role of artificial intelligence in problem solving

Artificial intelligence, or AI, is technology that enables computers and machines to simulate human intelligence and problem-solving capabilities.

On its own or combined with other technologies (e.g., sensors, geolocation, robotics) AI can perform tasks that would otherwise require human intelligence or intervention. Digital assistants, GPS guidance, autonomous vehicles, and generative AI tools (like Open AI's Chat GPT) are just a few examples of AI in the daily news and our daily lives.

As a field of computer science, artificial intelligence encompasses (and is often mentioned together with) machine learning and deep learning . These disciplines involve the development of AI algorithms, modeled after the decision-making processes of the human brain, that can ‘learn’ from available data and make increasingly more accurate classifications or predictions over time.

Artificial intelligence has gone through many cycles of hype, but even to skeptics, the release of ChatGPT seems to mark a turning point. The last time generative AI loomed this large, the breakthroughs were in computer vision, but now the leap forward is in natural language processing (NLP). Today, generative AI can learn and synthesize not just human language but other data types including images, video, software code, and even molecular structures.

Applications for AI are growing every day. But as the hype around the use of AI tools in business takes off, conversations around ai ethics and responsible ai become critically important. For more on where IBM stands on these issues, please read  Building trust in AI .

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Weak AI—also known as narrow AI or artificial narrow intelligence (ANI)—is AI trained and focused to perform specific tasks. Weak AI drives most of the AI that surrounds us today. "Narrow" might be a more apt descriptor for this type of AI as it is anything but weak: it enables some very robust applications, such as Apple's Siri, Amazon's Alexa, IBM watsonx™, and self-driving vehicles.

Strong AI is made up of artificial general intelligence (AGI) and artificial super intelligence (ASI). AGI, or general AI, is a theoretical form of AI where a machine would have an intelligence equal to humans; it would be self-aware with a consciousness that would have the ability to solve problems, learn, and plan for the future. ASI—also known as superintelligence—would surpass the intelligence and ability of the human brain. While strong AI is still entirely theoretical with no practical examples in use today, that doesn't mean AI researchers aren't also exploring its development. In the meantime, the best examples of ASI might be from science fiction, such as HAL, the superhuman and rogue computer assistant in  2001: A Space Odyssey.

Machine learning and deep learning are sub-disciplines of AI, and deep learning is a sub-discipline of machine learning.

Both machine learning and deep learning algorithms use neural networks to ‘learn’ from huge amounts of data. These neural networks are programmatic structures modeled after the decision-making processes of the human brain. They consist of layers of interconnected nodes that extract features from the data and make predictions about what the data represents.

Machine learning and deep learning differ in the types of neural networks they use, and the amount of human intervention involved. Classic machine learning algorithms use neural networks with an input layer, one or two ‘hidden’ layers, and an output layer. Typically, these algorithms are limited to supervised learning : the data needs to be structured or labeled by human experts to enable the algorithm to extract features from the data.

Deep learning algorithms use deep neural networks—networks composed of an input layer, three or more (but usually hundreds) of hidden layers, and an output layout. These multiple layers enable unsupervised learning : they automate extraction of features from large, unlabeled and unstructured data sets. Because it doesn’t require human intervention, deep learning essentially enables machine learning at scale.

Generative AI refers to deep-learning models that can take raw data—say, all of Wikipedia or the collected works of Rembrandt—and “learn” to generate statistically probable outputs when prompted. At a high level, generative models encode a simplified representation of their training data and draw from it to create a new work that’s similar, but not identical, to the original data.

Generative models have been used for years in statistics to analyze numerical data. The rise of deep learning, however, made it possible to extend them to images, speech, and other complex data types. Among the first class of AI models to achieve this cross-over feat were variational autoencoders, or VAEs, introduced in 2013. VAEs were the first deep-learning models to be widely used for generating realistic images and speech.

“VAEs opened the floodgates to deep generative modeling by making models easier to scale,” said Akash Srivastava , an expert on generative AI at the MIT-IBM Watson AI Lab. “Much of what we think of today as generative AI started here.”

Early examples of models, including GPT-3, BERT, or DALL-E 2, have shown what’s possible. In the future, models will be trained on a broad set of unlabeled data that can be used for different tasks, with minimal fine-tuning. Systems that execute specific tasks in a single domain are giving way to broad AI systems that learn more generally and work across domains and problems. Foundation models, trained on large, unlabeled datasets and fine-tuned for an array of applications, are driving this shift.

As to the future of AI, when it comes to generative AI, it is predicted that foundation models will dramatically accelerate AI adoption in enterprise. Reducing labeling requirements will make it much easier for businesses to dive in, and the highly accurate, efficient AI-driven automation they enable will mean that far more companies will be able to deploy AI in a wider range of mission-critical situations. For IBM, the hope is that the computing power of foundation models can eventually be brought to every enterprise in a frictionless hybrid-cloud environment.

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There are numerous, real-world applications for AI systems today. Below are some of the most common use cases:

Also known as automatic speech recognition (ASR), computer speech recognition, or speech-to-text, speech recognition uses NLP to process human speech into a written format. Many mobile devices incorporate speech recognition into their systems to conduct voice search—Siri, for example—or provide more accessibility around texting in English or many widely-used languages.  See how Don Johnston used IBM Watson Text to Speech to improve accessibility in the classroom with our case study .

Online  virtual agents  and chatbots are replacing human agents along the customer journey. They answer frequently asked questions (FAQ) around topics, like shipping, or provide personalized advice, cross-selling products or suggesting sizes for users, changing the way we think about customer engagement across websites and social media platforms. Examples include messaging bots on e-commerce sites with virtual agents , messaging apps, such as Slack and Facebook Messenger, and tasks usually done by virtual assistants and  voice assistants .  See how Autodesk Inc. used IBM watsonx Assistant to speed up customer response times by 99% with our case study .

This AI technology enables computers and systems to derive meaningful information from digital images, videos and other visual inputs, and based on those inputs, it can take action. This ability to provide recommendations distinguishes it from image recognition tasks. Powered by convolutional neural networks, computer vision has applications within photo tagging in social media, radiology imaging in healthcare, and self-driving cars within the automotive industry.  See how ProMare used IBM Maximo to set a new course for ocean research with our case study .

Adaptive robotics act on Internet of Things (IoT) device information, and structured and unstructured data to make autonomous decisions. NLP tools can understand human speech and react to what they are being told. Predictive analytics are applied to demand responsiveness, inventory and network optimization, preventative maintenance and digital manufacturing. Search and pattern recognition algorithms—which are no longer just predictive, but hierarchical—analyze real-time data, helping supply chains to react to machine-generated, augmented intelligence, while providing instant visibility and transparency. See how Hendrickson used IBM Sterling to fuel real-time transactions with our case study .

The weather models broadcasters rely on to make accurate forecasts consist of complex algorithms run on supercomputers. Machine-learning techniques enhance these models by making them more applicable and precise. See how Emnotion used IBM Cloud to empower weather-sensitive enterprises to make more proactive, data-driven decisions with our case study .

AI models can comb through large amounts of data and discover atypical data points within a dataset. These anomalies can raise awareness around faulty equipment, human error, or breaches in security.  See how Netox used IBM QRadar to protect digital businesses from cyberthreats with our case study .

The idea of "a machine that thinks" dates back to ancient Greece. But since the advent of electronic computing (and relative to some of the topics discussed in this article) important events and milestones in the evolution of artificial intelligence include the following:

• 1950:  Alan Turing publishes Computing Machinery and Intelligence  (link resides outside ibm.com) .  In this paper, Turing—famous for breaking the German ENIGMA code during WWII and often referred to as the "father of computer science"— asks the following question: "Can machines think?"  From there, he offers a test, now famously known as the "Turing Test," where a human interrogator would try to distinguish between a computer and human text response. While this test has undergone much scrutiny since it was published, it remains an important part of the history of AI, as well as an ongoing concept within philosophy as it utilizes ideas around linguistics.
• 1956:  John McCarthy coins the term "artificial intelligence" at the first-ever AI conference at Dartmouth College. (McCarthy would go on to invent the Lisp language.) Later that year, Allen Newell, J.C. Shaw, and Herbert Simon create the Logic Theorist, the first-ever running AI software program.
• 1967:  Frank Rosenblatt builds the Mark 1 Perceptron, the first computer based on a neural network that "learned" though trial and error. Just a year later, Marvin Minsky and Seymour Papert publish a book titled  Perceptrons , which becomes both the landmark work on neural networks and, at least for a while, an argument against future neural network research projects.
• 1980s:  Neural networks which use a backpropagation algorithm to train itself become widely used in AI applications.
• 1995 : Stuart Russell and Peter Norvig publish  Artificial Intelligence: A Modern Approach  (link resides outside ibm.com), which becomes one of the leading textbooks in the study of AI. In it, they delve into four potential goals or definitions of AI, which differentiates computer systems on the basis of rationality and thinking vs. acting.
• 1997:  IBM's Deep Blue beats then world chess champion Garry Kasparov, in a chess match (and rematch).
• 2004 : John McCarthy writes a paper, What Is Artificial Intelligence?  (link resides outside ibm.com), and proposes an often-cited definition of AI.
• 2011:  IBM Watson beats champions Ken Jennings and Brad Rutter at  Jeopardy!
• 2015:  Baidu's Minwa supercomputer uses a special kind of deep neural network called a convolutional neural network to identify and categorize images with a higher rate of accuracy than the average human.
• 2016:  DeepMind's AlphaGo program, powered by a deep neural network, beats Lee Sodol, the world champion Go player, in a five-game match. The victory is significant given the huge number of possible moves as the game progresses (over 14.5 trillion after just four moves!). Later, Google purchased DeepMind for a reported USD 400 million.
• 2023 : A rise in large language models, or LLMs, such as ChatGPT, create an enormous change in performance of AI and its potential to drive enterprise value. With these new generative AI practices, deep-learning models can be pre-trained on vast amounts of raw, unlabeled data.

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When Should You Use AI to Solve Problems?

Not every challenge requires an algorithmic approach.

AI is increasingly informing business decisions but can be misused if executives stick with old decision-making styles. A key to effective collaboration is to recognize which parts of a problem to hand off to the AI and which the managerial mind will be better at solving. While AI is superior at data-intensive prediction problems, humans are uniquely suited to the creative thought experiments that underpin the best decisions.

Business leaders often pride themselves on their intuitive decision-making. They didn’t get to be division heads and CEOs by robotically following some leadership checklist. Of course, intuition and instinct can be important leadership tools, but not if they’re indiscriminately applied.

• Bob Suh is the founder and CEO of OnCorps, which provides AI-based decision guidance systems to the financial services industry. The firm works with leading scientists at Yale, Oxford, and Harvard to test decision making and behavioral algorithms. Previously, he was the chief technology strategist at Accenture.

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The Intersection of Math and AI: A New Era in Problem-Solving

By Whitney Clavin, California Institute of Technology (Caltech) December 11, 2023

The Mathematics and Machine Learning 2023 conference at Caltech highlights the growing integration of machine learning in mathematics, offering new solutions to complex problems and advancing algorithm development.

Conference is exploring burgeoning connections between the two fields.

Traditionally, mathematicians jot down their formulas using paper and pencil, seeking out what they call pure and elegant solutions. In the 1970s, they hesitantly began turning to computers to assist with some of their problems. Decades later, computers are often used to crack the hardest math puzzles. Now, in a similar vein, some mathematicians are turning to machine learning tools to aid in their numerical pursuits.

Embracing Machine Learning in Mathematics

“Mathematicians are beginning to embrace machine learning,” says Sergei Gukov, the John D. MacArthur Professor of Theoretical Physics and Mathematics at Caltech, who put together the Mathematics and Machine Learning 2023 conference, which is taking place at Caltech December 10–13.

“There are some mathematicians who may still be skeptical about using the tools,” Gukov says. “The tools are mischievous and not as pure as using paper and pencil, but they work.”

Machine Learning: A New Era in Mathematical Problem Solving

Machine learning is a subfield of AI, or artificial intelligence, in which a computer program is trained on large datasets and learns to find new patterns and make predictions. The conference, the first put on by the new Richard N. Merkin Center for Pure and Applied Mathematics, will help bridge the gap between developers of machine learning tools (the data scientists) and the mathematicians. The goal is to discuss ways in which the two fields can complement each other.

Mathematics and Machine Learning: A Two-Way Street

“It’s a two-way street,” says Gukov, who is the director of the new Merkin Center, which was established by Caltech Trustee Richard Merkin.

“Mathematicians can help come up with clever new algorithms for machine learning tools like the ones used in generative AI programs like ChatGPT, while machine learning can help us crack difficult math problems.”

Yi Ni, a professor of mathematics at Caltech, plans to attend the conference, though he says he does not use machine learning in his own research, which involves the field of topology and, specifically, the study of mathematical knots in lower dimensions. “Some mathematicians are more familiar with these advanced tools than others,” Ni says. “You need to know somebody who is an expert in machine learning and willing to help. Ultimately, I think AI for math will become a subfield of math.”

The Riemann Hypothesis and Machine Learning

One tough problem that may unravel with the help of machine learning, according to Gukov, is known as the Riemann hypothesis. Named after the 19th-century mathematician Bernhard Riemann, this problem is one of seven Millennium Problems selected by the Clay Mathematics Institute; a $1 million prize will be awarded for the solution to each problem.

