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Artificial basic intelligence (AGI) is a kind of expert system (AI) that matches or exceeds human cognitive capabilities throughout a wide variety of cognitive tasks. This contrasts with narrow AI, which is limited to specific tasks. [1] Artificial superintelligence (ASI), on the other hand, describes AGI that considerably exceeds human cognitive abilities. AGI is considered one of the definitions of strong AI.

Creating AGI is a main objective of AI research study and of companies such as OpenAI [2] and Meta. [3] A 2020 survey recognized 72 active AGI research and development jobs throughout 37 countries. [4]
The timeline for achieving AGI remains a topic of ongoing debate amongst researchers and experts. As of 2023, some argue that it might be possible in years or years; others preserve it might take a century or longer; a minority think it may never be achieved; and another minority claims that it is currently here. [5] [6] Notable AI researcher Geoffrey Hinton has expressed issues about the rapid progress towards AGI, recommending it could be achieved faster than numerous anticipate. [7]
There is argument on the specific definition of AGI and relating to whether modern-day big language designs (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 professionals on AI have mentioned that reducing the threat of human termination postured by AGI should be a global concern. [14] [15] Others discover the development of AGI to be too remote to provide such a danger. [16] [17]
Terminology

AGI is likewise called strong AI, [18] [19] full AI, [20] human-level AI, [5] human-level smart AI, or general intelligent action. [21]
Some scholastic sources reserve the term "strong AI" for computer system programs that experience life or consciousness. [a] On the other hand, weak AI (or narrow AI) has the ability to resolve one particular issue but does not have basic cognitive capabilities. [22] [19] Some academic sources utilize "weak AI" to refer more broadly to any programs that neither experience awareness nor have a mind in the same sense as human beings. [a]
Related principles include synthetic superintelligence and transformative AI. A synthetic superintelligence (ASI) is a hypothetical kind of AGI that is far more generally smart than humans, [23] while the concept of transformative AI associates with AI having a big impact on society, for example, comparable to the agricultural or commercial revolution. [24]
A structure for categorizing AGI in levels was proposed in 2023 by Google DeepMind researchers. They define 5 levels of AGI: emerging, qualified, professional, virtuoso, and superhuman. For example, a skilled AGI is specified as an AI that surpasses 50% of skilled adults in a large range of non-physical tasks, and a superhuman AGI (i.e. a synthetic superintelligence) is likewise defined 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. One of the leading proposals is the Turing test. However, there are other widely known definitions, and some researchers disagree with the more popular techniques. [b]
Intelligence characteristics

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

• communicate in natural language
• if necessary, incorporate these skills in conclusion of any offered goal

Many interdisciplinary approaches (e.g. cognitive science, computational intelligence, and choice making) consider extra qualities such as creativity (the ability to form unique psychological images and principles) [28] and autonomy. [29]
Computer-based systems that show many of these capabilities exist (e.g. see computational imagination, automated thinking, decision support group, robotic, evolutionary computation, smart representative). There is argument about whether contemporary AI systems have them to a sufficient degree.

Physical qualities

Other abilities are thought about preferable in smart systems, as they might impact intelligence or help in its expression. These include: [30]
- the ability to sense (e.g. see, hear, and so on), and - the capability to act (e.g. relocation and control things, change place to explore, and so on).
This includes the ability to find and react to risk. [31]
Although the ability to sense (e.g. see, hear, etc) and the capability to act (e.g. relocation and control objects, modification location to explore, and so on) can be desirable for some smart systems, [30] these physical abilities are not strictly required for an entity to certify as AGI-particularly under the thesis that large language models (LLMs) might already be or end up being AGI. Even from a less positive viewpoint on LLMs, there is no company requirement for an AGI to have a human-like form; being a silicon-based computational system is enough, offered it can process input (language) from the external world in location of human senses. This interpretation lines up with the understanding that AGI has never ever been proscribed a particular physical embodiment and therefore does not demand a capacity for locomotion or standard "eyes and ears". [32]
Tests for human-level AGI

Several tests suggested to confirm human-level AGI have actually been considered, including: [33] [34]
The concept of the test is that the maker has to attempt and pretend to be a man, by answering questions put to it, and it will only pass if the pretence is reasonably convincing. A substantial portion of a jury, who ought to not be professional about devices, need to be taken in by the pretence. [37]
AI-complete issues

A problem is informally called "AI-complete" or "AI-hard" if it is thought that in order to fix it, one would need to carry out AGI, since the option is beyond the capabilities of a purpose-specific algorithm. [47]
There are numerous problems that have been conjectured to require basic intelligence to solve along with people. Examples consist of computer system vision, natural language understanding, and handling unforeseen scenarios while resolving any real-world problem. [48] Even a particular task like translation needs a machine to check out and write in both languages, follow the author's argument (factor), comprehend the context (knowledge), and faithfully reproduce the author's initial intent (social intelligence). All of these problems need to be solved simultaneously in order to reach human-level maker efficiency.

