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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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Everyone thinks they know but no one can agree. And that’s a problem.

• Will Douglas Heaven archive page

Internet nastiness, name-calling, and other not-so-petty, world-altering disagreements

AI is sexy, AI is cool. AI is entrenching inequality, upending the job market, and wrecking education. AI is a theme-park ride, AI is a magic trick. AI is our final invention, AI is a moral obligation. AI is the buzzword of the decade, AI is marketing jargon from 1955. AI is humanlike, AI is alien. AI is super-smart and as dumb as dirt. The AI boom will boost the economy, the AI bubble is about to burst. AI will increase abundance and empower humanity to maximally flourish in the universe. AI will kill us all.

What the hell is everybody talking about?

Artificial intelligence is the hottest technology of our time. But what is it? It sounds like a stupid question, but it’s one that’s never been more urgent. Here’s the short answer: AI is a catchall term for a set of technologies that make computers do things that are thought to require intelligence when done by people. Think of recognizing faces, understanding speech, driving cars, writing sentences, answering questions, creating pictures. But even that definition contains multitudes.

And that right there is the problem. What does it mean for machines to understand speech or write a sentence? What kinds of tasks could we ask such machines to do? And how much should we trust the machines to do them?

As this technology moves from prototype to product faster and faster, these have become questions for all of us. But (spoilers!) I don’t have the answers. I can’t even tell you what AI is. The people making it don’t know what AI is either. Not really. “These are the kinds of questions that are important enough that everyone feels like they can have an opinion,” says Chris Olah, chief scientist at the San Francisco–based AI lab Anthropic. “I also think you can argue about this as much as you want and there’s no evidence that’s going to contradict you right now.”

But if you’re willing to buckle up and come for a ride, I can tell you why nobody really knows, why everybody seems to disagree, and why you’re right to care about it.

Let’s start with an offhand joke.

Back in 2022, partway through the first episode of Mystery AI Hype Theater 3000 , a party-pooping podcast in which the irascible cohosts Alex Hanna and Emily Bender have a lot of fun sticking “the sharpest needles’’ into some of Silicon Valley’s most inflated sacred cows, they make a ridiculous suggestion. They’re hate-reading aloud from a 12,500-word Medium post by a Google VP of engineering, Blaise Agüera y Arcas, titled “ Can machines learn how to behave? ” Agüera y Arcas makes a case that AI can understand concepts in a way that’s somehow analogous to the way humans understand concepts—concepts such as moral values. In short, perhaps machines can be taught to behave. 

Hanna and Bender are having none of it. They decide to replace the term “AI’’ with “mathy math”—you know, just lots and lots of math.

The irreverent phrase is meant to collapse what they see as bombast and anthropomorphism in the sentences being quoted. Pretty soon Hanna, a sociologist and director of research at the Distributed AI Research Institute, and Bender, a computational linguist at the University of Washington (and internet-famous critic of tech industry hype), open a gulf between what Agüera y Arcas wants to say and how they choose to hear it.

“How should AIs, their creators, and their users be held morally accountable?” asks Agüera y Arcas.

How should mathy math be held morally accountable? asks Bender.

“There’s a category error here,” she says. Hanna and Bender don’t just reject what Agüera y Arcas says; they claim it makes no sense. “Can we please stop it with the ‘an AI’ or ‘the AIs’ as if they are, like, individuals in the world?” Bender says.

It might sound as if they’re talking about different things, but they’re not. Both sides are talking about large language models, the technology behind the current AI boom. It’s just that the way we talk about AI is more polarized than ever. In May, OpenAI CEO Sam Altman teased the latest update to GPT-4 , his company’s flagship model, by tweeting , “Feels like magic to me.”

There’s a lot of road between math and magic.

AI has acolytes, with a faith-like belief in the technology’s current power and inevitable future improvement. Artificial general intelligence is in sight, they say; superintelligence is coming behind it. And it has heretics, who pooh-pooh such claims as mystical mumbo-jumbo.

The buzzy popular narrative is shaped by a pantheon of big-name players, from Big Tech marketers in chief like Sundar Pichai and Satya Nadella to edgelords of industry like Elon Musk and Altman to celebrity computer scientists like Geoffrey Hinton . Sometimes these boosters and doomers are one and the same, telling us that the technology is so good it’s bad .

As AI hype has ballooned, a vocal anti-hype lobby has risen in opposition, ready to smack down its ambitious, often wild claims. Pulling in this direction are a raft of researchers, including Hanna and Bender, and also outspoken industry critics like influential computer scientist and former Googler Timnit Gebru and NYU cognitive scientist Gary Marcus. All have a chorus of followers bickering in their replies.

In short, AI has come to mean all things to all people, splitting the field into fandoms. It can feel as if different camps are talking past one another, not always in good faith.

Maybe you find all this silly or tiresome. But given the power and complexity of these technologies—which are already used to determine how much we pay for insurance, how we look up information, how we do our jobs, etc. etc. etc.—it’s about time we at least agreed on what it is we’re even talking about.

Yet in all the conversations I’ve had with people at the cutting edge of this technology, no one has given a straight answer about exactly what it is they’re building. (A quick side note: This piece focuses on the AI debate in the US and Europe, largely because many of the best-funded, most cutting-edge AI labs are there. But of course there’s important research happening elsewhere, too, in countries with their own varying perspectives on AI, particularly China.) Partly, it’s the pace of development. But the science is also wide open. Today’s large language models can do amazing things . The field just can’t find common ground on what’s really going on under the hood .

These models are trained to complete sentences. They appear to be able to do a lot more—from solving high school math problems to writing computer code to passing law exams to composing poems. When a person does these things, we take it as a sign of intelligence. What about when a computer does it? Is the appearance of intelligence enough?

These questions go to the heart of what we mean by “artificial intelligence,” a term people have actually been arguing about for decades. But the discourse around AI has become more acrimonious with the rise of large language models that can mimic the way we talk and write with thrilling/chilling (delete as applicable) realism.

We have built machines with humanlike behavior but haven’t shrugged off the habit of imagining a humanlike mind behind them. This leads to over-egged evaluations of what AI can do; it hardens gut reactions into dogmatic positions, and it plays into the wider culture wars between techno-optimists and techno-skeptics.

Add to this stew of uncertainty a truckload of cultural baggage, from the science fiction that I’d bet many in the industry were raised on, to far more malign ideologies that influence the way we think about the future. Given this heady mix, arguments about AI are no longer simply academic (and perhaps never were). AI inflames people’s passions and makes grownups call each other names.

“It’s not in an intellectually healthy place right now,” Marcus says of the debate. For years Marcus has pointed out the flaws and limitations of deep learning, the tech that launched AI into the mainstream, powering everything from LLMs to image recognition to self-driving cars. His 2001 book The Algebraic Mind argued that neural networks, the foundation on which deep learning is built, are incapable of reasoning by themselves. (We’ll skip over it for now, but I’ll come back to it later and we’ll see just how much a word like “reasoning” matters in a sentence like this.)

Marcus says that he has tried to engage Hinton—who last year went public with existential fears about the technology he helped invent—in a proper debate about how good large language models really are. “He just won’t do it,” says Marcus. “He calls me a twit.” (Having talked to Hinton about Marcus in the past, I can confirm that. “ChatGPT clearly understands neural networks better than he does,” Hinton told me last year.) Marcus also drew ire when he wrote an essay titled “Deep learning is hitting a wall.” Altman responded to it with a tweet : “Give me the confidence of a mediocre deep learning skeptic.”

At the same time, banging his drum has made Marcus a one-man brand and earned him an invitation to sit next to Altman and give testimony last year before the US Senate’s AI oversight committee.

And that’s why all these fights matter more than your average internet nastiness. Sure, there are big egos and vast sums of money at stake. But more than that, these disputes matter when industry leaders and opinionated scientists are summoned by heads of state and lawmakers to explain what this technology is and what it can do (and how scared we should be). They matter when this technology is being built into software we use every day, from search engines to word-processing apps to assistants on your phone. AI is not going away. But if we don’t know what we’re being sold, who’s the dupe?

“It is hard to think of another technology in history about which such a debate could be had—a debate about whether it is everywhere, or nowhere at all,” Stephen Cave and Kanta Dihal write in Imagining AI , a 2023 collection of essays about how different cultural beliefs shape people’s views of artificial intelligence. “That it can be held about AI is a testament to its mythic quality.”

Above all else, AI is an idea—an ideal—shaped by worldviews and sci-fi tropes as much as by math and computer science. Figuring out what we are talking about when we talk about AI will clarify many things. We won’t agree on them, but common ground on what AI is would be a great place to start talking about what AI should be .

What is everyone really fighting about, anyway?

In late 2022, soon after OpenAI released ChatGPT , a new meme started circulating online that captured the weirdness of this technology better than anything else. In most versions , a Lovecraftian monster called the Shoggoth, all tentacles and eyeballs, holds up a bland smiley-face emoji as if to disguise its true nature. ChatGPT presents as humanlike and accessible in its conversational wordplay, but behind that façade lie unfathomable complexities—and horrors. (“It was a terrible, indescribable thing vaster than any subway train—a shapeless congeries of protoplasmic bubbles,” H.P. Lovecraft wrote of the Shoggoth in his 1936 novella At the Mountains of Madness .)  

For years one of the best-known touchstones for AI in pop culture was The Terminator , says Dihal. But by putting ChatGPT online for free, OpenAI gave millions of people firsthand experience of something different. “AI has always been a sort of really vague concept that can expand endlessly to encompass all kinds of ideas,” she says. But ChatGPT made those ideas tangible: “Suddenly, everybody has a concrete thing to refer to.” What is AI? For millions of people the answer was now: ChatGPT.

The AI industry is selling that smiley face hard. Consider how The Daily Show recently skewered the hype, as expressed by industry leaders. Silicon Valley’s VC in chief, Marc Andreessen: “This has the potential to make life much better … I think it’s honestly a layup.” Altman: “I hate to sound like a utopic tech bro here, but the increase in quality of life that AI can deliver is extraordinary.” Pichai: “AI is the most profound technology that humanity is working on. More profound than fire.”

Jon Stewart: “Yeah, suck a dick, fire!”

But as the meme points out, ChatGPT is a friendly mask. Behind it is a monster called GPT-4, a large language model built from a vast neural network that has ingested more words than most of us could read in a thousand lifetimes. During training, which can last months and cost tens of millions of dollars, such models are given the task of filling in blanks in sentences taken from millions of books and a significant fraction of the internet. They do this task over and over again. In a sense, they are trained to be supercharged autocomplete machines. The result is a model that has turned much of the world’s written information into a statistical representation of which words are most likely to follow other words, captured across billions and billions of numerical values.

It’s math—a hell of a lot of math. Nobody disputes that. But is it just that, or does this complex math encode algorithms capable of something akin to human reasoning or the formation of concepts?

Many of the people who answer yes to that question believe we’re close to unlocking something called artificial general intelligence , or AGI, a hypothetical future technology that can do a wide range of tasks as well as humans can. A few of them have even set their sights on what they call superintelligence , sci-fi technology that can do things far better than humans. This cohort believes AGI will drastically change the world—but to what end? That’s yet another point of tension. It could fix all the world’s problems—or bring about its doom. 

kinda mad how the so called godfathers of AI managed to convince seemingly smart people within AI field & many regulators to buy into the absurd idea that a sophisticated curve fitting (to a dataset) machine can have the urge to exterminate humans — Abeba Birhane (@Abebab) June 30, 2024

Today AGI appears in the mission statements of the world’s top AI labs. But the term was invented in 2007 as a niche attempt to inject some pizzazz into a field that was then best known for applications that read handwriting on bank deposit slips or recommended your next book to buy. The idea was to reclaim the original vision of an artificial intelligence that could do humanlike things (more on that soon).

It was really an aspiration more than anything else, Google DeepMind cofounder Shane Legg, who coined the term, told me last year: “I didn’t have an especially clear definition.”

AGI became the most controversial idea in AI . Some talked it up as the next big thing: AGI was AI but, you know, much better . Others claimed the term was so vague that it was meaningless.

“AGI used to be a dirty word,” Ilya Sutskever told me, before he resigned as chief scientist at OpenAI.

But large language models, and ChatGPT in particular, changed everything. AGI went from dirty word to marketing dream.

Which brings us to what I think is one of the most illustrative disputes of the moment—one that sets up the sides of the argument and the stakes in play. 

Seeing magic in the machine

A few months before the public launch of OpenAI’s large language model GPT-4 in March 2023, the company shared a prerelease version with Microsoft, which wanted to use the new model to revamp its search engine Bing.

At the time, Sebastian Bubeck was studying the limitations of LLMs and was somewhat skeptical of their abilities. In particular, Bubeck—the vice president of generative AI research at Microsoft Research in Redmond, Washington—had been trying and failing to get the technology to solve middle school math problems. Things like: x – y = 0; what are x and y ? “My belief was that reasoning was a bottleneck, an obstacle,” he says. “I thought that you would have to do something really fundamentally different to get over that obstacle.”

Then he got his hands on GPT-4. The first thing he did was try those math problems. “The model nailed it,” he says. “Sitting here in 2024, of course GPT-4 can solve linear equations. But back then, this was crazy. GPT-3 cannot do that.”

But Bubeck’s real road-to-Damascus moment came when he pushed it to do something new.

The thing about middle school math problems is that they are all over the internet, and GPT-4 may simply have memorized them. “How do you study a model that may have seen everything that human beings have written?” asks Bubeck. His answer was to test GPT-4 on a range of problems that he and his colleagues believed to be novel.

Playing around with Ronen Eldan, a mathematician at Microsoft Research, Bubeck asked GPT-4 to give, in verse, a mathematical proof that there are an infinite number of primes.

Here’s a snippet of GPT-4’s response: “If we take the smallest number in S that is not in P / And call it p, we can add it to our set, don’t you see? / But this process can be repeated indefinitely. / Thus, our set P must also be infinite, you’ll agree.”

Cute, right? But Bubeck and Eldan thought it was much more than that. “We were in this office,” says Bubeck, waving at the room behind him via Zoom. “Both of us fell from our chairs. We couldn’t believe what we were seeing. It was just so creative and so, like, you know, different.” 

The Microsoft team also got GPT-4 to generate the code to add a horn to a cartoon picture of a unicorn drawn in Latex, a word processing program. Bubeck thinks this shows that the model could read the existing Latex code, understand what it depicted, and identify where the horn should go.

“There are many examples, but a few of them are smoking guns of reasoning,” he says—reasoning being a crucial building block of human intelligence.

Bubeck, Eldan, and a team of other Microsoft researchers described their findings in a paper that they called “ Spark s of artificial general intelligence ”: “We believe that GPT-4’s intelligence signals a true paradigm shift in the field of computer science and beyond.” When Bubeck shared the paper online, he tweeted : “time to face it, the sparks of #AGI have been ignited.”

The Sparks paper quickly became infamous—and a touchstone for AI boosters. Agüera y Arcas and Peter Norvig, a former director of research at Google and coauthor of Artificial Intelligence: A Modern Approach , perhaps the most popular AI textbook in the world, cowrote an article called “ Artificial General Intelligence Is Already Here .” Published in Noema , a magazine backed by an LA think tank called the Berggruen Institute, their argument uses the Sparks paper as a jumping-off point: “Artificial General Intelligence (AGI) means many different things to different people, but the most important parts of it have already been achieved by the current generation of advanced AI large language models,” they wrote. “Decades from now, they will be recognized as the first true examples of AGI.”

Since then, the hype has continued to balloon. Leopold Aschenbrenner, who at the time was a researcher at OpenAI focusing on superintelligence, told me last year: “AI progress in the last few years has been just extraordinarily rapid. We’ve been crushing all the benchmarks, and that progress is continuing unabated. But it won’t stop there. We’re going to have superhuman models, models that are much smarter than us.” (He was fired from OpenAI in April because, he claims, he raised security concerns about the tech he was building and “ ruffled some feathers .” He has since set up a Silicon Valley investment fund.)

