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Artificial intelligence was founded as an academic discipline in 1956.[2] The field went through multiple cycles of optimism[3][4] followed by disappointment and loss of funding.[5][6] After 2012, deep learning surpassed all previous AI techniques,[7] leading to a vast increase in funding and interest.
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.[a]
Reasoning, problem-solving
Early researchers developed algorithms that imitated step-by-step reasoning that humans use when they solve puzzles or make logical deductions.[10] By the late 1980s and 1990s, methods were developed for dealing with uncertain or incomplete information, employing concepts from probability and economics.[11]
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.[12] 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.[13] Accurate and efficient reasoning is an unsolved problem.
Knowledge representation
An ontology represents knowledge as a set of concepts within a domain and the relationships between those concept
Knowledge representation and knowledge engineering[14] 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,[15] scene interpretation,[16] clinical decision support,[17] knowledge discovery (mining "interesting" and actionable inferences from large databases),[18] and other areas.[19]
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 domain of knowledge.[20] The most general ontologies are called upper ontologies, which attempt to provide a foundation for all other knowledge and act as mediators between domain ontologies that cover specific knowledge about a particular domain (field of interest or area of concern).
Knowledge bases need to represent things such as: objects, properties, categories and relations between objects; [21] situations, events, states and time;[22] causes and effects;[23] knowledge about knowledge (what we know about what other people know);[24]default reasoning (things that humans assume are true until they are told differently and will remain true even when other facts are changing);[25] 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;[26] the difficulty of knowledge acquisition 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).[13]
Planning and decision making
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Machine learning is the study of programs that can improve their performance on a given task automatically.[29] It has been a part of AI from the beginning.[b]
There are several kinds of machine learning. Unsupervised learning analyzes a stream of data and finds patterns and makes predictions without any other guidance.[32]Supervised learning requires a human to label the input data first, and comes in two main varieties: classification (where the program must learn to predict what category the input belongs in) and regression (where the program must deduce a numeric function based on numeric input).[33] In reinforcement learning the agent is rewarded for good responses and punished for bad ones. The agent learns to choose responses that are classified as "good".[34]Transfer learning is when the knowledge gained from one problem is applied to a new problem.[35]Deep learning uses artificial neural networks for all of these types of learning.
Kismet, a robot with rudimentary social skills[51]
Affective computing is an interdisciplinary umbrella that comprises systems that recognize, interpret, process or simulate human feeling, emotion and mood.[52] For example, some virtual assistants are programmed to speak conversationally or even to banter humorously; it makes them appear more sensitive to the emotional dynamics of human interaction, or to otherwise facilitate human–computer interaction. However, this tends to give naïve users an unrealistic conception of how intelligent existing computer agents actually are.[53] Moderate successes related to affective computing include textual sentiment analysis and, more recently, multimodal sentiment analysis, wherein AI classifies the affects displayed by a videotaped subject.[54]
General intelligence
A machine with artificial general intelligence should be able to solve a wide variety of problems with breadth and versatility similar to human intelligence.[8]
Tools
Search and optimization
AI can solve many problems by intelligently searching through many possible solutions.[55] 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.[56] 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.[57]
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.[60]
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.[61]
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).[62]
Neural networks and statistical classifiers (discussed below), also use a form of local search, where the "landscape" to be searched is formed by learning.
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).[67] A logical knowledge base also handles queries and assertions as a special case of inference.[68] An inference rule describes what is a valid step in a proof. The most general inference rule is resolution.[69] 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.[70] Inference performed this way is intractable except for short proofs in restricted domains. No efficient, powerful and general method has been discovered.[71]
Expectation-maximization clustering of Old Faithful eruption data starts from a random guess but then successfully converges on an accurate clustering of the two physically distinct modes of eruption.
