Artificial basic intelligence (AGI) is a kind of expert system (AI) that matches or goes beyond human cognitive capabilities across a vast array of cognitive tasks. This contrasts with narrow AI, which is limited to specific jobs. [1] Artificial superintelligence (ASI), on the other hand, refers to AGI that significantly goes beyond human cognitive capabilities. AGI is considered one of the definitions of strong AI.

Creating AGI is a primary goal of AI research study and of business such as OpenAI [2] and Meta. [3] A 2020 survey recognized 72 active AGI research study and development jobs throughout 37 countries. [4]
The timeline for attaining AGI remains a subject of ongoing debate amongst researchers and professionals. 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 ever be attained; and another minority declares that it is already here. [5] [6] Notable AI scientist Geoffrey Hinton has revealed concerns about the fast development towards AGI, suggesting it might be attained faster than numerous expect. [7]
There is dispute on the exact meaning of AGI and concerning whether contemporary large language models (LLMs) such as GPT-4 are early types of AGI. [8] AGI is a typical subject in science fiction and futures research studies. [9] [10]
Contention exists over whether AGI represents an existential danger. [11] [12] [13] Many experts on AI have actually mentioned that reducing the danger of human extinction posed by AGI ought to be a worldwide priority. [14] [15] Others find the development of AGI to be too remote to present such a risk. [16] [17]
Terminology

AGI is also understood as strong AI, [18] [19] full AI, [20] human-level AI, [5] human-level intelligent AI, or basic smart action. [21]
Some scholastic sources book the term "strong AI" for computer system programs that experience sentience or consciousness. [a] On the other hand, weak AI (or narrow AI) has the ability to resolve one specific problem but lacks general cognitive capabilities. [22] [19] Some scholastic sources use "weak AI" to refer more broadly to any programs that neither experience awareness nor have a mind in the same sense as humans. [a]
Related concepts include artificial superintelligence and transformative AI. A synthetic superintelligence (ASI) is a hypothetical kind of AGI that is far more normally intelligent than people, [23] while the idea of transformative AI relates to AI having a large effect on society, for instance, comparable to the farming or industrial transformation. [24]
A framework for categorizing AGI in levels was proposed in 2023 by Google DeepMind scientists. They define 5 levels of AGI: emerging, proficient, professional, virtuoso, and superhuman. For example, a qualified AGI is specified as an AI that exceeds 50% of knowledgeable adults in a wide variety of non-physical jobs, and a superhuman AGI (i.e. an artificial superintelligence) is likewise specified however with a limit of 100%. They consider big language models like ChatGPT or LLaMA 2 to be instances of emerging AGI. [25]
Characteristics

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

Researchers normally hold that intelligence is required to do all of the following: [27]
factor, usage strategy, solve puzzles, and make judgments under uncertainty represent knowledge, including sound judgment knowledge plan find out

• interact in natural language
• if needed, integrate these abilities in completion of any provided goal

Many interdisciplinary methods (e.g. cognitive science, computational intelligence, and choice making) consider extra qualities such as creativity (the ability to form unique mental images and ideas) [28] and autonomy. [29]
Computer-based systems that display a number of these capabilities exist (e.g. see computational creativity, automated reasoning, choice support system, robotic, evolutionary calculation, smart representative). There is argument about whether contemporary AI systems possess them to an adequate degree.

Physical traits

Other abilities are thought about preferable in smart systems, as they may affect intelligence or aid in its expression. These include: [30]
- the capability to sense (e.g. see, hear, and so on), and - the ability to act (e.g. relocation and manipulate items, change area to explore, and so on).
This consists of the ability to identify and react to risk. [31]
Although the ability to sense (e.g. see, hear, and so on) and the capability to act (e.g. relocation and manipulate items, change location to explore, and so on) can be desirable for some intelligent systems, [30] these physical capabilities are not strictly required for an entity to certify as AGI-particularly under the thesis that big language designs (LLMs) may currently be or end up being AGI. Even from a less optimistic perspective on LLMs, there is no firm requirement for an AGI to have a human-like form; 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 been proscribed a particular physical personification and hence does not require a capacity for locomotion or standard "eyes and ears". [32]
Tests for human-level AGI

Several tests meant to validate human-level AGI have been thought about, consisting of: [33] [34]
The concept of the test is that the machine has to try and pretend to be a guy, by responding to questions put to it, and it will just pass if the pretence is reasonably persuading. A substantial part of a jury, who need to not be skilled about devices, should 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 fix it, one would need to implement AGI, since the solution is beyond the capabilities of a purpose-specific algorithm. [47]
There are numerous issues that have actually been conjectured to require basic intelligence to solve as well as human beings. Examples include computer system vision, natural language understanding, and dealing with unforeseen situations while resolving any real-world issue. [48] Even a specific task like translation requires a device to read and write in both languages, follow the author's argument (reason), understand the context (understanding), and faithfully reproduce the author's original intent (social intelligence). All of these issues require to be solved all at once in order to reach human-level device efficiency.

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 criteria for reading comprehension 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 general intelligence was possible and that it would exist in simply a few years. [51] AI leader Herbert A. Simon composed in 1965: "makers will be capable, within twenty years, of doing any work a man can do." [52]
Their predictions were the inspiration for Stanley Kubrick and Arthur C. Clarke's character HAL 9000, who embodied what AI scientists believed they could produce by the year 2001. AI pioneer Marvin Minsky was a specialist [53] on the job of making HAL 9000 as realistic as possible according to the consensus forecasts of the time. He said in 1967, "Within a generation ... the issue of developing 'synthetic intelligence' will considerably be solved". [54]
Several classical AI jobs, such as Doug Lenat's Cyc project (that started in 1984), and Allen Newell's Soar job, were directed at AGI.

However, in the early 1970s, it became apparent that scientists had grossly ignored the trouble of the job. Funding agencies became doubtful of AGI and put scientists under increasing pressure to produce helpful "applied AI". [c] In the early 1980s, Japan's Fifth Generation Computer Project restored interest in AGI, setting out a ten-year timeline that included AGI goals like "carry on a casual conversation". [58] In reaction to this and the success of specialist 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 objectives of the Fifth Generation Computer Project were never fulfilled. [60] For the 2nd time in twenty years, AI researchers who predicted the imminent accomplishment of AGI had actually been misinterpreted. By the 1990s, AI scientists had a credibility for making vain promises. They ended up being hesitant to make predictions at all [d] and prevented mention of "human level" expert system for worry of being identified "wild-eyed dreamer [s]. [62]
Narrow AI research study

In the 1990s and early 21st century, mainstream AI accomplished commercial success and academic respectability by concentrating on particular sub-problems where AI can produce verifiable outcomes and industrial applications, such as speech recognition and suggestion algorithms. [63] These "applied AI" systems are now used thoroughly throughout the technology market, and research in this vein is greatly moneyed in both academia and market. As of 2018 [update], advancement in this field was considered an emerging trend, and a mature stage was anticipated to be reached in more than ten years. [64]
At the millenium, numerous traditional AI researchers [65] hoped that strong AI could be established by integrating programs that solve numerous sub-problems. Hans Moravec wrote in 1988:

I am positive that this bottom-up route to expert system will one day satisfy the standard top-down path majority method, prepared to provide the real-world proficiency and the commonsense knowledge that has actually been so frustratingly elusive in reasoning programs. Fully smart machines will result when the metaphorical golden spike is driven unifying the 2 efforts. [65]
However, even at the time, this was challenged. For instance, Stevan Harnad of Princeton University concluded his 1990 paper on the sign grounding hypothesis by mentioning:

The expectation has actually often been voiced that "top-down" (symbolic) approaches to modeling cognition will somehow fulfill "bottom-up" (sensory) approaches somewhere in between. If the grounding considerations in this paper stand, then this expectation is hopelessly modular and there is really only one feasible path from sense to symbols: from the ground up. A free-floating symbolic level like the software application level of a computer will never be reached by this path (or vice versa) - nor is it clear why we need to even try to reach such a level, because it looks as if getting there would just amount to uprooting our symbols from their intrinsic significances (consequently simply minimizing ourselves to the practical equivalent of a programmable computer). [66]
Modern synthetic basic intelligence research

The term "artificial general intelligence" was used as early as 1997, by Mark Gubrud [67] in a discussion 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 agent maximises "the capability to please goals in a large range of environments". [68] This kind of AGI, defined by the ability to maximise a mathematical meaning of intelligence instead of show human-like behaviour, [69] was likewise called universal artificial intelligence. [70]
The term AGI was re-introduced and popularized by Shane Legg and Ben Goertzel around 2002. [71] AGI research activity in 2006 was described by Pei Wang and Ben Goertzel [72] as "producing publications and initial outcomes". The first summertime school in AGI was organized in Xiamen, China in 2009 [73] by the Xiamen university's Artificial Brain Laboratory and OpenCog. The first university course was given up 2010 [74] and 2011 [75] at Plovdiv University, Bulgaria by Todor Arnaudov. MIT presented a course on AGI in 2018, arranged by Lex Fridman and including a number of visitor speakers.

As of 2023 [upgrade], a little number of computer system researchers are active in AGI research, and lots of contribute to a series of AGI conferences. However, increasingly more scientists have an interest in open-ended knowing, [76] [77] which is the idea of allowing AI to constantly find out and innovate like people do.

Feasibility

As of 2023, the advancement and prospective achievement of AGI stays a topic of intense dispute within the AI neighborhood. While traditional consensus held that AGI was a remote goal, recent developments have led some scientists and market figures to claim that early types of AGI might currently exist. [78] AI pioneer Herbert A. Simon hypothesized in 1965 that "machines will be capable, within twenty years, of doing any work a male can do". This prediction stopped working to come true. Microsoft co-founder Paul Allen thought that such intelligence is unlikely in the 21st century since it would need "unforeseeable and fundamentally unforeseeable breakthroughs" and a "scientifically deep understanding of cognition". [79] Writing in The Guardian, roboticist Alan Winfield declared the gulf in between modern-day computing and human-level expert system is as large as the gulf between existing area flight and practical faster-than-light spaceflight. [80]
An additional obstacle is the absence of clarity in specifying what intelligence involves. Does it need awareness? Must it display the ability to set goals as well as pursue them? Is it purely a matter of scale such that if model sizes increase sufficiently, intelligence will emerge? Are facilities such as planning, thinking, and causal understanding needed? Does intelligence require clearly replicating the brain and its particular professors? Does it need emotions? [81]
Most AI researchers think strong AI can be attained in the future, but some thinkers, like Hubert Dreyfus and Roger Penrose, reject the possibility of attaining strong AI. [82] [83] John McCarthy is amongst those who think human-level AI will be achieved, but that the present level of development is such that a date can not accurately be forecasted. [84] AI professionals' views on the feasibility of AGI wax and subside. Four surveys performed in 2012 and 2013 recommended that the mean estimate among specialists for when they would be 50% confident AGI would get here was 2040 to 2050, depending on the survey, with the mean being 2081. Of the experts, 16.5% responded to with "never ever" when asked the exact same concern but with a 90% confidence rather. [85] [86] Further current AGI development considerations can be found above Tests for verifying human-level AGI.

A report by Stuart Armstrong and Kaj Sotala of the Machine Intelligence Research Institute discovered that "over [a] 60-year time frame there is a strong bias towards forecasting the arrival of human-level AI as in between 15 and 25 years from the time the forecast was made". They examined 95 forecasts made in between 1950 and 2012 on when human-level AI will come about. [87]
In 2023, Microsoft scientists published a comprehensive assessment of GPT-4. They concluded: "Given the breadth and depth of GPT-4's abilities, our company believe that it might reasonably be viewed as an early (yet still incomplete) variation of an artificial general intelligence (AGI) system." [88] Another research study in 2023 reported that GPT-4 outperforms 99% of human beings on the Torrance tests of creative thinking. [89] [90]
Blaise Agüera y Arcas and Peter Norvig wrote in 2023 that a substantial level of basic intelligence has actually currently been attained with frontier designs. They wrote that hesitation to this view comes from 4 primary factors: a "healthy apprehension about metrics for AGI", an "ideological dedication to alternative AI theories or methods", a "devotion to human (or biological) exceptionalism", or a "concern about the economic ramifications of AGI". [91]
2023 also marked the emergence of large multimodal designs (big language models capable of processing or creating numerous methods such as text, audio, and images). [92]
In 2024, OpenAI released o1-preview, the very first of a series of designs that "spend more time believing before they respond". According to Mira Murati, this ability to believe before reacting represents a brand-new, extra paradigm. It improves model outputs by spending more computing power when producing the answer, whereas the model scaling paradigm enhances outputs by increasing the design size, training information and training calculate power. [93] [94]
An OpenAI staff member, Vahid Kazemi, claimed in 2024 that the business had actually accomplished AGI, stating, "In my viewpoint, we have already accomplished AGI and it's much more clear with O1." Kazemi clarified that while the AI is not yet "much better than any human at any job", it is "better than the majority of human beings at most tasks." He also resolved criticisms that large language models (LLMs) merely follow predefined patterns, comparing their learning procedure to the clinical technique of observing, hypothesizing, and validating. These statements have actually triggered argument, as they rely on a broad and non-traditional definition of AGI-traditionally understood as AI that matches human intelligence across all domains. Critics argue that, while OpenAI's models demonstrate amazing adaptability, they might not totally satisfy this standard. Notably, Kazemi's remarks came soon after OpenAI eliminated "AGI" from the regards to its collaboration with Microsoft, prompting speculation about the company's tactical intentions. [95]
Timescales

