Artificial Intelligence
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 the development of the digital computer in the 1940s, it has been demonstrated that computers can be programmed to carry out very complex tasks—such as discovering proofs for mathematical theorems or playing chess—with great proficiency. Still, 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 performing 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.
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Goals
The general problem of simulating (or creating) intelligence has been broken down into sub-problems. These consist of particular traits or capabilities that researchers expect an intelligent system to display. The traits described below have received the most attention and cover the scope of AI research.
Reasoning, problem-solving
Early researchers developed algorithms that imitated step-by-step reasoning that humans use when they solve puzzles or make logical deductions. By the late 1980s and 1990s, methods were developed for dealing with uncertain or incomplete information, employing concepts from probability and economics.
Many of these algorithms are insufficient for solving large reasoning problems because they experience a “combinatorial explosion”: they became exponentially slower as the problems grew larger. Even humans rarely use the step-by-step deduction that early AI research could model. They solve most of their problems using fast, intuitive judgments. Accurate and efficient reasoning is an unsolved problem.
Knowledge representation
Knowledge representation and knowledge engineering allow AI programs to answer questions intelligently and make deductions about real-world facts. Formal knowledge representations are used in content-based indexing and retrieval, scene interpretation, clinical decision support, knowledge discovery (mining “interesting” and actionable inferences from large databases), and other areas.
A knowledge base is a body of knowledge represented in a form that can be used by a program. An ontology is the set of objects, relations, concepts, and properties used by a particular domain of knowledge. Knowledge bases need to represent things such as: objects, properties, categories and relations between objects; situations, events, states and time; causes and effects; knowledge about knowledge (what we know about what other people know); default reasoning (things that humans assume are true until they are told differently and will remain true even when other facts are changing); and many other aspects and domains of knowledge.
Among the most difficult problems in KR are: the breadth of commonsense knowledge (the set of atomic facts that the average person knows is enormous); and the sub-symbolic form of most commonsense knowledge (much of what people know is not represented as “facts” or “statements” that they could express verbally).
Knowledge acquisition is the difficult problem of obtaining knowledge for AI applications. Modern AI gathers knowledge by “scraping” the internet (including Wikipedia). The knowledge itself was collected by the volunteers and professionals who published the information (who may or may not have agreed to provide their work to AI companies).
This “crowd sourced” technique does not guarantee that the knowledge is correct or reliable. The knowledge of Large Language Models (such as ChatGPT) is highly unreliable — it generates misinformation and falsehoods (known as “hallucinations”). Providing accurate knowledge for these modern AI applications is an unsolved problem.
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Artificial Intelligence
on the globe 2021 – 2024
($B) venture funding in AI
0
($B) corporate investment in AI
0
($B) overall investment in AI
0
Tools
ToolsAI research uses a wide variety of tools to accomplish the goals above.
Search and optimization
AI can solve many problems by intelligently searching through many possible solutions. There are two very different kinds of search used in AI: state space search and local search.
State space search
State space search searches through a tree of possible states to try to find a goal state. For example, Planning algorithms search through trees of goals and subgoals, attempting to find a path to a target goal, a process called means-ends analysis.
Simple exhaustive searches are rarely sufficient for most real-world problems: the search space (the number of places to search) quickly grows to astronomical numbers. The result is a search that is too slow or never completes. “Heuristics” or “rules of thumb” can help to prioritize choices that are more likely to reach a goal.
Adversarial search is used for game-playing programs, such as chess or Go. It searches through a tree of possible moves and counter-moves, looking for a winning position.
Local search
Local search uses mathematical optimization to find a numeric solution to a problem. It begins with some form of a guess and then refines the guess incrementally until no more refinements can be made. These algorithms can be visualized as blind hill climbing: we begin the search at a random point on the landscape, and then, by jumps or steps, we keep moving our guess uphill, until we reach the top. This process is called stochastic gradient descent.
