Artificial General Intelligence

Artificial general intelligence (AGI) is a kind of expert system (AI) that matches or exceeds human cognitive abilities throughout a large range of cognitive tasks. This contrasts with narrow AI, which is limited to particular jobs. [1] Artificial superintelligence (ASI), on the other hand, describes AGI that considerably exceeds human cognitive capabilities. AGI is thought about among the meanings of strong AI.

Creating AGI is a primary goal of AI research study and of business such as OpenAI [2] and Meta. [3] A 2020 survey identified 72 active AGI research and advancement tasks across 37 nations. [4]

The timeline for accomplishing AGI stays a subject of continuous dispute among scientists and professionals. Since 2023, some argue that it may be possible in years or decades; others keep it may take a century or longer; a minority think it may never ever be achieved; and another minority declares that it is currently here. [5] [6] Notable AI researcher Geoffrey Hinton has actually revealed issues about the fast development towards AGI, suggesting it might be attained faster than lots of expect. [7]

There is argument on the exact definition of AGI and concerning whether contemporary big language designs (LLMs) such as GPT-4 are early types of AGI. [8] AGI is a typical subject in sci-fi and futures studies. [9] [10]

Contention exists over whether AGI represents an existential risk. [11] [12] [13] Many professionals on AI have stated that alleviating the danger of human extinction positioned by AGI should be a global top priority. [14] [15] Others discover the development of AGI to be too remote to provide such a danger. [16] [17]

Terminology

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

Some academic sources reserve the term “strong AI” for computer system programs that experience life or awareness. [a] On the other hand, weak AI (or narrow AI) has the ability to resolve one particular problem but lacks basic cognitive capabilities. [22] [19] Some academic sources utilize “weak AI” to refer more broadly to any programs that neither experience awareness nor have a mind in the same sense as humans. [a]

Related concepts consist of artificial superintelligence and transformative AI. A synthetic superintelligence (ASI) is a theoretical kind of AGI that is far more normally smart than human beings, [23] while the notion of transformative AI relates to AI having a big influence on society, for example, comparable to the farming or commercial revolution. [24]

A structure for categorizing AGI in levels was proposed in 2023 by Google DeepMind researchers. They define five levels of AGI: emerging, qualified, specialist, virtuoso, and superhuman. For example, a competent AGI is defined as an AI that outperforms 50% of knowledgeable grownups in a large range of non-physical tasks, and a superhuman AGI (i.e. an artificial superintelligence) is similarly defined but with a limit of 100%. They consider large language models like ChatGPT or LLaMA 2 to be circumstances of emerging AGI. [25]

Characteristics

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

Intelligence qualities

Researchers typically hold that intelligence is required to do all of the following: [27]

reason, use strategy, resolve puzzles, and make judgments under uncertainty
represent knowledge, consisting of common sense knowledge
plan
learn
– interact in natural language
– if needed, incorporate these abilities in completion of any provided goal

Many interdisciplinary approaches (e.g. cognitive science, computational intelligence, and decision making) consider extra qualities such as creativity (the ability to form unique mental images and principles) [28] and autonomy. [29]

Computer-based systems that exhibit a number of these abilities exist (e.g. see computational imagination, automated thinking, choice support system, robot, evolutionary calculation, intelligent agent). There is argument about whether modern-day AI systems possess them to an adequate degree.

Physical qualities

Other capabilities are thought about preferable in intelligent systems, as they might impact intelligence or help in its expression. These consist of: [30]

– the ability to sense (e.g. see, hear, etc), and
– the capability to act (e.g. relocation and manipulate things, modification location to explore, etc).

This includes the capability to identify and react to hazard. [31]

Although 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) can be preferable for some intelligent systems, [30] these physical abilities are not strictly needed for an entity to qualify as AGI-particularly under the thesis that big language models (LLMs) might currently be or end up being AGI. Even from a less positive perspective on LLMs, there is no company requirement for an AGI to have a human-like type; being a silicon-based computational system suffices, provided it can process input (language) from the external world in location of human senses. This interpretation aligns with the understanding that AGI has actually never ever been proscribed a specific physical embodiment and hence does not require a capability for locomotion or traditional “eyes and ears”. [32]

Tests for human-level AGI

Several tests suggested to verify human-level AGI have been thought about, including: [33] [34]

The concept of the test is that the maker has to attempt and pretend to be a male, by responding to questions put to it, and it will only pass if the pretence is reasonably convincing. A substantial portion of a jury, who ought to not be expert about devices, must be taken in by the pretence. [37]

AI-complete issues

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

There are numerous problems that have been conjectured to need general intelligence to fix along with human beings. Examples include computer vision, natural language understanding, and dealing with unexpected circumstances while fixing any real-world issue. [48] Even a particular job like translation requires a maker to read and write in both languages, follow the author’s argument (factor), understand the context (understanding), and consistently reproduce the author’s initial intent (social intelligence). All of these problems require to be fixed at the same time in order to reach human-level machine performance.

However, a lot of these jobs can now be carried out by contemporary big language models. According to Stanford University’s 2024 AI index, AI has reached human-level performance on many standards for reading understanding and visual reasoning. [49]

History

Classical AI

Modern AI research started in the mid-1950s. [50] The first generation of AI scientists were encouraged that artificial general intelligence was possible which it would exist in just a few years. [51] AI leader Herbert A. Simon wrote in 1965: “devices will be capable, within twenty years, of doing any work a male can do.” [52]

Their predictions were the motivation for Stanley Kubrick and Arthur C. Clarke’s character HAL 9000, who embodied what AI researchers believed they might develop by the year 2001. AI pioneer Marvin Minsky was a specialist [53] on the task of making HAL 9000 as practical as possible according to the consensus predictions of the time. He stated in 1967, “Within a generation … the problem of creating ‘expert system’ will substantially be solved”. [54]

