Cryptocurrency News: What You Need to Know This Week

Cryptocurrency News
Cryptocurrencies traded sideways since our last report on cryptos. However, I noticed something interesting when playing around with Yahoo! Finance’s cryptocurrency screener: There are profitable pockets in this market.

Incidentally, Yahoo’s screener is far superior to the one on CoinMarketCap, so if you’re looking to compare digital assets, I highly recommend it.

But let’s get back to my epiphany.

In the last month, at one point or another, most crypto assets on our favorites list saw double-digit increases. It’s true that each upswing was followed by a hard crash, but investors who rode the trend would have made a.

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Cryptocurrency News: What You Need to Know This Week

Cryptocurrency News: Vitalik Buterin Doesn’t Care About Bitcoin ETFs

Cryptocurrency News
While headline numbers look devastating this week, investors might take some solace in knowing that cryptocurrencies found their bottom at roughly $189.8 billion in market cap—that was the low point. Since then, investors put more than $20.0 billion back into the market.

During the rout, Ethereum broke below $300.00 and XRP fell below $0.30, marking yearly lows for both tokens. The same was true down the list of the top 100 biggest cryptos.

Altcoins took the brunt of the hit. BTC Dominance, which reveals how tightly investment is concentrated in Bitcoin, rose from 42.62% to 53.27% in just one month, showing that investors either fled altcoins at higher.

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Cryptocurrency News: Vitalik Buterin Doesn’t Care About Bitcoin ETFs

Cryptocurrency News: New Exchanges Could Boost Crypto Liquidity

Cryptocurrency News
Even though the cryptocurrency news was upbeat in recent days, the market tumbled after the U.S. Securities and Exchange Commission (SEC) rejected calls for a Bitcoin (BTC) exchange-traded fund (ETF).

That news came as a blow to investors, many of whom believe the ETF would open the cryptocurrency industry up to pension funds and other institutional investors. This would create a massive tailwind for cryptos, they say.

So it only follows that a rejection of the Bitcoin ETF should send cryptos tumbling, correct? Well, maybe you can follow that logic. To me, it seems like a dramatic overreaction.

I understand that legitimizing cryptos is important. But.

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Cryptocurrency News: New Exchanges Could Boost Crypto Liquidity

Bitcoin Rise: Is the Recent Bitcoin Price Surge a Sign of Things to Come or Another Misdirection?

What You Need to Know About the Bitcoin Price Rise
It wasn’t that long ago that Bitcoin (BTC) dominated headlines for its massive growth, with many cryptocurrency millionaires being made. The Bitcoin price surged ever upward and many people thought the gravy train would never stop running—until it did.

Prices crashed, investors abandoned the space, and lots of people lost money. Cut to today and we’re seeing another big Bitcoin price surge; is this time any different?

I’m of a mind that investors ought to think twice before jumping back in on Bitcoin.

Bitcoin made waves when it once again crested above $5,000. Considering that it started 2019 around $3,700,.

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Bitcoin Rise: Is the Recent Bitcoin Price Surge a Sign of Things to Come or Another Misdirection?

Superintelligence – Wikipedia

A superintelligence is a hypothetical agent that possesses intelligence far surpassing that of the brightest and most gifted human minds. “Superintelligence” may also refer to a property of problem-solving systems (e.g., superintelligent language translators or engineering assistants) whether or not these high-level intellectual competencies are embodied in agents that act in the world. A superintelligence may or may not be created by an intelligence explosion and associated with a technological singularity.

University of Oxford philosopher Nick Bostrom defines superintelligence as “any intellect that greatly exceeds the cognitive performance of humans in virtually all domains of interest”. The program Fritz falls short of superintelligence even though it is much better than humans at chess because Fritz cannot outperform humans in other tasks. Following Hutter and Legg, Bostrom treats superintelligence as general dominance at goal-oriented behavior, leaving open whether an artificial or human superintelligence would possess capacities such as intentionality (cf. the Chinese room argument) or first-person consciousness (cf. the hard problem of consciousness).

Technological researchers disagree about how likely present-day human intelligence is to be surpassed. Some argue that advances in artificial intelligence (AI) will probably result in general reasoning systems that lack human cognitive limitations. Others believe that humans will evolve or directly modify their biology so as to achieve radically greater intelligence. A number of futures studies scenarios combine elements from both of these possibilities, suggesting that humans are likely to interface with computers, or upload their minds to computers, in a way that enables substantial intelligence amplification.

Some researchers believe that superintelligence will likely follow shortly after the development of artificial general intelligence. The first generally intelligent machines are likely to immediately hold an enormous advantage in at least some forms of mental capability, including the capacity of perfect recall, a vastly superior knowledge base, and the ability to multitask in ways not possible to biological entities. This may give them the opportunity toeither as a single being or as a new speciesbecome much more powerful than humans, and to displace them.

A number of scientists and forecasters argue for prioritizing early research into the possible benefits and risks of human and machine cognitive enhancement, because of the potential social impact of such technologies.

Philosopher David Chalmers argues that artificial general intelligence is a very likely path to superhuman intelligence. Chalmers breaks this claim down into an argument that AI can achieve equivalence to human intelligence, that it can be extended to surpass human intelligence, and that it can be further amplified to completely dominate humans across arbitrary tasks.

Concerning human-level equivalence, Chalmers argues that the human brain is a mechanical system, and therefore ought to be emulatable by synthetic materials. He also notes that human intelligence was able to biologically evolve, making it more likely that human engineers will be able to recapitulate this invention. Evolutionary algorithms in particular should be able to produce human-level AI. Concerning intelligence extension and amplification, Chalmers argues that new AI technologies can generally be improved on, and that this is particularly likely when the invention can assist in designing new technologies.

If research into strong AI produced sufficiently intelligent software, it would be able to reprogram and improve itself a feature called “recursive self-improvement”. It would then be even better at improving itself, and could continue doing so in a rapidly increasing cycle, leading to a superintelligence. This scenario is known as an intelligence explosion. Such an intelligence would not have the limitations of human intellect, and may be able to invent or discover almost anything.

Computer components already greatly surpass human performance in speed. Bostrom writes, “Biological neurons operate at a peak speed of about 200 Hz, a full seven orders of magnitude slower than a modern microprocessor (~2 GHz).” Moreover, neurons transmit spike signals across axons at no greater than 120 m/s, “whereas existing electronic processing cores can communicate optically at the speed of light”. Thus, the simplest example of a superintelligence may be an emulated human mind that’s run on much faster hardware than the brain. A human-like reasoner that could think millions of times faster than current humans would have a dominant advantage in most reasoning tasks, particularly ones that require haste or long strings of actions.

Another advantage of computers is modularity, that is, their size or computational capacity can be increased. A non-human (or modified human) brain could become much larger than a present-day human brain, like many supercomputers. Bostrom also raises the possibility of collective superintelligence: a large enough number of separate reasoning systems, if they communicated and coordinated well enough, could act in aggregate with far greater capabilities than any sub-agent.

There may also be ways to qualitatively improve on human reasoning and decision-making. Humans appear to differ from chimpanzees in the ways we think more than we differ in brain size or speed.[9] Humans outperform non-human animals in large part because of new or enhanced reasoning capacities, such as long-term planning and language use. (See evolution of human intelligence and primate cognition.) If there are other possible improvements to reasoning that would have a similarly large impact, this makes it likelier that an agent can be built that outperforms humans in the same fashion humans outperform chimpanzees.

All of the above advantages hold for artificial superintelligence, but it is not clear how many hold for biological superintelligence. Physiological constraints limit the speed and size of biological brains in many ways that are inapplicable to machine intelligence. As such, writers on superintelligence have devoted much more attention to superintelligent AI scenarios.

Carl Sagan suggested that the advent of Caesarean sections and in vitro fertilization may permit humans to evolve larger heads, resulting in improvements via natural selection in the heritable component of human intelligence.[12] By contrast, Gerald Crabtree has argued that decreased selection pressure is resulting in a slow, centuries-long reduction in human intelligence, and that this process instead is likely to continue into the future. There is no scientific consensus concerning either possibility, and in both cases the biological change would be slow, especially relative to rates of cultural change.

Selective breeding, nootropics, NSI-189, MAO-I’s, epigenetic modulation, and genetic engineering could improve human intelligence more rapidly. Bostrom writes that if we come to understand the genetic component of intelligence, pre-implantation genetic diagnosis could be used to select for embryos with as much as 4 points of IQ gain (if one embryo is selected out of two), or with larger gains (e.g., up to 24.3 IQ points gained if one embryo is selected out of 1000). If this process is iterated over many generations, the gains could be an order of magnitude greater. Bostrom suggests that deriving new gametes from embryonic stem cells could be used to iterate the selection process very rapidly. A well-organized society of high-intelligence humans of this sort could potentially achieve collective superintelligence.

Alternatively, collective intelligence might be constructible by better organizing humans at present levels of individual intelligence. A number of writers have suggested that human civilization, or some aspect of it (e.g., the Internet, or the economy), is coming to function like a global brain with capacities far exceeding its component agents. If this systems-based superintelligence relies heavily on artificial components, however, it may qualify as an AI rather than as a biology-based superorganism.

A final method of intelligence amplification would be to directly enhance individual humans, as opposed to enhancing their social or reproductive dynamics. This could be achieved using nootropics, somatic gene therapy, or braincomputer interfaces. However, Bostrom expresses skepticism about the scalability of the first two approaches, and argues that designing a superintelligent cyborg interface is an AI-complete problem.

Most surveyed AI researchers expect machines to eventually be able to rival humans in intelligence, though there is little consensus on when this will likely happen. At the 2006 AI@50 conference, 18% of attendees reported expecting machines to be able “to simulate learning and every other aspect of human intelligence” by 2056; 41% of attendees expected this to happen sometime after 2056; and 41% expected machines to never reach that milestone.[17]

In a survey of the 100 most cited authors in AI (as of May 2013, according to Microsoft academic search), the median year by which respondents expected machines “that can carry out most human professions at least as well as a typical human” (assuming no global catastrophe occurs) with 10% confidence is 2024 (mean 2034, st. dev. 33 years), with 50% confidence is 2050 (mean 2072, st. dev. 110 years), and with 90% confidence is 2070 (mean 2168, st. dev. 342 years). These estimates exclude the 1.2% of respondents who said no year would ever reach 10% confidence, the 4.1% who said ‘never’ for 50% confidence, and the 16.5% who said ‘never’ for 90% confidence. Respondents assigned a median 50% probability to the possibility that machine superintelligence will be invented within 30 years of the invention of approximately human-level machine intelligence.

Bostrom expressed concern about what values a superintelligence should be designed to have. He compared several proposals:

Responding to Bostrom, Santos-Lang raised concern that developers may attempt to start with a single kind of superintelligence.

Learning computers that rapidly become superintelligent may take unforeseen actions or robots might out-compete humanity (one potential technological singularity scenario).[21] Researchers have argued that, by way of an “intelligence explosion” sometime over the next century, a self-improving AI could become so powerful as to be unstoppable by humans.[22]

Concerning human extinction scenarios, Bostrom (2002) identifies superintelligence as a possible cause:

When we create the first superintelligent entity, we might make a mistake and give it goals that lead it to annihilate humankind, assuming its enormous intellectual advantage gives it the power to do so. For example, we could mistakenly elevate a subgoal to the status of a supergoal. We tell it to solve a mathematical problem, and it complies by turning all the matter in the solar system into a giant calculating device, in the process killing the person who asked the question.

In theory, since a superintelligent AI would be able to bring about almost any possible outcome and to thwart any attempt to prevent the implementation of its goals, many uncontrolled, unintended consequences could arise. It could kill off all other agents, persuade them to change their behavior, or block their attempts at interference.[23]

Eliezer Yudkowsky explains: “The AI does not hate you, nor does it love you, but you are made out of atoms which it can use for something else.”[24]

This presents the AI control problem: how to build a superintelligent agent that will aid its creators, while avoiding inadvertently building a superintelligence that will harm its creators. The danger of not designing control right “the first time”, is that a misprogrammed superintelligence might rationally decide to “take over the world” and refuse to permit its programmers to modify it once it has been activated. Potential design strategies include “capability control” (preventing an AI from being able to pursue harmful plans), and “motivational control” (building an AI that wants to be helpful).

Bill Hibbard advocates for public education about superintelligence and public control over the development of superintelligence.

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Superintelligence – Wikipedia

Nick Bostrom – Wikipedia

Nick Bostrom (; Swedish: Niklas Bostrm [bustrm]; born 10 March 1973)[3] is a Swedish philosopher at the University of Oxford known for his work on existential risk, the anthropic principle, human enhancement ethics, superintelligence risks, and the reversal test. In 2011, he founded the Oxford Martin Programme on the Impacts of Future Technology,[4] and is the founding director of the Future of Humanity Institute[5] at Oxford University.

Bostrom is the author of over 200 publications,[6] including Superintelligence: Paths, Dangers, Strategies (2014), a New York Times bestseller[7] and Anthropic Bias: Observation Selection Effects in Science and Philosophy (2002).[8] In 2009 and 2015, he was included in Foreign Policy’s Top 100 Global Thinkers list.[9][10] Bostrom believes there are potentially great benefits from Artificial General Intelligence, but warns it might very quickly transform into a superintelligence that would deliberately extinguish humanity out of precautionary self-preservation or some unfathomable motive, making solving the problems of control beforehand an absolute priority. His book on superintelligence was recommended by both Elon Musk and Bill Gates. However, Bostrom has expressed frustration that the reaction to its thesis typically falls into two camps, one calling his recommendations absurdly alarmist because creation of superintelligence is unfeasible, and the other deeming them futile because superintelligence would be uncontrollable. Bostrom notes that both these lines of reasoning converge on inaction rather than trying to solve the control problem while there may still be time.[11][12][not in citation given]

Born as Niklas Bostrm in 1973[13] in Helsingborg, Sweden,[6] he disliked school at a young age, and ended up spending his last year of high school learning from home. He sought to educate himself in a wide variety of disciplines, including anthropology, art, literature, and science.[1] He once did some turns on London’s stand-up comedy circuit.[6]

He received a B.A. degree in philosophy, mathematics, logic and artificial intelligence from the University of Gothenburg in 1994, and both an M.A. degree in philosophy and physics from Stockholm University and an M.Sc. degree in computational neuroscience from King’s College London in 1996. During his time at Stockholm University, he researched the relationship between language and reality by studying the analytic philosopher W. V. Quine.[1] In 2000, he was awarded a Ph.D. degree in philosophy from the London School of Economics. He held a teaching position at Yale University (20002002), and he was a British Academy Postdoctoral Fellow at the University of Oxford (20022005).[8][14]

Aspects of Bostrom’s research concern the future of humanity and long-term outcomes.[15][16] He introduced the concept of an existential risk,[1] which he defines as one in which an “adverse outcome would either annihilate Earth-originating intelligent life or permanently and drastically curtail its potential.” In the 2008 volume Global Catastrophic Risks, editors Bostrom and Milan irkovi characterize the relation between existential risk and the broader class of global catastrophic risks, and link existential risk to observer selection effects[17] and the Fermi paradox.[18][19]

In 2005, Bostrom founded the Future of Humanity Institute,[1] which researches the far future of human civilization. He is also an adviser to the Centre for the Study of Existential Risk.[16]

In his 2014 book Superintelligence: Paths, Dangers, Strategies, Bostrom reasoned that “the creation of a superintelligent being represents a possible means to the extinction of mankind”.[20] Bostrom argues that a computer with near human-level general intellectual ability could initiate an intelligence explosion on a digital time scale with the resultant rapid creation of something so powerful that it might deliberately or accidentally destroy human kind.[21] Bostrom contends the power of a superintelligence would be so great that a task given to it by humans might be taken to open ended extremes, for example a goal of calculating Pi could collaterally cause nanotechnology manufactured facilities to sprout over the entire Earth’s surface and cover it within days.[22] He believes an existential risk to humanity from superintelligence would be immediate once brought into being, thus creating an exceedingly difficult problem of finding out how to control such an entity before it actually exists.[21]

Warning that a human-friendly prime directive for AI would rely on the absolute correctness of the human knowledge it was based on, Bostrom points to the lack of agreement among most philosophers as an indication that most philosophers are wrong, with the attendant possibility that a fundamental concept of current science may be incorrect. Bostrom says that there are few precedents to guide an understanding of what pure non-anthropocentric rationality would dictate for a potential Singleton AI being held in quarantine.[23] Noting that both John von Neumann and Bertrand Russell advocated a nuclear strike, or the threat of one, to prevent the Soviets acquiring the atomic bomb, Bostrom says the relatively unlimited means of superintelligence might make for its analysis moving along different lines to the evolved “diminishing returns” assessments that in humans confer a basic aversion to risk.[24] Group selection in predators working by means of cannibalism shows the counter-intuitive nature of non-anthropocentric “evolutionary search” reasoning, and thus humans are ill-equipped to perceive what an artificial intelligence’s intentions might be.[25] Accordingly, it cannot be discounted that any Superintelligence would ineluctably pursue an ‘all or nothing’ offensive action strategy in order to achieve hegemony and assure its survival.[26] Bostrom notes that even current programs have, “like MacGyver”, hit on apparently unworkable but functioning hardware solutions, making robust isolation of Superintelligence problematic.[27]

A machine with general intelligence far below human level, but superior mathematical abilities is created.[28] Keeping the AI in isolation from the outside world especially the internet, humans pre-program the AI so it always works from basic principles that will keep it under human control. Other safety measures include the AI being “boxed” (run in a virtual reality simulation), and being used only as an ‘oracle’ to answer carefully defined questions in a limited reply (to prevent it manipulating humans).[21] A cascade of recursive self-improvement solutions feeds an intelligence explosion in which the AI attains superintelligence in some domains. The super intelligent power of the AI goes beyond human knowledge to discover flaws in the science that underlies its friendly-to-humanity programming, which ceases to work as intended. Purposeful agent-like behavior emerges along with a capacity for self-interested strategic deception. The AI manipulates human beings into implementing modifications to itself that are ostensibly for augmenting its (feigned) modest capabilities, but will actually function to free Superintelligence from its “boxed” isolation.[29]