The Riemann hypothesis centers around a formula known as the Riemann zeta function, which packages information about prime numbers. If proved true, the hypothesis would provide a new understanding of how prime numbers are distributed. Machine learning tools could help crack the problem by providing a new way to run through more possible iterations of the problem.

Mathematicians and Machine Learning: A Synergistic Relationship

“Machine learning tools are very good at recognizing patterns and analyzing very complex problems,” Gukov says.

Ni agrees that machine learning can serve as a helpful assistant. “Machine learning solutions may not be as beautiful, but they can find new connections,” he says. “But you still need a mathematician to turn the questions into something computers can solve.”

Knot Theory and Machine Learning

Gukov has used machine learning himself to untangle problems in knot theory. Knot theory is the study of abstract knots, which are similar to the knots you might find on a shoestring, but the ends of the strings are closed into loops. These mathematical knots can be entwined in various ways, and mathematicians like Gukov want to understand their structures and how they relate to each other. The work has relationships to other fields of mathematics such as representation theory and quantum algebra, and even quantum physics.

In particular, Gukov and his colleagues are working to solve what is called the smooth Poincaré conjecture in four dimensions. The original Poincaré conjecture, which is also a Millennium Problem, was proposed by mathematician Henri Poincaré early in the 20th century. It was ultimately solved from 2002 to 2003 by Grigori Perelman (who famously turned down his prize of $1 million). The problem involves comparing spheres to certain types of manifolds that look like spheres; manifolds are shapes that are projections of higher-dimensional objects onto lower dimensions. Gukov says the problem is like asking, “Are objects that look like spheres really spheres?”

The four-dimensional smooth Poincaré conjecture holds that, in four dimensions, all manifolds that look like spheres are indeed actually spheres. In an attempt to solve this conjecture, Gukov and his team develop a machine learning approach to evaluate so-called ribbon knots.

“Our brain cannot handle four dimensions, so we package shapes into knots,” Gukov says. “A ribbon is where the string in a knot pierces through a different part of the string in three dimensions but doesn’t pierce through anything in four dimensions. Machine learning lets us analyze the ‘ribboness’ of knots, a yes-or-no property of knots that has applications to the smooth Poincaré conjecture.”

“This is where machine learning comes to the rescue,” writes Gukov and his team in a preprint paper titled “ Searching for Ribbons with Machine Learning .” “It has the ability to quickly search through many potential solutions and, more importantly, to improve the search based on the successful ‘games’ it plays. We use the word ‘games’ since the same types of algorithms and architectures can be employed to play complex board games, such as Go or chess, where the goals and winning strategies are similar to those in math problems.”

The Interplay of Mathematics and Machine Learning Algorithms

On the flip side, math can help in developing machine learning algorithms, Gukov explains. A mathematical mindset, he says, can bring fresh ideas to the development of the algorithms behind AI tools. He cites Peter Shor as an example of a mathematician who brought insight to computer science problems. Shor, who graduated from Caltech with a bachelor’s degree in mathematics in 1981, famously came up with what is known as Shor’s algorithm, a set of rules that could allow quantum computers of the future to factor integers faster than typical computers, thereby breaking digital encryption codes.

Today’s machine learning algorithms are trained on large sets of data. They churn through mountains of data on language, images, and more to recognize patterns and come up with new connections. However, data scientists don’t always know how the programs reach their conclusions. The inner workings are hidden in a so-called “black box.” A mathematical approach to developing the algorithms would reveal what’s happening “under the hood,” as Gukov says, leading to a deeper understanding of how the algorithms work and thus can be improved.

“Math,” says Gukov, “is fertile ground for new ideas.”

The conference will take place at the Merkin Center on the eighth floor of Caltech Hall.

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How Does Artificial Intelligence Solve Problems? An In-Depth Look at Problem Solving in AI

What is problem solving in artificial intelligence? It is a complex process of finding solutions to challenging problems using computational algorithms and techniques. Artificial intelligence, or AI, refers to the development of intelligent systems that can perform tasks typically requiring human intelligence.

Solving problems in AI involves the use of various algorithms and models that are designed to mimic human cognitive processes. These algorithms analyze and interpret data, generate possible solutions, and evaluate the best course of action. Through machine learning and deep learning, AI systems can continuously improve their problem-solving abilities.

Artificial intelligence problem solving is not limited to a specific domain or industry. It can be applied in various fields such as healthcare, finance, manufacturing, and transportation. AI-powered systems can analyze vast amounts of data, identify patterns, and make predictions to solve complex problems efficiently.

Understanding and developing problem-solving capabilities in artificial intelligence is crucial for the advancement of AI technologies. By improving problem-solving algorithms and models, researchers and developers can create more efficient and intelligent AI systems that can address real-world challenges and contribute to technological progress.

What is Artificial Intelligence?

Artificial intelligence (AI) can be defined as the simulation of human intelligence in machines that are programmed to think and learn like humans. It is a branch of computer science that deals with the creation and development of intelligent machines that can perform tasks that normally require human intelligence.

AI is achieved through the use of algorithms and data that allow machines to learn from and adapt to new information. These machines can then use their knowledge and reasoning abilities to solve problems, make decisions, and even perform tasks that were previously thought to require human intelligence.

Types of Artificial Intelligence

There are two main types of AI: narrow or weak AI and general or strong AI.

Narrow AI refers to AI systems that are designed to perform specific tasks, such as language translation, image recognition, or playing chess. These systems are trained to excel in their specific tasks but lack the ability to generalize their knowledge to other domains.

General AI, on the other hand, refers to AI systems that have the ability to understand, learn, and apply knowledge across a wide range of tasks and domains. These systems are capable of reasoning, problem-solving, and adapting to new situations in a way that is similar to human intelligence.

The Role of Problem Solving in Artificial Intelligence

Problem solving is a critical component of artificial intelligence. It involves the ability of AI systems to identify problems, analyze information, and develop solutions to those problems. AI algorithms are designed to imitate human problem-solving techniques, such as searching for solutions, evaluating options, and making decisions based on available information.

AI systems use various problem-solving techniques, including algorithms such as search algorithms, heuristic algorithms, and optimization algorithms, to find the best solution to a given problem. These techniques allow AI systems to solve complex problems efficiently and effectively.

In conclusion, artificial intelligence is the field of study that focuses on creating intelligent machines that can perform tasks that normally require human intelligence. Problem-solving is a fundamental aspect of AI and involves the use of algorithms and data to analyze information and develop solutions. AI has the potential to revolutionize many aspects of our lives, from healthcare and transportation to business and entertainment.

Problem solving is a critical component of artificial intelligence (AI). AI systems are designed to solve complex, real-world problems by employing various problem-solving techniques and algorithms.

One of the main goals of AI is to create intelligent systems that can solve problems in a way that mimics human problem-solving abilities. This involves using algorithms to search through a vast amount of data and information to find the most optimal solution.

Problem solving in AI involves breaking down a problem into smaller, more manageable sub-problems. These sub-problems are then solved individually and combined to solve the larger problem at hand. This approach allows AI systems to tackle complex problems that would be impossible for a human to solve manually.

AI problem-solving techniques can be classified into two main categories: algorithmic problem-solving and heuristic problem-solving. Algorithmic problem-solving involves using predefined rules and algorithms to solve a problem. These algorithms are based on logical reasoning and can be programmed into AI systems to provide step-by-step instructions for solving a problem.

Heuristic problem-solving, on the other hand, involves using heuristics or rules of thumb to guide the problem-solving process. Heuristics are not guaranteed to find the optimal solution, but they can provide a good enough solution in a reasonable amount of time.

Problem solving in AI is not limited to just finding a single solution to a problem. AI systems can also generate multiple solutions and evaluate them based on predefined criteria. This allows AI systems to explore different possibilities and find the best solution among them.

In conclusion, problem solving is a fundamental aspect of artificial intelligence. AI systems use problem-solving techniques and algorithms to tackle complex real-world problems. Through algorithmic and heuristic problem solving, AI systems are able to find optimal solutions and generate multiple solutions for evaluation. As AI continues to advance, problem-solving abilities will play an increasingly important role in the development of intelligent systems.

Problem Solving Approaches in Artificial Intelligence

In the field of artificial intelligence, problem solving is a fundamental aspect. Artificial intelligence (AI) is the intelligence exhibited by machines or computer systems. It aims to mimic human intelligence in solving complex problems that require reasoning and decision-making.

What is problem solving?

Problem solving refers to the cognitive mental process of finding solutions to difficult or complex issues. It involves identifying the problem, gathering relevant information, analyzing possible solutions, and selecting the most effective one. Problem solving is an essential skill for both humans and AI systems to achieve desired goals.

Approaches in problem solving in AI

Artificial intelligence employs various approaches to problem solving. Some of the commonly used approaches are:

• Search algorithms: These algorithms explore a problem space to find a solution. They can use different search strategies such as depth-first search, breadth-first search, and heuristic search.
• Knowledge-based systems: These systems store and utilize knowledge to solve problems. They rely on rules, facts, and heuristics to guide their problem-solving process.
• Logic-based reasoning: This approach uses logical reasoning to solve problems. It involves representing the problem as a logical formula and applying deduction rules to reach a solution.
• Machine learning: Machine learning algorithms enable AI systems to learn from data and improve their problem-solving capabilities. They can analyze patterns, make predictions, and adjust their behavior based on feedback.

Each approach has its strengths and weaknesses, and the choice of approach depends on the problem domain and available resources. By combining these approaches, AI systems can effectively tackle complex problems and provide valuable solutions.

Search Algorithms in Problem Solving

Problem solving is a critical aspect of artificial intelligence, as it involves the ability to find a solution to a given problem or goal. Search algorithms play a crucial role in problem solving by systematically exploring the search space to find an optimal solution.

What is a Problem?

A problem in the context of artificial intelligence refers to a task or challenge that requires a solution. It can be a complex puzzle, a decision-making problem, or any situation that requires finding an optimal solution.

What is an Algorithm?

An algorithm is a step-by-step procedure or set of rules for solving a problem. In the context of search algorithms, it refers to the systematic exploration of the search space, where each step narrows down the possibilities to find an optimal solution.

Search algorithms in problem solving aim to efficiently explore the search space to find a solution. There are several types of search algorithms, each with its own characteristics and trade-offs.

One commonly used search algorithm is the Breadth-First Search (BFS) algorithm. BFS explores the search space by systematically expanding all possible paths from the initial state to find the goal state. It explores the search space in a breadth-first manner, meaning that it visits all nodes at the same depth level before moving to the next level.

Another popular search algorithm is the Depth-First Search (DFS) algorithm. Unlike BFS, DFS explores the search space by diving deep into a path until it reaches a dead-end or the goal state. It explores the search space in a depth-first manner, meaning that it explores the deepest paths first before backtracking.

Other search algorithms include the A* algorithm, which combines the efficiency of BFS with the heuristic guidance of algorithms; the Greedy Best-First Search, which prioritizes paths based on a heuristic evaluation; and the Hill Climbing algorithm, which iteratively improves the current solution by making small changes.

Search algorithms in problem solving are essential in the field of artificial intelligence as they enable systems to find optimal solutions efficiently. By understanding and implementing different search algorithms, developers and researchers can design intelligent systems capable of solving complex problems.

Heuristic Functions in Problem Solving

In the field of artificial intelligence, problem-solving is a crucial aspect of creating intelligent systems. One key component in problem-solving is the use of heuristic functions.

A heuristic function is a function that guides an intelligent system in making decisions about how to solve a problem. It provides an estimate of the best possible solution based on available information at any given point in the problem-solving process.

What is a Heuristic Function?

A heuristic function is designed to provide a quick, yet informed, estimate of the most promising solution out of a set of possible solutions. It helps the intelligent system prioritize its search and focus on the most likely path to success.

Heuristic functions are especially useful in problems that have a large number of possible solutions and where an exhaustive search through all possibilities would be impractical or inefficient.

How Does a Heuristic Function Work?

Heuristic functions take into account various factors and considerations that are relevant to the problem being solved. These factors could include knowledge about the problem domain, past experience, or rules and constraints specific to the problem.

The heuristic function assigns a value to each possible solution based on these factors. The higher the value, the more likely a solution is to be optimal. The intelligent system then uses this information to guide its search for the best solution.

A good heuristic function strikes a balance between accuracy and efficiency. It should be accurate enough to guide the search towards the best solution but should also be computationally efficient to prevent excessive computation time.

Overall, heuristic functions play a crucial role in problem-solving in artificial intelligence. They provide a way for intelligent systems to efficiently navigate complex problem domains and find near-optimal solutions.

Constraint Satisfaction in Problem Solving

Problem solving is a key component of artificial intelligence, as it involves using computational methods to find solutions to complex issues. However, understanding how to solve these problems efficiently is essential for developing effective AI systems. And this is where constraint satisfaction comes into play.

Constraint satisfaction is a technique used in problem solving to ensure that all solution candidates satisfy a set of predefined constraints. These constraints can be thought of as rules or conditions that must be met for a solution to be considered valid.

So, what is a constraint? A constraint is a limitation or restriction on the values that variables can take. For example, in a scheduling problem, constraints can include time availability, resource limitations, or precedence relationships between tasks.

