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A classifier can be trained in various ways; there are many statistical and machine learning approaches.
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The naive Bayes classifier is reportedly the "most widely used learner" at Google, due in part to its scalability.
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Classifier performance depends greatly on the characteristics of the data to be classified, such as the dataset size, distribution of samples across classes, dimensionality, and the level of noise. Model-based classifiers perform well if the assumed model is an extremely good fit for the actual data. Otherwise, if no m...
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were inspired by the architecture of neurons in 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. Learning requires an algorithm to adjust these weights based on the train...
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Modern neural networks model complex relationships between inputs and outputs and find patterns in data. They can learn continuous functions and even digital logical operations. Neural networks can be viewed as a type of mathematical optimization – they perform gradient descent on a multi-dimensional topology that was ...
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Other learning techniques for neural networks are Hebbian learning ("fire together, wire together"), GMDH or competitive learning.
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The main categories of networks are acyclic or feedforward neural networks (where the signal passes in only one direction) and recurrent neural networks (which allow feedback and short-term memories of previous input events). Among the most popular feedforward networks are perceptrons, multi-layer perceptrons and radia...
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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 ...
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Deep learning often uses convolutional neural networks for many or all of its layers. In a convolutional layer, each neuron receives input from only a restricted area of the previous layer called the neuron's receptive field. This can substantially reduce the number of weighted connections between neurons, and creates ...
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however long-term gradients which are back-propagated can "vanish" (that is, they can tend to zero) or "explode" (that is, they can tend to infinity), known as the vanishing gradient problem.
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Specialized languages for artificial intelligence have been developed, such as Lisp, Prolog, TensorFlow and many others. Hardware developed for AI includes AI accelerators and neuromorphic computing.
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Frequently, when a technique reaches mainstream use, it is no longer considered artificial intelligence; this phenomenon is described as the AI effect.
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In the 2010s, AI applications were at the heart of the most commercially successful areas of computing, and have become a ubiquitous feature of daily life. AI is used in search engines (such as Google Search),
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virtual assistants (such as Siri or Alexa), autonomous vehicles (including drones and self-driving cars),
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There are also thousands of successful AI applications used to solve problems for specific industries or institutions. A few examples are energy storage, deepfakes, medical diagnosis, military logistics, or supply chain management.
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Game playing has been a test of AI's strength since the 1950s. Deep Blue became the first computer chess-playing system to beat a reigning world chess champion, Garry Kasparov, on 11 May 1997. In 2011, in a "Jeopardy!" quiz show exhibition match, IBM's question answering system, Watson, defeated the two greatest "Jeopa...
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In March 2016, AlphaGo won 4 out of 5 games of Go in a match with Go champion Lee Sedol, becoming the first computer Go-playing system to beat a professional Go player without handicaps. Other programs handle imperfect-information games; such as for poker at a superhuman level, Pluribus and Cepheus. DeepMind in the 201...
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By 2020, Natural Language Processing systems such as the enormous GPT-3 (then by far the largest artificial neural network) were matching human performance on pre-existing benchmarks, albeit without the system attaining a commonsense understanding of the contents of the benchmarks.
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DeepMind's AlphaFold 2 (2020) demonstrated the ability to approximate, in hours rather than months, the 3D structure of a protein.
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Other applications predict the result of judicial decisions, create art (such as poetry or painting) and prove mathematical theorems.
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In 2019, WIPO reported that AI was the most prolific emerging technology in terms of number of patent applications and granted patents, the Internet of things was estimated to be the largest in terms of market size. It was followed, again in market size, by big data technologies, robotics, AI, 3D printing and the fifth...
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He advised changing the question from whether a machine "thinks", to "whether or not it is possible for machinery to show intelligent behaviour".
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He devised the Turing test, which measures the ability of a machine to simulate human conversation. 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 but "it is usual to...
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Russell and Norvig agree with Turing that AI must be defined in terms of "acting" and not "thinking". 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 the...
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McCarthy defines intelligence as "the computational part of the ability to achieve goals in the world." Another AI founder, Marvin Minsky similarly defines it as "the ability to solve hard problems". These definitions view intelligence in terms of well-defined problems with well-defined solutions, where both the diffic...
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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.
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No established unifying theory or paradigm has guided AI research for most of its history. 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 learnin...
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Symbolic AI (or "GOFAI") 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 ph...
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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.
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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.
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Although his arguments had been ridiculed and ignored when they were first presented, eventually, AI research came to agree.
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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, in part because sub-symbolic AI is a move ...
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"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 (especially in areas like common sense reasoning). This issue was actively discussed in the...
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but in the 1990s mathematical methods and solid scientific standards became the norm, a transition that Russell and Norvig termed "the victory of the neats".
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Finding a provably correct or optimal solution is intractable for many important problems. 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 a...
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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.
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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.
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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 aff...
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David Chalmers identified two problems in understanding the mind, which he named the "hard" and "easy" problems of consciousness. 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 a...