However, a number of these jobs can now be carried out by modern big language designs. According to Stanford University's 2024 AI index, AI has actually reached human-level performance on lots of criteria for checking out understanding and visual thinking. [49]
History

Classical AI

Modern AI research started in the mid-1950s. [50] The very first generation of AI scientists were encouraged that artificial general intelligence was possible and that it would exist in just a few decades. [51] AI leader Herbert A. Simon wrote in 1965: "makers will be capable, within twenty years, of doing any work a male can do." [52]
Their predictions were the motivation for Stanley Kubrick and Arthur C. Clarke's character HAL 9000, who embodied what AI scientists thought they might produce by the year 2001. AI pioneer Marvin Minsky was a specialist [53] on the job of making HAL 9000 as reasonable as possible according to the consensus forecasts of the time. He stated in 1967, "Within a generation ... the problem of creating 'expert system' will considerably be resolved". [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 ended up being obvious that researchers had actually grossly ignored the trouble of the task. Funding companies ended up being hesitant of AGI and put researchers under increasing pressure to produce beneficial "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 objectives like "continue a casual discussion". [58] In action to this and the success of expert systems, both market and federal government pumped cash into the field. [56] [59] However, self-confidence in AI spectacularly collapsed in the late 1980s, and the objectives of the Fifth Generation Computer Project were never ever fulfilled. [60] For the 2nd time in twenty years, AI researchers who forecasted the impending accomplishment of AGI had actually been misinterpreted. By the 1990s, AI scientists had a credibility for making vain promises. They became reluctant to make forecasts at all [d] and prevented reference of "human level" expert system for worry of being labeled "wild-eyed dreamer [s]. [62]
Narrow AI research

In the 1990s and early 21st century, mainstream AI achieved commercial success and scholastic respectability by focusing on particular sub-problems where AI can produce proven outcomes and commercial applications, such as speech recognition and recommendation algorithms. [63] These "applied AI" systems are now utilized extensively throughout the technology industry, and research study in this vein is greatly funded in both academia and market. As of 2018 [upgrade], advancement in this field was thought about an emerging trend, and a fully grown phase was anticipated to be reached in more than 10 years. [64]
At the turn of the century, lots of mainstream AI researchers [65] hoped that strong AI could be developed by integrating programs that solve various sub-problems. Hans Moravec composed in 1988:

I am positive that this bottom-up route to synthetic intelligence will one day fulfill the standard top-down route more than half way, ready to provide the real-world competence and the commonsense knowledge that has actually been so frustratingly evasive in reasoning programs. Fully intelligent machines will result when the metaphorical golden spike is driven uniting the 2 efforts. [65]
However, even at the time, this was challenged. For example, Stevan Harnad of Princeton University concluded his 1990 paper on the symbol grounding hypothesis by specifying:

The expectation has typically been voiced that "top-down" (symbolic) approaches to modeling cognition will somehow satisfy "bottom-up" (sensory) approaches somewhere in between. If the grounding factors to consider in this paper are valid, then this expectation is hopelessly modular and there is truly just one viable path from sense to symbols: from the ground up. A free-floating symbolic level like the software application level of a computer will never be reached by this route (or vice versa) - nor is it clear why we must even try to reach such a level, since it appears arriving would simply amount to uprooting our signs from their intrinsic meanings (thus simply minimizing ourselves to the practical equivalent of a programmable computer). [66]
Modern synthetic general intelligence research

The term "synthetic basic intelligence" was utilized as early as 1997, by Mark Gubrud [67] in a conversation of the implications of completely automated military production and operations. A mathematical formalism of AGI was proposed by Marcus Hutter in 2000. Named AIXI, the proposed AGI representative maximises "the ability to satisfy goals in a wide range of environments". [68] This kind of AGI, identified by the ability to maximise a mathematical meaning 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 study activity in 2006 was described by Pei Wang and Ben Goertzel [72] as "producing publications and initial results". The very first summertime 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 including a variety of visitor lecturers.