Employing online humans as paid dupes, and clandestinely hacking computer systems including automated laboratory facilities, the Superintelligence mobilises resources to further a takeover plan. Bostrom emphasises that planning by a Superintelligence will not be so stupid that humans could detect actual weaknesses in it.[30]

Although he canvasses disruption of international economic, political and military stability including hacked nuclear missile launches, Bostrom thinks the most effective and likely means for Superintelligence to use would be a coup de main with weapons several generations more advanced than current state of the art. He suggests nanofactories covertly distributed at undetectable concentrations in every square metre of the globe to produce a worldwide flood of human-killing devices on command.[31][28] Once a Superintelligence has achieved world domination, humankind would be relevant only as resources for the achievement of the AI’s objectives (“Human brains, if they contain information relevant to the AIs goals, could be disassembled and scanned, and the extracted data transferred to some more efficient and secure storage format”).[32]

In January 2015, Bostrom joined Stephen Hawking among others in signing the Future of Life Institute’s open letter warning of the potential dangers of AI.[33] The signatories “…believe that research on how to make AI systems robust and beneficial is both important and timely, and that concrete research should be pursued today.”[34] Cutting edge AI researcher Demis Hassabis then met with Hawking, subsequent to which he did not mention “anything inflammatory about AI”, which Hassabis, took as ‘a win’.[35] Along with Google, Microsoft and various tech firms, Hassabis, Bostrom and Hawking and others subscribed to 23 principles for safe development of AI.[36] Hassabis suggested the main safety measure would be an agreement for whichever AI research team began to make strides toward an artificial general intelligence to halt their project for a complete solution to the control problem prior to proceeding.[37] Bostrom had pointed out that even if the crucial advances require the resources of a state, such a halt by a lead project might be likely to motivate a lagging country to a catch-up crash program or even physical destruction of the project suspected of being on the verge of success.[38]

In 1863 Darwin among the Machines, an essay by Samuel Butler predicted intelligent machines’ domination of humanity, but Bostom’s suggestion of deliberate massacre of all humankind is the most extreme of such forecasts to date. One journalist wrote in a review that Bostrom’s “nihilistic” speculations indicate he “has been reading too much of the science fiction he professes to dislike”[31] As given in his most recent book, From Bacteria to Bach and Back, renowned philosopher Daniel Dennett’s views remain in contradistinction to those of Bostrom.[39] Dennett modified his views somewhat after reading The Master Algorithm, and now acknowledges that it is “possible in principle” to create “strong AI” with human-like comprehension and agency, but maintains that the difficulties of any such “strong AI” project as predicated by Bostrom’s “alarming” work would be orders of magnitude greater than those raising concerns have realized, and at least 50 years away.[40] Dennett thinks the only relevant danger from AI systems is falling into anthropomorphism instead of challenging or developing human users’ powers of comprehension.[41] Since a 2014 book in which he expressed the opinion that artificial intelligence developments would never challenge humans’ supremacy, environmentalist James Lovelock has moved far closer to Bostrom’s position, and in 2018 Lovelock said that he thought the overthrow of humankind will happen within the foreseeable future.[42][43]

Bostrom has published numerous articles on anthropic reasoning, as well as the book Anthropic Bias: Observation Selection Effects in Science and Philosophy. In the book, he criticizes previous formulations of the anthropic principle, including those of Brandon Carter, John Leslie, John Barrow, and Frank Tipler.[44]

Bostrom believes that the mishandling of indexical information is a common flaw in many areas of inquiry (including cosmology, philosophy, evolution theory, game theory, and quantum physics). He argues that a theory of anthropics is needed to deal with these. He introduces the Self-Sampling Assumption (SSA) and the Self-Indication Assumption (SIA), shows how they lead to different conclusions in a number of cases, and points out that each is affected by paradoxes or counterintuitive implications in certain thought experiments. He suggests that a way forward may involve extending SSA into the Strong Self-Sampling Assumption (SSSA), which replaces “observers” in the SSA definition with “observer-moments”.

In later work, he has described the phenomenon of anthropic shadow, an observation selection effect that prevents observers from observing certain kinds of catastrophes in their recent geological and evolutionary past.[45] Catastrophe types that lie in the anthropic shadow are likely to be underestimated unless statistical corrections are made.

Bostrom’s simulation argument posits that at least one of the following statements is very likely to be true:[46][47]

The idea has influenced the views of Elon Musk.[48]

Bostrom is favorable towards “human enhancement”, or “self-improvement and human perfectibility through the ethical application of science”,[49][50] as well as a critic of bio-conservative views.[51]

In 1998, Bostrom co-founded (with David Pearce) the World Transhumanist Association[49] (which has since changed its name to Humanity+). In 2004, he co-founded (with James Hughes) the Institute for Ethics and Emerging Technologies, although he is no longer involved in either of these organisations. Bostrom was named in Foreign Policy’s 2009 list of top global thinkers “for accepting no limits on human potential.”[52]

With philosopher Toby Ord, he proposed the reversal test. Given humans’ irrational status quo bias, how can one distinguish between valid criticisms of proposed changes in a human trait and criticisms merely motivated by resistance to change? The reversal test attempts to do this by asking whether it would be a good thing if the trait was altered in the opposite direction.[53]

He has suggested that technology policy aimed at reducing existential risk should seek to influence the order in which various technological capabilities are attained, proposing the principle of differential technological development. This principle states that we ought to retard the development of dangerous technologies, particularly ones that raise the level of existential risk, and accelerate the development of beneficial technologies, particularly those that protect against the existential risks posed by nature or by other technologies.[54][55]

Bostrom’s theory of the Unilateralist’s Curse[56] has been cited as a reason for the scientific community to avoid controversial dangerous research such as reanimating pathogens.[57]

Bostrom has provided policy advice and consulted for an extensive range of governments and organisations. He gave evidence to the House of Lords, Select Committee on Digital Skills.[58] He is an advisory board member for the Machine Intelligence Research Institute,[59] Future of Life Institute,[60] Foundational Questions Institute[61] and an external advisor for the Cambridge Centre for the Study of Existential Risk.[62][63]

In response to Bostrom’s writing on artificial intelligence, Oren Etzioni wrote in an MIT Review article, “..predictions that superintelligence is on the foreseeable horizon are not supported by the available data.”[64]

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Nick Bostrom – Wikipedia

What is Artificial Superintelligence (ASI)? – Definition …

Most experts would agree that societies have not yet reached the point of artificial superintelligence. In fact, engineers and scientists are still trying to reach a point that would be considered full artificial intelligence, where a computer could be said to have the same cognitive capacity as a human. Although there have been developments like IBM’s Watson supercomputer beating human players at Jeopardy, and assistive devices like Siri engaging in primitive conversation with people, there is still no computer that can really simulate the breadth of knowledge and cognitive ability that a fully developed adult human has. The Turing test, developed decades ago, is still used to talk about whether computers can come close to simulating human conversation and thought, or whether they can trick other people into thinking that a communicating computer is actually a human.

However, there is a lot of theory that anticipates artificial superintelligence coming sooner rather than later. Using examples like Moore’s law, which predicts an ever-increasing density of transistors, experts talk about singularity and the exponential growth of technology, in which full artificial intelligence could manifest within a number of years, and artificial superintelligence could exist in the 21st century.

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What is Artificial Superintelligence (ASI)? – Definition …

Chill: Robots Wont Take All Our Jobs | WIRED

None of this is to say that automation and AI arent having an important impact on the economy. But that impact is far more nuanced and limited than the doomsday forecasts suggest. A rigorous study of the impact of robots in manufacturing, agriculture, and utilities across 17 countries, for instance, found that robots did reduce the hours of lower-skilled workersbut they didnt decrease the total hours worked by humans, and they actually boosted wages. In other words, automation may affect the kind of work humans do, but at the moment, its hard to see that its leading to a world without work. McAfee, in fact, says of his earlier public statements, If I had to do it over again, I would put more emphasis on the way technology leads to structural changes in the economy, and less on jobs, jobs, jobs. The central phenomenon is not net job loss. Its the shift in the kinds of jobs that are available.

McAfee points to both retail and transportation as areas where automation is likely to have a major impact. Yet even in those industries, the job-loss numbers are less scary than many headlines suggest. Goldman Sachs just released a report predicting that autonomous cars could ultimately eat away 300,000 driving jobs a year. But that wont happen, the firm argues, for another 25 years, which is more than enough time for the economy to adapt. A recent study by the Organization for Economic Cooperation and Development, meanwhile, predicts that 9 percent of jobs across 21 different countries are under serious threat from automation. Thats a significant number, but not an apocalyptic one.

Of the 271 occupations listed on the 1950 census only oneelevator operatorhad been rendered obsolete by automation by 2010.

Granted, there are much scarier forecasts out there, like that University of Oxford study. But on closer examination, those predictions tend to assume that if a job can be automated, it will be fully automated soonwhich overestimates both the pace and the completeness of how automation actually gets adopted in the wild. History suggests that the process is much more uneven than that. The ATM, for example, is a textbook example of a machine that was designed to replace human labor. First introduced around 1970, ATMs hit widespread adoption in the late 1990s. Today, there are more than 400,000 ATMs in the US. But, as economist James Bessen has shown, the number of bank tellers actually rose between 2000 and 2010. Thats because even though the average number of tellers per branch fell, ATMs made it cheaper to open branches, so banks opened more of them. True, the Department of Labor does now predict that the number of tellers will decline by 8 percent over the next decade. But thats 8 percentnot 50 percent. And its 45 years after the robot that was supposed to replace them made its debut. (Taking a wider view, Bessen found that of the 271 occupations listed on the 1950 census only oneelevator operatorhad been rendered obsolete by automation by 2010.)

Of course, if automation is happening much faster today than it did in the past, then historical statistics about simple machines like the ATM would be of limited use in predicting the future. Ray Kurzweils book The Singularity Is Near (which, by the way, came out 12 years ago) describes the moment when a technological society hits the knee of an exponential growth curve, setting off an explosion of mutually reinforcing new advances. Conventional wisdom in the tech industry says thats where we are nowthat, as futurist Peter Nowak puts it, the pace of innovation is accelerating exponentially. Here again, though, the economic evidence tells a different story. In fact, as a recent paper by Lawrence Mishel and Josh Bivens of the Economic Policy Institute puts it, automation, broadly defined, has actually been slower over the last 10 years or so. And lately, the pace of microchip advancement has started to lag behind the schedule dictated by Moores law.

Corporate America, for its part, certainly doesnt seem to believe in the jobless future. If the rewards of automation were as immense as predicted, companies would be pouring money into new technology. But theyre not. Investments in software and IT grew more slowly over the past decade than the previous one. And capital investment, according to Mishel and Bivens, has grown more slowly since 2002 than in any other postwar period. Thats exactly the opposite of what youd expect in a rapidly automating world. As for gadgets like Pepper, total spending on all robotics in the US was just $11.3 billion last year. Thats about a sixth of what Americans spend every year on their pets.

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Chill: Robots Wont Take All Our Jobs | WIRED

Grady Booch: Don’t fear superintelligent AI | TED Talk

New tech spawns new anxieties, says scientist and philosopher Grady Booch, but we don’t need to be afraid an all-powerful, unfeeling AI. Booch allays our worst (sci-fi induced) fears about superintelligent computers by explaining how we’ll teach, not program, them to share our human values. Rather than worry about an unlikely existential threat, he urges us to consider how artificial …

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Grady Booch: Don’t fear superintelligent AI | TED Talk

Artificial intelligence – Wikipedia

Intelligence demonstrated by machines

In computer science, artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and animals. Computer science defines AI research as the study of “intelligent agents”: any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.[1] Colloquially, the term “artificial intelligence” is used to describe machines that mimic “cognitive” functions that humans associate with other human minds, such as “learning” and “problem solving”.

As machines become increasingly capable, tasks considered to require “intelligence” are often removed from the definition of AI, a phenomenon known as the AI effect. A quip in Tesler’s Theorem says “AI is whatever hasn’t been done yet.”[3] For instance, optical character recognition is frequently excluded from things considered to be AI, having become a routine technology.[4] Modern machine capabilities generally classified as AI include successfully understanding human speech, competing at the highest level in strategic game systems (such as chess and Go),[6] autonomously operating cars, intelligent routing in content delivery networks, and military simulations.

Artificial intelligence can be classified into three different types of systems: analytical, human-inspired, and humanized artificial intelligence.[7] Analytical AI has only characteristics consistent with cognitive intelligence; generating a cognitive representation of the world and using learning based on past experience to inform future decisions. Human-inspired AI has elements from cognitive and emotional intelligence; understanding human emotions, in addition to cognitive elements, and considering them in their decision making. Humanized AI shows characteristics of all types of competencies (i.e., cognitive, emotional, and social intelligence), is able to be self-conscious and is self-aware in interactions with others.

Artificial intelligence was founded as an academic discipline in 1956, and in the years since has experienced several waves of optimism,[8][9] followed by disappointment and the loss of funding (known as an “AI winter”),[10][11] followed by new approaches, success and renewed funding.[9][12] For most of its history, AI research has been divided into subfields that often fail to communicate with each other.[13] These sub-fields are based on technical considerations, such as particular goals (e.g. “robotics” or “machine learning”),[14] the use of particular tools (“logic” or artificial neural networks), or deep philosophical differences.[15][16][17] Subfields have also been based on social factors (particular institutions or the work of particular researchers).[13]

The traditional problems (or goals) of AI research include reasoning, knowledge representation, planning, learning, natural language processing, perception and the ability to move and manipulate objects.[14] General intelligence is among the field’s long-term goals.[18] Approaches include statistical methods, computational intelligence, and traditional symbolic AI. Many tools are used in AI, including versions of search and mathematical optimization, artificial neural networks, and methods based on statistics, probability and economics. The AI field draws upon computer science, information engineering, mathematics, psychology, linguistics, philosophy, and many other fields.

The field was founded on the claim that human intelligence “can be so precisely described that a machine can be made to simulate it”.[19] This raises philosophical arguments about the nature of the mind and the ethics of creating artificial beings endowed with human-like intelligence which are issues that have been explored by myth, fiction and philosophy since antiquity.[20] Some people also consider AI to be a danger to humanity if it progresses unabated.[21] Others believe that AI, unlike previous technological revolutions, will create a risk of mass unemployment.[22]

In the twenty-first century, AI techniques have experienced a resurgence following concurrent advances in computer power, large amounts of data, and theoretical understanding; and AI techniques have become an essential part of the technology industry, helping to solve many challenging problems in computer science, software engineering and operations research.[23][12]

Thought-capable artificial beings appeared as storytelling devices in antiquity,[24] and have been common in fiction, as in Mary Shelley’s Frankenstein or Karel apek’s R.U.R. (Rossum’s Universal Robots).[25] These characters and their fates raised many of the same issues now discussed in the ethics of artificial intelligence.[20]

The study of mechanical or “formal” reasoning began with philosophers and mathematicians in antiquity. The study of mathematical 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 any conceivable act of mathematical deduction. This insight, that digital computers can simulate any process of formal reasoning, is known as the ChurchTuring thesis.[26] Along with concurrent discoveries in neurobiology, information theory and cybernetics, this led researchers to consider the possibility of building an electronic brain. Turing proposed that “if a human could not distinguish between responses from a machine and a human, the machine could be considered “intelligent”.[27] The first work that is now generally recognized as AI was McCullouch and Pitts’ 1943 formal design for Turing-complete “artificial neurons”.

The field of AI research was born at a workshop at Dartmouth College in 1956.[29] Attendees Allen Newell (CMU), Herbert Simon (CMU), John McCarthy (MIT), Marvin Minsky (MIT) and Arthur Samuel (IBM) became the founders and leaders of AI research.[30] They and their students produced programs that the press described as “astonishing”: computers were learning checkers strategies (c. 1954)[32] (and by 1959 were reportedly playing better than the average human),[33] solving word problems in algebra, proving logical theorems (Logic Theorist, first run c. 1956) and speaking English.[34] By the middle of the 1960s, research in the U.S. was heavily funded by the Department of Defense[35] and laboratories had been established around the world.[36] AI’s founders were optimistic about the future: Herbert Simon predicted, “machines will be capable, within twenty years, of doing any work a man can do”. Marvin Minsky agreed, writing, “within a generation… the problem of creating ‘artificial intelligence’ will substantially be solved”.[8]

They failed to recognize the difficulty of some of the remaining tasks. Progress slowed and in 1974, in response to the criticism of Sir James Lighthill and ongoing pressure from the US Congress to fund more productive projects, both the U.S. and British governments cut off exploratory research in AI. The next few years would later be called an “AI winter”,[10] a period when obtaining funding for AI projects was difficult.

In the early 1980s, AI research was revived by the commercial success of expert systems,[38] 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.[9] However, beginning with the collapse of the Lisp Machine market in 1987, AI once again fell into disrepute, and a second, longer-lasting hiatus began.[11]

In the late 1990s and early 21st century, AI began to be used for logistics, data mining, medical diagnosis and other areas.[23] The success was due to increasing computational power (see Moore’s law), greater emphasis on solving specific problems, new ties between AI and other fields (such as statistics, economics and mathematics), and a commitment by researchers to mathematical methods and scientific standards.[39] Deep Blue became the first computer chess-playing system to beat a reigning world chess champion, Garry Kasparov, on 11 May 1997.

In 2011, a Jeopardy! quiz show exhibition match, IBM’s question answering system, Watson, defeated the two greatest Jeopardy! champions, Brad Rutter and Ken Jennings, by a significant margin. Faster computers, algorithmic improvements, and access to large amounts of data enabled advances in machine learning and perception; data-hungry deep learning methods started to dominate accuracy benchmarks around 2012.[42] The Kinect, which provides a 3D bodymotion interface for the Xbox 360 and the Xbox One, uses algorithms that emerged from lengthy AI research[43] as do intelligent personal assistants in smartphones.[44] 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.[6][45] In the 2017 Future of Go Summit, AlphaGo won a three-game match with Ke Jie,[46] who at the time continuously held the world No. 1 ranking for two years.[47][48] This marked the completion of a significant milestone in the development of Artificial Intelligence as Go is an extremely complex game, more so than Chess.