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

However, in the early 1970s, it ended up being apparent that researchers had grossly underestimated the difficulty of the project. Funding agencies became hesitant 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 goals like “continue a table talk”. [58] In reaction to this and the success of specialist systems, both industry and government pumped cash into the field. [56] [59] However, confidence in AI amazingly 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 researchers who anticipated the impending achievement of AGI had been mistaken. By the 1990s, AI scientists had a reputation for making vain guarantees. They ended up being hesitant to make forecasts at all [d] and avoided mention of “human level” synthetic intelligence for fear of being labeled “wild-eyed dreamer [s]. [62]

Narrow AI research study

In the 1990s and early 21st century, mainstream AI achieved business success and academic respectability by concentrating on particular sub-problems where AI can produce verifiable outcomes and business applications, such as speech acknowledgment and recommendation algorithms. [63] These “applied AI” systems are now utilized extensively throughout the technology industry, and research in this vein is heavily funded in both academia and industry. As of 2018 [upgrade], development in this field was considered an emerging trend, and a fully grown phase was expected to be reached in more than 10 years. [64]

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

I am positive that this bottom-up path to expert system will one day fulfill the conventional top-down path more than half method, all set to provide the real-world competence and the commonsense knowledge that has actually been so frustratingly evasive in reasoning programs. Fully intelligent machines will result when the metaphorical golden spike is driven unifying the 2 efforts. [65]

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

The expectation has actually typically been voiced that “top-down” (symbolic) approaches to modeling cognition will somehow fulfill “bottom-up” (sensory) approaches somewhere in between. If the grounding considerations in this paper stand, then this expectation is hopelessly modular and there is actually just one viable path from sense to signs: from the ground up. A free-floating symbolic level like the software level of a computer system will never ever be reached by this path (or vice versa) – nor is it clear why we need to even try to reach such a level, because it looks as if arriving would just amount to uprooting our signs from their intrinsic significances (therefore simply lowering ourselves to the practical equivalent of a programmable computer system). [66]

Modern synthetic basic intelligence research study

The term “artificial general intelligence” was used 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 agent increases “the capability to satisfy objectives in a large range of environments”. [68] This type of AGI, identified by the ability to increase a mathematical definition of intelligence rather than show human-like behaviour, [69] was also called universal expert system. [70]

The term AGI was re-introduced and promoted by Shane Legg and Ben Goertzel around 2002. [71] AGI research activity in 2006 was described by Pei Wang and Ben Goertzel [72] as “producing publications and preliminary results”. The very first summer season school in AGI was organized in Xiamen, China in 2009 [73] by the Xiamen university’s Artificial Brain Laboratory and OpenCog. The first university course was given up 2010 [74] and 2011 [75] at Plovdiv University, Bulgaria by Todor Arnaudov. MIT presented a course on AGI in 2018, arranged by Lex Fridman and including a number of visitor lecturers.

Since 2023 [upgrade], a small number of computer scientists are active in AGI research study, and many contribute to a series of AGI conferences. However, increasingly more scientists have an interest in open-ended learning, [76] [77] which is the idea of enabling AI to continually discover and innovate like human beings do.

Feasibility

As of 2023, the advancement and prospective achievement of AGI remains a topic of intense debate within the AI neighborhood. While conventional consensus held that AGI was a distant goal, recent advancements have actually led some researchers and market figures to claim that early kinds of AGI may already exist. [78] AI leader Herbert A. Simon speculated in 1965 that “devices will be capable, within twenty years, of doing any work a male can do”. This prediction stopped working to come real. Microsoft co-founder Paul Allen thought that such intelligence is not likely in the 21st century since it would require “unforeseeable and basically unforeseeable breakthroughs” and a “scientifically deep understanding of cognition”. [79] Writing in The Guardian, roboticist Alan Winfield claimed the gulf in between contemporary computing and human-level synthetic intelligence is as wide as the gulf between current space flight and practical faster-than-light spaceflight. [80]

A more obstacle is the lack of clarity in defining what intelligence requires. Does it require awareness? Must it show the ability to set goals as well as pursue them? Is it purely a matter of scale such that if design sizes increase sufficiently, intelligence will emerge? Are facilities such as preparation, thinking, and causal understanding required? Does intelligence require explicitly replicating the brain and its specific professors? Does it require feelings? [81]

Most AI researchers believe strong AI can be accomplished in the future, however 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, but that the present level of progress is such that a date can not precisely be anticipated. [84] AI specialists’ views on the feasibility of AGI wax and subside. Four polls conducted in 2012 and 2013 recommended that the mean price quote among experts for when they would be 50% confident AGI would arrive was 2040 to 2050, depending upon the survey, with the mean being 2081. Of the professionals, 16.5% addressed with “never” when asked the exact same concern but with a 90% self-confidence instead. [85] [86] Further present AGI development considerations can be found above Tests for confirming human-level AGI.

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

In 2023, Microsoft researchers released a comprehensive examination of GPT-4. They concluded: “Given the breadth and depth of GPT-4’s capabilities, we think that it could reasonably be viewed as an early (yet still insufficient) variation of an artificial basic intelligence (AGI) system.” [88] Another research study in 2023 reported that GPT-4 exceeds 99% of humans on the Torrance tests of creative thinking. [89] [90]

Blaise Agüera y Arcas and Peter Norvig composed in 2023 that a significant level of basic intelligence has currently been achieved with frontier models. They wrote that unwillingness to this view originates from 4 main factors: a “healthy uncertainty about metrics for AGI”, an “ideological commitment to alternative AI theories or strategies”, a “dedication to human (or biological) exceptionalism”, or a “issue about the financial implications of AGI”. [91]

2023 also marked the emergence of big multimodal models (large language designs capable of processing or producing multiple techniques such as text, audio, and images). [92]

In 2024, OpenAI released o1-preview, the first of a series of designs that “spend more time believing before they respond”. According to Mira Murati, this capability to think before responding represents a new, additional paradigm. It enhances design outputs by investing more computing power when generating the answer, whereas the design scaling paradigm improves outputs by increasing the design size, training information and training compute power. [93] [94]