Progress in expert system has historically gone through durations of fast progress separated by periods when development appeared to stop. [82] Ending each hiatus were fundamental advances in hardware, software application or both to develop area for additional progress. [82] [98] [99] For example, the computer system hardware offered in the twentieth century was not adequate to implement deep knowing, which requires great deals of GPU-enabled CPUs. [100]
In the introduction to his 2006 book, [101] Goertzel says that quotes of the time needed before a truly flexible AGI is constructed differ from ten years to over a century. As of 2007 [upgrade], the agreement in the AGI research neighborhood seemed to be that the timeline gone over by Ray Kurzweil in 2005 in The Singularity is Near [102] (i.e. in between 2015 and 2045) was plausible. [103] Mainstream AI scientists have provided a wide range of opinions on whether progress will be this quick. A 2012 meta-analysis of 95 such viewpoints discovered a bias towards anticipating that the start of AGI would take place within 16-26 years for contemporary and historical forecasts alike. That paper has been slammed 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%, considerably much better than the second-best entry's rate of 26.3% (the traditional technique utilized a weighted amount of ratings from different pre-defined classifiers). [105] AlexNet was considered as the initial ground-breaker of the existing deep learning wave. [105]
In 2017, researchers Feng Liu, Yong Shi, and Ying Liu conducted intelligence tests on openly readily available and easily available weak AI such as Google AI, Apple's Siri, and others. At the optimum, these AIs reached an IQ worth of about 47, which corresponds around to a six-year-old child in first grade. A grownup comes to about 100 typically. Similar tests were performed in 2014, with the IQ rating reaching an optimum worth of 27. [106] [107]
In 2020, OpenAI established GPT-3, a language model capable of performing many varied jobs without specific training. According to Gary Grossman in a VentureBeat article, while there is consensus that GPT-3 is not an example of AGI, it is considered by some to be too advanced to be classified as a narrow AI system. [108]
In the same year, Jason Rohrer utilized his GPT-3 account to establish a chatbot, and supplied a chatbot-developing platform called "Project December". OpenAI asked for changes to the chatbot to comply with their security standards; Rohrer detached Project December from the GPT-3 API. [109]
In 2022, DeepMind established Gato, a "general-purpose" system capable of performing more than 600 various tasks. [110]
In 2023, Microsoft Research released a study on an early variation of OpenAI's GPT-4, competing that it exhibited more general intelligence than previous AI models and demonstrated human-level efficiency in jobs spanning numerous domains, such as mathematics, coding, and law. This research sparked a dispute on whether GPT-4 might be considered an early, insufficient variation of synthetic general intelligence, highlighting the need for additional expedition and evaluation of such systems. [111]
In 2023, the AI researcher Geoffrey Hinton stated that: [112]
The idea that this stuff could really get smarter than people - a few people believed that, [...] But the majority of individuals thought it was way off. And I believed it was way off. I thought it was 30 to 50 years and even longer away. Obviously, I no longer believe that.

In May 2023, Demis Hassabis likewise stated that "The progress in the last couple of years has actually been pretty incredible", and that he sees no reason that it would slow down, expecting AGI within a years or perhaps a few 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 in addition to people. [114] In June 2024, the AI researcher Leopold Aschenbrenner, a previous OpenAI staff member, approximated AGI by 2027 to be "strikingly plausible". [115]
Whole brain emulation

While the development of transformer models like in ChatGPT is considered the most appealing path to AGI, [116] [117] whole brain emulation can act as an alternative approach. With whole brain simulation, a brain design is developed by scanning and mapping a biological brain in detail, and after that copying and imitating it on a computer system or another computational device. The simulation design need to be sufficiently loyal to the initial, so that it behaves in almost the very same way 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 actually been gone over in artificial intelligence research study [103] as a method to strong AI. Neuroimaging innovations that could provide the essential in-depth understanding are improving quickly, and futurist Ray Kurzweil in the book The Singularity Is Near [102] forecasts that a map of adequate quality will end up being available on a similar timescale to the computing power needed to emulate it.

Early approximates

For low-level brain simulation, an extremely powerful cluster of computers or GPUs would be required, given the massive 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, stabilizing by adulthood. Estimates vary for an adult, varying from 1014 to 5 × 1014 synapses (100 to 500 trillion). [120] A price quote of the brain's processing power, based on a simple switch design for nerve cell activity, is around 1014 (100 trillion) synaptic updates per second (SUPS). [121]
In 1997, Kurzweil took a look at various estimates for the hardware needed to equal the human brain and adopted a figure of 1016 calculations per 2nd (cps). [e] (For comparison, if a "calculation" was comparable to one "floating-point operation" - a measure utilized to rate existing supercomputers - then 1016 "calculations" would be equivalent to 10 petaFLOPS, attained in 2011, while 1018 was accomplished in 2022.) He utilized this figure to anticipate the required hardware would be offered at some point between 2015 and 2025, if the exponential development in computer system power at the time of composing continued.

Current research study

The Human Brain Project, an EU-funded effort active from 2013 to 2023, has established an especially detailed and publicly accessible atlas of the human brain. [124] In 2023, researchers from Duke University carried out a high-resolution scan of a mouse brain.

Criticisms of simulation-based approaches

The synthetic neuron model assumed by Kurzweil and utilized in lots of present artificial neural network executions is easy compared to biological nerve cells. A brain simulation would likely have to capture the in-depth cellular behaviour of biological neurons, currently comprehended just in broad overview. The overhead presented by full modeling of the biological, chemical, and physical information of neural behaviour (particularly on a molecular scale) would need computational powers several orders of magnitude bigger than Kurzweil's estimate. In addition, the quotes do not represent glial cells, which are understood to play a function in cognitive processes. [125]
A basic criticism of the simulated brain approach originates from embodied cognition theory which asserts that human personification is an important aspect of human intelligence and is required to ground significance. [126] [127] If this theory is right, any completely functional brain design will need to encompass more than simply the nerve cells (e.g., a robotic body). Goertzel [103] proposes virtual personification (like in metaverses like Second Life) as an alternative, however it is unidentified whether this would be enough.

Philosophical perspective

"Strong AI" as specified in philosophy

In 1980, philosopher John Searle coined the term "strong AI" as part of his Chinese space argument. [128] He proposed a distinction in between 2 hypotheses about expert system: [f]
Strong AI hypothesis: An expert system system can have "a mind" and "consciousness". Weak AI hypothesis: An expert system system can (only) act like it thinks and has a mind and consciousness.
The first one he called "strong" because it makes a more powerful statement: it assumes something special has taken place to the maker that goes beyond those abilities that we can check. The behaviour of a "weak AI" device would be specifically identical to a "strong AI" machine, but the latter would also have subjective mindful experience. This usage is likewise typical in scholastic AI research and textbooks. [129]
In contrast to Searle and traditional AI, some futurists such as Ray Kurzweil utilize the term "strong AI" to indicate "human level artificial basic intelligence". [102] This is not the very same as Searle's strong AI, unless it is presumed that awareness is essential for human-level AGI. Academic theorists such as Searle do not believe that holds true, and to most expert system researchers the concern is out-of-scope. [130]
Mainstream AI is most interested in how a program acts. [131] According to Russell and Norvig, "as long as the program works, they do not care if you call it genuine or a simulation." [130] If the program can act as if it has a mind, then there is no need to understand if it in fact has mind - certainly, there would be no chance to tell. For AI research, Searle's "weak AI hypothesis" is equivalent to the declaration "synthetic general intelligence is possible". Thus, according to Russell and Norvig, "most AI scientists take the weak AI hypothesis for approved, and don't care about the strong AI hypothesis." [130] Thus, for scholastic AI research, "Strong AI" and "AGI" are two various things.

