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Artificial basic intelligence (AGI) is a kind of expert system (AI) that matches or goes beyond human cognitive capabilities throughout a wide variety of cognitive jobs. This contrasts with narrow AI, which is limited to specific jobs. [1] Artificial superintelligence (ASI), on the other hand, refers to AGI that considerably surpasses human cognitive abilities. AGI is thought about one of the meanings of strong AI.

Creating AGI is a primary objective of AI research and of business such as OpenAI [2] and Meta. [3] A 2020 study determined 72 active AGI research study and advancement jobs across 37 countries. [4]
The timeline for achieving AGI stays a topic of continuous debate among researchers and specialists. As of 2023, some argue that it might be possible in years or decades; others maintain it might take a century or longer; a minority think it might never be attained; and another minority claims that it is already here. [5] [6] Notable AI scientist Geoffrey Hinton has actually expressed issues about the rapid progress towards AGI, suggesting it might be accomplished faster than lots of anticipate. [7]
There is argument on the specific definition of AGI and regarding whether modern big language models (LLMs) such as GPT-4 are early forms 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 specialists on AI have actually specified that reducing the risk of human extinction posed by AGI should be a worldwide concern. [14] [15] Others find the development of AGI to be too remote to present such a risk. [16] [17]
Terminology

AGI is also referred to as strong AI, [18] [19] complete AI, [20] human-level AI, [5] human-level smart AI, or general smart action. [21]
Some scholastic sources reserve the term "strong AI" for computer programs that experience sentience or consciousness. [a] In contrast, weak AI (or narrow AI) has the ability to solve one specific issue but does not have basic cognitive capabilities. [22] [19] Some scholastic sources use "weak AI" to refer more broadly to any programs that neither experience consciousness nor have a mind in the exact same sense as humans. [a]
Related ideas consist of artificial superintelligence and transformative AI. An artificial superintelligence (ASI) is a theoretical kind of AGI that is a lot more usually smart than people, [23] while the notion of transformative AI connects to AI having a big impact on society, for example, comparable to the agricultural or industrial transformation. [24]
A structure for categorizing AGI in levels was proposed in 2023 by Google DeepMind scientists. They define five levels of AGI: emerging, proficient, expert, virtuoso, and superhuman. For example, a proficient AGI is defined as an AI that exceeds 50% of competent grownups in a wide variety of non-physical tasks, and a superhuman AGI (i.e. a synthetic superintelligence) is likewise specified but with a threshold of 100%. They think about big 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 propositions is the Turing test. However, there are other widely known definitions, and some scientists disagree with the more popular methods. [b]
Intelligence traits

Researchers normally hold that intelligence is needed to do all of the following: [27]
factor, use technique, resolve puzzles, and make judgments under unpredictability represent understanding, including sound judgment knowledge plan find out

• interact in natural language
• if required, incorporate these abilities in conclusion of any given objective

Many interdisciplinary approaches (e.g. cognitive science, computational intelligence, and choice making) think about additional traits such as imagination (the capability to form unique psychological images and principles) [28] and autonomy. [29]
Computer-based systems that display a lot of these abilities exist (e.g. see computational imagination, automated thinking, decision support group, robotic, evolutionary computation, intelligent representative). There is dispute about whether contemporary AI systems have them to a sufficient degree.

Physical qualities

Other abilities are thought about preferable in smart systems, as they may affect intelligence or aid in its expression. These consist of: [30]
- the ability to sense (e.g. see, hear, and so on), and - the ability to act (e.g. move and manipulate items, modification place to explore, and so on).
This includes the capability to find and respond to threat. [31]
Although the ability to sense (e.g. see, hear, drapia.org and so on) and the capability to act (e.g. move and control items, change location to explore, etc) can be preferable for some smart systems, [30] these physical abilities are not strictly needed for an entity to qualify as AGI-particularly under the thesis that big language designs (LLMs) might already be or become AGI. Even from a less optimistic 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 enough, supplied it can process input (language) from the external world in location of human senses. This analysis lines up with the understanding that AGI has never ever been proscribed a specific physical embodiment and thus does not require a capability for mobility or traditional "eyes and ears". [32]
Tests for human-level AGI

Several tests implied to verify human-level AGI have actually been considered, including: [33] [34]
The concept of the test is that the maker needs to attempt and pretend to be a guy, by addressing concerns put to it, and it will only pass if the pretence is reasonably convincing. A considerable part of a jury, who must not be expert about machines, need to be taken in by the pretence. [37]
AI-complete issues

An issue is informally called "AI-complete" or "AI-hard" if it is thought that in order to resolve it, one would require to implement AGI, because the service is beyond the capabilities of a purpose-specific algorithm. [47]
There are numerous problems that have actually been conjectured to need general intelligence to fix in addition to people. Examples consist of computer system vision, natural language understanding, and dealing with unexpected scenarios while solving any real-world issue. [48] Even a specific task like translation requires a machine to read and compose in both languages, follow the author's argument (reason), understand the context (knowledge), and consistently recreate the author's initial intent (social intelligence). All of these issues need to be fixed simultaneously in order to reach human-level device efficiency.

However, much of these tasks can now be performed by modern-day large language models. According to Stanford University's 2024 AI index, AI has reached human-level efficiency on numerous criteria for checking out understanding and visual thinking. [49]
History

Classical AI

Modern AI research study began in the mid-1950s. [50] The very first generation of AI researchers were persuaded that artificial basic intelligence was possible and that it would exist in simply a couple of decades. [51] AI leader Herbert A. Simon wrote in 1965: "makers will be capable, within twenty years, of doing any work a guy can do." [52]
Their forecasts were the inspiration for Stanley Kubrick and Arthur C. Clarke's character HAL 9000, who embodied what AI scientists believed they might produce by the year 2001. AI pioneer Marvin Minsky was a specialist [53] on the job 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 considerably be resolved". [54]
Several classical AI projects, such as Doug Lenat's Cyc task (that began in 1984), and Allen Newell's Soar job, were directed at AGI.