In June, Aschenbrenner put out a 165-page manifesto arguing that AI will outpace college graduates by “2025/2026” and that “we will have superintelligence, in the true sense of the word” by the end of the decade. But others in the industry scoff at such claims. When Aschenbrenner tweeted a chart to show how fast he thought AI would continue to improve given how fast it had improved in last few years, the tech investor Christian Keil replied that by the same logic, his baby son, who had doubled in size since he was born, would weigh 7.5 trillion tons by the time he was 10.

It’s no surprise that “sparks of AGI” has also become a byword for over-the-top buzz. “I think they got carried away,” says Marcus, speaking about the Microsoft team. “They got excited, like ‘Hey, we found something! This is amazing!’ They didn’t vet it with the scientific community.” Bender refers to the Sparks paper as a “fan fiction novella.”

Not only was it provocative to claim that GPT-4’s behavior showed signs of AGI, but Microsoft, which uses GPT-4 in its own products, has a clear interest in promoting the capabilities of the technology. “This document is marketing fluff masquerading as research,” one tech COO posted on LinkedIn.

Some also felt the paper’s methodology was flawed. Its evidence is hard to verify because it comes from interactions with a version of GPT-4 that was not made available outside OpenAI and Microsoft. The public version has guardrails that restrict the model’s capabilities, admits Bubeck. This made it impossible for other researchers to re-create his experiments.

One group tried to re-create the unicorn example with a coding language called Processing, which GPT-4 can also use to generate images . They found that the public version of GPT-4 could produce a passable unicorn but not flip or rotate that image by 90 degrees. It may seem like a small difference, but such things really matter when you’re claiming that the ability to draw a unicorn is a sign of AGI.

The key thing about the examples in the Sparks paper, including the unicorn, is that Bubeck and his colleagues believe they are genuine examples of creative reasoning. This means the team had to be certain that examples of these tasks, or ones very like them, were not included anywhere in the vast data sets that OpenAI amassed to train its model. Otherwise, the results could be interpreted instead as instances where GPT-4 reproduced patterns it had already seen.

Bubeck insists that they set the model only tasks that would not be found on the internet. Drawing a cartoon unicorn in Latex was surely one such task. But the internet is a big place. Other researchers soon pointed out that there are indeed online forums dedicated to drawing animals in Latex . “Just fyi we knew about this,” Bubeck replied on X. “Every single query of the Sparks paper was thoroughly looked for on the internet.”

(This didn’t stop the name-calling: “I’m asking you to stop being a charlatan,” Ben Recht, a computer scientist at the University of California, Berkeley, tweeted back before accusing Bubeck of “being caught flat-out lying.”)

Bubeck insists the work was done in good faith, but he and his coauthors admit in the paper itself that their approach was not rigorous—notebook observations rather than foolproof experiments. 

Still, he has no regrets: “The paper has been out for more than a year and I have yet to see anyone give me a convincing argument that the unicorn, for example, is not a real example of reasoning.”

That’s not to say he can give me a straight answer to the big question—though his response reveals what kind of answer he’d like to give. “What is AI?” Bubeck repeats back to me. “I want to be clear with you. The question can be simple, but the answer can be complex.”

“There are many simple questions out there to which we still don’t know the answer. And some of those simple questions are the most profound ones,” he says. “I’m putting this on the same footing as, you know, What is the origin of life? What is the origin of the universe? Where did we come from? Big, big questions like this.”

Seeing only math in the machine

Before Bender became one of the chief antagonists of AI’s boosters, she made her mark on the AI world as a coauthor on two influential papers. (Both peer-reviewed, she likes to point out—unlike the Sparks paper and many of the others that get much of the attention.) The first, written with Alexander Koller, a fellow computational linguist at Saarland University in Germany, and published in 2020, was called “ Climbing towards NLU ” (NLU is natural-language understanding).

“The start of all this for me was arguing with other people in computational linguistics whether or not language models understand anything,” she says. (Understanding, like reasoning, is typically taken to be a basic ingredient of human intelligence.)

Bender and Koller argue that a model trained exclusively on text will only ever learn the form of a language, not its meaning. Meaning, they argue, consists of two parts: the words (which could be marks or sounds) plus the reason those words were uttered. People use language for many reasons, such as sharing information, telling jokes, flirting, warning somebody to back off, and so on. Stripped of that context, the text used to train LLMs like GPT-4 lets them mimic the patterns of language well enough for many sentences generated by the LLM to look exactly like sentences written by a human. But there’s no meaning behind them, no spark . It’s a remarkable statistical trick, but completely mindless.

They illustrate their point with a thought experiment. Imagine two English-speaking people stranded on neighboring deserted islands. There is an underwater cable that lets them send text messages to each other. Now imagine that an octopus, which knows nothing about English but is a whiz at statistical pattern matching, wraps its suckers around the cable and starts listening in to the messages. The octopus gets really good at guessing what words follow other words. So good that when it breaks the cable and starts replying to messages from one of the islanders, she believes that she is still chatting with her neighbor. (In case you missed it, the octopus in this story is a chatbot.)

The person talking to the octopus would stay fooled for a reasonable amount of time, but could that last? Does the octopus understand what comes down the wire? 

Imagine that the islander now says she has built a coconut catapult and asks the octopus to build one too and tell her what it thinks. The octopus cannot do this. Without knowing what the words in the messages refer to in the world, it cannot follow the islander’s instructions. Perhaps it guesses a reply: “Okay, cool idea!” The islander will probably take this to mean that the person she is speaking to understands her message. But if so, she is seeing meaning where there is none. Finally, imagine that the islander gets attacked by a bear and sends calls for help down the line. What is the octopus to do with these words?

Bender and Koller believe that this is how large language models learn and why they are limited. “The thought experiment shows why this path is not going to lead us to a machine that understands anything,” says Bender. “The deal with the octopus is that we have given it its training data, the conversations between those two people, and that’s it. But then here’s something that comes out of the blue and it won’t be able to deal with it because it hasn’t understood.”

The other paper Bender is known for, “ On the Dangers of Stochastic Parrots ,” highlights a series of harms that she and her coauthors believe the companies making large language models are ignoring. These include the huge computational costs of making the models and their environmental impact; the racist, sexist, and other abusive language the models entrench; and the dangers of building a system that could fool people by “haphazardly stitching together sequences of linguistic forms … according to probabilistic information about how they combine, but without any reference to meaning: a stochastic parrot.”

Google senior management wasn’t happy with the paper, and the resulting conflict led two of Bender’s coauthors, Timnit Gebru and Margaret Mitchell, to be forced out of the company, where they had led the AI Ethics team. It also made “stochastic parrot” a popular put-down for large language models—and landed Bender right in the middle of the name-calling merry-go-round.

The bottom line for Bender and for many like-minded researchers is that the field has been taken in by smoke and mirrors: “I think that they are led to imagine autonomous thinking entities that can make decisions for themselves and ultimately be the kind of thing that could actually be accountable for those decisions.”

Always the linguist, Bender is now at the point where she won’t even use the term AI “without scare quotes,” she tells me. Ultimately, for her, it’s a Big Tech buzzword that distracts from the many associated harms. “I’ve got skin in the game now,” she says. “I care about these issues, and the hype is getting in the way.”

Extraordinary evidence?

Agüera y Arcas calls people like Bender “AI denialists”—the implication being that they won’t ever accept what he takes for granted. Bender’s position is that extraordinary claims require extraordinary evidence, which we do not have.

But there are people looking for it, and until they find something clear-cut—sparks or stochastic parrots or something in between—they’d prefer to sit out the fight. Call this the wait-and-see camp.

As Ellie Pavlick, who studies neural networks at Brown University, tells me: “It’s offensive to some people to suggest that human intelligence could be re-created through these kinds of mechanisms.”

She adds, “People have strong-held beliefs about this issue—it almost feels religious. On the other hand, there’s people who have a little bit of a God complex. So it’s also offensive to them to suggest that they just can’t do it.”

Pavlick is ultimately agnostic. She’s a scientist, she insists, and will follow wherever the science leads. She rolls her eyes at the wilder claims, but she believes there’s something exciting going on. “That’s where I would disagree with Bender and Koller,” she tells me. “I think there’s actually some sparks—maybe not of AGI, but like, there’s some things in there that we didn’t expect to find.”

The problem is finding agreement on what those exciting things are and why they’re exciting. With so much hype, it’s easy to be cynical.

Researchers like Bubeck seem a lot more cool-headed when you hear them out. He thinks the infighting misses the nuance in his work. “I don’t see any problem in holding simultaneous views,” he says. “There is stochastic parroting; there is reasoning—it’s a spectrum. It’s very complex. We don’t have all the answers.”

“We need a completely new vocabulary to describe what’s going on,” he says. “One reason why people push back when I talk about reasoning in large language models is because it’s not the same reasoning as in human beings. But I think there is no way we can not call it reasoning. It is reasoning.”

Anthropic’s Olah plays it safe when pushed on what we’re seeing in LLMs, though his company, one of the hottest AI labs in the world right now, built Claude 3, an LLM that has received just as much hyperbolic praise as GPT-4 (if not more) since its release earlier this year.

“I feel like a lot of these conversations about the capabilities of these models are very tribal,” he says. “People have preexisting opinions, and it’s not very informed by evidence on any side. Then it just becomes kind of vibes-based, and I think vibes-based arguments on the internet tend to go in a bad direction.”

Olah tells me he has hunches of his own. “My subjective impression is that these things are tracking pretty sophisticated ideas,” he says. “We don’t have a comprehensive story of how very large models work, but I think it’s hard to reconcile what we’re seeing with the extreme ‘stochastic parrots’ picture.”

That’s as far as he’ll go: “I don’t want to go too much beyond what can be really strongly inferred from the evidence that we have.”

Last month, Anthropic released results from a study in which researchers gave Claude 3 the neural network equivalent of an MRI. By monitoring which bits of the model turned on and off as they ran it, they identified specific patterns of neurons that activated when the model was shown specific inputs.

Anthropic also reported patterns that it says correlate with inputs that attempt to describe or show abstract concepts. “We see features related to deception and honesty, to sycophancy, to security vulnerabilities, to bias,” says Olah. “We find features related to power seeking and manipulation and betrayal.”

ASK IT FOR A RECIPE pic.twitter.com/0ZM3uGRJi9 — heron (@iamaheron_) May 23, 2024

These results give one of the clearest looks yet at what’s inside a large language model. It’s a tantalizing glimpse at what look like elusive humanlike traits. But what does it really tell us? As Olah admits, they do not know what the model does with these patterns. “It’s a relatively limited picture, and the analysis is pretty hard,” he says.

Even if Olah won’t spell out exactly what he thinks goes on inside a large language model like Claude 3, it’s clear why the question matters to him. Anthropic is known for its work on AI safety—making sure that powerful future models will behave in ways we want them to and not in ways we don’t (known as “alignment” in industry jargon). Figuring out how today’s models work is not only a necessary first step if you want to control future ones; it also tells you how much you need to worry about doomer scenarios in the first place. “If you don’t think that models are going to be very capable,” says Olah, “then they’re probably not going to be very dangerous.”

Why we all can’t get along

In a 2014 interview with the BBC that looked back on her career, the influential cognitive scientist Margaret Boden, now 87, was asked if she thought there were any limits that would prevent computers (or “tin cans,” as she called them) from doing what humans can do.

“I certainly don’t think there’s anything in principle,” she said. “Because to deny that is to say that [human thinking] happens by magic, and I don’t believe that it happens by magic.”

But, she cautioned, powerful computers won’t be enough to get us there: the AI field will also need “powerful ideas”—new theories of how thinking happens, new algorithms that might reproduce it. “But these things are very, very difficult and I see no reason to assume that we will one of these days be able to answer all of those questions. Maybe we will; maybe we won’t.” 

Boden was reflecting on the early days of the current boom, but this will-we-or-won’t-we teetering speaks to decades in which she and her peers grappled with the same hard questions that researchers struggle with today. AI began as an ambitious aspiration 70-odd years ago and we are still disagreeing about what is and isn’t achievable, and how we’ll even know if we have achieved it. Most—if not all—of these disputes come down to this: We don’t have a good grasp on what intelligence is or how to recognize it. The field is full of hunches, but no one can say for sure.

We’ve been stuck on this point ever since people started taking the idea of AI seriously. Or even before that, when the stories we consumed started planting the idea of humanlike machines deep in our collective imagination. The long history of these disputes means that today’s fights often reinforce rifts that have been around since the beginning, making it even more difficult for people to find common ground.

To understand how we got here, we need to understand where we’ve been. So let’s dive into AI’s origin story—one that also played up the hype in a bid for cash.

A brief history of AI spin

The computer scientist John McCarthy is credited with coming up with the term “artificial intelligence” in 1955 when writing a funding application for a summer research program at Dartmouth College in New Hampshire.

The plan was for McCarthy and a small group of fellow researchers, a who’s-who of postwar US mathematicians and computer scientists—or “John McCarthy and the boys,” as Harry Law, a researcher who studies the history of AI at the University of Cambridge and ethics and policy at Google DeepMind, puts it—to get together for two months (not a typo) and make some serious headway on this new research challenge they’d set themselves.

“The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it,” McCarthy and his coauthors wrote. “An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves.”

That list of things they wanted to make machines do—what Bender calls “the starry-eyed dream”—hasn’t changed much. Using language, forming concepts, and solving problems are defining goals for AI today. The hubris hasn’t changed much either: “We think that a significant advance can be made in one or more of these problems if a carefully selected group of scientists work on it together for a summer,” they wrote. That summer, of course, has stretched to seven decades. And the extent to which these problems are in fact now solved is something that people still shout about on the internet. 

But what’s often left out of this canonical history is that artificial intelligence almost wasn’t called “artificial intelligence” at all.

More than one of McCarthy’s colleagues hated the term he had come up with. “The word ‘artificial’ makes you think there’s something kind of phony about this,” Arthur Samuel, a Dartmouth participant and creator of the first checkers-playing computer, is quoted as saying in historian Pamela McCorduck’s 2004 book Machines Who Think . The mathematician Claude Shannon, a coauthor of the Dartmouth proposal who is sometimes billed as “the father of the information age,” preferred the term “automata studies.” Herbert Simon and Allen Newell, two other AI pioneers, continued to call their own work “complex information processing” for years afterwards.

In fact, “artificial intelligence” was just one of several labels that might have captured the hodgepodge of ideas that the Dartmouth group was drawing on. The historian Jonnie Penn has identified possible alternatives that were in play at the time, including “engineering psychology,” “applied epistemology,” “neural cybernetics,” “non-numerical computing,” “neuraldynamics,” “advanced automatic programming,” and “hypothetical automata.” This list of names reveals how diverse the inspiration for their new field was, pulling from biology, neuroscience, statistics, and more. Marvin Minsky, another Dartmouth participant, has described AI as a “suitcase word” because it can hold so many divergent interpretations.

But McCarthy wanted a name that captured the ambitious scope of his vision. Calling this new field “artificial intelligence” grabbed people’s attention—and money. Don’t forget: AI is sexy, AI is cool.

In addition to terminology, the Dartmouth proposal codified a split between rival approaches to artificial intelligence that has divided the field ever since—a divide Law calls the “core tension in AI.”

McCarthy and his colleagues wanted to describe in computer code “every aspect of learning or any other feature of intelligence” so that machines could mimic them. In other words, if they could just figure out how thinking worked—the rules of reasoning—and write down the recipe, they could program computers to follow it. This laid the foundation of what came to be known as rule-based or symbolic AI (sometimes referred to now as GOFAI, “good old-fashioned AI”). But coming up with hard-coded rules that captured the processes of problem-solving for actual, nontrivial problems proved too hard.