Many problems in AI (including in reasoning, planning, learning, perception, and robotics) require the agent to operate with incomplete or uncertain information. AI researchers have devised a number of tools to solve these problems using methods from probability theory and economics.[73]
Probabilistic algorithms can also be used for filtering, prediction, smoothing and finding explanations for streams of data, helping perception systems to analyze processes that occur over time (e.g., hidden Markov models or Kalman filters).[80]
The simplest AI applications can be divided into two types: classifiers (e.g. "if shiny then diamond"), on one hand, and controllers (e.g. "if diamond then pick up"), on the other hand. Classifiers[85] are functions that use pattern matching to determine the closest match. They can be fine-tuned based on chosen examples using supervised learning. Each pattern (also called an "observation") is labeled with a certain predefined class. All the observations combined with their class labels are known as a data set. When a new observation is received, that observation is classified based on previous experience.[33]
There many kinds of classifiers in use. The decision tree is the simplest and most widely used symbolic machine learning algorithm.[86]K-nearest neighbor algorithm was the most widely used analogical AI until the mid-1990s, and Kernel methods such as the support vector machine (SVM) displaced k-nearest neighbor in the 1990s.[87] The naive Bayes classifier is reportedly the "most widely used learner"[88] at Google, due in part to its scalability.[89]Neural networks are also used as classifiers.[90]
Artificial neural networks
A neural network is an interconnected group of nodes, akin to the vast network of neurons in the human brain.
Artificial neural networks[90] were inspired by the design of the human brain: a simple "neuron" N accepts input from other neurons, each of which, when activated (or "fired"), casts a weighted "vote" for or against whether neuron N should itself activate. In practice, the "neurons" are a list of numbers, the weights are matrixes, and learning is performed by linear algebra operations on the matrixes and vectors. Neural networks perform a type of mathematical optimization -- they perform stochastic gradient descent on a multi-dimensional topology that is created by training the network.[f]
Neural networks learn to model complex relationships between inputs and outputs and find patterns in data. In theory, a neural network can learn any function.[92] The most common training technique is the backpropagation algorithm.[93] The earliest learning technique for neural networks was Hebbian learning ("fire together, wire together").[94]
Representing images on multiple layers of abstraction in deep learning[100]
Deep learning[98] uses several layers of neurons between the network's inputs and outputs. The multiple layers can progressively extract higher-level features from the raw input. For example, in image processing, lower layers may identify edges, while higher layers may identify the concepts relevant to a human such as digits or letters or faces.[101]
In late 2010s, graphics processing units (GPUs) that were increasingly designed with AI-specific enhancements and used with specialized TensorFlow software, had replaced previously used central processing unit (CPUs) as the dominant means for large-scale (commercial and academic) machine learning models' training.[103]
Historically, specialized languages, such as Lisp, Prolog, and others, had been used.
For this 2018 project of the artist Joseph Ayerle the AI had to learn the typical patterns in the colors and brushstrokes of Renaissance painter Raphael. The portrait shows the face of the actress Ornella Muti, "painted" by AI in the style of Raphael.
There are also thousands of successful AI applications used to solve specific problems for specific industries or institutions. In a 2017 survey, one in five companies reported they had incorporated "AI" in some offerings or processes.[108] A few examples are energy storage,[109] medical diagnosis, military logistics, applications that predict the result of judicial decisions,[110]foreign policy,[111] or supply chain management.
AI programs can become biased after learning from real-world data.[127] It may not be introduced by the system designers but learned by the program, and thus the programmers may not be aware that the bias exists.[128] Bias can be inadvertently introduced by the way training data is selected and by the way a model is deployed.[129][127] It can also emerge from correlations: AI is used to classify individuals into groups and then make predictions assuming that the individual will resemble other members of the group. In some cases, this assumption may be unfair.[130] An example of this is COMPAS, a commercial program widely used by U.S. courts to assess the likelihood of a defendant becoming a recidivist. ProPublica claims that the COMPAS-assigned recidivism risk level of black defendants is far more likely to be overestimated than that of white defendants, despite the fact that the program was not told the races of the defendants.[131]
Health equity issues may also be exacerbated when many-to-many mapping is done without taking steps to ensure equity for populations at risk for bias. At this time equity-focused tools and regulations are not in place to ensure equity application representation and usage.[132] Other examples where algorithmic bias can lead to unfair outcomes are when AI is used for credit rating, CV screening, hiring and applications for public housing.[127]
