Artificial General Intelligence

Artificial basic intelligence (AGI) is a type of expert system (AI) that matches or goes beyond human cognitive abilities across a large range of cognitive jobs. This contrasts with narrow AI, which is restricted to particular tasks. [1] Artificial superintelligence (ASI), on the other hand, refers to AGI that considerably exceeds human cognitive capabilities. AGI is thought about among the definitions of strong AI.

Creating AGI is a primary objective of AI research and of companies such as OpenAI [2] and Meta. [3] A 2020 study identified 72 active AGI research and advancement jobs across 37 countries. [4]

The timeline for achieving AGI stays a topic of continuous argument among researchers and professionals. Since 2023, some argue that it may be possible in years or decades; others keep it might take a century or longer; a minority believe it may never be achieved; and another minority declares that it is already here. [5] [6] Notable AI scientist Geoffrey Hinton has actually expressed concerns about the rapid development towards AGI, suggesting it might be attained quicker than lots of anticipate. [7]

There is dispute on the precise meaning of AGI and regarding whether modern large language designs (LLMs) such as GPT-4 are early forms of AGI. [8] AGI is a typical topic in sci-fi and futures research studies. [9] [10]

Contention exists over whether AGI represents an existential danger. [11] [12] [13] Many specialists on AI have actually mentioned that alleviating the danger of human termination positioned by AGI must be an international concern. [14] [15] Others discover the advancement of AGI to be too remote to provide such a threat. [16] [17]

Terminology

AGI is likewise known as strong AI, [18] [19] full AI, [20] human-level AI, [5] human-level smart AI, or basic smart action. [21]

Some academic sources schedule the term “strong AI” for computer system programs that experience life or awareness. [a] In contrast, weak AI (or narrow AI) is able to resolve one particular problem but does not have basic cognitive capabilities. [22] [19] Some academic sources use “weak AI” to refer more broadly to any programs that neither experience awareness nor have a mind in the exact same sense as people. [a]

Related concepts include synthetic superintelligence and transformative AI. An artificial superintelligence (ASI) is a theoretical kind of AGI that is a lot more generally intelligent than humans, [23] while the concept of transformative AI associates with AI having a large influence on society, for example, comparable to the farming or industrial transformation. [24]

A framework for categorizing AGI in levels was proposed in 2023 by Google DeepMind scientists. They specify 5 levels of AGI: emerging, skilled, specialist, virtuoso, and superhuman. For example, a proficient AGI is specified as an AI that outperforms 50% of experienced grownups in a large variety of non-physical jobs, and a superhuman AGI (i.e. a synthetic superintelligence) is similarly specified but with a threshold of 100%. They think about big language designs like ChatGPT or LLaMA 2 to be instances of emerging AGI. [25]

Characteristics

Various popular meanings of intelligence have actually been proposed. Among the leading propositions is the Turing test. However, there are other well-known definitions, and some researchers disagree with the more popular approaches. [b]

Intelligence qualities

Researchers generally hold that intelligence is needed to do all of the following: [27]

reason, usage technique, resolve puzzles, and make judgments under uncertainty
represent understanding, consisting of sound judgment understanding
strategy
discover
– interact in natural language
– if needed, integrate these abilities in conclusion of any provided objective

Many interdisciplinary approaches (e.g. cognitive science, suvenir51.ru computational intelligence, and choice making) think about additional characteristics such as imagination (the ability to form novel psychological images and concepts) [28] and autonomy. [29]

Computer-based systems that display a lot of these abilities exist (e.g. see computational imagination, automated thinking, choice assistance system, robot, evolutionary computation, intelligent representative). There is argument about whether contemporary AI systems possess them to an adequate degree.

Physical traits

Other abilities are considered preferable in smart systems, as they might affect intelligence or aid in its expression. These include: [30]

– the ability to sense (e.g. see, hear, and so on), and
– the ability to act (e.g. relocation and control things, modification area to explore, etc).

This consists of the ability to identify and react to threat. [31]

Although the capability to sense (e.g. see, hear, etc) and the capability to act (e.g. move and akropolistravel.com control items, modification location to explore, and so on) can be desirable for some intelligent systems, [30] these physical abilities are not strictly needed for an entity to certify as AGI-particularly under the thesis that large language models (LLMs) might currently be or end up being AGI. Even from a less optimistic perspective on LLMs, there is no company requirement for an AGI to have a human-like form; being a silicon-based computational system suffices, supplied it can process input (language) from the external world in location of human senses. This interpretation lines up with the understanding that AGI has never ever been proscribed a particular physical personification and hence does not require a capability for locomotion or standard “eyes and ears”. [32]

Tests for human-level AGI

Several tests implied to verify human-level AGI have been considered, including: [33] [34]

The concept of the test is that the machine needs to try and pretend to be a male, by answering questions put to it, and it will just pass if the pretence is fairly convincing. A substantial part of a jury, who should not be expert about makers, must be taken in by the pretence. [37]

AI-complete issues

A problem is informally called “AI-complete” or “AI-hard” if it is believed that in order to resolve it, one would require to implement AGI, because the service is beyond the capabilities of a purpose-specific algorithm. [47]

There are numerous problems that have been conjectured to need general intelligence to resolve as well as human beings. Examples consist of computer vision, natural language understanding, and handling unforeseen scenarios while resolving any real-world problem. [48] Even a particular job like translation needs a machine to check out and write in both languages, follow the author’s argument (factor), understand the context (understanding), and faithfully replicate the author’s initial intent (social intelligence). All of these problems require to be resolved concurrently in order to reach human-level device performance.

However, numerous of these jobs can now be carried out by modern-day large language models. According to Stanford University’s 2024 AI index, AI has reached human-level efficiency on many benchmarks for reading understanding and visual reasoning. [49]

History

Classical AI

Modern AI research started in the mid-1950s. [50] The first generation of AI researchers were encouraged that synthetic basic intelligence was possible which it would exist in simply a couple of years. [51] AI leader Herbert A. Simon wrote in 1965: “machines will be capable, within twenty years, of doing any work a guy can do.” [52]

Their forecasts were the motivation for Stanley Kubrick and Arthur C. Clarke’s character HAL 9000, who embodied what AI scientists believed they might produce by the year 2001. AI leader Marvin Minsky was a consultant [53] on the job of making HAL 9000 as sensible as possible according to the consensus forecasts of the time. He stated in 1967, “Within a generation … the issue of developing ‘synthetic intelligence’ will substantially be resolved”. [54]

Several classical AI projects, such as Doug Lenat’s Cyc project (that began in 1984), and Allen Newell’s Soar job, were directed at AGI.

However, in the early 1970s, it became obvious that scientists had actually grossly undervalued the problem of the task. Funding agencies became doubtful of AGI and put scientists under increasing pressure to produce beneficial “used AI“. [c] In the early 1980s, Japan’s Fifth Generation Computer Project restored interest in AGI, setting out a ten-year timeline that included AGI objectives like “carry on a casual discussion”. [58] In action to this and the success of expert systems, both market and government pumped cash into the field. [56] [59] However, self-confidence in AI stunningly collapsed in the late 1980s, and the goals of the Fifth Generation Computer Project were never ever fulfilled. [60] For the 2nd time in 20 years, AI scientists who predicted the imminent accomplishment of AGI had actually been misinterpreted. By the 1990s, AI scientists had a reputation for making vain guarantees. They ended up being reluctant to make forecasts at all [d] and prevented reference of “human level” expert system for fear of being identified “wild-eyed dreamer [s]. [62]

Narrow AI research study

In the 1990s and early 21st century, mainstream AI accomplished business success and academic respectability by focusing on particular sub-problems where AI can produce verifiable results and commercial applications, such as speech acknowledgment and recommendation algorithms. [63] These “applied AI” systems are now used extensively throughout the innovation market, and research study in this vein is heavily moneyed in both academic community and industry. Since 2018 [upgrade], development in this field was thought about an emerging trend, and a mature stage was expected to be reached in more than ten years. [64]

At the millenium, numerous mainstream AI researchers [65] hoped that strong AI might be developed by combining programs that solve various sub-problems. Hans Moravec wrote in 1988:

I am confident that this bottom-up path to expert system will one day meet the traditional top-down route majority method, all set to offer the real-world proficiency and the commonsense knowledge that has been so frustratingly elusive in thinking programs. Fully smart makers will result when the metaphorical golden spike is driven uniting the 2 efforts. [65]

However, even at the time, this was contested. For instance, Stevan Harnad of Princeton University concluded his 1990 paper on the symbol grounding hypothesis by specifying:

The expectation has often been voiced that “top-down” (symbolic) approaches to modeling cognition will in some way meet “bottom-up” (sensory) approaches somewhere in between. If the grounding considerations in this paper are valid, then this expectation is hopelessly modular and there is really only one feasible path from sense to symbols: from the ground up. A free-floating symbolic level like the software application level of a computer will never be reached by this route (or vice versa) – nor is it clear why we should even attempt to reach such a level, given that it appears getting there would just amount to uprooting our symbols from their intrinsic meanings (thus simply lowering ourselves to the practical equivalent of a programmable computer). [66]

Modern artificial general intelligence research

The term “artificial general intelligence” was utilized as early as 1997, by Mark Gubrud [67] in a conversation of the implications of completely automated military production and operations. A mathematical formalism of AGI was proposed by Marcus Hutter in 2000. Named AIXI, the proposed AGI representative maximises “the capability to satisfy goals in a large range of environments”. [68] This type of AGI, characterized by the capability to increase a mathematical meaning of intelligence instead of exhibit human-like behaviour, [69] was likewise called universal artificial intelligence. [70]

The term AGI was re-introduced and popularized by Shane Legg and Ben Goertzel around 2002. [71] AGI research activity in 2006 was explained by Pei Wang and Ben Goertzel [72] as “producing publications and initial outcomes”. The first summer school in AGI was arranged in Xiamen, China in 2009 [73] by the Xiamen university’s Artificial Brain Laboratory and OpenCog. The very first university course was given up 2010 [74] and 2011 [75] at Plovdiv University, Bulgaria by Todor Arnaudov. MIT provided a course on AGI in 2018, organized by Lex Fridman and featuring a variety of guest speakers.