Consciousness

Consciousness can have different meanings, and some aspects play significant roles in sci-fi and the ethics of expert system:

Sentience (or "extraordinary awareness"): The ability to "feel" perceptions or feelings subjectively, instead of the ability to reason about understandings. Some philosophers, such as David Chalmers, utilize the term "consciousness" to refer exclusively to extraordinary awareness, which is roughly comparable to sentience. [132] Determining why and how subjective experience occurs is called the tough issue of consciousness. [133] Thomas Nagel discussed 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 smartly ask "what does it feel 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 seems conscious (i.e., has awareness) however a toaster does not. [134] In 2022, a Google engineer declared that the business's AI chatbot, LaMDA, had actually accomplished sentience, though this claim was widely contested by other specialists. [135]
Self-awareness: To have mindful awareness of oneself as a different person, specifically to be knowingly mindful of one's own ideas. This is opposed to merely being the "subject of one's believed"-an os or debugger has the ability to be "familiar with itself" (that is, to represent itself in the same way it represents whatever else)-however this is not what people usually indicate when they utilize the term "self-awareness". [g]
These traits have an ethical measurement. AI sentience would offer increase to concerns of well-being and legal defense, similarly to animals. [136] Other aspects of consciousness related to cognitive abilities are also relevant to the concept of AI rights. [137] Determining how to incorporate advanced AI with existing legal and social frameworks is an emergent issue. [138]
Benefits

AGI might have a broad variety of applications. If oriented towards such objectives, AGI might assist reduce various problems on the planet such as hunger, hardship and health issue. [139]
AGI could improve performance and performance in many tasks. For example, in public health, AGI could accelerate medical research study, significantly versus cancer. [140] It could take care of the elderly, [141] and democratize access to fast, high-quality medical diagnostics. It could offer fun, low-cost and individualized education. [141] The requirement to work to subsist might end up being obsolete if the wealth produced is properly rearranged. [141] [142] This likewise raises the question of the place of people in a drastically automated society.

AGI could likewise assist to make reasonable choices, and to prepare for and prevent catastrophes. It could also help to profit of potentially catastrophic innovations such as nanotechnology or climate engineering, while avoiding the associated dangers. [143] If an AGI's primary goal is to prevent existential catastrophes such as human extinction (which could be difficult if the Vulnerable World Hypothesis turns out to be true), [144] it might take procedures to considerably decrease the threats [143] while decreasing the impact of these measures on our lifestyle.

Risks

Existential threats

AGI might represent several kinds of existential threat, which are threats that threaten "the early termination of Earth-originating intelligent life or the permanent and drastic damage of its potential for desirable future development". [145] The threat of human extinction from AGI has been the topic of lots of disputes, but there is likewise the possibility that the development of AGI would cause a completely problematic future. Notably, it might be utilized to spread out and protect the set of values of whoever develops it. If humanity still has moral blind spots similar to slavery in the past, AGI might irreversibly entrench it, avoiding ethical progress. [146] Furthermore, AGI might help with mass security and brainwashing, which might be used to create a stable repressive around the world totalitarian program. [147] [148] There is likewise a danger for the makers themselves. If devices that are sentient or otherwise worthwhile of moral consideration are mass produced in the future, participating in a civilizational course that indefinitely disregards their welfare and interests could be an existential catastrophe. [149] [150] Considering how much AGI might improve mankind's future and help in reducing other existential risks, Toby Ord calls these existential threats "an argument for continuing with due care", not for "deserting AI". [147]
Risk of loss of control and human termination

The thesis that AI positions an existential risk for humans, and that this threat requires more attention, is controversial however has actually been endorsed in 2023 by lots of public figures, AI scientists and CEOs of AI business such as Elon Musk, Bill Gates, Geoffrey Hinton, Yoshua Bengio, Demis Hassabis and Sam Altman. [151] [152]
In 2014, Stephen Hawking slammed extensive indifference:

So, dealing with possible futures of enormous benefits and dangers, the professionals are surely doing everything possible to ensure the very best result, right? Wrong. If an exceptional alien civilisation sent us a message stating, 'We'll show up in a couple of years,' would we just reply, 'OK, call us when you get here-we'll leave the lights on?' Probably not-but this is basically what is taking place with AI. [153]
The prospective fate of mankind has sometimes been compared to the fate of gorillas threatened by human activities. The comparison specifies that greater intelligence permitted humanity to control gorillas, which are now vulnerable in manner ins which they might not have actually anticipated. As an outcome, the gorilla has become a threatened species, not out of malice, but just as a collateral damage from human activities. [154]
The skeptic Yann LeCun considers that AGIs will have no desire to control humanity and that we must take care not to anthropomorphize them and analyze their intents as we would for humans. He said that individuals won't be "smart enough to create super-intelligent machines, yet unbelievably stupid to the point of offering it moronic goals without any safeguards". [155] On the other side, the idea of important merging suggests that practically whatever their goals, intelligent representatives will have reasons to attempt to make it through and obtain more power as intermediary actions to attaining these objectives. Which this does not need having emotions. [156]
Many scholars who are worried about existential risk supporter for more research into solving the "control problem" to respond to the question: what kinds of safeguards, algorithms, or architectures can programmers carry out to maximise the possibility that their recursively-improving AI would continue to behave in a friendly, rather than harmful, manner after it reaches superintelligence? [157] [158] Solving the control problem is made complex by the AI arms race (which might result in a race to the bottom of security preventative measures in order to release products before rivals), [159] and using AI in weapon systems. [160]
The thesis that AI can posture existential threat also has detractors. Skeptics usually state that AGI is not likely in the short-term, or that issues about AGI distract from other issues related to current AI. [161] Former Google fraud czar Shuman Ghosemajumder thinks about that for lots of people beyond the innovation market, existing chatbots and LLMs are currently viewed as though they were AGI, leading to more misconception and worry. [162]
Skeptics sometimes charge that the thesis is crypto-religious, with an illogical belief in the possibility of superintelligence changing an illogical belief in a supreme God. [163] Some researchers believe that the communication projects on AI existential risk by specific AI groups (such as OpenAI, Anthropic, DeepMind, and Conjecture) might be an at effort at regulative 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, provided a joint declaration asserting that "Mitigating the threat of extinction from AI must be an international top priority alongside other societal-scale risks such as pandemics and nuclear war." [152]
Mass joblessness

Researchers from OpenAI estimated that "80% of the U.S. workforce could have at least 10% of their work jobs impacted by the intro of LLMs, while around 19% of employees might see at least 50% of their jobs impacted". [166] [167] They think about office workers to be the most exposed, for example mathematicians, accounting professionals or web designers. [167] AGI could have a better autonomy, ability to make decisions, to interface with other computer tools, however also to control robotized bodies.