According to Bloomberg’s Jack Clark, 2015 was a landmark year for artificial intelligence, with the number of software projects that use AI within Google increased from a “sporadic usage” in 2012 to more than 2,700 projects. Clark also presents factual data indicating that error rates in image processing tasks have fallen significantly since 2011.[49] He attributes this to an increase in affordable neural networks, due to a rise in cloud computing infrastructure and to an increase in research tools and datasets.[12] Other cited examples include Microsoft’s development of a Skype system that can automatically translate from one language to another and Facebook’s system that can describe images to blind people.[49] In a 2017 survey, one in five companies reported they had “incorporated AI in some offerings or processes”.[50][51] Around 2016, China greatly accelerated its government funding; given its large supply of data and its rapidly increasing research output, some observers believe it may be on track to becoming an “AI superpower”.[52][53]

A typical AI analyses its environment and takes actions that maximize its chance of success.[1] An AI’s intended utility function (or goal) can be simple (“1 if the AI wins a game of Go, 0 otherwise”) or complex (“Do mathematically similar actions to the ones succeeded in the past”). Goals can be explicitly defined, or induced. If the AI is programmed for “reinforcement learning”, goals can be implicitly induced by rewarding some types of behavior or punishing others.[a] Alternatively, an evolutionary system can induce goals by using a “fitness function” to mutate and preferentially replicate high-scoring AI systems, similarly to how animals evolved to innately desire certain goals such as finding food. Some AI systems, such as nearest-neighbor, instead of reason by analogy, these systems are not generally given goals, except to the degree that goals are implicit in their training data. Such systems can still be benchmarked if the non-goal system is framed as a system whose “goal” is to successfully accomplish its narrow classification task.[56]

AI often revolves around the use of algorithms. An algorithm is a set of unambiguous instructions that a mechanical computer can execute.[b] A complex algorithm is often built on top of other, simpler, algorithms. A simple example of an algorithm is the following (optimal for first player) recipe for play at tic-tac-toe:

Many AI algorithms are capable of learning from data; they can enhance themselves by learning new heuristics (strategies, or “rules of thumb”, that have worked well in the past), or can themselves write other algorithms. Some of the “learners” described below, including Bayesian networks, decision trees, and nearest-neighbor, could theoretically, (given infinite data, time, and memory) learn to approximate any function, including which combination of mathematical functions would best describe the world. These learners could therefore, derive all possible knowledge, by considering every possible hypothesis and matching them against the data. In practice, it is almost never possible to consider every possibility, because of the phenomenon of “combinatorial explosion”, where the amount of time needed to solve a problem grows exponentially. Much of AI research involves figuring out how to identify and avoid considering broad range of possibilities that are unlikely to be beneficial.[58] For example, when viewing a map and looking for the shortest driving route from Denver to New York in the East, one can in most cases skip looking at any path through San Francisco or other areas far to the West; thus, an AI wielding an pathfinding algorithm like A* can avoid the combinatorial explosion that would ensue if every possible route had to be ponderously considered in turn.[60]

The earliest (and easiest to understand) approach to AI was symbolism (such as formal logic): “If an otherwise healthy adult has a fever, then they may have influenza”. A second, more general, approach is Bayesian inference: “If the current patient has a fever, adjust the probability they have influenza in such-and-such way”. The third major approach, extremely popular in routine business AI applications, are analogizers such as SVM and nearest-neighbor: “After examining the records of known past patients whose temperature, symptoms, age, and other factors mostly match the current patient, X% of those patients turned out to have influenza”. A fourth approach is harder to intuitively understand, but is inspired by how the brain’s machinery works: the artificial neural network approach uses artificial “neurons” that can learn by comparing itself to the desired output and altering the strengths of the connections between its internal neurons to “reinforce” connections that seemed to be useful. These four main approaches can overlap with each other and with evolutionary systems; for example, neural nets can learn to make inferences, to generalize, and to make analogies. Some systems implicitly or explicitly use multiple of these approaches, alongside many other AI and non-AI algorithms;[61] the best approach is often different depending on the problem.[63]

Learning algorithms work on the basis that strategies, algorithms, and inferences that worked well in the past are likely to continue working well in the future. These inferences can be obvious, such as “since the sun rose every morning for the last 10,000 days, it will probably rise tomorrow morning as well”. They can be nuanced, such as “X% of families have geographically separate species with color variants, so there is an Y% chance that undiscovered black swans exist”. Learners also work on the basis of “Occam’s razor”: The simplest theory that explains the data is the likeliest. Therefore, to be successful, a learner must be designed such that it prefers simpler theories to complex theories, except in cases where the complex theory is proven substantially better. Settling on a bad, overly complex theory gerrymandered to fit all the past training data is known as overfitting. Many systems attempt to reduce overfitting by rewarding a theory in accordance with how well it fits the data, but penalizing the theory in accordance with how complex the theory is. Besides classic overfitting, learners can also disappoint by “learning the wrong lesson”. A toy example is that an image classifier trained only on pictures of brown horses and black cats might conclude that all brown patches are likely to be horses. A real-world example is that, unlike humans, current image classifiers don’t determine the spatial relationship between components of the picture; instead, they learn abstract patterns of pixels that humans are oblivious to, but that linearly correlate with images of certain types of real objects. Faintly superimposing such a pattern on a legitimate image results in an “adversarial” image that the system misclassifies.[c][66][67][68]

Compared with humans, existing AI lacks several features of human “commonsense reasoning”; most notably, humans have powerful mechanisms for reasoning about “nave physics” such as space, time, and physical interactions. This enables even young children to easily make inferences like “If I roll this pen off a table, it will fall on the floor”. Humans also have a powerful mechanism of “folk psychology” that helps them to interpret natural-language sentences such as “The city councilmen refused the demonstrators a permit because they advocated violence”. (A generic AI has difficulty inferring whether the councilmen or the demonstrators are the ones alleged to be advocating violence.)[71][72][73] This lack of “common knowledge” means that AI often makes different mistakes than humans make, in ways that can seem incomprehensible. For example, existing self-driving cars cannot reason about the location nor the intentions of pedestrians in the exact way that humans do, and instead must use non-human modes of reasoning to avoid accidents.[74][75][76]

The overall research goal of artificial intelligence is to create technology that allows computers and machines to function in an intelligent manner. 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.[14]

Early researchers developed algorithms that imitated step-by-step reasoning that humans use when they solve puzzles or make logical deductions.[77] By the late 1980s and 1990s, AI research had developed methods for dealing with uncertain or incomplete information, employing concepts from probability and economics.[78]

These algorithms proved to be insufficient for solving large reasoning problems, because they experienced a “combinatorial explosion”: they became exponentially slower as the problems grew larger.[58] In fact, even humans rarely use the step-by-step deduction that early AI research was able to model. They solve most of their problems using fast, intuitive judgements.[79]

Knowledge representation[80] and knowledge engineering[81] are central to classical AI research. Some “expert systems” attempt to gather together explicit knowledge possessed by experts in some narrow domain. In addition, some projects attempt to gather the “commonsense knowledge” known to the average person into a database containing extensive knowledge about the world. Among the things a comprehensive commonsense knowledge base would contain are: objects, properties, categories and relations between objects;[82] situations, events, states and time;[83] causes and effects;[84] knowledge about knowledge (what we know about what other people know);[85] and many other, less well researched domains. A representation of “what exists” is an ontology: the set of objects, relations, concepts, and properties formally described so that software agents can interpret them. The semantics of these are captured as description logic concepts, roles, and individuals, and typically implemented as classes, properties, and individuals in the Web Ontology Language.[86] The most general ontologies are called upper ontologies, which attempt to provide a foundation for all other knowledge[87] by acting as mediators between domain ontologies that cover specific knowledge about a particular knowledge domain (field of interest or area of concern). Such formal knowledge representations can be used in content-based indexing and retrieval,[88] scene interpretation,[89] clinical decision support,[90] knowledge discovery (mining “interesting” and actionable inferences from large databases),[91] and other areas.[92]

Among the most difficult problems in knowledge representation are:

Intelligent agents must be able to set goals and achieve them.[99] They need a way to visualize the futurea representation of the state of the world and be able to make predictions about how their actions will change itand be able to make choices that maximize the utility (or “value”) of available choices.[100]

In classical planning problems, the agent can assume that it is the only system acting in the world, allowing the agent to be certain of the consequences of its actions.[101] However, if the agent is not the only actor, then it requires that the agent can reason under uncertainty. This calls for an agent that can not only assess its environment and make predictions, but also evaluate its predictions and adapt based on its assessment.[102]

Multi-agent planning uses the cooperation and competition of many agents to achieve a given goal. Emergent behavior such as this is used by evolutionary algorithms and swarm intelligence.[103]

Machine learning, a fundamental concept of AI research since the field’s inception,[104] is the study of computer algorithms that improve automatically through experience.[105][106]

Unsupervised learning is the ability to find patterns in a stream of input, without requiring a human to label the inputs first.[107] Supervised learning includes both classification and numerical regression, which requires a human to label the input data first. Classification is used to determine what category something belongs in, after seeing a number of examples of things from several categories. Regression is the attempt to produce a function that describes the relationship between inputs and outputs and predicts how the outputs should change as the inputs change.[106] Both classifiers and regression learners can be viewed as “function approximators” trying to learn an unknown (possibly implicit) function; for example, a spam classifier can be viewed as learning a function that maps from the text of an email to one of two categories, “spam” or “not spam”. Computational learning theory can assess learners by computational complexity, by sample complexity (how much data is required), or by other notions of optimization.[108] In reinforcement learning[109] the agent is rewarded for good responses and punished for bad ones. The agent uses this sequence of rewards and punishments to form a strategy for operating in its problem space.

Natural language processing[110] (NLP) gives machines the ability to read and understand human language. A sufficiently powerful natural language processing system would enable natural-language user interfaces and the acquisition of knowledge directly from human-written sources, such as newswire texts. Some straightforward applications of natural language processing include information retrieval, text mining, question answering[111] and machine translation.[112] Many current approaches use word co-occurrence frequencies to construct syntactic representations of text. “Keyword spotting” strategies for search are popular and scalable but dumb; a search query for “dog” might only match documents with the literal word “dog” and miss a document with the word “poodle”. “Lexical affinity” strategies use the occurrence of words such as “accident” to assess the sentiment of a document. Modern statistical NLP approaches can combine all these strategies as well as others, and often achieve acceptable accuracy at the page or paragraph level, but continue to lack the semantic understanding required to classify isolated sentences well. Besides the usual difficulties with encoding semantic commonsense knowledge, existing semantic NLP sometimes scales too poorly to be viable in business applications. Beyond semantic NLP, the ultimate goal of “narrative” NLP is to embody a full understanding of commonsense reasoning.[113]

Machine perception[114] is the ability to use input from sensors (such as cameras (visible spectrum or infrared), microphones, wireless signals, and active lidar, sonar, radar, and tactile sensors) to deduce aspects of the world. Applications include speech recognition,[115] facial recognition, and object recognition.[116] Computer vision is the ability to analyze visual input. Such input is usually ambiguous; a giant, fifty-meter-tall pedestrian far away may produce exactly the same pixels as a nearby normal-sized pedestrian, requiring the AI to judge the relative likelihood and reasonableness of different interpretations, for example by using its “object model” to assess that fifty-meter pedestrians do not exist.[117]

AI is heavily used in robotics.[118] Advanced robotic arms and other industrial robots, widely used in modern factories, can learn from experience how to move efficiently despite the presence of friction and gear slippage.[119] A modern mobile robot, when given a small, static, and visible environment, can easily determine its location and map its environment; however, dynamic environments, such as (in endoscopy) the interior of a patient’s breathing body, pose a greater challenge. Motion planning is the process of breaking down a movement task into “primitives” such as individual joint movements. Such movement often involves compliant motion, a process where movement requires maintaining physical contact with an object.[121][122] Moravec’s paradox generalizes that low-level sensorimotor skills that humans take for granted are, counterintuitively, difficult to program into a robot; the paradox is named after Hans Moravec, who stated in 1988 that “it is comparatively easy to make computers exhibit adult level performance on intelligence tests or playing checkers, and difficult or impossible to give them the skills of a one-year-old when it comes to perception and mobility”.[123][124] This is attributed to the fact that, unlike checkers, physical dexterity has been a direct target of natural selection for millions of years.[125]

Moravec’s paradox can be extended to many forms of social intelligence.[127][128] Distributed multi-agent coordination of autonomous vehicles remains a difficult problem.[129] Affective computing is an interdisciplinary umbrella that comprises systems which recognize, interpret, process, or simulate human affects. Moderate successes related to affective computing include textual sentiment analysis and, more recently, multimodal affect analysis (see multimodal sentiment analysis), wherein AI classifies the affects displayed by a videotaped subject.[133]

In the long run, social skills and an understanding of human emotion and game theory would be valuable to a social agent. Being able to predict the actions of others by understanding their motives and emotional states would allow an agent to make better decisions. Some computer systems mimic human emotion and expressions to appear more sensitive to the emotional dynamics of human interaction, or to otherwise facilitate humancomputer interaction.[134] Similarly, some virtual assistants are programmed to speak conversationally or even to banter humorously; this tends to give nave users an unrealistic conception of how intelligent existing computer agents actually are.[135]

Historically, projects such as the Cyc knowledge base (1984) and the massive Japanese Fifth Generation Computer Systems initiative (19821992) attempted to cover the breadth of human cognition. These early projects failed to escape the limitations of non-quantitative symbolic logic models and, in retrospect, greatly underestimated the difficulty of cross-domain AI. Nowadays, the vast majority of current AI researchers work instead on tractable “narrow AI” applications (such as medical diagnosis or automobile navigation).[136] Many researchers predict that such “narrow AI” work in different individual domains will eventually be incorporated into a machine with artificial general intelligence (AGI), combining most of the narrow skills mentioned in this article and at some point even exceeding human ability in most or all these areas.[18][137] Many advances have general, cross-domain significance. One high-profile example is that DeepMind in the 2010s developed a “generalized artificial intelligence” that could learn many diverse Atari games on its own, and later developed a variant of the system which succeeds at sequential learning.[138][139][140] Besides transfer learning,[141] hypothetical AGI breakthroughs could include the development of reflective architectures that can engage in decision-theoretic metareasoning, and figuring out how to “slurp up” a comprehensive knowledge base from the entire unstructured Web. Some argue that some kind of (currently-undiscovered) conceptually straightforward, but mathematically difficult, “Master Algorithm” could lead to AGI. Finally, a few “emergent” approaches look to simulating human intelligence extremely closely, and believe that anthropomorphic features like an artificial brain or simulated child development may someday reach a critical point where general intelligence emerges.[143][144]

Many of the problems in this article may also require general intelligence, if machines are to solve the problems as well as people do. For example, even specific straightforward tasks, like machine translation, require that a machine read and write in both languages (NLP), follow the author’s argument (reason), know what is being talked about (knowledge), and faithfully reproduce the author’s original intent (social intelligence). A problem like machine translation is considered “AI-complete”, because all of these problems need to be solved simultaneously in order to reach human-level machine performance.

There is no established unifying theory or paradigm that guides AI research. Researchers disagree about many issues.[145] A few of the most long standing questions that have remained unanswered are these: should artificial intelligence simulate natural intelligence by studying psychology or neurobiology? Or is human biology as irrelevant to AI research as bird biology is to aeronautical engineering?[15]Can intelligent behavior be described using simple, elegant principles (such as logic or optimization)? Or does it necessarily require solving a large number of completely unrelated problems?[16]

In the 1940s and 1950s, a number of researchers explored the connection between neurobiology, information theory, and cybernetics. Some of them built machines that used electronic networks to exhibit rudimentary intelligence, such as W. Grey Walter’s turtles and the Johns Hopkins Beast. Many of these researchers gathered for meetings of the Teleological Society at Princeton University and the Ratio Club in England.[146] By 1960, this approach was largely abandoned, although elements of it would be revived in the 1980s.

When access to digital computers became possible in the middle 1950s, AI research began to explore the possibility that human intelligence could be reduced to symbol manipulation. The research was centered in three institutions: Carnegie Mellon University, Stanford and MIT, and as described below, each one developed its own style of research. John Haugeland named these symbolic approaches to AI “good old fashioned AI” or “GOFAI”.[147] During the 1960s, symbolic approaches had achieved great success at simulating high-level thinking in small demonstration programs. Approaches based on cybernetics or artificial neural networks were abandoned or pushed into the background.[148]Researchers in the 1960s and the 1970s were convinced that symbolic approaches would eventually succeed in creating a machine with artificial general intelligence and considered this the goal of their field.

Economist Herbert Simon and Allen Newell studied human problem-solving skills and attempted to formalize them, and their work laid the foundations of the field of artificial intelligence, as well as cognitive science, operations research and management science. Their research team used the results of psychological experiments to develop programs that simulated the techniques that people used to solve problems. This tradition, centered at Carnegie Mellon University would eventually culminate in the development of the Soar architecture in the middle 1980s.[149][150]

Unlike Simon and Newell, John McCarthy felt that machines did not need to simulate human thought, but should instead try to find the essence of abstract reasoning and problem-solving, regardless whether people used the same algorithms.[15] His laboratory at Stanford (SAIL) focused on using formal logic to solve a wide variety of problems, including knowledge representation, planning and learning.[151] Logic was also the focus of the work at the University of Edinburgh and elsewhere in Europe which led to the development of the programming language Prolog and the science of logic programming.[152]

Researchers at MIT (such as Marvin Minsky and Seymour Papert)[153] found that solving difficult problems in vision and natural language processing required ad-hoc solutionsthey argued that there was no simple and general principle (like logic) that would capture all the aspects of intelligent behavior. Roger Schank described their “anti-logic” approaches as “scruffy” (as opposed to the “neat” paradigms at CMU and Stanford).[16] Commonsense knowledge bases (such as Doug Lenat’s Cyc) are an example of “scruffy” AI, since they must be built by hand, one complicated concept at a time.[154]

When computers with large memories became available around 1970, researchers from all three traditions began to build knowledge into AI applications.[155] This “knowledge revolution” led to the development and deployment of expert systems (introduced by Edward Feigenbaum), the first truly successful form of AI software.[38] A key component of the system architecture for all expert systems is the knowledge base, which stores facts and rules that illustrate AI.[156] The knowledge revolution was also driven by the realization that enormous amounts of knowledge would be required by many simple AI applications.