At its 2022 Conference on Fairness, Accountability, and Transparency (ACM FAccT 2022) the Association for Computing Machinery, in Seoul, South Korea, presented and published findings recommending that until AI and robotics systems are demonstrated to be free of bias mistakes, they are unsafe and the use of self-learning neural networks trained on vast, unregulated sources of flawed internet data should be curtailed.[133]
Lack of transparency
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Terrorists, criminals and rogue states may use other forms of weaponized AI such as advanced digital warfare and lethal autonomous weapons. By 2015, over fifty countries were reported to be researching battlefield robots.[135]
Machine-learning AI is also able to design tens of thousands of toxic molecules in a matter of hours.[136]
Economists have frequently highlighted the risks of redundancies from AI, and speculated about unemployment if there is no adequate social policy for full employment.[137]
In the past, technology has tended to increase rather than reduce total employment, but economists acknowledge that "we're in uncharted territory" with AI.[138] A survey of economists showed disagreement about whether the increasing use of robots and AI will cause a substantial increase in long-term unemployment, but they generally agree that it could be a net benefit if productivity gains are redistributed.[139] Risk estimates vary; for example, in the 2010s Michael Osborne and Carl Benedikt Frey estimated 47% of U.S. jobs are at "high risk" of potential automation, while an OECD report classified only 9% of U.S. jobs as "high risk".[h][141] The methodology of speculating about future employment levels has been criticised as lacking evidential foundation, and for implying that technology (rather than social policy) creates unemployment (as opposed to redundancies).[137]
Unlike previous waves of automation, many middle-class jobs may be eliminated by artificial intelligence; The Economist stated in 2015 that "the worry that AI could do to white-collar jobs what steam power did to blue-collar ones during the Industrial Revolution" is "worth taking seriously".[142] Jobs at extreme risk range from paralegals to fast food cooks, while job demand is likely to increase for care-related professions ranging from personal healthcare to the clergy.[143]
In order to leverage as large a dataset as is feasible, generative AI is often trained on unlicensed copyrighted works, including in domains such as images or computer code; the output is then used under a rationale of "fair use". Experts disagree about how well, and under what circumstances, this rationale will hold up in courts of law; relevant factors may include "the purpose and character of the use of the copyrighted work" and "the effect upon the potential market for the copyrighted work".[144]
Friendly AI are machines that have been designed from the beginning to minimize risks and to make choices that benefit humans. Eliezer Yudkowsky, who coined the term, argues that developing friendly AI should be a higher research priority: it may require a large investment and it must be completed before AI becomes an existential risk.[145]
Machines with intelligence have the potential to use their intelligence to make ethical decisions. The field of machine ethics provides machines with ethical principles and procedures for resolving ethical dilemmas.[146] The field of machine ethics is also called computational morality,[146] and was founded at an AAAI symposium in 2005.[147]
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.[150] The regulatory and policy landscape for AI is an emerging issue in jurisdictions globally.[151] 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.[152][153] Between 2016 and 2020, more than 30 countries adopted dedicated strategies for AI.[154] 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.[154] 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.[154]Henry Kissinger, Eric Schmidt, and Daniel Huttenlocher published a joint statement in November 2021 calling for a government commission to regulate AI.[155] In 2023, OpenAI leaders published recommendations for the governance of superintelligence, which they believe may happen in less than 10 years.[156]
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".[152] A 2023 Reuters/Ipsos poll found that 61% of Americans agree, and 22% disagree, that AI poses risks to humanity.[157] 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".[158][159]
The study of mechanical or "formal" reasoning began with philosophers and mathematicians in antiquity. The study of logic led directly to Alan Turing's theory of computation, which suggested that a machine, by shuffling symbols as simple as "0" and "1", could simulate both mathematical deduction and formal reasoning, which is known as the Church–Turing thesis.[160] This, along with at the time new discoveries in cybernetics and information theory, led researchers to consider the possibility of building an "electronic brain".[i][162] The first paper later recognized as "AI" was McCullouch and Pitts design for Turing-complete "artificial neurons" in 1943.[163]
The field of AI research was founded at a workshop at Dartmouth College in 1956.[j][2] The attendees became the leaders of AI research in the 1960s.[k] They and their students produced programs that the press described as "astonishing":[l] computers were learning checkers strategies, solving word problems in algebra, proving logical theorems and speaking English.[m][3]
By the middle of the 1960s, research in the U.S. was heavily funded by the Department of Defense[167] and laboratories had been established around the world.[168]Herbert Simon predicted, "machines will be capable, within twenty years, of doing any work a man can do".[169]Marvin Minsky agreed, writing, "within a generation ... the problem of creating 'artificial intelligence' will substantially be solved".[170]