The other path favored neural networks, computer programs that would try to learn those rules by themselves in the form of statistical patterns. The Dartmouth proposal mentions it almost as an aside (referring variously to “neuron nets” and “nerve nets”). Though the idea seemed less promising at first, some researchers nevertheless continued to work on versions of neural networks alongside symbolic AI. But it would take decades—plus vast amounts of computing power and much of the data on the internet—before they really took off. Fast-forward to today and this approach underpins the entire AI boom.

The big takeaway here is that, just like today’s researchers, AI’s innovators fought about foundational concepts and got caught up in their own promotional spin. Even team GOFAI was plagued by squabbles. Aaron Sloman, a philosopher and fellow AI pioneer now in his late 80s, recalls how “old friends” Minsky and McCarthy “disagreed strongly” when he got to know them in the ’70s: “Minsky thought McCarthy’s claims about logic could not work, and McCarthy thought Minsky’s mechanisms could not do what could be done using logic. I got on well with both of them, but I was saying, ‘Neither of you have got it right.’” (Sloman still thinks no one can account for the way human reasoning uses intuition as much as logic, but that’s yet another tangent!)

As the fortunes of the technology waxed and waned, the term “AI” went in and out of fashion. In the early ’70s, both research tracks were effectively put on ice after the UK government published a report arguing that the AI dream had gone nowhere and wasn’t worth funding. All that hype, effectively, had led to nothing. Research projects were shuttered, and computer scientists scrubbed the words “artificial intelligence” from their grant proposals.

When I was finishing a computer science PhD in 2008, only one person in the department was working on neural networks. Bender has a similar recollection: “When I was in college, a running joke was that AI is anything that we haven’t figured out how to do with computers yet. Like, as soon as you figure out how to do it, it wasn’t magic anymore, so it wasn’t AI.”

But that magic—the grand vision laid out in the Dartmouth proposal—remained alive and, as we can now see, laid the foundations for the AGI dream.

Good and bad behavior

In 1950, five years before McCarthy started talking about artificial intelligence, Alan Turing had published a paper that asked: Can machines think? To address that question, the famous mathematician proposed a hypothetical test, which he called the imitation game. The setup imagines a human and a computer behind a screen and a second human who types questions to each. If the questioner cannot tell which answers come from the human and which come from the computer, Turing claimed, the computer may as well be said to think.

What Turing saw—unlike McCarthy’s crew—was that thinking is a really difficult thing to describe. The Turing test was a way to sidestep that problem. “He basically said: Instead of focusing on the nature of intelligence itself, I’m going to look for its manifestation in the world. I’m going to look for its shadow ,” says Law.

In 1952, BBC Radio convened a panel to explore Turing’s ideas further. Turing was joined in the studio by two of his Manchester University colleagues—professor of mathematics Maxwell Newman and professor of neurosurgery Geoffrey Jefferson—and Richard Braithwaite, a philosopher of science, ethics, and religion at the University of Cambridge.

Braithwaite kicked things off: “Thinking is ordinarily regarded as so much the specialty of man, and perhaps of other higher animals, the question may seem too absurd to be discussed. But of course, it all depends on what is to be included in ‘thinking.’”

The panelists circled Turing’s question but never quite pinned it down.

When they tried to define what thinking involved, what its mechanisms were, the goalposts moved. “As soon as one can see the cause and effect working themselves out in the brain, one regards it as not being thinking but a sort of unimaginative donkey work,” said Turing.

Here was the problem: When one panelist proposed some behavior that might be taken as evidence of thought—reacting to a new idea with outrage, say—another would point out that a computer could be made to do it.

As Newman said, it would be easy enough to program a computer to print “I don’t like this new program.” But he admitted that this would be a trick.

Exactly, Jefferson said: He wanted a computer that would print “I don’t like this new program” because it didn’t like the new program. In other words, for Jefferson, behavior was not enough. It was the process leading to the behavior that mattered.

But Turing disagreed. As he had noted, uncovering a specific process—the donkey work, to use his phrase—did not pinpoint what thinking was either. So what was left?

“From this point of view, one might be tempted to define thinking as consisting of those mental processes that we don’t understand,” said Turing. “If this is right, then to make a thinking machine is to make one which does interesting things without our really understanding quite how it is done.”

It is strange to hear people grapple with these ideas for the first time. “The debate is prescient,” says Tomer Ullman, a cognitive scientist at Harvard University. “Some of the points are still alive—perhaps even more so. What they seem to be going round and round on is that the Turing test is first and foremost a behaviorist test.”

For Turing, intelligence was hard to define but easy to recognize. He proposed that the appearance of intelligence was enough—and said nothing about how that behavior should come about.

And yet most people, when pushed, will have a gut instinct about what is and isn’t intelligent. There are dumb ways and clever ways to come across as intelligent. In 1981, Ned Block, a philosopher at New York University, showed that Turing’s proposal fell short of those gut instincts. Because it said nothing of what caused the behavior, the Turing test can be beaten through trickery (as Newman had noted in the BBC broadcast).

“Could the issue of whether a machine in fact thinks or is intelligent depend on how gullible human interrogators tend to be?” asked Block. (Or as computer scientist Mark Reidl has remarked : “The Turing test is not for AI to pass but for humans to fail.”)

Imagine, Block said, a vast look-up table in which human programmers had entered all possible answers to all possible questions. Type a question into this machine, and it would look up a matching answer in its database and send it back. Block argued that anyone using this machine would judge its behavior to be intelligent: “But actually, the machine has the intelligence of a toaster,” he wrote. “All the intelligence it exhibits is that of its programmers.”

Block concluded that whether behavior is intelligent behavior is a matter of how it is produced, not how it appears. Block’s toasters, which became known as Blockheads, are one of the strongest counterexamples to the assumptions behind Turing’s proposal.

Looking under the hood

The Turing test is not meant to be a practical metric, but its implications are deeply ingrained in the way we think about artificial intelligence today. This has become particularly relevant as LLMs have exploded in the past several years. These models get ranked by their outward behaviors, specifically how well they do on a range of tests. When OpenAI announced GPT-4, it published an impressive-looking scorecard that detailed the model’s performance on multiple high school and professional exams. Almost nobody talks about how these models get those results.

That’s because we don’t know. Today’s large language models are too complex for anybody to say exactly how their behavior is produced. Researchers outside the small handful of companies making those models don’t know what’s in their training data; none of the model makers have shared details. That makes it hard to say what is and isn’t a kind of memorization—a stochastic parroting. But even researchers on the inside, like Olah, don’t know what’s really going on when faced with a bridge-obsessed bot.

This leaves the question wide open: Yes, large language models are built on math—but are they doing something intelligent with it?

And the arguments begin again.

“Most people are trying to armchair through it,” says Brown University’s Pavlick, meaning that they are arguing about theories without looking at what’s really happening. “Some people are like, ‘I think it’s this way,’ and some people are like, ‘Well, I don’t.’ We’re kind of stuck and everyone’s unsatisfied.”

Bender thinks that this sense of mystery plays into the mythmaking. (“Magicians do not explain their tricks,” she says.) Without a proper appreciation of where the LLM’s words come from, we fall back on familiar assumptions about humans, since that is our only real point of reference. When we talk to another person, we try to make sense of what that person is trying to tell us. “That process necessarily entails imagining a life behind the words,” says Bender. That’s how language works.

“The parlor trick of ChatGPT is so impressive that when we see these words coming out of it, we do the same thing instinctively,” she says. “It’s very good at mimicking the form of language. The problem is that we are not at all good at encountering the form of language and not imagining the rest of it.”

For some researchers, it doesn’t really matter if we can’t understand the how . Bubeck used to study large language models to try to figure out how they worked, but GPT-4 changed the way he thought about them. “It seems like these questions are not so relevant anymore,” he says. “The model is so big, so complex, that we can’t hope to open it up and understand what’s really happening.”

But Pavlick, like Olah, is trying to do just that. Her team has found that models seem to encode abstract relationships between objects, such as that between a country and its capital. Studying one large language model, Pavlick and her colleagues found that it used the same encoding to map France to Paris and Poland to Warsaw. That almost sounds smart, I tell her. “No, it’s literally a lookup table,” she says.

But what struck Pavlick was that, unlike a Blockhead, the model had learned this lookup table on its own. In other words, the LLM figured out itself that Paris is to France as Warsaw is to Poland. But what does this show? Is encoding its own lookup table instead of using a hard-coded one a sign of intelligence? Where do you draw the line?

“Basically, the problem is that behavior is the only thing we know how to measure reliably,” says Pavlick. “Anything else requires a theoretical commitment, and people don’t like having to make a theoretical commitment because it’s so loaded.”

Not all people. A lot of influential scientists are just fine with theoretical commitment. Hinton, for example, insists that neural networks are all you need to re-create humanlike intelligence. “Deep learning is going to be able to do everything,” he told MIT Technology Review in 2020 . 

It’s a commitment that Hinton seems to have held onto from the start. Sloman, who recalls the two of them arguing when Hinton was a graduate student in his lab, remembers being unable to persuade him that neural networks cannot learn certain crucial abstract concepts that humans and some other animals seem to have an intuitive grasp of, such as whether something is impossible. We can just see when something’s ruled out, Sloman says. “Despite Hinton’s outstanding intelligence, he never seemed to understand that point. I don’t know why, but there are large numbers of researchers in neural networks who share that failing.”

And then there’s Marcus, whose view of neural networks is the exact opposite of Hinton’s. His case draws on what he says scientists have discovered about brains.

Brains, Marcus points out, are not blank slates that learn fully from scratch—they come ready-made with innate structures and processes that guide learning. It’s how babies can learn things that the best neural networks still can’t, he argues.

“Neural network people have this hammer, and now everything is a nail,” says Marcus. “They want to do all of it with learning, which many cognitive scientists would find unrealistic and silly. You’re not going to learn everything from scratch.”

Not that Marcus—a cognitive scientist—is any less sure of himself. “If one really looked at who’s predicted the current situation well, I think I would have to be at the top of anybody’s list,” he tells me from the back of an Uber on his way to catch a flight to a speaking gig in Europe. “I know that doesn’t sound very modest, but I do have this perspective that turns out to be very important if what you’re trying to study is artificial intelligence.”

Given his well-publicized attacks on the field, it might surprise you that Marcus still believes AGI is on the horizon. It’s just that he thinks today’s fixation on neural networks is a mistake. “We probably need a breakthrough or two or four,” he says. “You and I might not live that long, I’m sorry to say. But I think it’ll happen this century. Maybe we’ve got a shot at it.”

The power of a technicolor dream

Over Dor Skuler’s shoulder on the Zoom call from his home in Ramat Gan, Israel, a little lamp-like robot is winking on and off while we talk about it. “You can see ElliQ behind me here,” he says. Skuler’s company, Intuition Robotics, develops these devices for older people, and the design—part Amazon Alexa, part R2-D2—must make it very clear that ElliQ is a computer. If any of his customers show signs of being confused about that, Intuition Robotics takes the device back, says Skuler.

ElliQ has no face, no humanlike shape at all. Ask it about sports, and it will crack a joke about having no hand-eye coordination because it has no hands and no eyes. “For the life of me, I don’t understand why the industry is trying to fulfill the Turing test,” Skuler says. “Why is it in the best interest of humanity for us to develop technology whose goal is to dupe us?”

Instead, Skuler’s firm is betting that people can form relationships with machines that present as machines. “Just like we have the ability to build a real relationship with a dog,” he says. “Dogs provide a lot of joy for people. They provide companionship. People love their dog—but they never confuse it to be a human.”

ElliQ’s users, many in their 80s and 90s, refer to the robot as an entity or a presence—sometimes a roommate. “They’re able to create a space for this in-between relationship, something between a device or a computer and something that’s alive,” says Skuler.

But no matter how hard ElliQ’s designers try to control the way people view the device, they are competing with decades of pop culture that have shaped our expectations. Why are we so fixated on AI that’s humanlike? “Because it’s hard for us to imagine something else,” says Skuler (who indeed refers to ElliQ as “she” throughout our conversation). “And because so many people in the tech industry are fans of science fiction. They try to make their dream come true.”

How many developers grew up today thinking that building a smart machine was seriously the coolest thing—if not the most important thing—that they could possibly do?

It was not long ago that OpenAI launched its new voice-controlled version of ChatGPT with a voice that sounded like Scarlett Johansson, after which many people—including Altman—flagged the connection to Spike Jonze’s 2013 movie Her .

Science fiction co-invents what AI is understood to be. As Cave and Dihal write in Imagining AI : “AI was a cultural phenomenon long before it was a technological one.”

Stories and myths about remaking humans as machines have been around for centuries. People have been dreaming of artificial humans for probably as long as they have dreamed of flight, says Dihal. She notes that Daedalus, the figure in Greek mythology famous for building a pair of wings for himself and his son, Icarus, also built what was effectively a giant bronze robot called Talos that threw rocks at passing pirates.

The word robot comes from robota , a term for “forced labor” coined by the Czech playwright Karel Čapek in his 1920 play Rossum’s Universal Robots . The “laws of robotics” outlined in Isaac Asimov’s science fiction, forbidding machines from harming humans, are inverted by movies like The Terminator , which is an iconic reference point for popular fears about real-world technology. The 2014 film Ex Machina is a dramatic riff on the Turing test. Last year’s blockbuster The Creator imagines a future world in which AI has been outlawed because it set off a nuclear bomb, an event that some doomers consider at least an outside possibility.

Cave and Dihal relate how another movie, 2014’s Transcendence , in which an AI expert played by Johnny Depp gets his mind uploaded to a computer, served a narrative pushed by ur-doomers Stephen Hawking, fellow physicist Max Tegmark, and AI researcher Stuart Russell. In an article published in the Huffington Post on the movie’s opening weekend, the trio wrote: “As the Hollywood blockbuster Transcendence debuts this weekend with … clashing visions for the future of humanity, it’s tempting to dismiss the notion of highly intelligent machines as mere science fiction. But this would be a mistake, and potentially our worst mistake ever.”

Right around the same time, Tegmark founded the Future of Life Institute, with a remit to study and promote AI safety. Depp’s costar in the movie, Morgan Freeman, was on the institute’s board, and Elon Musk, who had a cameo in the film, donated $10 million in its first year. For Cave and Dihal, Transcendence is a perfect example of the multiple entanglements between popular culture, academic research, industrial production, and “the billionaire-funded fight to shape the future.”

On the London leg of his world tour last year, Altman was asked what he’d meant when he tweeted : “AI is the tech the world has always wanted.” Standing at the back of the room that day, behind an audience of hundreds, I listened to him offer his own kind of origin story: “I was, like, a very nervous kid. I read a lot of sci-fi. I spent a lot of Friday nights home, playing on the computer. But I was always really interested in AI and I thought it’d be very cool.” He went to college, got rich, and watched as neural networks became better and better. “This can be tremendously good but also could be really bad. What are we going to do about that?” he recalled thinking in 2015. “I ended up starting OpenAI.”

Why you should care that a bunch of nerds are fighting about AI

Okay, you get it: No one can agree on what AI is. But what everyone does seem to agree on is that the current debate around AI has moved far beyond the academic and the scientific. There are political and moral components in play—which doesn’t help with everyone thinking everyone else is wrong.

Untangling this is hard. It can be difficult to see what’s going on when some of those moral views take in the entire future of humanity and anchor them in a technology that nobody can quite define.

But we can't just throw our hands up and walk away. Because no matter what this technology is, it’s coming, and unless you live under a rock, you’ll use it in one form or another. And the form that technology takes—and the problems it both solves and creates—will be shaped by the thinking and the motivations of people like the ones you just read about. In particular, by the people with the most power, the most cash, and the biggest megaphones.

Which leads me to the TESCREALists. Wait, come back! I realize it’s unfair to introduce yet another new concept so late in the game. But to understand how the people in power may mold the technologies they build, and how they explain them to the world’s regulators and lawmakers, you need to really understand their mindset.

