Artificial General Intelligence

Artificial basic intelligence (AGI) is a type of artificial intelligence (AI) that matches or exceeds human cognitive capabilities across a large variety of cognitive tasks. This contrasts with narrow AI, which is limited to specific tasks. [1] Artificial superintelligence (ASI), on the other hand, refers to AGI that significantly goes beyond human cognitive capabilities. AGI is thought about among the definitions of strong AI.


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

The timeline for accomplishing AGI remains a topic of continuous dispute amongst scientists and experts. As of 2023, some argue that it might be possible in years or decades; others preserve it might take a century or longer; a minority think it might never ever be attained; and another minority claims that it is already here. [5] [6] Notable AI researcher Geoffrey Hinton has expressed concerns about the fast development towards AGI, recommending it might be accomplished sooner than lots of expect. [7]

There is dispute on the specific meaning of AGI and relating to whether modern large language designs (LLMs) such as GPT-4 are early forms of AGI. [8] AGI is a common topic in science fiction and futures research studies. [9] [10]

Contention exists over whether AGI represents an existential risk. [11] [12] [13] Many professionals on AI have actually mentioned that reducing the risk of human termination presented by AGI needs to be a worldwide top priority. [14] [15] Others find the advancement of AGI to be too remote to provide such a danger. [16] [17]

Terminology


AGI is also called strong AI, [18] [19] full AI, [20] human-level AI, [5] human-level intelligent AI, or general intelligent action. [21]

Some scholastic sources book the term "strong AI" for computer system programs that experience sentience or consciousness. [a] In contrast, weak AI (or narrow AI) is able to fix one specific issue but lacks general 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 exact same sense as people. [a]

Related principles consist of artificial superintelligence and transformative AI. An artificial superintelligence (ASI) is a theoretical kind of AGI that is a lot more usually intelligent than humans, [23] while the idea of transformative AI to AI having a large influence on society, for example, similar to the agricultural or industrial revolution. [24]

A framework for categorizing AGI in levels was proposed in 2023 by Google DeepMind scientists. They define five levels of AGI: emerging, skilled, professional, virtuoso, and superhuman. For example, a qualified AGI is defined as an AI that outshines 50% of experienced adults in a large range of non-physical jobs, and a superhuman AGI (i.e. an artificial superintelligence) is likewise specified but with a limit of 100%. They think about large language designs like ChatGPT or LLaMA 2 to be circumstances 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 widely known definitions, and some researchers disagree with the more popular techniques. [b]

Intelligence characteristics


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

factor, usage strategy, fix puzzles, and make judgments under unpredictability
represent understanding, consisting of good sense knowledge
plan
find out
- communicate in natural language
- if required, incorporate these abilities in conclusion of any given objective


Many interdisciplinary techniques (e.g. cognitive science, computational intelligence, and decision making) think about extra characteristics such as imagination (the ability to form unique psychological images and ideas) [28] and autonomy. [29]

Computer-based systems that show numerous of these capabilities exist (e.g. see computational imagination, automated thinking, choice support group, robot, evolutionary calculation, intelligent agent). There is debate about whether modern-day AI systems possess them to a sufficient degree.


Physical qualities


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

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


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

Although the capability to sense (e.g. see, hear, etc) and the capability to act (e.g. relocation and manipulate things, change location to check out, etc) can be desirable 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 point of view on LLMs, there is no firm requirement for an AGI to have a human-like kind; being a silicon-based computational system is adequate, provided 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 been proscribed a specific physical personification and thus does not require a capacity for locomotion or christianpedia.com conventional "eyes and ears". [32]

Tests for classicrock.awardspace.biz human-level AGI


Several tests suggested to confirm human-level AGI have been considered, consisting of: [33] [34]

The concept of the test is that the maker has to try and pretend to be a man, by addressing questions put to it, and it will only pass if the pretence is fairly persuading. A substantial part of a jury, who should not be skilled about devices, need to be taken in by the pretence. [37]

AI-complete problems


A problem is informally called "AI-complete" or "AI-hard" if it is believed that in order to solve it, one would require to carry out AGI, since the solution is beyond the capabilities of a purpose-specific algorithm. [47]

There are numerous issues that have been conjectured to require general intelligence to resolve as well as people. Examples include computer system vision, natural language understanding, and dealing with unanticipated situations while resolving any real-world problem. [48] Even a particular job like translation needs a machine to check out and compose in both languages, follow the author's argument (factor), understand the context (knowledge), and faithfully reproduce the author's initial intent (social intelligence). All of these problems need to be fixed simultaneously in order to reach human-level device performance.