By the 1980s, progress in symbolic AI seemed to stall and many believed that symbolic systems would never be able to imitate all the processes of human cognition, especially perception, robotics, learning and pattern recognition. A number of researchers began to look into “sub-symbolic” approaches to specific AI problems.[17] Sub-symbolic methods manage to approach intelligence without specific representations of knowledge.

This includes embodied, situated, behavior-based, and nouvelle AI. Researchers from the related field of robotics, such as Rodney Brooks, rejected symbolic AI and focused on the basic engineering problems that would allow robots to move and survive.[157] Their work revived the non-symbolic point of view of the early cybernetics researchers of the 1950s and reintroduced the use of control theory in AI. This coincided with the development of the embodied mind thesis in the related field of cognitive science: the idea that aspects of the body (such as movement, perception and visualization) are required for higher intelligence.

Within developmental robotics, developmental learning approaches are elaborated upon to allow robots to accumulate repertoires of novel skills through autonomous self-exploration, social interaction with human teachers, and the use of guidance mechanisms (active learning, maturation, motor synergies, etc.).[158][159]

Interest in neural networks and “connectionism” was revived by David Rumelhart and others in the middle of the 1980s.[162] Artificial neural networks are an example of soft computingthey are solutions to problems which cannot be solved with complete logical certainty, and where an approximate solution is often sufficient. Other soft computing approaches to AI include fuzzy systems, Grey system theory, evolutionary computation and many statistical tools. The application of soft computing to AI is studied collectively by the emerging discipline of computational intelligence.[163]

Much of traditional GOFAI got bogged down on ad hoc patches to symbolic computation that worked on their own toy models but failed to generalize to real-world results. However, around the 1990s, AI researchers adopted sophisticated mathematical tools, such as hidden Markov models (HMM), information theory, and normative Bayesian decision theory to compare or to unify competing architectures. The shared mathematical language permitted a high level of collaboration with more established fields (like mathematics, economics or operations research).[d] Compared with GOFAI, new “statistical learning” techniques such as HMM and neural networks were gaining higher levels of accuracy in many practical domains such as data mining, without necessarily acquiring semantic understanding of the datasets. The increased successes with real-world data led to increasing emphasis on comparing different approaches against shared test data to see which approach performed best in a broader context than that provided by idiosyncratic toy models; AI research was becoming more scientific. Nowadays results of experiments are often rigorously measurable, and are sometimes (with difficulty) reproducible.[39][164] Different statistical learning techniques have different limitations; for example, basic HMM cannot model the infinite possible combinations of natural language. Critics note that the shift from GOFAI to statistical learning is often also a shift away from Explainable AI. In AGI research, some scholars caution against over-reliance on statistical learning, and argue that continuing research into GOFAI will still be necessary to attain general intelligence.

AI has developed a large number of tools to solve the most difficult problems in computer science. A few of the most general of these methods are discussed below.

Many problems in AI can be solved in theory by intelligently searching through many possible solutions:[173] Reasoning can be reduced to performing a search. For example, logical proof can be viewed as searching for a path that leads from premises to conclusions, where each step is the application of an inference rule.[174] Planning algorithms search through trees of goals and subgoals, attempting to find a path to a target goal, a process called means-ends analysis.[175] Robotics algorithms for moving limbs and grasping objects use local searches in configuration space.[119] Many learning algorithms use search algorithms based on optimization.

Simple exhaustive searches[176] 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. The solution, for many problems, is to use “heuristics” or “rules of thumb” that prioritize choices in favor of those that are more likely to reach a goal and to do so in a shorter number of steps. In some search methodologies heuristics can also serve to entirely eliminate some choices that are unlikely to lead to a goal (called “pruning the search tree”). Heuristics supply the program with a “best guess” for the path on which the solution lies.[177] Heuristics limit the search for solutions into a smaller sample size.

A very different kind of search came to prominence in the 1990s, based on the mathematical theory of optimization. For many problems, it is possible to begin the search with some form of a guess and then refine 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. Other optimization algorithms are simulated annealing, beam search and random optimization.[178]

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). Classic evolutionary algorithms include genetic algorithms, gene expression programming, and genetic programming.[179] Alternatively, 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).[180][181]

Logic[182] is used for knowledge representation and problem solving, but it can be applied to other problems as well. For example, the satplan algorithm uses logic for planning[183] and inductive logic programming is a method for learning.[184]

Several different forms of logic are used in AI research. Propositional logic[185] involves truth functions such as “or” and “not”. First-order logic[186] adds quantifiers and predicates, and can express facts about objects, their properties, and their relations with each other. Fuzzy set theory assigns a “degree of truth” (between 0 and 1) to vague statements such as “Alice is old” (or rich, or tall, or hungry) that are too linguistically imprecise to be completely true or false. Fuzzy logic is successfully used in control systems to allow experts to contribute vague rules such as “if you are close to the destination station and moving fast, increase the train’s brake pressure”; these vague rules can then be numerically refined within the system. Fuzzy logic fails to scale well in knowledge bases; many AI researchers question the validity of chaining fuzzy-logic inferences.[e][188][189]

Default logics, non-monotonic logics and circumscription[94] are forms of logic designed to help with default reasoning and the qualification problem. Several extensions of logic have been designed to handle specific domains of knowledge, such as: description logics;[82] situation calculus, event calculus and fluent calculus (for representing events and time);[83] causal calculus;[84] belief calculus;[190] and modal logics.[85]

Overall, qualitiative symbolic logic is brittle and scales poorly in the presence of noise or other uncertainty. Exceptions to rules are numerous, and it is difficult for logical systems to function in the presence of contradictory rules.[192]

Many problems in AI (in reasoning, planning, learning, perception, and robotics) require the agent to operate with incomplete or uncertain information. AI researchers have devised a number of powerful tools to solve these problems using methods from probability theory and economics.[193]

Bayesian networks[194] are a very general tool that can be used for a large number of problems: reasoning (using the Bayesian inference algorithm),[195] learning (using the expectation-maximization algorithm),[f][197] planning (using decision networks)[198] and perception (using dynamic Bayesian networks).[199] 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).[199] Compared with symbolic logic, formal Bayesian inference is computationally expensive. For inference to be tractable, most observations must be conditionally independent of one another. Complicated graphs with diamonds or other “loops” (undirected cycles) can require a sophisticated method such as Markov Chain Monte Carlo, which spreads an ensemble of random walkers throughout the Bayesian network and attempts to converge to an assessment of the conditional probabilities. Bayesian networks are used on Xbox Live to rate and match players; wins and losses are “evidence” of how good a player is. AdSense uses a Bayesian network with over 300 million edges to learn which ads to serve.

A key concept from the science of economics is “utility”: a measure of how valuable something is to an intelligent agent. Precise mathematical tools have been developed that analyze how an agent can make choices and plan, using decision theory, decision analysis,[200] and information value theory.[100] These tools include models such as Markov decision processes,[201] dynamic decision networks,[199] game theory and mechanism design.[202]

The simplest AI applications can be divided into two types: classifiers (“if shiny then diamond”) and controllers (“if shiny then pick up”). Controllers do, however, also classify conditions before inferring actions, and therefore classification forms a central part of many AI systems. Classifiers are functions that use pattern matching to determine a closest match. They can be tuned according to examples, making them very attractive for use in AI. These examples are known as observations or patterns. In supervised learning, each pattern belongs to a certain predefined class. A class can be seen as a decision that has to be made. 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.[203]

A classifier can be trained in various ways; there are many statistical and machine learning approaches. The decision tree[204] is perhaps the most widely used machine learning algorithm. Other widely used classifiers are the neural network,[206]k-nearest neighbor algorithm,[g][208]kernel methods such as the support vector machine (SVM),[h][210]Gaussian mixture model,[211] and the extremely popular naive Bayes classifier.[i][213] Classifier performance depends greatly on the characteristics of the data to be classified, such as the dataset size, distribution of samples across classes, the 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 matching model is available, and if accuracy (rather than speed or scalability) is the sole concern, conventional wisdom is that discriminative classifiers (especially SVM) tend to be more accurate than model-based classifiers such as “naive Bayes” on most practical data sets.[214]

Neural networks, or neural nets, were inspired by the architecture of neurons in the human brain. A simple “neuron” N accepts input from multiple other neurons, each of which, when activated (or “fired”), cast a weighted “vote” for or against whether neuron N should itself activate. Learning requires an algorithm to adjust these weights based on the training data; one simple algorithm (dubbed “fire together, wire together”) is to increase the weight between two connected neurons when the activation of one triggers the successful activation of another. The net forms “concepts” that are distributed among a subnetwork of shared[j] neurons that tend to fire together; a concept meaning “leg” might be coupled with a subnetwork meaning “foot” that includes the sound for “foot”. Neurons have a continuous spectrum of activation; in addition, neurons can process inputs in a nonlinear way rather than weighing straightforward votes. Modern neural nets can learn both continuous functions and, surprisingly, digital logical operations. Neural networks’ early successes included predicting the stock market and (in 1995) a mostly self-driving car.[k] In the 2010s, advances in neural networks using deep learning thrust AI into widespread public consciousness and contributed to an enormous upshift in corporate AI spending; for example, AI-related M&A in 2017 was over 25 times as large as in 2015.[217][218]

The study of non-learning artificial neural networks[206] began in the decade before the field of AI research was founded, in the work of Walter Pitts and Warren McCullouch. Frank Rosenblatt invented the perceptron, a learning network with a single layer, similar to the old concept of linear regression. Early pioneers also include Alexey Grigorevich Ivakhnenko, Teuvo Kohonen, Stephen Grossberg, Kunihiko Fukushima, Christoph von der Malsburg, David Willshaw, Shun-Ichi Amari, Bernard Widrow, John Hopfield, Eduardo R. Caianiello, and others.

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 radial basis networks.[219] Neural networks can be applied to the problem of intelligent control (for robotics) or learning, using such techniques as Hebbian learning (“fire together, wire together”), GMDH or competitive learning.[220]

Today, neural networks are often trained by the backpropagation algorithm, which had been around since 1970 as the reverse mode of automatic differentiation published by Seppo Linnainmaa,[221][222] and was introduced to neural networks by Paul Werbos.[223][224][225]

Hierarchical temporal memory is an approach that models some of the structural and algorithmic properties of the neocortex.[226]

To summarize, most neural networks use some form of gradient descent on a hand-created neural topology. However, some research groups, such as Uber, argue that simple neuroevolution to mutate new neural network topologies and weights may be competitive with sophisticated gradient descent approaches. One advantage of neuroevolution is that it may be less prone to get caught in “dead ends”.[227]

Deep learning is any artificial neural network that can learn a long chain of causal links. For example, a feedforward network with six hidden layers can learn a seven-link causal chain (six hidden layers + output layer) and has a “credit assignment path” (CAP) depth of seven. Many deep learning systems need to be able to learn chains ten or more causal links in length.[228] Deep learning has transformed many important subfields of artificial intelligence, including computer vision, speech recognition, natural language processing and others.[229][230][228]

According to one overview,[231] the expression “Deep Learning” was introduced to the Machine Learning community by Rina Dechter in 1986[232] and gained traction afterIgor Aizenberg and colleagues introduced it to Artificial Neural Networks in 2000.[233] The first functional Deep Learning networks were published by Alexey Grigorevich Ivakhnenko and V. G. Lapa in 1965.[234][pageneeded] These networks are trained one layer at a time. Ivakhnenko’s 1971 paper[235] describes the learning of a deep feedforward multilayer perceptron with eight layers, already much deeper than many later networks. In 2006, a publication by Geoffrey Hinton and Ruslan Salakhutdinov introduced another way of pre-training many-layered feedforward neural networks (FNNs) one layer at a time, treating each layer in turn as an unsupervised restricted Boltzmann machine, then using supervised backpropagation for fine-tuning. Similar to shallow artificial neural networks, deep neural networks can model complex non-linear relationships. Over the last few years, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks that contain many layers of non-linear hidden units and a very large output layer.[237]

Deep learning often uses convolutional neural networks (CNNs), whose origins can be traced back to the Neocognitron introduced by Kunihiko Fukushima in 1980.[238] In 1989, Yann LeCun and colleagues applied backpropagation to such an architecture. In the early 2000s, in an industrial application CNNs already processed an estimated 10% to 20% of all the checks written in the US.[239]Since 2011, fast implementations of CNNs on GPUs havewon many visual pattern recognition competitions.[228]

CNNs with 12 convolutional layers were used in conjunction with reinforcement learning by Deepmind’s “AlphaGo Lee”, the program that beat a top Go champion in 2016.[240]

Early on, deep learning was also applied to sequence learning with recurrent neural networks (RNNs)[241] which are in theory Turing complete[242] and can run arbitrary programs to process arbitrary sequences of inputs. The depth of an RNN is unlimited and depends on the length of its input sequence; thus, an RNN is an example of deep learning.[228] RNNs can be trained by gradient descent[243][244][245] but suffer from the vanishing gradient problem.[229][246] In 1992, it was shown that unsupervised pre-training of a stack of recurrent neural networks can speed up subsequent supervised learning of deep sequential problems.[247]

Numerous researchers now use variants of a deep learning recurrent NN called the long short-term memory (LSTM) network published by Hochreiter & Schmidhuber in 1997.[248] LSTM is often trained by Connectionist Temporal Classification (CTC).[249] At Google, Microsoft and Baidu this approach has revolutionised speech recognition.[250][251][252] For example, in 2015, Google’s speech recognition experienced a dramatic performance jump of 49% through CTC-trained LSTM, which is now available through Google Voice to billions of smartphone users.[253] Google also used LSTM to improve machine translation,[254] Language Modeling[255] and Multilingual Language Processing.[256] LSTM combined with CNNs also improved automatic image captioning[257] and a plethora of other applications.

AI, like electricity or the steam engine, is a general purpose technology. There is no consensus on how to characterize which tasks AI tends to excel at.[258] While projects such as AlphaZero have succeeded in generating their own knowledge from scratch, many other machine learning projects require large training datasets.[259][260] Researcher Andrew Ng has suggested, as a “highly imperfect rule of thumb”, that “almost anything a typical human can do with less than one second of mental thought, we can probably now or in the near future automate using AI.”[261] Moravec’s paradox suggests that AI lags humans at many tasks that the human brain has specifically evolved to perform well.[125]

Games provide a well-publicized benchmark for assessing rates of progress. AlphaGo around 2016 brought the era of classical board-game benchmarks to a close. Games of imperfect knowledge provide new challenges to AI in the area of game theory.[262][263] E-sports such as StarCraft continue to provide additional public benchmarks.[264][265] There are many competitions and prizes, such as the Imagenet Challenge, to promote research in artificial intelligence. The most common areas of competition include general machine intelligence, conversational behavior, data-mining, robotic cars, and robot soccer as well as conventional games.[266]

The “imitation game” (an interpretation of the 1950 Turing test that assesses whether a computer can imitate a human) is nowadays considered too exploitable to be a meaningful benchmark.[267] A derivative of the Turing test is the Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA). As the name implies, this helps to determine that a user is an actual person and not a computer posing as a human. In contrast to the standard Turing test, CAPTCHA is administered by a machine and targeted to a human as opposed to being administered by a human and targeted to a machine. A computer asks a user to complete a simple test then generates a grade for that test. Computers are unable to solve the problem, so correct solutions are deemed to be the result of a person taking the test. A common type of CAPTCHA is the test that requires the typing of distorted letters, numbers or symbols that appear in an image undecipherable by a computer.

Proposed “universal intelligence” tests aim to compare how well machines, humans, and even non-human animals perform on problem sets that are generic as possible. At an extreme, the test suite can contain every possible problem, weighted by Kolmogorov complexity; unfortunately, these problem sets tend to be dominated by impoverished pattern-matching exercises where a tuned AI can easily exceed human performance levels.[269][270]

AI is relevant to any intellectual task. Modern artificial intelligence techniques are pervasive and are too numerous to list here. Frequently, when a technique reaches mainstream use, it is no longer considered artificial intelligence; this phenomenon is described as the AI effect.