Gebru, who founded the Distributed AI Research Institute after leaving Google, and Émile Torres, a philosopher and historian at Case Western Reserve University, have traced the influence of several techno-utopian belief systems on Silicon Valley. The pair argue that to understand what’s going on with AI right now—both why companies such as Google DeepMind and OpenAI are in a race to build AGI and why doomers like Tegmark and Hinton warn of a coming catastrophe—the field must be seen through the lens of what Torres has dubbed the TESCREAL framework .

The clunky acronym (pronounced tes-cree-all ) replaces an even clunkier list of labels: transhumanism , extropianism , singularitarianism , cosmism , rationalism , effective altruism , and longtermism . A lot has been written (and will be written) about each of these worldviews, so I’ll spare you here. (There are rabbit holes within rabbit holes for anyone wanting to dive deeper. Pick your forum and pack your spelunking gear.)

This constellation of overlapping ideologies is attractive to a certain kind of galaxy-brain mindset common in the Western tech world. Some anticipate human immortality; others predict humanity’s colonization of the stars. The common tenet is that an all-powerful technology—AGI or superintelligence, choose your team—is not only within reach but inevitable. You can see this in the do-or-die attitude that’s ubiquitous inside cutting-edge labs like OpenAI: If we don’t make AGI, someone else will.

What’s more, TESCREALists believe that AGI could not only fix the world’s problems but level up humanity. “The development and proliferation of AI—far from a risk that we should fear—is a moral obligation that we have to ourselves, to our children and to our future,” Andreessen wrote in a much-dissected manifesto last year. I have been told many times over that AGI is the way to make the world a better place—by Demis Hassabis , CEO and cofounder of Google DeepMind; by Mustafa Suleyman , CEO of the newly minted Microsoft AI and another cofounder of DeepMind; by Sutskever , Altman , and more.

But as Andreessen noted, it’s a yin-yang mindset. The flip side of techno-utopia is techno-hell. If you believe that you are building a technology so powerful that it will solve all the world’s problems, you probably also believe there’s a non-zero chance it will all go very wrong. When asked at the World Government Summit in February what keeps him up at night, Altman replied: “It’s all the sci-fi stuff.”

It’s a tension that Hinton has been talking up for the last year. It’s what companies like Anthropic claim to address. It’s what Sutskever is focusing on in his new lab , and what he wanted a special in-house team at OpenAI to focus on last year before disagreements over the way the company balanced risk and reward led most members of that team to leave.

Sure, doomerism is part of the spin. (“Claiming that you have created something that is super-intelligent is good for sales figures,” says Dihal. “It’s like, ‘Please, someone stop me from being so good and so powerful.’”) But boom or doom, exactly what (and whose) problems are these guys supposedly solving? Are we really expected to trust what they build and what they tell our leaders?

Gebru and Torres (and others) are adamant: No, we should not. They are highly critical of these ideologies and how they may influence the development of future technology, especially AI. Fundamentally, they link several of these worldviews—with their common focus on “improving” humanity—to the racist eugenics movements of the 20th century.

One danger, they argue, is that a shift of resources toward the kind of technological innovations that these ideologies demand, from building AGI to extending life spans to colonizing other planets, will ultimately benefit people who are Western and white at the cost of billions of people who aren’t. If your sight is set on fantastical futures, it’s easy to overlook the present-day costs of innovation, such as labor exploitation, the entrenchment of racist and sexist bias, and environmental damage.  

“Are we trying to build a tool that’s useful to us in some way?” asks Bender, reflecting on the casualties of this race to AGI. If so, who’s it for, how do we test it, how well does it work? “But if what we’re building it for is just so that we can say that we’ve done it, that’s not a goal that I can get behind. That’s not a goal that’s worth billions of dollars.”

Bender says that seeing the connections between the TESCREAL ideologies is what made her realize there was something more to these debates. “Tangling with those people was—” she stops. “Okay, there’s more here than just academic ideas. There’s a moral code tied up in it as well.”

Of course, laid out like this without nuance, it doesn’t sound as if we—as a society, as individuals—are getting the best deal. It also all sounds rather silly. When Gebru described parts of the TESCREAL bundle in a talk last year, her audience laughed. It’s also true that few people would identify themselves as card-carrying students of these schools of thought, at least in their extremes.

But if we don’t understand how those building this tech approach it, how can we decide what deals we want to make? What apps we decide to use, what chatbots we want to give personal information to, what data centers we support in our neighborhoods, what politicians we want to vote for?

It used to be like this: There was a problem in the world, and we built something to fix it. Here, everything is backward: The goal seems to be to build a machine that can do everything, and to skip the slow, hard work that goes into figuring out what the problem is before building the solution.

And as Gebru said in that same talk, “A machine that solves all problems: if that’s not magic, what is it?”

Semantics, semantics … semantics?

When asked outright what AI is, a lot of people dodge the question. Not Suleyman. In April, the CEO of Microsoft AI stood on the TED stage and told the audience what he’d told his six-year-old nephew in response to that question. The best answer he could give, Suleyman explained, was that AI was “a new kind of digital species”—a technology so universal, so powerful, that calling it a tool no longer captured what it could do for us.

“On our current trajectory, we are heading toward the emergence of something we are all struggling to describe, and yet we cannot control what we don’t understand,” he said. “And so the metaphors, the mental models, the names—these all matter if we are to get the most out of AI whilst limiting its potential downsides.”

Language matters! I hope that’s clear from the twists and turns and tantrums we’ve been through to get to this point. But I also hope you’re asking: Whose language? And whose downsides? Suleyman is an industry leader at a technology giant that stands to make billions from its AI products. Describing the technology behind those products as a new kind of species conjures something wholly unprecedented, something with agency and capabilities that we have never seen before. That makes my spidey sense tingle. You?

I can’t tell you if there’s magic here (ironically or not). And I can’t tell you how math can realize what Bubeck and many others see in this technology (no one can yet). You’ll have to make up your own mind. But I can pull back the curtain on my own point of view.

Writing about GPT-3 back in 2020, I said that the greatest trick AI ever pulled was convincing the world it exists. I still think that: We are hardwired to see intelligence in things that behave in certain ways, whether it’s there or not. In the last few years, the tech industry has found reasons of its own to convince us that AI exists, too. This makes me skeptical of many of the claims made for this technology.

With large language models—via their smiley-face masks—we are confronted by something we’ve never had to think about before. “It’s taking this hypothetical thing and making it really concrete,” says Pavlick. “I’ve never had to think about whether a piece of language required intelligence to generate because I’ve just never dealt with language that didn’t.”

AI is many things. But I don’t think it’s humanlike. I don’t think it’s the solution to all (or even most) of our problems. It isn’t ChatGPT or Gemini or Copilot. It isn’t neural networks. It’s an idea, a vision, a kind of wish fulfillment. And ideas get shaped by other ideas, by morals, by quasi-religious convictions, by worldviews, by politics, and by gut instinct. “Artificial intelligence” is a helpful shorthand to describe a raft of different technologies. But AI is not one thing; it never has been, no matter how often the branding gets seared into the outside of the box. 

“The truth is these words”—intelligence, reasoning, understanding, and more—“were defined before there was a need to be really precise about it,” says Pavlick. “I don’t really like when the question becomes ‘Does the model understand—yes or no?’ because, well, I don’t know. Words get redefined and concepts evolve all the time.”

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Learn Artificial Intelligence

Ai - introduction, ai - intelligent system, ai - types of artificial intelligence, ai - agents, ai - agent environment, ai - turing test, ai - alternatives to the turing test, ai - problem solving, ai - algorithms, ai - searching algorithms, ai - informed searching algorithms, ai - hill climbing algorithm, ai - adversarial search.

• Problem Solving in AI

Problem-solving in AI includes various techniques like efficient algorithms, heuristics, and performing root cause analysis to obtain desirable solutions. For a given problem in AI, there may be various solutions by different heuristics and at the same time, there are problems with unique solutions. These all depend on the nature of the problem to be solved.

Characteristics of problem in AI

Each problem given to an AI is with different aspects of representation and explanation. The given problem has to be analyzed along with several dimensions to choose the most acceptable method to solve. Some of the key features of a problem are listed below.

• Can the problem decompose into subproblems?
• Can any of the solution steps be ignored?
• Is the given problem universally predictable?
• Can we select a good solution for the given problem without comparing it to all the possible solutions?
• Whether the desired output is a state of the world or a path to a state?
• Did the problem require a large amount of knowledge to solve?
• Does it require any interaction with computers and humans?

The above-listed characteristics of a problem are called 7-problem characteristics. The solution for the given problem must take place under these characteristics. 

Problems in AI can basically be divided into two types. Toy Problems and Real-World Problems.

Toy Problem:

It can also be called a puzzle-like problem which can be used as a way to explain a more general problem-solving technique. For testing and demonstrating methodologies, or to compare the performance of different algorithms, Toy problems can be used. Toy problems are often useful in providing divination about specific phenomena in complicated problems. Large complicated problems are divided into many smaller toy problems that can be understood in detail easily. Sliding-block puzzles, N-Queens problem, Tower of Hanoi are some examples.

Real-World Problem:

 As with the name, it is a problem based on the real world. Real-world problems require a solution. It doesn't depend on descriptions, but with a general formulation. Online shopping, Fraud detection, Medical diagnosis are some examples of real-world problems in AI.

Steps performed in problem-solving

• Goal formulation : The first step in problem-solving is to identify the problem. It involves selecting the steps to formulate the perfect goal out of multiple goals and selecting actions to achieve the goal.
• Problem formulation : The most important step in problem-solving is choosing the action to be taken to achieve the goal formulated.
• Initial State : The starting state of the agent towards the goal.
• Actions :  The list of the possible actions available to the agent.
• Transition Model : What each action does is described.
• Goal Test : The given state is tested whether it is a goal state.
• Path cost :  A numeric cost to each path that follows the goal is assigned. It reflects its performance. Solution with the lowest path cost is the optimal solution. 

Example for the problem-solving procedure

Eight queens puzzle

The problem here is to place eight chess queens on an 8*8 chessboard in a way that no queens will threaten the other. If two queens will come in the same row, column, or diagonal one will attack another.

Consider the Incremental formulation for this problem: It starts from an empty state and the operator expands a queen at each step. Following are the steps involved in this formulation:

• States : Arranging  0 to 8 queens on the chessboard.
• Initial State : An empty chessboard
• Actions : Adding a queen to any of the empty boxes on the chessboard.
• Transition model : Returns the new state of the chessboard with the queen added in a box.
• Goal test : Checks whether 8-queens are kept on the chessboard without any possibility of attack.
• Path cost : Path costs are neglected because only final states are counted.

Problem-solving methods in Artificial Intelligence

Let us discuss the techniques like Heuristics, Algorithms, Root cause analysis used by AI as problem-solving methods to find a desirable solution for the given problem.

1. ALGORITHMS

A problem-solving algorithm can be said as a procedure that is guaranteed to solve if its steps are strictly followed.  Let's have a simple example to get to know what it means: A person wants to find a book on display among the vast collections of the library. He does not know where the book is kept. By tracing a sequential examination of every book displayed in every rack of the library, the person will eventually find the book. But the approach will consume a considerable amount of time. Thus an algorithmic approach will succeed but are often slow.

Types of AI Algorithms

• Regression Algorithms.
• Instance-Based Algorithms. 
• Decision Tree Algorithms. 
• Clustering Algorithms. 
• Association Rule Learning Algorithms. 
• Artificial Neural Network Algorithms. 
• Deep Learning Algorithms.
• Search Algorithms

2. HEURISTICS.

A problem-solving heuristic can be said as an informal, ideational, impulsive procedure that leads to the desired solution in some cases only. The fact is that the outcome of a heuristic operation is unpredictable. Using a heuristic approach may be more or less effective than using an algorithm. Consider the same example discussed above. If he had an idea of where to look for the book, a great deal of time could be saved. This can be said as searching heuristically. But if one happens to be wrong on the first trial, He has to try another heuristic. The frequently used problem-solving heuristics are 

WORKING FORWARD 

It is a forward approach problem-solving solution. In this method, the problem is solved from the beginning itself and working to the end.

WORKING BACKWARD

It is a backward approach problem-solving solution. Here the problem is solved from the endpoint or goal to steps that led to the goal.

MEANS-ENDS ANALYSIS.

A problem-solving technique where a mixture of the above two directions. This method is appropriate for solving complex and large problems. MEA is a strategy to control the searching procedure in problem-solving. It is centered on evaluating the difference between the current state and the goal state. First, the difference between the initial state and the final state is calculated. Then select various operators for each difference. The application of these operators will reduce the difference between the current state and the goal state.

GENERATE-AND-TEST.

The Generate-and-test method is a problem-solving heuristics that involve an alternate set of actions in a random method. Each alternate method is to check whether it will solve the problem.  It ensures that the selected best possible solution is checked against the generated possible solutions.

Difference between algorithm and heuristics

The terms heuristics and algorithms overlap somewhat in AI. A heuristic may be a subroutine that can be used to determine where to look first for an optimal algorithm. Heuristic algorithms can be listed under the categories of algorithms. In a sense of heuristics are algorithms, heuristic makes a guessing approach to solve the problem, granting a good enough answer rather than finding the best possible outcome. The level of indirection is the main difference between the two.

3. Root Cause Analysis

Like the name itself, it is the process of identifying the root cause of the problem. The root cause of the problem is analyzed to identify the appropriate solutions. A collection of principles, techniques, and methodologies are used to identify the root causes of a problem. RCA can identify an issue in the first place.

The first goal of RCA is to identify the root cause of the problem. The second goal is to understand how to fix the underlying issues within the root cause. The third goal is to prevent future issues or to repeat the success.

The core principles of RCA are:

• Focus on correcting the root causes.
• Treating symptoms for short-term relief.
• There can be multiple root causes.
• Focus on HOW? and WHY?
• Root cause claims are kept back up with concrete cause-effect.
• Provides information to get a corrective course of action.
• Analyze how to avoid a root cause in the future.

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Related Tutorials

• Agent environment in AI
• Types of artificial intelligence
• Searching Algorithms in AI ( Uninformed )

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• Part 2 Problem-solving »
• Chapter 3 Solving Problems by Searching
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Chapter 3 Solving Problems by Searching 

When the correct action to take is not immediately obvious, an agent may need to plan ahead : to consider a sequence of actions that form a path to a goal state. Such an agent is called a problem-solving agent , and the computational process it undertakes is called search .

Problem-solving agents use atomic representations, that is, states of the world are considered as wholes, with no internal structure visible to the problem-solving algorithms. Agents that use factored or structured representations of states are called planning agents .

We distinguish between informed algorithms, in which the agent can estimate how far it is from the goal, and uninformed algorithms, where no such estimate is available.

3.1 Problem-Solving Agents 

If the agent has no additional information—that is, if the environment is unknown —then the agent can do no better than to execute one of the actions at random. For now, we assume that our agents always have access to information about the world. With that information, the agent can follow this four-phase problem-solving process:

GOAL FORMULATION : Goals organize behavior by limiting the objectives and hence the actions to be considered.

PROBLEM FORMULATION : The agent devises a description of the states and actions necessary to reach the goal—an abstract model of the relevant part of the world.

SEARCH : Before taking any action in the real world, the agent simulates sequences of actions in its model, searching until it finds a sequence of actions that reaches the goal. Such a sequence is called a solution .

EXECUTION : The agent can now execute the actions in the solution, one at a time.

It is an important property that in a fully observable, deterministic, known environment, the solution to any problem is a fixed sequence of actions . The open-loop system means that ignoring the percepts breaks the loop between agent and environment. If there is a chance that the model is incorrect, or the environment is nondeterministic, then the agent would be safer using a closed-loop approach that monitors the percepts.

In partially observable or nondeterministic environments, a solution would be a branching strategy that recommends different future actions depending on what percepts arrive.