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

History


Classical AI


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

Their predictions were the motivation for Stanley Kubrick and Arthur C. Clarke's character HAL 9000, who embodied what AI scientists believed they could create by the year 2001. AI leader Marvin Minsky was an expert [53] on the task of making HAL 9000 as practical as possible according to the agreement predictions of the time. He stated in 1967, "Within a generation ... the issue of producing 'expert system' will substantially be fixed". [54]

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


However, in the early 1970s, it ended up being apparent that researchers had actually grossly undervalued the problem of the project. Funding companies ended up being doubtful of AGI and put researchers under increasing pressure to produce beneficial "used AI". [c] In the early 1980s, Japan's Fifth Generation Computer Project revived interest in AGI, setting out a ten-year timeline that consisted of AGI goals like "bring on a casual discussion". [58] In action to this and the success of expert systems, both market and federal government pumped money into the field. [56] [59] However, self-confidence in AI amazingly collapsed in the late 1980s, and the goals of the Fifth Generation Computer Project were never fulfilled. [60] For the 2nd time in twenty years, AI researchers who predicted the imminent achievement of AGI had actually been misinterpreted. By the 1990s, AI researchers had a track record for making vain guarantees. They became reluctant to make predictions at all [d] and avoided reference of "human level" artificial intelligence for fear of being labeled "wild-eyed dreamer [s]. [62]

Narrow AI research


In the 1990s and early 21st century, mainstream AI achieved industrial success and academic respectability by focusing on specific sub-problems where AI can produce verifiable results and commercial applications, such as speech recognition and recommendation algorithms. [63] These "applied AI" systems are now used extensively throughout the technology market, and research in this vein is greatly moneyed in both academia and market. Since 2018 [upgrade], advancement in this field was thought about an emerging trend, and a fully grown phase was anticipated to be reached in more than ten years. [64]

At the turn of the century, numerous mainstream AI researchers [65] hoped that strong AI could be developed by combining programs that fix various sub-problems. Hans Moravec composed in 1988:


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

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


The expectation has actually typically been voiced that "top-down" (symbolic) approaches to modeling cognition will somehow satisfy "bottom-up" (sensory) approaches somewhere in between. If the grounding factors to consider in this paper are legitimate, then this expectation is hopelessly modular and there is really just 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 try to reach such a level, because it appears arriving would simply total up to uprooting our symbols from their intrinsic meanings (therefore merely minimizing ourselves to the practical equivalent of a programmable computer system). [66]

Modern synthetic general intelligence research


The term "synthetic general intelligence" was utilized as early as 1997, by Mark Gubrud [67] in a conversation of the implications of fully automated military production and operations. A mathematical formalism of AGI was proposed by Marcus Hutter in 2000. Named AIXI, the proposed AGI representative increases "the ability to please objectives in a wide variety of environments". [68] This type of AGI, identified by the ability to increase a mathematical definition of intelligence instead of show human-like behaviour, [69] was also called universal expert system. [70]

The term AGI was re-introduced and popularized 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 outcomes". The first summertime 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, arranged by Lex Fridman and featuring a number of guest speakers.


As of 2023 [upgrade], a little number of computer scientists are active in AGI research study, and many add to a series of AGI conferences. However, progressively more researchers are interested in open-ended learning, [76] [77] which is the concept of permitting AI to constantly find out and innovate like people do.


Feasibility


As of 2023, the development and prospective achievement of AGI remains a topic of extreme debate within the AI neighborhood. While standard agreement held that AGI was a distant goal, current advancements have actually led some researchers and industry figures to claim that early types of AGI may currently exist. [78] AI leader Herbert A. Simon hypothesized in 1965 that "makers will be capable, within twenty years, of doing any work a male can do". This forecast stopped working to come true. Microsoft co-founder Paul Allen believed that such intelligence is unlikely in the 21st century due to the fact that it would need "unforeseeable and fundamentally unpredictable developments" and a "scientifically deep understanding of cognition". [79] Writing in The Guardian, roboticist Alan Winfield claimed the gulf between modern computing and human-level artificial intelligence is as large as the gulf in between existing space flight and practical faster-than-light spaceflight. [80]

A more obstacle is the absence of clarity in specifying what intelligence involves. Does it need consciousness? Must it show the capability to set goals in addition to pursue them? Is it simply a matter of scale such that if design sizes increase sufficiently, intelligence will emerge? Are centers such as preparation, reasoning, and causal understanding required? Does intelligence need explicitly duplicating the brain and its specific faculties? Does it require emotions? [81]

Most AI scientists believe strong AI can be attained in the future, however some thinkers, like Hubert Dreyfus and Roger Penrose, reject the possibility of achieving strong AI. [82] [83] John McCarthy is amongst those who think human-level AI will be accomplished, but that today level of development is such that a date can not properly be predicted. [84] AI experts' views on the expediency of AGI wax and subside. Four surveys performed in 2012 and 2013 recommended that the mean quote among professionals for when they would be 50% confident AGI would show up was 2040 to 2050, depending on the poll, with the mean being 2081. Of the experts, 16.5% responded to with "never" when asked the very same concern but with a 90% self-confidence instead. [85] [86] Further existing AGI progress factors to consider can be discovered above Tests for validating 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 in between 15 and 25 years from the time the prediction was made". They examined 95 predictions made between 1950 and 2012 on when human-level AI will come about. [87]

In 2023, Microsoft researchers published a detailed evaluation of GPT-4. They concluded: "Given the breadth and depth of GPT-4's abilities, our company believe that it could reasonably be considered as an early (yet still incomplete) version of an artificial general intelligence (AGI) system." [88] Another research study in 2023 reported that GPT-4 surpasses 99% of humans on the Torrance tests of imaginative thinking. [89] [90]

