

Step 3. Click on the "Page Break" option.

What is the difference between page break and section break?

The key difference between a page break and a section break in Word lies in their impact on your document's layout and formatting. Here's a breakdown:

In this guide, I've shown you how to easily remove section breaks in Word. I've explained common problems and provided simple steps to solve them. Throughout, I've emphasized the convenience of using WPS Office , which makes editing documents a breeze. With clear instructions and practical tips, you can now handle section breaks with confidence and efficiency using WPS Office.

• 1. Inserting section break in Word document (Mac and PC)
• 2. How to Remove Page Breaks in Word[2024]
• 3. How to insert a section break in word
• 4. How to insert a section break in Word
• 5. How to remove all page breaks from word document
• 6. How can we insert next page section break in WPS Writer

15 years of office industry experience, tech lover and copywriter. Follow me for product reviews, comparisons, and recommendations for new apps and software.

Labākie veidi, kā palielināt vārdu skaitu esejā

• Smodin redakcijas komanda
• Publicēts: 23. gada 2024. maijs

Vai rakstāt mājasdarbu un cenšaties izpildīt minimālās vārdu skaita prasības? Vai varbūt jums ir grūti pievienot saturu, nezaudējot kvalitāti.

Šajā rokasgrāmatā mēs apskatīsim vienkāršas stratēģijas, kā palielināt vārdu skaitu esejā, vienlaikus uzlabojot jūsu rakstīšanas kvalitāti. Šīs metodes ietver AI rīku, piemēram, Smodin, izmantošanu, pārfrāzēšanas uzlabošanu un teikumu apguvi.

1. Izmantojiet AI rakstīšanas rīkus

AI rakstīšanas rīku izmantošana var palīdzēt padarīt jūsu eseju garāku. Šie rīki var nodrošināt palīgus dažādos veidos:

• AI rīki, piemēram, Smodin, var analizēt jūsu tekstu. Viņi piedāvā alternatīvas, ļaujot jums paplašināt idejas, nekaitējot jūsu rakstīšanai.
• AI rīki var palīdzēt atrast iespējas sadalīt vai apvienot teikumus. Tas palielinās nepieciešamo vārdu skaitu, vienlaikus saglabājot lietas skaidras un plūstošas.
• Šie rīki var ieteikt sinonīmus. Tie aizstāj vispārīgos terminus, pievienojot jūsu esejai dziļumu.
• AI rakstīšanas palīgi, piemēram Smodins rakstnieks , var sniegt pielāgotus ieteikumus, pamatojoties uz jūsu esejas tēmu un toni. Tie nodrošina, ka jūsu vēlamais vārdu skaits palielinās un atbilst jūsu rakstīšanas mērķiem.

AI rakstīšanas rīku pievienošana eseju veidošanas procesam var būt spēcīgs veids, kā viegli palielināt vārdu skaitu. Tie var arī uzlabot jūsu darba kvalitāti.

2. Rakstīt īsus stāstus

Stāstu pievienošana esejai ir pārliecinošs veids, kā pievienot vārdus un piesaistīt lasītājus, jo īpaši, ja runa ir par radošo rakstīšanu. Lūk, kā jūs varat efektīvi izmantot stāstu:

• Pievienojiet anekdotes : tie piešķir jūsu punktiem kontekstu un detalizētu informāciju, vienlaikus pievienojot papildu vārdus.
• Izstrādājiet spilgtus varoņus un scenārijus : izmantojiet tos, lai ilustrētu savas idejas un pievienotu rakstīšanai dziļumu.
• Izmantojiet spilgtu valodu, lai uzzīmētu attēlu saviem lasītājiem : tas iegremdēs lasītājus stāstā, vienlaikus palielinot vārdu skaitu.
• Izmantojiet emocionālo pievilcību : sazinieties ar lasītāju, izmantojot stāstus. Stāstiem vajadzētu izraisīt jūtas un rezonēt ar viņu pieredzi.

Stāstu iekļaušana esejā palielina vārdu skaitu. Tas arī padara jūsu saturu saistošāku un neaizmirstamāku. Apsveriet iespēju izmantot AI rīkus, piemēram, Smodin. Tie var uzlabot jūsu stāstījumu un uzlabot jūsu stāstījuma plūsmu.

3. Izvērsiet rindkopas

Punktu paplašināšana ir stratēģiska pieeja. Tas palielinās vārdu skaitu un uzlabos jūsu esejas dziļumu un saskaņotību. Šeit ir galvenās taktikas, lai efektīvi paplašinātu rindkopas:

• Pievienojiet sīkāku informāciju un paskaidrojumus saviem galvenajiem punktiem : Tas padarīs jūsu rakstīšanu saturīgāku un garāku.
• Likvidējiet pildījuma vārdus : paplašināšanas laikā pievērsiet uzmanību vārdiem, kas nepievieno nozīmi, kas negatīvi ietekmēs jūsu esejas garumu.
• Stiprināt ķermeņa rindkopas : attīstiet savas rindkopas, loģiski un saskaņoti savienojot idejas.
• Izmantojiet pārejas frāzes : tie palīdz pārvietoties starp idejām un rindkopām. Tie uztur jūsu eseju plūstošu un pozitīvi palielina vārdu skaitu.