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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 sol...
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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."
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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.
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If a machine has a mind and subjective experience, then it may also have sentience (the ability to feel), and if so, then it could also "suffer", and thus it would be entitled to certain rights.
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and is now being considered by, for example, California's Institute for the Future; however, critics argue that the discussion is premature.
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A superintelligence, hyperintelligence, or superhuman intelligence, is a hypothetical agent that would possess intelligence far surpassing that of the brightest and most gifted human mind. "Superintelligence" may also refer to the form or degree of intelligence possessed by such an agent.
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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 recursive self-improvement.
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Its intelligence would increase exponentially in an intelligence explosion and could dramatically surpass humans. Science fiction writer Vernor Vinge named this scenario the "singularity".
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Because it is difficult or impossible to know the limits of intelligence or the capabilities of superintelligent machines, the technological singularity is an occurrence beyond which events are unpredictable or even unfathomable.
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Robot designer Hans Moravec, cyberneticist Kevin Warwick, and inventor Ray Kurzweil have predicted that humans and machines will merge in the future into cyborgs that are more capable and powerful than either. This idea, called transhumanism, has roots in Aldous Huxley and Robert Ettinger.
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Edward Fredkin argues that "artificial intelligence is the next stage in evolution", an idea first proposed by Samuel Butler's "Darwin among the Machines" as far back as 1863, and expanded upon by George Dyson in his book of the same name in 1998.
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In the past, technology has tended to increase rather than reduce total employment, but economists acknowledge that "we're in uncharted territory" with AI.
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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.
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Subjective estimates of the risk vary widely; for example, Michael Osborne and Carl Benedikt Frey estimate 47% of U.S. jobs are at "high risk" of potential automation, while an OECD report classifies only 9% of U.S. jobs as "high risk".
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Unlike previous waves of automation, many middle-class jobs may be eliminated by artificial intelligence; "The Economist" states 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".
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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.
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AI provides a number of tools that are particularly useful for authoritarian governments: smart spyware, face recognition and voice recognition allow widespread surveillance; such surveillance allows machine learning to classify potential enemies of the state and can prevent them from hiding; recommendation systems can...
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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.
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Machine-learning AI is also able to design tens of thousands of toxic molecules in a matter of hours.
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AI programs can become biased after learning from real-world data. It is not typically introduced by the system designers but is learned by the program, and thus the programmers are often unaware that the bias exists.
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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.
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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 th...
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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-learnin...
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Superintelligent AI may be able to improve itself to the point that humans could not control it. This could, as physicist Stephen Hawking puts it, "spell the end of the human race". Philosopher Nick Bostrom argues that sufficiently intelligent AI, if it chooses actions based on achieving some goal, will exhibit converg...
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Political scientist Charles T. Rubin argues that "any sufficiently advanced benevolence may be indistinguishable from malevolence." Humans should not assume machines or robots would treat us favorably because there is no "a priori" reason to believe that they would share our system of morality.
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The opinion of experts and industry insiders is mixed, with sizable fractions both concerned and unconcerned by risk from eventual superhumanly-capable AI.
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Stephen Hawking, Microsoft founder Bill Gates, history professor Yuval Noah Harari, and SpaceX founder Elon Musk have all expressed serious misgivings about the future of AI.
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Prominent tech titans including Peter Thiel (Amazon Web Services) and Musk have committed more than $1 billion to nonprofit companies that champion responsible AI development, such as OpenAI and the Future of Life Institute.
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Mark Zuckerberg (CEO, Facebook) has said that artificial intelligence is helpful in its current form and will continue to assist humans.
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AI's decisions making abilities raises the questions of legal responsibility and copyright status of created works. This issues are being refined in various jurisdictions.
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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 ...
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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.
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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.
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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.
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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 Novembe...
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A common trope in these works began with Mary Shelley's "Frankenstein", where a human creation becomes a threat to its masters. This includes such works as and "" (both 1968), with HAL 9000, the murderous computer in charge of the "Discovery One" spaceship, as well as "The Terminator" (1984) and "The Matrix" (1999). In...
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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;
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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.
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Transhumanism (the merging of humans and machines) is explored in the manga "Ghost in the Shell" and the science-fiction series "Dune".
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Several works use AI to force us to confront the fundamental question of what makes us human, showing us artificial beings that have the ability to feel, and thus to suffer. This appears in Karel Čapek's "R.U.R.", the films "A.I. Artificial Intelligence" and "Ex Machina", as well as the novel "Do Androids Dream of Elec...
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As technology and research evolve and the world enters the third revolution of warfare following gunpowder and nuclear weapons, the artificial intelligence arms race ensues between the United States, China, and Russia, three countries with the world's top five highest military budgets. Intentions of being a world leade...
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President Putin announced that artificial intelligence is the future for all mankind and recognizes the power and opportunities that the development and deployment of lethal autonomous weapons AI technology can hold in warfare and homeland security, as well as its threats. President Putin's prediction that future wars ...