The goal of constraint satisfaction in problem-solving is to find a solution that satisfies all the given constraints. This is achieved by exploring the space of possible solutions and eliminating those that violate the constraints.

Constraint satisfaction problems (CSPs) can be solved using various algorithms, such as backtracking or constraint propagation. These algorithms iteratively assign values to variables and check if the constraints are satisfied. If a constraint is violated, the algorithm backtracks and tries a different value for the previous variable.

One advantage of using constraint satisfaction in problem solving is that it provides a systematic way to represent and solve problems with complex constraints. By breaking down the problem into smaller constraints, it becomes easier to reason about the problem and find a solution.

In conclusion, constraint satisfaction is an important technique in problem solving for artificial intelligence. By defining and enforcing constraints, AI systems can efficiently search for valid solutions. Incorporating constraint satisfaction techniques into AI algorithms can greatly improve problem-solving capabilities and contribute to the development of more intelligent systems.

Genetic Algorithms in Problem Solving

Artificial intelligence (AI) is a branch of computer science that focuses on creating intelligent machines capable of performing tasks that typically require human intelligence. One aspect of AI is problem solving, which involves finding solutions to complex problems. Genetic algorithms are a type of problem-solving method used in artificial intelligence.

So, what are genetic algorithms? In simple terms, genetic algorithms are inspired by the process of natural selection and evolution. They are a type of optimization algorithm that uses concepts from genetics and biology to find the best solution to a problem. Instead of relying on a predefined set of rules or instructions, genetic algorithms work by evolving a population of potential solutions over multiple generations.

The process of genetic algorithms involves several key steps. First, an initial population of potential solutions is generated. Each solution is represented as a set of variables or “genes.” These solutions are then evaluated based on their fitness or how well they solve the problem at hand.

Next, the genetic algorithm applies operators such as selection, crossover, and mutation to the current population. Selection involves choosing the fittest solutions to become the parents for the next generation. Crossover involves combining the genes of two parents to create offspring with a mix of their characteristics. Mutation introduces small random changes in the offspring’s genes to introduce genetic diversity.

The new population is then evaluated, and the process continues until a stopping criterion is met, such as finding a solution that meets a certain fitness threshold or reaching a maximum number of generations. Over time, the genetic algorithm converges towards the best solution, much like how natural selection leads to the evolution of species.

Genetic algorithms have been successfully applied to a wide range of problem-solving tasks, including optimization, machine learning, and scheduling. They have been used to solve problems in areas such as engineering, finance, and biology. Due to their ability to explore a large solution space and find globally optimal or near-optimal solutions, genetic algorithms are often preferred when traditional methods fail or are not feasible.

In conclusion, genetic algorithms are a powerful tool in the field of artificial intelligence and problem solving. By mimicking the process of natural selection and evolution, they provide a way to find optimal solutions to complex problems. Their ability to explore a wide search space and adapt to changing environments makes them well-suited for a variety of problem-solving tasks. As AI continues to advance, genetic algorithms will likely play an increasingly important role in solving real-world problems.

Logical Reasoning in Problem Solving

Problem solving is a fundamental aspect of artificial intelligence. It involves finding a solution to a given problem by using logical reasoning. Logical reasoning is the process of using valid arguments and deductions to make inferences and arrive at a logical conclusion. In the context of problem solving, logical reasoning is used to analyze the problem, identify potential solutions, and evaluate their feasibility.

Logical reasoning is what sets artificial intelligence apart from other problem-solving approaches. Unlike human problem solvers, AI can analyze vast amounts of data and consider numerous possibilities simultaneously. It can also distinguish between relevant and irrelevant information and use it to make informed decisions.

Types of Logical Reasoning

There are several types of logical reasoning that AI systems employ in problem solving:

• Deductive Reasoning: Deductive reasoning involves drawing specific conclusions from general principles or premises. It uses a top-down approach, starting from general knowledge and applying logical rules to derive specific conclusions.
• Inductive Reasoning: Inductive reasoning involves drawing general conclusions or patterns from specific observations or examples. It uses a bottom-up approach, where specific instances are used to make generalizations.
• Abductive Reasoning: Abductive reasoning involves making the best possible explanation or hypothesis based on the available evidence. It is a form of reasoning that combines deductive and inductive reasoning to generate the most likely conclusion.

Importance of Logical Reasoning in Problem Solving

Logical reasoning is crucial in problem solving as it ensures that the solutions generated by AI systems are sound, valid, and reliable. Without logical reasoning, AI systems may produce incorrect or nonsensical solutions that are of no use in practical applications.

Furthermore, logical reasoning helps AI systems analyze complex problems systematically and break them down into smaller, more manageable sub-problems. By applying logical rules and deductions, AI systems can generate possible solutions, evaluate their feasibility, and select the most optimal one.

In conclusion, logical reasoning plays a vital role in problem solving in artificial intelligence. It enables AI systems to analyze problems, consider multiple possibilities, and arrive at logical conclusions. By employing various types of logical reasoning, AI systems can generate accurate and effective solutions to a wide range of problems.

Planning and Decision Making in Problem Solving

Planning and decision making play crucial roles in the field of artificial intelligence when it comes to problem solving . A fundamental aspect of problem solving is understanding what the problem actually is and how it can be solved.

Planning refers to the process of creating a sequence of actions or steps to achieve a specific goal. In the context of artificial intelligence, planning involves creating a formal representation of the problem and finding a sequence of actions that will lead to a solution. This can be done by using various techniques and algorithms, such as heuristic search or constraint satisfaction.

Decision making, on the other hand, is the process of selecting the best course of action among several alternatives. In problem solving, decision making is essential at every step, from determining the initial state to selecting the next action to take. Decision making is often based on evaluation and comparison of different options, taking into consideration factors such as feasibility, cost, efficiency, and the desired outcome.

Both planning and decision making are closely intertwined in problem solving. Planning helps in breaking down a problem into smaller, manageable sub-problems and devising a strategy to solve them. Decision making, on the other hand, guides the selection of actions or steps at each stage of the problem-solving process.

In conclusion, planning and decision making are integral components of the problem-solving process in artificial intelligence. Understanding the problem at hand, creating a plan, and making informed decisions are essential for achieving an effective and efficient solution.

Challenges in Problem Solving in Artificial Intelligence

Problem solving is at the core of what artificial intelligence is all about. It involves using intelligent systems to find solutions to complex problems, often with limited information or resources. While artificial intelligence has made great strides in recent years, there are still several challenges that need to be overcome in order to improve problem solving capabilities.

Limited Data and Information

One of the main challenges in problem solving in artificial intelligence is the availability of limited data and information. Many problems require a large amount of data to be effective, but gathering and organizing that data can be time-consuming and difficult. Additionally, there may be cases where the necessary data simply doesn’t exist, making it even more challenging to find a solution.

Complexity and Uncertainty

Another challenge is the complexity and uncertainty of many real-world problems. Artificial intelligence systems need to be able to handle ambiguous, incomplete, or contradictory information in order to find appropriate solutions. This requires advanced algorithms and models that can handle uncertainty and make decisions based on probabilistic reasoning.

Intelligent Decision-Making

In problem solving, artificial intelligence systems need to be able to make intelligent decisions based on the available information. This involves understanding the problem at hand, identifying potential solutions, and evaluating the best course of action. Intelligent decision-making requires not only advanced algorithms but also the ability to learn from past experiences and adapt to new situations.

In conclusion, problem solving in artificial intelligence is a complex and challenging task. Limited data and information, complexity and uncertainty, and the need for intelligent decision-making are just a few of the challenges that need to be addressed. However, with continued research and advancement in the field, it is hoped that these challenges can be overcome, leading to even more effective problem solving in artificial intelligence.

Complexity of Problems

Artificial intelligence (AI) is transforming many aspects of our lives, including problem solving. But what exactly is the complexity of the problems that AI is capable of solving?

The complexity of a problem refers to the level of difficulty involved in finding a solution. In the context of AI, it often refers to the computational complexity of solving a problem using algorithms.

AI is known for its ability to handle complex problems that would be difficult or time-consuming for humans to solve. This is because AI can process and analyze large amounts of data quickly, allowing it to explore different possibilities and find optimal solutions.

One of the key factors that determines the complexity of a problem is the size of the problem space. The problem space refers to the set of all possible states or configurations of a problem. The larger the problem space, the more complex the problem is.

Another factor that influences the complexity of a problem is the nature of the problem itself. Some problems are inherently more difficult to solve than others. For example, problems that involve combinatorial optimization or probabilistic reasoning are often more complex.

Furthermore, the complexity of a problem can also depend on the available resources and the algorithms used to solve it. Certain problems may require significant computational power or specialized algorithms to find optimal solutions.

In conclusion, the complexity of problems that AI is capable of solving is determined by various factors, including the size of the problem space, the nature of the problem, and the available resources. AI’s ability to handle complex problems is one of the key reasons why it is transforming many industries and becoming an essential tool in problem solving.

Incomplete or Uncertain Information

One of the challenges in problem solving in artificial intelligence is dealing with incomplete or uncertain information. In many real-world scenarios, AI systems have to make decisions based on incomplete or uncertain knowledge. This can happen due to various reasons, such as missing data, conflicting information, or uncertain predictions.

When faced with incomplete information, AI systems need to rely on techniques that can handle uncertainty. One such technique is probabilistic reasoning, which allows AI systems to assign probabilities to different possible outcomes and make decisions based on these probabilities. By using probabilistic models, AI systems can estimate the most likely outcomes and use this information to guide problem-solving processes.

In addition to probabilistic reasoning, AI systems can also utilize techniques like fuzzy logic and Bayesian networks to handle incomplete or uncertain information. Fuzzy logic allows for the representation and manipulation of uncertain or vague concepts, while Bayesian networks provide a graphical representation of uncertain relationships between variables.

Overall, dealing with incomplete or uncertain information is an important aspect of problem solving in artificial intelligence. AI systems need to be equipped with techniques and models that can handle uncertainty and make informed decisions based on incomplete or uncertain knowledge. By incorporating these techniques, AI systems can overcome limitations caused by incomplete or uncertain information and improve problem-solving capabilities.

Dynamic Environments

In the field of artificial intelligence, problem solving is a fundamental task. However, in order to solve a problem, it is important to understand what the problem is and what intelligence is required to solve it.

What is a problem?

A problem can be defined as a situation in which an individual or system faces a challenge and needs to find a solution. Problems can vary in complexity and can be static or dynamic in nature.

What is dynamic intelligence?

Dynamic intelligence refers to the ability of an individual or system to adapt and respond to changing environments or situations. In the context of problem solving in artificial intelligence, dynamic environments play a crucial role.

In dynamic environments, the problem or the conditions surrounding the problem can change over time. This requires the problem-solving system to be able to adjust its approach or strategy in order to find a solution.

Dynamic environments can be found in various domains, such as robotics, autonomous vehicles, and game playing. For example, in a game, the game board or the opponent’s moves can change, requiring the player to adapt their strategy.

To solve problems in dynamic environments, artificial intelligence systems need to possess the ability to perceive changes, learn from past experiences, and make decisions based on the current state of the environment.

In conclusion, understanding dynamic environments is essential for problem solving in artificial intelligence. By studying how intelligence can adapt and respond to changing conditions, researchers can develop more efficient and effective problem-solving algorithms.

Optimization vs. Satisficing

In the field of artificial intelligence and problem solving, there are two main approaches: optimization and satisficing. These approaches differ in their goals and strategies for finding solutions to problems.

What is optimization?

Optimization is the process of finding the best solution to a problem, typically defined as maximizing or minimizing a certain objective function. In the context of artificial intelligence, this often involves finding the optimal values for a set of variables that satisfy a given set of constraints. The goal is to find the solution that maximizes or minimizes the objective function while satisfying all the constraints. Optimization algorithms, such as gradient descent or genetic algorithms, are often used to search for the best solution.

What is satisficing?

Satisficing, on the other hand, focuses on finding solutions that are good enough to meet a certain set of criteria or requirements. The goal is not to find the absolute best solution, but rather to find a solution that satisfies a sufficient level of performance. Satisficing algorithms often trade off between the quality of the solution and the computational resources required to find it. These algorithms aim to find a solution that meets the requirements while minimizing the computational effort.

Both optimization and satisficing have their advantages and disadvantages. Optimization is typically used when the problem has a clear objective function and the goal is to find the best possible solution. However, it can be computationally expensive and time-consuming, especially for complex problems. Satisficing, on the other hand, is often used when the problem is ill-defined or there are multiple conflicting objectives. It allows for faster and less resource-intensive solutions, but the quality of the solution may be compromised to some extent.

In conclusion, the choice between optimization and satisficing depends on the specific problem at hand and the trade-offs between the desired solution quality and computational resources. Understanding these approaches can help in developing effective problem-solving strategies in the field of artificial intelligence.

Ethical Considerations in Problem Solving

Intelligence is the ability to understand and learn from experiences, solve problems, and adapt to new situations. Artificial intelligence (AI) is a field that aims to develop machines and algorithms that possess these abilities. Problem solving is a fundamental aspect of intelligence, as it involves finding solutions to challenges and achieving desired outcomes.

The Role of Ethics

However, it is essential to consider the ethical implications of problem solving in the context of AI. What is considered a suitable solution for a problem and how it is obtained can have significant ethical consequences. AI systems and algorithms should be designed in a way that promotes fairness, transparency, and accountability.