However, in the early 1970s, demo.qkseo.in it ended up being apparent that scientists had actually grossly undervalued the difficulty of the job. Funding agencies became hesitant of AGI and put researchers under increasing pressure to produce helpful "applied 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 "continue a casual conversation". [58] In reaction to this and the success of expert systems, both market and federal government pumped money into the field. [56] [59] However, confidence in AI amazingly collapsed in the late 1980s, and the objectives of the Fifth Generation Computer Project were never ever satisfied. [60] For the second time in 20 years, AI scientists who anticipated the impending accomplishment of AGI had been mistaken. By the 1990s, AI researchers had a track record for making vain pledges. They became reluctant to make predictions at all [d] and avoided reference of "human level" synthetic intelligence for worry of being identified "wild-eyed dreamer [s]. [62]
Narrow AI research

In the 1990s and early 21st century, mainstream AI attained business success and academic respectability by focusing on particular sub-problems where AI can produce proven results and industrial applications, such as speech recognition and recommendation algorithms. [63] These "applied AI" systems are now utilized thoroughly throughout the technology market, and research in this vein is heavily moneyed in both academic community and industry. Since 2018 [upgrade], development in this field was thought about an emerging pattern, and a fully grown phase was expected to be reached in more than ten years. [64]
At the millenium, numerous mainstream AI scientists [65] hoped that strong AI could be developed by combining programs that resolve different 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, ready to supply the real-world skills and the commonsense knowledge that has been so frustratingly elusive in reasoning programs. Fully intelligent machines will result when the metaphorical golden spike is driven joining the two efforts. [65]
However, even at the time, this was disputed. For example, Stevan Harnad of Princeton University concluded his 1990 paper on the sign grounding hypothesis by specifying:

The expectation has typically been voiced that "top-down" (symbolic) approaches to modeling cognition will somehow fulfill "bottom-up" (sensory) approaches someplace in between. If the grounding considerations in this paper are valid, then this expectation is hopelessly modular and there is truly only one practical route from sense to symbols: from the ground up. A free-floating symbolic level like the software application level of a computer system will never ever be reached by this path (or vice versa) - nor is it clear why we ought to even attempt to reach such a level, considering that it appears getting there would just amount to uprooting our signs from their intrinsic significances (thus merely minimizing ourselves to the functional equivalent of a programmable computer). [66]
Modern synthetic basic intelligence research study

The term "artificial basic intelligence" was used as early as 1997, by Mark Gubrud [67] in a conversation of the ramifications of fully 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 goals in a vast array of environments". [68] This kind of AGI, defined by the ability to maximise a mathematical definition of intelligence rather than exhibit human-like behaviour, [69] was likewise 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 explained by Pei Wang and Ben Goertzel [72] as "producing publications and preliminary results". The very first summer season school in AGI was arranged 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 provided a course on AGI in 2018, arranged by Lex Fridman and featuring a number of guest lecturers.

As of 2023 [upgrade], a small number of computer scientists are active in AGI research, and many contribute to a series of AGI conferences. However, significantly more researchers are interested in open-ended knowing, [76] [77] which is the concept of enabling AI to continually find out and innovate like humans do.

Feasibility

As of 2023, the advancement and possible accomplishment of AGI remains a topic of extreme argument within the AI community. While traditional consensus held that AGI was a remote goal, current advancements have led some scientists and market figures to declare 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 guy can do". This prediction stopped working to come true. Microsoft co-founder Paul Allen believed that such intelligence is not likely in the 21st century due to the fact that it would require "unforeseeable and basically unpredictable breakthroughs" and a "clinically deep understanding of cognition". [79] Writing in The Guardian, roboticist Alan Winfield declared the gulf between contemporary computing and human-level artificial intelligence is as large as the gulf in between current space flight and practical faster-than-light spaceflight. [80]
A more difficulty is the lack of clearness in specifying what intelligence entails. Does it require consciousness? Must it display the capability to set objectives along with pursue them? Is it purely a matter of scale such that if design sizes increase adequately, intelligence will emerge? Are centers such as preparation, thinking, and causal understanding needed? Does intelligence need clearly duplicating the brain and its particular faculties? Does it require feelings? [81]
Most AI scientists believe strong AI can be achieved in the future, however some thinkers, like Hubert Dreyfus and Roger Penrose, reject the possibility of accomplishing strong AI. [82] [83] John McCarthy is among those who think human-level AI will be achieved, but that the present level of development is such that a date can not precisely be anticipated. [84] AI professionals' views on the expediency of AGI wax and subside. Four surveys carried out in 2012 and 2013 suggested that the mean estimate among specialists for when they would be 50% positive AGI would get here was 2040 to 2050, depending on the survey, with the mean being 2081. Of the experts, 16.5% addressed with "never" when asked the very same concern but with a 90% confidence instead. [85] [86] Further current AGI development considerations can be discovered above Tests for validating human-level AGI.