An OpenAI staff member, Vahid Kazemi, declared in 2024 that the business had actually achieved AGI, mentioning, “In my viewpoint, we have currently achieved AGI and it’s even 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 human beings at many tasks.” He likewise addressed criticisms that large language designs (LLMs) merely follow predefined patterns, comparing their learning procedure to the clinical technique of observing, assuming, and validating. These statements have stimulated dispute, as they rely on a broad and non-traditional definition of AGI-traditionally comprehended as AI that matches human intelligence across all domains. Critics argue that, while OpenAI’s models show amazing flexibility, they may not fully meet this standard. Notably, Kazemi’s comments came shortly after OpenAI got rid of “AGI” from the regards to its partnership with Microsoft, triggering speculation about the business’s tactical objectives. [95]

Timescales

Progress in expert system has actually historically gone through periods of fast progress separated by durations when development appeared to stop. [82] Ending each hiatus were fundamental advances in hardware, software application or both to develop space for further progress. [82] [98] [99] For example, the hardware available in the twentieth century was not enough to carry out deep learning, which requires great deals of GPU-enabled CPUs. [100]

In the introduction to his 2006 book, [101] Goertzel states that price quotes of the time required before a genuinely versatile AGI is developed vary from 10 years to over a century. Since 2007 [upgrade], the agreement in the AGI research neighborhood seemed to be that the timeline talked about by Ray Kurzweil in 2005 in The Singularity is Near [102] (i.e. in between 2015 and 2045) was possible. [103] Mainstream AI scientists have actually given a wide variety of opinions on whether progress will be this rapid. A 2012 meta-analysis of 95 such viewpoints found a bias towards predicting that the beginning of AGI would occur within 16-26 years for modern-day and historic forecasts alike. That paper has actually been slammed for how it classified opinions as professional or non-expert. [104]

In 2012, Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton developed a neural network called AlexNet, which won the ImageNet competitors with a top-5 test error rate of 15.3%, significantly much better than the second-best entry’s rate of 26.3% (the standard technique utilized a weighted sum of ratings from different pre-defined classifiers). [105] AlexNet was considered as the initial ground-breaker of the existing deep learning wave. [105]

In 2017, researchers Feng Liu, Yong Shi, and Ying Liu carried out intelligence tests on publicly readily available and easily available weak AI such as Google AI, Apple’s Siri, and others. At the maximum, these AIs reached an IQ value of about 47, which corresponds approximately to a six-year-old kid in very first grade. A grownup concerns about 100 usually. Similar tests were performed in 2014, with the IQ score reaching a maximum worth of 27. [106] [107]

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

In the very same year, Jason Rohrer utilized his GPT-3 account to establish a chatbot, and supplied a chatbot-developing platform called “Project December”. OpenAI asked for changes to the chatbot to abide by their safety standards; Rohrer detached Project December from the GPT-3 API. [109]

In 2022, DeepMind developed Gato, a “general-purpose” system capable of performing more than 600 different jobs. [110]

In 2023, Microsoft Research released a study on an early variation of OpenAI’s GPT-4, contending that it displayed more basic intelligence than previous AI models and showed human-level efficiency in tasks covering several domains, such as mathematics, coding, and law. This research study triggered a dispute on whether GPT-4 might be thought about an early, insufficient version of artificial general intelligence, stressing the need for further expedition and assessment of such systems. [111]

In 2023, the AI researcher Geoffrey Hinton mentioned that: [112]

The idea that this things could actually get smarter than people – a few people thought that, […] But a lot of individuals thought it was way off. And I thought it was way off. I believed it was 30 to 50 years or perhaps longer away. Obviously, I no longer believe that.

In May 2023, Demis Hassabis likewise stated that “The development in the last couple of years has actually been pretty extraordinary”, which he sees no reason that it would slow down, expecting AGI within a decade or even a few years. [113] In March 2024, Nvidia’s CEO, Jensen Huang, stated his expectation that within 5 years, AI would can passing any test a minimum of as well as human beings. [114] In June 2024, the AI researcher Leopold Aschenbrenner, a previous OpenAI employee, estimated AGI by 2027 to be “noticeably possible”. [115]

Whole brain emulation

While the development of transformer models like in ChatGPT is considered the most promising course to AGI, [116] [117] entire brain emulation can serve as an alternative approach. With entire brain simulation, a brain design is built by scanning and mapping a biological brain in detail, and after that copying and replicating it on a computer system or another computational gadget. The simulation design need to be sufficiently faithful to the initial, so that it behaves in almost the same way as the initial brain. [118] Whole brain emulation is a type of brain simulation that is gone over in computational neuroscience and neuroinformatics, and for medical research functions. It has actually been discussed in expert system research study [103] as a method to strong AI. Neuroimaging innovations that might deliver the required comprehensive understanding are enhancing quickly, and futurist Ray Kurzweil in the book The Singularity Is Near [102] anticipates that a map of sufficient quality will become offered on a comparable timescale to the computing power needed to replicate it.

Early approximates

For low-level brain simulation, an extremely effective cluster of computers or GPUs would be required, provided the massive 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 nerve cells. The brain of a three-year-old kid has about 1015 synapses (1 quadrillion). This number decreases with age, supporting by the adult years. Estimates vary for an adult, ranging from 1014 to 5 × 1014 synapses (100 to 500 trillion). [120] An estimate of the brain’s processing power, based on a simple switch design for neuron activity, is around 1014 (100 trillion) synaptic updates per second (SUPS). [121]

In 1997, Kurzweil looked at numerous estimates for the hardware required to equate to the human brain and adopted a figure of 1016 computations per 2nd (cps). [e] (For contrast, if a “calculation” was equivalent to one “floating-point operation” – a measure utilized to rate existing supercomputers – then 1016 “calculations” would be comparable to 10 petaFLOPS, accomplished in 2011, while 1018 was accomplished in 2022.) He utilized this figure to forecast the needed hardware would be available sometime in between 2015 and 2025, if the rapid growth in computer system power at the time of writing continued.