As of 2023 [upgrade], a little number of computer researchers are active in AGI research study, and lots of contribute to a series of AGI conferences. However, progressively more scientists are interested in open-ended learning, [76] [77] which is the idea of allowing AI to continually learn and innovate like humans do.

Feasibility

Since 2023, the advancement and potential achievement of AGI stays a topic of intense dispute within the AI neighborhood. While traditional consensus held that AGI was a distant goal, recent advancements have led some researchers and industry figures to declare that early forms of AGI might already exist. [78] AI leader Herbert A. Simon hypothesized in 1965 that “makers will be capable, within twenty years, of doing any work a guy can do”. This forecast stopped working to come true. Microsoft co-founder Paul Allen thought that such intelligence is unlikely in the 21st century because it would require “unforeseeable and fundamentally unpredictable advancements” and a “scientifically deep understanding of cognition”. [79] Writing in The Guardian, roboticist Alan Winfield declared the gulf in between contemporary computing and human-level expert system is as broad as the gulf in between existing space flight and practical faster-than-light spaceflight. [80]

An additional difficulty is the absence of clearness in specifying what intelligence involves. Does it require consciousness? Must it display the ability to set objectives along with pursue them? Is it purely a matter of scale such that if model sizes increase adequately, intelligence will emerge? Are centers such as preparation, reasoning, and causal understanding required? Does intelligence require clearly replicating the brain and its specific professors? Does it require emotions? [81]

Most AI researchers believe strong AI can be accomplished in the future, but some thinkers, like Hubert Dreyfus and Roger Penrose, reject the possibility of attaining strong AI. [82] [83] John McCarthy is amongst those who believe human-level AI will be accomplished, however that the present level of progress is such that a date can not accurately be anticipated. [84] AI professionals’ views on the feasibility of AGI wax and wane. Four polls performed in 2012 and 2013 suggested that the mean price quote among professionals for when they would be 50% confident AGI would arrive was 2040 to 2050, depending on the survey, with the mean being 2081. Of the experts, 16.5% responded to with “never” when asked the same question however with a 90% self-confidence rather. [85] [86] Further current AGI progress considerations can be discovered above Tests for verifying human-level AGI.

A report by Stuart Armstrong and Kaj Sotala of the Machine Intelligence Research Institute found that “over [a] 60-year time frame there is a strong bias towards forecasting the arrival of human-level AI as in between 15 and 25 years from the time the prediction was made”. They examined 95 forecasts made between 1950 and 2012 on when human-level AI will come about. [87]

In 2023, Microsoft scientists published an in-depth evaluation of GPT-4. They concluded: “Given the breadth and depth of GPT-4’s abilities, we think that it might fairly be considered as an early (yet still insufficient) version of an artificial basic intelligence (AGI) system.” [88] Another study in 2023 reported that GPT-4 outshines 99% of humans on the Torrance tests of creativity. [89] [90]

Blaise Agüera y Arcas and Peter Norvig wrote in 2023 that a substantial level of basic intelligence has already been attained with frontier models. They wrote that hesitation to this view originates from 4 primary reasons: a “healthy apprehension about metrics for AGI”, an “ideological commitment to alternative AI theories or methods”, a “devotion to human (or biological) exceptionalism”, or a “concern about the financial ramifications of AGI”. [91]

2023 likewise marked the development of large multimodal models (big language models capable of processing or creating numerous techniques such as text, audio, and images). [92]

In 2024, OpenAI launched o1-preview, the first of a series of models that “invest more time believing before they react”. According to Mira Murati, this ability to think before responding represents a new, extra paradigm. It enhances design outputs by investing more computing power when producing the response, whereas the design scaling paradigm enhances outputs by increasing the model size, training information and training compute power. [93] [94]

An OpenAI worker, Vahid Kazemi, declared in 2024 that the business had attained AGI, specifying, “In my viewpoint, we have already achieved AGI and it’s much more clear with O1.” Kazemi clarified that while the AI is not yet “better than any human at any task”, it is “better than a lot of people at many tasks.” He also dealt with criticisms that large language designs (LLMs) simply follow predefined patterns, comparing their knowing process to the scientific method of observing, assuming, and verifying. These statements have actually sparked dispute, as they count on a broad and unconventional meaning of AGI-traditionally understood as AI that matches human intelligence throughout all domains. Critics argue that, while OpenAI’s designs show remarkable versatility, they may not totally meet this requirement. Notably, Kazemi’s comments came shortly after OpenAI eliminated “AGI” from the terms of its collaboration with Microsoft, triggering speculation about the business’s strategic intents. [95]

Timescales

Progress in expert system has traditionally gone through durations of rapid progress separated by durations when development appeared to stop. [82] Ending each hiatus were fundamental advances in hardware, software application or both to create area for additional development. [82] [98] [99] For instance, the hardware readily available in the twentieth century was not sufficient to execute deep knowing, which requires great deals of GPU-enabled CPUs. [100]

In the intro to his 2006 book, [101] Goertzel states that estimates of the time required before a truly versatile AGI is constructed vary from 10 years to over a century. Since 2007 [upgrade], the consensus in the AGI research community appeared to be that the timeline discussed by Ray Kurzweil in 2005 in The Singularity is Near [102] (i.e. in between 2015 and 2045) was plausible. [103] Mainstream AI researchers have given a wide variety of viewpoints on whether development will be this fast. A 2012 meta-analysis of 95 such opinions found a bias towards anticipating that the start of AGI would occur within 16-26 years for modern and historical forecasts alike. That paper has actually been slammed for how it classified opinions as specialist or non-expert. [104]

In 2012, Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton developed a neural network called AlexNet, which won the ImageNet competition with a top-5 test error rate of 15.3%, substantially better than the second-best entry’s rate of 26.3% (the standard approach used a weighted sum of scores from various pre-defined classifiers). [105] AlexNet was related to as the preliminary ground-breaker of the present deep learning wave. [105]

In 2017, scientists Feng Liu, Yong Shi, and Ying Liu carried out intelligence tests on publicly available and easily accessible weak AI such as Google AI, Apple’s Siri, and others. At the maximum, these AIs reached an IQ worth of about 47, which corresponds around to a six-year-old kid in first grade. An adult concerns about 100 typically. Similar tests were brought out in 2014, with the IQ rating reaching an optimum value of 27. [106] [107]

In 2020, OpenAI established GPT-3, a language design efficient in carrying out numerous varied jobs without specific training. According to Gary Grossman in a VentureBeat article, while there is consensus that GPT-3 is not an example of AGI, it is thought about by some to be too advanced to be classified as a narrow AI system. [108]

In the very same year, Jason Rohrer used his GPT-3 account to develop a chatbot, and supplied a chatbot-developing platform called “Project December”. OpenAI requested modifications to the chatbot to comply with their security guidelines; Rohrer detached Project December from the GPT-3 API. [109]

In 2022, DeepMind established Gato, a “general-purpose” system capable of carrying out more than 600 various tasks. [110]

In 2023, Microsoft Research released a study on an early variation of OpenAI’s GPT-4, contending that it exhibited more general intelligence than previous AI designs and demonstrated human-level efficiency in tasks spanning numerous domains, such as mathematics, coding, and law. This research stimulated an argument on whether GPT-4 could be considered an early, insufficient version of artificial basic intelligence, emphasizing the need for additional expedition and evaluation of such systems. [111]

In 2023, the AI scientist Geoffrey Hinton stated that: [112]

The concept that this things might actually get smarter than people – a couple of people thought that, […] But the majority of people believed it was way off. And I thought it was method off. I thought it was 30 to 50 years or perhaps longer away. Obviously, I no longer think that.

In May 2023, Demis Hassabis similarly stated that “The development in the last couple of years has been pretty incredible”, which he sees no reason that it would slow down, expecting AGI within a years or even a few years. [113] In March 2024, Nvidia’s CEO, Jensen Huang, mentioned his expectation that within 5 years, AI would be capable of passing any test a minimum of along with humans. [114] In June 2024, the AI researcher Leopold Aschenbrenner, a former OpenAI worker, approximated AGI by 2027 to be “noticeably possible”. [115]

Whole brain emulation

While the advancement of transformer models like in ChatGPT is thought about the most appealing course to AGI, [116] [117] entire brain emulation can act as an alternative technique. With whole brain simulation, a brain design is developed by scanning and mapping a biological brain in information, and after that copying and replicating it on a computer system or another computational gadget. The simulation design need to be adequately faithful to the original, so that it behaves in practically the same way as the initial brain. [118] Whole brain emulation is a type of brain simulation that is talked about in computational neuroscience and neuroinformatics, and for medical research study purposes. It has actually been talked about in expert system research study [103] as an approach to strong AI. Neuroimaging innovations that could deliver the essential detailed understanding are improving quickly, and futurist Ray Kurzweil in the book The Singularity Is Near [102] predicts that a map of enough quality will end up being available on a comparable timescale to the computing power required to replicate it.