A report by Stuart Armstrong and Kaj Sotala of the Machine Intelligence Research Institute discovered 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 evaluated 95 forecasts made between 1950 and 2012 on when human-level AI will come about. [87]
In 2023, Microsoft scientists published a detailed assessment of GPT-4. They concluded: "Given the breadth and depth of GPT-4's capabilities, we think that it might reasonably be seen as an early (yet still incomplete) version of a synthetic basic intelligence (AGI) system." [88] Another study in 2023 reported that GPT-4 exceeds 99% of human beings on the Torrance tests of innovative thinking. [89] [90]
Blaise Agüera y Arcas and Peter Norvig composed in 2023 that a substantial level of general intelligence has actually already been accomplished with frontier designs. They composed that reluctance to this view comes from 4 main factors: a "healthy hesitation about metrics for AGI", an "ideological dedication to alternative AI theories or strategies", a "devotion to human (or biological) exceptionalism", or a "concern about the economic implications of AGI". [91]
2023 likewise marked the introduction of big multimodal designs (big language models capable of processing or producing several methods such as text, audio, and images). [92]
In 2024, OpenAI released o1-preview, the first of a series of designs that "invest more time thinking before they respond". According to Mira Murati, this capability to think before reacting represents a new, extra paradigm. It enhances model outputs by investing more computing power when creating the answer, whereas the design scaling paradigm improves outputs by increasing the design size, training data and training compute power. [93] [94]
An OpenAI worker, Vahid Kazemi, claimed in 2024 that the company had accomplished AGI, mentioning, "In my viewpoint, we have actually already accomplished AGI and it's much 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 the majority of humans at many jobs." He also dealt with criticisms that large language models (LLMs) merely follow predefined patterns, comparing their knowing procedure to the clinical approach of observing, assuming, and verifying. These declarations have stimulated argument, as they count on a broad and unconventional meaning of AGI-traditionally comprehended as AI that matches human intelligence throughout all domains. Critics argue that, while OpenAI's designs show amazing adaptability, they might not completely meet this standard. Notably, Kazemi's comments came quickly after OpenAI got rid of "AGI" from the regards to its collaboration with Microsoft, triggering speculation about the business's tactical objectives. [95]
Timescales

Progress in synthetic intelligence has actually historically gone through periods of rapid progress separated by periods when development appeared to stop. [82] Ending each hiatus were basic advances in hardware, software application or both to create area for more development. [82] [98] [99] For instance, the hardware readily available in the twentieth century was not enough to implement deep knowing, which needs big numbers of GPU-enabled CPUs. [100]
In the introduction to his 2006 book, [101] Goertzel states that estimates of the time needed before a really versatile AGI is built differ from 10 years to over a century. As of 2007 [upgrade], the consensus in the AGI research study community appeared to be that the timeline discussed by Ray Kurzweil in 2005 in The Singularity is Near [102] (i.e. in between 2015 and 2045) was plausible. [103] Mainstream AI researchers have actually provided a large range of opinions on whether progress will be this fast. A 2012 meta-analysis of 95 such viewpoints found a bias towards predicting that the onset of AGI would occur within 16-26 years for modern-day and historical forecasts alike. That paper has been criticized for how it categorized viewpoints 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 competitors with a top-5 test mistake rate of 15.3%, substantially much better than the second-best entry's rate of 26.3% (the conventional method used a weighted amount of ratings from various pre-defined classifiers). [105] AlexNet was considered the preliminary ground-breaker of the current deep learning wave. [105]
In 2017, scientists Feng Liu, Yong Shi, and Ying Liu carried out intelligence tests on publicly offered 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 around to a six-year-old child in very first grade. A grownup pertains to about 100 on average. 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 design efficient in carrying out lots of diverse jobs without specific training. According to Gary Grossman in a VentureBeat post, 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 supplied a chatbot-developing platform called "Project December". OpenAI requested for modifications to the chatbot to comply with their safety standards; Rohrer disconnected Project December from the GPT-3 API. [109]
In 2022, DeepMind established Gato, a "general-purpose" system capable of carrying out more than 600 different tasks. [110]
In 2023, Microsoft Research released a research study on an early variation of OpenAI's GPT-4, contending that it showed more basic intelligence than previous AI designs and showed human-level performance in jobs spanning multiple domains, such as mathematics, coding, and law. This research study sparked a debate on whether GPT-4 could be considered an early, insufficient variation of synthetic general intelligence, emphasizing the requirement for additional expedition and examination of such systems. [111]
In 2023, the AI scientist Geoffrey Hinton specified that: [112]
The idea that this stuff could in fact get smarter than individuals - a few people thought that, [...] But many people thought it was method off. And I believed it was way off. I believed it was 30 to 50 years or even longer away. Obviously, I no longer believe that.

In May 2023, Demis Hassabis similarly said that "The development in the last couple of years has been pretty unbelievable", which he sees no reason it would decrease, anticipating AGI within a years or perhaps a couple of years. [113] In March 2024, Nvidia's CEO, Jensen Huang, specified his expectation that within five years, AI would be capable of passing any test at least along with human beings. [114] In June 2024, the AI scientist Leopold Aschenbrenner, a previous OpenAI worker, approximated AGI by 2027 to be "strikingly plausible". [115]
Whole brain emulation

While the advancement of transformer designs like in ChatGPT is thought about the most promising path to AGI, [116] [117] entire brain emulation can act as an alternative technique. With entire brain simulation, a brain model is built by scanning and mapping a biological brain in information, and then copying and replicating it on a computer system or another computational gadget. The simulation model must be adequately faithful to the original, so that it behaves in practically the very same method as the original 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 actually been discussed in expert system research study [103] as an approach to strong AI. Neuroimaging innovations that could provide the needed comprehensive understanding are enhancing rapidly, and futurist Ray Kurzweil in the book The Singularity Is Near [102] anticipates that a map of adequate quality will appear on a comparable timescale to the computing power needed to replicate it.

Early estimates

For low-level brain simulation, an extremely effective cluster of computer systems or GPUs would be needed, given the huge amount of synapses within the human brain. Each of the 1011 (one hundred billion) nerve cells has on average 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, 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 basic switch design for neuron activity, is around 1014 (100 trillion) synaptic updates per second (SUPS). [121]
In 1997, Kurzweil took a look at different estimates for the hardware required to equate to the human brain and adopted a figure of 1016 computations per second (cps). [e] (For contrast, if a "computation" was comparable to one "floating-point operation" - a step used to rate existing supercomputers - then 1016 "computations" would be comparable to 10 petaFLOPS, attained in 2011, while 1018 was attained in 2022.) He utilized this figure to anticipate the required hardware would be available sometime in between 2015 and 2025, if the exponential development in computer power at the time of writing continued.