Current research

The Human Brain Project, an EU-funded initiative active from 2013 to 2023, has actually developed an especially in-depth and openly accessible atlas of the human brain. [124] In 2023, scientists from Duke University carried out 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 present artificial neural network implementations is simple compared to biological nerve cells. A brain simulation would likely have to capture the in-depth cellular behaviour of biological nerve cells, presently understood just in broad outline. The overhead presented by full modeling of the biological, chemical, and physical information of neural behaviour (especially on a molecular scale) would require computational powers several orders of magnitude bigger than Kurzweil’s estimate. In addition, the price quotes do not represent glial cells, which are known to contribute in cognitive procedures. [125]

An essential criticism of the simulated brain method stems from embodied cognition theory which asserts that human embodiment is an essential aspect of human intelligence and is needed to ground significance. [126] [127] If this theory is appropriate, any completely practical brain design will require to encompass more than simply the nerve cells (e.g., a robotic body). Goertzel [103] proposes virtual embodiment (like in metaverses like Second Life) as an option, however it is unknown whether this would be enough.

Philosophical point of view

“Strong AI” as defined in approach

In 1980, theorist John Searle created the term “strong AI” as part of his Chinese space argument. [128] He proposed a distinction between two hypotheses about expert system: [f]

Strong AI hypothesis: A synthetic intelligence system can have “a mind” and “awareness”.
Weak AI hypothesis: An artificial intelligence system can (only) imitate it believes and has a mind and consciousness.

The very first one he called “strong” since it makes a more powerful declaration: it presumes something special has occurred to the maker that goes beyond those abilities that we can evaluate. The behaviour of a “weak AI” maker would be exactly similar to a “strong AI” maker, however the latter would likewise have subjective conscious experience. This use is likewise typical in academic AI research study and textbooks. [129]

In contrast to Searle and traditional AI, some futurists such as Ray Kurzweil use the term “strong AI” to mean “human level artificial general intelligence”. [102] This is not the exact same as Searle’s strong AI, unless it is presumed that consciousness is required for human-level AGI. Academic theorists such as Searle do not think that is the case, and to most synthetic intelligence researchers the question is out-of-scope. [130]

Mainstream AI is most interested in how a program behaves. [131] According to Russell and Norvig, “as long as the program works, they do not care if you call it genuine or a simulation.” [130] If the program can act as if it has a mind, then there is no need to know if it really has mind – indeed, there would be no other way to inform. For AI research study, Searle’s “weak AI hypothesis” is equivalent to the declaration “artificial general intelligence is possible”. Thus, according to Russell and Norvig, “most AI scientists take the weak AI hypothesis for approved, and don’t care about the strong AI hypothesis.” [130] Thus, for academic AI research study, “Strong AI” and “AGI” are 2 various things.

Consciousness

Consciousness can have various meanings, and some aspects play substantial roles in science fiction and the ethics of synthetic intelligence:

Sentience (or “phenomenal awareness”): The capability to “feel” perceptions or emotions subjectively, as opposed to the ability to reason about understandings. Some philosophers, such as David Chalmers, use the term “awareness” to refer specifically to sensational awareness, which is roughly equivalent to life. [132] Determining why and how subjective experience occurs is referred to as the tough problem of consciousness. [133] Thomas Nagel discussed in 1974 that it “seems like” something to be conscious. If we are not mindful, then it doesn’t feel like anything. Nagel utilizes the example of a bat: we can sensibly ask “what does it feel 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) but a toaster does not. [134] In 2022, a Google engineer claimed that the business’s AI chatbot, LaMDA, had actually accomplished life, though this claim was extensively challenged by other professionals. [135]
Self-awareness: To have conscious awareness of oneself as a separate individual, particularly to be consciously aware of one’s own ideas. This is opposed to just being the “topic of one’s believed”-an os or debugger has the ability to be “mindful of itself” (that is, to represent itself in the same method it represents whatever else)-but this is not what people generally suggest when they use the term “self-awareness”. [g]
These characteristics have an ethical dimension. AI sentience would generate issues of well-being and legal defense, similarly to animals. [136] Other aspects of consciousness associated to cognitive abilities are likewise pertinent to the principle of AI rights. [137] Finding out how to incorporate innovative AI with existing legal and social frameworks is an emergent problem. [138]

Benefits

AGI might have a variety of applications. If oriented towards such goals, AGI could help mitigate different issues worldwide such as appetite, poverty and health problems. [139]

AGI could improve efficiency and efficiency in many tasks. For example, in public health, AGI could accelerate medical research, especially against cancer. [140] It might take care of the elderly, [141] and equalize access to fast, top quality medical diagnostics. It might offer fun, cheap and personalized education. [141] The requirement to work to subsist could end up being outdated if the wealth produced is effectively redistributed. [141] [142] This likewise raises the question of the place of people in a significantly automated society.

AGI could likewise help to make rational choices, and to expect and avoid catastrophes. It could likewise assist to profit of possibly catastrophic innovations such as nanotechnology or environment engineering, while preventing the associated risks. [143] If an AGI’s main objective is to avoid existential catastrophes such as human termination (which might be difficult if the Vulnerable World Hypothesis turns out to be true), [144] it could take steps to drastically minimize the threats [143] while reducing the impact of these steps on our quality of life.