Early approximates

For low-level brain simulation, an extremely powerful cluster of computers or GPUs would be required, given the huge quantity of synapses within the human brain. Each of the 1011 (one hundred billion) nerve cells has on typical 7,000 synaptic connections (synapses) to other neurons. The brain of a three-year-old kid has about 1015 synapses (1 quadrillion). This number declines with age, supporting by adulthood. Estimates differ for an adult, ranging from 1014 to 5 × 1014 synapses (100 to 500 trillion). [120] A price quote of the brain’s processing power, based on an easy switch design for neuron activity, is around 1014 (100 trillion) synaptic updates per second (SUPS). [121]

In 1997, Kurzweil took a look at numerous quotes for the hardware required to equal the human brain and adopted a figure of 1016 calculations per 2nd (cps). [e] (For comparison, if a “computation” was equivalent to one “floating-point operation” – a measure utilized to rate current supercomputers – then 1016 “computations” would be comparable to 10 petaFLOPS, accomplished in 2011, while 1018 was achieved in 2022.) He utilized this figure to forecast the needed hardware would be offered sometime between 2015 and 2025, if the rapid growth in computer power at the time of composing continued.

Current research

The Human Brain Project, an EU-funded initiative active from 2013 to 2023, has actually established an especially comprehensive and publicly available atlas of the human brain. [124] In 2023, scientists from Duke University performed a high-resolution scan of a mouse brain.

Criticisms of simulation-based approaches

The synthetic nerve cell model assumed by Kurzweil and utilized in numerous current artificial neural network executions is easy compared with biological neurons. A brain simulation would likely have to capture the in-depth cellular behaviour of biological neurons, presently comprehended only in broad summary. The overhead introduced by complete modeling of the biological, chemical, and physical details of neural behaviour (specifically on a molecular scale) would need computational powers a number of orders of magnitude bigger than Kurzweil’s estimate. In addition, the quotes do not represent glial cells, which are known to play a function in cognitive processes. [125]

A fundamental criticism of the simulated brain approach stems from embodied cognition theory which asserts that human personification is an essential aspect of human intelligence and is needed to ground significance. [126] [127] If this theory is proper, any totally practical brain model will require to include more than just the nerve cells (e.g., a robotic body). Goertzel [103] proposes virtual embodiment (like in metaverses like Second Life) as an alternative, but it is unidentified whether this would suffice.

Philosophical perspective

“Strong AI” as specified in philosophy

In 1980, philosopher John Searle coined the term “strong AI” as part of his Chinese space argument. [128] He proposed a distinction between 2 hypotheses about synthetic intelligence: [f]

Strong AI hypothesis: A synthetic intelligence system can have “a mind” and “consciousness”.
Weak AI hypothesis: An expert system system can (just) act like it believes and has a mind and awareness.

The first one he called “strong” because it makes a stronger statement: it assumes something special has actually happened to the device that surpasses those capabilities that we can test. The behaviour of a “weak AI” device would be precisely similar to a “strong AI” device, however the latter would likewise have subjective mindful experience. This use is likewise typical in academic AI research and books. [129]

In contrast to Searle and mainstream AI, some futurists such as Ray Kurzweil use the term “strong AI” to imply “human level synthetic general intelligence”. [102] This is not the like Searle’s strong AI, unless it is presumed that awareness is necessary for human-level AGI. Academic philosophers such as Searle do not think that is the case, and to most artificial intelligence scientists the question is out-of-scope. [130]

Mainstream AI is most thinking about how a program behaves. [131] According to Russell and Norvig, “as long as the program works, they don’t care if you call it real or a simulation.” [130] If the program can act as if it has a mind, then there is no need to know if it actually has mind – certainly, there would be no other way to tell. For AI research study, Searle’s “weak AI hypothesis” is comparable to the declaration “artificial general intelligence is possible”. Thus, according to Russell and Norvig, “most AI researchers take the weak AI hypothesis for granted, and do not care about the strong AI hypothesis.” [130] Thus, for academic AI research, “Strong AI” and “AGI” are two various things.

Consciousness

Consciousness can have various meanings, and some elements play significant functions in science fiction and the principles of expert system:

Sentience (or “sensational consciousness”): The capability to “feel” perceptions or emotions subjectively, as opposed to the capability to factor about understandings. Some thinkers, such as David Chalmers, utilize the term “awareness” to refer specifically to remarkable consciousness, which is roughly comparable to life. [132] Determining why and how subjective experience arises is understood as the tough problem of awareness. [133] Thomas Nagel discussed in 1974 that it “seems like” something to be conscious. If we are not mindful, then it does not feel like anything. Nagel uses the example of a bat: we can smartly ask “what does it seem like to be a bat?” However, we are not likely to ask “what does it seem like to be a toaster?” Nagel concludes that a bat appears to be conscious (i.e., has awareness) however a toaster does not. [134] In 2022, a Google engineer claimed that the company’s AI chatbot, LaMDA, had actually accomplished life, though this claim was extensively contested by other experts. [135]
Self-awareness: To have conscious awareness of oneself as a different individual, specifically to be purposely familiar with one’s own ideas. This is opposed to simply being the “topic of one’s thought”-an operating system or debugger has the ability to be “knowledgeable about itself” (that is, to represent itself in the same way it represents whatever else)-but this is not what individuals normally mean when they utilize the term “self-awareness”. [g]
These characteristics have a moral dimension. AI sentience would offer rise to issues of well-being and legal security, similarly to animals. [136] Other aspects of awareness associated to cognitive abilities are also pertinent to the concept of AI rights. [137] Determining how to incorporate advanced AI with existing legal and social frameworks is an emergent concern. [138]

Benefits

AGI could have a wide range of applications. If oriented towards such objectives, AGI might help reduce numerous problems worldwide such as appetite, poverty and illness. [139]

AGI might enhance performance and efficiency in many tasks. For example, in public health, AGI might speed up medical research, significantly versus cancer. [140] It could look after the elderly, [141] and democratize access to quick, top quality medical diagnostics. It could provide fun, inexpensive and tailored education. [141] The requirement to work to subsist could end up being obsolete if the wealth produced is effectively rearranged. [141] [142] This also raises the question of the place of human beings in a significantly automated society.

AGI might likewise assist to make rational decisions, and to prepare for and avoid catastrophes. It could also help to reap the benefits of potentially catastrophic innovations such as nanotechnology or environment engineering, while avoiding the associated threats. [143] If an AGI’s primary goal is to avoid existential disasters such as human extinction (which might be challenging if the Vulnerable World Hypothesis turns out to be real), [144] it might take steps to drastically minimize the dangers [143] while lessening the impact of these procedures on our lifestyle.

Risks

Existential risks

AGI might represent multiple types of existential threat, which are threats that threaten “the premature termination of Earth-originating intelligent life or the long-term and drastic damage of its potential for desirable future development”. [145] The risk of human extinction from AGI has been the subject of lots of disputes, however there is also the possibility that the advancement of AGI would result in a completely problematic future. Notably, it might be utilized to spread and maintain the set of values of whoever develops it. If mankind still has ethical blind areas similar to slavery in the past, AGI might irreversibly entrench it, avoiding moral progress. [146] Furthermore, AGI might facilitate mass surveillance and indoctrination, which could be utilized to create a stable repressive worldwide totalitarian regime. [147] [148] There is likewise a risk for the machines themselves. If makers that are sentient or otherwise worthwhile of moral factor to consider are mass produced in the future, taking part in a civilizational course that forever overlooks their well-being and interests could be an existential disaster. [149] [150] Considering just how much AGI could enhance humankind’s future and aid lower other existential threats, Toby Ord calls these existential threats “an argument for proceeding with due care”, not for “deserting AI”. [147]

Risk of loss of control and human extinction

The thesis that AI poses an existential risk for humans, and that this danger requires more attention, is controversial but has been backed in 2023 by many public figures, AI researchers and CEOs of AI companies such as Elon Musk, Bill Gates, Geoffrey Hinton, Yoshua Bengio, Demis Hassabis and Sam Altman. [151] [152]

In 2014, Stephen Hawking criticized widespread indifference:

So, dealing with possible futures of incalculable advantages and risks, the experts are surely doing everything possible to make sure the finest result, right? Wrong. If a remarkable alien civilisation sent us a message stating, ‘We’ll get here in a couple of years,’ would we just reply, ‘OK, call us when you get here-we’ll leave the lights on?’ Probably not-but this is basically what is happening with AI. [153]

The potential fate of humankind has actually in some cases been compared to the fate of gorillas threatened by human activities. The comparison states that higher intelligence allowed mankind to dominate gorillas, which are now susceptible in ways that they might not have actually anticipated. As an outcome, the gorilla has actually become an endangered species, not out of malice, however merely as a civilian casualties from human activities. [154]

The skeptic Yann LeCun thinks about that AGIs will have no desire to dominate humankind and that we should be careful not to anthropomorphize them and translate their intents as we would for humans. He stated that people won’t be “wise enough to create super-intelligent machines, yet extremely foolish to the point of offering it moronic goals with no safeguards”. [155] On the other side, the principle of critical convergence recommends that almost whatever their goals, intelligent agents will have factors to attempt to survive and acquire more power as intermediary steps to accomplishing these objectives. Which this does not need having emotions. [156]

Many scholars who are concerned about existential risk advocate for more research into resolving the “control problem” to respond to the concern: what types of safeguards, algorithms, or architectures can developers execute to increase the likelihood that their recursively-improving AI would continue to act in a friendly, instead of destructive, manner after it reaches superintelligence? [157] [158] Solving the control problem is made complex by the AI arms race (which could result in a race to the bottom of security preventative measures in order to release products before competitors), [159] and the use of AI in weapon systems. [160]

The thesis that AI can posture existential danger likewise has critics. Skeptics normally state that AGI is not likely in the short-term, or that concerns about AGI sidetrack from other concerns associated with current AI. [161] Former Google scams czar Shuman Ghosemajumder thinks about that for many individuals outside of the innovation industry, existing chatbots and LLMs are currently perceived as though they were AGI, leading to more misunderstanding and fear. [162]

Skeptics often charge that the thesis is crypto-religious, with an illogical belief in the possibility of superintelligence changing an unreasonable belief in an omnipotent God. [163] Some researchers think that the interaction projects on AI existential risk by specific AI groups (such as OpenAI, Anthropic, DeepMind, and Conjecture) might be an at attempt at regulatory capture and to inflate interest in their products. [164] [165]

In 2023, the CEOs of Google DeepMind, OpenAI and Anthropic, in addition to other market leaders and researchers, provided a joint statement asserting that “Mitigating the danger of extinction from AI should be a global priority together with other societal-scale dangers such as pandemics and nuclear war.” [152]

Mass joblessness

Researchers from OpenAI approximated that “80% of the U.S. labor force could have at least 10% of their work jobs impacted by the intro of LLMs, while around 19% of employees might see a minimum of 50% of their jobs impacted”. [166] [167] They consider office workers to be the most exposed, for example mathematicians, accountants or web designers. [167] AGI could have a much better autonomy, ability to make choices, to interface with other computer system tools, but also to control robotized bodies.