High-profile examples of AI include autonomous vehicles (such as drones and self-driving cars), medical diagnosis, creating art (such as poetry), proving mathematical theorems, playing games (such as Chess or Go), search engines (such as Google search), online assistants (such as Siri), image recognition in photographs, spam filtering, predicting flight delays,[273] prediction of judicial decisions[274] and targeting online advertisements.[275][276]

With social media sites overtaking TV as a source for news for young people and news organisations increasingly reliant on social media platforms for generating distribution,[277] major publishers now use artificial intelligence (AI) technology to post stories more effectively and generate higher volumes of traffic.[278]

AI is being applied to the high cost problem of dosage issueswhere findings suggested that AI could save $16 billion. In 2016, a ground breaking study in California found that a mathematical formula developed with the help of AI correctly determined the accurate dose of immunosuppressant drugs to give to organ patients.[279]

Artificial intelligence is breaking into the healthcare industry by assisting doctors. According to Bloomberg Technology, Microsoft has developed AI to help doctors find the right treatments for cancer.[280] There is a great amount of research and drugs developed relating to cancer. In detail, there are more than 800 medicines and vaccines to treat cancer. This negatively affects the doctors, because there are too many options to choose from, making it more difficult to choose the right drugs for the patients. Microsoft is working on a project to develop a machine called “Hanover”. Its goal is to memorize all the papers necessary to cancer and help predict which combinations of drugs will be most effective for each patient. One project that is being worked on at the moment is fighting myeloid leukemia, a fatal cancer where the treatment has not improved in decades. Another study was reported to have found that artificial intelligence was as good as trained doctors in identifying skin cancers.[281] Another study is using artificial intelligence to try and monitor multiple high-risk patients, and this is done by asking each patient numerous questions based on data acquired from live doctor to patient interactions.[282] One study was done with transfer learning, the machine performed a diagnosis similarly to a well-trained ophthalmologist, and could generate a decision within 30 seconds on whether or not the patient should be referred for treatment, with more than 95% percent accuracy.[283]

According to CNN, a recent study by surgeons at the Children’s National Medical Center in Washington successfully demonstrated surgery with an autonomous robot. The team supervised the robot while it performed soft-tissue surgery, stitching together a pig’s bowel during open surgery, and doing so better than a human surgeon, the team claimed.[284] IBM has created its own artificial intelligence computer, the IBM Watson, which has beaten human intelligence (at some levels). Watson not only won at the game show Jeopardy! against former champions,[285] but was declared a hero after successfully diagnosing a woman who was suffering from leukemia.[286]

Advancements in AI have contributed to the growth of the automotive industry through the creation and evolution of self-driving vehicles. As of 2016[update], there are over 30 companies utilizing AI into the creation of driverless cars. A few companies involved with AI include Tesla, Google, and Apple.[287]

Many components contribute to the functioning of self-driving cars. These vehicles incorporate systems such as braking, lane changing, collision prevention, navigation and mapping. Together, these systems, as well as high performance computers, are integrated into one complex vehicle.[288]

Recent developments in autonomous automobiles have made the innovation of self-driving trucks possible, though they are still in the testing phase. The UK government has passed legislation to begin testing of self-driving truck platoons in 2018.[289] Self-driving truck platoons are a fleet of self-driving trucks following the lead of one non-self-driving truck, so the truck platoons aren’t entirely autonomous yet. Meanwhile, the Daimler, a German automobile corporation, is testing the Freightliner Inspiration which is a semi-autonomous truck that will only be used on the highway.[290]

One main factor that influences the ability for a driver-less automobile to function is mapping. In general, the vehicle would be pre-programmed with a map of the area being driven. This map would include data on the approximations of street light and curb heights in order for the vehicle to be aware of its surroundings. However, Google has been working on an algorithm with the purpose of eliminating the need for pre-programmed maps and instead, creating a device that would be able to adjust to a variety of new surroundings.[291] Some self-driving cars are not equipped with steering wheels or brake pedals, so there has also been research focused on creating an algorithm that is capable of maintaining a safe environment for the passengers in the vehicle through awareness of speed and driving conditions.[292]

Another factor that is influencing the ability for a driver-less automobile is the safety of the passenger. To make a driver-less automobile, engineers must program it to handle high-risk situations. These situations could include a head-on collision with pedestrians. The car’s main goal should be to make a decision that would avoid hitting the pedestrians and saving the passengers in the car. But there is a possibility the car would need to make a decision that would put someone in danger. In other words, the car would need to decide to save the pedestrians or the passengers.[293] The programming of the car in these situations is crucial to a successful driver-less automobile.

Financial institutions have long used artificial neural network systems to detect charges or claims outside of the norm, flagging these for human investigation. The use of AI in banking can be traced back to 1987 when Security Pacific National Bank in US set-up a Fraud Prevention Task force to counter the unauthorised use of debit cards. Programs like Kasisto and Moneystream are using AI in financial services.

Banks use artificial intelligence systems today to organize operations, maintain book-keeping, invest in stocks, and manage properties. AI can react to changes overnight or when business is not taking place.[294] In August 2001, robots beat humans in a simulated financial trading competition.[295] AI has also reduced fraud and financial crimes by monitoring behavioral patterns of users for any abnormal changes or anomalies.[296]

The use of AI machines in the market in applications such as online trading and decision making has changed major economic theories.[297] For example, AI based buying and selling platforms have changed the law of supply and demand in that it is now possible to easily estimate individualized demand and supply curves and thus individualized pricing. Furthermore, AI machines reduce information asymmetry in the market and thus making markets more efficient while reducing the volume of trades. Furthermore, AI in the markets limits the consequences of behavior in the markets again making markets more efficient. Other theories where AI has had impact include in rational choice, rational expectations, game theory, Lewis turning point, portfolio optimization and counterfactual thinking.

In video games, artificial intelligence is routinely used to generate dynamic purposeful behavior in non-player characters (NPCs). In addition, well-understood AI techniques are routinely used for pathfinding. Some researchers consider NPC AI in games to be a “solved problem” for most production tasks. Games with more atypical AI include the AI director of Left 4 Dead (2008) and the neuroevolutionary training of platoons in Supreme Commander 2 (2010).[298][299]

Worldwide annual military spending on robotics rose from US$5.1 billion in 2010 to US$7.5 billion in 2015.[300][301] Military drones capable of autonomous action are widely considered a useful asset.[302] Many artificial intelligence researchers seek to distance themselves from military applications of AI.[303]

For financial statements audit, AI makes continuous audit possible. AI tools could analyze many sets of different information immediately. The potential benefit would be the overall audit risk will be reduced, the level of assurance will be increased and the time duration of audit will be reduced.[304]

It is possible to use AI to predict or generalize the behavior of customers from their digital footprints in order to target them with personalized promotions or build customer personas automatically.[305] A documented case reports that online gambling companies were using AI to improve customer targeting.[306]

Moreover, the application of Personality computing AI models can help reducing the cost of advertising campaigns by adding psychological targeting to more traditional sociodemographic or behavioral targeting.[307]

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Artificial intelligence – Wikipedia

What is Artificial Intelligence (AI)? – Definition from …

Artificial intelligence is a branch of computer science that aims to create intelligent machines. It has become an essential part of the technology industry.

Research associated with artificial intelligence is highly technical and specialized. The core problems of artificial intelligence include programming computers for certain traits such as:

Knowledge engineering is a core part of AI research. Machines can often act and react like humans only if they have abundant information relating to the world. Artificial intelligence must have access to objects, categories, properties and relations between all of them to implement knowledge engineering. Initiating common sense, reasoning and problem-solving power in machines is a difficult and tedious task.

Machine learning is also a core part of AI. Learning without any kind of supervision requires an ability to identify patterns in streams of inputs, whereas learning with adequate supervision involves classification and numerical regressions. Classification determines the category an object belongs to and regression deals with obtaining a set of numerical input or output examples, thereby discovering functions enabling the generation of suitable outputs from respective inputs. Mathematical analysis of machine learning algorithms and their performance is a well-defined branch of theoretical computer science often referred to as computational learning theory.

Machine perception deals with the capability to use sensory inputs to deduce the different aspects of the world, while computer vision is the power to analyze visual inputs with a few sub-problems such as facial, object and gesture recognition.

Robotics is also a major field related to AI. Robots require intelligence to handle tasks such as object manipulation and navigation, along with sub-problems of localization, motion planning and mapping.

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What is Artificial Intelligence (AI)? – Definition from …

Benefits & Risks of Artificial Intelligence – Future of …

Many AI researchers roll their eyes when seeing this headline:Stephen Hawking warns that rise of robots may be disastrous for mankind. And as many havelost count of how many similar articles theyveseen.Typically, these articles are accompanied by an evil-looking robot carrying a weapon, and they suggest we should worry about robots rising up and killing us because theyve become conscious and/or evil.On a lighter note, such articles are actually rather impressive, because they succinctly summarize the scenario that AI researchers dontworry about. That scenario combines as many as three separate misconceptions: concern about consciousness, evil, androbots.

If you drive down the road, you have a subjective experience of colors, sounds, etc. But does a self-driving car have a subjective experience? Does it feel like anything at all to be a self-driving car?Although this mystery of consciousness is interesting in its own right, its irrelevant to AI risk. If you get struck by a driverless car, it makes no difference to you whether it subjectively feels conscious. In the same way, what will affect us humans is what superintelligent AIdoes, not how it subjectively feels.

The fear of machines turning evil is another red herring. The real worry isnt malevolence, but competence. A superintelligent AI is by definition very good at attaining its goals, whatever they may be, so we need to ensure that its goals are aligned with ours. Humans dont generally hate ants, but were more intelligent than they are so if we want to build a hydroelectric dam and theres an anthill there, too bad for the ants. The beneficial-AI movement wants to avoid placing humanity in the position of those ants.

The consciousness misconception is related to the myth that machines cant have goals.Machines can obviously have goals in the narrow sense of exhibiting goal-oriented behavior: the behavior of a heat-seeking missile is most economically explained as a goal to hit a target.If you feel threatened by a machine whose goals are misaligned with yours, then it is precisely its goals in this narrow sense that troubles you, not whether the machine is conscious and experiences a sense of purpose.If that heat-seeking missile were chasing you, you probably wouldnt exclaim: Im not worried, because machines cant have goals!

I sympathize with Rodney Brooks and other robotics pioneers who feel unfairly demonized by scaremongering tabloids,because some journalists seem obsessively fixated on robots and adorn many of their articles with evil-looking metal monsters with red shiny eyes. In fact, the main concern of the beneficial-AI movement isnt with robots but with intelligence itself: specifically, intelligence whose goals are misaligned with ours. To cause us trouble, such misaligned superhuman intelligence needs no robotic body, merely an internet connection this may enable outsmarting financial markets, out-inventing human researchers, out-manipulating human leaders, and developing weapons we cannot even understand. Even if building robots were physically impossible, a super-intelligent and super-wealthy AI could easily pay or manipulate many humans to unwittingly do its bidding.

The robot misconception is related to the myth that machines cant control humans. Intelligence enables control: humans control tigers not because we are stronger, but because we are smarter. This means that if we cede our position as smartest on our planet, its possible that we might also cede control.

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Benefits & Risks of Artificial Intelligence – Future of …

A.I. Artificial Intelligence (2001) – IMDb

Nominated for 2 Oscars. Another 17 wins & 68 nominations. See more awards Learn more More Like This

Comedy | Drama | Sci-Fi

An android endeavors to become human as he gradually acquires emotions.

Director:Chris Columbus

Stars:Robin Williams,Embeth Davidtz,Sam Neill

Drama | Sci-Fi

Roy Neary, an electric lineman, watches how his quiet and ordinary daily life turns upside down after a close encounter with a UFO.

Director:Steven Spielberg

Stars:Richard Dreyfuss,Franois Truffaut,Teri Garr

Action | Crime | Mystery

In a future where a special police unit is able to arrest murderers before they commit their crimes, an officer from that unit is himself accused of a future murder.

Director:Steven Spielberg

Stars:Tom Cruise,Colin Farrell,Samantha Morton

Drama | History | Thriller

Based on the true story of the Black September aftermath, about the five men chosen to eliminate the ones responsible for that fateful day.

Director:Steven Spielberg

Stars:Eric Bana,Daniel Craig,Marie-Jose Croze

Drama | Mystery | Sci-Fi

Dr. Ellie Arroway, after years of searching, finds conclusive radio proof of extraterrestrial intelligence, sending plans for a mysterious machine.

Director:Robert Zemeckis

Stars:Jodie Foster,Matthew McConaughey,Tom Skerritt

Drama

A black Southern woman struggles to find her identity after suffering abuse from her father and others over four decades.

Director:Steven Spielberg

Stars:Danny Glover,Whoopi Goldberg,Oprah Winfrey

Drama | History | War

A young English boy struggles to survive under Japanese occupation during World War II.

Director:Steven Spielberg

Stars:Christian Bale,John Malkovich,Miranda Richardson

Adventure | Sci-Fi | Thriller

As Earth is invaded by alien tripod fighting machines, one family fights for survival.

Director:Steven Spielberg

Stars:Tom Cruise,Dakota Fanning,Tim Robbins

Drama | Sci-Fi | Thriller

A genetically inferior man assumes the identity of a superior one in order to pursue his lifelong dream of space travel.

Director:Andrew Niccol

Stars:Ethan Hawke,Uma Thurman,Jude Law

Drama | History

In 1839, the revolt of Mende captives aboard a Spanish owned ship causes a major controversy in the United States when the ship is captured off the coast of Long Island. The courts must decide whether the Mende are slaves or legally free.

Director:Steven Spielberg

Stars:Djimon Hounsou,Matthew McConaughey,Anthony Hopkins

Comedy | Drama | Romance

An Eastern European tourist unexpectedly finds himself stranded in JFK airport, and must take up temporary residence there.

Director:Steven Spielberg

Stars:Tom Hanks,Catherine Zeta-Jones,Chi McBride

Drama | History | War

Young Albert enlists to serve in World War I after his beloved horse is sold to the cavalry. Albert’s hopeful journey takes him out of England and to the front lines as the war rages on.

Director:Steven Spielberg

Stars:Jeremy Irvine,Emily Watson,David Thewlis

In the not-so-far future the polar ice caps have melted and the resulting rise of the ocean waters has drowned all the coastal cities of the world. Withdrawn to the interior of the continents, the human race keeps advancing, reaching the point of creating realistic robots (called mechas) to serve them. One of the mecha-producing companies builds David, an artificial kid which is the first to have real feelings, especially a never-ending love for his “mother”, Monica. Monica is the woman who adopted him as a substitute for her real son, who remains in cryo-stasis, stricken by an incurable disease. David is living happily with Monica and her husband, but when their real son returns home after a cure is discovered, his life changes dramatically. Written byChris Makrozahopoulos

Budget:$100,000,000 (estimated)

Opening Weekend USA: $29,352,630,1 July 2001, Wide Release

Gross USA: $78,616,689, 23 September 2001

Cumulative Worldwide Gross: $235,927,000

Runtime: 146 min

Aspect Ratio: 1.85 : 1

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A.I. Artificial Intelligence (2001) – IMDb

3 Top Artificial Intelligence Stocks to Watch in April …

“Artificial intelligence” might sound like an overused buzzword to many people. But astute investors know AI is already positively changing the way companies all over the world do business.

With that in mind, we asked three top Motley Fool contributors to each discuss an artificial intelligence stock you should be watching closely this month. Read on to see why they chose Facebook (NASDAQ:FB), Visa (NYSE:V), and Alphabet (NASDAQ:GOOG) (NASDAQ:GOOGL).

IMAGE SOURCE: GETTY IMAGES.

Steve Symington (Facebook): Facebook is set to release first-quarter 2019 results next Wednesday, April 24, 2019, and what’s contained in the report will help set the tone for both the online advertising and broader tech industries. Shares of the social media juggernaut are already up 36% so far in 2018 — albeit after plunging 26% last year as it traversed a difficult combination of data-privacy scandals, the spread of misinformation on its platforms, and concerns over decelerating top-line growth as Facebook builds on a larger base. With the help of AI, Facebook is striving to address those concerns.

That said, Facebook management did offer some color on what to expect during the January conference call, namely that first-quarter revenue growth should decelerate by a mid-single-digit-percent range (from 30% in Q4 2018). To that end — and though we don’t usually pay close attention to Wall Street’s demands — most analysts see the company delivering earnings of $1.63 per share on a 25.1% increase in revenue, to $14.97 billion.

Just as important will be the trends underlying that growth. Last quarter, the company saw daily and monthly active users each climb 9%, to 1.52 billion and 2.32 billion, respectively, and roughly 2.7 billion people hopped on at least one of its platforms — including Facebook, Instagram, WhatsApp, and Messenger — every month. If Facebook is seeing success with its recent efforts to combat last year’s problems and win back the trust of the public, we should hope to see its number of users continue to steadily climb.

Anders Bylund (Visa): Payment-processing veteran Visa may not be the first company that springs to mind in a discussion about artificial intelligence, but maybe it should. Take this snippet from a management presentation at a recent industry conference:

“The services that we offer that are extremely high value-added and in many ways have economics that are very similar to our core business and very much leverage of core business or things like risks services, fraud management and all that, authentication services, data analytics where we continue to add to our capabilities with AI,” said CFO Vasant Prabhu, according to a transcript compiled by Seeking Alpha. “We’ve always been doing machine learning, especially on fraud.”

Yep, you can thank artificial intelligence for the fraud warnings that drop in whenever your card issuer finds suspicious activity in your credit card accounts. Visa and its peers have actually been on the bleeding edge of AI tools in practical use for many years. These antifraud processes should continue improving over time as Visa takes advantage of more advanced technologies, ranging from neural networks to blockchain transactions.

Visa is gearing up for a second-quarter earnings report next Wednesday. AI probably won’t be a big talking point in that presentation, but you can rest assured that Visa takes advantage of the technology in a big way and will only continue to deepen its machine learning roots.

Chris Neiger (Alphabet): Alphabet makes the bulk of its revenue by selling ads across its various Google services platforms, but the company is also an artificial intelligence powerhouse, and it’s making considerable investments in AI now — so it can benefit later.

Take, for example, Alphabet’s self-driving vehicle company Waymo. The company has logged more than 10 million miles of autonomous driving, and at the end of 2018, Waymo launched one of the first commercial self-driving car services in the country. Without AI, Waymo’s vehicles wouldn’t be able to navigate traffic, avoid pedestrians, or learn from past driving experiences. The company stands to benefit from this AI pursuit by licensing some of its tech to other companies and selling its own services, all of which could help Waymo generate nearly $100 billion in sales over the next decade.

Autonomous vehicles aren’t Alphabet’s only AI bet though. Alphabet also owns DeepMind, an artificial-intelligence tech company that’s already proving itself invaluable. Just last month, DeepMind used some of its machine learning algorithms to predict when some of Google’s wind farms would produce the most wind, thereby generating the most energy, and scheduled some of the energy to be sent back to the electric grid. Why is this important? Because AI-powered tools like this are poised to help add $15.7 trillion to the global economy by 2030 by creating new services and making existing ones more efficient. Imagine the same tech being used not just for Google’s wind farms but in power grids across the country.

With Alphabet already knee deep in developing the AI tools and services that will make our lives more efficient, investors should consider snatching up shares of this tech giant now.