Current research

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

Criticisms of simulation-based techniques

The synthetic neuron design presumed by Kurzweil and utilized in numerous present synthetic neural network applications is simple compared with biological nerve cells. A brain simulation would likely have to capture the in-depth cellular behaviour of biological neurons, presently comprehended only in broad summary. The overhead introduced by complete modeling of the biological, chemical, and physical details of neural behaviour (especially on a molecular scale) would need computational powers a number of orders of magnitude larger than Kurzweil's estimate. In addition, the estimates do not represent glial cells, which are known to contribute in cognitive processes. [125]
A fundamental criticism of the simulated brain technique originates from embodied cognition theory which asserts that human embodiment is an important element of human intelligence and is necessary to ground significance. [126] [127] If this theory is appropriate, any fully practical brain model will need to incorporate more than simply the neurons (e.g., a robotic body). Goertzel [103] proposes virtual personification (like in metaverses like Second Life) as an option, but it is unknown whether this would suffice.

Philosophical point of view

"Strong AI" as defined in approach

In 1980, philosopher John Searle coined the term "strong AI" as part of his Chinese space argument. [128] He proposed a difference between 2 hypotheses about expert system: [f]
Strong AI hypothesis: An artificial intelligence system can have "a mind" and "consciousness". Weak AI hypothesis: An expert system system can (just) imitate it believes and has a mind and consciousness.
The first one he called "strong" because it makes a stronger statement: it assumes something special has occurred to the machine that surpasses those capabilities that we can check. The behaviour of a "weak AI" machine would be specifically identical to a "strong AI" device, however the latter would also have subjective conscious experience. This use is also common in scholastic AI research and textbooks. [129]
In contrast to Searle and mainstream AI, some futurists such as Ray Kurzweil use the term "strong AI" to imply "human level synthetic basic intelligence". [102] This is not the like Searle's strong AI, unless it is presumed that consciousness is essential for human-level AGI. Academic philosophers such as Searle do not think that holds true, and to most expert system 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 do not care if you call it genuine or a simulation." [130] If the program can behave as if it has a mind, then there is no requirement to know if it actually has mind - certainly, there would be no chance to tell. 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 researchers take the weak AI hypothesis for approved, and archmageriseswiki.com do not care about the strong AI hypothesis." [130] Thus, for scholastic AI research study, "Strong AI" and "AGI" are two different things.

Consciousness

Consciousness can have various significances, and some aspects play considerable roles in science fiction and the principles of synthetic intelligence:

Sentience (or "extraordinary consciousness"): The capability to "feel" understandings or feelings subjectively, rather than the capability to reason about understandings. Some theorists, such as David Chalmers, use the term "awareness" to refer solely to extraordinary consciousness, which is approximately comparable to life. [132] Determining why and how subjective experience develops is called the hard problem of consciousness. [133] Thomas Nagel discussed in 1974 that it "seems like" something to be conscious. If we are not conscious, then it doesn't seem 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 feel 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 declared that the company's AI chatbot, LaMDA, had achieved life, though this claim was commonly contested by other professionals. [135]
Self-awareness: To have conscious awareness of oneself as a separate person, specifically to be knowingly aware of one's own thoughts. This is opposed to just being the "subject of one's thought"-an operating system or debugger has the ability to be "knowledgeable about itself" (that is, to represent itself in the very same method it represents whatever else)-however this is not what people typically suggest when they use the term "self-awareness". [g]
These characteristics have a moral dimension. AI sentience would generate issues of welfare and legal defense, similarly to animals. [136] Other elements of consciousness associated to cognitive abilities are also appropriate to the principle of AI rights. [137] Figuring out how to integrate innovative AI with existing legal and social structures is an emergent concern. [138]
Benefits

AGI might have a wide variety of applications. If oriented towards such goals, AGI might help alleviate different issues in the world such as cravings, hardship and health issues. [139]
AGI could enhance efficiency and effectiveness in many jobs. For example, in public health, AGI could speed up medical research study, significantly against cancer. [140] It could take care of the elderly, [141] and equalize access to quick, top quality medical diagnostics. It could offer fun, inexpensive and customized education. [141] The requirement to work to subsist could become obsolete if the wealth produced is appropriately rearranged. [141] [142] This also raises the question of the place of humans in a drastically automated society.

AGI might also help to make logical decisions, and to expect and prevent catastrophes. It might likewise help to reap the advantages of possibly disastrous innovations such as nanotechnology or climate engineering, while preventing the associated risks. [143] If an AGI's main objective is to prevent existential disasters such as human termination (which might be tough if the Vulnerable World Hypothesis turns out to be real), [144] it could take procedures to considerably reduce the threats [143] while reducing the effect of these measures on our lifestyle.

Risks

Existential threats

AGI might represent numerous types of existential danger, which are threats that threaten "the early extinction of Earth-originating smart life or the long-term and extreme destruction of its potential for desirable future advancement". [145] The danger of human termination from AGI has been the topic of lots of debates, but there is likewise the possibility that the advancement of AGI would lead to a completely problematic future. Notably, it could be utilized to spread out and preserve the set of worths of whoever develops it. If humanity still has ethical blind areas comparable to slavery in the past, AGI might irreversibly entrench it, preventing moral development. [146] Furthermore, AGI might assist in mass surveillance and brainwashing, which might be used to create a stable repressive worldwide totalitarian program. [147] [148] There is also a threat for the makers themselves. If devices that are sentient or otherwise worthy of moral factor to consider are mass created in the future, participating in a civilizational path that forever neglects their well-being and interests could be an existential catastrophe. [149] [150] Considering just how much AGI might enhance mankind's future and aid decrease other existential risks, Toby Ord calls these existential dangers "an argument for proceeding with due caution", not for "deserting AI". [147]
Risk of loss of control and human extinction

The thesis that AI postures an existential danger for humans, and that this risk requires more attention, is controversial however has been backed in 2023 by numerous public figures, AI researchers 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 widespread indifference:

So, dealing with possible futures of incalculable benefits and threats, the experts are surely doing whatever possible to ensure the finest outcome, right? Wrong. If an exceptional alien civilisation sent us a message stating, 'We'll arrive in a few years,' would we simply respond, 'OK, call us when you get here-we'll leave the lights on?' Probably not-but this is basically what is occurring with AI. [153]
The potential fate of mankind has actually often been compared to the fate of gorillas threatened by human activities. The comparison states that higher intelligence allowed humankind to dominate gorillas, which are now vulnerable in ways that they might not have actually anticipated. As a result, the gorilla has actually become an endangered types, not out of malice, but simply as a civilian casualties from human activities. [154]
The skeptic Yann LeCun considers that AGIs will have no desire to control mankind which we ought to be careful not to anthropomorphize them and interpret their intents as we would for humans. He stated that individuals will not be "clever sufficient to develop super-intelligent machines, yet extremely silly to the point of offering it moronic goals without any safeguards". [155] On the other side, the principle of important convergence recommends that practically whatever their objectives, smart representatives will have reasons 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 address the concern: what kinds of safeguards, algorithms, or architectures can developers carry out to increase the possibility that their recursively-improving AI would continue to act in a friendly, rather than devastating, manner after it reaches superintelligence? [157] [158] Solving the control problem is complicated by the AI arms race (which might result in a race to the bottom of security precautions in order to release items before competitors), [159] and using AI in weapon systems. [160]
The thesis that AI can pose existential risk also has detractors. Skeptics typically say that AGI is unlikely in the short-term, or that issues about AGI sidetrack from other issues associated with existing AI. [161] Former Google fraud czar Shuman Ghosemajumder considers that for many individuals outside of the innovation market, existing chatbots and LLMs are currently perceived as though they were AGI, resulting in additional misconception and worry. [162]
Skeptics in some cases charge that the thesis is crypto-religious, with an illogical belief in the possibility of superintelligence replacing an unreasonable belief in an omnipotent God. [163] Some researchers believe that the interaction campaigns on AI existential risk by certain AI groups (such as OpenAI, Anthropic, DeepMind, and Conjecture) may be an at effort at regulatory capture and to inflate interest in their products. [164] [165]
In 2023, the CEOs of Google DeepMind, OpenAI and Anthropic, in addition to other market leaders and scientists, released a joint declaration asserting that "Mitigating the danger of termination from AI ought to be a worldwide top priority along with other societal-scale dangers such as pandemics and nuclear war." [152]
Mass joblessness

Researchers from OpenAI approximated that "80% of the U.S. workforce might have at least 10% of their work jobs affected by the intro of LLMs, while around 19% of workers might see a minimum of 50% of their tasks impacted". [166] [167] They think about 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 interface with other computer tools, but also 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 miserably poor if the machine-owners effectively lobby versus wealth redistribution. Up until now, the pattern seems to be toward the 2nd choice, with technology driving ever-increasing inequality

Elon Musk considers that the automation of society will need governments to adopt a universal fundamental earnings. [168]
See likewise