According to Stephen Hawking, the outcome of automation on the quality of life will depend on how the wealth will be rearranged: [142]

Everyone can take pleasure in a life of luxurious leisure if the machine-produced wealth is shared, or the majority of individuals can wind up miserably poor if the machine-owners effectively lobby against wealth redistribution. Up until now, the trend seems to be towards the second alternative, with innovation driving ever-increasing inequality

Elon Musk thinks about that the automation of society will need federal governments to embrace a universal basic earnings. [168]

See likewise

Artificial brain – Software and hardware with cognitive capabilities similar to those of the animal or human brain
AI effect
AI safety – Research area on making AI safe and beneficial
AI positioning – AI conformance to the designated goal
A.I. Rising – 2018 film directed by Lazar Bodroža
Expert system
Automated machine learning – Process of automating the application of artificial intelligence
BRAIN Initiative – Collaborative public-private research initiative revealed by the Obama administration
China Brain Project
Future of Humanity Institute – Defunct Oxford interdisciplinary research study centre
General game playing – Ability of artificial intelligence to play different games
Generative expert system – AI system efficient in creating material in response to triggers
Human Brain Project – Scientific research study project
Intelligence amplification – Use of infotech to augment human intelligence (IA).
Machine ethics – Moral behaviours of manufactured machines.
Moravec’s paradox.
Multi-task knowing – Solving numerous maker discovering tasks at the same time.
Neural scaling law – Statistical law in machine learning.
Outline of expert system – Overview of and topical guide to artificial intelligence.
Transhumanism – Philosophical movement.
Synthetic intelligence – Alternate term for or form of expert system.
Transfer learning – Machine knowing technique.
Loebner Prize – Annual AI competition.
Hardware for synthetic intelligence – Hardware specially designed and enhanced for artificial intelligence.
Weak expert system – Form of artificial intelligence.

Notes

^ a b See below for the origin of the term “strong AI”, and see the academic definition of “strong AI” and weak AI in the short article Chinese space.
^ AI founder John McCarthy writes: “we can not yet define in general what type of computational procedures we wish to call intelligent. ” [26] (For a discussion of some meanings of intelligence used by artificial intelligence scientists, see approach of expert system.).
^ The Lighthill report specifically criticized AI‘s “grandiose objectives” and led the taking apart of AI research study in England. [55] In the U.S., DARPA became identified to fund just “mission-oriented direct research, instead of fundamental undirected research”. [56] [57] ^ As AI founder John McCarthy writes “it would be an excellent relief to the remainder of the workers in AI if the innovators of new basic formalisms would reveal their hopes in a more secured form than has actually sometimes been the case.” [61] ^ In “Mind Children” [122] 1015 cps is utilized. More just recently, in 1997, [123] Moravec argued for 108 MIPS which would approximately correspond to 1014 cps. Moravec talks in regards to MIPS, not “cps”, which is a non-standard term Kurzweil introduced.
^ As defined in a standard AI textbook: “The assertion that machines might perhaps act wisely (or, possibly much better, act as if they were intelligent) is called the ‘weak AI’ hypothesis by thinkers, and the assertion that makers that do so are actually thinking (instead of thinking) is called the ‘strong AI‘ hypothesis.” [121] ^ Alan Turing made this point in 1950. [36] References