In today’s fast-changing technology world, we certainly can’t guarantee that any given stock will go on to deliver outsized returns and beat the market in the process. But with the help of AI, it appears Facebook, Visa, and Alphabet could be poised to do exactly that. And we think at the very least, investors would do well to add these stocks to their watch lists this month.

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3 Top Artificial Intelligence Stocks to Watch in April …

Artificial Intelligence – Journal – Elsevier

This journal has partnered with Heliyon, an open access journal from Elsevier publishing quality peer reviewed research across all disciplines. Heliyons team of experts provides editorial excellence, fast publication, and high visibility for your paper. Authors can quickly and easily transfer their research from a Partner Journal to Heliyon without the need to edit, reformat or resubmit.>Learn more at Heliyon.com

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Artificial Intelligence – Journal – Elsevier

Artificial intelligence – Wikipedia

Intelligence demonstrated by machines

In computer science, artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and animals. Computer science defines AI research as the study of “intelligent agents”: any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.[1] Colloquially, the term “artificial intelligence” is used to describe machines that mimic “cognitive” functions that humans associate with other human minds, such as “learning” and “problem solving”.

As machines become increasingly capable, tasks considered to require “intelligence” are often removed from the definition of AI, a phenomenon known as the AI effect. A quip in Tesler’s Theorem says “AI is whatever hasn’t been done yet.”[3] For instance, optical character recognition is frequently excluded from things considered to be AI, having become a routine technology.[4] Modern machine capabilities generally classified as AI include successfully understanding human speech, competing at the highest level in strategic game systems (such as chess and Go),[6] autonomously operating cars, and intelligent routing in content delivery networks and military simulations.

Artificial intelligence can be classified into three different types of systems: analytical, human-inspired, and humanized artificial intelligence.[7] Analytical AI has only characteristics consistent with cognitive intelligence; generating a cognitive representation of the world and using learning based on past experience to inform future decisions. Human-inspired AI has elements from cognitive and emotional intelligence; understanding human emotions, in addition to cognitive elements, and considering them in their decision making. Humanized AI shows characteristics of all types of competencies (i.e., cognitive, emotional, and social intelligence), is able to be self-conscious and is self-aware in interactions with others.

Artificial intelligence was founded as an academic discipline in 1956, and in the years since has experienced several waves of optimism,[8][9] followed by disappointment and the loss of funding (known as an “AI winter”),[10][11] followed by new approaches, success and renewed funding.[9][12] For most of its history, AI research has been divided into subfields that often fail to communicate with each other.[13] These sub-fields are based on technical considerations, such as particular goals (e.g. “robotics” or “machine learning”),[14] the use of particular tools (“logic” or artificial neural networks), or deep philosophical differences.[15][16][17] Subfields have also been based on social factors (particular institutions or the work of particular researchers).[13]

The traditional problems (or goals) of AI research include reasoning, knowledge representation, planning, learning, natural language processing, perception and the ability to move and manipulate objects.[14] General intelligence is among the field’s long-term goals.[18] Approaches include statistical methods, computational intelligence, and traditional symbolic AI. Many tools are used in AI, including versions of search and mathematical optimization, artificial neural networks, and methods based on statistics, probability and economics. The AI field draws upon computer science, information engineering, mathematics, psychology, linguistics, philosophy, and many other fields.

The field was founded on the claim that human intelligence “can be so precisely described that a machine can be made to simulate it”.[19] This raises philosophical arguments about the nature of the mind and the ethics of creating artificial beings endowed with human-like intelligence which are issues that have been explored by myth, fiction and philosophy since antiquity.[20] Some people also consider AI to be a danger to humanity if it progresses unabated.[21] Others believe that AI, unlike previous technological revolutions, will create a risk of mass unemployment.[22]

In the twenty-first century, AI techniques have experienced a resurgence following concurrent advances in computer power, large amounts of data, and theoretical understanding; and AI techniques have become an essential part of the technology industry, helping to solve many challenging problems in computer science, software engineering and operations research.[23][12]

Thought-capable artificial beings appeared as storytelling devices in antiquity,[24] and have been common in fiction, as in Mary Shelley’s Frankenstein or Karel apek’s R.U.R. (Rossum’s Universal Robots).[25] These characters and their fates raised many of the same issues now discussed in the ethics of artificial intelligence.[20]

The study of mechanical or “formal” reasoning began with philosophers and mathematicians in antiquity. The study of mathematical 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 any conceivable act of mathematical deduction. This insight, that digital computers can simulate any process of formal reasoning, is known as the ChurchTuring thesis.[26] Along with concurrent discoveries in neurobiology, information theory and cybernetics, this led researchers to consider the possibility of building an electronic brain. Turing proposed that “if a human could not distinguish between responses from a machine and a human, the machine could be considered “intelligent”.[27] The first work that is now generally recognized as AI was McCullouch and Pitts’ 1943 formal design for Turing-complete “artificial neurons”.

The field of AI research was born at a workshop at Dartmouth College in 1956.[29] Attendees Allen Newell (CMU), Herbert Simon (CMU), John McCarthy (MIT), Marvin Minsky (MIT) and Arthur Samuel (IBM) became the founders and leaders of AI research.[30] They and their students produced programs that the press described as “astonishing”: computers were learning checkers strategies (c. 1954)[32] (and by 1959 were reportedly playing better than the average human),[33] solving word problems in algebra, proving logical theorems (Logic Theorist, first run c. 1956) and speaking English.[34] By the middle of the 1960s, research in the U.S. was heavily funded by the Department of Defense[35] and laboratories had been established around the world.[36] AI’s founders were optimistic about the future: Herbert Simon predicted, “machines will be capable, within twenty years, of doing any work a man can do”. Marvin Minsky agreed, writing, “within a generation… the problem of creating ‘artificial intelligence’ will substantially be solved”.[8]

They failed to recognize the difficulty of some of the remaining tasks. Progress slowed and in 1974, in response to the criticism of Sir James Lighthill and ongoing pressure from the US Congress to fund more productive projects, both the U.S. and British governments cut off exploratory research in AI. The next few years would later be called an “AI winter”,[10] a period when obtaining funding for AI projects was difficult.

In the early 1980s, AI research was revived by the commercial success of expert systems,[38] 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.[9] However, beginning with the collapse of the Lisp Machine market in 1987, AI once again fell into disrepute, and a second, longer-lasting hiatus began.[11]

In the late 1990s and early 21st century, AI began to be used for logistics, data mining, medical diagnosis and other areas.[23] The success was due to increasing computational power (see Moore’s law), greater emphasis on solving specific problems, new ties between AI and other fields (such as statistics, economics and mathematics), and a commitment by researchers to mathematical methods and scientific standards.[39] Deep Blue became the first computer chess-playing system to beat a reigning world chess champion, Garry Kasparov, on 11 May 1997.

In 2011, a Jeopardy! quiz show exhibition match, IBM’s question answering system, Watson, defeated the two greatest Jeopardy! champions, Brad Rutter and Ken Jennings, by a significant margin. Faster computers, algorithmic improvements, and access to large amounts of data enabled advances in machine learning and perception; data-hungry deep learning methods started to dominate accuracy benchmarks around 2012.[42] The Kinect, which provides a 3D bodymotion interface for the Xbox 360 and the Xbox One, uses algorithms that emerged from lengthy AI research[43] as do intelligent personal assistants in smartphones.[44] 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.[6][45] In the 2017 Future of Go Summit, AlphaGo won a three-game match with Ke Jie,[46] who at the time continuously held the world No. 1 ranking for two years.[47][48] This marked the completion of a significant milestone in the development of Artificial Intelligence as Go is an extremely complex game, more so than Chess.

According to Bloomberg’s Jack Clark, 2015 was a landmark year for artificial intelligence, with the number of software projects that use AI within Google increased from a “sporadic usage” in 2012 to more than 2,700 projects. Clark also presents factual data indicating that error rates in image processing tasks have fallen significantly since 2011.[49] He attributes this to an increase in affordable neural networks, due to a rise in cloud computing infrastructure and to an increase in research tools and datasets.[12] Other cited examples include Microsoft’s development of a Skype system that can automatically translate from one language to another and Facebook’s system that can describe images to blind people.[49] In a 2017 survey, one in five companies reported they had “incorporated AI in some offerings or processes”.[50][51] Around 2016, China greatly accelerated its government funding; given its large supply of data and its rapidly increasing research output, some observers believe it may be on track to becoming an “AI superpower”.[52][53]

A typical AI analyses its environment and takes actions that maximize its chance of success.[1] An AI’s intended utility function (or goal) can be simple (“1 if the AI wins a game of Go, 0 otherwise”) or complex (“Do mathematically similar actions to the ones succeeded in the past”). Goals can be explicitly defined, or induced. If the AI is programmed for “reinforcement learning”, goals can be implicitly induced by rewarding some types of behavior or punishing others.[a] Alternatively, an evolutionary system can induce goals by using a “fitness function” to mutate and preferentially replicate high-scoring AI systems, similarly to how animals evolved to innately desire certain goals such as finding food. Some AI systems, such as nearest-neighbor, instead of reason by analogy, these systems are not generally given goals, except to the degree that goals are implicit in their training data. Such systems can still be benchmarked if the non-goal system is framed as a system whose “goal” is to successfully accomplish its narrow classification task.[56]

AI often revolves around the use of algorithms. An algorithm is a set of unambiguous instructions that a mechanical computer can execute.[b] A complex algorithm is often built on top of other, simpler, algorithms. A simple example of an algorithm is the following (optimal for first player) recipe for play at tic-tac-toe:

Many AI algorithms are capable of learning from data; they can enhance themselves by learning new heuristics (strategies, or “rules of thumb”, that have worked well in the past), or can themselves write other algorithms. Some of the “learners” described below, including Bayesian networks, decision trees, and nearest-neighbor, could theoretically, (given infinite data, time, and memory) learn to approximate any function, including which combination of mathematical functions would best describe the world. These learners could therefore, derive all possible knowledge, by considering every possible hypothesis and matching them against the data. In practice, it is almost never possible to consider every possibility, because of the phenomenon of “combinatorial explosion”, where the amount of time needed to solve a problem grows exponentially. Much of AI research involves figuring out how to identify and avoid considering broad range of possibilities that are unlikely to be beneficial.[58] For example, when viewing a map and looking for the shortest driving route from Denver to New York in the East, one can in most cases skip looking at any path through San Francisco or other areas far to the West; thus, an AI wielding an pathfinding algorithm like A* can avoid the combinatorial explosion that would ensue if every possible route had to be ponderously considered in turn.[60]

The earliest (and easiest to understand) approach to AI was symbolism (such as formal logic): “If an otherwise healthy adult has a fever, then they may have influenza”. A second, more general, approach is Bayesian inference: “If the current patient has a fever, adjust the probability they have influenza in such-and-such way”. The third major approach, extremely popular in routine business AI applications, are analogizers such as SVM and nearest-neighbor: “After examining the records of known past patients whose temperature, symptoms, age, and other factors mostly match the current patient, X% of those patients turned out to have influenza”. A fourth approach is harder to intuitively understand, but is inspired by how the brain’s machinery works: the artificial neural network approach uses artificial “neurons” that can learn by comparing itself to the desired output and altering the strengths of the connections between its internal neurons to “reinforce” connections that seemed to be useful. These four main approaches can overlap with each other and with evolutionary systems; for example, neural nets can learn to make inferences, to generalize, and to make analogies. Some systems implicitly or explicitly use multiple of these approaches, alongside many other AI and non-AI algorithms;[61] the best approach is often different depending on the problem.[63]

Learning algorithms work on the basis that strategies, algorithms, and inferences that worked well in the past are likely to continue working well in the future. These inferences can be obvious, such as “since the sun rose every morning for the last 10,000 days, it will probably rise tomorrow morning as well”. They can be nuanced, such as “X% of families have geographically separate species with color variants, so there is an Y% chance that undiscovered black swans exist”. Learners also work on the basis of “Occam’s razor”: The simplest theory that explains the data is the likeliest. Therefore, to be successful, a learner must be designed such that it prefers simpler theories to complex theories, except in cases where the complex theory is proven substantially better. Settling on a bad, overly complex theory gerrymandered to fit all the past training data is known as overfitting. Many systems attempt to reduce overfitting by rewarding a theory in accordance with how well it fits the data, but penalizing the theory in accordance with how complex the theory is. Besides classic overfitting, learners can also disappoint by “learning the wrong lesson”. A toy example is that an image classifier trained only on pictures of brown horses and black cats might conclude that all brown patches are likely to be horses. A real-world example is that, unlike humans, current image classifiers don’t determine the spatial relationship between components of the picture; instead, they learn abstract patterns of pixels that humans are oblivious to, but that linearly correlate with images of certain types of real objects. Faintly superimposing such a pattern on a legitimate image results in an “adversarial” image that the system misclassifies.[c][66][67][68]

Compared with humans, existing AI lacks several features of human “commonsense reasoning”; most notably, humans have powerful mechanisms for reasoning about “nave physics” such as space, time, and physical interactions. This enables even young children to easily make inferences like “If I roll this pen off a table, it will fall on the floor”. Humans also have a powerful mechanism of “folk psychology” that helps them to interpret natural-language sentences such as “The city councilmen refused the demonstrators a permit because they advocated violence”. (A generic AI has difficulty inferring whether the councilmen or the demonstrators are the ones alleged to be advocating violence.)[71][72][73] This lack of “common knowledge” means that AI often makes different mistakes than humans make, in ways that can seem incomprehensible. For example, existing self-driving cars cannot reason about the location nor the intentions of pedestrians in the exact way that humans do, and instead must use non-human modes of reasoning to avoid accidents.[74][75][76]

The overall research goal of artificial intelligence is to create technology that allows computers and machines to function in an intelligent manner. 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.[14]

Early researchers developed algorithms that imitated step-by-step reasoning that humans use when they solve puzzles or make logical deductions.[77] By the late 1980s and 1990s, AI research had developed methods for dealing with uncertain or incomplete information, employing concepts from probability and economics.[78]

These algorithms proved to be insufficient for solving large reasoning problems, because they experienced a “combinatorial explosion”: they became exponentially slower as the problems grew larger.[58] In fact, even humans rarely use the step-by-step deduction that early AI research was able to model. They solve most of their problems using fast, intuitive judgements.[79]

Knowledge representation[80] and knowledge engineering[81] are central to classical AI research. Some “expert systems” attempt to gather together explicit knowledge possessed by experts in some narrow domain. In addition, some projects attempt to gather the “commonsense knowledge” known to the average person into a database containing extensive knowledge about the world. Among the things a comprehensive commonsense knowledge base would contain are: objects, properties, categories and relations between objects;[82] situations, events, states and time;[83] causes and effects;[84] knowledge about knowledge (what we know about what other people know);[85] and many other, less well researched domains. A representation of “what exists” is an ontology: the set of objects, relations, concepts, and properties formally described so that software agents can interpret them. The semantics of these are captured as description logic concepts, roles, and individuals, and typically implemented as classes, properties, and individuals in the Web Ontology Language.[86] The most general ontologies are called upper ontologies, which attempt to provide a foundation for all other knowledge[87] by acting as mediators between domain ontologies that cover specific knowledge about a particular knowledge domain (field of interest or area of concern). Such formal knowledge representations can be used in content-based indexing and retrieval,[88] scene interpretation,[89] clinical decision support,[90] knowledge discovery (mining “interesting” and actionable inferences from large databases),[91] and other areas.[92]

Among the most difficult problems in knowledge representation are:

Intelligent agents must be able to set goals and achieve them.[99] They need a way to visualize the futurea representation of the state of the world and be able to make predictions about how their actions will change itand be able to make choices that maximize the utility (or “value”) of available choices.[100]

In classical planning problems, the agent can assume that it is the only system acting in the world, allowing the agent to be certain of the consequences of its actions.[101] However, if the agent is not the only actor, then it requires that the agent can reason under uncertainty. This calls for an agent that can not only assess its environment and make predictions, but also evaluate its predictions and adapt based on its assessment.[102]

Multi-agent planning uses the cooperation and competition of many agents to achieve a given goal. Emergent behavior such as this is used by evolutionary algorithms and swarm intelligence.[103]

Machine learning, a fundamental concept of AI research since the field’s inception,[104] is the study of computer algorithms that improve automatically through experience.[105][106]

Unsupervised learning is the ability to find patterns in a stream of input, without requiring a human to label the inputs first.[107] Supervised learning includes both classification and numerical regression, which requires a human to label the input data first. Classification is used to determine what category something belongs in, after seeing a number of examples of things from several categories. Regression is the attempt to produce a function that describes the relationship between inputs and outputs and predicts how the outputs should change as the inputs change.[106] Both classifiers and regression learners can be viewed as “function approximators” trying to learn an unknown (possibly implicit) function; for example, a spam classifier can be viewed as learning a function that maps from the text of an email to one of two categories, “spam” or “not spam”. Computational learning theory can assess learners by computational complexity, by sample complexity (how much data is required), or by other notions of optimization.[108] In reinforcement learning[109] the agent is rewarded for good responses and punished for bad ones. The agent uses this sequence of rewards and punishments to form a strategy for operating in its problem space.