Risks

Existential threats

AGI might represent multiple types of existential threat, which are risks that threaten “the early termination of Earth-originating intelligent life or the long-term and extreme damage of its potential for preferable future advancement”. [145] The threat of human extinction from AGI has actually been the topic of numerous debates, but there is also the possibility that the development of AGI would cause a permanently problematic future. Notably, it might be utilized to spread and maintain the set of values of whoever establishes it. If mankind still has ethical blind areas similar to slavery in the past, AGI may irreversibly entrench it, avoiding moral development. [146] Furthermore, AGI could help with mass surveillance and brainwashing, which might be utilized to develop a stable repressive worldwide totalitarian regime. [147] [148] There is also a threat for the machines themselves. If makers that are sentient or otherwise worthy of moral consideration are mass produced in the future, taking part in a civilizational path that forever disregards their well-being and interests could be an existential catastrophe. [149] [150] Considering just how much AGI could improve mankind’s future and help in reducing other existential risks, Toby Ord calls these existential dangers “an argument for continuing with due care”, not for “abandoning AI“. [147]

Risk of loss of control and human extinction

The thesis that AI presents an existential threat for human beings, which this risk needs more attention, is controversial but has actually been backed in 2023 by lots of public figures, AI scientists and CEOs of AI business such as Elon Musk, Bill Gates, Geoffrey Hinton, Yoshua Bengio, Demis Hassabis and Sam Altman. [151] [152]

In 2014, Stephen Hawking criticized extensive indifference:

So, facing possible futures of enormous benefits and threats, the specialists are surely doing whatever possible to ensure the finest result, right? Wrong. If a superior alien civilisation sent us a message saying, ‘We’ll show up 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 more or less what is taking place with AI. [153]

The potential fate of humanity has actually often been compared to the fate of gorillas threatened by human activities. The contrast mentions that greater intelligence permitted humanity to control gorillas, which are now susceptible in manner ins which they could not have actually expected. As a result, the gorilla has become an endangered types, not out of malice, but just as a collateral damage from human activities. [154]

The skeptic Yann LeCun considers that AGIs will have no desire to dominate mankind and that we ought to take care not to anthropomorphize them and interpret their intents as we would for people. He stated that individuals won’t be “wise enough to create super-intelligent machines, yet unbelievably silly to the point of giving it moronic goals without any safeguards”. [155] On the other side, the concept of critical merging suggests that almost whatever their objectives, smart agents will have reasons to try to survive and get more power as intermediary steps to accomplishing these goals. Which this does not require having emotions. [156]

Many scholars who are worried about existential risk supporter for more research into resolving the “control problem” to respond to the question: what kinds of safeguards, algorithms, or architectures can developers execute to increase the possibility that their recursively-improving AI would continue to act in a friendly, rather than harmful, manner after it reaches superintelligence? [157] [158] Solving the control problem is made complex by the AI arms race (which could cause a race to the bottom of safety preventative measures in order to release items before competitors), [159] and using AI in weapon systems. [160]

The thesis that AI can position existential danger also has detractors. Skeptics normally state that AGI is not likely in the short-term, or that concerns about AGI sidetrack from other issues associated with present AI. [161] Former Google scams czar Shuman Ghosemajumder thinks about that for many individuals outside of the technology industry, existing chatbots and LLMs are currently viewed as though they were AGI, causing further misunderstanding and fear. [162]

Skeptics often charge that the thesis is crypto-religious, with an irrational belief in the possibility of superintelligence changing an unreasonable belief in an omnipotent God. [163] Some researchers believe that the communication campaigns on AI existential threat by certain AI groups (such as OpenAI, Anthropic, DeepMind, and Conjecture) may be an at attempt at regulatory capture and to pump up interest in their products. [164] [165]

In 2023, the CEOs of Google DeepMind, OpenAI and Anthropic, along with other market leaders and researchers, issued a joint statement asserting that “Mitigating the risk of termination from AI must be a global concern together with other societal-scale risks such as pandemics and nuclear war.” [152]

Mass unemployment

Researchers from OpenAI approximated that “80% of the U.S. labor force might have at least 10% of their work jobs impacted by the intro of LLMs, while around 19% of employees may see at least 50% of their tasks affected”. [166] [167] They consider workplace employees to be the most exposed, for example mathematicians, accounting professionals or web designers. [167] AGI might have a better autonomy, ability to make choices, to interface with other computer tools, however likewise to manage robotized bodies.

According to Stephen Hawking, the result of automation on the lifestyle will depend upon how the wealth will be redistributed: [142]

Everyone can delight in a life of elegant leisure if the machine-produced wealth is shared, or the majority of people can end up badly poor if the machine-owners successfully lobby versus wealth redistribution. Up until now, the pattern seems to be towards the 2nd alternative, with innovation driving ever-increasing inequality

Elon Musk thinks about that the automation of society will require governments to adopt a universal standard income. [168]