They had, however, underestimated the difficulty of the problem.[n] Both the U.S. and British governments cut off exploratory research in response to the criticism of Sir James Lighthill[172] and ongoing pressure from the US Congress to fund more productive projects. Minsky's and Papert's book Perceptrons was understood as proving that artificial neural networks approach would never be useful for solving real-world tasks, thus discrediting the approach altogether.[173] The "AI winter", a period when obtaining funding for AI projects was difficult, followed.[5]
In the early 1980s, AI research was revived by the commercial success of expert systems,[174] a form of AI program that simulated the knowledge and analytical skills of human experts. By 1985, the market for AI had reached over a billion dollars. At the same time, Japan's fifth generation computer project inspired the U.S. and British governments to restore funding for academic research.[4] However, beginning with the collapse of the Lisp Machine market in 1987, AI once again fell into disrepute, and a second, longer-lasting winter began.[6]
Many researchers began to doubt that the current practices would be able to imitate all the processes of human cognition, especially perception, robotics, learning and pattern recognition.[175] A number of researchers began to look into "sub-symbolic" approaches.[176]Robotics researchers, such as Rodney Brooks, rejected "representation" in general and focussed directly on engineering machines that move and survive.[o]. Judea Pearl, Lofti Zadeh and others developed methods that handled incomplete and uncertain information by making reasonable guesses rather than precise logic.[73][181] But the most important development was the revival of "connectionism", including neural network research, by Geoffrey Hinton and others.[182] In 1990, Yann LeCun successfully showed that convolutional neural networks can recognize handwritten digits, the first of many successful applications of neural networks.[183]
AI gradually restored its reputation in the late 1990s and early 21st century by exploiting formal mathematical methods and by finding specific solutions to specific problems. This "narrow" and "formal" focus allowed researchers to produce verifiable results and collaborate with other fields (such as statistics, economics and mathematics).[184] By 2000, solutions developed by AI researchers were being widely used, although in the 1990s they were rarely described as "artificial intelligence".[185]
The machine learning achievements made it safe for media and businesses to refer to them as "AI" again. [q] The number of software projects that use machine learning at Google increased from a "sporadic usage" in 2012 to more than 2,700 projects in 2015.[188]
In a 2017 survey, one in five companies reported they had incorporated "AI" in some offerings or processes".[190] The amount of machine learning research (measured by total publications) increased by 50% in the years 2015–2019.[154] According to 'AI Impacts', about $50 billion annually was invested in "AI" around 2022 in the US alone and about 20% of new US Computer Science PhD graduates have specialized in "AI";[191] about 800,000 "AI"-related US job openings existed in 2022.[192]
In 2016, issues of fairness and the misuse of technology were catapulted into center stage at machine learning conferences, publications vastly increased, funding became available, and many researchers re-focussed their careers on these issues. The alignment problem became a serious field of academic study.[193]
Alan Turing wrote in 1950 "I propose to consider the question 'can machines think'?"[194] He advised changing the question from whether a machine "thinks", to "whether or not it is possible for machinery to show intelligent behaviour".[194] He devised the Turing test, which measures the ability of a machine to simulate human conversation.[195] Since we can only observe the behavior of the machine, it does not matter if it is "actually" thinking or literally has a "mind". Turing notes that we can not determine these things about other people[r] but "it is usual to have a polite convention that everyone thinks"[196]
Russell and Norvig agree with Turing that AI must be defined in terms of "acting" and not "thinking".[197] However, they are critical that the test compares machines to people. "Aeronautical engineering texts," they wrote, "do not define the goal of their field as making 'machines that fly so exactly like pigeons that they can fool other pigeons.'"[198] AI founder John McCarthy agreed, writing that "Artificial intelligence is not, by definition, simulation of human intelligence".[199]
McCarthy defines intelligence as "the computational part of the ability to achieve goals in the world."[200] Another AI founder, Marvin Minsky similarly defines it as "the ability to solve hard problems".[201] These definitions view intelligence in terms of well-defined problems with well-defined solutions, where both the difficulty of the problem and the performance of the program are direct measures of the "intelligence" of the machine—and no other philosophical discussion is required, or may not even be possible.
A definition that has also been adopted by Google[202][better source needed] – major practitionary in the field of AI. This definition stipulated the ability of systems to synthesize information as the manifestation of intelligence, similar to the way it is defined in biological intelligence.