Rūpīgi paplašinot rindkopas, varat palielināt vārdu skaitu. Tas bagātinās jūsu rakstīšanas kvalitāti un struktūru. Šeit var palīdzēt arī AI rīki, piemēram, Smodin. Tie racionalizēs un uzlabos rindkopu izvēršanu.

4. Pievienojiet piemērus

Piemēru pievienošana esejai ir spēcīgs veids, kā pamatot savus argumentus. Tas arī padara jūsu rakstīšanu saistošāku. Šeit ir dažas stratēģijas, lai efektīvi iekļautu piemērus savā rakstā:

• Izmantojiet atbilstošus piemērus : Tiem jābūt saistītiem ar tēmu un jāatbalsta jūsu argumenti. Neizmantojiet piemērus, kas nav saistīti vai mulsinoši.
• Izmantojiet daudzus piemērus : tie var uzsvērt dažādas jūsu argumentācijas daļas un padarīt jūsu rakstīšanu pilnīgāku.
• Izmantojiet piemērus no dažādiem avotiem : Tie sniedz pilnīgu priekšstatu par tēmu. Viņi parāda jūsu prasmi analizēt un novērtēt dažādus viedokļus.
• Izmantojiet piemērus, lai kontrastētu un salīdzinātu : Izmantojot piemērus ideju kontrastēšanai un salīdzināšanai, var izcelt to stiprās un vājās puses. Tas var sniegt niansētāku izpratni par tēmu.
• Izmantojiet piemērus, lai precizētu sarežģītus jēdzienus : Tie var padarīt jēdzienus vieglāk saprotamus.

Piemēru pievienošana esejai sniedz pierādījumus jūsu argumentiem. Tas arī padara jūsu rakstīšanu saistošāku un pārliecinošāku.

5. Precizējiet teikumus

Ja vēlaties palielināt esejas vārdu skaitu, ļoti svarīgi ir skaidri teikumi. Tie piešķir jūsu rakstīšanai saturu un dziļumu. Tālāk ir norādītas galvenās stratēģijas, lai uzlabotu skaidrību un efektīvi paplašinātu saturu.

• Sniedziet sīkāku informāciju : precizējiet galvenos punktus, pievienojot sīkāku informāciju un piemērus, lai bagātinātu savus paskaidrojumus.
• Izmantojiet aprakstošu valodu : Tas ilustrē jēdzienus un dziļi iesaista lasītājus.
• Precizējiet savus apgalvojumus : padariet tos skaidrus un īsus. Izvairieties no neskaidrības un sarežģītības.
• Uzsveriet galvenos punktus : Dariet to, lai nostiprinātu savus argumentus un nodrošinātu pilnīgu izpratni.
• Pievienojiet vairāk dziļuma : iedziļinieties tēmās, izpētot dažādus leņķus un perspektīvas, lai bagātinātu analīzi un precizētu apgalvojumus.
• Izvairieties no nevajadzīgas informācijas : izgrieziet detaļas, kurām nav nozīmes vai kas papildina galvenās idejas. Tādējādi jūsu rakstīšana ir skaidra.

Šīs stratēģijas palīdzēs jums precizēt teikumus un padarīt saturu dziļāku. Tie arī palielinās vārdu skaitu, vienlaikus saglabājot jūsu eseju atbilstošu un saskaņotu.

6. Izmantojiet citātus

Citāti izmantošana esejā var palielināt vārdu skaitu un pievienot argumentiem ticamību un dziļumu. Šeit ir daži efektīvi veidi, kā izmantot citātus savā rakstīšanā:

• Izmantojiet citātus no kvalitatīviem avotiem : Viņi sniegs pārliecinošus pierādījumus jūsu apgalvojumiem.
• Autoritātes pievienošana : Citāti no ekspertiem vai labi zināmām personām var pievienot jūsu rakstam autoritātes sajūtu un palielināt jūsu argumentu pamatotību.
• Uzsverot galvenos punktus: Līdzīgi kā izmantojot piemērus, citātus var izmantot, lai izceltu galvenās idejas vai perspektīvas, kas atbilst jūsu argumentam.
• Sniedziet dažādus viedokļus : integrējiet citātus ar dažādiem viedokļiem. Tie bagātina diskusiju un parāda pilnīgu izpratni par tēmu.
• Izmantojiet pēdiņas stratēģiski : Tie stiprinās jūsu argumentus un pārliecinās jūsu lasītājus.
• Citējiet atbilstošos citātus : atcerieties pareizi citēt citātus atbilstoši savas skolas vai universitātes vadlīnijām.