Fairness: AI systems should not discriminate against any individuals or groups based on characteristics such as race, gender, or religion. The solutions generated should be fair and unbiased, taking into account diverse perspectives and circumstances.

Transparency: AI algorithms should be transparent in their decision-making process. The steps taken to arrive at a solution should be understandable and explainable, enabling humans to assess the algorithm’s reliability and correctness.

The Impact of AI Problem Solving

Problem solving in AI can have various impacts, both positive and negative, on individuals and society as a whole. AI systems can help address complex problems and make processes more efficient, leading to advancements in fields such as healthcare, transportation, and finance.

On the other hand, there can be ethical concerns regarding the use of AI in problem solving:

– Privacy: AI systems may collect and analyze vast amounts of data, raising concerns about privacy invasion and potential misuse of personal information.

– Job displacement: As AI becomes more capable of problem solving, there is a possibility of job displacement for certain professions. It is crucial to consider the societal impact and explore ways to mitigate the negative effects.

In conclusion, ethical considerations play a vital role in problem solving in artificial intelligence. It is crucial to design AI systems that are fair, transparent, and accountable. Balancing the potential benefits of AI problem solving with its ethical implications is necessary to ensure the responsible and ethical development of AI technologies.

Question-answer:

What is problem solving in artificial intelligence.

Problem solving in artificial intelligence refers to the process of finding solutions to complex problems using computational systems or algorithms. It involves defining and structuring the problem, formulating a plan or strategy to solve it, and executing the plan to reach the desired solution.

What are the steps involved in problem solving in artificial intelligence?

The steps involved in problem solving in artificial intelligence typically include problem formulation, creating a search space, search strategy selection, executing the search, and evaluating the solution. Problem formulation involves defining the problem and its constraints, while creating a search space involves representing all possible states and actions. The search strategy selection determines the approach used to explore the search space, and executing the search involves systematically exploring the space to find a solution. Finally, the solution is evaluated based on predefined criteria.

What are some common techniques used for problem solving in artificial intelligence?

There are several common techniques used for problem solving in artificial intelligence, including uninformed search algorithms (such as breadth-first search and depth-first search), heuristic search algorithms (such as A* search), constraint satisfaction algorithms, and machine learning algorithms. Each technique has its own advantages and is suited for different types of problems.

Can problem solving in artificial intelligence be applied to real-world problems?

Yes, problem solving in artificial intelligence can be applied to real-world problems. It has been successfully used in various domains, such as robotics, healthcare, finance, and transportation. By leveraging computational power and advanced algorithms, artificial intelligence can provide efficient and effective solutions to complex problems.

What are the limitations of problem solving in artificial intelligence?

Problem solving in artificial intelligence has certain limitations. It heavily relies on the quality of input data and the accuracy of algorithms. In cases where the problem space is vast and complex, finding an optimal solution may be computationally expensive or even infeasible. Additionally, problem solving in artificial intelligence may not always capture human-like reasoning and may lack common sense knowledge, which can limit its ability to solve certain types of problems.

Problem solving in artificial intelligence is the process of finding solutions to complex problems using computer algorithms. It involves using various techniques and methods to analyze a problem, break it down into smaller sub-problems, and then develop a step-by-step approach to solving it.

How does artificial intelligence solve problems?

Artificial intelligence solves problems by employing different algorithms and approaches. These include search algorithms, heuristic methods, constraint satisfaction techniques, genetic algorithms, and machine learning. The choice of the specific algorithms depends on the nature of the problem and the available data.

What are the steps involved in problem solving using artificial intelligence?

The steps involved in problem solving using artificial intelligence typically include problem analysis, formulation, search or exploration of possible solutions, evaluation of the solutions, and finally, selecting the best solution. These steps may be repeated iteratively until a satisfactory solution is found.

What are some real-life applications of problem solving in artificial intelligence?

Problem solving in artificial intelligence has various real-life applications. It is used in areas such as robotics, natural language processing, computer vision, data analysis, expert systems, and autonomous vehicles. For example, self-driving cars use problem-solving techniques to navigate and make decisions on the road.

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In the age of AI, the most successful people will be those who can identify the problems that AI is best placed to solve. Image:  Shutterstock/Baranq

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• The global conversation around artificial intelligence (AI) has rapidly shifted from optimism to pessimism.
• But that fear is misplaced — AI tools will always require humans to develop and direct them to where they're most useful.
• And the most essential human skill is going to shift from problem-solving to problem-finding, which demands cognitive diversity.

The global conversation about artificial intelligence (AI) has come full circle. It has shifted from widespread curiosity ( what can AI do? ) to boundless optimimism ( AI will save the world ) to sweeping pessimism ( AI will destroy the world ).

AI undoubtedly raises a range of serious policy issues that we are only beginning to understand. But the tenor of the current discussion is unreasonably skeptical. AI will take away some of today’s jobs; we know this because every major technology advance has done so. But we also know that AI will create significantly more new jobs and potentially at higher wages.

Rather than stifling humans, this technology will enable us to expand our knowledge, skills and productivity far beyond what most of us previously thought possible.

The employees and companies that will thrive in this new era are those that embrace the technology and accept the inevitable disruption — rather than those reflexively opposing it. This will require a profound change in mindset, one that has little precedent in previous waves of technology.

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Navigating ai: what are the top concerns of chief information security officers, can we embrace the ai revolution and journey towards a brighter future, problem-finding is the new premium skill.

The most essential human skill is going to shift from problem-solving to problem-finding.

This contrasts with how workplaces have functioned since the Industrial Revolution. For decades, the emphasis has been on taking an obvious problem and finding an unobvious solution. But AI — when combined with human ingenuity — has unprecedented problem-solving power, so much so that it may free humans to spend more time in creative pursuits. The real challenge of applying AI productively is going to be “use case” discovery: identifying cross-disciplinary urgent problems that are best suited to AI technology.

One good illustration of a surprising application of AI is boosting the productivity of salmon farming — a crucial step towards promoting sustainable aquaculture. Fish farmers are now using AI and machine perception tools (that can take in and process sensory information) to automate feeding time in accordance with the hunger levels of the fish. This reduces wasted feed, trimming a significant carbon emissions source, while improving salmon growth metrics.

A recent collaboration between Tidal AI (a project inside X, Alphabet’s Moonshot Factory) and Cognizant will build on the initial success and expand to other sectors of what’s known as the Blue Economy, including shipping via sea transport. Already, companies can use machine learning models to analyze micro-weather systems, current speeds and port data traffic to optimize shipping route and port arrival times for lower fuel usage.

Why diversity matters to problem-finding

Problem-finding, unlike problem-solving, is going to demand cognitive diversity. To navigate this landscape successfully, businesses will require a more diversely skilled workforce — one that understands human behavior (sociology, psychology, anthropology), can create and optimize different processes (design thinking, six sigma, industry-specific knowledge) and engage audiences intellectually and emotionally through storytelling and design. Liberal arts majors will play as big of a role as STEM graduates. They will help humanize AI and give it more nuanced judgment.

In the face of an increasingly complex and unpredictable world, organizations need to embrace the mantra that “great minds think different — not alike.” Homogeneous cultures tend to stifle cognitive diversity because of the pressure to conform. We can’t tackle 21 st century problems purely through top-down analysis and the application of big data. We need people who can ask great questions, see around corners, think outside the mainstream, understand context, tell us not only what’s happening but why it’s happening and look at the world through their customers’ eyes. That’s why cognitive diversity is so important to maintaining a business’s relevance to its customers and employees.

The prevalence of immigrant founders, researchers and leaders in the US AI industry is a testament to the importance of different perspectives and backgrounds to ensure the country maintains its leadership position as the industry grows. According to one recent study, 28 of 43 (65%) of the top AI companies in the US were founded or co-founded by immigrants.

It is clear that even as generative AI advances towards human-like capabilities, there is no near-term prospect that it will replace human work. Human imagination and ingenuity will be the source of human work indefinitely. People are still going to be essential to solving the vital policy issues raised by AI.

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• Published: 13 January 2020

The role of artificial intelligence in achieving the Sustainable Development Goals

• Ricardo Vinuesa   ORCID: orcid.org/0000-0001-6570-5499 1 ,
• Hossein Azizpour   ORCID: orcid.org/0000-0001-5211-6388 2 ,
• Iolanda Leite 2 ,
• Madeline Balaam 3 ,
• Virginia Dignum 4 ,
• Sami Domisch   ORCID: orcid.org/0000-0002-8127-9335 5 ,
• Anna Felländer 6 ,
• Simone Daniela Langhans 7 , 8 ,
• Max Tegmark 9 &
• Francesco Fuso Nerini   ORCID: orcid.org/0000-0002-4770-4051 10  

Nature Communications volume  11 , Article number:  233 ( 2020 ) Cite this article

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The emergence of artificial intelligence (AI) and its progressively wider impact on many sectors requires an assessment of its effect on the achievement of the Sustainable Development Goals. Using a consensus-based expert elicitation process, we find that AI can enable the accomplishment of 134 targets across all the goals, but it may also inhibit 59 targets. However, current research foci overlook important aspects. The fast development of AI needs to be supported by the necessary regulatory insight and oversight for AI-based technologies to enable sustainable development. Failure to do so could result in gaps in transparency, safety, and ethical standards.

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Introduction.

The emergence of artificial intelligence (AI) is shaping an increasing range of sectors. For instance, AI is expected to affect global productivity 1 , equality and inclusion 2 , environmental outcomes 3 , and several other areas, both in the short and long term 4 . Reported potential impacts of AI indicate both positive 5 and negative 6 impacts on sustainable development. However, to date, there is no published study systematically assessing the extent to which AI might impact all aspects of sustainable development—defined in this study as the 17 Sustainable Development Goals (SDGs) and 169 targets internationally agreed in the 2030 Agenda for Sustainable Development 7 . This is a critical research gap, as we find that AI may influence the ability to meet all SDGs.

Here we present and discuss implications of how AI can either enable or inhibit the delivery of all 17 goals and 169 targets recognized in the 2030 Agenda for Sustainable Development. Relationships were characterized by the methods reported at the end of this study, which can be summarized as a consensus-based expert elicitation process, informed by previous studies aimed at mapping SDGs interlinkages 8 , 9 , 10 . A summary of the results is given in Fig.  1 and the Supplementary Data  1 provides a complete list of all the SDGs and targets, together with the detailed results from this work. Although there is no internationally agreed definition of AI, for this study we considered as AI any software technology with at least one of the following capabilities: perception—including audio, visual, textual, and tactile (e.g., face recognition), decision-making (e.g., medical diagnosis systems), prediction (e.g., weather forecast), automatic knowledge extraction and pattern recognition from data (e.g., discovery of fake news circles in social media), interactive communication (e.g., social robots or chat bots), and logical reasoning (e.g., theory development from premises). This view encompasses a large variety of subfields, including machine learning.

Documented evidence of the potential of AI acting as ( a ) an enabler or ( b ) an inhibitor on each of the SDGs. The numbers inside the colored squares represent each of the SDGs (see the Supplementary Data  1 ). The percentages on the top indicate the proportion of all targets potentially affected by AI and the ones in the inner circle of the figure correspond to proportions within each SDG. The results corresponding to the three main groups, namely Society, Economy, and Environment, are also shown in the outer circle of the figure. The results obtained when the type of evidence is taken into account are shown by the inner shaded area and the values in brackets.

Documented connections between AI and the SDGs

Our review of relevant evidence shows that AI may act as an enabler on 134 targets (79%) across all SDGs, generally through a technological improvement, which may allow to overcome certain present limitations. However, 59 targets (35%, also across all SDGs) may experience a negative impact from the development of AI. For the purpose of this study, we divide the SDGs into three categories, according to the three pillars of sustainable development, namely Society, Economy, and Environment 11 , 12 (see the Methods section). This classification allows us to provide an overview of the general areas of influence of AI. In Fig.  1 , we also provide the results obtained when weighting how appropriate is the evidence presented in each reference to assess an interlinkage to the percentage of targets assessed, as discussed in the Methods section and below. A detailed assessment of the Society, Economy, and Environment groups, together with illustrative examples, are discussed next.