^ Krishna, Sri (9 February 2023). “What is artificial narrow intelligence (ANI)?”. VentureBeat. Retrieved 1 March 2024. ANI is created to perform a single task.
^ “OpenAI Charter”. OpenAI. Retrieved 6 April 2023. Our objective is to make sure that synthetic general intelligence benefits all of mankind.
^ Heath, Alex (18 January 2024). “Mark Zuckerberg’s brand-new objective is creating artificial basic intelligence”. The Verge. Retrieved 13 June 2024. Our vision is to develop AI that is better than human-level at all of the human senses.
^ Baum, Seth D. (2020 ). A Survey of Artificial General Intelligence Projects for Ethics, Risk, and Policy (PDF) (Report). Global Catastrophic Risk Institute. Retrieved 28 November 2024. 72 AGI R&D projects were determined as being active in 2020.
^ a b c “AI timelines: What do professionals in synthetic intelligence anticipate for the future?”. Our World in Data. Retrieved 6 April 2023.
^ Metz, Cade (15 May 2023). “Some Researchers Say A.I. Is Already Here, Stirring Debate in Tech Circles”. The New York Times. Retrieved 18 May 2023.
^ “AI pioneer Geoffrey Hinton stops Google and warns of threat ahead”. The New York City Times. 1 May 2023. Retrieved 2 May 2023. It is tough to see how you can avoid the bad actors from using it for bad things.
^ Bubeck, Sébastien; Chandrasekaran, Varun; Eldan, Ronen; Gehrke, Johannes; Horvitz, Eric (2023 ). “Sparks of Artificial General Intelligence: Early explores GPT-4”. arXiv preprint. arXiv:2303.12712. GPT-4 reveals triggers of AGI.
^ Butler, Octavia E. (1993 ). Parable of the Sower. Grand Central Publishing. ISBN 978-0-4466-7550-5. All that you touch you change. All that you alter modifications you.
^ Vinge, Vernor (1992 ). A Fire Upon the Deep. Tor Books. ISBN 978-0-8125-1528-2. The Singularity is coming.
^ Morozov, Evgeny (30 June 2023). “The True Threat of Expert System”. The New York Times. The real threat is not AI itself however the method we release it.
^ “Impressed by synthetic intelligence? Experts say AGI is coming next, and it has ‘existential’ dangers”. ABC News. 23 March 2023. Retrieved 6 April 2023. AGI might present existential risks to humankind.
^ Bostrom, Nick (2014 ). Superintelligence: Paths, Dangers, Strategies. Oxford University Press. ISBN 978-0-1996-7811-2. The first superintelligence will be the last innovation that humankind requires to make.
^ Roose, Kevin (30 May 2023). “A.I. Poses ‘Risk of Extinction,’ Industry Leaders Warn”. The New York City Times. Mitigating the risk of termination from AI must be an international priority.
^ “Statement on AI Risk”. Center for AI Safety. Retrieved 1 March 2024. AI professionals caution of threat of extinction from AI.
^ Mitchell, Melanie (30 May 2023). “Are AI‘s Doomsday Scenarios Worth Taking Seriously?”. The New York City Times. We are far from developing machines that can outthink us in basic methods.
^ LeCun, Yann (June 2023). “AGI does not present an existential threat”. Medium. There is no factor to fear AI as an existential threat.
^ Kurzweil 2005, p. 260.
^ a b Kurzweil, Ray (5 August 2005), “Long Live AI”, Forbes, archived from the original on 14 August 2005: Kurzweil describes strong AI as “maker intelligence with the complete series of human intelligence.”.
^ “The Age of Expert System: George John at TEDxLondonBusinessSchool 2013”. Archived from the original on 26 February 2014. Retrieved 22 February 2014.
^ Newell & Simon 1976, This is the term they use for “human-level” intelligence in the physical sign system hypothesis.
^ “The Open University on Strong and Weak AI”. Archived from the initial on 25 September 2009. Retrieved 8 October 2007.
^ “What is artificial superintelligence (ASI)?|Definition from TechTarget”. Enterprise AI. Retrieved 8 October 2023.
^ “Artificial intelligence is transforming our world – it is on everybody to ensure that it works out”. Our World in Data. Retrieved 8 October 2023.
^ Dickson, Ben (16 November 2023). “Here is how far we are to achieving AGI, according to DeepMind”. VentureBeat.
^ McCarthy, John (2007a). “Basic Questions”. Stanford University. Archived from the initial on 26 October 2007. Retrieved 6 December 2007.
^ This list of smart traits is based upon the topics covered by major AI textbooks, including: Russell & Norvig 2003, Luger & Stubblefield 2004, Poole, Mackworth & Goebel 1998 and Nilsson 1998.
^ Johnson 1987.
^ de Charms, R. (1968 ). Personal causation. New York: Academic Press.
^ a b Pfeifer, R. and Bongard J. C., How the body shapes the way we believe: a new view of intelligence (The MIT Press, 2007). ISBN 0-2621-6239-3.
^ White, R. W. (1959 ). “Motivation reevaluated: The principle of competence”. Psychological Review. 66 (5 ): 297-333. doi:10.1037/ h0040934. PMID 13844397. S2CID 37385966.
^ White, R. W. (1959 ). “Motivation reevaluated: The concept of competence”. Psychological Review. 66 (5 ): 297-333. doi:10.1037/ h0040934. PMID 13844397. S2CID 37385966.
^ Muehlhauser, Luke (11 August 2013). “What is AGI?”. Machine Intelligence Research Institute. Archived from the initial on 25 April 2014. Retrieved 1 May 2014.
^ “What is Artificial General Intelligence (AGI)?|4 Tests For Ensuring Artificial General Intelligence”. Talky Blog. 13 July 2019. Archived from the initial on 17 July 2019. Retrieved 17 July 2019.
^ Kirk-Giannini, Cameron Domenico; Goldstein, Simon (16 October 2023). “AI is closer than ever to passing the Turing test for ‘intelligence’. What occurs when it does?”. The Conversation. Retrieved 22 September 2024.
^ a b Turing 1950.
^ Turing, Alan (1952 ). B. Jack Copeland (ed.). Can Automatic Calculating Machines Be Said To Think?. Oxford: Oxford University Press. pp. 487-506. ISBN 978-0-1982-5079-1.
^ “Eugene Goostman is a genuine boy – the Turing Test states so”. The Guardian. 9 June 2014. ISSN 0261-3077. Retrieved 3 March 2024.
^ “Scientists contest whether computer system ‘Eugene Goostman’ passed Turing test”. BBC News. 9 June 2014. Retrieved 3 March 2024.
^ Jones, Cameron R.; Bergen, Benjamin K. (9 May 2024). “People can not identify GPT-4 from a human in a Turing test”. arXiv:2405.08007 [cs.HC]
^ Varanasi, Lakshmi (21 March 2023). “AI models like ChatGPT and GPT-4 are acing everything from the bar test to AP Biology. Here’s a list of tough examinations both AI versions have passed”. Business Insider. Retrieved 30 May 2023.
^ Naysmith, Caleb (7 February 2023). “6 Jobs Expert System Is Already Replacing and How Investors Can Profit From It”. Retrieved 30 May 2023.
^ Turk, Victoria (28 January 2015). “The Plan to Replace the Turing Test with a ‘Turing Olympics'”. Vice. Retrieved 3 March 2024.
^ Gopani, Avi (25 May 2022). “Turing Test is unreliable. The Winograd Schema is outdated. Coffee is the response”. Analytics India Magazine. Retrieved 3 March 2024.
^ Bhaimiya, Sawdah (20 June 2023). “DeepMind’s co-founder recommended checking an AI chatbot’s capability to turn $100,000 into $1 million to measure human-like intelligence”. Business Insider. Retrieved 3 March 2024.
^ Suleyman, Mustafa (14 July 2023). “Mustafa Suleyman: My new Turing test would see if AI can make $1 million”. MIT Technology Review. Retrieved 3 March 2024.
^ Shapiro, Stuart C. (1992 ). “Artificial Intelligence” (PDF). In Stuart C. Shapiro (ed.). Encyclopedia of Expert System (Second ed.). New York City: John Wiley. pp. 54-57. Archived (PDF) from the original on 1 February 2016. (Section 4 is on “AI-Complete Tasks”.).
^ Yampolskiy, Roman V. (2012 ). Xin-She Yang (ed.). “Turing Test as a Defining Feature of AI-Completeness” (PDF). Expert System, Evolutionary Computation and Metaheuristics (AIECM): 3-17. Archived (PDF) from the original on 22 May 2013.
^ “AI Index: State of AI in 13 Charts”. Stanford University Human-Centered Artificial Intelligence. 15 April 2024. Retrieved 27 May 2024.
^ Crevier 1993, pp. 48-50.
^ Kaplan, Andreas (2022 ). “Expert System, Business and Civilization – Our Fate Made in Machines”. Archived from the original on 6 May 2022. Retrieved 12 March 2022.
^ Simon 1965, p. 96 priced quote in Crevier 1993, p. 109.
^ “Scientist on the Set: An Interview with Marvin Minsky”. Archived from the original on 16 July 2012. Retrieved 5 April 2008.
^ Marvin Minsky to Darrach (1970 ), quoted in Crevier (1993, p. 109).
^ Lighthill 1973; Howe 1994.
^ a b NRC 1999, “Shift to Applied Research Increases Investment”.
^ Crevier 1993, pp. 115-117; Russell & Norvig 2003, pp. 21-22.
^ Crevier 1993, p. 211, Russell & Norvig 2003, p. 24 and see also Feigenbaum & McCorduck 1983.
^ Crevier 1993, pp. 161-162, 197-203, 240; Russell & Norvig 2003, p. 25.
^ Crevier 1993, pp. 209-212.
^ McCarthy, John (2000 ). “Respond to Lighthill”. Stanford University. Archived from the initial on 30 September 2008. Retrieved 29 September 2007.
^ Markoff, John (14 October 2005). “Behind Expert system, a Squadron of Bright Real People”. The New York City Times. Archived from the initial on 2 February 2023. Retrieved 18 February 2017. At its low point, some computer system scientists and software application engineers avoided the term artificial intelligence for fear of being seen as wild-eyed dreamers.
^ Russell & Norvig 2003, pp. 25-26
^ “Trends in the Emerging Tech Hype Cycle”. Gartner Reports. Archived from the original on 22 May 2019. Retrieved 7 May 2019.
^ a b Moravec 1988, p. 20
^ Harnad, S. (1990 ). “The Symbol Grounding Problem”. Physica D. 42 (1-3): 335-346. arXiv: cs/9906002. Bibcode:1990 PhyD … 42..335 H. doi:10.1016/ 0167-2789( 90 )90087-6. S2CID 3204300.
^ Gubrud 1997
^ Hutter, Marcus (2005 ). Universal Artificial Intelligence: Sequential Decisions Based Upon Algorithmic Probability. Texts in Theoretical Computer Science an EATCS Series. Springer. doi:10.1007/ b138233. ISBN 978-3-5402-6877-2. S2CID 33352850. Archived from the original on 19 July 2022. Retrieved 19 July 2022.
^ Legg, Shane (2008 ). Machine Super Intelligence (PDF) (Thesis). University of Lugano. Archived (PDF) from the initial on 15 June 2022. Retrieved 19 July 2022.
^ Goertzel, Ben (2014 ). Artificial General Intelligence. Lecture Notes in Computer Technology. Vol. 8598. Journal of Artificial General Intelligence. doi:10.1007/ 978-3-319-09274-4. ISBN 978-3-3190-9273-7. S2CID 8387410.
^ “Who created the term “AGI”?”. goertzel.org. Archived from the original on 28 December 2018. Retrieved 28 December 2018., by means of Life 3.0: ‘The term “AGI” was promoted by … Shane Legg, Mark Gubrud and Ben Goertzel’
^ Wang & Goertzel 2007
^ “First International Summer School in Artificial General Intelligence, Main summer school: June 22 – July 3, 2009, OpenCog Lab: July 6-9, 2009”. Archived from the original on 28 September 2020. Retrieved 11 May 2020.
^ “Избираеми дисциплини 2009/2010 – пролетен триместър” [Elective courses 2009/2010 – spring trimester] Факултет по математика и информатика [Faculty of Mathematics and Informatics] (in Bulgarian). Archived from the original on 26 July 2020. Retrieved 11 May 2020.
^ “Избираеми дисциплини 2010/2011 – зимен триместър” [Elective courses 2010/2011 – winter trimester] Факултет по математика и информатика [Faculty of Mathematics and Informatics] (in Bulgarian). Archived from the original on 26 July 2020. Retrieved 11 May 2020.
^ Shevlin, Henry; Vold, Karina; Crosby, Matthew; Halina, Marta (4 October 2019). “The limits of machine intelligence: Despite progress in device intelligence, synthetic basic intelligence is still a major challenge”. EMBO Reports. 20 (10 ): e49177. doi:10.15252/ embr.201949177. ISSN 1469-221X. PMC 6776890. PMID 31531926.
^ Bubeck, Sébastien; Chandrasekaran, Varun; Eldan, Ronen; Gehrke, Johannes; Horvitz, Eric; Kamar, Ece; Lee, Peter; Lee, Yin Tat; Li, Yuanzhi; Lundberg, Scott; Nori, Harsha; Palangi, Hamid; Ribeiro, Marco Tulio; Zhang, Yi (27 March 2023). “Sparks of Artificial General Intelligence: Early explores GPT-4”. arXiv:2303.12712 [cs.CL]
^ “Microsoft Researchers Claim GPT-4 Is Showing “Sparks” of AGI”. Futurism. 23 March 2023. Retrieved 13 December 2023.
^ Allen, Paul; Greaves, Mark (12 October 2011). “The Singularity Isn’t Near”. MIT Technology Review. Retrieved 17 September 2014.
^ Winfield, Alan. “Expert system will not turn into a Frankenstein’s monster”. The Guardian. Archived from the initial on 17 September 2014. Retrieved 17 September 2014.
^ Deane, George (2022 ). “Machines That Feel and Think: The Role of Affective Feelings and Mental Action in (Artificial) General Intelligence”. Artificial Life. 28 (3 ): 289-309. doi:10.1162/ artl_a_00368. ISSN 1064-5462. PMID 35881678. S2CID 251069071.
^ a b c Clocksin 2003.
^ Fjelland, Ragnar (17 June 2020). “Why basic synthetic intelligence will not be realized”. Humanities and Social Sciences Communications. 7 (1 ): 1-9. doi:10.1057/ s41599-020-0494-4. hdl:11250/ 2726984. ISSN 2662-9992. S2CID 219710554.
^ McCarthy 2007b.
^ Khatchadourian, Raffi (23 November 2015). “The Doomsday Invention: Will artificial intelligence bring us paradise or destruction?”. The New Yorker. Archived from the original on 28 January 2016. Retrieved 7 February 2016.
^ Müller, V. C., & Bostrom, N. (2016 ). Future progress in expert system: A study of skilled opinion. In Fundamental concerns of expert system (pp. 555-572). Springer, Cham.
^ Armstrong, Stuart, and Kaj Sotala. 2012. “How We’re Predicting AI-or Failing To.” In Beyond AI: Artificial Dreams, edited by Jan Romportl, Pavel Ircing, Eva Žáčková, Michal Polák and Radek Schuster, 52-75. Plzeň: University of West Bohemia
^ “Microsoft Now Claims GPT-4 Shows ‘Sparks’ of General Intelligence”. 24 March 2023.
^ Shimek, Cary (6 July 2023). “AI Outperforms Humans in Creativity Test”. Neuroscience News. Retrieved 20 October 2023.
^ Guzik, Erik E.; Byrge, Christian; Gilde, Christian (1 December 2023). “The creativity of machines: AI takes the Torrance Test”. Journal of Creativity. 33 (3 ): 100065. doi:10.1016/ j.yjoc.2023.100065. ISSN 2713-3745. S2CID 261087185.
^ Arcas, Blaise Agüera y (10 October 2023). “Artificial General Intelligence Is Already Here”. Noema.
^ Zia, Tehseen (8 January 2024). “Unveiling of Large Multimodal Models: Shaping the Landscape of Language Models in 2024”. Unite.ai. Retrieved 26 May 2024.
^ “Introducing OpenAI o1-preview”. OpenAI. 12 September 2024.
^ Knight, Will. “OpenAI Announces a Brand-new AI Model, Code-Named Strawberry, That Solves Difficult Problems Step by Step”. Wired. ISSN 1059-1028. Retrieved 17 September 2024.
^ “OpenAI Employee Claims AGI Has Been Achieved”. Orbital Today. 13 December 2024. Retrieved 27 December 2024.
^ “AI Index: State of AI in 13 Charts”. hai.stanford.edu. 15 April 2024. Retrieved 7 June 2024.
^ “Next-Gen AI: OpenAI and Meta’s Leap Towards Reasoning Machines”. Unite.ai. 19 April 2024. Retrieved 7 June 2024.
^ James, Alex P. (2022 ). “The Why, What, and How of Artificial General Intelligence Chip Development”. IEEE Transactions on Cognitive and Developmental Systems. 14 (2 ): 333-347. arXiv:2012.06338. doi:10.1109/ TCDS.2021.3069871. ISSN 2379-8920. S2CID 228376556. Archived from the initial on 28 August 2022. Retrieved 28 August 2022.
^ Pei, Jing; Deng, Lei; Song, Sen; Zhao, Mingguo; Zhang, Youhui; Wu, Shuang; Wang, Guanrui; Zou, Zhe; Wu, Zhenzhi; He, Wei; Chen, Feng; Deng, Ning; Wu, Si; Wang, Yu; Wu, Yujie (2019 ). “Towards synthetic basic intelligence with hybrid Tianjic chip architecture”. Nature. 572 (7767 ): 106-111. Bibcode:2019 Natur.572..106 P. doi:10.1038/ s41586-019-1424-8. ISSN 1476-4687. PMID 31367028. S2CID 199056116. Archived from the initial on 29 August 2022. Retrieved 29 August 2022.
^ Pandey, Mohit; Fernandez, Michael; Gentile, Francesco; Isayev, Olexandr; Tropsha, Alexander; Stern, Abraham C.; Cherkasov, Artem (March 2022). “The transformational function of GPU computing and deep learning in drug discovery”. Nature Machine Intelligence. 4 (3 ): 211-221. doi:10.1038/ s42256-022-00463-x. ISSN 2522-5839. S2CID 252081559.
^ Goertzel & Pennachin 2006.
^ a b c (Kurzweil 2005, p. 260).
^ a b c Goertzel 2007.
^ Grace, Katja (2016 ). “Error in Armstrong and Sotala 2012”. AI Impacts (blog). Archived from the original on 4 December 2020. Retrieved 24 August 2020.
^ a b Butz, Martin V. (1 March 2021). “Towards Strong AI”. KI – Künstliche Intelligenz. 35 (1 ): 91-101. doi:10.1007/ s13218-021-00705-x. ISSN 1610-1987. S2CID 256065190.
^ Liu, Feng; Shi, Yong; Liu, Ying (2017 ). “Intelligence Quotient and Intelligence Grade of Artificial Intelligence”. Annals of Data Science. 4 (2 ): 179-191. arXiv:1709.10242. doi:10.1007/ s40745-017-0109-0. S2CID 37900130.
^ Brien, Jörn (5 October 2017). “Google-KI doppelt so schlau wie Siri” [Google AI is two times as wise as Siri – but a six-year-old beats both] (in German). Archived from the original on 3 January 2019. Retrieved 2 January 2019.
^ Grossman, Gary (3 September 2020). “We’re entering the AI twilight zone between narrow and forum.batman.gainedge.org general AI“. VentureBeat. Archived from the original on 4 September 2020. Retrieved 5 September 2020. Certainly, too, there are those who declare we are currently seeing an early example of an AGI system in the just recently announced GPT-3 natural language processing (NLP) neural network. … So is GPT-3 the first example of an AGI system? This is debatable, however the agreement is that it is not AGI. … If nothing else, GPT-3 informs us there is a happy medium between narrow and basic AI.
^ Quach, Katyanna. “A designer developed an AI chatbot utilizing GPT-3 that helped a guy speak again to his late fiancée. OpenAI shut it down”. The Register. Archived from the initial on 16 October 2021. Retrieved 16 October 2021.
^ Wiggers, Kyle (13 May 2022), “DeepMind’s brand-new AI can perform over 600 tasks, from playing video games to controlling robotics”, TechCrunch, archived from the initial on 16 June 2022, retrieved 12 June 2022.
^ Bubeck, Sébastien; Chandrasekaran, Varun; Eldan, Ronen; Gehrke, Johannes; Horvitz, Eric; Kamar, Ece; Lee, Peter; Lee, Yin Tat; Li, Yuanzhi; Lundberg, Scott; Nori, Harsha; Palangi, Hamid; Ribeiro, Marco Tulio; Zhang, Yi (22 March 2023). “Sparks of Artificial General Intelligence: Early try outs GPT-4”. arXiv:2303.12712 [cs.CL]
^ Metz, Cade (1 May 2023). “‘ The Godfather of A.I.’ Leaves Google and Warns of Danger Ahead”. The New York City Times. ISSN 0362-4331. Retrieved 7 June 2023.
^ Bove, Tristan. “A.I. might measure up to human intelligence in ‘just a couple of years,’ says CEO of Google’s primary A.I. research study lab”. Fortune. Retrieved 4 September 2024.
^ Nellis, Stephen (2 March 2024). “Nvidia CEO states AI could pass human tests in five years”. Reuters. ^ Aschenbrenner, Leopold. “SITUATIONAL AWARENESS, The Decade Ahead”.
^ Sullivan, Mark (18 October 2023). “Why everyone appears to disagree on how to specify Artificial General Intelligence”. Fast Company.
^ Nosta, John (5 January 2024). “The Accelerating Path to Artificial General Intelligence”. Psychology Today. Retrieved 30 March 2024.
^ Hickey, Alex. “Whole Brain Emulation: A Huge Step for Neuroscience”. Tech Brew. Retrieved 8 November 2023.
^ Sandberg & Boström 2008.
^ Drachman 2005.
^ a b Russell & Norvig 2003.
^ Moravec 1988, p. 61.
^ Moravec 1998.
^ Holmgaard Mersh, Amalie (15 September 2023). “Decade-long European research task maps the human brain”. euractiv.
^ Swaminathan, Nikhil (January-February 2011). “Glia-the other brain cells”. Discover. Archived from the initial on 8 February 2014. Retrieved 24 January 2014.
^ de Vega, Glenberg & Graesser 2008. A large variety of views in present research, all of which need grounding to some degree
^ Thornton, Angela (26 June 2023). “How publishing our minds to a computer system may end up being possible”. The Conversation. Retrieved 8 November 2023.
^ Searle 1980
^ For instance: Russell & Norvig 2003,
Oxford University Press Dictionary of Psychology Archived 3 December 2007 at the Wayback Machine (priced estimate in” Encyclopedia.com”),.
MIT Encyclopedia of Cognitive Science Archived 19 July 2008 at the Wayback Machine (quoted in “AITopics”),.
Will Biological Computers Enable Artificially Intelligent Machines to Become Persons? Archived 13 May 2008 at the Wayback Machine Anthony Tongen.