3.1.1 Search problems and solutions 

A search problem can be defined formally as follows:

A set of possible states that the environment can be in. We call this the state space .

The initial state that the agent starts in.

A set of one or more goal states . We can account for all three of these possibilities by specifying an \(Is\-Goal\) method for a problem.

The actions available to the agent. Given a state \(s\) , \(Actions(s)\) returns a finite set of actions that can be executed in \(s\) . We say that each of these actions is applicable in \(s\) .

A transition model , which describes what each action does. \(Result(s,a)\) returns the state that results from doing action \(a\) in state \(s\) .

An action cost function , denote by \(Action\-Cost(s,a,s\pr)\) when we are programming or \(c(s,a,s\pr)\) when we are doing math, that gives the numeric cost of applying action \(a\) in state \(s\) to reach state \(s\pr\) .

A sequence of actions forms a path , and a solution is a path from the initial state to a goal state. We assume that action costs are additive; that is, the total cost of a path is the sum of the individual action costs. An optimal solution has the lowest path cost among all solutions.

The state space can be represented as a graph in which the vertices are states and the directed edges between them are actions.

3.1.2 Formulating problems 

The process of removing detail from a representation is called abstraction . The abstraction is valid if we can elaborate any abstract solution into a solution in the more detailed world. The abstraction is useful if carrying out each of the actions in the solution is easier than the original problem.

3.2 Example Problems 

A standardized problem is intended to illustrate or exercise various problem-solving methods. It can be given a concise, exact description and hence is suitable as a benchmark for researchers to compare the performance of algorithms. A real-world problem , such as robot navigation, is one whose solutions people actually use, and whose formulation is idiosyncratic, not standardized, because, for example, each robot has different sensors that produce different data.

3.2.1 Standardized problems 

A grid world problem is a two-dimensional rectangular array of square cells in which agents can move from cell to cell.

Vacuum world

Sokoban puzzle

Sliding-tile puzzle

3.2.2 Real-world problems 

Route-finding problem

Touring problems

Trveling salesperson problem (TSP)

VLSI layout problem

Robot navigation

Automatic assembly sequencing

3.3 Search Algorithms 

A search algorithm takes a search problem as input and returns a solution, or an indication of failure. We consider algorithms that superimpose a search tree over the state-space graph, forming various paths from the initial state, trying to find a path that reaches a goal state. Each node in the search tree corresponds to a state in the state space and the edges in the search tree correspond to actions. The root of the tree corresponds to the initial state of the problem.

The state space describes the (possibly infinite) set of states in the world, and the actions that allow transitions from one state to another. The search tree describes paths between these states, reaching towards the goal. The search tree may have multiple paths to (and thus multiple nodes for) any given state, but each node in the tree has a unique path back to the root (as in all trees).

The frontier separates two regions of the state-space graph: an interior region where every state has been expanded, and an exterior region of states that have not yet been reached.

3.3.1 Best-first search 

In best-first search we choose a node, \(n\) , with minimum value of some evaluation function , \(f(n)\) .

3.3.2 Search data structures 

A node in the tree is represented by a data structure with four components

\(node.State\) : the state to which the node corresponds;

\(node.Parent\) : the node in the tree that generated this node;

\(node.Action\) : the action that was applied to the parent’s state to generate this node;

\(node.Path\-Cost\) : the total cost of the path from the initial state to this node. In mathematical formulas, we use \(g(node)\) as a synonym for \(Path\-Cost\) .

Following the \(PARENT\) pointers back from a node allows us to recover the states and actions along the path to that node. Doing this from a goal node gives us the solution.

We need a data structure to store the frontier . The appropriate choice is a queue of some kind, because the operations on a frontier are:

\(Is\-Empty(frontier)\) returns true only if there are no nodes in the frontier.

\(Pop(frontier)\) removes the top node from the frontier and returns it.

\(Top(frontier)\) returns (but does not remove) the top node of the frontier.

\(Add(node, frontier)\) inserts node into its proper place in the queue.

Three kinds of queues are used in search algorithms:

A priority queue first pops the node with the minimum cost according to some evaluation function, \(f\) . It is used in best-first search.

A FIFO queue or first-in-first-out queue first pops the node that was added to the queue first; we shall see it is used in breadth-first search.

A LIFO queue or last-in-first-out queue (also known as a stack ) pops first the most recently added node; we shall see it is used in depth-first search.

3.3.3 Redundant paths 

A cycle is a special case of a redundant path .

As the saying goes, algorithms that cannot remember the past are doomed to repeat it . There are three approaches to this issue.

First, we can remember all previously reached states (as best-first search does), allowing us to detect all redundant paths, and keep only the best path to each state.

Second, we can not worry about repeating the past. We call a search algorithm a graph search if it checks for redundant paths and a tree-like search if it does not check.

Third, we can compromise and check for cycles, but not for redundant paths in general.

3.3.4 Measuring problem-solving performance 

COMPLETENESS : Is the algorithm guaranteed to find a solution when there is one, and to correctly report failure when there is not?

COST OPTIMALITY : Does it find a solution with the lowest path cost of all solutions?

TIME COMPLEXITY : How long does it take to find a solution?

SPACE COMPLEXITY : How much memory is needed to perform the search?

To be complete, a search algorithm must be systematic in the way it explores an infinite state space, making sure it can eventually reach any state that is connected to the initial state.

In theoretical computer science, the typical measure of time and space complexity is the size of the state-space graph, \(|V|+|E|\) , where \(|V|\) is the number of vertices (state nodes) of the graph and \(|E|\) is the number of edges (distinct state/action pairs). For an implicit state space, complexity can be measured in terms of \(d\) , the depth or number of actions in an optimal solution; \(m\) , the maximum number of actions in any path; and \(b\) , the branching factor or number of successors of a node that need to be considered.

3.4 Uninformed Search Strategies 

3.4.1 breadth-first search .

When all actions have the same cost, an appropriate strategy is breadth-first search , in which the root node is expanded first, then all the successors of the root node are expanded next, then their successors, and so on.

Breadth-first search always finds a solution with a minimal number of actions, because when it is generating nodes at depth \(d\) , it has already generated all the nodes at depth \(d-1\) , so if one of them were a solution, it would have been found.

All the nodes remain in memory, so both time and space complexity are \(O(b^d)\) . The memory requirements are a bigger problem for breadth-first search than the execution time . In general, exponential-complexity search problems cannot be solved by uninformed search for any but the smallest instances .

3.4.2 Dijkstra’s algorithm or uniform-cost search 

When actions have different costs, an obvious choice is to use best-first search where the evaluation function is the cost of the path from the root to the current node. This is called Dijkstra’s algorithm by the theoretical computer science community, and uniform-cost search by the AI community.

The complexity of uniform-cost search is characterized in terms of \(C^*\) , the cost of the optimal solution, and \(\epsilon\) , a lower bound on the cost of each action, with \(\epsilon>0\) . Then the algorithm’s worst-case time and space complexity is \(O(b^{1+\lfloor C^*/\epsilon\rfloor})\) , which can be much greater than \(b^d\) .

When all action costs are equal, \(b^{1+\lfloor C^*/\epsilon\rfloor}\) is just \(b^{d+1}\) , and uniform-cost search is similar to breadth-first search.

3.4.3 Depth-first search and the problem of memory 

Depth-first search always expands the deepest node in the frontier first. It could be implemented as a call to \(Best\-First\-Search\) where the evaluation function \(f\) is the negative of the depth.

For problems where a tree-like search is feasible, depth-first search has much smaller needs for memory. A depth-first tree-like search takes time proportional to the number of states, and has memory complexity of only \(O(bm)\) , where \(b\) is the branching factor and \(m\) is the maximum depth of the tree.

A variant of depth-first search called backtracking search uses even less memory.

3.4.4 Depth-limited and iterative deepening search 

To keep depth-first search from wandering down an infinite path, we can use depth-limited search , a version of depth-first search in which we supply a depth limit, \(l\) , and treat all nodes at depth \(l\) as if they had no successors. The time complexity is \(O(b^l)\) and the space complexity is \(O(bl)\)

Iterative deepening search solves the problem of picking a good value for \(l\) by trying all values: first 0, then 1, then 2, and so on—until either a solution is found, or the depth- limited search returns the failure value rather than the cutoff value.

Its memory requirements are modest: \(O(bd)\) when there is a solution, or \(O(bm)\) on finite state spaces with no solution. The time complexity is \(O(bd)\) when there is a solution, or \(O(bm)\) when there is none.

In general, iterative deepening is the preferred uninformed search method when the search state space is larger than can fit in memory and the depth of the solution is not known .

3.4.5 Bidirectional search 

An alternative approach called bidirectional search simultaneously searches forward from the initial state and backwards from the goal state(s), hoping that the two searches will meet.

3.4.6 Comparing uninformed search algorithms 

3.5 Informed (Heuristic) Search Strategies 

An informed search strategy uses domain–specific hints about the location of goals to find colutions more efficiently than an uninformed strategy. The hints come in the form of a heuristic function , denoted \(h(n)\) :

\(h(n)\) = estimated cost of the cheapest path from the state at node \(n\) to a goal state.

3.5.1 Greedy best-first search 

Greedy best-first search is a form of best-first search that expands first the node with the lowest \(h(n)\) value—the node that appears to be closest to the goal—on the grounds that this is likely to lead to a solution quickly. So the evaluation function \(f(n)=h(n)\) .

Problem-Solving Agents In Artificial Intelligence

In artificial intelligence, a problem-solving agent refers to a type of intelligent agent designed to address and solve complex problems or tasks in its environment. These agents are a fundamental concept in AI and are used in various applications, from game-playing algorithms to robotics and decision-making systems. Here are some key characteristics and components of a problem-solving agent:

• Perception : Problem-solving agents typically have the ability to perceive or sense their environment. They can gather information about the current state of the world, often through sensors, cameras, or other data sources.
• Knowledge Base : These agents often possess some form of knowledge or representation of the problem domain. This knowledge can be encoded in various ways, such as rules, facts, or models, depending on the specific problem.
• Reasoning : Problem-solving agents employ reasoning mechanisms to make decisions and select actions based on their perception and knowledge. This involves processing information, making inferences, and selecting the best course of action.
• Planning : For many complex problems, problem-solving agents engage in planning. They consider different sequences of actions to achieve their goals and decide on the most suitable action plan.
• Actuation : After determining the best course of action, problem-solving agents take actions to interact with their environment. This can involve physical actions in the case of robotics or making decisions in more abstract problem-solving domains.
• Feedback : Problem-solving agents often receive feedback from their environment, which they use to adjust their actions and refine their problem-solving strategies. This feedback loop helps them adapt to changing conditions and improve their performance.
• Learning : Some problem-solving agents incorporate machine learning techniques to improve their performance over time. They can learn from experience, adapt their strategies, and become more efficient at solving similar problems in the future.

Problem-solving agents can vary greatly in complexity, from simple algorithms that solve straightforward puzzles to highly sophisticated AI systems that tackle complex, real-world problems. The design and implementation of problem-solving agents depend on the specific problem domain and the goals of the AI application.

Hello, I’m Hridhya Manoj. I’m passionate about technology and its ever-evolving landscape. With a deep love for writing and a curious mind, I enjoy translating complex concepts into understandable, engaging content. Let’s explore the world of tech together

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AI accelerates problem-solving in complex scenarios

By adam zewe.

December 5, 2023 | MIT News

While Santa Claus may have a magical sleigh and nine plucky reindeer to help him deliver presents, for companies like FedEx, the optimization problem of efficiently routing holiday packages is so complicated that they often employ specialized software to find a solution.

This software, called a mixed-integer linear programming (MILP) solver, splits a massive optimization problem into smaller pieces and uses generic algorithms to try and find the best solution. However, the solver could take hours — or even days — to arrive at a solution.

The process is so onerous that a company often must stop the software partway through, accepting a solution that is not ideal but the best that could be generated in a set amount of time.

Researchers from MIT and ETH Zurich used machine learning to speed things up.

They identified a key intermediate step in MILP solvers that has so many potential solutions it takes an enormous amount of time to unravel, which slows the entire process. The researchers employed a filtering technique to simplify this step, then used machine learning to find the optimal solution for a specific type of problem.

Their data-driven approach enables a company to use its own data to tailor a general-purpose MILP solver to the problem at hand.

This new technique sped up MILP solvers between 30 and 70 percent, without any drop in accuracy. One could use this method to obtain an optimal solution more quickly or, for especially complex problems, a better solution in a tractable amount of time.

This approach could be used wherever MILP solvers are employed, such as by ride-hailing services, electric grid operators, vaccination distributors, or any entity faced with a thorny resource-allocation problem.

“Sometimes, in a field like optimization, it is very common for folks to think of solutions as either purely machine learning or purely classical. I am a firm believer that we want to get the best of both worlds, and this is a really strong instantiation of that hybrid approach,” says senior author Cathy Wu, the Gilbert W. Winslow Career Development Assistant Professor in Civil and Environmental Engineering (CEE), and a member of a member of the Laboratory for Information and Decision Systems (LIDS) and the Institute for Data, Systems, and Society (IDSS).

Wu wrote the  paper  with co-lead authors Sirui Li, an IDSS graduate student, and Wenbin Ouyang, a CEE graduate student; as well as Max Paulus, a graduate student at ETH Zurich. The research will be presented at the Conference on Neural Information Processing Systems.

Tough to solve

MILP problems have an exponential number of potential solutions. For instance, say a traveling salesperson wants to find the shortest path to visit several cities and then return to their city of origin. If there are many cities which could be visited in any order, the number of potential solutions might be greater than the number of atoms in the universe.  

“These problems are called NP-hard, which means it is very unlikely there is an efficient algorithm to solve them. When the problem is big enough, we can only hope to achieve some suboptimal performance,” Wu explains.

An MILP solver employs an array of techniques and practical tricks that can achieve reasonable solutions in a tractable amount of time.

A typical solver uses a divide-and-conquer approach, first splitting the space of potential solutions into smaller pieces with a technique called branching. Then, the solver employs a technique called cutting to tighten up these smaller pieces so they can be searched faster.

Cutting uses a set of rules that tighten the search space without removing any feasible solutions. These rules are generated by a few dozen algorithms, known as separators, that have been created for different kinds of MILP problems. 

Wu and her team found that the process of identifying the ideal combination of separator algorithms to use is, in itself, a problem with an exponential number of solutions.

“Separator management is a core part of every solver, but this is an underappreciated aspect of the problem space. One of the contributions of this work is identifying the problem of separator management as a machine learning task to begin with,” she says.

Shrinking the solution space

She and her collaborators devised a filtering mechanism that reduces this separator search space from more than 130,000 potential combinations to around 20 options. This filtering mechanism draws on the principle of diminishing marginal returns, which says that the most benefit would come from a small set of algorithms, and adding additional algorithms won’t bring much extra improvement.

Then they use a machine-learning model to pick the best combination of algorithms from among the 20 remaining options.

This model is trained with a dataset specific to the user’s optimization problem, so it learns to choose algorithms that best suit the user’s particular task. Since a company like FedEx has solved routing problems many times before, using real data gleaned from past experience should lead to better solutions than starting from scratch each time.

The model’s iterative learning process, known as contextual bandits, a form of reinforcement learning, involves picking a potential solution, getting feedback on how good it was, and then trying again to find a better solution.

This data-driven approach accelerated MILP solvers between 30 and 70 percent without any drop in accuracy. Moreover, the speedup was similar when they applied it to a simpler, open-source solver and a more powerful, commercial solver.

In the future, Wu and her collaborators want to apply this approach to even more complex MILP problems, where gathering labeled data to train the model could be especially challenging. Perhaps they can train the model on a smaller dataset and then tweak it to tackle a much larger optimization problem, she says. The researchers are also interested in interpreting the learned model to better understand the effectiveness of different separator algorithms.

This research is supported, in part, by Mathworks, the National Science Foundation (NSF), the MIT Amazon Science Hub, and MIT’s Research Support Committee.