See also

Artificial brain – Software and hardware with cognitive capabilities comparable to those of the animal or human brain
AI effect
AI security – Research area on making AI safe and helpful
AI positioning – AI conformance to the desired goal
A.I. Rising – 2018 film directed by Lazar Bodroža
Expert system
Automated artificial intelligence – Process of automating the application of artificial intelligence
BRAIN Initiative – Collaborative public-private research effort revealed by the Obama administration
China Brain Project
Future of Humanity Institute – Defunct Oxford interdisciplinary research centre
General video game playing – Ability of synthetic intelligence to play different video games
Generative expert system – AI system capable of producing material in response to prompts
Human Brain Project – Scientific research study job
Intelligence amplification – Use of information technology to augment human intelligence (IA).
Machine ethics – Moral behaviours of manufactured makers.
Moravec’s paradox.
Multi-task knowing – Solving numerous maker learning jobs at the exact same time.
Neural scaling law – Statistical law in artificial intelligence.
Outline of expert system – Overview of and topical guide to expert system.
Transhumanism – Philosophical motion.
Synthetic intelligence – Alternate term for or type of expert system.
Transfer knowing – Machine knowing technique.
Loebner Prize – Annual AI competition.
Hardware for expert system – Hardware specifically developed and optimized for artificial intelligence.
Weak synthetic intelligence – 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 room.
^ AI founder John McCarthy composes: “we can not yet characterize in general what sort of computational procedures we desire to call smart. ” [26] (For a conversation of some meanings of intelligence used by expert system researchers, see approach of synthetic intelligence.).
^ The Lighthill report particularly criticized AI‘s “grandiose objectives” and led the dismantling of AI research study in England. [55] In the U.S., DARPA ended up being determined to money just “mission-oriented direct research, rather than standard undirected research”. [56] [57] ^ As AI creator John McCarthy composes “it would be a fantastic relief to the rest of the employees in AI if the innovators of new basic formalisms would express their hopes in a more guarded form than has actually sometimes held true.” [61] ^ In “Mind Children” [122] 1015 cps is utilized. More recently, in 1997, [123] Moravec argued for 108 MIPS which would approximately correspond to 1014 cps. Moravec talks in terms of MIPS, not “cps”, which is a non-standard term Kurzweil presented.
^ As defined in a basic AI textbook: “The assertion that machines could possibly act wisely (or, perhaps better, act as if they were intelligent) is called the ‘weak AI‘ hypothesis by thinkers, and the assertion that makers that do so are really thinking (rather than replicating 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 developed to carry out a single task.
^ “OpenAI Charter”. OpenAI. Retrieved 6 April 2023. Our mission is to make sure that synthetic general intelligence benefits all of humanity.
^ Heath, Alex (18 January 2024). “Mark Zuckerberg’s new objective is producing synthetic basic intelligence”. The Verge. Retrieved 13 June 2024. Our vision is to build AI that is better than human-level at all of the human senses.
^ Baum, Seth D. (2020 ). A Study 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 identified as being active in 2020.
^ a b c “AI timelines: What do professionals in expert system 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 City Times. Retrieved 18 May 2023.
^ “AI pioneer Geoffrey Hinton stops Google and warns of danger ahead”. The New York Times. 1 May 2023. Retrieved 2 May 2023. It is hard to see how you can prevent 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 try outs 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 alter. All that you alter changes 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 Artificial Intelligence”. The New York City Times. The real danger is not AI itself but the way we deploy it.
^ “Impressed by expert system? Experts say AGI is following, and it has ‘existential’ dangers”. ABC News. 23 March 2023. Retrieved 6 April 2023. AGI might pose existential threats to mankind.
^ Bostrom, Nick (2014 ). Superintelligence: Paths, Dangers, Strategies. Oxford University Press. ISBN 978-0-1996-7811-2. The very first superintelligence will be the last invention that mankind requires to make.
^ Roose, Kevin (30 May 2023). “A.I. Poses ‘Risk of Extinction,’ Industry Leaders Warn”. The New York Times. Mitigating the threat of termination from AI must be an international concern.
^ “Statement on AI Risk”. Center for AI Safety. Retrieved 1 March 2024. AI professionals alert of risk of termination from AI.
^ Mitchell, Melanie (30 May 2023). “Are AI‘s Doomsday Scenarios Worth Taking Seriously?”. The New York Times. We are far from creating machines that can outthink us in basic methods.
^ LeCun, Yann (June 2023). “AGI does not provide an existential danger”. Medium. There is no reason to fear AI as an existential risk.
^ 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 variety of human intelligence.”.
^ “The Age of Expert System: George John at TEDxLondonBusinessSchool 2013”. Archived from the initial on 26 February 2014. Retrieved 22 February 2014.
^ Newell & Simon 1976, This is the term they utilize for “human-level” intelligence in the physical sign system hypothesis.
^ “The Open University on Strong and Weak AI“. Archived from the original on 25 September 2009. Retrieved 8 October 2007.
^ “What is artificial superintelligence (ASI)?|Definition from TechTarget”. Enterprise AI. Retrieved 8 October 2023.
^ “Expert system is transforming our world – it is on everyone 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 attaining 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 intelligent traits is based on the subjects covered by major AI books, consisting of: 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 forms the method we believe: a new view of intelligence (The MIT Press, 2007). ISBN 0-2621-6239-3.
^ White, R. W. (1959 ). “Motivation reevaluated: The concept of skills”. Psychological Review. 66 (5 ): 297-333. doi:10.1037/ h0040934. PMID 13844397. S2CID 37385966.
^ White, R. W. (1959 ). “Motivation reconsidered: 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 original 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 takes place 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 dispute whether computer ‘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 differentiate GPT-4 from a human in a Turing test”. arXiv:2405.08007 [cs.HC]
^ Varanasi, Lakshmi (21 March 2023). “AI designs like ChatGPT and GPT-4 are acing everything from the bar test to AP Biology. Here’s a list of challenging examinations both AI variations have passed”. Business Insider. Retrieved 30 May 2023.
^ Naysmith, Caleb (7 February 2023). “6 Jobs Artificial Intelligence Is Already Replacing and How Investors Can Take Advantage Of 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 undependable. The Winograd Schema is outdated. Coffee is the answer”. Analytics India Magazine. Retrieved 3 March 2024.
^ Bhaimiya, Sawdah (20 June 2023). “DeepMind’s co-founder suggested testing an AI chatbot’s ability to turn $100,000 into $1 million to determine human-like intelligence”. Business Insider. Retrieved 3 March 2024.
^ Suleyman, Mustafa (14 July 2023). “Mustafa Suleyman: My brand-new Turing test would see if AI can make $1 million”. MIT Technology Review. Retrieved 3 March 2024.
^ Shapiro, Stuart C. (1992 ). “Expert System” (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 ), estimated 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 ). “Reply to Lighthill”. Stanford University. Archived from the initial on 30 September 2008. Retrieved 29 September 2007.