Blaise Agüera y Arcas and Peter Norvig composed in 2023 that a substantial level of basic intelligence has already been attained with frontier designs. They composed that unwillingness to this view comes from four main factors: a "healthy skepticism about metrics for AGI", an "ideological dedication to alternative AI theories or methods", a "dedication to human (or biological) exceptionalism", or a "concern about the economic implications of AGI". [91]

2023 also marked the emergence of large multimodal models (large language designs efficient in processing or producing multiple modalities 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 ability to think before reacting represents a brand-new, extra paradigm. It enhances design outputs by spending more computing power when generating the response, whereas the design scaling paradigm enhances outputs by increasing the design size, training information and training calculate power. [93] [94]

An OpenAI employee, Vahid Kazemi, claimed in 2024 that the company had actually achieved AGI, specifying, "In my viewpoint, we have already accomplished AGI and it's a lot more clear with O1." Kazemi clarified that while the AI is not yet "better than any human at any job", it is "much better than many people at most jobs." He likewise attended to criticisms that big language designs (LLMs) simply follow predefined patterns, comparing their learning process to the clinical technique of observing, assuming, and verifying. These declarations have stimulated dispute, as they depend 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 demonstrate remarkable adaptability, they may not fully satisfy this standard. Notably, Kazemi's comments came soon after OpenAI eliminated "AGI" from the terms of its partnership with Microsoft, prompting speculation about the business's strategic objectives. [95]

Timescales


Progress in expert system has historically gone through periods of fast progress separated by durations when development appeared to stop. [82] Ending each hiatus were essential advances in hardware, software or both to create area for more progress. [82] [98] [99] For instance, the computer system hardware available in the twentieth century was not sufficient to implement deep learning, which requires big numbers of GPU-enabled CPUs. [100]

In the intro to his 2006 book, [101] Goertzel states that price quotes of the time required before a truly flexible AGI is constructed vary from ten years to over a century. As of 2007 [update], the agreement in the AGI research community appeared 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 offered a wide variety of opinions on whether progress will be this fast. A 2012 meta-analysis of 95 such opinions discovered a predisposition towards anticipating that the onset of AGI would happen within 16-26 years for modern-day and historical forecasts alike. That paper has actually been criticized for how it classified opinions as specialist or non-expert. [104]

In 2012, Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton established a neural network called AlexNet, which won the ImageNet competition with a top-5 test error rate of 15.3%, substantially much better than the second-best entry's rate of 26.3% (the conventional technique used a weighted amount of scores from different pre-defined classifiers). [105] AlexNet was considered the initial 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 readily 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 approximately to a six-year-old kid in first grade. An adult comes to about 100 usually. Similar tests were brought out in 2014, with the IQ rating reaching a maximum worth of 27. [106] [107]

In 2020, OpenAI developed GPT-3, a language design capable of performing numerous diverse tasks without specific training. According to Gary Grossman in a VentureBeat article, while there is agreement that GPT-3 is not an example of AGI, it is considered by some to be too advanced to be classified as a narrow AI system. [108]

In the exact same year, Jason Rohrer used his GPT-3 account to develop a chatbot, and provided a chatbot-developing platform called "Project December". OpenAI requested modifications to the chatbot to abide by their safety guidelines; Rohrer disconnected Project December from the GPT-3 API. [109]

In 2022, DeepMind established Gato, a "general-purpose" system efficient in carrying out more than 600 different jobs. [110]

In 2023, Microsoft Research released a research study on an early variation of OpenAI's GPT-4, contending that it displayed more general intelligence than previous AI models and showed human-level performance in jobs covering several domains, such as mathematics, coding, and law. This research sparked an argument on whether GPT-4 could be considered an early, insufficient variation of synthetic basic intelligence, highlighting the need for further exploration and examination of such systems. [111]

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

The idea that this stuff might actually get smarter than people - a few individuals believed that, [...] But many people believed it was method off. And I thought it was method off. I thought it was 30 to 50 years or even longer away. Obviously, I no longer believe that.


In May 2023, Demis Hassabis likewise stated that "The progress in the last few years has been quite amazing", which he sees no factor why it would decrease, expecting AGI within a years or perhaps a few years. [113] In March 2024, Nvidia's CEO, Jensen Huang, specified his expectation that within 5 years, AI would be capable of passing any test at least in addition to people. [114] In June 2024, the AI researcher Leopold Aschenbrenner, a previous OpenAI worker, approximated AGI by 2027 to be "strikingly possible". [115]

Whole brain emulation


While the development of transformer models like in ChatGPT is considered the most promising course to AGI, [116] [117] whole brain emulation can function as an alternative approach. With whole brain simulation, a brain model is developed by scanning and mapping a biological brain in information, and after that copying and mimicing it on a computer system or another computational device. The simulation model should be adequately loyal to the original, so that it behaves in virtually the exact same method as the initial brain. [118] Whole brain emulation is a kind of brain simulation that is discussed in computational neuroscience and neuroinformatics, and for medical research purposes. It has been talked about in artificial intelligence research [103] as a method to strong AI. Neuroimaging technologies that could provide the necessary comprehensive understanding are improving rapidly, and futurist Ray Kurzweil in the book The Singularity Is Near [102] predicts that a map of adequate quality will appear on a comparable timescale to the computing power required to replicate it.