Related topics

• Artificial Intelligence + Machine Learning

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Updated : 16 August 2024 Contributors : Cole Stryker, Eda Kavlakoglu

Artificial intelligence (AI) is technology that enables computers and machines to simulate human learning, comprehension, problem solving, decision making, creativity and autonomy.

Applications and devices equipped with AI can see and identify objects. They can understand and respond to human language. They can learn from new information and experience. They can make detailed recommendations to users and experts. They can act independently, replacing the need for human intelligence or intervention (a classic example being a self-driving car). 

But in 2024, most AI researchers and practitioners—and most AI-related headlines—are focused on breakthroughs in generative AI  (gen AI), a technology that can create original text, images, video and other content. To fully understand generative AI, it’s important to first understand the technologies on which generative AI tools are built: machine learning  (ML) and deep learning .

Learn how to choose the right approach in preparing data sets and employing AI models.

A simple way to think about AI is as a series of nested or derivative concepts that have emerged over more than 70 years:  

Directly underneath AI, we have machine learning, which involves creating models by training an algorithm to make predictions or decisions based on data. It encompasses a broad range of techniques that enable computers to learn from and make inferences based on data without being explicitly programmed for specific tasks. 

There are many types of machine learning techniques or algorithms, including linear regression ,  logistic regression , decision trees , random forest , support vector machines   (SVMs) , k-nearest neighbor (KNN), clustering and more. Each of these approaches is suited to different kinds of problems and data.

But one of the most popular types of machine learning algorithm is called a neural network (or artificial neural network). Neural networks are modeled after the human brain's structure and function. A neural network consists of interconnected layers of nodes (analogous to neurons) that work together to process and analyze complex data. Neural networks are well suited to tasks that involve identifying complex patterns and relationships in large amounts of data.

The simplest form of machine learning is called supervised learning , which involves the use of labeled data sets to train algorithms to classify data or predict outcomes accurately. In supervised learning, humans pair each training example with an output label. The goal is for the model to learn the mapping between inputs and outputs in the training data, so it can predict the labels of new, unseen data.  

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Deep learning is a subset of machine learning that uses multilayered neural networks, called deep neural networks, that more closely simulate the complex decision-making power of the human brain.

Deep neural networks include an input layer, at least three but usually hundreds of hidden layers, and an output layer, unlike neural networks used in classic machine learning models, which usually have only one or two hidden layers.

These multiple layers enable unsupervised learning : they can automate the extraction of features from large, unlabeled and unstructured data sets, and make their own predictions about what the data represents.

Because deep learning doesn’t require human intervention, it enables machine learning at a tremendous scale. It is well suited to natural language processing (NLP) , computer vision , and other tasks that involve the fast, accurate identification complex patterns and relationships in large amounts of data. Some form of deep learning powers most of the artificial intelligence (AI) applications in our lives today.  

Deep learning also enables:

• Semi-supervised learning , which combines supervised and unsupervised learning by using both labeled and unlabeled data to train AI models for classification and regression tasks.
• Self-supervised learning , which generates implicit labels from unstructured data, rather than relying on labeled data sets for supervisory signals.
• Reinforcement learning , which learns by trial-and-error and reward functions rather than by extracting information from hidden patterns.
• Transfer learning , in which knowledge gained through one task or data set is used to improve model performance on another related task or different data set.

Generative AI, sometimes called "gen AI" , refers to deep learning models that can create complex original content—such as long-form text, high-quality images, realistic video or audio and more—in response to a user’s prompt or request.

At a high level, generative models encode a simplified representation of their training data, and then draw from that representation to create 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. But over the last decade, they evolved to analyze and generate more complex data types. This evolution coincided with the emergence of three sophisticated deep learning model types:

• Variational autoencoders  or VAEs, which were introduced in 2013, and enabled models that could generate multiple variations of content in response to a prompt or instruction.
• Diffusion models, first seen in 2014, which add "noise" to images until they are unrecognizable, and then remove the noise to generate original images in response to prompts.
• Transformers (also called transformer models), which are trained on sequenced data to generate extended sequences of content (such as words in sentences, shapes in an image, frames of a video or commands in software code). Transformers are at the core of most of today’s headline-making generative AI tools, including ChatGPT and GPT-4, Copilot, BERT, Bard and Midjourney. 

In general, generative AI operates in three phases:

• Training, to create a foundation model.
• Tuning, to adapt the model to a specific application.
• Generation, evaluation and more tuning, to improve accuracy.

Generative AI begins with a "foundation model"; a deep learning model that serves as the basis for multiple different types of generative AI applications.

The most common foundation models today are large language models (LLMs) , created for text generation applications. But there are also foundation models for image, video, sound or music generation, and multimodal foundation models that support several kinds of content.

To create a foundation model, practitioners train a deep learning algorithm on huge volumes of relevant raw, unstructured, unlabeled data, such as terabytes or petabytes of data text or images or video from the internet. The training yields a neural network of billions of parameters —encoded representations of the entities, patterns and relationships in the data—that can generate content autonomously in response to prompts. This is the foundation model.

This training process is compute-intensive, time-consuming and expensive. It requires thousands of clustered graphics processing units (GPUs) and weeks of processing, all of which typically costs millions of dollars. Open source foundation model projects, such as Meta's Llama-2, enable gen AI developers to avoid this step and its costs.

Next, the model must be tuned to a specific content generation task. This can be done in various ways, including:

• Fine-tuning, which involves feeding the model application-specific labeled data—questions or prompts the application is likely to receive, and corresponding correct answers in the wanted format.
• Reinforcement learning with human feedback (RLHF), in which human users evaluate the accuracy or relevance of model outputs so that the model can improve itself. This can be as simple as having people type or talk back corrections to a chatbot or virtual assistant.

Generation, evaluation and more tuning  

Developers and users regularly assess the outputs of their generative AI apps, and further tune the model—even as often as once a week—for greater accuracy or relevance. In contrast, the foundation model itself is updated much less frequently, perhaps every year or 18 months.

Another option for improving a gen AI app's performance is retrieval augmented generation (RAG), a technique for extending the foundation model to use relevant sources outside of the training data to refine the parameters for greater accuracy or relevance.

AI offers numerous benefits across various industries and applications. Some of the most commonly cited benefits include:

• Automation of repetitive tasks.
• More and faster insight from data.
• Enhanced decision-making.
• Fewer human errors.
• 24x7 availability.
• Reduced physical risks.

Automation of repetitive tasks  

AI can automate routine, repetitive and often tedious tasks—including digital tasks such as data collection, entering and preprocessing, and physical tasks such as warehouse stock-picking and manufacturing processes. This automation frees to work on higher value, more creative work.

Enhanced decision-making  

Whether used for decision support or for fully automated decision-making, AI enables faster, more accurate predictions and reliable, data-driven decisions . Combined with automation, AI enables businesses to act on opportunities and respond to crises as they emerge, in real time and without human intervention.

Fewer human errors  

AI can reduce human errors in various ways, from guiding people through the proper steps of a process, to flagging potential errors before they occur, and fully automating processes without human intervention. This is especially important in industries such as healthcare where, for example, AI-guided surgical robotics enable consistent precision.

Machine learning algorithms can continually improve their accuracy and further reduce errors as they're exposed to more data and "learn" from experience.

Round-the-clock availability and consistency  

AI is always on, available around the clock, and delivers consistent performance every time. Tools such as AI chatbots or virtual assistants can lighten staffing demands for customer service or support. In other applications—such as materials processing or production lines—AI can help maintain consistent work quality and output levels when used to complete repetitive or tedious tasks.

Reduced physical risk  

By automating dangerous work—such as animal control, handling explosives, performing tasks in deep ocean water, high altitudes or in outer space—AI can eliminate the need to put human workers at risk of injury or worse. While they have yet to be perfected, self-driving cars and other vehicles offer the potential to reduce the risk of injury to passengers.

The real-world applications of AI are many. Here is just a small sampling of use cases across various industries to illustrate its potential:

Customer experience, service and support  

Companies can implement AI-powered chatbots and virtual assistants to handle customer inquiries, support tickets and more. These tools use natural language processing (NLP) and generative AI capabilities to understand and respond to customer questions about order status, product details and return policies.

Chatbots and virtual assistants enable always-on support, provide faster answers to frequently asked questions (FAQs), free human agents to focus on higher-level tasks, and give customers faster, more consistent service.

Fraud detection  

Machine learning and deep learning algorithms can analyze transaction patterns and flag anomalies, such as unusual spending or login locations, that indicate fraudulent transactions. This enables organizations to respond more quickly to potential fraud and limit its impact, giving themselves and customers greater peace of mind.

Personalized marketing  

Retailers, banks and other customer-facing companies can use AI to create personalized customer experiences and marketing campaigns that delight customers, improve sales and prevent churn. Based on data from customer purchase history and behaviors, deep learning algorithms can recommend products and services customers are likely to want, and even generate personalized copy and special offers for individual customers in real time.

Human resources and recruitment  

AI-driven recruitment platforms can streamline hiring by screening resumes, matching candidates with job descriptions, and even conducting preliminary interviews using video analysis. These and other tools can dramatically reduce the mountain of administrative paperwork associated with fielding a large volume of candidates. It can also reduce response times and time-to-hire, improving the experience for candidates whether they get the job or not.

Application development and modernization  

Generative AI code generation tools and automation tools can streamline repetitive coding tasks associated with application development, and accelerate the migration and modernization (reformatting and replatorming) of legacy applications at scale. These tools can speed up tasks, help ensure code consistency and reduce errors.

Predictive maintenance  

Machine learning models can analyze data from sensors, Internet of Things (IoT) devices and operational technology (OT) to forecast when maintenance will be required and predict equipment failures before they occur. AI-powered preventive maintenance helps prevent downtime and enables you to stay ahead of supply chain issues before they affect the bottom line.

Organizations are scrambling to take advantage of the latest AI technologies and capitalize on AI's many benefits. This rapid adoption is necessary, but adopting and maintaining AI workflows comes with challenges and risks. 

Data risks  

AI systems rely on data sets that might be vulnerable to data poisoning, data tampering, data bias or cyberattacks that can lead to data breaches. Organizations can mitigate these risks by protecting data integrity and implementing security and availability throughout the entire AI lifecycle, from development to training and deployment and postdeployment.

Model risks  

Threat actors can target AI models for theft, reverse engineering or unauthorized manipulation. Attackers might compromise a model’s integrity by tampering with its architecture, weights or parameters; the core components that determine a model’s behavior, accuracy and performance.

Operational risks  

Like all technologies, models are susceptible to operational risks such as model drift, bias and breakdowns in the governance structure. Left unaddressed, these risks can lead to system failures and cybersecurity vulnerabilities that threat actors can use.

Ethics and legal risks  

If organizations don’t prioritize safety and ethics when developing and deploying AI systems, they risk committing privacy violations and producing biased outcomes. For example, biased training data used for hiring decisions might reinforce gender or racial stereotypes and create AI models that favor certain demographic groups over others.  

AI ethics is a multidisciplinary field that studies how to optimize AI's beneficial impact while reducing risks and adverse outcomes. Principles of AI ethics are applied through a system of AI governance consisted of guardrails that help ensure that AI tools and systems remain safe and ethical.  

AI governance encompasses oversight mechanisms that address risks. An ethical approach to AI governance requires the involvement of a wide range of stakeholders, including developers, users, policymakers and ethicists, helping to ensure that AI-related systems are developed and used to align with society's values.

Here are common values associated with AI ethics and responsible AI :

As AI becomes more advanced, humans are challenged to comprehend and retrace how the algorithm came to a result. Explainable AI is a set of processes and methods that enables human users to interpret, comprehend and trust the results and output created by algorithms.

Although machine learning, by its very nature, is a form of statistical discrimination, the discrimination becomes objectionable when it places privileged groups at systematic advantage and certain unprivileged groups at systematic disadvantage, potentially causing varied harms. To encourage fairness, practitioners can try to minimize algorithmic bias across data collection and model design, and to build more diverse and inclusive teams.

Robust AI effectively handles exceptional conditions, such as abnormalities in input or malicious attacks, without causing unintentional harm. It is also built to withstand intentional and unintentional interference by protecting against exposed vulnerabilities.

Organizations should implement clear responsibilities and governance structures for the development, deployment and outcomes of AI systems. In addition, users should be able to see how an AI service works, evaluate its functionality, and comprehend its strengths and limitations. Increased transparency provides information for AI consumers to better understand how the AI model or service was created.

Many regulatory frameworks, including GDPR, mandate that organizations abide by certain privacy principles when processing personal information. It is crucial to be able to protect AI models that might contain personal information, control what data goes into the model in the first place, and to build adaptable systems that can adjust to changes in regulation and attitudes around AI ethics.

In order to contextualize the use of AI at various levels of complexity and sophistication, researchers have defined several types of AI that refer to its level of sophistication:

Weak AI : Also known as “narrow AI,” defines AI systems designed to perform a specific task or a set of tasks. Examples might include “smart” voice assistant apps, such as Amazon’s Alexa, Apple’s Siri, a social media chatbot or the autonomous vehicles promised by Tesla. 

Strong AI : Also known as “artificial general intelligence” (AGI) or “general AI,” possess the ability to understand, learn and apply knowledge across a wide range of tasks at a level equal to or surpassing human intelligence . This level of AI is currently theoretical and no known AI systems approach this level of sophistication. Researchers argue that if AGI is even possible, it requires major increases in computing power. Despite recent advances in AI development, self-aware AI systems of science fiction remain firmly in that realm. 

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 AI 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, and an ongoing concept within philosophy as it uses ideas around linguistics. 

1956 John McCarthy coins the term "artificial intelligence" at the first-ever AI conference at Dartmouth College. (McCarthy went 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 computer program.

1967 Frank Rosenblatt builds the Mark 1 Perceptron, the first computer based on a neural network that "learned" through 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 initiatives. 

1980 Neural networks, which use a backpropagation algorithm to train itself, became 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 based on rationality and thinking versus 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. By this time, the era of big data and cloud computing is underway, enabling organizations to manage ever-larger data estates, which will one day be used to train AI models. 

2011 IBM Watson® beats champions Ken Jennings and Brad Rutter at Jeopardy! Also, around this time, data science begins to emerge as a popular discipline.

2015 Baidu's Minwa supercomputer uses a special 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.

2022 A rise in large language models  or LLMs, such as OpenAI’s ChatGPT, creates 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 pretrained on large amounts of data.

2024 The latest AI trends point to a continuing AI renaissance. Multimodal models that can take multiple types of data as input are providing richer, more robust experiences. These models bring together computer vision image recognition and NLP speech recognition capabilities. Smaller models are also making strides in an age of diminishing returns with massive models with large parameter counts. 

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Home » Machine Learning/Artificial Intelligence

Problem Solving in Artificial Intelligence

In this tutorial, you will study about the problem-solving approach in Artificial Intelligence. You will learn how an agent tackles the problem and what steps are involved in solving it? By Monika Sharma Last updated : April 12, 2023

Problem Solving in AI

The aim of Artificial Intelligence is to develop a system which can solve the various problems on its own. But the challenge is, to understand a problem, a system must predict and convert the problem in its understandable form. That is, when an agent confronts a problem, it should first sense the problem, and this information that the agent gets through the sensing should be converted into machine-understandable form. For this, a particular sequence should be followed by the agent in which a particular format for the representation of agent's knowledge is defined and each time a problem arises, the agent can follow that particular approach to find a solution to it .

Types of Problems in AI

The types of problems in artificial intelligence are:

1. Ignorable Problems

In ignorable problems, the solution steps can be ignored.

2. Recoverable Problems

In recoverable problems, the solution steps which you have already implemented can be undone.

3. Irrecoverable Problems

In irrecoverable problems, the solution steps which you have already implemented cannot be undone.