Artificial brain - Software and hardware with cognitive abilities comparable to those of the animal or human brain AI effect AI safety - Research location on making AI safe and useful AI alignment - AI conformance to the designated goal A.I. Rising - 2018 movie directed by Lazar Bodroža Artificial intelligence Automated device knowing - Process of automating the application of machine learning BRAIN Initiative - Collaborative public-private research initiative revealed by the Obama administration China Brain Project Future of Humanity Institute - Defunct Oxford interdisciplinary research study centre General game playing - Ability of expert system to play various games Generative expert system - AI system capable of creating material in reaction to triggers Human Brain Project - Scientific research study job Intelligence amplification - Use of information technology to augment human intelligence (IA). Machine principles - Moral behaviours of man-made machines. Moravec's paradox. Multi-task knowing - Solving numerous machine discovering jobs at the same time. Neural scaling law - Statistical law in artificial intelligence. Outline of expert system - Overview of and topical guide to expert system. Transhumanism - Philosophical movement. Synthetic intelligence - Alternate term for or type of artificial intelligence. Transfer learning - Machine learning technique. Loebner Prize - Annual AI competition. Hardware for expert system - Hardware specifically designed and enhanced for expert system. Weak artificial intelligence - 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 founder John McCarthy composes: "we can not yet define in general what sort of computational procedures we want to call intelligent. " [26] (For a discussion of some meanings of intelligence utilized by synthetic intelligence scientists, see philosophy of expert system.). ^ The Lighthill report specifically criticized AI's "grand objectives" and led the taking apart of AI research study in England. [55] In the U.S., DARPA ended up being determined to money only "mission-oriented direct research study, rather than basic undirected research". [56] [57] ^ As AI creator John McCarthy composes "it would be an excellent relief to the rest of the employees in AI if the innovators of brand-new basic formalisms would reveal their hopes in a more secured form than has actually often been the case." [61] ^ In "Mind Children" [122] 1015 cps is used. More recently, in 1997, [123] Moravec argued for 108 MIPS which would roughly correspond to 1014 cps. Moravec talks in terms of MIPS, not "cps", which is a non-standard term Kurzweil introduced. ^ As defined in a basic AI textbook: "The assertion that devices could potentially act smartly (or, maybe much better, act as if they were smart) is called the 'weak AI' hypothesis by thinkers, and the assertion that devices that do so are in fact thinking (instead of 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 objective is to ensure that synthetic general intelligence advantages all of humanity. ^ Heath, Alex (18 January 2024). "Mark Zuckerberg's new objective is developing synthetic general intelligence". The Verge. Retrieved 13 June 2024. Our vision is to develop AI that is better than human-level at all of the human senses. ^ Baum, Seth D. (2020 ). A 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 tasks were identified as being active in 2020. ^ a b c "AI timelines: What do professionals in expert system expect for christianpedia.com the future?". Our World in Data. Retrieved 6 April 2023. ^ Metz, Cade (15 May 2023). "Some Researchers Say A.I. Is Already Here, Stirring Debate in Tech Circles". The New York Times. Retrieved 18 May 2023. ^ "AI leader Geoffrey Hinton gives up Google and warns of danger ahead". The New York City Times. 1 May 2023. Retrieved 2 May 2023. It is difficult 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 explores GPT-4". arXiv preprint. arXiv:2303.12712. GPT-4 reveals stimulates of AGI. ^ Butler, Octavia E. (1993 ). Parable of the Sower. Grand Central Publishing. ISBN 978-0-4466-7550-5. All that you touch you change. All that you alter 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 Times. The genuine threat is not AI itself however the method we release it. ^ "Impressed by synthetic intelligence? Experts state AGI is following, and it has 'existential' dangers". ABC News. 23 March 2023. Retrieved 6 April 2023. AGI might present existential dangers to humanity. ^ 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 humanity needs 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 worldwide 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 general methods. ^ LeCun, Yann (June 2023). "AGI does not present an existential threat". 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 initial on 14 August 2005: Kurzweil explains strong AI as "machine intelligence with the complete range 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 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 synthetic superintelligence (ASI)?|Definition from TechTarget". Enterprise AI. Retrieved 8 October 2023. ^ "Artificial intelligence is changing our world - it is on all of us to make certain that it goes well". Our World in Data. Retrieved 8 October 2023. ^ Dickson, Ben (16 November 2023). "Here is how far we are to achieving AGI, according to DeepMind". VentureBeat. ^ McCarthy, John (2007a). "Basic Questions". Stanford University. Archived from the initial on 26 October 2007. Retrieved 6 December 2007. ^ This list of smart traits is based on the topics 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: Academic Press. ^ a b Pfeifer, R. and Bongard J. C., How the body forms the method we believe: a brand-new view of intelligence (The MIT Press, 2007). ISBN 0-2621-6239-3. ^ White, R. W. (1959 ). "Motivation reconsidered: The concept of proficiency". Psychological Review. 66 (5 ): 297-333. doi:10.1037/ h0040934. PMID 13844397. S2CID 37385966. ^ White, R. W. (1959 ). "Motivation reevaluated: The principle 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 occurs when it does?". The Conversation. Retrieved 22 September 2024. ^ a b Turing 1950. ^ Turing, Alan (1952 ). B. Jack Copeland (ed.). Can Automatic Calculating Machines Be Said To Think?. Oxford: Oxford University Press. pp. 487-506. ISBN 978-0-1982-5079-1. ^ "Eugene Goostman is a real young boy - the Turing Test states so". The Guardian. 9 June 2014. ISSN 0261-3077. Retrieved 3 March 2024. ^ "Scientists challenge whether computer system 'Eugene Goostman' passed Turing test". BBC News. 9 June 2014. Retrieved 3 March 2024. ^ Jones, Cameron R.; Bergen, Benjamin K. (9 May 2024). "People can not identify GPT-4 from a human in a Turing test". arXiv:2405.08007 [cs.HC] ^ Varanasi, Lakshmi (21 March 2023). "AI models like ChatGPT and GPT-4 are acing everything from the bar exam to AP Biology. Here's a list of hard tests both AI versions have passed". Business Insider. Retrieved 30 May 2023. ^ Naysmith, Caleb (7 February 2023). "6 Jobs Expert System Is Already Replacing and How Investors Can Profit From It". Retrieved 30 May 2023. ^ Turk, Victoria (28 January 2015). "The Plan to Replace the Turing Test with a 'Turing Olympics'". Vice. Retrieved 3 March 2024. ^ Gopani, Avi (25 May 2022). "Turing Test is unreliable. The Winograd Schema is outdated. Coffee is the answer". Analytics India Magazine. Retrieved 3 March 2024. ^ Bhaimiya, Sawdah (20 June 2023). "DeepMind's co-founder recommended checking an AI chatbot's capability to turn $100,000 into $1 million to 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 ). "Artificial Intelligence" (PDF). In Stuart C. Shapiro (ed.). Encyclopedia of Artificial Intelligence (Second ed.). New York: 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 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 ). "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 City Times. Archived from the initial on 2 February 2023. Retrieved 18 February 2017. At its low point, some computer system researchers and software engineers avoided the term expert system for worry of being seen as wild-eyed dreamers. ^ Russell & Norvig 2003, pp. 25-26 ^ "Trends in the Emerging Tech Hype Cycle". Gartner Reports. Archived from the original on 22 May 2019. Retrieved 7 May 2019. ^ a b Moravec 1988, p. 20 ^ Harnad, S. (1990 ). "The Symbol Grounding Problem". Physica D. 42 (1-3): 335-346. arXiv: cs/9906002. Bibcode:1990 PhyD ... 42..335 H. doi:10.1016/ 0167-2789( 90 )90087-6. S2CID 3204300. ^ Gubrud 1997 ^ Hutter, Marcus (2005 ). Universal Artificial Intelligence: Sequential Decisions Based Upon Algorithmic Probability. Texts in Theoretical Computer Science an EATCS Series. Springer. doi:10.1007/ b138233. ISBN 978-3-5402-6877-2. S2CID 33352850. Archived from the initial 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 Technology. Vol. 8598. Journal of Artificial General Intelligence. doi:10.1007/ 978-3-319-09274-4. ISBN 978-3-3190-9273-7. S2CID 8387410. ^ "Who created the term "AGI"?". goertzel.org. Archived from the original on 28 December 2018. Retrieved 28 December 2018., by means of Life 3.0: 'The term "AGI" was popularized by ... Shane Legg, Mark Gubrud and Ben Goertzel' ^ Wang & Goertzel 2007 ^ "First International Summer School in Artificial General Intelligence, Main summer season school: June 22 - July 3, 2009, OpenCog Lab: July 6-9, 2009". Archived from the initial 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 original on 26 July 2020. Retrieved 11 May 2020. ^ Shevlin, Henry; Vold, Karina; Crosby, Matthew; Halina, Marta (4 October 2019). "The limits of machine intelligence: Despite progress in device intelligence, synthetic basic intelligence is still a major challenge". EMBO Reports. 20 (10 ): e49177. doi:10.15252/ embr.201949177. ISSN 1469-221X. PMC 6776890. PMID 31531926. ^ Bubeck, Sébastien; Chandrasekaran, Varun; Eldan, Ronen; Gehrke, Johannes; Horvitz, Eric; Kamar, Ece; Lee, Peter; Lee, Yin Tat; Li, Yuanzhi; Lundberg, Scott; Nori, Harsha; Palangi, Hamid; Ribeiro, Marco Tulio; Zhang, Yi (27 March 2023). "Sparks of Artificial General Intelligence: Early 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 develop into a Frankenstein's beast". The Guardian. Archived from the original 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 recognized". 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 paradise or destruction?". The New Yorker. Archived from the original on 28 January 2016. Retrieved 7 February 2016. ^ Müller, V. C., & Bostrom, N. (2016 ). Future development in synthetic intelligence: A study of skilled viewpoint. In Fundamental problems of expert system (pp. 555-572). Springer, Cham. ^ Armstrong, Stuart, and Kaj Sotala. 2012. "How We're Predicting AI-or Failing To." In Beyond AI: Artificial Dreams, edited by Jan Romportl, Pavel Ircing, Eva Žáčková, Michal Polák and Radek Schuster, 52-75. Plzeň: University of West Bohemia ^ "Microsoft Now Claims GPT-4 Shows 'Sparks' of General Intelligence". 24 March 2023. ^ Shimek, Cary (6 July 2023). "AI Outperforms Humans in Creativity Test". Neuroscience News. Retrieved 20 October 2023. ^ Guzik, Erik E.; Byrge, Christian; Gilde, Christian (1 December 2023). "The originality of devices: 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 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 artificial 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 twice as wise as Siri - but a six-year-old beats both] (in German). Archived from the original on 3 January 2019. Retrieved 2 January 2019. ^ Grossman, Gary (3 September 2020). "We're getting in the AI twilight zone between narrow and general AI". VentureBeat. Archived from the original on 4 September 2020. Retrieved 5 September 2020. Certainly, too, there are those who claim we are currently seeing an early example of an AGI system in the recently revealed GPT-3 natural language processing (NLP) neural network. ... So is GPT-3 the very first example of an AGI system? This is debatable, but the agreement is that it is not AGI. ... If absolutely nothing else, GPT-3 informs us there is a middle ground in between narrow and general AI. ^ Quach, Katyanna. "A designer constructed an AI chatbot using GPT-3 that helped a man 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 new AI can perform over 600 jobs, from playing video games to controlling robotics", 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 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 Times. ISSN 0362-4331. Retrieved 7 June 2023. ^ Bove, Tristan. "A.I. could equal human intelligence in 'simply a couple of years,' states CEO of Google's main A.I. research study laboratory". Fortune. Retrieved 4 September 2024. ^ Nellis, Stephen (2 March 2024). "Nvidia CEO states AI might pass human tests in 5 years". Reuters. ^ Aschenbrenner, Leopold. "SITUATIONAL AWARENESS, The Decade Ahead". ^ Sullivan, Mark (18 October 2023). "Why everyone appears 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 vast array of views in present research, 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 example: 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 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 expert system for interest by the field about why a program behaves 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 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 Artificial Intelligence Have Moral or Legal Rights?". The Wall Street Journal. ^ "Artificial General Intelligence - Do [es] the cost outweigh advantages?". 23 August 2021. Retrieved 7 June 2023. ^ "How we can Benefit from Advancing Artificial General Intelligence (AGI) - Unite.AI". www.unite.ai. 7 April 2020. Retrieved 7 June 2023. ^ a b c Talty, Jules; Julien, Stephan. "What Will Our Society Appear 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: paths, risks, strategies (Reprinted with corrections 2017 ed.). Oxford, United Kingdom; New York, New York City, USA: Oxford University Press. ISBN 978-0-1996-7811-2. ^ Piper, Kelsey (19 November 2018). "How technological progress is making it likelier than ever that humans will ruin ourselves". Vox. Retrieved 8 June 2023. ^ Doherty, Ben (17 May 2018). "Climate alter an 'existential security risk' 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 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 artificial intelligence - but 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 remarkable Facebook argument in between Yann LeCun, Stuart Russel and Yoshua Bengio about the dangers of strong AI". The remarkable Facebook debate between Yann LeCun, Stuart Russel and Yoshua Bengio about the risks 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 devastating 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 alerts". Financial Times. Archived from the original on 11 April 2022. Retrieved 24 December 2023. ^ Milmo, Dan; Stacey, Kiran (25 September 2023). "Experts disagree over risk positioned however artificial 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 the End 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 worries over AI, warn scientists". The Telegraph. Retrieved 7 December 2023. ^ Davidson, John (30 October 2023). "Google Brain creator says big tech is lying about AI termination 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 large language models". OpenAI. Retrieved 7 June 2023. ^ a b Hurst, Luke (23 March 2023). "OpenAI says 80% of employees might see their jobs affected by AI. These are the tasks most affected". euronews. Retrieved 8 June 2023. ^ Sheffey, Ayelet (20 August 2021). "Elon Musk states we require universal standard income due to the fact that 'in the future, physical work will be a choice'". Business Insider. Archived from the original on 9 July 2023. Retrieved 8 June 2023. Sources