Early estimates


For low-level brain simulation, a really powerful cluster of computers or GPUs would be needed, given the enormous amount of synapses within the human brain. Each of the 1011 (one hundred billion) neurons has on typical 7,000 synaptic connections (synapses) to other nerve cells. The brain of a three-year-old child has about 1015 synapses (1 quadrillion). This number decreases with age, stabilizing by the adult years. Estimates vary for an adult, varying from 1014 to 5 × 1014 synapses (100 to 500 trillion). [120] An estimate of the brain's processing power, based upon a simple switch design for nerve cell activity, is around 1014 (100 trillion) synaptic updates per second (SUPS). [121]

In 1997, Kurzweil looked at various price quotes for the hardware needed to equate to the human brain and embraced a figure of 1016 calculations per 2nd (cps). [e] (For comparison, if a "calculation" was comparable to one "floating-point operation" - a measure used to rate current supercomputers - then 1016 "computations" would be comparable to 10 petaFLOPS, attained in 2011, while 1018 was accomplished in 2022.) He utilized this figure to anticipate the essential hardware would be available at some point in between 2015 and 2025, if the exponential growth in computer system power at the time of writing continued.


Current research study


The Human Brain Project, an EU-funded effort active from 2013 to 2023, has actually developed a particularly in-depth and publicly accessible 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 methods


The synthetic neuron model presumed by Kurzweil and used in numerous existing artificial neural network executions is simple compared to biological nerve cells. A brain simulation would likely need to record the comprehensive cellular behaviour of biological nerve cells, currently understood only in broad summary. The overhead presented by full modeling of the biological, chemical, and physical details of neural behaviour (specifically on a molecular scale) would require computational powers a number of orders of magnitude bigger than Kurzweil's price quote. In addition, the price quotes do not represent glial cells, which are understood to play a function in cognitive processes. [125]

A fundamental criticism of the simulated brain approach originates from embodied cognition theory which asserts that human personification is an important element of human intelligence and is essential to ground significance. [126] [127] If this theory is appropriate, any fully practical brain design will require to include more than simply the neurons (e.g., a robotic body). Goertzel [103] proposes virtual personification (like in metaverses like Second Life) as a choice, but it is unidentified whether this would suffice.


Philosophical perspective


"Strong AI" as defined in viewpoint


In 1980, philosopher John Searle coined the term "strong AI" as part of his Chinese room argument. [128] He proposed a distinction between two hypotheses about expert system: [f]

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


The first one he called "strong" since it makes a stronger declaration: it presumes something special has occurred to the device that exceeds those abilities that we can check. The behaviour of a "weak AI" maker would be exactly similar to a "strong AI" machine, however the latter would also have subjective mindful experience. This usage is also common in academic AI research and textbooks. [129]

In contrast to Searle and mainstream AI, some futurists such as Ray Kurzweil use the term "strong AI" to mean "human level artificial general intelligence". [102] This is not the very same as Searle's strong AI, unless it is presumed that consciousness is needed for human-level AGI. Academic thinkers such as Searle do not believe that holds true, and to most synthetic intelligence scientists the concern 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 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 requirement to know if it in fact has mind - indeed, there would be no chance to inform. For AI research, Searle's "weak AI hypothesis" is equivalent to the declaration "artificial basic intelligence is possible". Thus, according to Russell and Norvig, "most AI researchers 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 different things.


Consciousness


Consciousness can have different meanings, and some aspects play significant functions in sci-fi and the ethics of synthetic intelligence:


Sentience (or "sensational awareness"): The capability to "feel" perceptions or feelings subjectively, rather than the capability to reason about perceptions. Some thinkers, such as David Chalmers, use the term "consciousness" to refer exclusively to remarkable awareness, which is roughly comparable to life. [132] Determining why and how subjective experience occurs is called the difficult problem of consciousness. [133] Thomas Nagel explained in 1974 that it "feels like" something to be mindful. If we are not conscious, then it doesn't seem like anything. Nagel uses the example of a bat: we can sensibly ask "what does it seem like to be a bat?" However, we are unlikely to ask "what does it seem like to be a toaster?" Nagel concludes that a bat appears to be mindful (i.e., has consciousness) however a toaster does not. [134] In 2022, a Google engineer declared that the business's AI chatbot, LaMDA, had accomplished life, though this claim was widely disputed by other experts. [135]

Self-awareness: To have conscious awareness of oneself as a different person, specifically to be knowingly familiar with one's own ideas. This is opposed to merely being the "topic of one's thought"-an operating system or debugger has the ability to be "familiar with itself" (that is, to represent itself in the exact same method it represents whatever else)-but this is not what individuals normally suggest when they use the term "self-awareness". [g]

These qualities have a moral measurement. AI life would trigger concerns of well-being and legal defense, likewise to animals. [136] Other elements of awareness associated to cognitive abilities are likewise relevant to the concept of AI rights. [137] Finding out how to integrate advanced AI with existing legal and social structures is an emergent issue. [138]

Benefits


AGI might have a variety of applications. If oriented towards such goals, AGI might help reduce numerous issues worldwide such as cravings, hardship and health issues. [139]

AGI could improve performance and performance in many tasks. For instance, in public health, AGI could speed up medical research study, notably versus cancer. [140] It might take care of the elderly, [141] and democratize access to rapid, high-quality medical diagnostics. It could offer fun, inexpensive and tailored education. [141] The need to work to subsist could become outdated if the wealth produced is properly redistributed. [141] [142] This likewise raises the question of the location of people in a drastically automated society.