^ Markoff, John (14 October 2005). “Behind Artificial Intelligence, a Squadron of Bright Real People”. The New York Times. Archived from the original on 2 February 2023. Retrieved 18 February 2017. At its low point, some computer system researchers and software engineers avoided the term synthetic intelligence for worry of being considered as wild-eyed dreamers.
^ Russell & Norvig 2003, pp. 25-26
^ “Trends in the Emerging Tech Hype Cycle”. Gartner Reports. Archived from the initial 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 Expert System: Sequential Decisions Based on Algorithmic Probability. Texts in Theoretical Computer Technology an EATCS Series. Springer. doi:10.1007/ b138233. ISBN 978-3-5402-6877-2. S2CID 33352850. Archived from the initial 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 Science. Vol. 8598. Journal of Artificial General Intelligence. doi:10.1007/ 978-3-319-09274-4. ISBN 978-3-3190-9273-7. S2CID 8387410.
^ “Who coined the term “AGI”?”. goertzel.org. Archived from the initial on 28 December 2018. Retrieved 28 December 2018., through Life 3.0: ‘The term “AGI” was popularized by … Shane Legg, Mark Gubrud and Ben Goertzel’
^ Wang & Goertzel 2007
^ “First International Summer School in Artificial General Intelligence, Main summertime 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 limitations of device intelligence: Despite development in maker intelligence, artificial basic intelligence is still a significant obstacle”. 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 try outs 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. “Artificial intelligence will not become a Frankenstein’s beast”. 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 expert system will not be understood”. 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 utopia or destruction?”. The New Yorker. Archived from the initial on 28 January 2016. Retrieved 7 February 2016.
^ Müller, V. C., & Bostrom, N. (2016 ). Future progress in synthetic intelligence: A study of skilled opinion. In Fundamental concerns of artificial intelligence (pp. 555-572). Springer, Cham.
^ Armstrong, Stuart, and Kaj Sotala. 2012. “How We’re Predicting AI-or Failing To.” In Beyond AI: Artificial Dreams, modified 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 originality 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 general 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 initial 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 smart 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 basic AI“. VentureBeat. Archived from the original on 4 September 2020. Retrieved 5 September 2020. Certainly, too, there are those who declare we are already seeing an early example of an AGI system in the recently announced GPT-3 natural language processing (NLP) neural network. … So is GPT-3 the very first example of an AGI system? This is arguable, however the agreement is that it is not AGI. … If nothing else, GPT-3 informs us there is a middle ground in between narrow and general AI.
^ Quach, Katyanna. “A developer developed an AI chatbot utilizing GPT-3 that assisted a male speak once 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 jobs, from playing video games to managing 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 explores 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. could match human intelligence in ‘just a few years,’ states CEO of Google’s main A.I. research laboratory”. 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 study job 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 wide variety of views in existing research study, all of which need grounding to some degree
^ Thornton, Angela (26 June 2023). “How submitting our minds to a computer might 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 (estimated in” Encyclopedia.com”),.
MIT Encyclopedia of Cognitive Science Archived 19 July 2008 at the Wayback Machine (priced quote 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 artificial intelligence for curiosity by the field about why a program acts the method it does.
^ Chalmers, David J. (9 August 2023). “Could a Big Language Model Be Conscious?”. Boston Review.
^ Seth, Anil. “Consciousness”. New Scientist. Retrieved 5 September 2024.
^ Nagel 1974.
^ “The Google engineer who believes the company’s AI has 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 Expert System Have Rights?”. Psychology Today. Retrieved 5 September 2024.
^ Akst, Daniel (10 April 2023). “Should Robots With Expert System Have Moral or Legal Rights?”. The Wall Street Journal.
^ “Artificial General Intelligence – Do [es] the cost exceed benefits?”. 23 August 2021. Retrieved 7 June 2023.
^ “How we can Gain 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 Appear 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, threats, strategies (Reprinted with corrections 2017 ed.). Oxford, UK; 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 human beings will destroy ourselves”. Vox. Retrieved 8 June 2023.
^ Doherty, Ben (17 May 2018). “Climate alter an ‘existential security threat’ to Australia, Senate inquiry says”. The Guardian. ISSN 0261-3077. Retrieved 16 July 2023.
^ MacAskill, William (2022 ). What we owe the future. New York, 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 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 artificial 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 in between Yann LeCun, Stuart Russel and Yoshua Bengio about the dangers of strong AI“. The interesting Facebook debate between Yann LeCun, Stuart Russel and Yoshua Bengio about the dangers of strong AI (in French). Retrieved 8 June 2023.
^ “Will Expert System 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 disastrous AGI danger: 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 cautions”. Financial Times. Archived from the initial on 11 April 2022. Retrieved 24 December 2023.
^ Milmo, Dan; Stacey, Kiran (25 September 2023). “Experts disagree over danger posed but expert system can not be overlooked”. 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 completion of Humanity Mean for Me?”. The Atlantic. Archived from the initial on 4 June 2014. Retrieved 12 December 2015.
^ Titcomb, James (30 October 2023). “Big Tech is stiring fears 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 extinction danger”. 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 potential of big language designs”. OpenAI. Retrieved 7 June 2023.
^ a b Hurst, Luke (23 March 2023). “OpenAI says 80% of employees could see their jobs affected by AI. These are the jobs most impacted”. euronews. Retrieved 8 June 2023.
^ Sheffey, Ayelet (20 August 2021). “Elon Musk says we need universal basic income due to the fact that ‘in the future, manual labor will be an option'”. 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 initial 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 Artificial Intelligence. 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: Expert System 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 initial (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 critique of Kurzweil”, Artificial Intelligence, vol. 171, no. 18, Special Review Issue, pp. 1161-1173, doi:10.1016/ j.artint.2007.10.011, archived from the original on 7 January 2016, recovered 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), Artificial Intelligence at Edinburgh University: a Perspective, archived from the original on 17 August 2007, recovered 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 ), “Artificial Intelligence: A General Survey”, Expert System: a paper symposium, Science Research Council.
Luger, George; Stubblefield, William (2004 ), Expert System: Structures and Strategies for Complex Problem Solving (fifth 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 ultimate effort is to make computer system programs that can resolve issues and achieve goals on the planet as well as humans.