According to Stephen Hawking, the result of automation on the quality of life will depend on how the wealth will be redistributed: [142]
Everyone can take pleasure in a life of luxurious leisure if the machine-produced wealth is shared, or a lot of individuals can end up miserably bad if the machine-owners effectively lobby versus wealth redistribution. Up until now, the trend appears to be toward the 2nd option, with technology driving ever-increasing inequality

Elon Musk considers that the automation of society will require federal governments to embrace a universal fundamental income. [168]
See likewise

Artificial brain - Software and hardware with cognitive abilities comparable to those of the animal or human brain AI result AI safety - Research area on making AI safe and helpful AI alignment - AI conformance to the desired objective A.I. Rising - 2018 movie directed by Lazar Bodroža Expert system Automated artificial intelligence - Process of automating the application of device knowing BRAIN Initiative - Collaborative public-private research study effort revealed by the Obama administration China Brain Project Future of Humanity Institute - Defunct Oxford interdisciplinary research study centre General video game playing - Ability of expert system to play different video games Generative expert system - AI system capable of producing content in reaction to prompts Human Brain Project - Scientific research study project Intelligence amplification - Use of info technology to augment human intelligence (IA). Machine ethics - Moral behaviours of man-made devices. Moravec's paradox. Multi-task knowing - Solving several device discovering jobs at the exact same time. Neural scaling law - Statistical law in machine learning. Outline of expert system - Overview of and topical guide to expert system. Transhumanism - Philosophical motion. Synthetic intelligence - Alternate term for or type of expert system. Transfer learning - Artificial intelligence strategy. Loebner Prize - Annual AI competitors. Hardware for synthetic intelligence - Hardware specially created and optimized for expert system. 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 definition of "strong AI" and weak AI in the article Chinese space. ^ AI creator John McCarthy composes: "we can not yet in basic what kinds of computational procedures we want to call intelligent. " [26] (For a conversation of some definitions of intelligence used by artificial intelligence researchers, see philosophy of expert system.). ^ The Lighthill report specifically criticized AI's "grandiose objectives" and led the dismantling of AI research in England. [55] In the U.S., DARPA ended up being determined to money only "mission-oriented direct research, rather than fundamental undirected research study". [56] [57] ^ As AI founder John McCarthy composes "it would be a great relief to the rest of the workers in AI if the innovators of new basic formalisms would reveal their hopes in a more guarded type than has sometimes held true." [61] ^ In "Mind Children" [122] 1015 cps is utilized. More just recently, in 1997, [123] Moravec argued for 108 MIPS which would roughly correspond to 1014 cps. Moravec talks in regards to MIPS, not "cps", which is a non-standard term Kurzweil introduced. ^ As specified in a standard AI book: "The assertion that makers could possibly act intelligently (or, possibly 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 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 synthetic 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 guarantee that synthetic general intelligence benefits all of humanity. ^ Heath, Alex (18 January 2024). "Mark Zuckerberg's new objective is producing 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 Survey of Artificial General Intelligence Projects for Ethics, Risk, and Policy (PDF) (Report). Global Catastrophic Risk Institute. Retrieved 28 November 2024. 72 AGI R&D projects were recognized as being active in 2020. ^ a b c "AI timelines: What do professionals in expert system expect for the future?". Our World in Data. Retrieved 6 April 2023. ^ Metz, Cade (15 May 2023). "Some Researchers Say A.I. Is Already Here, Stirring Debate in Tech Circles". The New York Times. Retrieved 18 May 2023. ^ "AI pioneer Geoffrey Hinton stops Google and alerts of risk ahead". The New York City Times. 1 May 2023. Retrieved 2 May 2023. It is tough to see how you can avoid the bad actors from using it for bad things. ^ Bubeck, Sébastien; Chandrasekaran, Varun; Eldan, Ronen; Gehrke, Johannes; Horvitz, Eric (2023 ). "Sparks of Artificial General Intelligence: Early explores GPT-4". arXiv preprint. arXiv:2303.12712. GPT-4 reveals triggers of AGI. ^ Butler, Octavia E. (1993 ). Parable of the Sower. Grand Central Publishing. ISBN 978-0-4466-7550-5. All that you touch you change. All that you alter changes you. ^ Vinge, Vernor (1992 ). A Fire Upon the Deep. Tor Books. ISBN 978-0-8125-1528-2. The Singularity is coming. ^ Morozov, Evgeny (30 June 2023). "The True Threat of Artificial Intelligence". The New York City Times. The genuine hazard is not AI itself however the way we release it. ^ "Impressed by expert system? Experts say AGI is following, and it has 'existential' dangers". ABC News. 23 March 2023. Retrieved 6 April 2023. AGI might position existential risks to mankind. ^ Bostrom, Nick (2014 ). Superintelligence: Paths, Dangers, Strategies. Oxford University Press. ISBN 978-0-1996-7811-2. The very first superintelligence will be the last innovation 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 risk of termination from AI should be a global top priority. ^ "Statement on AI Risk". Center for AI Safety. Retrieved 1 March 2024. AI experts 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 developing machines that can outthink us in general ways. ^ LeCun, Yann (June 2023). "AGI does not present an existential threat". Medium. There is no reason to fear AI as an existential risk. ^ Kurzweil 2005, p. 260. ^ a b Kurzweil, Ray (5 August 2005), "Long Live AI", Forbes, archived from the original on 14 August 2005: Kurzweil describes strong AI as "maker intelligence with the full variety 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 use for "human-level" intelligence in the physical symbol system hypothesis. ^ "The Open University on Strong and Weak AI". Archived from the initial on 25 September 2009. Retrieved 8 October 2007. ^ "What is artificial superintelligence (ASI)?|Definition from TechTarget". Enterprise AI. Retrieved 8 October 2023. ^ "Artificial intelligence is transforming our world - it is on 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 intelligent traits 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 forms 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 reevaluated: The concept of proficiency". 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 original 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 happens when it does?". The Conversation. Retrieved 22 September 2024. ^ a b Turing 1950. ^ Turing, Alan (1952 ). B. Jack Copeland (ed.). Can Automatic Calculating Machines Be Said To Think?. Oxford: Oxford University Press. pp. 487-506. ISBN 978-0-1982-5079-1. ^ "Eugene Goostman is a genuine boy - the Turing Test says so". The Guardian. 