AGI might likewise help to make reasonable decisions, and to anticipate and avoid disasters. It might likewise help to enjoy the benefits of possibly devastating innovations such as nanotechnology or climate engineering, while avoiding the associated risks. [143] If an AGI's main objective is to prevent existential catastrophes such as human termination (which could be hard if the Vulnerable World Hypothesis ends up being true), [144] it could take steps to drastically minimize the threats [143] while decreasing the effect of these measures on our quality of life.


Risks


Existential risks


AGI may represent several types of existential danger, which are risks that threaten "the premature termination of Earth-originating smart life or the long-term and extreme damage of its capacity for desirable future advancement". [145] The threat of human termination from AGI has actually been the subject of many arguments, but there is likewise the possibility that the advancement of AGI would lead to a permanently flawed future. Notably, it might be utilized to spread out and preserve the set of values of whoever establishes it. If humankind still has moral blind areas similar to slavery in the past, AGI might irreversibly entrench it, preventing moral development. [146] Furthermore, AGI could facilitate mass monitoring and brainwashing, which might be utilized to create a stable repressive worldwide totalitarian routine. [147] [148] There is also a threat for the makers themselves. If makers that are sentient or otherwise deserving of ethical consideration are mass created in the future, engaging in a civilizational path that indefinitely disregards their well-being and interests might be an existential catastrophe. [149] [150] Considering just how much AGI could improve mankind's future and aid minimize other existential dangers, Toby Ord calls these existential dangers "an argument for proceeding with due care", not for "abandoning AI". [147]

Risk of loss of control and human termination


The thesis that AI positions an existential risk for human beings, which this danger needs more attention, is questionable but has been endorsed in 2023 by numerous 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 dangers, the specialists are definitely doing everything possible to make sure the finest outcome, right? Wrong. If a superior alien civilisation sent us a message stating, 'We'll arrive in a few years,' would we simply reply, 'OK, call us when you get here-we'll leave the lights on?' Probably not-but this is more or less what is occurring with AI. [153]

The prospective fate of humankind has actually often been compared to the fate of gorillas threatened by human activities. The contrast states that higher intelligence allowed humanity to dominate gorillas, which are now susceptible in methods that they might not have anticipated. As a result, the gorilla has ended up being an endangered types, not out of malice, however just as a security damage from human activities. [154]

The skeptic Yann LeCun thinks about that AGIs will have no desire to dominate humanity which we need to beware not to anthropomorphize them and interpret their intents as we would for human beings. He stated that people will not be "wise enough to design super-intelligent makers, yet ridiculously stupid to the point of giving it moronic objectives with no safeguards". [155] On the other side, the idea of critical merging suggests that almost whatever their goals, smart agents will have factors to try to survive and obtain more power as intermediary steps to achieving these goals. Which this does not require having feelings. [156]

Many scholars who are worried about existential threat supporter for more research into resolving the "control issue" to answer the question: what types of safeguards, algorithms, or architectures can programmers implement to maximise the possibility that their recursively-improving AI would continue to behave in a friendly, instead of damaging, manner after it reaches superintelligence? [157] [158] Solving the control issue is complicated by the AI arms race (which might cause a race to the bottom of security preventative measures in order to release items before rivals), [159] and using AI in weapon systems. [160]

The thesis that AI can pose existential danger likewise has critics. Skeptics usually state that AGI is unlikely in the short-term, or that concerns about AGI distract from other problems associated with existing AI. [161] Former Google scams czar Shuman Ghosemajumder thinks about that for many people outside of the innovation market, existing chatbots and LLMs are already perceived as though they were AGI, resulting in further misunderstanding and fear. [162]

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

In 2023, the CEOs of Google DeepMind, OpenAI and Anthropic, along with other industry leaders and researchers, issued a joint statement asserting that "Mitigating the threat of extinction from AI must be a global concern along with other societal-scale threats 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 tasks affected by the introduction of LLMs, while around 19% of workers may see a minimum of 50% of their tasks affected". [166] [167] They consider workplace employees to be the most exposed, for instance mathematicians, accountants or web designers. [167] AGI might have a much better autonomy, ability to make decisions, to user interface with other computer tools, but likewise to control robotized bodies.