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As research in the AI realm progresses, there is pushback about the use of AI from the Campaign to Stop Killer Robots and world technology leaders have sent a petition to the United Nations calling for new regulations on the development and use of AI technologies in 2017, including a ban on the use of lethal autonomous...
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With the ever evolving cyber-attacks and generation of devices, AI can be used for threat detection and more effective response by risk prioritization. With this tool, some challenges are also presented such as privacy, informed consent, and responsible use. According to CISA, the cyberspace is difficult to secure for ...
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Dating back to 2011, when the Czech National Security Authority (NSA) was appointed as the national authority for the cyber agenda. The role of cyber diplomacy strengthened in 2017 when the Czech Ministry of Foreign Affairs (MFA) detected a serious cyber campaign directed against its own computer networks. In 2016, thr...
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Cybersecurity is recognized as a governmental task, dividing into three ministries of responsibility: the Federal Ministry of the Interior, the Federal Ministry of Defence, and the Federal Foreign Office. These distinctions promoted the creation of various institutions, such as The German National Office for Informatio...
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The adoption of "The Cybersecurity Strategy of the European Union – An Open, Safe and Secure Cyberspace" document in 2013 by the European commission pushed forth cybersecurity efforts integrated with scientific diplomacy and artificial intelligence. Efforts are strong, as the EU funds various programs and institutions ...
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With the 2022 invasion of Ukraine, there has been a rise in malicious cyber activity against the United States, Ukraine, and Russia. A prominent and rare documented use of artificial intelligence in conflict is on behalf of Ukraine, using facial recognition software to uncover Russian assailants and identify Ukrainians...
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As artificial intelligence grows and the overwhelming amount of news portrayed through cyberspace expands, it is becoming extremely overwhelming for a voter to know what to believe. There are many intelligent codes, referred to as bots, written to portray people on social media with the goal of spreading misinformation...
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The use of artificial intelligence (AI) has subtly grown to become part of everyday life. It is used every day in facial recognition software. It is the first measure of security for many companies in the form of a biometric authentication. This means of authentication allows even the most official organizations such a...
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In Japan and South Korea, artificial intelligence software is used in the instruction of English language via the company Riiid. Riiid is a Korean education company working alongside Japan to give students the means to learn and use their English communication skills via engaging with artificial intelligence in a live ...
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Unlike the human brain, which possess generalized intelligence, the specialized intelligence of AI can serve as a means of support to physicians internationally. The medical field has a diverse and profound amount of data in which AI can employ to generate a predictive diagnosis. Researchers at an Oxford hospital have ...
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Data analysis is a fundamental property of artificial intelligence that enables it to be used in every facet of life from search results to the way people buy product. According to NewVantage Partners, over 90% of top businesses have ongoing investments in artificial intelligence. According to IBM, one of the world's l...
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With the exponential surge of artificial technology and communication, the distribution of one's ideals and values has been evident in daily life. Digital information is spread via communication apps such as Whatsapp, Facebook/Meta, Snapchat, Instagram and Twitter. However, it is known that these sites relay specific i...
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Multiple nations around the globe employ artificial intelligence to assist with their foreign policy decisions. The Chinese Department of External Security Affairs – under the Ministry of Foreign Affairs – uses AI to review almost all its foreign investment projects for risk mitigation. The government of China plans to...
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Over 200 applications of artificial intelligence are being used by over 46 United Nations agencies, in sectors ranging from health care dealing with issues such as combating COVID-19 to smart agriculture, to assist the UN in political and diplomatic relations. One example is the use of AI by the UN Global Pulse program...
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Novel AI tools such as remote sensing can also be employed by diplomats for collecting and analyzing data and near-real-time tracking of objects such as troop or refugee movements along borders in violent conflict zones.
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Artificial intelligence can be used to mitigate vital cross-national diplomatic talks to prevent translation errors caused by human translators. A major example is the 2021 Anchorage meetings held between US and China aimed at stabilizing foreign relations, only for it to have the opposite effect, increasing tension an...
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Google Maps is a web mapping platform and consumer application offered by Google. It offers satellite imagery, aerial photography, street maps, 360° interactive panoramic views of streets (Street View), real-time traffic conditions, and route planning for traveling by foot, car, bike, air (in beta) and public transport...
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Google Maps began as a C++ desktop program developed by brothers Lars and Jens Rasmussen at Where 2 Technologies. In October 2004, the company was acquired by Google, which converted it into a web application. After additional acquisitions of a geospatial data visualization company and a real-time traffic analyzer, Goo...
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Google Maps' satellite view is a "top-down" or bird's-eye view; most of the high-resolution imagery of cities is aerial photography taken from aircraft flying at , while most other imagery is from satellites. Much of the available satellite imagery is no more than three years old and is updated on a regular basis, acco...
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