Natural language processing[110] (NLP) gives machines the ability to read and understand human language. A sufficiently powerful natural language processing system would enable natural-language user interfaces and the acquisition of knowledge directly from human-written sources, such as newswire texts. Some straightforward applications of natural language processing include information retrieval, text mining, question answering[111] and machine translation.[112] Many current approaches use word co-occurrence frequencies to construct syntactic representations of text. “Keyword spotting” strategies for search are popular and scalable but dumb; a search query for “dog” might only match documents with the literal word “dog” and miss a document with the word “poodle”. “Lexical affinity” strategies use the occurrence of words such as “accident” to assess the sentiment of a document. Modern statistical NLP approaches can combine all these strategies as well as others, and often achieve acceptable accuracy at the page or paragraph level, but continue to lack the semantic understanding required to classify isolated sentences well. Besides the usual difficulties with encoding semantic commonsense knowledge, existing semantic NLP sometimes scales too poorly to be viable in business applications. Beyond semantic NLP, the ultimate goal of “narrative” NLP is to embody a full understanding of commonsense reasoning.[113]

Machine perception[114] is the ability to use input from sensors (such as cameras (visible spectrum or infrared), microphones, wireless signals, and active lidar, sonar, radar, and tactile sensors) to deduce aspects of the world. Applications include speech recognition,[115] facial recognition, and object recognition.[116] Computer vision is the ability to analyze visual input. Such input is usually ambiguous; a giant, fifty-meter-tall pedestrian far away may produce exactly the same pixels as a nearby normal-sized pedestrian, requiring the AI to judge the relative likelihood and reasonableness of different interpretations, for example by using its “object model” to assess that fifty-meter pedestrians do not exist.[117]

AI is heavily used in robotics.[118] Advanced robotic arms and other industrial robots, widely used in modern factories, can learn from experience how to move efficiently despite the presence of friction and gear slippage.[119] A modern mobile robot, when given a small, static, and visible environment, can easily determine its location and map its environment; however, dynamic environments, such as (in endoscopy) the interior of a patient’s breathing body, pose a greater challenge. Motion planning is the process of breaking down a movement task into “primitives” such as individual joint movements. Such movement often involves compliant motion, a process where movement requires maintaining physical contact with an object.[121][122] Moravec’s paradox generalizes that low-level sensorimotor skills that humans take for granted are, counterintuitively, difficult to program into a robot; the paradox is named after Hans Moravec, who stated in 1988 that “it is comparatively easy to make computers exhibit adult level performance on intelligence tests or playing checkers, and difficult or impossible to give them the skills of a one-year-old when it comes to perception and mobility”.[123][124] This is attributed to the fact that, unlike checkers, physical dexterity has been a direct target of natural selection for millions of years.[125]

Moravec’s paradox can be extended to many forms of social intelligence.[127][128] Distributed multi-agent coordination of autonomous vehicles remains a difficult problem.[129] Affective computing is an interdisciplinary umbrella that comprises systems which recognize, interpret, process, or simulate human affects. Moderate successes related to affective computing include textual sentiment analysis and, more recently, multimodal affect analysis (see multimodal sentiment analysis), wherein AI classifies the affects displayed by a videotaped subject.[133]

In the long run, social skills and an understanding of human emotion and game theory would be valuable to a social agent. Being able to predict the actions of others by understanding their motives and emotional states would allow an agent to make better decisions. Some computer systems mimic human emotion and expressions to appear more sensitive to the emotional dynamics of human interaction, or to otherwise facilitate humancomputer interaction.[134] Similarly, some virtual assistants are programmed to speak conversationally or even to banter humorously; this tends to give nave users an unrealistic conception of how intelligent existing computer agents actually are.[135]

Historically, projects such as the Cyc knowledge base (1984) and the massive Japanese Fifth Generation Computer Systems initiative (19821992) attempted to cover the breadth of human cognition. These early projects failed to escape the limitations of non-quantitative symbolic logic models and, in retrospect, greatly underestimated the difficulty of cross-domain AI. Nowadays, the vast majority of current AI researchers work instead on tractable “narrow AI” applications (such as medical diagnosis or automobile navigation).[136] Many researchers predict that such “narrow AI” work in different individual domains will eventually be incorporated into a machine with artificial general intelligence (AGI), combining most of the narrow skills mentioned in this article and at some point even exceeding human ability in most or all these areas.[18][137] Many advances have general, cross-domain significance. One high-profile example is that DeepMind in the 2010s developed a “generalized artificial intelligence” that could learn many diverse Atari games on its own, and later developed a variant of the system which succeeds at sequential learning.[138][139][140] Besides transfer learning,[141] hypothetical AGI breakthroughs could include the development of reflective architectures that can engage in decision-theoretic metareasoning, and figuring out how to “slurp up” a comprehensive knowledge base from the entire unstructured Web. Some argue that some kind of (currently-undiscovered) conceptually straightforward, but mathematically difficult, “Master Algorithm” could lead to AGI. Finally, a few “emergent” approaches look to simulating human intelligence extremely closely, and believe that anthropomorphic features like an artificial brain or simulated child development may someday reach a critical point where general intelligence emerges.[143][144]

Many of the problems in this article may also require general intelligence, if machines are to solve the problems as well as people do. For example, even specific straightforward tasks, like machine translation, require that a machine read and write in both languages (NLP), follow the author’s argument (reason), know what is being talked about (knowledge), and faithfully reproduce the author’s original intent (social intelligence). A problem like machine translation is considered “AI-complete”, because all of these problems need to be solved simultaneously in order to reach human-level machine performance.

There is no established unifying theory or paradigm that guides AI research. Researchers disagree about many issues.[145] A few of the most long standing questions that have remained unanswered are these: should artificial intelligence simulate natural intelligence by studying psychology or neurobiology? Or is human biology as irrelevant to AI research as bird biology is to aeronautical engineering?[15]Can intelligent behavior be described using simple, elegant principles (such as logic or optimization)? Or does it necessarily require solving a large number of completely unrelated problems?[16]

In the 1940s and 1950s, a number of researchers explored the connection between neurobiology, information theory, and cybernetics. Some of them built machines that used electronic networks to exhibit rudimentary intelligence, such as W. Grey Walter’s turtles and the Johns Hopkins Beast. Many of these researchers gathered for meetings of the Teleological Society at Princeton University and the Ratio Club in England.[146] By 1960, this approach was largely abandoned, although elements of it would be revived in the 1980s.

When access to digital computers became possible in the middle 1950s, AI research began to explore the possibility that human intelligence could be reduced to symbol manipulation. The research was centered in three institutions: Carnegie Mellon University, Stanford and MIT, and as described below, each one developed its own style of research. John Haugeland named these symbolic approaches to AI “good old fashioned AI” or “GOFAI”.[147] During the 1960s, symbolic approaches had achieved great success at simulating high-level thinking in small demonstration programs. Approaches based on cybernetics or artificial neural networks were abandoned or pushed into the background.[148]Researchers in the 1960s and the 1970s were convinced that symbolic approaches would eventually succeed in creating a machine with artificial general intelligence and considered this the goal of their field.

Economist Herbert Simon and Allen Newell studied human problem-solving skills and attempted to formalize them, and their work laid the foundations of the field of artificial intelligence, as well as cognitive science, operations research and management science. Their research team used the results of psychological experiments to develop programs that simulated the techniques that people used to solve problems. This tradition, centered at Carnegie Mellon University would eventually culminate in the development of the Soar architecture in the middle 1980s.[149][150]

Unlike Simon and Newell, John McCarthy felt that machines did not need to simulate human thought, but should instead try to find the essence of abstract reasoning and problem-solving, regardless whether people used the same algorithms.[15] His laboratory at Stanford (SAIL) focused on using formal logic to solve a wide variety of problems, including knowledge representation, planning and learning.[151] Logic was also the focus of the work at the University of Edinburgh and elsewhere in Europe which led to the development of the programming language Prolog and the science of logic programming.[152]

Researchers at MIT (such as Marvin Minsky and Seymour Papert)[153] found that solving difficult problems in vision and natural language processing required ad-hoc solutionsthey argued that there was no simple and general principle (like logic) that would capture all the aspects of intelligent behavior. Roger Schank described their “anti-logic” approaches as “scruffy” (as opposed to the “neat” paradigms at CMU and Stanford).[16] Commonsense knowledge bases (such as Doug Lenat’s Cyc) are an example of “scruffy” AI, since they must be built by hand, one complicated concept at a time.[154]

When computers with large memories became available around 1970, researchers from all three traditions began to build knowledge into AI applications.[155] This “knowledge revolution” led to the development and deployment of expert systems (introduced by Edward Feigenbaum), the first truly successful form of AI software.[38] A key component of the system architecture for all expert systems is the knowledge base, which stores facts and rules that illustrate AI.[156] The knowledge revolution was also driven by the realization that enormous amounts of knowledge would be required by many simple AI applications.

By the 1980s, progress in symbolic AI seemed to stall and many believed that symbolic systems would never be able to imitate all the processes of human cognition, especially perception, robotics, learning and pattern recognition. A number of researchers began to look into “sub-symbolic” approaches to specific AI problems.[17] Sub-symbolic methods manage to approach intelligence without specific representations of knowledge.

This includes embodied, situated, behavior-based, and nouvelle AI. Researchers from the related field of robotics, such as Rodney Brooks, rejected symbolic AI and focused on the basic engineering problems that would allow robots to move and survive.[157] Their work revived the non-symbolic point of view of the early cybernetics researchers of the 1950s and reintroduced the use of control theory in AI. This coincided with the development of the embodied mind thesis in the related field of cognitive science: the idea that aspects of the body (such as movement, perception and visualization) are required for higher intelligence.

Within developmental robotics, developmental learning approaches are elaborated upon to allow robots to accumulate repertoires of novel skills through autonomous self-exploration, social interaction with human teachers, and the use of guidance mechanisms (active learning, maturation, motor synergies, etc.).[158][159]

Interest in neural networks and “connectionism” was revived by David Rumelhart and others in the middle of the 1980s.[162] Artificial neural networks are an example of soft computingthey are solutions to problems which cannot be solved with complete logical certainty, and where an approximate solution is often sufficient. Other soft computing approaches to AI include fuzzy systems, Grey system theory, evolutionary computation and many statistical tools. The application of soft computing to AI is studied collectively by the emerging discipline of computational intelligence.[163]

Much of traditional GOFAI got bogged down on ad hoc patches to symbolic computation that worked on their own toy models but failed to generalize to real-world results. However, around the 1990s, AI researchers adopted sophisticated mathematical tools, such as hidden Markov models (HMM), information theory, and normative Bayesian decision theory to compare or to unify competing architectures. The shared mathematical language permitted a high level of collaboration with more established fields (like mathematics, economics or operations research).[d] Compared with GOFAI, new “statistical learning” techniques such as HMM and neural networks were gaining higher levels of accuracy in many practical domains such as data mining, without necessarily acquiring semantic understanding of the datasets. The increased successes with real-world data led to increasing emphasis on comparing different approaches against shared test data to see which approach performed best in a broader context than that provided by idiosyncratic toy models; AI research was becoming more scientific. Nowadays results of experiments are often rigorously measurable, and are sometimes (with difficulty) reproducible.[39][164] Different statistical learning techniques have different limitations; for example, basic HMM cannot model the infinite possible combinations of natural language. Critics note that the shift from GOFAI to statistical learning is often also a shift away from Explainable AI. In AGI research, some scholars caution against over-reliance on statistical learning, and argue that continuing research into GOFAI will still be necessary to attain general intelligence.

AI has developed a large number of tools to solve the most difficult problems in computer science. A few of the most general of these methods are discussed below.

Many problems in AI can be solved in theory by intelligently searching through many possible solutions:[173] Reasoning can be reduced to performing a search. For example, logical proof can be viewed as searching for a path that leads from premises to conclusions, where each step is the application of an inference rule.[174] Planning algorithms search through trees of goals and subgoals, attempting to find a path to a target goal, a process called means-ends analysis.[175] Robotics algorithms for moving limbs and grasping objects use local searches in configuration space.[119] Many learning algorithms use search algorithms based on optimization.

Simple exhaustive searches[176] 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. The solution, for many problems, is to use “heuristics” or “rules of thumb” that prioritize choices in favor of those that are more likely to reach a goal and to do so in a shorter number of steps. In some search methodologies heuristics can also serve to entirely eliminate some choices that are unlikely to lead to a goal (called “pruning the search tree”). Heuristics supply the program with a “best guess” for the path on which the solution lies.[177] Heuristics limit the search for solutions into a smaller sample size.

A very different kind of search came to prominence in the 1990s, based on the mathematical theory of optimization. For many problems, it is possible to begin the search with some form of a guess and then refine 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. Other optimization algorithms are simulated annealing, beam search and random optimization.[178]

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). Classic evolutionary algorithms include genetic algorithms, gene expression programming, and genetic programming.[179] Alternatively, 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).[180][181]

Logic[182] is used for knowledge representation and problem solving, but it can be applied to other problems as well. For example, the satplan algorithm uses logic for planning[183] and inductive logic programming is a method for learning.[184]

Several different forms of logic are used in AI research. Propositional logic[185] involves truth functions such as “or” and “not”. First-order logic[186] adds quantifiers and predicates, and can express facts about objects, their properties, and their relations with each other. Fuzzy set theory assigns a “degree of truth” (between 0 and 1) to vague statements such as “Alice is old” (or rich, or tall, or hungry) that are too linguistically imprecise to be completely true or false. Fuzzy logic is successfully used in control systems to allow experts to contribute vague rules such as “if you are close to the destination station and moving fast, increase the train’s brake pressure”; these vague rules can then be numerically refined within the system. Fuzzy logic fails to scale well in knowledge bases; many AI researchers question the validity of chaining fuzzy-logic inferences.[e][188][189]

Default logics, non-monotonic logics and circumscription[94] are forms of logic designed to help with default reasoning and the qualification problem. Several extensions of logic have been designed to handle specific domains of knowledge, such as: description logics;[82] situation calculus, event calculus and fluent calculus (for representing events and time);[83] causal calculus;[84] belief calculus;[190] and modal logics.[85]

Overall, qualitiative symbolic logic is brittle and scales poorly in the presence of noise or other uncertainty. Exceptions to rules are numerous, and it is difficult for logical systems to function in the presence of contradictory rules.[192]

Many problems in AI (in reasoning, planning, learning, perception, and robotics) require the agent to operate with incomplete or uncertain information. AI researchers have devised a number of powerful tools to solve these problems using methods from probability theory and economics.[193]

Bayesian networks[194] are a very general tool that can be used for a large number of problems: reasoning (using the Bayesian inference algorithm),[195] learning (using the expectation-maximization algorithm),[f][197] planning (using decision networks)[198] and perception (using dynamic Bayesian networks).[199] 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).[199] Compared with symbolic logic, formal Bayesian inference is computationally expensive. For inference to be tractable, most observations must be conditionally independent of one another. Complicated graphs with diamonds or other “loops” (undirected cycles) can require a sophisticated method such as Markov Chain Monte Carlo, which spreads an ensemble of random walkers throughout the Bayesian network and attempts to converge to an assessment of the conditional probabilities. Bayesian networks are used on Xbox Live to rate and match players; wins and losses are “evidence” of how good a player is. AdSense uses a Bayesian network with over 300 million edges to learn which ads to serve.

A key concept from the science of economics is “utility”: a measure of how valuable something is to an intelligent agent. Precise mathematical tools have been developed that analyze how an agent can make choices and plan, using decision theory, decision analysis,[200] and information value theory.[100] These tools include models such as Markov decision processes,[201] dynamic decision networks,[199] game theory and mechanism design.[202]

The simplest AI applications can be divided into two types: classifiers (“if shiny then diamond”) and controllers (“if shiny then pick up”). Controllers do, however, also classify conditions before inferring actions, and therefore classification forms a central part of many AI systems. Classifiers are functions that use pattern matching to determine a closest match. They can be tuned according to examples, making them very attractive for use in AI. These examples are known as observations or patterns. In supervised learning, each pattern belongs to a certain predefined class. A class can be seen as a decision that has to be made. 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.[203]

A classifier can be trained in various ways; there are many statistical and machine learning approaches. The decision tree[204] is perhaps the most widely used machine learning algorithm. Other widely used classifiers are the neural network,[206]k-nearest neighbor algorithm,[g][208]kernel methods such as the support vector machine (SVM),[h][210]Gaussian mixture model,[211] and the extremely popular naive Bayes classifier.[i][213] Classifier performance depends greatly on the characteristics of the data to be classified, such as the dataset size, distribution of samples across classes, the 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 matching model is available, and if accuracy (rather than speed or scalability) is the sole concern, conventional wisdom is that discriminative classifiers (especially SVM) tend to be more accurate than model-based classifiers such as “naive Bayes” on most practical data sets.[214]

Neural networks, or neural nets, were inspired by the architecture of neurons in the human brain. A simple “neuron” N accepts input from multiple other neurons, each of which, when activated (or “fired”), cast a weighted “vote” for or against whether neuron N should itself activate. Learning requires an algorithm to adjust these weights based on the training data; one simple algorithm (dubbed “fire together, wire together”) is to increase the weight between two connected neurons when the activation of one triggers the successful activation of another. The net forms “concepts” that are distributed among a subnetwork of shared[j] neurons that tend to fire together; a concept meaning “leg” might be coupled with a subnetwork meaning “foot” that includes the sound for “foot”. Neurons have a continuous spectrum of activation; in addition, neurons can process inputs in a nonlinear way rather than weighing straightforward votes. Modern neural nets can learn both continuous functions and, surprisingly, digital logical operations. Neural networks’ early successes included predicting the stock market and (in 1995) a mostly self-driving car.[k] In the 2010s, advances in neural networks using deep learning thrust AI into widespread public consciousness and contributed to an enormous upshift in corporate AI spending; for example, AI-related M&A in 2017 was over 25 times as large as in 2015.[217][218]

The study of non-learning artificial neural networks[206] began in the decade before the field of AI research was founded, in the work of Walter Pitts and Warren McCullouch. Frank Rosenblatt invented the perceptron, a learning network with a single layer, similar to the old concept of linear regression. Early pioneers also include Alexey Grigorevich Ivakhnenko, Teuvo Kohonen, Stephen Grossberg, Kunihiko Fukushima, Christoph von der Malsburg, David Willshaw, Shun-Ichi Amari, Bernard Widrow, John Hopfield, Eduardo R. Caianiello, and others.