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

Everyone can take pleasure in a life of elegant leisure if the machine-produced wealth is shared, or many people can wind up badly poor if the machine-owners successfully lobby against wealth redistribution. Up until now, the trend seems to be toward the second alternative, with innovation driving ever-increasing inequality


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

See likewise


Artificial brain - Software and hardware with cognitive abilities similar to those of the animal or human brain
AI impact
AI security - Research location on making AI safe and advantageous
AI positioning - AI conformance to the designated goal
A.I. Rising - 2018 film directed by Lazar Bodroža
Expert system
Automated machine knowing - Process of automating the application of artificial intelligence
BRAIN Initiative - Collaborative public-private research effort announced by the Obama administration
China Brain Project
Future of Humanity Institute - Defunct Oxford interdisciplinary research study centre
General game playing - Ability of expert system to play various games
Generative artificial intelligence - AI system capable of creating material in action to triggers
Human Brain Project - Scientific research study project
Intelligence amplification - Use of infotech to enhance human intelligence (IA).
Machine ethics - Moral behaviours of manufactured makers.
Moravec's paradox.
Multi-task knowing - Solving several device finding out tasks at the very same time.
Neural scaling law - Statistical law in device knowing.
Outline of synthetic intelligence - Overview of and topical guide to expert system.
Transhumanism - Philosophical motion.
Synthetic intelligence - Alternate term for or form of expert system.
Transfer learning - Machine learning method.
Loebner Prize - Annual AI competition.
Hardware for expert system - Hardware specially created and optimized for artificial intelligence.
Weak expert system - Form of expert system.


Notes


^ a b See listed below for the origin of the term "strong AI", and see the academic meaning of "strong AI" and weak AI in the post Chinese space.
^ AI creator John McCarthy writes: "we can not yet characterize in general what type of computational procedures we desire to call intelligent. " [26] (For a discussion of some definitions of intelligence utilized by artificial intelligence researchers, see philosophy of synthetic intelligence.).
^ The Lighthill report specifically criticized AI's "grandiose goals" and led the taking apart of AI research in England. [55] In the U.S., DARPA became identified to fund only "mission-oriented direct research study, rather than basic undirected research". [56] [57] ^ As AI founder John McCarthy composes "it would be a fantastic relief to the rest of the employees in AI if the inventors of brand-new basic formalisms would reveal their hopes in a more safeguarded type than has actually often 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 represent 1014 cps. Moravec talks in regards to MIPS, not "cps", which is a non-standard term Kurzweil introduced.
^ As defined in a basic AI book: "The assertion that makers could potentially act smartly (or, possibly much better, act as if they were intelligent) is called the 'weak AI' hypothesis by philosophers, and the assertion that makers that do so are really believing (rather than imitating thinking) is called the 'strong AI' hypothesis." [121] ^ Alan Turing made this point in 1950. [36] References