Moravec, Hans (1988 ), Mind Children, Harvard University Press
Moravec, Hans (1998 ), “When will hardware match the human brain?”, Journal of Evolution and Technology, vol. 1, archived from the original on 15 June 2006, retrieved 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 Science as Inquiry: Symbols and Search”. Communications of the ACM. 19 (3 ): 113-126. doi:10.1145/ 360018.360022.
Nilsson, Nils (1998 ), Artificial Intelligence: A New Synthesis, Morgan Kaufmann Publishers, ISBN 978-1-5586-0467-4
NRC (1999 ), “Developments in Expert System”, Funding a Transformation: Government Support for Computing Research, National Academy Press, archived from the original on 12 January 2008, recovered 29 September 2007
Poole, David; Mackworth, Alan; Goebel, Randy (1998 ), Computational Intelligence: A Rational Approach, New York City: Oxford University Press, archived from the initial on 25 July 2009, retrieved 6 December 2007
Russell, Stuart J.; Norvig, Peter (2003 ), Expert System: A Modern Approach (2nd 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 original on 17 March 2019, obtained 3 September 2020
Simon, H. A. (1965 ), The Shape of Automation for Men and Management, New York City: 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 significance 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 original on 18 February 2021. Retrieved 13 December 2020 – by means of 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 varieties 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 original on 18 February 2021, obtained 4 September 2013 – via ResearchGate
Berglas, Anthony (January 2012) [2008], Artificial Intelligence Will Kill Our Grandchildren (Singularity), archived from the original 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 might be called “Dyson’s Law”) that “Any system easy sufficient to be easy to understand will not be made complex enough to behave intelligently, while any system complicated enough to act intelligently will be too made complex to understand.” (p. 197.) Computer scientist Alex Pentland writes: “Current AI machine-learning algorithms are, at their core, dead easy silly. They work, however they work by strength.” (p. 198.).
Gelernter, David, Dream-logic, the Internet and Artificial Thought, Edge, archived from the original on 26 July 2010, recovered 25 July 2010.
Gleick, James, “The Fate of Free Choice” (review of Kevin J. Mitchell, Free Agents: How Evolution Gave Us Free Will, 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 differentiates us from devices. For biological animals, reason and function originate from acting worldwide and experiencing the effects. Artificial intelligences – disembodied, strangers to blood, sweat, and tears – have no celebration for that.” (p. 30.).
Halal, William E. “TechCast Article Series: The Automation of Thought” (PDF). Archived from the initial (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 anticipate that those who want to get abundant from AI are going to have the interests of the rest of us close at heart,’ … composes [Gary Marcus] ‘We can’t depend on federal governments driven by project finance contributions [from tech companies] to press back.’ … Marcus details the demands that residents must make from their governments and the tech companies. They consist of openness on how AI systems work; settlement for individuals if their data [are] utilized to train LLMs (big language model) s and the right to authorization to this use; and the ability to hold tech business responsible for the damages they trigger by getting rid of Section 230, imposing cash penalites, and passing more stringent item liability laws … Marcus also suggests … that a brand-new, AI-specific federal agency, comparable 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 program for engineers that would function in a comparable method to medical licenses, malpractice matches, and the Hippocratic oath in medicine. ‘What if, like medical professionals,’ she asks …, forum.pinoo.com.tr ‘AI engineers also vowed to do no harm?'” (p. 46.).
Holte, R. C.; Choueiry, B. Y. (2003 ), “Abstraction and reformulation in expert system”, 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 actually stumped humans for decades, reveals the restrictions of natural-language-processing algorithms”, Scientific American, vol. 329, no. 4 (November 2023), pp. 81-82. “This murder secret competitors has actually revealed that although NLP (natural-language processing) models are capable of unbelievable accomplishments, their abilities are extremely much limited by the amount of context they receive. This […] could trigger [problems] for scientists who wish to use them to do things such as evaluate ancient languages. Sometimes, there are few historical records on long-gone civilizations to function as training data for such a purpose.” (p. 82.).
Immerwahr, Daniel, “Your Lying Eyes: People now use A.I. to create fake videos identical from genuine ones. How much does it matter?”, The New Yorker, 20 November 2023, pp. 54-59. “If by ‘deepfakes’ we suggest sensible videos produced using artificial intelligence that really trick individuals, then they hardly exist. The phonies aren’t deep, and the deeps aren’t phony. […] A.I.-generated videos are not, in general, running in our media as counterfeited evidence. Their role much better looks like that of animations, especially smutty ones.” (p. 59.).
– Leffer, Lauren, “The Risks of Trusting AI: We should prevent humanizing machine-learning designs used in scientific research”, Scientific American, vol. 330, no. 6 (June 2024), pp. 80-81.
Lepore, Jill, “The Chit-Chatbot: Is talking with a maker a conversation?”, The New Yorker, 7 October 2024, pp. 12-16.
Marcus, Gary, “Artificial Confidence: Even the newest, buzziest systems of artificial general intelligence are stymmied by the same old problems”, Scientific American, vol. 327, no. 4 (October 2022), pp. 42-45.
McCarthy, John (October 2007), “From here to human-level AI“, Artificial Intelligence, 171 (18 ): 1174-1182, doi:10.1016/ j.artint.2007.10.009.
McCorduck, Pamela (2004 ), Machines Who Think (second ed.), Natick, Massachusetts: A. K. Peters, ISBN 1-5688-1205-1.
Moravec, Hans (1976 ), The Role of Raw Power in Intelligence, archived from the initial on 3 March 2016, obtained 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 distributed at the 2007 Singularity Summit, San Francisco, California.
Press, Eyal, “In Front of Their Faces: Does facial-recognition innovation lead cops to disregard inconsistent evidence?”, The New Yorker, 20 November 2023, pp. 20-26.
Roivainen, Eka, “AI‘s IQ: ChatGPT aced a [basic intelligence] test however revealed that intelligence can not be determined by IQ alone”, Scientific American, vol. 329, no. 1 (July/August 2023), p. 7. “Despite its high IQ, ChatGPT fails at tasks that need genuine humanlike reasoning or an understanding of the physical and social world … ChatGPT appeared not able to reason logically and tried to depend on its huge database of … realities obtained 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 innovations are effective but unreliable. Rules-based systems can not deal with situations their programmers did not anticipate. Learning systems are limited by the data on which they were trained. AI failures have actually currently resulted in tragedy. Advanced autopilot features in cars, although they perform well in some scenarios, have driven cars and trucks without alerting into trucks, concrete barriers, and parked automobiles. In the wrong scenario, AI systems go from supersmart to superdumb in an immediate. When an opponent is attempting to control 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 made possible by new technologies however rely on the timelelss human propensity to anthropomorphise.” (p. 29.).
Williams, R. W.; Herrup, K.