Evaluating approaches to AI
No established unifying theory or paradigm has guided AI research for most of its history.[s] The unprecedented success of statistical machine learning in the 2010s eclipsed all other approaches (so much so that some sources, especially in the business world, use the term "artificial intelligence" to mean "machine learning with neural networks"). This approach is mostly sub-symbolic, neat, soft and narrow (see below). Critics argue that these questions may have to be revisited by future generations of AI researchers.
Symbolic AI and its limits
Symbolic AI (or "GOFAI")[204] simulated the high-level conscious reasoning that people use when they solve puzzles, express legal reasoning and do mathematics. They were highly successful at "intelligent" tasks such as algebra or IQ tests. In the 1960s, Newell and Simon proposed the physical symbol systems hypothesis: "A physical symbol system has the necessary and sufficient means of general intelligent action."[205]
However, the symbolic approach failed on many tasks that humans solve easily, such as learning, recognizing an object or commonsense reasoning. Moravec's paradox is the discovery that high-level "intelligent" tasks were easy for AI, but low level "instinctive" tasks were extremely difficult.[206] Philosopher Hubert Dreyfus had argued since the 1960s that human expertise depends on unconscious instinct rather than conscious symbol manipulation, and on having a "feel" for the situation, rather than explicit symbolic knowledge.[207] Although his arguments had been ridiculed and ignored when they were first presented, eventually, AI research came to agree.[t][13]
The issue is not resolved: sub-symbolic reasoning can make many of the same inscrutable mistakes that human intuition does, such as algorithmic bias. Critics such as Noam Chomsky argue continuing research into symbolic AI will still be necessary to attain general intelligence,[209][210] in part because sub-symbolic AI is a move away from explainable AI: it can be difficult or impossible to understand why a modern statistical AI program made a particular decision. The emerging field of neuro-symbolic artificial intelligence attempts to bridge the two approaches.
"Neats" hope that intelligent behavior is described using simple, elegant principles (such as logic, optimization, or neural networks). "Scruffies" expect that it necessarily requires solving a large number of unrelated problems. Neats defend their programs with theoretical rigor, scruffies rely only on incremental testing to see if they work. This issue was actively discussed in the 70s and 80s,[211] but eventually was seen as irrelevant. In the 1990s mathematical methods and solid scientific standards became the norm, a transition that Russell and Norvig termed in 2003 as "the victory of the neats".[212] However in 2020 they wrote "deep learning may represent a resurgence of the scruffies".[175] Modern AI has elements of both.
Finding a provably correct or optimal solution is intractable for many important problems.[12] Soft computing is a set of techniques, including genetic algorithms, fuzzy logic and neural networks, that are tolerant of imprecision, uncertainty, partial truth and approximation. Soft computing was introduced in the late 80s and most successful AI programs in the 21st century are examples of soft computing with neural networks.
AI researchers are divided as to whether to pursue the goals of artificial general intelligence and superintelligence (general AI) directly or to solve as many specific problems as possible (narrow AI) in hopes these solutions will lead indirectly to the field's long-term goals.[213][214] General intelligence is difficult to define and difficult to measure, and modern AI has had more verifiable successes by focusing on specific problems with specific solutions. The experimental sub-field of artificial general intelligence studies this area exclusively.
The philosophy of mind does not know whether a machine can have a mind, consciousness and mental states, in the same sense that human beings do. This issue considers the internal experiences of the machine, rather than its external behavior. Mainstream AI research considers this issue irrelevant because it does not affect the goals of the field: to build machines that can solve problems using intelligence. Russell and Norvig add that "[t]he additional project of making a machine conscious in exactly the way humans are is not one that we are equipped to take on."[215] However, the question has become central to the philosophy of mind. It is also typically the central question at issue in artificial intelligence in fiction.