^ Krishna, Sri (9 February 2023). "What is synthetic narrow intelligence (ANI)?". VentureBeat. Retrieved 1 March 2024. ANI is designed to perform a single job.
^ "OpenAI Charter". OpenAI. Retrieved 6 April 2023. Our objective is to guarantee that synthetic general intelligence advantages all of humanity.
^ 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 construct AI that is much 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 determined 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 quits Google and alerts of threat ahead". The New York Times. 1 May 2023. Retrieved 2 May 2023. It is hard to see how you can avoid the bad stars from using it for bad things.
^ Bubeck, Sébastien; Chandrasekaran, Varun; Eldan, Ronen; Gehrke, Johannes; Horvitz, Eric (2023 ). "Sparks of Artificial General Intelligence: Early experiments with GPT-4". arXiv preprint. arXiv:2303.12712. GPT-4 shows 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 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 City Times. The real hazard is not AI itself but the method we release it.
^ "Impressed by synthetic intelligence? Experts say AGI is coming next, and it has 'existential' risks". ABC News. 23 March 2023. Retrieved 6 April 2023. AGI might position existential risks to humankind.
^ Bostrom, Nick (2014 ). Superintelligence: Paths, Dangers, Strategies. Oxford University Press. ISBN 978-0-1996-7811-2. The very first superintelligence will be the last development 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 danger of termination from AI need to be a global priority.
^ "Statement on AI Risk". Center for AI Safety. Retrieved 1 March 2024. AI experts warn of danger 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 developing makers that can outthink us in basic methods.
^ LeCun, Yann (June 2023). "AGI does not present an existential danger". Medium. There is no factor to fear AI as an existential danger.
^ Kurzweil 2005, p. 260.
^ a b Kurzweil, Ray (5 August 2005), "Long Live AI", Forbes, archived from the original on 14 August 2005: Kurzweil explains strong AI as "maker intelligence with the full series of human intelligence.".
^ "The Age of Artificial Intelligence: 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 symbol 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.
^ "Artificial intelligence is changing our world - it is on everyone to ensure that it goes well". 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 smart characteristics is based upon the subjects covered by significant AI textbooks, 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 City: Academic Press.
^ a b Pfeifer, R. and Bongard J. C., How the body shapes the way we believe: a brand-new view of intelligence (The MIT Press, 2007). ISBN 0-2621-6239-3.
^ White, R. W. (1959 ). "Motivation reevaluated: The idea of proficiency". Psychological Review. 66 (5 ): 297-333. doi:10.1037/ h0040934. PMID 13844397. S2CID 37385966.
^ White, R. W. (1959 ). "Motivation reassessed: The idea 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 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 kid - the Turing Test states so". The Guardian. 9 June 2014. ISSN 0261-3077. Retrieved 3 March 2024.
^ "Scientists contest 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 models like ChatGPT and GPT-4 are acing everything from the bar test to AP Biology. Here's a list of challenging tests both AI variations have passed". Business Insider. Retrieved 30 May 2023.
^ Naysmith, Caleb (7 February 2023). "6 Jobs Expert System Is Already Replacing and How Investors Can Capitalize on 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 response". Analytics India Magazine. Retrieved 3 March 2024.
^ Bhaimiya, Sawdah (20 June 2023). "DeepMind's co-founder recommended checking an AI chatbot's ability 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 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 initial 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). Artificial Intelligence, Evolutionary Computation and Metaheuristics (AIECM): 3-17. Archived (PDF) from the initial on 22 May 2013.
^ "AI Index: State of AI in 13 Charts". Stanford University Human-Centered Expert System. 15 April 2024. Retrieved 27 May 2024.
^ Crevier 1993, pp. 48-50.
^ Kaplan, Andreas (2022 ). "Artificial Intelligence, Business and Civilization - Our Fate Made in Machines". Archived from the initial on 6 May 2022. Retrieved 12 March 2022.
^ Simon 1965, p. 96 priced estimate in Crevier 1993, p. 109.
^ "Scientist on the Set: An Interview with Marvin Minsky". Archived from the initial 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 likewise 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 original 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 City Times. Archived from the original on 2 February 2023. Retrieved 18 February 2017. At its low point, some computer system scientists and software engineers avoided the term artificial intelligence for fear 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 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 Technology 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 original 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 created 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 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 initial 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 initial on 26 July 2020. Retrieved 11 May 2020.
^ Shevlin, Henry; Vold, Karina; Crosby, Matthew; Halina, Marta (4 October 2019). "The limits of device intelligence: Despite progress in machine intelligence, synthetic basic intelligence is still a major difficulty". 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. "Expert system 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 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 expert system bring us utopia or forum.batman.gainedge.org damage?". The New Yorker. Archived from the initial on 28 January 2016. Retrieved 7 February 2016.
^ Müller, V. C., & Bostrom, N. (2016 ). Future development in expert system: A study of expert viewpoint. In Fundamental problems of synthetic 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 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 original 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 role 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 site). 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 twice as smart as Siri - but a six-year-old beats both] (in German). Archived from the initial on 3 January 2019. Retrieved 2 January 2019.
^ Grossman, Gary (3 September 2020). "We're entering the AI twilight zone in between narrow and 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 very first example of an AGI system? This is arguable, however the agreement is that it is not AGI. ... If absolutely nothing else, GPT-3 tells us there is a middle ground in between narrow and basic AI.
^ Quach, Katyanna. "A designer developed an AI chatbot utilizing GPT-3 that assisted a male 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 jobs, from playing video games to managing robots", TechCrunch, archived from the original on 16 June 2022, recovered 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 experiments with 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 Times. ISSN 0362-4331. Retrieved 7 June 2023.
^ Bove, Tristan. "A.I. might match human intelligence in 'just a few years,' says CEO of Google's main A.I. research laboratory". Fortune. Retrieved 4 September 2024.
^ Nellis, Stephen (2 March 2024). "Nvidia CEO states AI might pass human tests in five years". Reuters. ^ Aschenbrenner, Leopold. "SITUATIONAL AWARENESS, The Decade Ahead".
^ Sullivan, Mark (18 October 2023). "Why everyone seems to disagree on how to define 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 original on 8 February 2014. Retrieved 24 January 2014.
^ de Vega, Glenberg & Graesser 2008. A wide variety of views in present research study, all of which require grounding to some degree
^ Thornton, Angela (26 June 2023). "How publishing our minds to a computer might become 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 (quoted in" Encyclopedia.com"),.
MIT Encyclopedia of Cognitive Science Archived 19 July 2008 at the Wayback Machine (priced estimate 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 interest by the field about why a program acts the way 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 business'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 cost exceed advantages?". 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 Look Like When Expert System 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: courses, dangers, strategies (Reprinted with corrections 2017 ed.). Oxford, United Kingdom; New York, New York, 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 humans will damage ourselves". Vox. Retrieved 8 June 2023.