Steps for Problem Solving in AI

The steps involved in solving a problem (by an agent based on Artificial Intelligence ) are:

1. Define a problem

Whenever a problem arises, the agent must first define a problem to an extent so that a particular state space can be represented through it. Analyzing and defining the problem is a very important step because if the problem is understood something which is different than the actual problem, then the whole problem-solving process by the agent is of no use.

2. Form the state space

Convert the problem statement into state space. A state space is the collection of all the possible valid states that an agent can reside in. But here, all the possible states are chosen which can exist according to the current problem. The rest are ignored while dealing with this particular problem.

3. Gather knowledge

collect and isolate the knowledge which is required by the agent to solve the current problem. This knowledge gathering is done from both the pre-embedded knowledge in the system and the knowledge it has gathered through the past experiences in solving the same type of problem earlier.

4. Planning-(Decide data structure and control strategy)

A problem may not always be an isolated problem. It may contain various related problems as well or some related areas where the decision made with respect to the current problem can affect those areas. So, a well-suited data structure and a relevant control strategy must be decided before attempting to solve the problem.

5. Applying and executing

After all the gathering of knowledge and planning the strategies, the knowledge should be applied and the plans should be executed in a systematic way so s to reach the goal state in the most efficient and fruitful manner.

Components to Formulate the Associated Problem

• Initial State
• Path Costing

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Artificial intelligence is the ability of a computer or computer-controlled robot to perform tasks that are commonly associated with the  intellectual processes characteristic of humans , such as the ability to reason. Although there are as yet no AIs that match full human flexibility over wider domains or in tasks requiring much everyday knowledge, some AIs perform specific tasks as well as humans. Learn more.

Are artificial intelligence and machine learning the same?

No, artificial intelligence and machine learning are not the same, but they are closely related. Machine learning is the method to train a computer to learn from its inputs but without explicit programming for every circumstance. Machine learning helps a computer to achieve artificial intelligence.

Recent News

artificial intelligence (AI) , the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. The term is frequently applied to the project of developing systems endowed with the intellectual processes characteristic of humans, such as the ability to reason, discover meaning, generalize, or learn from past experience. Since their development in the 1940s, digital computers have been programmed to carry out very complex tasks—such as discovering proofs for mathematical theorems or playing chess —with great proficiency. Despite continuing advances in computer processing speed and memory capacity, there are as yet no programs that can match full human flexibility over wider domains or in tasks requiring much everyday knowledge. On the other hand, some programs have attained the performance levels of human experts and professionals in executing certain specific tasks, so that artificial intelligence in this limited sense is found in applications as diverse as medical diagnosis , computer search engines , voice or handwriting recognition, and chatbots .

What is intelligence?

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All but the simplest human behavior is ascribed to intelligence, while even the most complicated insect behavior is usually not taken as an indication of intelligence. What is the difference? Consider the behavior of the digger wasp , Sphex ichneumoneus . When the female wasp returns to her burrow with food, she first deposits it on the threshold , checks for intruders inside her burrow, and only then, if the coast is clear, carries her food inside. The real nature of the wasp’s instinctual behavior is revealed if the food is moved a few inches away from the entrance to her burrow while she is inside: on emerging, she will repeat the whole procedure as often as the food is displaced. Intelligence—conspicuously absent in the case of the wasp—must include the ability to adapt to new circumstances.

(Read Ray Kurzweil’s Britannica essay on the future of “Nonbiological Man.”)

Psychologists generally characterize human intelligence not by just one trait but by the combination of many diverse abilities. Research in AI has focused chiefly on the following components of intelligence: learning, reasoning, problem solving , perception , and using language.

There are a number of different forms of learning as applied to artificial intelligence. The simplest is learning by trial and error. For example, a simple computer program for solving mate-in-one chess problems might try moves at random until mate is found. The program might then store the solution with the position so that, the next time the computer encountered the same position, it would recall the solution. This simple memorizing of individual items and procedures—known as rote learning—is relatively easy to implement on a computer. More challenging is the problem of implementing what is called generalization . Generalization involves applying past experience to analogous new situations. For example, a program that learns the past tense of regular English verbs by rote will not be able to produce the past tense of a word such as jump unless the program was previously presented with jumped , whereas a program that is able to generalize can learn the “add -ed ” rule for regular verbs ending in a consonant and so form the past tense of jump on the basis of experience with similar verbs.

(Read Yuval Noah Harari’s Britannica essay on the future of “Nonconscious Man.”)

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Title: artificial intelligence for science: the easy and hard problems.

Abstract: A suite of impressive scientific discoveries have been driven by recent advances in artificial intelligence. These almost all result from training flexible algorithms to solve difficult optimization problems specified in advance by teams of domain scientists and engineers with access to large amounts of data. Although extremely useful, this kind of problem solving only corresponds to one part of science - the "easy problem." The other part of scientific research is coming up with the problem itself - the "hard problem." Solving the hard problem is beyond the capacities of current algorithms for scientific discovery because it requires continual conceptual revision based on poorly defined constraints. We can make progress on understanding how humans solve the hard problem by studying the cognitive science of scientists, and then use the results to design new computational agents that automatically infer and update their scientific paradigms.

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AI accelerates problem-solving in complex scenarios

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While Santa Claus may have a magical sleigh and nine plucky reindeer to help him deliver presents, for companies like FedEx, the optimization problem of efficiently routing holiday packages is so complicated that they often employ specialized software to find a solution.

This software, called a mixed-integer linear programming (MILP) solver, splits a massive optimization problem into smaller pieces and uses generic algorithms to try and find the best solution. However, the solver could take hours — or even days — to arrive at a solution.

The process is so onerous that a company often must stop the software partway through, accepting a solution that is not ideal but the best that could be generated in a set amount of time.

Researchers from MIT and ETH Zurich used machine learning to speed things up.

They identified a key intermediate step in MILP solvers that has so many potential solutions it takes an enormous amount of time to unravel, which slows the entire process. The researchers employed a filtering technique to simplify this step, then used machine learning to find the optimal solution for a specific type of problem.

Their data-driven approach enables a company to use its own data to tailor a general-purpose MILP solver to the problem at hand.

This new technique sped up MILP solvers between 30 and 70 percent, without any drop in accuracy. One could use this method to obtain an optimal solution more quickly or, for especially complex problems, a better solution in a tractable amount of time.

This approach could be used wherever MILP solvers are employed, such as by ride-hailing services, electric grid operators, vaccination distributors, or any entity faced with a thorny resource-allocation problem.

“Sometimes, in a field like optimization, it is very common for folks to think of solutions as either purely machine learning or purely classical. I am a firm believer that we want to get the best of both worlds, and this is a really strong instantiation of that hybrid approach,” says senior author Cathy Wu, the Gilbert W. Winslow Career Development Assistant Professor in Civil and Environmental Engineering (CEE), and a member of a member of the Laboratory for Information and Decision Systems (LIDS) and the Institute for Data, Systems, and Society (IDSS).

Wu wrote the paper with co-lead authors Sirui Li, an IDSS graduate student, and Wenbin Ouyang, a CEE graduate student; as well as Max Paulus, a graduate student at ETH Zurich. The research will be presented at the Conference on Neural Information Processing Systems.

Tough to solve

MILP problems have an exponential number of potential solutions. For instance, say a traveling salesperson wants to find the shortest path to visit several cities and then return to their city of origin. If there are many cities which could be visited in any order, the number of potential solutions might be greater than the number of atoms in the universe.  

“These problems are called NP-hard, which means it is very unlikely there is an efficient algorithm to solve them. When the problem is big enough, we can only hope to achieve some suboptimal performance,” Wu explains.

An MILP solver employs an array of techniques and practical tricks that can achieve reasonable solutions in a tractable amount of time.

A typical solver uses a divide-and-conquer approach, first splitting the space of potential solutions into smaller pieces with a technique called branching. Then, the solver employs a technique called cutting to tighten up these smaller pieces so they can be searched faster.

Cutting uses a set of rules that tighten the search space without removing any feasible solutions. These rules are generated by a few dozen algorithms, known as separators, that have been created for different kinds of MILP problems. 

Wu and her team found that the process of identifying the ideal combination of separator algorithms to use is, in itself, a problem with an exponential number of solutions.

“Separator management is a core part of every solver, but this is an underappreciated aspect of the problem space. One of the contributions of this work is identifying the problem of separator management as a machine learning task to begin with,” she says.

Shrinking the solution space

She and her collaborators devised a filtering mechanism that reduces this separator search space from more than 130,000 potential combinations to around 20 options. This filtering mechanism draws on the principle of diminishing marginal returns, which says that the most benefit would come from a small set of algorithms, and adding additional algorithms won’t bring much extra improvement.

Then they use a machine-learning model to pick the best combination of algorithms from among the 20 remaining options.

This model is trained with a dataset specific to the user’s optimization problem, so it learns to choose algorithms that best suit the user’s particular task. Since a company like FedEx has solved routing problems many times before, using real data gleaned from past experience should lead to better solutions than starting from scratch each time.

The model’s iterative learning process, known as contextual bandits, a form of reinforcement learning, involves picking a potential solution, getting feedback on how good it was, and then trying again to find a better solution.

This data-driven approach accelerated MILP solvers between 30 and 70 percent without any drop in accuracy. Moreover, the speedup was similar when they applied it to a simpler, open-source solver and a more powerful, commercial solver.

In the future, Wu and her collaborators want to apply this approach to even more complex MILP problems, where gathering labeled data to train the model could be especially challenging. Perhaps they can train the model on a smaller dataset and then tweak it to tackle a much larger optimization problem, she says. The researchers are also interested in interpreting the learned model to better understand the effectiveness of different separator algorithms.

This research is supported, in part, by Mathworks, the National Science Foundation (NSF), the MIT Amazon Science Hub, and MIT’s Research Support Committee.

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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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How leaders are using ai as a problem-solving tool.

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Leaders face more complex decisions than ever before. For example, many must deliver new and better services for their communities while meeting sustainability and equity goals. At the same time, many need to find ways to operate and manage their budgets more efficiently. So how can these leaders make complex decisions and get them right in an increasingly tricky business landscape? The answer lies in harnessing technological tools like Artificial Intelligence (AI).

CHONGQING, CHINA - AUGUST 22: A visitor interacts with a NewGo AI robot during the Smart China Expo ... [+] 2022 on August 22, 2022 in Chongqing, China. The expo, held annually in Chongqing since 2018, is a platform to promote global exchanges of smart technologies and international cooperation in the smart industry. (Photo by Chen Chao/China News Service via Getty Images)

What is AI?

AI can help leaders in several different ways. It can be used to process and make decisions on large amounts of data more quickly and accurately. AI can also help identify patterns and trends that would otherwise be undetectable. This information can then be used to inform strategic decision-making, which is why AI is becoming an increasingly important tool for businesses and governments. A recent study by PwC found that 52% of companies accelerated their AI adoption plans in the last year. In addition, 86% of companies believe that AI will become a mainstream technology at their company imminently. As AI becomes more central in the business world, leaders need to understand how this technology works and how they can best integrate it into their operations.

At its simplest, AI is a computer system that can learn and work independently without human intervention. This ability makes AI a powerful tool. With AI, businesses and public agencies can automate tasks, get insights from data, and make decisions with little or no human input. Consequently, AI can be a valuable problem-solving tool for leaders across the private and public sectors, primarily through three methods.

1) Automation

One of AI’s most beneficial ways to help leaders is by automating tasks. This can free up time to focus on other essential things. For example, AI can help a city save valuable human resources by automating parking enforcement. In addition, this will help improve the accuracy of detecting violations and prevent costly mistakes. Automation can also help with things like appointment scheduling and fraud detection.

2) Insights from data

Another way AI can help leaders solve problems is by providing insights from data. With AI, businesses can gather large amounts of data and then use that data to make better decisions. For example, suppose a company is trying to decide which products to sell. In that case, AI can be used to gather data about customer buying habits and then use that data to make recommendations about which products to market.

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3) Simulations

Finally, AI can help leaders solve problems by allowing them to create simulations. With AI, organizations can test out different decision scenarios and see what the potential outcomes could be. This can help leaders make better decisions by examining the consequences of their choices. For example, a city might use AI to simulate different traffic patterns to see how a new road layout would impact congestion.

Choosing the Right Tools

Artificial intelligence and machine learning technologies can revolutionize how governments and businesses solve real-world problems,” said Chris Carson, CEO of Hayden AI, a global leader in intelligent enforcement technologies powered by artificial intelligence. His company addresses a problem once thought unsolvable in the transit world: managing illegal parking in bus lanes in a cost effective, scalable way.

Illegal parking in bus lanes is a major problem for cities and their transit agencies. Cars and trucks illegally parked in bus lanes force buses to merge into general traffic lanes, significantly slowing down transit service and making riders’ trips longer. That’s where a company like Hayden AI comes in. “Hayden AI uses artificial intelligence and machine learning algorithms to detect and process illegal parking in bus lanes in real-time so that cities can take proactive measures to address the problem ,” Carson observes.

Illegal parking in bus lanes is a huge problem for transit agencies. Hayden AI works with transit ... [+] agencies to fix this problem by installing its AI-powered camera systems on buses to conduct automated enforcement of parking violations in bus lanes

In this case, an AI-powered camera system is installed on each bus. The camera system uses computer vision to “watch” the street for illegal parking in the bus lane. When it detects a traffic violation, it sends the data back to the parking authority. This allows the parking authority to take action, such as sending a ticket to the offending vehicle’s owner.

The effectiveness of AI is entirely dependent on how you use it. As former Accenture chief technology strategist Bob Suh notes in the Harvard Business Review, problem-solving is best when combined with AI and human ingenuity. “In other words, it’s not about the technology itself; it’s about how you use the technology that matters. AI is not a panacea for all ills. Still, when incorporated into a company’s problem-solving repertoire, it can be an enormously powerful tool,” concludes Terence Mauri, founder of Hack Future Lab, a global think tank.

Split the Responsibility

Huda Khan, an academic researcher from the University of Aberdeen, believes that AI is critical for international companies’ success, especially in the era of disruption. Khan is calling international marketing academics’ research attention towards exploring such transformative approaches in terms of how these inform competitive business practices, as are international marketing academics Michael Christofi from the Cyprus University of Technology; Richard Lee from the University of South Australia; Viswanathan Kumar from St. John University; and Kelly Hewett from the University of Tennessee. “AI is very good at automating repetitive tasks, such as customer service or data entry. But it’s not so good at creative tasks, such as developing new products,” Khan says. “So, businesses need to think about what tasks they want to automate and what tasks they want to keep for humans.”

Khan believes that businesses need to split the responsibility between AI and humans. For example, Hayden AI’s system is highly accurate and only sends evidence packages of potential violations for human review. Once the data is sent, human analysis is still needed to make the final decision. But with much less work to do, government agencies can devote their employees to tasks that can’t be automated.

Backed up by efficient, effective data analysis, human problem-solving can be more innovative than ever. Like all business transitions, developing the best system for combining human and AI work might take some experimentation, but it can significantly impact future success. For example, if a company is trying to improve its customer service, it can use AI startup Satisfi’s natural language processing technology . This technology can understand a customer’s question and find the best answer from a company’s knowledge base. Likewise, if a company tries to increase sales, it can use AI startup Persado’s marketing language generation technology . This technology can be used to create more effective marketing campaigns by understanding what motivates customers and then generating language that is more likely to persuade them to make a purchase.

Look at the Big Picture

A technological solution can frequently improve performance in multiple areas simultaneously. For instance, Hayden AI’s automated enforcement system doesn’t just help speed up transit by keeping bus lanes clear for buses; it also increases data security by limiting how much data is kept for parking enforcement, which allows a city to increase the efficiency of its transportation while also protecting civil liberties.

This is the case with many technological solutions. For example, an e-commerce business might adopt a better data architecture to power a personalized recommendation option and benefit from improved SEO. As a leader, you can use your big-picture view of your company to identify critical secondary benefits of technologies. Once you have the technologies in use, you can also fine-tune your system to target your most important priorities at once.