9 June 2014. ISSN 0261-3077. Retrieved 3 March 2024. ^ "Scientists dispute whether computer 'Eugene Goostman' passed Turing test". BBC News. 9 June 2014. Retrieved 3 March 2024. ^ Jones, Cameron R.; Bergen, Benjamin K. (9 May 2024). "People can not differentiate GPT-4 from a human in a Turing test". arXiv:2405.08007 [cs.HC] ^ Varanasi, Lakshmi (21 March 2023). "AI models like ChatGPT and GPT-4 are acing everything from the bar examination to AP Biology. Here's a list of tough examinations 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 Take Advantage Of It". Retrieved 30 May 2023. ^ Turk, Victoria (28 January 2015). "The Plan to Replace the Turing Test with a 'Turing Olympics'". Vice. Retrieved 3 March 2024. ^ Gopani, Avi (25 May 2022). "Turing Test is unreliable. The Winograd Schema is outdated. Coffee is the response". Analytics India Magazine. Retrieved 3 March 2024. ^ Bhaimiya, Sawdah (20 June 2023). "DeepMind's co-founder suggested 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 original 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 ). "Expert System, Business and forum.batman.gainedge.org Civilization - Our Fate Made in Machines". Archived from the original on 6 May 2022. Retrieved 12 March 2022. ^ Simon 1965, p. 96 quoted 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 ), priced estimate in Crevier (1993, p. 109). ^ Lighthill 1973; Howe 1994. ^ a b NRC 1999, "Shift to Applied Research Increases Investment". ^ Crevier 1993, pp. 115-117; Russell & Norvig 2003, pp. 21-22. ^ Crevier 1993, p. 211, Russell & Norvig 2003, p. 24 and see also Feigenbaum & McCorduck 1983. ^ Crevier 1993, pp. 161-162, 197-203, 240; Russell & Norvig 2003, p. 25. ^ Crevier 1993, pp. 209-212. ^ McCarthy, John (2000 ). "Respond to Lighthill". Stanford University. Archived from the initial on 30 September 2008. Retrieved 29 September 2007. ^ Markoff, John (14 October 2005). "Behind Expert system, a Squadron of Bright Real People". The New York Times. Archived from the original on 2 February 2023. Retrieved 18 February 2017. At its low point, some computer system scientists and software engineers prevented the term expert system for worry of being deemed 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 on 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., via Life 3.0: 'The term "AGI" was promoted by ... Shane Legg, Mark Gubrud and Ben Goertzel' ^ Wang & Goertzel 2007 ^ "First International Summer School in Artificial General Intelligence, Main summertime school: June 22 - July 3, 2009, OpenCog Lab: July 6-9, 2009". Archived from the original on 28 September 2020. Retrieved 11 May 2020. ^ "Избираеми дисциплини 2009/2010 - пролетен триместър" [Elective courses 2009/2010 - spring trimester] Факултет по математика и информатика [Faculty of Mathematics and Informatics] (in Bulgarian). Archived from the 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 limitations of machine intelligence: Despite development in maker intelligence, synthetic general 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 experiments with GPT-4". arXiv:2303.12712 [cs.CL] ^ "Microsoft Researchers Claim GPT-4 Is Showing "Sparks" of AGI". Futurism. 23 March 2023. Retrieved 13 December 2023. ^ Allen, Paul; Greaves, Mark (12 October 2011). "The Singularity Isn't Near". MIT Technology Review. Retrieved 17 September 2014. ^ Winfield, Alan. "Expert system will not turn into a Frankenstein's 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 wakewiki.de destruction?". The New Yorker. Archived from the initial on 28 January 2016. Retrieved 7 February 2016. ^ Müller, V. C., & Bostrom, N. (2016 ). Future development in synthetic intelligence: A survey of professional opinion. In Fundamental concerns of expert system (pp. 555-572). Springer, Cham. ^ Armstrong, Stuart, and Kaj Sotala. 2012. "How We're Predicting AI-or Failing To." In Beyond AI: Artificial Dreams, edited by Jan Romportl, Pavel Ircing, Eva Žáčková, Michal Polák and Radek Schuster, 52-75. Plzeň: University of West Bohemia ^ "Microsoft Now Claims GPT-4 Shows 'Sparks' of General Intelligence". 24 March 2023. ^ Shimek, Cary (6 July 2023). "AI Outperforms Humans in Creativity Test". Neuroscience News. Retrieved 20 October 2023. ^ Guzik, Erik E.; Byrge, Christian; Gilde, Christian (1 December 2023). "The creativity of machines: AI takes the Torrance Test". Journal of Creativity. 33 (3 ): 100065. doi:10.1016/ j.yjoc.2023.100065. ISSN 2713-3745. S2CID 261087185. ^ Arcas, Blaise Agüera y (10 October 2023). "Artificial General Intelligence Is Already Here". Noema. ^ Zia, Tehseen (8 January 2024). "Unveiling of Large Multimodal Models: Shaping the Landscape of Language Models in 2024". Unite.ai. Retrieved 26 May 2024. ^ "Introducing OpenAI o1-preview". OpenAI. 12 September 2024. ^ Knight, Will. "OpenAI Announces a Brand-new AI Model, Code-Named Strawberry, That Solves Difficult Problems Step by Step". Wired. ISSN 1059-1028. Retrieved 17 September 2024. ^ "OpenAI Employee Claims AGI Has Been Achieved". Orbital Today. 13 December 2024. Retrieved 27 December 2024. ^ "AI Index: State of AI in 13 Charts". hai.stanford.edu. 15 April 2024. Retrieved 7 June 2024. ^ "Next-Gen AI: OpenAI and Meta's Leap Towards Reasoning Machines". Unite.ai. 19 April 2024. Retrieved 7 June 2024. ^ James, Alex P. (2022 ). "The Why, What, and How of Artificial General Intelligence Chip Development". IEEE Transactions on Cognitive and Developmental Systems. 14 (2 ): 333-347. arXiv:2012.06338. doi:10.1109/ TCDS.2021.3069871. ISSN 2379-8920. S2CID 228376556. Archived from the original 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 basic intelligence with hybrid Tianjic chip architecture". Nature. 572 (7767 ): 106-111. Bibcode:2019 Natur.572..106 P. doi:10.1038/ s41586-019-1424-8. ISSN 1476-4687. PMID 31367028. S2CID 199056116. Archived from the initial on 29 August 2022. Retrieved 29 August 2022. ^ Pandey, Mohit; Fernandez, Michael; Gentile, Francesco; Isayev, Olexandr; Tropsha, Alexander; Stern, Abraham C.; Cherkasov, Artem (March 2022). "The transformational 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 two times as wise as Siri - but a six-year-old beats both] (in German). Archived from the original on 3 January 2019. Retrieved 2 January 2019. ^ Grossman, Gary (3 September 2020). "We're getting in 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 claim we are already seeing an early example of an AGI system in the just recently announced GPT-3 natural language processing (NLP) neural network. ... So is GPT-3 the first example of an AGI system? This is arguable, however the agreement is that it is not AGI. ... If nothing else, GPT-3 tells us there is a happy medium between narrow and basic AI. ^ Quach, Katyanna. "A developer 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 brand-new AI can perform over 600 jobs, library.kemu.ac.ke from playing video games to controlling robotics", TechCrunch, archived from the original on 16 June 2022, retrieved 12 June 2022. ^ Bubeck, Sébastien; Chandrasekaran, Varun; Eldan, Ronen; Gehrke, Johannes; Horvitz, Eric; Kamar, Ece; Lee, Peter; Lee, Yin Tat; Li, Yuanzhi; Lundberg, Scott; Nori, Harsha; Palangi, Hamid; Ribeiro, Marco Tulio; Zhang, Yi (22 March 2023). "Sparks of Artificial General Intelligence: Early try outs GPT-4". arXiv:2303.12712 [cs.CL] ^ Metz, Cade (1 May 2023). "' The Godfather of A.I.' Leaves Google and Warns of Danger Ahead". The New York Times. ISSN 0362-4331. Retrieved 7 June 2023. ^ Bove, Tristan. "A.I. could match human intelligence in 'just a couple of years,' says CEO of Google's primary A.I. research study lab". Fortune. Retrieved 4 September 2024. ^ Nellis, Stephen (2 March 2024). "Nvidia CEO says AI could pass human tests in five years". Reuters. ^ Aschenbrenner, Leopold. "SITUATIONAL AWARENESS, The Decade Ahead". ^ Sullivan, Mark (18 October 2023). "Why everybody 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 Giant 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 project 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 current research, all of which require grounding to some degree ^ Thornton, Angela (26 June 2023). "How uploading our minds to a computer system 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 (priced quote in" Encyclopedia.com"),. MIT Encyclopedia of Cognitive Science Archived 19 July 2008 at the Wayback Machine (estimated 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 synthetic intelligence 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 Artificial Intelligence Have Rights?". Psychology Today. Retrieved 5 September 2024. ^ Akst, Daniel (10 April 2023). "Should Robots With Artificial Intelligence Have Moral or Legal Rights?". The Wall Street Journal. ^ "Artificial General Intelligence - Do [es] the expense surpass advantages?". 