^ a b c Russell & Norvig 2003, p. 947.
^ though see Explainable expert system for curiosity by the field about why a program behaves the method it does.
^ Chalmers, David J. (9 August 2023). “Could a Large Language Model Be Conscious?”. Boston Review.
^ Seth, Anil. “Consciousness”. New Scientist. Retrieved 5 September 2024.
^ Nagel 1974.
^ “The Google engineer who thinks the company’s AI has actually come to life”. The Washington Post. 11 June 2022. Retrieved 12 June 2023.
^ Kateman, Brian (24 July 2023). “AI Should Be Terrified of Humans”. TIME. Retrieved 5 September 2024.
^ Nosta, John (18 December 2023). “Should Artificial Intelligence Have Rights?”. Psychology Today. Retrieved 5 September 2024.
^ Akst, Daniel (10 April 2023). “Should Robots With Artificial Intelligence Have Moral or Legal Rights?”. The Wall Street Journal.
^ “Artificial General Intelligence – Do [es] the expense surpass advantages?”. 23 August 2021. Retrieved 7 June 2023.
^ “How we can Benefit from Advancing Artificial General Intelligence (AGI) – Unite.AI”. www.unite.ai. 7 April 2020. Retrieved 7 June 2023.
^ a b c Talty, Jules; Julien, Stephan. “What Will Our Society Look Like When Artificial Intelligence Is Everywhere?”. Smithsonian Magazine. Retrieved 7 June 2023.
^ a b Stevenson, Matt (8 October 2015). “Answers to Stephen Hawking’s AMA are Here!”. Wired. ISSN 1059-1028. Retrieved 8 June 2023.
^ a b Bostrom, Nick (2017 ). ” § Preferred order of arrival”. Superintelligence: paths, risks, methods (Reprinted with corrections 2017 ed.). Oxford, United Kingdom; New York City, New York City, USA: Oxford University Press. ISBN 978-0-1996-7811-2.
^ Piper, Kelsey (19 November 2018). “How technological development is making it likelier than ever that people will ruin ourselves”. Vox. Retrieved 8 June 2023.
^ Doherty, Ben (17 May 2018). “Climate change an ‘existential security danger’ to Australia, Senate query says”. The Guardian. ISSN 0261-3077. Retrieved 16 July 2023.
^ MacAskill, William (2022 ). What we owe the future. New York City, NY: Basic Books. ISBN 978-1-5416-1862-6.
^ a b Ord, Toby (2020 ). “Chapter 5: Future Risks, Unaligned Expert System”. The Precipice: Existential Risk and the Future of Humanity. Bloomsbury Publishing. ISBN 978-1-5266-0021-9.
^ Al-Sibai, Noor (13 February 2022). “OpenAI Chief Scientist Says Advanced AI May Already Be Conscious”. Futurism. Retrieved 24 December 2023.
^ Samuelsson, Paul Conrad (2019 ). “Artificial Consciousness: Our Greatest Ethical Challenge”. Philosophy Now. Retrieved 23 December 2023.
^ Kateman, Brian (24 July 2023). “AI Should Be Terrified of Humans”. TIME. Retrieved 23 December 2023.
^ Roose, Kevin (30 May 2023). “A.I. Poses ‘Risk of Extinction,’ Industry Leaders Warn”. The New York City Times. ISSN 0362-4331. Retrieved 24 December 2023.
^ a b “Statement on AI Risk”. Center for AI Safety. 30 May 2023. Retrieved 8 June 2023.
^ “Stephen Hawking: ‘Transcendence looks at the ramifications of synthetic intelligence – however are we taking AI seriously enough?'”. The Independent (UK). Archived from the initial on 25 September 2015. Retrieved 3 December 2014.
^ Herger, Mario. “The Gorilla Problem – Enterprise Garage”. Retrieved 7 June 2023.
^ “The interesting Facebook debate between Yann LeCun, Stuart Russel and Yoshua Bengio about the dangers of strong AI”. The fascinating Facebook dispute in between Yann LeCun, Stuart Russel and Yoshua Bengio about the dangers of strong AI (in French). Retrieved 8 June 2023.
^ “Will Artificial Intelligence Doom The Mankind Within The Next 100 Years?”. HuffPost. 22 August 2014. Retrieved 8 June 2023.
^ Sotala, Kaj; Yampolskiy, Roman V. (19 December 2014). “Responses to catastrophic AGI threat: a study”. Physica Scripta. 90 (1 ): 018001. doi:10.1088/ 0031-8949/90/ 1/018001. ISSN 0031-8949.
^ Bostrom, Nick (2014 ). Superintelligence: Paths, Dangers, Strategies (First ed.). Oxford University Press. ISBN 978-0-1996-7811-2.
^ Chow, Andrew R.; Perrigo, Billy (16 February 2023). “The AI Arms Race Is On. Start Worrying”. TIME. Retrieved 24 December 2023.
^ Tetlow, Gemma (12 January 2017). “AI arms race threats spiralling out of control, report alerts”. Financial Times. Archived from the initial on 11 April 2022. Retrieved 24 December 2023.
^ Milmo, Dan; Stacey, Kiran (25 September 2023). “Experts disagree over threat posed however expert system can not be neglected”. The Guardian. ISSN 0261-3077. Retrieved 24 December 2023.
^ “Humanity, Security & AI, Oh My! (with Ian Bremmer & Shuman Ghosemajumder)”. CAFE. 20 July 2023. Retrieved 15 September 2023.
^ Hamblin, James (9 May 2014). “But What Would the End of Humanity Mean for Me?”. The Atlantic. Archived from the original on 4 June 2014. Retrieved 12 December 2015.
^ Titcomb, James (30 October 2023). “Big Tech is stiring worries over AI, alert scientists”. The Telegraph. Retrieved 7 December 2023.
^ Davidson, John (30 October 2023). “Google Brain founder states huge tech is lying about AI termination risk”. Australian Financial Review. Archived from the original on 7 December 2023. Retrieved 7 December 2023.
^ Eloundou, Tyna; Manning, Sam; Mishkin, Pamela; Rock, Daniel (17 March 2023). “GPTs are GPTs: An early look at the labor market impact capacity of large language designs”. OpenAI. Retrieved 7 June 2023.
^ a b Hurst, Luke (23 March 2023). “OpenAI states 80% of employees could see their jobs impacted by AI. These are the tasks most affected”. euronews. Retrieved 8 June 2023.
^ Sheffey, Ayelet (20 August 2021). “Elon Musk says we require universal fundamental income since ‘in the future, physical work will be a choice'”. Business Insider. Archived from the original on 9 July 2023. Retrieved 8 June 2023.
Sources