^ Doherty, Ben (17 May 2018). "Climate change an 'existential security threat' to Australia, Senate questions states". 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 Artificial Intelligence". 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 implications of expert system - however are we taking AI seriously enough?'". The Independent (UK). Archived from the original on 25 September 2015. Retrieved 3 December 2014.
^ Herger, Mario. "The Gorilla Problem - Enterprise Garage". Retrieved 7 June 2023.
^ "The fascinating Facebook debate between Yann LeCun, Stuart Russel and Yoshua Bengio about the risks of strong AI". The remarkable Facebook argument between Yann LeCun, Stuart Russel and Yoshua Bengio about the threats 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 devastating AGI risk: 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 dangers spiralling out of control, report warns". Financial Times. Archived from the original on 11 April 2022. Retrieved 24 December 2023.
^ Milmo, Dan; Stacey, Kiran (25 September 2023). "Experts disagree over danger positioned however synthetic intelligence can not be ignored". 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 stoking fears over AI, warn scientists". The Telegraph. Retrieved 7 December 2023.
^ Davidson, John (30 October 2023). "Google Brain founder says big tech is lying about AI termination danger". Australian Financial Review. Archived from the initial 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 effect capacity of large language models". OpenAI. Retrieved 7 June 2023.
^ a b Hurst, Luke (23 March 2023). "OpenAI states 80% of employees could see their tasks affected by AI. These are the tasks most impacted". euronews. Retrieved 8 June 2023.
^ Sheffey, Ayelet (20 August 2021). "Elon Musk says we need universal standard 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), "Expert system 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 Search 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: 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 original (PDF) on 20 March 2013.
Goertzel, Ben (December 2007), "Human-level artificial basic intelligence and wiki.snooze-hotelsoftware.de the possibility of a technological singularity: a reaction to Ray Kurzweil's The Singularity Is Near, and McDermott's review 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, 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, retrieved 7 May 2011.
Howe, J. (November 1994), Artificial Intelligence at Edinburgh University: a Point of view, 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", Expert System: a paper seminar, 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 supreme effort is to make computer system programs that can resolve issues and attain goals worldwide as well as people.
Moravec, Hans (1988 ), Mind Children, Harvard University Press
Moravec, Hans (1998 ), "When will computer system hardware match the human brain?", Journal of Evolution and Technology, vol. 1, archived from the original 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 original on 16 October 2011, retrieved 7 November 2009
Newell, Allen; Simon, H. A. (1976 ). "Computer Science 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 original on 12 January 2008, retrieved 29 September 2007
Poole, David; Mackworth, Alan; Goebel, Randy (1998 ), Computational Intelligence: A Sensible Approach, New York: Oxford University Press, archived from the initial on 25 July 2009, obtained 6 December 2007
Russell, Stuart J.; Norvig, Peter (2003 ), Artificial Intelligence: 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, recovered 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, retrieved 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 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 initial 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 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 original 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 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 may be called "Dyson's Law") that "Any system easy enough to be reasonable will not be complicated enough to act wisely, while any system complicated enough to behave smartly will be too complicated to understand." (p. 197.) Computer researcher Alex Pentland composes: "Current AI machine-learning algorithms are, at their core, dead easy 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, recovered 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 identifies us from machines. For biological animals, reason and purpose come from acting in the world and experiencing the repercussions. Artificial intelligences - disembodied, complete 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" (review 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: Living 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 reasonably expect that those who hope to get rich from AI are going to have the interests of the rest people close at heart,' ... writes [Gary Marcus] 'We can't rely on governments driven by project finance contributions [from tech business] to push back.' ... Marcus information the demands that people must make from their federal governments and the tech companies. They consist of openness on how AI systems work; payment for people if their information [are] utilized to train LLMs (big language model) s and the right to consent to this usage; and the ability to hold tech companies responsible for the damages they trigger by eliminating Section 230, imposing cash penalites, and passing more stringent item liability laws ... Marcus likewise recommends ... that a new, AI-specific federal agency, similar to the FDA, the FCC, or the FTC, may offer the most robust oversight ... [T] he Fordham law teacher Chinmayi Sharma ... suggests ... establish [ing] a professional licensing program for engineers that would work in a comparable way to medical licenses, malpractice suits, and the Hippocratic oath in medication. 'What if, like physicians,' she asks ..., 'AI engineers also pledged 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 stymied humans for decades, reveals the limitations of natural-language-processing algorithms", Scientific American, vol. 329, no. 4 (November 2023), pp. 81-82. "This murder mystery competition has revealed that although NLP (natural-language processing) designs are capable of unbelievable feats, their capabilities are quite limited by the amount of context they receive. This [...] could trigger [troubles] for scientists who hope to use them to do things such as examine ancient languages. In many cases, there are few historical records on long-gone civilizations to work as training information for such a function." (p. 82.).
Immerwahr, Daniel, "Your Lying Eyes: People now use A.I. to create fake videos equivalent from real ones. Just how much does it matter?", The New Yorker, 20 November 2023, pp. 54-59. "If by 'deepfakes' we mean sensible videos produced utilizing artificial intelligence that really trick individuals, then they barely exist. The fakes aren't deep, and the deeps aren't fake. [...] A.I.-generated videos are not, in general, operating in our media as counterfeited proof. Their role much better looks like that of animations, especially smutty ones." (p. 59.).
- Leffer, Lauren, "The Risks of Trusting AI: We should avoid humanizing machine-learning models used in clinical research study", Scientific American, vol. 330, no. 6 (June 2024), pp. 80-81.
Lepore, Jill, "The Chit-Chatbot: Is talking with a maker a discussion?", 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 issues", 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 (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 original 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, presented and dispersed at the 2007 Singularity Summit, San Francisco, California.
Press, Eyal, "In Front of Their Faces: Does facial-recognition innovation lead police to ignore contradictory proof?", The New Yorker, 20 November 2023, pp. 20-26.
Roivainen, Eka, "AI's IQ: ChatGPT aced a [standard intelligence] test but showed 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 stops working at tasks that require real humanlike reasoning or an understanding of the physical and social world ... ChatGPT seemed not able to reason realistically and tried to rely on its vast database of ... facts derived 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 but undependable. 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 already resulted in tragedy. Advanced autopilot functions in cars and trucks, although they perform well in some circumstances, have actually driven vehicles without cautioning into trucks, concrete barriers, and parked vehicles. In the wrong circumstance, AI systems go from supersmart to superdumb in an instant. When an opponent is attempting to manipulate and hack an AI system, the risks are even higher." (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 brand-new innovations but depend on the timelelss human tendency to anthropomorphise." (p. 29.).
Williams, R. W.; Herrup, K.