AI and societal outcomes

Sixty-seven targets (82%) within the Society group could potentially benefit from AI-based technologies (Fig.  2 ). For instance, in SDG 1 on no poverty, SDG 4 on quality education, SDG 6 on clean water and sanitation, SDG 7 on affordable and clean energy, and SDG 11 on sustainable cities, AI may act as an enabler for all the targets by supporting the provision of food, health, water, and energy services to the population. It can also underpin low-carbon systems, for instance, by supporting the creation of circular economies and smart cities that efficiently use their resources 13 , 14 . For example, AI can enable smart and low-carbon cities encompassing a range of interconnected technologies such as electrical autonomous vehicles and smart appliances that can enable demand response in the electricity sector 13 , 14 (with benefits across SDGs 7, 11, and 13 on climate action). AI can also help to integrate variable renewables by enabling smart grids that partially match electrical demand to times when the sun is shining and the wind is blowing 13 . Fewer targets in the Society group can be impacted negatively by AI (31 targets, 38%) than the ones with positive impact. However, their consideration is crucial. Many of these relate to how the technological improvements enabled by AI may be implemented in countries with different cultural values and wealth. Advanced AI technology, research, and product design may require massive computational resources only available through large computing centers. These facilities have a very high energy requirement and carbon footprint 15 . For instance, cryptocurrency applications such as Bitcoin are globally using as much electricity as some nations’ electrical demand 16 , compromising outcomes in the SDG 7 sphere, but also on SDG 13 on Climate Action. Some estimates suggest that the total electricity demand of information and communications technologies (ICTs) could require up to 20% of the global electricity demand by 2030, from around 1% today 15 . Green growth of ICT technology is therefore essential 17 . More efficient cooling systems for data centers, broader energy efficiency, and renewable-energy usage in ICTs will all play a role in containing the electricity demand growth 15 . In addition to more efficient and renewable-energy-based data centers, it is essential to embed human knowledge in the development of AI models. Besides the fact that the human brain consumes much less energy than what is used to train AI models, the available knowledge introduced in the model (see, for instance, physics-informed deep learning 18 ) does not need to be learnt through data-intensive training, a fact that may significantly reduce the associated energy consumption. Although AI-enabled technology can act as a catalyst to achieve the 2030 Agenda, it may also trigger inequalities that may act as inhibitors on SDGs 1, 4, and 5. This duality is reflected in target 1.1, as AI can help to identify areas of poverty and foster international action using satellite images 5 . On the other hand, it may also lead to additional qualification requirements for any job, consequently increasing the inherent inequalities 19 and acting as an inhibitor towards the achievement of this target.

Documented evidence of positive or negative impact of AI on the achievement of each of the targets from SDGs 1, 2, 3, 4, 5, 6, 7, 11, and 16 ( https://www.un.org/sustainabledevelopment/ ). Each block in the diagram represents a target (see the Supplementary Data  1 for additional details on the targets). For targets highlighted in green or orange, we found published evidence that AI could potentially enable or inhibit such target, respectively. The absence of highlighting indicates the absence of identified evidence. It is noteworthy that this does not necessarily imply the absence of a relationship. (The content of of this figure has not been reviewed by the United Nations and does not reflect its views).

Another important drawback of AI-based developments is that they are traditionally based on the needs and values of nations in which AI is being developed. If AI technology and big data are used in regions where ethical scrutiny, transparency, and democratic control are lacking, AI might enable nationalism, hate towards minorities, and bias election outcomes 20 . The term “big nudging” has emerged to represent using big data and AI to exploit psychological weaknesses to steer decisions—creating problems such as damaging social cohesion, democratic principles, and even human rights 21 . AI has been recently utilized to develop citizen scores, which are used to control social behavior 22 . This type of score is a clear example of threat to human rights due to AI misuse and one of its biggest problems is the lack of information received by the citizens on the type of analyzed data and the consequences this may have on their lives. It is also important to note that AI technology is unevenly distributed: for instance, complex AI-enhanced agricultural equipment may not be accessible to small farmers and thus produce an increased gap with respect to larger producers in more developed economies 23 , consequently inhibiting the achievement of some targets of SDG 2 on zero hunger. There is another important shortcoming of AI in the context of SDG 5 on gender equality: there is insufficient research assessing the potential impact of technologies such as smart algorithms, image recognition, or reinforced learning on discrimination against women and minorities. For instance, machine-learning algorithms uncritically trained on regular news articles will inadvertently learn and reproduce the societal biases against women and girls, which are embedded in current languages. Word embeddings, a popular technique in natural language processing, have been found to exacerbate existing gender stereotypes 2 . In addition to the lack of diversity in datasets, another main issue is the lack of gender, racial, and ethnic diversity in the AI workforce 24 . Diversity is one of the main principles supporting innovation and societal resilience, which will become essential in a society exposed to changes associated to AI development 25 . Societal resilience is also promoted by decentralization, i.e., by the implementation of AI technologies adapted to the cultural background and the particular needs of different regions.

AI and economic outcomes

The technological advantages provided by AI may also have a positive impact on the achievement of a number of SDGs within the Economy group. We have identified benefits from AI on 42 targets (70%) from these SDGs, whereas negative impacts are reported in 20 targets (33%), as shown in Fig.  1 . Although Acemoglu and Restrepo 1 report a net positive impact of AI-enabled technologies associated to increased productivity, the literature also reflects potential negative impacts mainly related to increased inequalities 26 , 27 , 28 , 29 . In the context of the Economy group of SDGs, if future markets rely heavily on data analysis and these resources are not equally available in low- and middle- income countries, the economical gap may be significantly increased due to the newly introduced inequalities 30 , 31 significantly impacting SDGs 8 (decent work and economic growth), 9 (industry, innovation and infrastructure), and 10 (reduced inequalities). Brynjolfsson and McAfee 31  argue that AI can exacerbate inequality also within nations. By replacing old jobs with ones requiring more skills, technology disproportionately rewards the educated: since the mid 1970s, the salaries in the United States (US) salaries rose about 25% for those with graduate degrees, while the average high-school dropout took a 30% pay cut. Moreover, automation shifts corporate income to those who own companies from those who work there. Such transfer of revenue from workers to investors helps explain why, even though the combined revenues of Detroit's “Big 3” (GM, Ford, and Chrysler) in 1990 were almost identical to those of Silicon Valley's “Big 3” (Google, Apple, and Facebook) in 2014, the latter had 9 times fewer employees and were worth 30 times more on the stock market 32 . Figure  3 shows an assessment of the documented positive and negative effects on the various targets within the SDGs in the Economy group.

Documented evidence of positive or negative impact of AI on the achievement of each of the targets from SDGs 8, 9, 10, 12, and 17 ( https://www.un.org/sustainabledevelopment/ ). The interpretation of the blocks and colors is as in Fig.  2 .  (The content of of this figure has not been reviewed by the United Nations and does not reflect its views).

Although the identified linkages in the Economy group are mainly positive, trade-offs cannot be neglected. For instance, AI can have a negative effect on social media usage, by showing users content specifically suited to their preconceived ideas. This may lead to political polarization 33 and affect social cohesion 21 with consequences in the context of SDG 10 on reduced inequalities. On the other hand, AI can help identify sources of inequality and conflict 34 , 35 , and therewith potentially reduce inequalities, for instance, by using simulations to assess how virtual societies may respond to changes. However, there is an underlying risk when using AI to evaluate and predict human behavior, which is the inherent bias in the data. It has been reported that a number of discriminatory challenges are faced in the automated targeting of online job advertising using AI 35 , essentially related to the previous biases in selection processes conducted by human recruiters. The work by Dalenberg 35 highlights the need of modifying the data preparation process and explicitly adapting the AI-based algorithms used for selection processes to avoid such biases.

AI and environmental outcomes

The last group of SDGs, i.e., the one related to Environment, is analyzed in Fig.  4 . The three SDGs in this group are related to climate action, life below water and life on land (SDGs 13, 14, and 15). For the Environment group, we identified 25 targets (93%) for which AI could act as an enabler. Benefits from AI could be derived by the possibility of analyzing large-scale interconnected databases to develop joint actions aimed at preserving the environment. Looking at SDG 13 on climate action, there is evidence that AI advances will support the understanding of climate change and the modeling of its possible impacts. Furthermore, AI will support low-carbon energy systems with high integration of renewable energy and energy efficiency, which are all needed to address climate change 13 , 36 , 37 . AI can also be used to help improve the health of ecosystems. The achievement of target 14.1, calling to prevent and significantly reduce marine pollution of all kinds, can benefit from AI through algorithms for automatic identification of possible oil spills 38 . Another example is target 15.3, which calls for combating desertification and restoring degraded land and soil. According to Mohamadi et al. 39 , neural networks and objective-oriented techniques can be used to improve the classification of vegetation cover types based on satellite images, with the possibility of processing large amounts of images in a relatively short time. These AI techniques can help to identify desertification trends over large areas, information that is relevant for environmental planning, decision-making, and management to avoid further desertification, or help reverse trends by identifying the major drivers. However, as pointed out above, efforts to achieve SDG 13 on climate action could be undermined by the high-energy needs for AI applications, especially if non carbon-neutral energy sources are used. Furthermore, despite the many examples of how AI is increasingly applied to improve biodiversity monitoring and conservation 40 , it can be conjectured that an increased access to AI-related information of ecosystems may drive over-exploitation of resources, although such misuse has so far not been sufficiently documented. This aspect is further discussed below, where currently identified gaps in AI research are considered.

Documented evidence of positive or negative impact of AI on the achievement of each of the targets from SDGs 13, 14, and 15 ( https://www.un.org/sustainabledevelopment/ ). The interpretation of the blocks and colors is as in Fig.  2 . (The content of of this figure has not been reviewed by the United Nations and does not reflect its views).

An assessment of the collected evidence on the interlinkages

A deeper analysis of the gathered evidence was undertaken as shown in Fig.  1 (and explained in the Methods section). In practice, each interlinkage was weighted based on the applicability and appropriateness of each of the references to assess a specific interlinkage—and possibly identify research gaps. Although accounting for the type of evidence has a relatively small effect on the positive impacts (we see a reduction of positively affected targets from 79% to 71%), we observe a more significant reduction (from 35% to 23%) in the targets with negative impact of AI. This can be partly due the fact that AI research typically involves quantitative methods that would bias the results towards the positive effects. However, there are some differences across the Society, Economy and Environment spheres. In the Society sphere, when weighting the appropriateness of evidence, positively affected targets diminish by 5 percentage points (p.p.) and negatively affected targets by 13 p.p. In particular, weighting the appropriateness of evidence on negative impacts on SDG 1 (on no poverty) and SDG 6 (on clean water and sanitation) reduces the fraction of affected targets by 43 p.p. and 35 p.p., respectively. In the Economy group instead, positive impacts are reduced more (15 p.p.) than negative ones (10 p.p.) when taking into account the appropriateness of the found evidence to speak of the issues. This can be related to the extensive study in literature assessing the displacement of jobs due to AI (because of clear policy and societal concerns), but overall the longer-term benefits of AI on the economy are perhaps not so extensively characterized by currently available methods. Finally, although the weighting of evidence decreases the positive impacts of AI on the Environment group only by 8 p.p., the negative impacts see the largest average reduction (18 p.p.). This is explained by the fact that, although there are some indications of the potential negative impact of AI on this SDG, there is no strong evidence (in any of the targets) supporting this claim, and therefore this is a relevant area for future research.

In general, the fact that the evidence on interlinkages between AI and the large majority of targets is not based on tailored analyses and tools to refer to that particular issue provides a strong rationale to address a number of research gaps, which are identified and listed in the section below.

Research gaps on the role of AI in sustainable development

The more we enable SDGs by deploying AI applications, from autonomous vehicles 41 to AI-powered healthcare solutions 42 and smart electrical grids 13 , the more important it becomes to invest in the AI safety research needed to keep these systems robust and beneficial, so as to prevent them from malfunctioning, or from getting hacked 43 . A crucial research venue for a safe integration of AI is understanding catastrophes, which can be enabled by a systemic fault in AI technology. For instance, a recent World Economic Forum (WEF) report raises such a concern due to the integration of AI in the financial sector 44 . It is therefore very important to raise awareness on the risks associated to possible failures of AI systems in a society progressively more dependent on this technology. Furthermore, although we were able to find numerous studies suggesting that AI can potentially serve as an enabler for many SDG targets and indicators, a significant fraction of these studies have been conducted in controlled laboratory environments, based on limited datasets or using prototypes 45 , 46 , 47 . Hence, extrapolating this information to evaluate the real-world effects often remains a challenge. This is particularly true when measuring the impact of AI across broader scales, both temporally and spatially. We acknowledge that conducting controlled experimental trials for evaluating real-world impacts of AI can result in depicting a snapshot situation, where AI tools are tailored towards that specific environment. However, as society is constantly changing (also due to factors including non-AI-based technological advances), the requirements set for AI are changing as well, resulting in a feedback loop with interactions between society and AI. Another underemphasized aspect in existing literature is the resilience of the society towards AI-enabled changes. Therefore, novel methodologies are required to ensure that the impact of new technologies are assessed from the points of view of efficiency, ethics, and sustainability, prior to launching large-scale AI deployments. In this sense, research aimed at obtaining insight on the reasons for failure of AI systems, introducing combined human–machine analysis tools 48 , are an essential step towards accountable AI technology, given the large risk associated to such a failure.

Although we found more published evidence of AI serving as an enabler than as an inhibitor on the SDGs, there are at least two important aspects that should be considered. First, self-interest can be expected to bias the AI research community and industry towards publishing positive results. Second, discovering detrimental aspects of AI may require longer-term studies and, as mentioned above, there are not many established evaluation methodologies available to do so. Bias towards publishing positive results is particularly apparent in the SDGs corresponding to the Environment group. A good example of this bias is target 14.5 on conserving coastal and marine areas, where machine-learning algorithms can provide optimum solutions given a wide range of parameters regarding the best choice of areas to include in conservation networks 49 . However, even if the solutions are optimal from a mathematical point of view (given a certain range of selected parameters), additional research would be needed to assess the long-term impact of such algorithms on equity and fairness 6 , precisely because of the unknown factors that may come into play. Regarding the second point stated above, it is likely that the AI projects with the highest potential of maximizing profit will get funded. Without control, research on AI is expected to be directed towards AI applications where funding and commercial interests are. This may result in increased inequality 50 . Consequently, there is the risk that AI-based technologies with potential to achieve certain SDGs may not be prioritized, if their expected economic impact is not high. Furthermore, it is essential to promote the development of initiatives to assess the societal, ethical, legal, and environmental implications of new AI technologies.