UNESCO Science Report: the Race Against Time for Smarter Development. Paris: UNESCO. 11 June 2021. ISBN 978-9-2310-0450-6. Archived from the original on 18 June 2022. Retrieved 22 September 2021.
Chalmers, David (1996 ), The Conscious Mind, Oxford University Press.
Clocksin, William (August 2003), “Artificial intelligence and the future”, Philosophical Transactions of the Royal Society A, vol. 361, no. 1809, pp. 1721-1748, Bibcode:2003 RSPTA.361.1721 C, doi:10.1098/ rsta.2003.1232, PMID 12952683, S2CID 31032007.
Crevier, Daniel (1993 ). AI: The Tumultuous Look For Expert System. New York, NY: BasicBooks. ISBN 0-465-02997-3.
Darrach, Brad (20 November 1970), “Meet Shakey, the First Electronic Person”, Life Magazine, pp. 58-68.
Drachman, D. (2005 ), “Do we have brain to spare?”, Neurology, 64 (12 ): 2004-2005, doi:10.1212/ 01. WNL.0000166914.38327. BB, PMID 15985565, S2CID 38482114.
Feigenbaum, Edward A.; McCorduck, Pamela (1983 ), The Fifth Generation: Artificial Intelligence and Japan’s Computer Challenge to the World, Michael Joseph, ISBN 978-0-7181-2401-4.
Goertzel, Ben; Pennachin, Cassio, eds. (2006 ), Artificial General Intelligence (PDF), Springer, ISBN 978-3-5402-3733-4, archived from the original (PDF) on 20 March 2013.
Goertzel, Ben (December 2007), “Human-level synthetic basic intelligence and the possibility of a technological singularity: a reaction to Ray Kurzweil’s The Singularity Is Near, and McDermott’s review of Kurzweil”, Expert system, vol. 171, no. 18, Special Review Issue, pp. 1161-1173, doi:10.1016/ j.artint.2007.10.011, archived from the initial on 7 January 2016, obtained 1 April 2009.
Gubrud, Mark (November 1997), “Nanotechnology and International Security”, Fifth Foresight Conference on Molecular Nanotechnology, archived from the initial on 29 May 2011, obtained 7 May 2011.
Howe, J. (November 1994), Expert System at Edinburgh University: a Viewpoint, archived from the initial on 17 August 2007, obtained 30 August 2007.
Johnson, Mark (1987 ), The body in the mind, Chicago, ISBN 978-0-2264-0317-5.
Kurzweil, Ray (2005 ), The Singularity is Near, Viking Press.
Lighthill, Professor Sir James (1973 ), “Expert System: A General Survey”, Artificial Intelligence: a paper symposium, Science Research Council.
Luger, George; Stubblefield, William (2004 ), Artificial Intelligence: Structures and Strategies for Complex Problem Solving (5th ed.), The Benjamin/Cummings Publishing Company, Inc., p. 720, ISBN 978-0-8053-4780-7.
McCarthy, John (2007b). What is Expert system?. Stanford University. The supreme effort is to make computer system programs that can solve issues and attain objectives on the planet in addition to humans.
Moravec, Hans (1988 ), Mind Children, Harvard University Press
Moravec, Hans (1998 ), “When will computer hardware match the human brain?”, Journal of Evolution and Technology, vol. 1, archived from the initial on 15 June 2006, obtained 23 June 2006
Nagel (1974 ), “What Is it Like to Be a Bat” (PDF), Philosophical Review, 83 (4 ): 435-50, doi:10.2307/ 2183914, JSTOR 2183914, archived (PDF) from the initial on 16 October 2011, retrieved 7 November 2009
Newell, Allen; Simon, H. A. (1976 ). “Computer Technology as Empirical Inquiry: Symbols and Search”. Communications of the ACM. 19 (3 ): 113-126. doi:10.1145/ 360018.360022.
Nilsson, Nils (1998 ), Expert System: A New Synthesis, Morgan Kaufmann Publishers, ISBN 978-1-5586-0467-4
NRC (1999 ), “Developments in Artificial Intelligence”, Funding a Revolution: Government Support for Computing Research, National Academy Press, archived from the initial on 12 January 2008, obtained 29 September 2007
Poole, David; Mackworth, Alan; Goebel, Randy (1998 ), Computational Intelligence: A Logical Approach, New York City: Oxford University Press, archived from the initial on 25 July 2009, obtained 6 December 2007
Russell, Stuart J.; Norvig, Peter (2003 ), Expert System: A Modern Approach (second ed.), Upper Saddle River, New Jersey: Prentice Hall, ISBN 0-13-790395-2
Sandberg, Anders; Boström, Nick (2008 ), Whole Brain Emulation: A Roadmap (PDF), Technical Report # 2008-3, Future of Humanity Institute, Oxford University, archived (PDF) from the initial on 25 March 2020, obtained 5 April 2009
Searle, John (1980 ), “Minds, Brains and Programs” (PDF), Behavioral and Brain Sciences, 3 (3 ): 417-457, doi:10.1017/ S0140525X00005756, S2CID 55303721, archived (PDF) from the initial on 17 March 2019, obtained 3 September 2020
Simon, H. A. (1965 ), The Shape of Automation for Men and Management, New York: Harper & Row
Turing, Alan (October 1950). “Computing Machinery and Intelligence”. Mind. 59 (236 ): 433-460. doi:10.1093/ mind/LIX.236.433. ISSN 1460-2113. JSTOR 2251299. S2CID 14636783.

de Vega, Manuel; Glenberg, Arthur; Graesser, Arthur, eds. (2008 ), Symbols and Embodiment: Debates on meaning and cognition, Oxford University Press, ISBN 978-0-1992-1727-4
Wang, Pei; Goertzel, Ben (2007 ). “Introduction: Aspects of Artificial General Intelligence”. Advances in Artificial General Intelligence: Concepts, Architectures and Algorithms: Proceedings of the AGI Workshop 2006. IOS Press. pp. 1-16. ISBN 978-1-5860-3758-1. Archived from the initial on 18 February 2021. Retrieved 13 December 2020 – via ResearchGate.