UNESCO Science Report: the Race Against Time for Smarter Development. Paris: UNESCO. 11 June 2021. ISBN 978-9-2310-0450-6. Archived from the original on 18 June 2022. Retrieved 22 September 2021. Chalmers, David (1996 ), The Conscious Mind, Oxford University Press. Clocksin, William (August 2003), "Artificial intelligence and the future", Philosophical Transactions of the Royal Society A, vol. 361, no. 1809, pp. 1721-1748, Bibcode:2003 RSPTA.361.1721 C, doi:10.1098/ rsta.2003.1232, PMID 12952683, S2CID 31032007. Crevier, Daniel (1993 ). AI: The Tumultuous 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 synthetic general intelligence and the possibility of a technological singularity: a response 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, retrieved 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, recovered 7 May 2011. Howe, J. (November 1994), Artificial Intelligence at Edinburgh University: a Viewpoint, archived from the initial on 17 August 2007, retrieved 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 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 Artificial Intelligence?. Stanford University. The supreme effort is to make computer programs that can fix issues and attain goals on the planet as well as humans. Moravec, Hans (1988 ), Mind Children, Harvard University Press Moravec, Hans (1998 ), "When will computer hardware match the human brain?", Journal of Evolution and Technology, vol. 1, archived from the 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, obtained 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 ), Artificial Intelligence: A New Synthesis, Morgan Kaufmann Publishers, ISBN 978-1-5586-0467-4 NRC (1999 ), " in Expert System", Funding a Revolution: Government Support for Computing Research, National Academy Press, archived from the original on 12 January 2008, obtained 29 September 2007 Poole, David; Mackworth, Alan; Goebel, Randy (1998 ), Computational Intelligence: A Logical Approach, New York: Oxford University Press, archived from the original on 25 July 2009, recovered 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 original 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, recovered 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 meaning and cognition, Oxford University Press, ISBN 978-0-1992-1727-4 Wang, Pei; Goertzel, Ben (2007 ). "Introduction: Aspects of Artificial General Intelligence". Advances in Artificial General Intelligence: Concepts, Architectures and Algorithms: Proceedings of the AGI Workshop 2006. IOS Press. pp. 1-16. ISBN 978-1-5860-3758-1. Archived from the original on 18 February 2021. Retrieved 13 December 2020 - via ResearchGate.
Further reading