23 August 2021. Retrieved 7 June 2023. ^ "How we can Take advantage of 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: 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 development is making it likelier than ever that people will damage ourselves". Vox. Retrieved 8 June 2023. ^ Doherty, Ben (17 May 2018). "Climate alter an 'existential security threat' to Australia, Senate questions says". The Guardian. ISSN 0261-3077. Retrieved 16 July 2023. ^ MacAskill, William (2022 ). What we owe the future. New York, NY: Basic Books. ISBN 978-1-5416-1862-6. ^ a b Ord, Toby (2020 ). "Chapter 5: Future Risks, Unaligned Expert System". The Precipice: Existential Risk and the Future of Humanity. Bloomsbury Publishing. ISBN 978-1-5266-0021-9. ^ Al-Sibai, Noor (13 February 2022). "OpenAI Chief Scientist Says Advanced AI May Already Be Conscious". Futurism. Retrieved 24 December 2023. ^ Samuelsson, Paul Conrad (2019 ). "Artificial Consciousness: Our Greatest Ethical Challenge". Philosophy Now. Retrieved 23 December 2023. ^ Kateman, Brian (24 July 2023). "AI Should Be Terrified of Humans". TIME. Retrieved 23 December 2023. ^ Roose, Kevin (30 May 2023). "A.I. Poses 'Risk of Extinction,' Industry Leaders Warn". The New York Times. ISSN 0362-4331. Retrieved 24 December 2023. ^ a b "Statement on AI Risk". Center for AI Safety. 30 May 2023. Retrieved 8 June 2023. ^ "Stephen Hawking: 'Transcendence takes a look at the ramifications of expert system - however are we taking AI seriously enough?'". The Independent (UK). Archived from the initial on 25 September 2015. Retrieved 3 December 2014. ^ Herger, Mario. "The Gorilla Problem - Enterprise Garage". Retrieved 7 June 2023. ^ "The remarkable Facebook debate in between Yann LeCun, Stuart Russel and Yoshua Bengio about the threats of strong AI". The fascinating Facebook argument between Yann LeCun, Stuart Russel and Yoshua Bengio about the risks of strong AI (in French). Retrieved 8 June 2023. ^ "Will Artificial Intelligence Doom The Human Race 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 threat: a study". Physica Scripta. 90 (1 ): 018001. doi:10.1088/ 0031-8949/90/ 1/018001. ISSN 0031-8949. ^ Bostrom, Nick (2014 ). Superintelligence: Paths, Dangers, Strategies (First ed.). Oxford University Press. ISBN 978-0-1996-7811-2. ^ Chow, Andrew R.; Perrigo, Billy (16 February 2023). "The AI Arms Race Is On. Start Worrying". TIME. Retrieved 24 December 2023. ^ Tetlow, Gemma (12 January 2017). "AI arms race risks spiralling out of control, report cautions". 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 but 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 completion of Humanity Mean for Me?". The Atlantic. Archived from the initial on 4 June 2014. Retrieved 12 December 2015. ^ Titcomb, James (30 October 2023). "Big Tech is stiring fears over AI, warn researchers". The Telegraph. Retrieved 7 December 2023. ^ Davidson, John (30 October 2023). "Google Brain founder states huge tech is lying about AI extinction threat". 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 take a look at the labor market effect capacity of large language designs". OpenAI. Retrieved 7 June 2023. ^ a b Hurst, Luke (23 March 2023). "OpenAI states 80% of workers could see their tasks affected by AI. These are the jobs most impacted". euronews. Retrieved 8 June 2023. ^ Sheffey, Ayelet (20 August 2021). "Elon Musk states we need universal basic income due to the fact that 'in the future, manual labor will be a choice'". Business Insider. Archived from the initial 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), "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 Look For Artificial Intelligence. New York, NY: BasicBooks. ISBN 0-465-02997-3. Darrach, Brad (20 November 1970), "Meet Shakey, the First Electronic Person", Life Magazine, pp. 58-68. Drachman, D. (2005 ), "Do we have brain to spare?", Neurology, 64 (12 ): 2004-2005, doi:10.1212/ 01. WNL.0000166914.38327. BB, PMID 15985565, S2CID 38482114. Feigenbaum, Edward A.; McCorduck, Pamela (1983 ), The Fifth Generation: Artificial Intelligence and Japan's Computer Challenge to the World, Michael Joseph, ISBN 978-0-7181-2401-4. Goertzel, Ben; Pennachin, Cassio, eds. (2006 ), Artificial General Intelligence (PDF), Springer, ISBN 978-3-5402-3733-4, archived from the original (PDF) on 20 March 2013. Goertzel, Ben (December 2007), "Human-level artificial basic intelligence and the possibility of a technological singularity: a reaction to Ray Kurzweil's The Singularity Is Near, and McDermott's review of Kurzweil", Expert system, vol. 171, no. 18, Special Review Issue, pp. 1161-1173, doi:10.1016/ j.artint.2007.10.011, archived from the original on 7 January 2016, recovered 1 April 2009. Gubrud, Mark (November 1997), "Nanotechnology and International Security", Fifth Foresight Conference on Molecular Nanotechnology, archived from the initial on 29 May 2011, recovered 7 May 2011. Howe, J. (November 1994), Artificial Intelligence at Edinburgh University: a Perspective, archived from the original 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 ), "Artificial Intelligence: A General Survey", Expert System: a paper symposium, Science Research Council. Luger, George; Stubblefield, William (2004 ), Artificial Intelligence: Structures and Strategies for Complex Problem Solving (5th ed.), The Benjamin/Cummings Publishing Company, Inc., p. 720, ISBN 978-0-8053-4780-7. McCarthy, John (2007b). What is Expert system?. Stanford University. The ultimate effort is to make computer system programs that can resolve problems and accomplish objectives in the world as well as humans. Moravec, Hans (1988 ), Mind Children, Harvard University Press Moravec, Hans (1998 ), "When will hardware match the human brain?", Journal of Evolution and Technology, vol. 1, archived from the initial on 15 June 2006, retrieved 23 June 2006 Nagel (1974 ), "What Is it Like to Be a Bat" (PDF), Philosophical Review, 83 (4 ): 435-50, doi:10.2307/ 2183914, JSTOR 2183914, archived (PDF) from the original on 16 October 2011, recovered 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 ), "Developments in Expert System", Funding a Revolution: Government Support for Computing Research, National Academy Press, archived from the original on 12 January 2008, recovered 29 September 2007 Poole, David; Mackworth, Alan; Goebel, Randy (1998 ), Computational Intelligence: A Logical Approach, New York: Oxford University Press, archived from the original on 25 July 2009, obtained 6 December 2007 Russell, Stuart J.; Norvig, Peter (2003 ), Artificial Intelligence: A Modern Approach (2nd ed.), Upper Saddle River, New Jersey: Prentice Hall, ISBN 0-13-790395-2 Sandberg, Anders; Boström, Nick (2008 ), Whole Brain Emulation: A Roadmap (PDF), Technical Report # 2008-3, Future of Humanity Institute, Oxford University, archived (PDF) from the original on 25 March 2020, retrieved 5 April 2009 Searle, John (1980 ), "Minds, Brains and Programs" (PDF), Behavioral and Brain Sciences, 3 (3 ): 417-457, doi:10.1017/ S0140525X00005756, S2CID 55303721, archived (PDF) from the initial on 17 March 2019, obtained 3 September 2020 Simon, H. A. (1965 ), The Shape of Automation for Men and Management, New York: Harper & Row Turing, Alan (October 1950). "Computing Machinery and Intelligence". Mind. 59 (236 ): 433-460. doi:10.1093/ mind/LIX.236.433. ISSN 1460-2113. JSTOR 2251299. S2CID 14636783.