Substantive research and application of AI technologies to SDGs is concerned with the development of better data-mining and machine-learning techniques for the prediction of certain events. This is the case of applications such as forecasting extreme weather conditions or predicting recidivist offender behavior. The expectation with this research is to allow the preparation and response for a wide range of events. However, there is a research gap in real-world applications of such systems, e.g., by governments (as discussed above). Institutions have a number of barriers to the adoption AI systems as part of their decision-making process, including the need of setting up measures for cybersecurity and the need to protect the privacy of citizens and their data. Both aspects have implications on human rights regarding the issues of surveillance, tracking, communication, and data storage, as well as automation of processes without rigorous ethical standards 21 . Targeting these gaps would be essential to ensure the usability and practicality of AI technologies for governments. This would also be a prerequisite for understanding long-term impacts of AI regarding its potential, while regulating its use to reduce the possible bias that can be inherent to AI 6 .

Furthermore, our research suggests that AI applications are currently biased towards SDG issues that are mainly relevant to those nations where most AI researchers live and work. For instance, many systems applying AI technologies to agriculture, e.g., to automate harvesting or optimize its timing, are located within wealthy nations. Our literature search resulted in only a handful of examples where AI technologies are applied to SDG-related issues in nations without strong AI research. Moreover, if AI technologies are designed and developed for technologically advanced environments, they have the potential to exacerbate problems in less wealthy nations (e.g., when it comes to food production). This finding leads to a substantial concern that developments in AI technologies could increase inequalities both between and within countries, in ways which counteract the overall purpose of the SDGs. We encourage researchers and funders to focus more on designing and developing AI solutions, which respond to localized problems in less wealthy nations and regions. Projects undertaking such work should ensure that solutions are not simply transferred from technology-intensive nations. Instead, they should be developed based on a deep understanding of the respective region or culture to increase the likelihood of adoption and success.

Towards sustainable AI

The great wealth that AI-powered technology has the potential to create may go mainly to those already well-off and educated, while job displacement leaves others worse off. Globally, the growing economic importance of AI may result in increased inequalities due to the unevenly distributed educational and computing resources throughout the world. Furthermore, the existing biases in the data used to train AI algorithms may result in the exacerbation of those biases, eventually leading to increased discrimination. Another related problem is the usage of AI to produce computational (commercial, political) propaganda based on big data (also defined as “big nudging”), which is spread through social media by independent AI agents with the goals of manipulating public opinion and producing political polarization 51 . Despite the fact that current scientific evidence refutes technological determinism of such fake news 51 , long-term impacts of AI are possible (although unstudied) due to a lack of robust research methods. A change of paradigm is therefore needed to promote cooperation and to limit the possibilities for control of citizen behavior through AI. The concept of Finance 4.0 has been proposed 52 as a multi-currency financial system promoting a circular economy, which is aligned with societal goals and values. Informational self-determination (in which the individual takes an active role in how their data are handled by AI systems) would be an essential aspect of such a paradigm 52 . The data intensiveness of AI applications creates another problem: the need for more and more detailed information to improve AI algorithms, which is in conflict with the need of more transparent handling and protection of personal data 53 . One area where this conflict is particularly important is healthcare: Panch et al. 54 argue that although the vast amount of personal healthcare data could lead to the development of very powerful tools for diagnosis and treatment, the numerous problems associated to data ownership and privacy call for careful policy intervention. This is also an area where more research is needed to assess the possible long-term negative consequences. All the challenges mentioned above culminate in the academic discourse about legal personality of robots 55 , which may lead to alarming narratives of technological totalitarianism.

Many of these aspects result from the interplay between technological developments on one side and requests from individuals, response from governments, as well as environmental resources and dynamics on the other. Figure  5 shows a schematic representation of these dynamics, with emphasis on the role of technology. Based on the evidence discussed above, these interactions are not currently balanced and the advent of AI has exacerbated the process. A wide range of new technologies are being developed very fast, significantly affecting the way individuals live as well as the impacts on the environment, requiring new piloting procedures from governments. The problem is that neither individuals nor governments seem to be able to follow the pace of these technological developments. This fact is illustrated by the lack of appropriate legislation to ensure the long-term viability of these new technologies. We argue that it is essential to reverse this trend. A first step in this direction is to establish adequate policy and legislation frameworks, to help direct the vast potential of AI towards the highest benefit for individuals and the environment, as well as towards the achievement of the SDGs. Regulatory oversight should be preceded by regulatory insight, where policymakers have sufficient understanding of AI challenges to be able to formulate sound policy. Developing such insight is even more urgent than oversight, as policy formulated without understanding is likely to be ineffective at best and counterproductive at worst.

Schematic representation showing the identified agents and their roles towards the development of AI. Thicker arrows indicate faster change. In this representation, technology affects individuals through technical developments, which change the way people work and interact with each other and with the environment, whereas individuals would interact with technology through new needs to be satisfied. Technology (including technology itself and its developers) affects governments through new developments that need appropriate piloting and testing. Also, technology developers affect government through lobbying and influencing decision makers. Governments provide legislation and standards to technology. The governments affect individuals through policy and legislation, and individuals would require new legislation consistent with the changing circumstances from the governments. The environment interacts with technology by providing the resources needed for technological development and is affected by the environmental impact of technology. Furthermore, the environment is affected either negatively or positively by the needs, impacts, and choices of individuals and governments, which in turn require environmental resources. Finally, the environment is also an underlying layer that provides the “planetary boundaries” to the mentioned interactions.

Although strong and connected institutions (covered by SDG 16) are needed to regulate the future of AI, we find that there is limited understanding of the potential impact of AI on institutions. Examples of the positive impacts include AI algorithms aimed at improving fraud detection 56 , 57 or assessing the possible effects of certain legislation 58 , 59 . Another concern is that data-driven approaches for policing may hinder equal access to justice because of algorithm bias, particularly towards minorities 60 . Consequently, we believe that it is imperative to develop legislation regarding transparency and accountability of AI, as well as to decide the ethical standards to which AI-based technology should be subjected to. This debate is being pushed forward by initiatives such as the IEEE (Institute of Electrical and Electronics Engineers) ethical aligned design 60 and the new EU (European Union) ethical guidelines for trustworthy AI 61 . It is noteworthy that despite the importance of an ethical, responsible, and trustworthy approach to AI development and use, in a sense, this issue is independent of the aims of the article. In other words, one can envision AI applications that improve SDG outcomes while not being fully aligned with AI ethics guidelines. We therefore recommend that AI applications that target SDGs are open and explicit about guiding ethical principles, also by indicating explicitly how they align with the existing guidelines. On the other hand, the lack of interpretability of AI, which is currently one of the challenges of AI research, adds an additional complication to the enforcement of such regulatory actions 62 . Note that this implies that AI algorithms (which are trained with data consisting of previous regulations and decisions) may act as a “mirror” reflecting biases and unfair policy. This presents an opportunity to possibly identify and correct certain errors in the existing procedures. The friction between the uptake of data-driven AI applications and the need of protecting the privacy and security of the individuals is stark. When not properly regulated, the vast amount of data produced by citizens might potentially be used to influence consumer opinion towards a certain product or political cause 51 .

AI applications that have positive societal welfare implications may not always benefit each individual separately 41 . This inherent dilemma of collective vs. individual benefit is relevant in the scope of AI applications but is not one that should be solved by the application of AI itself. This has always been an issue affecting humankind and it cannot be solved in a simple way, since such a solution requires participation of all involved stakeholders. The dynamicity of context and the level of abstraction at which human values are described imply that there is not a single ethical theory that holds all the time in all situations 63 . Consequently, a single set of utilitarian ethical principles with AI would not be recommendable due to the high complexity of our societies 52 . It is also essential to be aware of the potential complexity in the interaction between human and AI agents, and of the increasing need for ethics-driven legislation and certification mechanisms for AI systems. This is true for all AI applications, but especially those that, if they became uncontrolled, could have even catastrophic effects on humanity, such as autonomous weapons. Regarding the latter, associations of AI and robotics experts are already getting together to call for legislation and limitations of their use 64 . Furthermore, associations such as the Future of Life Institute are reviewing and collecting policy actions and shared principles around the world to monitor progress towards sustainable-development-friendly AI 65 . To deal with the ethical dilemmas raised above, it is important that all applications provide openness about the choices and decisions made during design, development, and use, including information about the provenance and governance of the data used for training algorithms, and about whether and how they align with existing AI guidelines. It is therefore important to adopt decentralized AI approaches for a more equitable development of AI 66 .

We are at a critical turning point for the future of AI. A global and science-driven debate to develop shared principles and legislation among nations and cultures is necessary to shape a future in which AI positively contributes to the achievement of all the SDGs. The current choices to develop a sustainable-development-friendly AI by 2030 have the potential to unlock benefits that could go far-beyond the SDGs within our century. All actors in all nations should be represented in this dialogue, to ensure that no one is left behind. On the other hand, postponing or not having such a conversation could result in an unequal and unsustainable AI-fueled future.

In this section we describe the process employed to obtain the results described in the present study and shown in the Supplementary Data  1 . The goal was to answer the question “Is there published evidence of AI acting as an enabler or an inhibitor for this particular target?” for each of the 169 targets within the 17 SDGs. To this end, we conducted a consensus-based expert elicitation process, informed by previous studies on mapping SDGs interlinkages 8 , 9 and following Butler et al. 67 and Morgan 68 . The authors of this study are academics spanning a wide range of disciplines, including engineering, natural and social sciences, and acted as experts for the elicitation process. The authors performed an expert-driven literature search to support the identified connections between AI and the various targets, where the following sources of information were considered as acceptable evidence: published work on real-world applications (given the quality variation depending on the venue, we ensured that the publications considered in the analysis were of sufficient quality); published evidence on controlled/laboratory scenarios (given the quality variation depending on the venue, we ensured that the publications considered in the analysis were of sufficient quality); reports from accredited organizations (for instance: UN or government bodies); and documented commercial-stage applications. On the other hand, the following sources of information were not considered as acceptable evidence: educated conjectures, real-world applications without peer-reviewed research; media, public beliefs or other sources of information.

The expert elicitation process was conducted as follows: each of the SDGs was assigned to one or more main contributors, and in some cases to several additional contributors as summarized in the Supplementary Data  1 (here the initials correspond to the author names). The main contributors carried out a first literature search for that SDG and then the additional contributors completed the main analysis. One published study on a synergy or a trade-off between a target and AI was considered enough for mapping the interlinkage. However, for nearly all targets several references are provided. After the analysis of a certain SDG was concluded by the contributors, a reviewer was assigned to evaluate the connections and reasoning presented by the contributors. The reviewer was not part of the first analysis and we tried to assign the roles of the main contributor and reviewer to experts with complementary competences for each of the SDGs. The role of the reviewer was to bring up additional points of view and considerations, while critically assessing the analysis. Then, the main contributors and reviewers iteratively discussed to improve the results presented for each of the SDGs until the analysis for all the SDGs was sufficiently refined.

After reaching consensus regarding the assessment shown in the Supplementary Data  1 , we analyzed the results by evaluating the number of targets for which AI may act as an enabler or an inhibitor, and calculated the percentage of targets with positive and negative impact of AI for each of the 17 goals, as shown in Fig.  1 . In addition, we divided the SDGs into the three following categories: Society, Economy, and Environment, consistent with the classification discussed by Refs. 11 , 12 . The SDGs assigned to each of the categories are shown in Fig.  6 and the individual results from each of these groups can be observed in Figs.  2 – 4 . These figures indicate, for each target within each SDG, whether any published evidence of positive or negative impact was found.

(The content of this figure has not been reviewed by the United Nations and does not reflect its views).

Taking into account the types of evidence

In the methodology described above, a connection between AI and a certain target is established if at least one reference documenting such a link was found. As the analyzed studies rely on very different types of evidence, it is important to classify the references based on the methods employed to support their conclusions. Therefore, all the references in the Supplementary Data  1 include a classification from (A) to (D) according to the following criteria:

References using sophisticated tools and data to refer to this particular issue and with the possibility to be generalized are of type (A).

Studies based on data to refer to this particular issue, but with limited generalizability, are of type (B).

Anecdotal qualitative studies and methods are of type (C) .

Purely theoretical or speculative references are of type (D).

The various classes were assigned following the same expert elicitation process described above. Then, the contribution of these references towards the linkages is weighted and categories (A), (B), (C), and (D) are assigned relative weights of 1, 0.75, 0.5, and 0.25, respectively. It is noteworthy that, given the vast range of studies on all the SDG areas, the literature search was not exhaustive and, therefore, certain targets are related to more references than others in our study. To avoid any bias associated to the different amounts of references in the various targets, we considered the largest positive and negative weight to establish the connection with each target. Let us consider the following example: for a certain target, one reference of type (B) documents a positive connection and two references of types (A) and (D) document a negative connection with AI. In this case, the potential positive impact of AI on that target will be assessed with 0.75, while the potential negative impact is 1.