Aleksander, Igor (1996 ), Impossible Minds, World Scientific Publishing Company, ISBN 978-1-8609-4036-1 Azevedo FA, Carvalho LR, Grinberg LT, Farfel J, et al. (April 2009), "Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain", The Journal of Comparative Neurology, 513 (5 ): 532-541, doi:10.1002/ cne.21974, PMID 19226510, S2CID 5200449, archived from the initial on 18 February 2021, retrieved 4 September 2013 - by means of 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 Consider 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 simple sufficient to be understandable will not be complicated enough to behave wisely, while any system made complex enough to behave wisely will be too complicated to understand." (p. 197.) Computer scientist Alex Pentland composes: "Current AI machine-learning algorithms are, at their core, dead basic silly. They work, but 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 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, forum.batman.gainedge.org reason and photorum.eclat-mauve.fr function come from acting in the world and experiencing the effects. Expert systems - disembodied, complete strangers to blood, sweat, and tears - have no occasion for that." (p. 30.). Halal, William E. "TechCast Article Series: The Automation of Thought" (PDF). Archived from the 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 anticipate that those who want to get rich from AI are going to have the interests of the rest of us close at heart,' ... writes [Gary Marcus] 'We can't count on federal governments driven by campaign finance contributions [from tech business] to press back.' ... Marcus details the demands that people should make of their governments and the tech companies. They include transparency on how AI systems work; settlement for people if their data [are] used to train LLMs (large language design) s and the right to grant this usage; and the capability to hold tech business liable for the harms they bring on by eliminating Section 230, enforcing money penalites, and passing more stringent product liability laws ... Marcus also suggests ... that a new, AI-specific federal firm, similar to the FDA, the FCC, or the FTC, may supply the most robust oversight ... [T] he Fordham law professor 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 ..., 'AI engineers likewise promised 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 baffled human beings for years, exposes the limitations of natural-language-processing algorithms", Scientific American, vol. 329, no. 4 (November 2023), pp. 81-82. "This murder secret competition has exposed that although NLP (natural-language processing) designs are capable of amazing accomplishments, their capabilities are really much restricted by the quantity of context they get. This [...] could trigger [troubles] 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 act as training data for such a function." (p. 82.). Immerwahr, Daniel, "Your Lying Eyes: People now utilize A.I. to produce phony videos equivalent from genuine ones. How much does it matter?", The New Yorker, 20 November 2023, pp. 54-59. "If by 'deepfakes' we suggest practical videos produced using artificial intelligence that really deceive people, then they barely exist. The fakes aren't deep, and the deeps aren't fake. [...] A.I.-generated videos are not, in basic, photorum.eclat-mauve.fr running in our media as counterfeited evidence. Their function much better resembles that of cartoons, specifically smutty ones." (p. 59.). - Leffer, Lauren, "The Risks of Trusting AI: We should prevent humanizing machine-learning models utilized in clinical research", Scientific American, vol. 330, no. 6 (June 2024), pp. 80-81. Lepore, Jill, "The Chit-Chatbot: Is talking with a machine a discussion?", The New Yorker, 7 October 2024, pp. 12-16. Marcus, Gary, "Artificial Confidence: Even the most recent, buzziest systems of artificial basic intelligence are stymmied by the very same old problems", Scientific American, vol. 327, no. 4 (October 2022), pp. 42-45. McCarthy, John (October 2007), "From here to human-level AI", Expert System, 171 (18 ): 1174-1182, doi:10.1016/ j.artint.2007.10.009. McCorduck, Pamela (2004 ), Machines Who Think (2nd ed.), Natick, Massachusetts: A. K. Peters, ISBN 1-5688-1205-1. Moravec, Hans (1976 ), The Role of Raw Power in Intelligence, archived from the original on 3 March 2016, recovered 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 authorities to ignore contradictory evidence?", 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 determined 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 genuine humanlike thinking or an understanding of the physical and social world ... ChatGPT seemed not able to reason logically and tried to depend 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 innovations are powerful however unreliable. Rules-based systems can not handle situations their developers did not prepare for. Learning systems are restricted by the data on which they were trained. AI failures have already resulted in tragedy. Advanced autopilot functions in vehicles, although they perform well in some circumstances, have actually driven vehicles without warning into trucks, concrete barriers, and parked automobiles. In the incorrect situation, AI systems go from supersmart to superdumb in an immediate. When an opponent is attempting to control and hack an AI system, the threats 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 innovations but depend on the timelelss human tendency to anthropomorphise." (p. 29.). Williams, R. W.; Herrup, K.