de Vega, Manuel; Glenberg, Arthur; Graesser, Arthur, eds. (2008 ), Symbols and Embodiment: Debates on significance and cognition, Oxford University Press, ISBN 978-0-1992-1727-4 Wang, Pei; Goertzel, Ben (2007 ). "Introduction: Aspects of Artificial General Intelligence". Advances in Artificial General Intelligence: Concepts, Architectures and Algorithms: Proceedings of the AGI Workshop 2006. IOS Press. pp. 1-16. ISBN 978-1-5860-3758-1. Archived from the original on 18 February 2021. Retrieved 13 December 2020 - 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 original on 18 February 2021, obtained 4 September 2013 - via ResearchGate Berglas, Anthony (January 2012) [2008], Expert System Will Kill Our Grandchildren (Singularity), archived from the original on 23 July 2014, recovered 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, writes (in what might be called "Dyson's Law") that "Any system basic adequate to be understandable will not be made complex enough to behave smartly, while any system made complex enough to behave intelligently will be too complicated to understand." (p. 197.) Computer researcher Alex Pentland writes: "Current AI machine-learning algorithms are, at their core, dead basic stupid. They work, but they work by brute force." (p. 198.). Gelernter, David, Dream-logic, the Internet and Artificial Thought, Edge, archived from the original on 26 July 2010, recovered 25 July 2010. Gleick, James, "The Fate of Free Choice" (evaluation of Kevin J. Mitchell, Free Agents: How Evolution Gave Us Free Choice, Princeton University Press, 2023, 333 pp.), The New York Review of Books, vol. LXXI, no. 1 (18 January 2024), pp. 27-28, 30. "Agency is what distinguishes us from machines. For biological creatures, factor and purpose come from acting on the planet and experiencing the consequences. Expert systems - disembodied, strangers to blood, yewiki.org 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" (evaluation of Verity Harding, AI Needs You: How We Can Change AI's Future and Save Our Own, Princeton University Press, 274 pp.; Gary Marcus, Taming Silicon Valley: How We Can Ensure That AI Works for Us, MIT Press, 235 pp.; Daniela Rus and Gregory Mone, The Mind's Mirror: Risk and Reward in the Age of AI, Norton, 280 pp.; Madhumita Murgia, Code Dependent: Residing In the Shadow of AI, Henry Holt, 311 pp.), The New York Review of Books, vol. LXXI, no. 17 (7 November 2024), pp. 44-46. "' We can't reasonably anticipate that those who intend 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 rely on federal governments driven by campaign finance contributions [from tech companies] to press back.' ... Marcus information the demands that citizens ought to make of their federal governments and the tech business. They consist of openness on how AI systems work; compensation for people if their information [are] used to train LLMs (large language design) s and the right to approval to this use; and the capability to hold tech business accountable for the damages they trigger by eliminating Section 230, enforcing money penalites, and passing stricter item liability laws ... Marcus likewise suggests ... that a new, AI-specific federal company, comparable to the FDA, the FCC, or the FTC, may supply the most robust oversight ... [T] he Fordham law professor Chinmayi Sharma ... recommends ... develop [ing] a professional licensing routine for engineers that would function in a similar method to medical licenses, malpractice fits, and the Hippocratic oath in medicine. 'What if, like physicians,' she asks ..., 'AI engineers also pledged to do no damage?'" (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 actually baffled people for decades, reveals 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 can amazing tasks, their capabilities are very much restricted by the quantity of context they get. This [...] might trigger [problems] for scientists who want to use them to do things such as analyze ancient languages. In some cases, there are few historical records on long-gone civilizations to act as training information for such a purpose." (p. 82.). Immerwahr, Daniel, "Your Lying Eyes: People now use A.I. to produce fake videos identical from real ones. How much does it matter?", The New Yorker, 20 November 2023, pp. 54-59. "If by 'deepfakes' we mean sensible videos produced using expert system that actually deceive people, then they barely exist. The phonies aren't deep, and the deeps aren't phony. [...] A.I.-generated videos are not, in general, running in our media as counterfeited evidence. Their role better looks like that of cartoons, especially smutty ones." (p. 59.). - Leffer, Lauren, "The Risks of Trusting AI: We must avoid humanizing machine-learning models utilized in clinical research study", 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 newest, buzziest systems of synthetic basic intelligence are stymmied by the exact same old issues", Scientific American, vol. 327, no. 4 (October 2022), pp. 42-45. McCarthy, John (October 2007), "From here to human-level AI", Artificial Intelligence, 171 (18 ): 1174-1182, doi:10.1016/ j.artint.2007.10.009. McCorduck, Pamela (2004 ), Machines Who Think (second ed.), Natick, Massachusetts: A. K. Peters, ISBN 1-5688-1205-1. Moravec, Hans (1976 ), The Role of Raw Power in Intelligence, archived from the 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: 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 neglect contradictory evidence?", The New Yorker, 20 November 2023, pp. 20-26. Roivainen, Eka, "AI's IQ: ChatGPT aced a [basic intelligence] test however 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 fails at jobs that require genuine humanlike reasoning or an understanding of the physical and social world ... ChatGPT appeared unable to factor rationally and tried to count on its huge database of ... facts stemmed from online texts. " - Scharre, Paul, "Killer Apps: The Real Dangers of an AI Arms Race", Foreign Affairs, vol. 98, no. 3 (May/June 2019), pp. 135-44. "Today's AI innovations are powerful however unreliable. Rules-based systems can not handle circumstances their developers did not expect. Learning systems are restricted by the information on which they were trained. AI failures have actually already caused catastrophe. Advanced autopilot features in automobiles, although they perform well in some situations, have driven cars without cautioning into trucks, concrete barriers, and parked vehicles. In the incorrect scenario, AI systems go from supersmart to superdumb in an instant. When an enemy is attempting to control 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 new technologies however rely on the timelelss human propensity to anthropomorphise." (p. 29.). Williams, R. W.; Herrup, K.