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 radial basis networks.[219] Neural networks can be applied to the problem of intelligent control (for robotics) or learning, using such techniques as Hebbian learning (“fire together, wire together”), GMDH or competitive learning.[220]

Today, neural networks are often trained by the backpropagation algorithm, which had been around since 1970 as the reverse mode of automatic differentiation published by Seppo Linnainmaa,[221][222] and was introduced to neural networks by Paul Werbos.[223][224][225]

Hierarchical temporal memory is an approach that models some of the structural and algorithmic properties of the neocortex.[226]

To summarize, most neural networks use some form of gradient descent on a hand-created neural topology. However, some research groups, such as Uber, argue that simple neuroevolution to mutate new neural network topologies and weights may be competitive with sophisticated gradient descent approaches. One advantage of neuroevolution is that it may be less prone to get caught in “dead ends”.[227]

Deep learning is any artificial neural network that can learn a long chain of causal links. For example, a feedforward network with six hidden layers can learn a seven-link causal chain (six hidden layers + output layer) and has a “credit assignment path” (CAP) depth of seven. Many deep learning systems need to be able to learn chains ten or more causal links in length.[228] Deep learning has transformed many important subfields of artificial intelligence, including computer vision, speech recognition, natural language processing and others.[229][230][228]

According to one overview,[231] the expression “Deep Learning” was introduced to the Machine Learning community by Rina Dechter in 1986[232] and gained traction afterIgor Aizenberg and colleagues introduced it to Artificial Neural Networks in 2000.[233] The first functional Deep Learning networks were published by Alexey Grigorevich Ivakhnenko and V. G. Lapa in 1965.[234][pageneeded] These networks are trained one layer at a time. Ivakhnenko’s 1971 paper[235] describes the learning of a deep feedforward multilayer perceptron with eight layers, already much deeper than many later networks. In 2006, a publication by Geoffrey Hinton and Ruslan Salakhutdinov introduced another way of pre-training many-layered feedforward neural networks (FNNs) one layer at a time, treating each layer in turn as an unsupervised restricted Boltzmann machine, then using supervised backpropagation for fine-tuning. Similar to shallow artificial neural networks, deep neural networks can model complex non-linear relationships. Over the last few years, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks that contain many layers of non-linear hidden units and a very large output layer.[237]

Deep learning often uses convolutional neural networks (CNNs), whose origins can be traced back to the Neocognitron introduced by Kunihiko Fukushima in 1980.[238] In 1989, Yann LeCun and colleagues applied backpropagation to such an architecture. In the early 2000s, in an industrial application CNNs already processed an estimated 10% to 20% of all the checks written in the US.[239]Since 2011, fast implementations of CNNs on GPUs havewon many visual pattern recognition competitions.[228]

CNNs with 12 convolutional layers were used in conjunction with reinforcement learning by Deepmind’s “AlphaGo Lee”, the program that beat a top Go champion in 2016.[240]

Early on, deep learning was also applied to sequence learning with recurrent neural networks (RNNs)[241] which are in theory Turing complete[242] and can run arbitrary programs to process arbitrary sequences of inputs. The depth of an RNN is unlimited and depends on the length of its input sequence; thus, an RNN is an example of deep learning.[228] RNNs can be trained by gradient descent[243][244][245] but suffer from the vanishing gradient problem.[229][246] In 1992, it was shown that unsupervised pre-training of a stack of recurrent neural networks can speed up subsequent supervised learning of deep sequential problems.[247]

Numerous researchers now use variants of a deep learning recurrent NN called the long short-term memory (LSTM) network published by Hochreiter & Schmidhuber in 1997.[248] LSTM is often trained by Connectionist Temporal Classification (CTC).[249] At Google, Microsoft and Baidu this approach has revolutionised speech recognition.[250][251][252] For example, in 2015, Google’s speech recognition experienced a dramatic performance jump of 49% through CTC-trained LSTM, which is now available through Google Voice to billions of smartphone users.[253] Google also used LSTM to improve machine translation,[254] Language Modeling[255] and Multilingual Language Processing.[256] LSTM combined with CNNs also improved automatic image captioning[257] and a plethora of other applications.

AI, like electricity or the steam engine, is a general purpose technology. There is no consensus on how to characterize which tasks AI tends to excel at.[258] While projects such as AlphaZero have succeeded in generating their own knowledge from scratch, many other machine learning projects require large training datasets.[259][260] Researcher Andrew Ng has suggested, as a “highly imperfect rule of thumb”, that “almost anything a typical human can do with less than one second of mental thought, we can probably now or in the near future automate using AI.”[261] Moravec’s paradox suggests that AI lags humans at many tasks that the human brain has specifically evolved to perform well.[125]

Games provide a well-publicized benchmark for assessing rates of progress. AlphaGo around 2016 brought the era of classical board-game benchmarks to a close. Games of imperfect knowledge provide new challenges to AI in the area of game theory.[262][263] E-sports such as StarCraft continue to provide additional public benchmarks.[264][265] There are many competitions and prizes, such as the Imagenet Challenge, to promote research in artificial intelligence. The most common areas of competition include general machine intelligence, conversational behavior, data-mining, robotic cars, and robot soccer as well as conventional games.[266]

The “imitation game” (an interpretation of the 1950 Turing test that assesses whether a computer can imitate a human) is nowadays considered too exploitable to be a meaningful benchmark.[267] A derivative of the Turing test is the Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA). As the name implies, this helps to determine that a user is an actual person and not a computer posing as a human. In contrast to the standard Turing test, CAPTCHA is administered by a machine and targeted to a human as opposed to being administered by a human and targeted to a machine. A computer asks a user to complete a simple test then generates a grade for that test. Computers are unable to solve the problem, so correct solutions are deemed to be the result of a person taking the test. A common type of CAPTCHA is the test that requires the typing of distorted letters, numbers or symbols that appear in an image undecipherable by a computer.

Proposed “universal intelligence” tests aim to compare how well machines, humans, and even non-human animals perform on problem sets that are generic as possible. At an extreme, the test suite can contain every possible problem, weighted by Kolmogorov complexity; unfortunately, these problem sets tend to be dominated by impoverished pattern-matching exercises where a tuned AI can easily exceed human performance levels.[269][270]

AI is relevant to any intellectual task. Modern artificial intelligence techniques are pervasive and are too numerous to list here. Frequently, when a technique reaches mainstream use, it is no longer considered artificial intelligence; this phenomenon is described as the AI effect.

High-profile examples of AI include autonomous vehicles (such as drones and self-driving cars), medical diagnosis, creating art (such as poetry), proving mathematical theorems, playing games (such as Chess or Go), search engines (such as Google search), online assistants (such as Siri), image recognition in photographs, spam filtering, predicting flight delays,[273] prediction of judicial decisions[274] and targeting online advertisements.[275][276]

With social media sites overtaking TV as a source for news for young people and news organisations increasingly reliant on social media platforms for generating distribution,[277] major publishers now use artificial intelligence (AI) technology to post stories more effectively and generate higher volumes of traffic.[278]

AI is being applied to the high cost problem of dosage issueswhere findings suggested that AI could save $16 billion. In 2016, a ground breaking study in California found that a mathematical formula developed with the help of AI correctly determined the accurate dose of immunosuppressant drugs to give to organ patients.[279]

Artificial intelligence is breaking into the healthcare industry by assisting doctors. According to Bloomberg Technology, Microsoft has developed AI to help doctors find the right treatments for cancer.[280] There is a great amount of research and drugs developed relating to cancer. In detail, there are more than 800 medicines and vaccines to treat cancer. This negatively affects the doctors, because there are too many options to choose from, making it more difficult to choose the right drugs for the patients. Microsoft is working on a project to develop a machine called “Hanover”. Its goal is to memorize all the papers necessary to cancer and help predict which combinations of drugs will be most effective for each patient. One project that is being worked on at the moment is fighting myeloid leukemia, a fatal cancer where the treatment has not improved in decades. Another study was reported to have found that artificial intelligence was as good as trained doctors in identifying skin cancers.[281] Another study is using artificial intelligence to try and monitor multiple high-risk patients, and this is done by asking each patient numerous questions based on data acquired from live doctor to patient interactions.[282] One study was done with transfer learning, the machine performed a diagnosis similarly to a well-trained ophthalmologist, and could generate a decision within 30 seconds on whether or not the patient should be referred for treatment, with more than 95% percent accuracy.[283]

According to CNN, a recent study by surgeons at the Children’s National Medical Center in Washington successfully demonstrated surgery with an autonomous robot. The team supervised the robot while it performed soft-tissue surgery, stitching together a pig’s bowel during open surgery, and doing so better than a human surgeon, the team claimed.[284] IBM has created its own artificial intelligence computer, the IBM Watson, which has beaten human intelligence (at some levels). Watson not only won at the game show Jeopardy! against former champions,[285] but was declared a hero after successfully diagnosing a woman who was suffering from leukemia.[286]

Advancements in AI have contributed to the growth of the automotive industry through the creation and evolution of self-driving vehicles. As of 2016[update], there are over 30 companies utilizing AI into the creation of driverless cars. A few companies involved with AI include Tesla, Google, and Apple.[287]

Many components contribute to the functioning of self-driving cars. These vehicles incorporate systems such as braking, lane changing, collision prevention, navigation and mapping. Together, these systems, as well as high performance computers, are integrated into one complex vehicle.[288]

Recent developments in autonomous automobiles have made the innovation of self-driving trucks possible, though they are still in the testing phase. The UK government has passed legislation to begin testing of self-driving truck platoons in 2018.[289] Self-driving truck platoons are a fleet of self-driving trucks following the lead of one non-self-driving truck, so the truck platoons aren’t entirely autonomous yet. Meanwhile, the Daimler, a German automobile corporation, is testing the Freightliner Inspiration which is a semi-autonomous truck that will only be used on the highway.[290]

One main factor that influences the ability for a driver-less automobile to function is mapping. In general, the vehicle would be pre-programmed with a map of the area being driven. This map would include data on the approximations of street light and curb heights in order for the vehicle to be aware of its surroundings. However, Google has been working on an algorithm with the purpose of eliminating the need for pre-programmed maps and instead, creating a device that would be able to adjust to a variety of new surroundings.[291] Some self-driving cars are not equipped with steering wheels or brake pedals, so there has also been research focused on creating an algorithm that is capable of maintaining a safe environment for the passengers in the vehicle through awareness of speed and driving conditions.[292]

Another factor that is influencing the ability for a driver-less automobile is the safety of the passenger. To make a driver-less automobile, engineers must program it to handle high-risk situations. These situations could include a head-on collision with pedestrians. The car’s main goal should be to make a decision that would avoid hitting the pedestrians and saving the passengers in the car. But there is a possibility the car would need to make a decision that would put someone in danger. In other words, the car would need to decide to save the pedestrians or the passengers.[293] The programming of the car in these situations is crucial to a successful driver-less automobile.

Financial institutions have long used artificial neural network systems to detect charges or claims outside of the norm, flagging these for human investigation. The use of AI in banking can be traced back to 1987 when Security Pacific National Bank in US set-up a Fraud Prevention Task force to counter the unauthorised use of debit cards. Programs like Kasisto and Moneystream are using AI in financial services.

Banks use artificial intelligence systems today to organize operations, maintain book-keeping, invest in stocks, and manage properties. AI can react to changes overnight or when business is not taking place.[294] In August 2001, robots beat humans in a simulated financial trading competition.[295] AI has also reduced fraud and financial crimes by monitoring behavioral patterns of users for any abnormal changes or anomalies.[296]

The use of AI machines in the market in applications such as online trading and decision making has changed major economic theories.[297] For example, AI based buying and selling platforms have changed the law of supply and demand in that it is now possible to easily estimate individualized demand and supply curves and thus individualized pricing. Furthermore, AI machines reduce information asymmetry in the market and thus making markets more efficient while reducing the volume of trades. Furthermore, AI in the markets limits the consequences of behavior in the markets again making markets more efficient. Other theories where AI has had impact include in rational choice, rational expectations, game theory, Lewis turning point, portfolio optimization and counterfactual thinking.

In video games, artificial intelligence is routinely used to generate dynamic purposeful behavior in non-player characters (NPCs). In addition, well-understood AI techniques are routinely used for pathfinding. Some researchers consider NPC AI in games to be a “solved problem” for most production tasks. Games with more atypical AI include the AI director of Left 4 Dead (2008) and the neuroevolutionary training of platoons in Supreme Commander 2 (2010).[298][299]

Worldwide annual military spending on robotics rose from US$5.1 billion in 2010 to US$7.5 billion in 2015.[300][301] Military drones capable of autonomous action are widely considered a useful asset.[302] Many artificial intelligence researchers seek to distance themselves from military applications of AI.[303]

For financial statements audit, AI makes continuous audit possible. AI tools could analyze many sets of different information immediately. The potential benefit would be the overall audit risk will be reduced, the level of assurance will be increased and the time duration of audit will be reduced.[304]

It is possible to use AI to predict or generalize the behavior of customers from their digital footprints in order to target them with personalized promotions or build customer personas automatically.[305] A documented case reports that online gambling companies were using AI to improve customer targeting.[306]

Moreover, the application of Personality computing AI models can help reducing the cost of advertising campaigns by adding psychological targeting to more traditional sociodemographic or behavioral targeting.[307]

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Artificial intelligence – Wikipedia

Benefits & Risks of Artificial Intelligence – Future of …

Many AI researchers roll their eyes when seeing this headline:Stephen Hawking warns that rise of robots may be disastrous for mankind. And as many havelost count of how many similar articles theyveseen.Typically, these articles are accompanied by an evil-looking robot carrying a weapon, and they suggest we should worry about robots rising up and killing us because theyve become conscious and/or evil.On a lighter note, such articles are actually rather impressive, because they succinctly summarize the scenario that AI researchers dontworry about. That scenario combines as many as three separate misconceptions: concern about consciousness, evil, androbots.

If you drive down the road, you have a subjective experience of colors, sounds, etc. But does a self-driving car have a subjective experience? Does it feel like anything at all to be a self-driving car?Although this mystery of consciousness is interesting in its own right, its irrelevant to AI risk. If you get struck by a driverless car, it makes no difference to you whether it subjectively feels conscious. In the same way, what will affect us humans is what superintelligent AIdoes, not how it subjectively feels.

The fear of machines turning evil is another red herring. The real worry isnt malevolence, but competence. A superintelligent AI is by definition very good at attaining its goals, whatever they may be, so we need to ensure that its goals are aligned with ours. Humans dont generally hate ants, but were more intelligent than they are so if we want to build a hydroelectric dam and theres an anthill there, too bad for the ants. The beneficial-AI movement wants to avoid placing humanity in the position of those ants.

The consciousness misconception is related to the myth that machines cant have goals.Machines can obviously have goals in the narrow sense of exhibiting goal-oriented behavior: the behavior of a heat-seeking missile is most economically explained as a goal to hit a target.If you feel threatened by a machine whose goals are misaligned with yours, then it is precisely its goals in this narrow sense that troubles you, not whether the machine is conscious and experiences a sense of purpose.If that heat-seeking missile were chasing you, you probably wouldnt exclaim: Im not worried, because machines cant have goals!

I sympathize with Rodney Brooks and other robotics pioneers who feel unfairly demonized by scaremongering tabloids,because some journalists seem obsessively fixated on robots and adorn many of their articles with evil-looking metal monsters with red shiny eyes. In fact, the main concern of the beneficial-AI movement isnt with robots but with intelligence itself: specifically, intelligence whose goals are misaligned with ours. To cause us trouble, such misaligned superhuman intelligence needs no robotic body, merely an internet connection this may enable outsmarting financial markets, out-inventing human researchers, out-manipulating human leaders, and developing weapons we cannot even understand. Even if building robots were physically impossible, a super-intelligent and super-wealthy AI could easily pay or manipulate many humans to unwittingly do its bidding.

The robot misconception is related to the myth that machines cant control humans. Intelligence enables control: humans control tigers not because we are stronger, but because we are smarter. This means that if we cede our position as smartest on our planet, its possible that we might also cede control.

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Benefits & Risks of Artificial Intelligence – Future of …

What is Artificial Intelligence (AI)? – Definition from …

Artificial intelligence is a branch of computer science that aims to create intelligent machines. It has become an essential part of the technology industry.

Research associated with artificial intelligence is highly technical and specialized. The core problems of artificial intelligence include programming computers for certain traits such as:

Knowledge engineering is a core part of AI research. Machines can often act and react like humans only if they have abundant information relating to the world. Artificial intelligence must have access to objects, categories, properties and relations between all of them to implement knowledge engineering. Initiating common sense, reasoning and problem-solving power in machines is a difficult and tedious task.

Machine learning is also a core part of AI. Learning without any kind of supervision requires an ability to identify patterns in streams of inputs, whereas learning with adequate supervision involves classification and numerical regressions. Classification determines the category an object belongs to and regression deals with obtaining a set of numerical input or output examples, thereby discovering functions enabling the generation of suitable outputs from respective inputs. Mathematical analysis of machine learning algorithms and their performance is a well-defined branch of theoretical computer science often referred to as computational learning theory.

Machine perception deals with the capability to use sensory inputs to deduce the different aspects of the world, while computer vision is the power to analyze visual inputs with a few sub-problems such as facial, object and gesture recognition.

Robotics is also a major field related to AI. Robots require intelligence to handle tasks such as object manipulation and navigation, along with sub-problems of localization, motion planning and mapping.

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What is Artificial Intelligence (AI)? – Definition from …

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Director:Steven Spielberg

Stars:Jeremy Irvine,Emily Watson,David Thewlis

In the not-so-far future the polar ice caps have melted and the resulting rise of the ocean waters has drowned all the coastal cities of the world. Withdrawn to the interior of the continents, the human race keeps advancing, reaching the point of creating realistic robots (called mechas) to serve them. One of the mecha-producing companies builds David, an artificial kid which is the first to have real feelings, especially a never-ending love for his “mother”, Monica. Monica is the woman who adopted him as a substitute for her real son, who remains in cryo-stasis, stricken by an incurable disease. David is living happily with Monica and her husband, but when their real son returns home after a cure is discovered, his life changes dramatically. Written byChris Makrozahopoulos

Budget:$100,000,000 (estimated)

Opening Weekend USA: $29,352,630,1 July 2001, Wide Release

Gross USA: $78,616,689, 23 September 2001

Cumulative Worldwide Gross: $235,927,000

Runtime: 146 min

Aspect Ratio: 1.85 : 1

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A.I. Artificial Intelligence (2001) – IMDb

Artificial Intelligence – Journal – Elsevier

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Artificial Intelligence – Journal – Elsevier