Evolutionary computation uses a form of optimization search. For example, they may begin with a population of organisms (the guesses) and then allow them to mutate and recombine, selecting only the fittest to survive each generation (refining the guesses).
Distributed search processes can coordinate via swarm intelligence algorithms. Two popular swarm algorithms used in search are particle swarm optimization (inspired by bird flocking) and ant colony optimization (inspired by ant trails).
Neural networks and statistical classifiers (discussed below), also use a form of local search, where the “landscape” to be searched is formed by learning.
Logic
Formal Logic is used for reasoning and knowledge representation. Formal logic comes in two main forms: propositional logic (which operates on statements that are true or false and uses logical connectives such as “and”, “or”, “not” and “implies”) and predicate logic (which also operates on objects, predicates and relations and uses quantifiers such as “Every X is a Y” and “There are some Xs that are Ys”).
Logical inference (or deduction) is the process of proving a new statement (conclusion) from other statements that are already known to be true (the premises). A logical knowledge base also handles queries and assertions as a special case of inference. An inference rule describes what is a valid step in a proof.
The most general inference rule is resolution. Inference can be reduced to performing a search to find a path that leads from premises to conclusions, where each step is the application of an inference rule. Inference performed this way is intractable except for short proofs in restricted domains. No efficient, powerful and general method has been discovered.
Fuzzy logic assigns a “degree of truth” between 0 and 1 and handles uncertainty and probabilistic situations. Non-monotonic logics are designed to handle default reasoning. Other specialized versions of logic have been developed to describe many complex domains (see knowledge representation above).
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Applications
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Ethics
Artificial Intelligence (AI), like any powerful technology, has potential benefits and potential risks. AI may be able to advance science and find solutions for serious problems: Demis Hassabis of Deep Mind hopes to “solve intelligence, and then use that to solve everything else”. However, as the use of AI has become widespread, several unintended consequences and risks have been identified.
Anyone looking to use machine learning as part of real-world, in-production systems needs to factor ethics into their AI training processes and strive to avoid bias. This is especially true when using AI algorithms that are inherently unexplainable in deep learning.
Risks and harm
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Ethical machines and alignment
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Regulation
The regulation of artificial intelligence is the development of public sector policies and laws for promoting and regulating artificial intelligence (AI); it is therefore related to the broader regulation of algorithms. The regulatory and policy landscape for AI is an emerging issue in jurisdictions globally.
According to AI Index at Stanford, the annual number of AI-related laws passed in the 127 survey countries jumped from one passed in 2016 to 37 passed in 2022 alone.
Between 2016 and 2020, more than 30 countries adopted dedicated strategies for AI. Most EU member states had released national AI strategies, as had Canada, China, India, Japan, Mauritius, the Russian Federation, Saudi Arabia, United Arab Emirates, US and Vietnam. Others were in the process of elaborating their own AI strategy, including Bangladesh, Malaysia and Tunisia.
The Global Partnership on Artificial Intelligence was launched in June 2020, stating a need for AI to be developed in accordance with human rights and democratic values, to ensure public confidence and trust in the technology. Henry Kissinger, Eric Schmidt, and Daniel Huttenlocher published a joint statement in November 2021 calling for a government commission to regulate AI. In 2023, OpenAI leaders published recommendations for the governance of superintelligence, which they believe may happen in less than 10 years.
In a 2022 Ipsos survey, attitudes towards AI varied greatly by country; 78% of Chinese citizens, but only 35% of Americans, agreed that “products and services using AI have more benefits than drawbacks”. A 2023 Reuters/Ipsos poll found that 61% of Americans agree, and 22% disagree, that AI poses risks to humanity.
In a 2023 Fox News poll, 35% of Americans thought it “very important”, and an additional 41% thought it “somewhat important”, for the federal government to regulate AI, versus 13% responding “not very important” and 8% responding “not at all important”.
In November 2023, a global AI safety summit was held in Bletchley Park to discuss the near and far term risks of AI and the possibility of mandatory and voluntary regulatory frameworks.
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