Prasmīgi izmantojot citātus, varat uzlabot savu rakstīšanu, palielināt vārdu skaitu un bagātināt savu eseju ar vērtīgām atziņām un perspektīvām.

7. Izvērsiet ievadu un secinājumus

Jūsu esejas ievadam un noslēgumam ir izšķiroša nozīme. Paplašinot šīs sadaļas, varat palielināt savu vārdu skaitu un stiprināt rakstīšanas saskaņotību un ietekmi.

Ievada paplašināšana:

• Sniedziet sīkāku informāciju : Sāciet savu eseju ar detalizētāku un saistošāku āķi, lai piesaistītu lasītāja uzmanību.
• Rūpīgi ievadiet tēmu : pavadiet vairāk laika, nosakot kontekstu un sniedzot informāciju par to.
• Savienojiet idejas : izveidojiet skaidru saikni starp ievadu un esejas pamattekstu. Tas nodrošina vienmērīgu pāreju.
• Parādiet savu rakstīto : ievads nosaka toni visai esejai. Centieties labi parādīt savas rakstīšanas prasmes jau no pirmā teikuma.
• Ievadu rakstiet pēdējo : lai gan šī pieeja var šķist atgriezeniska, tas ir labākais veids, kā nodrošināt, ka ievadā iekļaujat visu nepieciešamo informāciju.

Secinājuma paplašināšana:

• Pārskatiet galvenos punktus : Apkopojiet savas esejas galvenos argumentus un idejas. Sniedziet pilnu kopsavilkumu saviem lasītājiem.
• Piedāvājiet vairāk ieskatu : izpētiet savas tēmas plašāku nozīmi. Vai arī iesakiet jaunus pētījumus un diskusiju tēmas.
• Saistiet savu secinājumu ar ievadu : Tas radīs vienotu eseju.
• Rakstiet ar nolūku : ieguldiet laiku, lai izstrādātu pārdomātu secinājumu. Padariet iespaidu, lai atstātu paliekošu iespaidu uz savu profesoru vai skolotāju.

Papildinot ievadu un noslēgumu, jūs varat palielināt savas esejas vārdu skaitu. Jūs arī uzlabosit sava raksta struktūru, saskaņotību un ietekmi.

8. Pievienojiet pārejas frāzes

Kā minēts, pārejas frāžu pievienošana skolas vai koledžas esejai ir stratēģisks veids, kā palielināt vārdu skaitu. Tas arī uzlabo jūsu rakstīšanas plūsmu un saskaņotību. Šīs frāzes darbojas kā tilti starp idejām. Tie palīdz jūsu lasītājiem netraucēti orientēties jūsu esejā.

Šeit ir daži efektīvi veidi, kā izmantot pārejas frāzes, lai palielinātu vārdu skaitu:

• Izmantojiet pārejas vārdus un frāzes, lai savienotu savas idejas. Dariet to gan rindkopām, gan sadaļām. Tas padarīs jūsu eseju vienotu un labi strukturētu.
• Izmantojiet dažādas pārejas. Izmēģiniet vairākas frāzes, piemēram, "papildus", "turklāt", "no otras puses" un "nobeigumā". Tie piešķirs jūsu rakstīšanai dziļumu un sarežģītību.
• Pārliecinieties, vai izmantotās frāzes atbilst kontekstam. Viņiem vajadzētu labi vadīt jūsu lasītājus ar jūsu argumentiem.

Pievienojot esejai pārejas frāzes, varat palielināt vārdu skaitu. Tas arī uzlabos jūsu rakstīšanas skaidrību, plūsmu un saskaņotību.

Ļaujiet Smodin palielināt jūsu vārdu skaitu

Mācīšanās palielināt vārdu skaitu esejās nav saistīta tikai ar kvantitāti. Tas ir arī par jūsu rakstīšanas kvalitātes un ietekmes uzlabošanu.

Šīs metodes mainīs jūsu rakstīšanas procesu un palīdzēs rakstīt esejas un pētnieciskos darbus, kas sasaucas ar jūsu profesoriem un skolotājiem neatkarīgi no tā, cik vārdu jums ir nepieciešams.

Tādas platformas kā Smodin izmanto AI, lai piedāvātu vienkāršu risinājumu eseju rakstīšanai. Tie palīdz viegli palielināt vārdu skaitu. Lūk, kā Smodin var jums palīdzēt:

• Smodin izmanto AI, lai analizētu jūsu tekstu, un iesaka veidus, kā pievienot vārdus papildus nevajadzīgo vārdu noņemšanai.
• Smodin var palīdzēt pārfrāzēt. Tas var arī pievienot teikumiem dziļumu un garumu.
• Izmantojiet Smodin, lai uzlabotu rakstīšanu. Tas sniedz ieteikumus par gramatiku un stilu.
• Pielāgoti ieteikumi, kas atbilst jūsu īpašajām rakstīšanas vajadzībām un mērķiem.

Izpētiet Smodin pakalpojumus jau šodien, lai uzlabotu rakstīšanu.