David Chalmers identified two problems in understanding the mind, which he named the "hard" and "easy" problems of consciousness.[216] The easy problem is understanding how the brain processes signals, makes plans and controls behavior. The hard problem is explaining how this feels or why it should feel like anything at all, assuming we are right in thinking that it truly does feel like something (Dennett's consciousness illusionism says this is an illusion). Human information processing is easy to explain, however, human subjective experience is difficult to explain. For example, it is easy to imagine a color-blind person who has learned to identify which objects in their field of view are red, but it is not clear what would be required for the person to know what red looks like.[217]
Computationalism is the position in the philosophy of mind that the human mind is an information processing system and that thinking is a form of computing. Computationalism argues that the relationship between mind and body is similar or identical to the relationship between software and hardware and thus may be a solution to the mind–body problem. This philosophical position was inspired by the work of AI researchers and cognitive scientists in the 1960s and was originally proposed by philosophers Jerry Fodor and Hilary Putnam.[218]
Philosopher John Searle characterized this position as "strong AI": "The appropriately programmed computer with the right inputs and outputs would thereby have a mind in exactly the same sense human beings have minds."[u] Searle counters this assertion with his Chinese room argument, which attempts to show that, even if a machine perfectly simulates human behavior, there is still no reason to suppose it also has a mind.[222]
If a machine has a mind and subjective experience, then it may also have sentience (the ability to feel), and if so it could also suffer; it has been argued that this could entitle it to certain rights.[223] Any hypothetical robot rights would lie on a spectrum with animal rights and human rights.[224] This issue has been considered in fiction for centuries,[225] and is now being considered by, for example, California's Institute for the Future; however, critics argue that the discussion is premature.[226]
Future
Superintelligence and the singularity
A superintelligence is a hypothetical agent that would possess intelligence far surpassing that of the brightest and most gifted human mind.[214]
If research into artificial general intelligence produced sufficiently intelligent software, it might be able to reprogram and improve itself. The improved software would be even better at improving itself, leading to what I. J. Good called an "intelligence explosion" and Vernor Vinge called a "singularity".[227] However, most technologies (such as transportation) do not improve exponentially indefinitely, but rather follow an S-curve, slowing when they reach the physical limits of what the technology can do.[228]
It has been argued AI will become so powerful that humanity may irreversibly lose control of it. This could, as the physicist Stephen Hawking puts it, "spell the end of the human race".[229] According to the philosopher Nick Bostrom, for almost any goals that a sufficiently intelligent AI may have, it is instrumentally incentivized to protect itself from being shut down and to acquire more resources, as intermediary steps to better achieve these goals. Sentience or emotions are then not required for an advanced AI to be dangerous. In order to be safe for humanity, a superintelligence would have to be genuinely aligned with humanity's morality and values so that it is "fundamentally on our side".[230] The political scientist Charles T. Rubin argued that "any sufficiently advanced benevolence may be indistinguishable from malevolence" and warned that we should not be confident that intelligent machines will by default treat us favorably.[231]
The opinions amongst experts and industry insiders are mixed, with sizable fractions both concerned and unconcerned by risk from eventual superintelligent AI.[232] Personalities such as Stephen Hawking, Bill Gates, Elon Musk have expressed concern about existential risk from AI.[233] In 2023, AI pioneers including Geoffrey Hinton, Yoshua Bengio, Demis Hassabis, and Sam Altman issued the joint statement that "Mitigating the risk of extinction from AI should be a global priority alongside other societal-scale risks such as pandemics and nuclear war"; some others such as Yann LeCun consider this to be unfounded.[234]Mark Zuckerberg said that AI will "unlock a huge amount of positive things", including curing diseases and improving the safety of self-driving cars.[235] Some experts have argued that the risks are too distant in the future to warrant research, or that humans will be valuable from the perspective of a superintelligent machine.[236]Rodney Brooks, in particular, said in 2014 that "malevolent" AI is still centuries away.[v]
The word "robot" itself was coined by Karel Čapek in his 1921 play R.U.R., the title standing for "Rossum's Universal Robots".
Thought-capable artificial beings have appeared as storytelling devices since antiquity,[240] and have been a persistent theme in science fiction.[241]
Isaac Asimov introduced the Three Laws of Robotics in many books and stories, most notably the "Multivac" series about a super-intelligent computer of the same name. Asimov's laws are often brought up during lay discussions of machine ethics;[243] while almost all artificial intelligence researchers are familiar with Asimov's laws through popular culture, they generally consider the laws useless for many reasons, one of which is their ambiguity.[244]
AI safety – Research area on making AI safe and beneficial
AI alignment – Conformance to the intended objective
Artificial intelligence in healthcare – Machine-learning algorithms and software in the analysis, presentation, and comprehension of complex medical and health care data
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