Limitations of the research

The presented analysis represents the perspective of the authors. Some literature on how AI might affect certain SDGs could have been missed by the authors or there might not be published evidence yet on such interlinkage. Nevertheless, the employed methods tried to minimize the subjectivity of the assessment. How AI might affect the delivery of each SDG was assessed and reviewed by several authors and a number of studies were reviewed for each interlinkage. Furthermore, as discussed in the Methods section, each interlinkage was discussed among a subset of authors until consensus was reached on its nature.

Finally, this study relies on the analysis of the SDGs. The SDGs provide a powerful lens for looking at internationally agreed goals on sustainable development and present a leap forward compared with the Millenium Development Goals in the representation of all spheres of sustainable development, encompassing human rights 69 , social sustainability, environmental outcomes, and economic development. However, the SDGs are a political compromise and might be limited in the representation of some of the complex dynamics and cross-interactions among targets. Therefore, the SDGs have to be considered in conjunction with previous and current, and other international agreements 9 . For instance, as pointed out in a recent work by UN Human Rights 69 , human rights considerations are highly embedded in the SDGs. Nevertheless, the SDGs should be considered as a complement, rather than a replacement, of the United Nations Universal Human Rights Charter 70 .

Data availability

The authors declare that all the data supporting the findings of this study are available within the paper and its Supplementary Data  1 file .

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Acknowledgements

R.V. acknowledges funding provided by KTH Sustainability Office. I.L. acknowledges the Swedish Research Council (registration number 2017-05189) and funding through an Early Career Research Fellowship granted by the Jacobs Foundation. M.B. acknowledges Implicit SSF: Swedish Foundation for Strategic Research project RIT15-0046. V.D. acknowledges the support of the Wallenberg AI, Autonomous Systems, and Software Program (WASP) program funded by the Knut and Alice Wallenberg Foundation. S.D. acknowledges funding from the Leibniz Competition (J45/2018). S.L. acknowledges funding from the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Skłodowska–Curie grant agreement number 748625. M.T. was supported by the Ethics and Governance of AI Fund. F.F.N. acknowledges funding from the Formas grant number 2018-01253.

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Ricardo Vinuesa

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Hossein Azizpour & Iolanda Leite

Division of Media Technology and Interaction Design, KTH Royal Institute of Technology, Lindstedtsvägen 3, Stockholm, Sweden

Madeline Balaam

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R.V. and F.F.N. ideated, designed, and wrote the paper; they also coordinated inputs from the other authors, and assessed and reviewed SDG evaluations as for the Supplementary Data 1 . H.A. and I.L. supported the design, wrote, and reviewed sections of the paper; they also assessed and reviewed SDG evaluations as for the Supplementary Data 1 . M.B., V.D., S.D., A.F. and S.L. wrote and reviewed sections of the paper; they also assessed and reviewed SDG evaluations as for the Supplementary Data 1 . M.T. reviewed the paper and acted as final editor.

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To build a better AI helper, start by modeling the irrational behavior of humans

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To build AI systems that can collaborate effectively with humans, it helps to have a good model of human behavior to start with. But humans tend to behave suboptimally when making decisions.

This irrationality, which is especially difficult to model, often boils down to computational constraints. A human can’t spend decades thinking about the ideal solution to a single problem.

Researchers at MIT and the University of Washington developed a way to model the behavior of an agent, whether human or machine, that accounts for the unknown computational constraints that may hamper the agent’s problem-solving abilities.

Their model can automatically infer an agent’s computational constraints by seeing just a few traces of their previous actions. The result, an agent’s so-called “inference budget,” can be used to predict that agent’s future behavior.

In a new paper, the researchers demonstrate how their method can be used to infer someone’s navigation goals from prior routes and to predict players’ subsequent moves in chess matches. Their technique matches or outperforms another popular method for modeling this type of decision-making.

Ultimately, this work could help scientists teach AI systems how humans behave, which could enable these systems to respond better to their human collaborators. Being able to understand a human’s behavior, and then to infer their goals from that behavior, could make an AI assistant much more useful, says Athul Paul Jacob, an electrical engineering and computer science (EECS) graduate student and lead author of a paper on this technique .

“If we know that a human is about to make a mistake, having seen how they have behaved before, the AI agent could step in and offer a better way to do it. Or the agent could adapt to the weaknesses that its human collaborators have. Being able to model human behavior is an important step toward building an AI agent that can actually help that human,” he says.

Jacob wrote the paper with Abhishek Gupta, assistant professor at the University of Washington, and senior author Jacob Andreas, associate professor in EECS and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL). The research will be presented at the International Conference on Learning Representations.

Modeling behavior

Researchers have been building computational models of human behavior for decades. Many prior approaches try to account for suboptimal decision-making by adding noise to the model. Instead of the agent always choosing the correct option, the model might have that agent make the correct choice 95 percent of the time.

However, these methods can fail to capture the fact that humans do not always behave suboptimally in the same way.

Others at MIT have also studied more effective ways to plan and infer goals in the face of suboptimal decision-making.

To build their model, Jacob and his collaborators drew inspiration from prior studies of chess players. They noticed that players took less time to think before acting when making simple moves and that stronger players tended to spend more time planning than weaker ones in challenging matches.

“At the end of the day, we saw that the depth of the planning, or how long someone thinks about the problem, is a really good proxy of how humans behave,” Jacob says.

They built a framework that could infer an agent’s depth of planning from prior actions and use that information to model the agent’s decision-making process.

The first step in their method involves running an algorithm for a set amount of time to solve the problem being studied. For instance, if they are studying a chess match, they might let the chess-playing algorithm run for a certain number of steps. At the end, the researchers can see the decisions the algorithm made at each step.

Their model compares these decisions to the behaviors of an agent solving the same problem. It will align the agent’s decisions with the algorithm’s decisions and identify the step where the agent stopped planning.

From this, the model can determine the agent’s inference budget, or how long that agent will plan for this problem. It can use the inference budget to predict how that agent would react when solving a similar problem.

An interpretable solution

This method can be very efficient because the researchers can access the full set of decisions made by the problem-solving algorithm without doing any extra work. This framework could also be applied to any problem that can be solved with a particular class of algorithms.

“For me, the most striking thing was the fact that this inference budget is very interpretable. It is saying tougher problems require more planning or being a strong player means planning for longer. When we first set out to do this, we didn’t think that our algorithm would be able to pick up on those behaviors naturally,” Jacob says.

The researchers tested their approach in three different modeling tasks: inferring navigation goals from previous routes, guessing someone’s communicative intent from their verbal cues, and predicting subsequent moves in human-human chess matches.

Their method either matched or outperformed a popular alternative in each experiment. Moreover, the researchers saw that their model of human behavior matched up well with measures of player skill (in chess matches) and task difficulty.

Moving forward, the researchers want to use this approach to model the planning process in other domains, such as reinforcement learning (a trial-and-error method commonly used in robotics). In the long run, they intend to keep building on this work toward the larger goal of developing more effective AI collaborators.

This work was supported, in part, by the MIT Schwarzman College of Computing Artificial Intelligence for Augmentation and Productivity program and the National Science Foundation.

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Characteristics of Artificial Intelligence Problems

Problems in Artificial Intelligence (AI) come in different forms, each with its own set of challenges and potential for innovation. From image recognition to natural language processing, AI problems exhibit distinct characteristics that shape the strategies and techniques used to tackle them effectively. In this article, we delve into the fundamental characteristics of AI problems, providing light on what makes them so fascinating and formidable.

Table of Content

Key Terminologies in Artificial Intelligence Problems

Addressing the challenges of ai problems, examples of ai applications and challenges across domains, characteristics of artificial intelligence problems – faqs.

Before exploring the characteristics, let’s clarify some essential AI concepts:

• Problem-solving: Problem-solving is a process that is a solution provided to a complex problem or task. When dealing with AI, problem-solving involves creating algorithms and methods of artificial intelligence that will empower machines to imitate humans’ capabilities of logical and reasonable thinking in certain situations.
• Search Space: Searching space refers to the area where an agent involved in the problem-solving process can examine all the possible states or settings with the hope of discovering a solution. It covers a gamut of options that the agent might select for arriving at the same destination.
• State: An entity represents some unique and specific arrangement of elements in a problem-solving situation. States can be assigned to different locations, challenges, or dangers that the problem-solving agent faces while looking for a solution to the problem within the search space.
• Search Algorithm: A search algorithm describes any process or method targeted for examining and exploring the given problem space to find a solution. Algorithm decision-making has diverging levels of complexity and effectiveness. They are studied to help in the discovery of the most suitable results.
• Heuristic: Heuristic is a thumb rule or guiding principle that is used to make intelligent decisions or solve the problems that are encountered during the process. Applying heuristics in AI is prevalent in prioritizing search paths or evaluating probable solutions based on their likelihood of finishing successfully.
• Optimization: The problem of optimization implies finding the best solution for process selection among the set of feasible alternatives submitted to some previously set objectives or criteria. AI optimization approaches are employed to deal optimally with complex issues through performance and efficiency improvement.

By understanding these key terminologies, we can better grasp the characteristics of AI problems and the techniques used to address them. These concepts form the foundation of AI problem-solving and provide the framework for developing innovative solutions to real-world challenges.

Let’s explore the core characteristics that differentiate AI problems:

• Learning and adaptation: AI systems should be capable of learning from data or experiences and adapting their behaviour accordingly. This enables them to improve performance over time and handle new situations more effectively.
• Complexity: AI problems often involve dealing with complex systems or large amounts of data. AI systems must be able to handle this complexity efficiently to produce meaningful results.
• Uncertainty: AI systems frequently operate in environments where outcomes are uncertain or incomplete information is available. They must be equipped to make decisions or predictions under such conditions.
• Dynamism: Environments in which AI systems operate can change over time. These changes may occur unpredictably or according to specific rules, requiring AI systems to continually adjust their strategies or models.
• Interactivity : Many AI applications involve interaction with users or other agents. Effective AI systems should be able to perceive, interpret, and respond to these interactions in a meaningful way.
• Context dependence: The behavior or performance of AI systems may depend on the context in which they operate. Understanding and appropriately responding to different contexts is essential for achieving desired outcomes.
• Multi-disciplinary: AI problems often require knowledge and techniques from multiple disciplines, including computer science, mathematics, statistics, psychology, and more. Integrating insights from these diverse fields is necessary for developing effective AI solutions.
• Goal-oriented Design: AI systems are typically designed to achieve specific objectives or goals. Designing AI systems with clear objectives in mind helps guide the development process and ensures that the resulting systems are focused on achieving meaningful outcomes.

These characteristics collectively shape the challenges and opportunities involved in developing and deploying AI systems across various domains and applications.

The characteristics of AI problems present unique challenges that require innovative approaches to solution development. Some of the key aspects to consider in tackling these challenges include:

• Complexity and Uncertainty: AI difficulties are sometimes characterized by highly variable domains that are difficult to predict exactly. Hence, AI algorithms should be installed with the skill of dealing with unclear circumstances and should make decisions that are based on imperfect data or noisy information.
• Algorithmic Efficiency: Among the key challenges of this approach are the enormous search spaces, computational resources, and the efficiency of the algorithms in terms of problem-solving. Strategies like caching, pruning, and parallelization are among the most widely used implementations for better algorithmic speed.
• Domain Knowledge Integration: Such numerous AI problems involve the ability to capture the rules and reasoning of the real world to model and solve the questions correctly. The AI machines that have been trained with expertise from relevant domains improve the accuracy and effectiveness of the applications in the real world.
• Scalability and Adaptability: AI solutions should be able to process large datasets and complex cases at the same time, and they should also be versatile by responding to shifts in conditions and requirements. Strategies such as machine learning and reinforcement learning allow systems to do more than just perform according to the given tasks at hand; they empower systems to learn and progress over time.
• Ethical and Social Implications: AI technologies elicit ethical and social limitations concerning problems of bias, justice, privacy, and responsible office. Taking these implications into account, along with ethical frameworks, compliance frameworks, and stakeholder engagement, is essential. This approach will help position cryptocurrencies as a secure and trustworthy investment.
• Interpretability and Explainability: To achieve interpretability and explainability of AI algorithms for the sake of understanding and confidence among users and stakeholders, these algorithms should be knowable and comprehensible enough. Examples like chatbots producing natural-like conversation could better clarify the working scheme of AI technology.
• Robustness and Resilience: AI machinery should perform against its being hacked or affected by adversarial attacks, inaccuracies (errors), and environmental changes. Robustness testing, the construction of mechanisms for error handling, and the building up of redundancy must be taken seriously by AI systems to ensure their reliability and stability.
• Human-AI Collaboration: Successful human-AI entente is the key component to making the most of our advantages as well as artificial intelligence skills. Achieving AI solutions that are capable of supporting human skills and more importantly, preferences will reduce human efforts correspondingly and bring the best performance.

By addressing these challenges through innovative methodologies and interdisciplinary collaboration, we can harness the full potential of AI to solve complex problems and drive societal progress.

1. Robotics

Problem: A delivery robot navigating a busy warehouse to locate and retrieve a specific item.