In summary, AI technology is constantly evolving, becoming more accessible and affordable for businesses of all sizes. By harnessing the power of AI, leaders can make better decisions, improve efficiency, and drive innovation. However, it’s important to remember that AI is not a silver bullet. Therefore, organizations must use AI and humans to get the best results.

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Problem-Solving Methods in Artificial Intelligence

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• Haigh T (2024) How the AI Boom Went Bust Communications of the ACM 10.1145/3634901 67 :2 (22-26) Online publication date: 25-Jan-2024 https://dl.acm.org/doi/10.1145/3634901
• Zheng L Xing Y Yu L Zhang J (2023) Uncovering the Dark Side of Artificial Intelligence in Electronic Markets Journal of Organizational and End User Computing 10.4018/JOEUC.327278 35 :1 (1-25) Online publication date: 1-Aug-2023 https://dl.acm.org/doi/10.4018/JOEUC.327278
• Sleeman D Gilhooly K (2023) Groups of experts often differ in their decisions AI Magazine 10.1002/aaai.12135 44 :4 (555-567) Online publication date: 8-Dec-2023 https://dl.acm.org/doi/10.1002/aaai.12135
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Artificial Intelligence Engineer Job Description | 6 Facts

Landing an artificial intelligence engineer job requires a deep understanding of theory and practice. This comprehensive guide will equip you with the essential questions and insights to showcase your skills and land your dream role.

We’ll delve into the key areas interviewers focus on, from technical proficiency to problem-solving abilities. So, whether you’re a seasoned data scientist or just starting, let’s get you ready to impress!

Let’s dive in!

Key Takeaways for the ‘Artificial Intelligence Engineer Job Description’ article:

Technical proficiency is essential. Demonstrate a strong understanding of algorithms, data structures, programming languages, machine learning frameworks, and AI techniques.

• Problem-solving skills are crucial: Be prepared to tackle complex AI problems and demonstrate your ability to break them down into smaller, manageable components.
• Practical experience matters: Highlight your projects and accomplishments to showcase your hands-on experience with AI tools and techniques.
• Communication and collaboration are important: Demonstrate your ability to work effectively with teams and communicate complex ideas.
• Continuous learning is essential: Stay up-to-date with the latest advancements in artificial intelligence and be willing to learn new techniques and technologies.
• Practice makes perfect: Prepare for your interview by practising technical questions, solving AI-related problems, and reviewing key concepts.
• Be enthusiastic and passionate: Show your genuine interest in artificial intelligence and your commitment to the field.

Table of Contents

Artificial Intelligence Engineer job description: Guide to Interview Preparation

In the rapidly evolving landscape of technology, artificial intelligence (AI) has become one of the most transformative fields. As businesses and industries increasingly rely on AI to drive innovation, the demand for skilled professionals in this area has surged. 

Among these professionals, the role of an Artificial Intelligence (AI) Engineer stands out as one of the most critical and sought-after positions. AI engineers are at the forefront of designing, developing, and implementing AI models and algorithms that enable machines to mimic human intelligence, automate tasks, and make data-driven decisions.

Overview of the Artificial Intelligence Engineer Roles

An AI engineer is responsible for creating and deploying AI models, leveraging machine learning algorithms, and working with large datasets to solve complex problems. Their role often involves:

Developing AI Models : AI Engineers design, build, and optimize AI models that can perform specific tasks, such as image recognition, natural language processing, or predictive analytics.

Working with Big Data : They must be adept at handling vast amounts of data, cleaning and preprocessing it to ensure accuracy and relevance before feeding it into models.

Implementing Machine Learning Algorithms : AI engineers implement various machine learning algorithms, including supervised, unsupervised, and reinforcement learning, to create systems that can learn and improve over time.

Collaborating with Cross-Functional Teams : They work closely with data scientists, software developers, and business analysts to align AI solutions with business goals.

Optimizing Performance : To improve accuracy, efficiency, and scalability, continuous monitoring and fine-tuning of AI systems are essential.

The role demands a deep understanding of both the theoretical and practical aspects of AI and machine learning, combined with programming skills and domain knowledge.

The importance of strong technical skills and problem-solving abilities

To succeed as an AI engineer, having strong technical skills is non-negotiable. This includes proficiency in programming languages such as Python, R, or Java, and familiarity with AI frameworks like TensorFlow, PyTorch, or Scikit-learn. A solid grasp of mathematics, especially in areas like linear algebra, calculus, probability, and statistics, is also essential since these are foundational to understanding machine learning algorithms.

Moreover, problem-solving abilities are crucial. AI engineers are often tasked with solving unprecedented challenges, which require innovative thinking and the ability to approach problems from different angles. They need to be able to deconstruct complex problems into manageable components, apply logical reasoning, and utilize appropriate tools and methods to devise effective solutions. 

Strong problem-solving skills also involve staying updated with the latest advancements in AI and machine learning, as the field is constantly evolving. Being able to quickly learn and adapt to new technologies, methodologies, and industry trends can significantly enhance an AI engineer’s effectiveness and career prospects.

Purpose of the Blog: Providing a Comprehensive Guide to Interview Preparation

The journey to becoming a successful AI engineer continues after acquiring the necessary skills; it also involves the interview process. AI Engineer interviews are known for being challenging, as they test both the technical expertise and problem-solving capabilities of candidates. This blog aims to be a comprehensive guide to interview preparation for aspiring AI engineers.

In this guide, we will cover:

Key Areas of Focus: Highlighting the core technical concepts and skills that candidates should master, including programming, algorithms, data structures, machine learning, and AI fundamentals.

Common Interview Questions: Provide examples of typical questions asked during AI Engineer interviews, along with explanations and strategies for crafting strong answers.

Preparation Strategies : Offering tips on how to effectively prepare for the interview, including resources for learning, practice exercises, and mock interview techniques.

Soft Skills and Communication : Emphasizing the importance of clear communication, teamwork, and the ability to articulate complex ideas, which are often evaluated in AI Engineer interviews.

Real-World Scenarios and Case Studies: Introducing problem-solving exercises based on real-world scenarios to help candidates apply their knowledge in practical contexts.

The Actual Artificial Intelligence Engineer Job Description

An Artificial Intelligence (AI) Engineer is a highly skilled professional responsible for developing, implementing, and maintaining AI systems and applications. They work at the intersection of computer science, mathematics, and engineering to create intelligent machines that can perform tasks that typically require human intelligence.

Key Responsibilities:

• Research and Development: Conduct research on AI algorithms, techniques, and frameworks to develop innovative solutions.
• Algorithm Development: Design, implement, and optimize AI algorithms, such as machine learning, deep learning, and neural networks.
• Data Analysis: Collect, clean, and prepare large datasets for AI model training and evaluation.
• Model Training: Train AI models using various techniques and tools, ensuring optimal performance and accuracy.
• Integration: Integrate AI models into existing software systems and applications.
• Testing and Evaluation: Develop and execute comprehensive testing procedures to assess AI model performance and identify areas for improvement.
• Problem-Solving: Diagnose and troubleshoot AI-related issues and find effective solutions.
• Collaboration: Work closely with data scientists, software engineers, and other team members to deliver AI projects.
• Stay Updated: Keep up-to-date with the latest advancements in AI and machine learning technologies.

Required Skills and Qualifications:

• Strong foundation in computer science: Proficiency in programming languages like Python, R, or Java.
• Deep understanding of AI and machine learning: Expertise in algorithms, techniques, and frameworks (e.g., TensorFlow, PyTorch, Keras).
• Data analysis and visualization skills: Ability to work with large datasets and extract meaningful insights.
• Problem-solving and critical thinking: Capacity to analyze complex problems and develop effective solutions.
• Excellent communication skills: Ability to explain technical concepts to both technical and non-technical audiences.
• Experience with cloud platforms: Familiarity with cloud-based AI tools and infrastructure (e.g., AWS, GCP, Azure).
• Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence, or a related field.

Additional Skills (Preferred):

• Experience with natural language processing (NLP) or computer vision.
• Knowledge of specific AI applications (e.g., robotics, autonomous vehicles, healthcare).
• Strong mathematical background (e.g., linear algebra, statistics).
• Experience with research and publication in AI journals or conferences.

By combining technical expertise with a passion for innovation, AI Engineers play a crucial role in shaping the future of technology and driving advancements in various industries.

Key Areas of Focus: Technical Proficiency

Technical proficiency is a cornerstone of artificial intelligence engineering. A successful candidate must demonstrate a strong understanding of the following areas:

Algorithms and Data Structures

• Fundamental algorithms: grass-core algorithms like linear regression, decision trees, random forests, support vector machines, and neural networks.
• Data structures: Understand data structures like arrays, linked lists, stacks, queues, trees, and graphs, as they are essential for efficient data manipulation and algorithm implementation.

Programming Languages

• Python: Master Python, the de facto language for machine learning, along with libraries like NumPy, Pandas, Matplotlib, and Scikit-learn.
• R: Familiarize yourself with R, another popular language for statistical computing and data analysis.

Machine Learning Frameworks

• TensorFlow and PyTorch: Gain expertise in these deep learning frameworks for building and training complex neural networks.
• Scikit-learn: Understand this versatile machine learning library for implementing various algorithms and tasks.

Natural Language Processing (NLP) and Computer Vision

• NLP techniques: Familiarize yourself with techniques like tokenization, stemming, lemmatization, and feature engineering for text data.
• Computer vision concepts: Understand image processing techniques, feature extraction, and deep learning architectures for computer vision tasks.

Deep Learning Architectures

• Neural networks: Grasp the fundamentals of neural networks, including feedforward networks, recurrent neural networks (RNNs), and convolutional neural networks (CNNs).
• Deep learning frameworks: Be proficient in using TensorFlow or PyTorch to build and train deep learning models.

Artificial Intelligence Engineer Job Roles in Top Indian MNCs

Artificial intelligence (AI) is rapidly transforming various industries in India, leading to a surge in demand for skilled AI engineers. Top Indian multinational companies (MNCs) are actively seeking talented AI professionals to drive innovation and gain a competitive edge.

Here are two key AI engineer job roles commonly found in top Indian MNCs:

1. AI Research Scientist

Job Description:

AI Research Scientists are responsible for conducting cutting-edge research in the field of artificial intelligence. They develop new algorithms, techniques, and models to address complex problems and advance the state-of-the-art in AI.

• Conduct independent research on AI topics, such as machine learning, deep learning, natural language processing, or computer vision.
• Develop and implement novel AI algorithms and models.
• Collaborate with other researchers and engineers to solve challenging AI problems.
• Publish research papers in top-tier conferences and journals.
• Stay updated on the latest advancements in AI technology.

Required Skills:

• Strong foundation in mathematics, statistics, and computer science.
• Expertise in machine learning algorithms and frameworks.
• Proficiency in programming languages like Python or R.
• Excellent problem-solving and analytical skills.
• Ability to think critically and creatively.

2. AI Product Engineer

AI Product Engineers are responsible for developing and deploying AI-powered products and applications. They work closely with data scientists and software engineers to translate research into practical solutions.

• Design and develop AI-driven products or features.
• Integrate AI models into existing software systems.
• Optimize AI models for performance and scalability.
• Collaborate with product managers, designers, and developers to deliver high-quality products.
• Ensure the ethical and responsible use of AI.
• Strong programming skills in languages like Python or Java.
• Knowledge of machine learning frameworks and libraries.
• Understanding of software development methodologies.
• Ability to work in a fast-paced and agile environment.
• Excellent communication and teamwork skills.

Sample interview questions

Technical questions.

1. Explain the difference between supervised and unsupervised learning.

• Supervised learning: involves training a model on a labelled dataset, where the input features and corresponding target variables are provided. The model learns to predict output values for new, unseen data evolves and trains a model on an unlabeled dataset, where only the input features are provided. The model learns to identify patterns, structures, or relationships within the data without explicit guidance. Examples include clustering and dimensionality reduction.  

2. Describe the steps involved in building a deep learning model.

• Data collection and preprocessing: Gather relevant data, clean it to remove noise and inconsistencies, and preprocess it for modelling (e.g., normalization, feature engineering).
• Model architecture design: Choose a suitable deep learning architecture (e.g., a convolutional neural network for images, a recurrent neural network for sequences) based on the problem and data.
• Model training: Train the model using an appropriate optimization algorithm (e.g., gradient descent) on the preprocessed data.
• Hyperparameter tuning: Experiment with different hyperparameters (e.g., learning rate, batch size) to optimize model performance.
• Evaluation: Evaluate the model’s performance using relevant metrics (e.g., accuracy, precision, recall, F1-score) on a validation or test dataset.
• Deployment: Deploy the trained model into a production environment for real-world use.

3. What are some common evaluation metrics for natural language processing tasks?

• Accuracy: Overall proportion of correct predictions.
• Precision: Proportion of correct positive predictions out of all positive predictions.
• Recall: Proportion of correct positive predictions out of all actual positive instances.
• F1-score: Harmonic mean of precision and recall.
• BLEU score: Commonly used for machine translation tasks.
• ROUGE score: Used for text summarization tasks.
• Word error rate (WER): Used for speech recognition tasks.

Problem-Solving Questions

1. How would you approach a problem with imbalanced data in a classification task?

• Data augmentation: Increase the number of samples from the minority class using techniques like oversampling or undersampling.
• SMOTE (Synthetic Minority Oversampling Technique): Generate synthetic samples for the minority class to balance the dataset.
• Class weighting: Assign higher weights to the minority class during training to address the imbalance.
• Ensemble methods: Combine multiple models to improve performance on imbalanced datasets.

2. Describe a time when you had to debug a complex neural network architecture.

• Checking for errors in the data preprocessing pipeline.
• Ensuring the network architecture was appropriate for the task.
• Experimenting with different hyperparameters like learning rate and batch size.
• Using visualization techniques to understand the model’s behaviour.

Behavioural Questions

1. Describe a project where you had to collaborate with a team of data scientists and engineers.

• Example: I was part of a team tasked with developing a recommendation system for an e-commerce platform. I worked closely with data scientists to extract relevant features from the data, and with software engineers to integrate the model into the existing system. I played a key role in designing the model architecture and evaluating its performance.

2. How do you stay up-to-date with the latest advancements in artificial intelligence?

• Example: I regularly follow research papers, blogs, and online courses to stay updated on the latest trends and techniques in AI. I also attend conferences and workshops to connect with other professionals in the field and learn about new developments. Additionally, I contribute to open-source projects and collaborate with other researchers to stay engaged with the AI community.

Tips for Success

• Practice technical skills: revise algorithms, data structures, and programming concepts.
• Prepare for problem-solving questions: Practice solving AI-related problems.
• Highlight your projects and accomplishments: Showcase your practical experience and skills.
• Be enthusiastic and passionate: Demonstrate your interest in artificial intelligence and your commitment to continuous learning.

To excel in your artificial intelligence engineer interview and achieve the job role, it is crucial to demonstrate a strong foundation in technical skills and problem-solving abilities. Focus on mastering algorithms and data structures, programming languages, machine learning frameworks, natural language processing, and computer vision.

The best way to prepare for your interview is through consistent practice and preparation. Review key concepts, work on coding challenges, and practice answering common interview questions. Consider joining online communities or forums to connect with other AI enthusiasts and seek guidance.

With dedication and preparation, you can confidently tackle artificial intelligence engineer interviews and achieve your career goals. Remember, the field of artificial intelligence is constantly evolving, so staying curious and committed to continuous learning will be essential for your success. Best of luck in your interview endeavours!

13+ Yrs Experienced Career Counsellor & Skill Development Trainer | Educator | Digital & Content Strategist. Helping freshers and graduates make sound career choices through practical consultation. Guest faculty and Digital Marketing trainer working on building a skill development brand in Softspace Solutions. A passionate writer in core technical topics related to career growth.

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