Further reading

Aleksander, Igor (1996 ), Impossible Minds, World Scientific Publishing Company, ISBN 978-1-8609-4036-1
Azevedo FA, Carvalho LR, Grinberg LT, Farfel J, et al. (April 2009), “Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain”, The Journal of Comparative Neurology, 513 (5 ): 532-541, doi:10.1002/ cne.21974, PMID 19226510, S2CID 5200449, archived from the initial on 18 February 2021, recovered 4 September 2013 – via ResearchGate
Berglas, Anthony (January 2012) [2008], Artificial Intelligence Will Kill Our Grandchildren (Singularity), archived from the initial on 23 July 2014, obtained 31 August 2012
Cukier, Kenneth, “Ready for Robots? How to Think about the Future of AI“, Foreign Affairs, vol. 98, no. 4 (July/August 2019), pp. 192-98. George Dyson, historian of computing, composes (in what may be called “Dyson’s Law”) that “Any system easy sufficient to be understandable will not be made complex enough to behave wisely, while any system complicated enough to act smartly will be too complicated to understand.” (p. 197.) Computer scientist Alex Pentland composes: “Current AI machine-learning algorithms are, at their core, dead basic stupid. They work, however they work by strength.” (p. 198.).
Gelernter, David, Dream-logic, the Internet and Artificial Thought, Edge, archived from the initial on 26 July 2010, obtained 25 July 2010.
Gleick, James, “The Fate of Free Will” (evaluation of Kevin J. Mitchell, Free Agents: How Evolution Gave Us Free Choice, Princeton University Press, 2023, 333 pp.), The New York City Review of Books, vol. LXXI, no. 1 (18 January 2024), pp. 27-28, 30. “Agency is what distinguishes us from devices. For biological animals, reason and function come from acting in the world and experiencing the effects. Artificial intelligences – disembodied, strangers to blood, sweat, and tears – have no occasion for that.” (p. 30.).
Halal, William E. “TechCast Article Series: The Automation of Thought” (PDF). Archived from the original (PDF) on 6 June 2013.
– Halpern, Sue, “The Coming Tech Autocracy” (evaluation of Verity Harding, AI Needs You: How We Can Change AI’s Future and Save Our Own, Princeton University Press, 274 pp.; Gary Marcus, Taming Silicon Valley: How We Can Ensure That AI Works for Us, MIT Press, 235 pp.; Daniela Rus and Gregory Mone, The Mind’s Mirror: Risk and Reward in the Age of AI, Norton, 280 pp.; Madhumita Murgia, Code Dependent: Residing In the Shadow of AI, Henry Holt, 311 pp.), The New York Review of Books, vol. LXXI, no. 17 (7 November 2024), pp. 44-46. “‘ We can’t realistically expect that those who wish to get abundant from AI are going to have the interests of the rest people close at heart,’ … composes [Gary Marcus] ‘We can’t count on federal governments driven by campaign finance contributions [from tech business] to push back.’ … Marcus details the needs that people should make of their governments and the tech business. They consist of transparency on how AI systems work; compensation for individuals if their information [are] utilized to train LLMs (large language design) s and the right to authorization to this use; and gratisafhalen.be the capability to hold tech business accountable for the harms they bring on by eliminating Section 230, enforcing cash penalites, and passing more stringent product liability laws … Marcus also recommends … that a new, AI-specific federal agency, similar to the FDA, the FCC, or the FTC, may provide the most robust oversight … [T] he Fordham law teacher Chinmayi Sharma … suggests … develop [ing] a professional licensing routine for engineers that would function in a similar method to medical licenses, malpractice suits, and the Hippocratic oath in medication. ‘What if, like doctors,’ she asks …, ‘AI engineers also promised to do no harm?'” (p. 46.).
Holte, R. C.; Choueiry, B. Y. (2003 ), “Abstraction and reformulation in synthetic intelligence”, Philosophical Transactions of the Royal Society B, vol. 358, no. 1435, pp. 1197-1204, doi:10.1098/ rstb.2003.1317, PMC 1693218, PMID 12903653.
Hughes-Castleberry, Kenna, “A Murder Mystery Puzzle: The literary puzzle Cain’s Jawbone, which has stumped human beings for decades, reveals the restrictions of natural-language-processing algorithms”, Scientific American, vol. 329, no. 4 (November 2023), pp. 81-82. “This murder secret competition has revealed that although NLP (natural-language processing) models are capable of unbelievable tasks, their abilities are extremely much limited by the amount of context they receive. This […] could trigger [problems] for researchers who intend to utilize them to do things such as examine ancient languages. Sometimes, there are few historic records on long-gone civilizations to function as training information for such a function.” (p. 82.).
Immerwahr, Daniel, “Your Lying Eyes: People now utilize A.I. to create phony videos indistinguishable from genuine ones. How much does it matter?”, The New Yorker, 20 November 2023, pp. 54-59. “If by ‘deepfakes’ we suggest practical videos produced using expert system that really trick individuals, then they hardly exist. The fakes aren’t deep, and the deeps aren’t phony. […] A.I.-generated videos are not, in general, running in our media as counterfeited proof. Their role much better looks like that of cartoons, specifically smutty ones.” (p. 59.).
– Leffer, Lauren, “The Risks of Trusting AI: wiki.whenparked.com We need to prevent humanizing machine-learning models used in scientific research”, Scientific American, vol. 330, no. 6 (June 2024), pp. 80-81.
Lepore, Jill, “The Chit-Chatbot: Is talking with a machine a conversation?”, The New Yorker, 7 October 2024, pp. 12-16.
Marcus, Gary, “Artificial Confidence: Even the latest, buzziest systems of artificial general intelligence are stymmied by the usual problems”, Scientific American, vol. 327, no. 4 (October 2022), pp. 42-45.
McCarthy, John (October 2007), “From here to human-level AI“, Expert System, 171 (18 ): 1174-1182, doi:10.1016/ j.artint.2007.10.009.
McCorduck, Pamela (2004 ), Machines Who Think (2nd ed.), Natick, Massachusetts: A. K. Peters, ISBN 1-5688-1205-1.
Moravec, Hans (1976 ), The Role of Raw Power in Intelligence, archived from the original on 3 March 2016, retrieved 29 September 2007.
Newell, Allen; Simon, H. A. (1963 ), “GPS: A Program that Simulates Human Thought”, in Feigenbaum, E. A.; Feldman, J. (eds.), Computers and Thought, New York City: McGraw-Hill.
Omohundro, Steve (2008 ), The Nature of Self-Improving Expert system, provided and dispersed at the 2007 Singularity Summit, San Francisco, California.
Press, Eyal, “In Front of Their Faces: Does facial-recognition technology lead police to disregard inconsistent proof?”, The New Yorker, 20 November 2023, pp. 20-26.
Roivainen, Eka, “AI‘s IQ: ChatGPT aced a [basic intelligence] test but revealed that intelligence can not be measured by IQ alone”, Scientific American, vol. 329, no. 1 (July/August 2023), p. 7. “Despite its high IQ, ChatGPT fails at jobs that need genuine humanlike reasoning or an understanding of the physical and social world … ChatGPT seemed unable to reason logically and attempted to depend on its vast database of … truths stemmed from online texts. ”
– Scharre, Paul, “Killer Apps: The Real Dangers of an AI Arms Race”, Foreign Affairs, vol. 98, no. 3 (May/June 2019), pp. 135-44. “Today’s AI technologies are effective however unreliable. Rules-based systems can not handle scenarios their developers did not anticipate. Learning systems are limited by the data on which they were trained. AI failures have actually already caused tragedy. Advanced autopilot functions in automobiles, although they carry out well in some scenarios, have driven vehicles without alerting into trucks, concrete barriers, and parked cars. In the incorrect scenario, AI systems go from supersmart to superdumb in an immediate. When an enemy is attempting to manipulate and hack an AI system, the dangers are even greater.” (p. 140.).
Sutherland, J. G. (1990 ), “Holographic Model of Memory, Learning, and Expression”, International Journal of Neural Systems, vol. 1-3, pp. 256-267.
– Vincent, James, “Horny Robot Baby Voice: James Vincent on AI chatbots”, London Review of Books, vol. 46, no. 19 (10 October 2024), pp. 29-32.” [AI chatbot] programs are enabled by brand-new technologies but count on the timelelss human tendency to anthropomorphise.” (p. 29.).
Williams, R. W.; Herrup, K.