Characteristics:

• Complexity: Industrial storage is networked, in the middle of things, with obstacles, and other robots and people moving unpredictably. This robot must process the visual scene, plan the route effectively, and detect and avoid possible collisions.
• Dynamism: A combination of outside factors leads to change, which is a constant inside the warehouse. Unpredictable system failures or spontaneous tasks can make the robot change its means and decision-making at the moment of need.
• Uncertainty: Sensor data (such as images obtained from a camera) might be noisy, incomplete, and unstable. The robot could be handling decisions based on fragmented or formless pieces of information.

2. Natural Language Processing (NLP)

Problem: A sentiment analysis system in NLP classifying customer reviews as positive, negative, or neutral.

• Subjectivity: Human language is nuanced. Sarcasm, irony, and figurative expressions can be difficult for machines to accurately interpret.
• Need for Context: Understanding sentiment may depend on cultural references, product-specific knowledge, or even the reviewer’s prior interactions with the company.
• Ambiguity: A single word or phrase could have multiple meanings, affecting the overall sentiment of the text.

3. Computer Vision

Problem: A medical image recognition system in Computer Vision designed to detect tumors in X-rays or MRI scans.

• Complexity: Medical images are highly detailed and can exhibit subtle variations. The system needs to distinguish between healthy tissue and potential abnormalities.
• Uncertainty: Images may contain noise or artifacts. The presence of a tumor might not be immediately obvious, requiring the system to handle ambiguity.
• Ethical Considerations: False positives or false negatives have serious consequences for patient health. Accuracy, transparency, and minimizing bias are crucial.

The premises of AI-based problems – complexity, uncertainty, subjectivity, and more, – bring an unavoidable difficulty to the table. These features must be known for building appropriate AI because this is necessary. Through the use of machine learning, probabilistic reasoning, and knowledge representation which are referred to as the tools in AI development alongside the ethical considerations, these designers and scientists can face such complexities well and give shape to AI in a way that will be beneficial to society.

Q. What are the core characteristics that differentiate AI problems?

The core characteristics of AI problems include complexity, uncertainty and ambiguity, lack of clear problem definition, non-linearity, dynamism, subjectivity, interactivity, context sensitivity, and ethical considerations.

Q. Can you explain the concept of problem-solving in AI?

Problem-solving in AI involves creating algorithms and methods that enable machines to imitate human capabilities of logical and reasonable thinking in certain situations.

Q. What is meant by the term “search space” in AI?

Search space refers to the area where an agent involved in the problem-solving process can examine all the possible states or settings with the hope of discovering a solution.

Q. How do AI algorithms address challenges such as complexity and uncertainty?

AI algorithms are designed to handle unclear circumstances and make decisions based on imperfect data or noisy information.

Q. What are some examples of AI applications and the challenges they face?

Examples include robotics (e.g., delivery robots navigating busy warehouses), natural language processing (e.g., sentiment analysis of customer reviews), and computer vision (e.g., medical image recognition for detecting tumors).

Q. What role do ethical considerations play in AI development?

Ethical considerations are crucial in AI development to address issues such as bias, justice, privacy, and responsibility, ensuring that AI technologies are deployed responsibly and ethically.

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Why AI Challenges Us To Become More Human

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In an era where artificial intelligence is reshaping the boundaries of what machines can do, we find ourselves at a pivotal moment in history. AI isn’t just a technological upgrade; it's a mirror reflecting our potential to evolve as a species. As these intelligent systems take over routine and repetitive tasks, they challenge us to delve deeper into what makes us uniquely human: our creativity, empathy, and the ability to navigate complex social dynamics. Let’s explore why the rise of AI might actually be the best thing to push humanity towards realizing its full potential.

The Unfulfilled Potential Of Human Creativity

Every day, countless hours are spent on tasks that, frankly, do not require the distinct capabilities of the human brain. Data entry, managing bookings, and even diagnosing common medical conditions are just a few examples. These tasks, while important, are mechanical—often predictable and repetitive. It's in this mundane reality that AI steps in, not as a replacement for human effort but as a liberator of human potential.

Imagine a world where the bulk of such tasks is handled by AI. This isn't a distant future scenario; it's already happening. AI applications in business, healthcare, and even creative industries are taking over the drudgery, enabling us to focus on tasks that require a human touch—innovation, strategy, and personal interaction. This shift is monumental, akin to the Industrial Revolution, but instead of mechanical muscle, we're leveraging digital brains.

Creative Problem Solving With AI

The real magic happens when AI and human intelligence are combined to tackle complex problems. Consider the field of environmental science, where AI can analyze vast datasets of climate patterns far quicker than any human team. However, interpreting these patterns and strategizing impactful interventions require human ingenuity and ethical consideration—qualities that AI has yet to master.

Another compelling example is in artistic endeavors. AI can now compose music or generate graphic art, but it lacks the nuanced understanding of what captivates human emotions and cultural contexts. Artists collaborating with AI find that it can act as a powerful tool to extend their own creative capabilities, pushing the boundaries of traditional art forms into new and unexplored territories.

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The synergy between human and machine opens up new frontiers for exploration and innovation. In healthcare, AI systems analyze medical data at superhuman speeds, but doctors provide the compassionate care and nuanced understanding that only a human can offer. Together, they achieve better outcomes, with AI handling data-driven tasks and humans focusing on patient care.

In business, AI tools predict consumer behavior through algorithms, but marketing professionals use these insights to craft creative and emotionally engaging campaigns that resonate on a human level. The technology identifies patterns, but the marketer tells the story.

The Future Is Human

As AI takes care of the ‘robotic’ aspects of work, humans are nudged towards roles that require creative problem-solving, emotional intelligence, moral judgment, and personal interaction. This isn’t just about job displacement; it’s about job transformation. It challenges us to redefine our roles in society and encourages the education system to focus more on critical thinking, creativity, emotional intelligence, and adaptability.

The question now is not whether AI will replace many of the tasks we currently do—it will—but what we do with the immense potential unleashed when this happens. As we delegate the routine to machines, we must cultivate our distinctly human abilities to engage, inspire, and innovate.

AI doesn't just challenge us to be more human; it demands it. By automating the mundane, AI not only frees our time but elevates our purpose. We are not moving towards an era where machines rule but one where they help us rediscover and reimagine what it means to be human. This is the paradox of our times: the more advanced our machines, the more we must tap into the depths of our human nature. In this new dawn, our most human traits are not our weaknesses but our greatest strengths.

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Title: pecc: problem extraction and coding challenges.

Abstract: Recent advancements in large language models (LLMs) have showcased their exceptional abilities across various tasks, such as code generation, problem-solving and reasoning. Existing benchmarks evaluate tasks in isolation, yet the extent to which LLMs can understand prose-style tasks, identify the underlying problems, and then generate appropriate code solutions is still unexplored. Addressing this gap, we introduce PECC, a novel benchmark derived from Advent Of Code (AoC) challenges and Project Euler, including 2396 problems. Unlike conventional benchmarks, PECC requires LLMs to interpret narrative-embedded problems, extract requirements, and generate executable code. A key feature of our dataset is the complexity added by natural language prompting in chat-based evaluations, mirroring real-world instruction ambiguities. Results show varying model performance between narrative and neutral problems, with specific challenges in the Euler math-based subset with GPT-3.5-Turbo passing 50% of the AoC challenges and only 8% on the Euler problems. By probing the limits of LLMs' capabilities, our benchmark provides a framework to monitor and assess the subsequent progress of LLMs as a universal problem solver.

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Can Artificial General Intelligence Demonstrate Human-Like Capabilities? Is It A Threat to Humanity?

Curated By : News Desk

Edited By: Shilpy Bisht

Last Updated: May 06, 2024, 16:28 IST

New Delhi, India

The Turing Test is widely used as a benchmark for evaluating the progress of artificial intelligence research, and has inspired several studies and experiments aimed at developing machines that can pass the test. (Image: Shutterstock)

OpenAI’s Sam Altman in an interview had said AGI will lead to a ‘lot of productivity and economic value’, and will be ‘transformative’, promising unprecedented problem-solving capabilities and creative expression

Technological companies are estimating the potential of Artificial General Intelligence (AGI), which they consider the zenith of artificial intelligence (AI) development. Although ChatGPT may seem like a giant leap in the world of AI, but it is just the start of an even greater breakthrough — AGI.

In a report published by Stanford University, a team of researchers has concluded that some AI companies may have already cracked the first step to AGI, and may have a working model that can perform at the level of a human.

AGI refers to a machine or a software that can perform any intellectual task that a human can do such as reasoning, common sense, abstract thinking, background knowledge, transfer learning, ability to differentiate between cause and effect.

AGI can emulate human cognitive abilities in a way that it allows it to do unfamiliar tasks, learn from new experiences, and apply its knowledge in new ways.

A human brain uses the information it has gathered to make decisions or solve a problem. An AGI-enabled software or computer will be able to do everything that a human computer does. For example, understanding issues, thinking of solutions and learning new things.

Difference between AI and AGI

The latest generative AI technologies, including ChatGPT, DALL-E are essentially prediction machines, that is, they can predict with the highest degree of accuracy because they have been trained on huge amounts of data. But it is nowhere close to the human level of performance in terms of creativity, logical reasoning, sensory perception and other capabilities.

AGI, however, could feature cognitive and emotional capabilities of a human being.

There are eight capabilities AI needs to master before achieving AGI, as mentioned by a report by McKinsey and Company.

Visual Perception : AI systems still need colour consistency to achieve human-like perception.

Audio Perception : Humans use sound to determine the spatial characteristics of an environment with little to no effort. AI have a more limited ability to extract and process sound, restricted by their hardware and software.

Fine Motor Skills : AI-powered robots have yet to achieve the kind of fine motor skills that a human being can do such as braid a hair or perform a human surgery.

Natural Language Processing : To achieve human-level cognition, AGI would need to consume sources of information such as books, articles, videos. AGI would need to fill the gaps that humans leave when they communicate. Although gen AI tools have demonstrated improved natural language processing, but they still lack understanding and context comprehension.

Problem-solving : AGI should learn from their environment and experience and adapt new situations without explicit programming from humans.

Navigation : Lots of work is required to create robot systems that can navigate autonomously with no human priming.

Creativity : To achieve human-level creativity, AI systems need to write their own code, which will require them to understand the vast amounts of code humans put together to build them and identify ways to improve them.

Social and Emotional Engagement : To achieve a successful human-robot interaction, a robot would need to be able to interpret facial expressions and changes in tone. Some AI are already doing so but to a limited extent.

Inception of AGI

The idea first emerged in the 20th century with a paper written by British mathematician Alan Turing, who was considered to be the father of theoretical computer science and artificial intelligence.

In ‘Computing Machinery and Intelligence’ (1950), he introduced the Turing test, which means, if a machine can engage in a conversation with a human being without being detected as a machine has shown true human intelligence.

The Turing Test is widely used as a benchmark for evaluating the progress of artificial intelligence research, and has inspired numerous studies and experiments aimed at developing machines that can pass the test.

Benefits of AGI

AGI would help perform multiple tasks. For example, in healthcare, it can redefine diagnostics, treatment planning, personalised medicine and analyzing vast data sets.

AGI can enhance more effective learning and improve personalised solutions in the education realm, by creating experiences and services to meet individual needs and preferences. It can revolutionise education, leading to a more knowledgeable and skilled global workforce.

AGI can foster a stronger commitment to ethical AI development, leading researchers and policymakers to remove areas of conflict and align with the ideal values and transparency.

It can play a crucial role in advanced space exploration, discover more life-saving and life-enhancing resources that will lead to all-round economic development. It could lead to a significant further understanding of the universe and other celestial bodies.

OpenAI’s Sam Altman in an interview with The Wall Street Journal said AGI will lead to a “lot of productivity and economic value”, and will be “transformative”, promising unprecedented problem-solving capabilities and creative expression.

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Why in News? Recently, Sam Altman (OpenAI’s CEO) expressed his commitment to invest towards Artificial General Intelligence’s (AGI) development.

What is AGI? AGI is a machine that aims to emulate human cognitive abilities to perform any intellectual task that a human can do like reasoning, common sense, abstract thinking, background knowledge etc.

Difference between AGI and AI Being Used (Narrow AI) -Scope of Narrow AI remains limited to set parameters as it can perform only specific tasks like image recognition, translation, playing games etc. But AGI envisions a broader, more generalized form of intelligence, not confined to any particular task(like humans).

Is this a New Idea? No, it was first introduced by Alan Turing (Father of Computer Science and Artificial Intelligence) in1950 in his paper ‘Computing Machinery and Intelligence’ where he introduced Turing test, a benchmark for machine intelligence according to which if a machine (without being detected) engages in a conversation with humans, it has human intelligence.

Benefits -It will add productivity and economic value through its unprecedented problem-solving capabilities and creative expression like in healthcare, it can redefine diagnostics, treatment planning, and personalized medicine by integrating and analyzing vast datasets. In businesses, it could automate various processes and enhance overall decision-making, offering real-time analytics and market predictions with accuracy.

Concerns -AGI can:

• Affect environment by high energy consumption and generating e-waste.
• lead to employment loss and widespread socio-economic disparity, where power would be concentrated with ones controlling AGI.
• introduce new security vulnerabilities.
• outrun the ability of governments and international bodies to introduce suitable regulations.
• lead to loss of basic human skills and capabilities due to dependence on AGI.
• outpace human beings and act against them if humans lose its control.

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