Since 2023 [upgrade], a small number of computer system researchers are active in AGI research, and many contribute to a series of AGI conferences. However, progressively more scientists are interested in open-ended learning, [76] [77] which is the idea of allowing AI to continually find out and innovate like humans do.

Feasibility

As of 2023, the development and possible accomplishment of AGI remains a subject of intense debate within the AI neighborhood. While traditional agreement held that AGI was a remote goal, recent improvements have actually led some researchers and market figures to claim that early types of AGI may already exist. [78] AI pioneer Herbert A. Simon speculated in 1965 that "machines will be capable, within twenty years, of doing any work a man can do". This prediction failed to come true. Microsoft co-founder Paul Allen believed that such intelligence is unlikely in the 21st century because it would require "unforeseeable and essentially unpredictable advancements" and a "scientifically deep understanding of cognition". [79] Writing in The Guardian, roboticist Alan Winfield claimed the gulf between modern computing and human-level synthetic intelligence is as wide as the gulf in between existing space flight and practical faster-than-light spaceflight. [80]
A more obstacle is the lack of clarity in defining what intelligence involves. Does it need awareness? Must it display the ability to set objectives as well as pursue them? Is it purely a matter of scale such that if design sizes increase sufficiently, intelligence will emerge? Are centers such as planning, reasoning, and causal understanding required? Does intelligence require explicitly replicating the brain and its specific faculties? Does it require emotions? [81]
Most AI scientists 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 among those who think human-level AI will be accomplished, but that today level of progress is such that a date can not precisely be anticipated. [84] AI experts' views on the feasibility of AGI wax and wane. Four surveys performed in 2012 and 2013 suggested that the median price quote among specialists for when they would be 50% positive AGI would show up was 2040 to 2050, depending on the survey, with the mean being 2081. Of the professionals, 16.5% answered with "never ever" when asked the exact same concern but with a 90% self-confidence instead. [85] [86] Further current AGI development considerations can be discovered above Tests for verifying human-level AGI.

A report by Stuart Armstrong and Kaj Sotala of the Machine Intelligence Research Institute 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 examined 95 predictions made in between 1950 and 2012 on when human-level AI will happen. [87]
In 2023, Microsoft researchers published an in-depth assessment of GPT-4. They concluded: "Given the breadth and depth of GPT-4's capabilities, our company believe that it could fairly be viewed as an early (yet still insufficient) variation of a synthetic general intelligence (AGI) system." [88] Another research study in 2023 reported that GPT-4 outshines 99% of human beings on the Torrance tests of creativity. [89] [90]
Blaise Agüera y Arcas and Peter Norvig composed in 2023 that a significant level of basic intelligence has already been attained with frontier models. They composed that hesitation to this view originates from four primary factors: a "healthy hesitation about metrics for AGI", an "ideological commitment to alternative AI theories or techniques", a "devotion to human (or biological) exceptionalism", or a "issue about the economic ramifications of AGI". [91]
2023 also marked the emergence of big multimodal models (big language designs capable of processing or producing several techniques such as text, audio, and images). [92]
In 2024, OpenAI launched o1-preview, the very first of a series of designs that "invest more time thinking before they react". According to Mira Murati, this ability to believe before reacting represents a new, additional paradigm. It improves design outputs by investing more computing power when creating the response, whereas the model scaling paradigm enhances outputs by increasing the design size, training data and training compute power. [93] [94]
An OpenAI worker, Vahid Kazemi, claimed in 2024 that the business had actually achieved AGI, stating, "In my opinion, we have already attained AGI and it's a lot more clear with O1." Kazemi clarified that while the AI is not yet "better than any human at any job", it is "better than most human beings at most jobs." He likewise resolved criticisms that big language models (LLMs) merely follow predefined patterns, comparing their learning process to the scientific method of observing, assuming, and verifying. These statements have actually triggered dispute, as they rely on a broad and unconventional definition of AGI-traditionally understood as AI that matches human intelligence throughout all domains. Critics argue that, while OpenAI's designs demonstrate impressive flexibility, they might not totally meet this requirement. Notably, Kazemi's remarks came quickly after OpenAI removed "AGI" from the terms of its partnership with Microsoft, prompting speculation about the business's strategic objectives. [95]
Timescales

Progress in artificial intelligence has historically gone through periods of rapid development 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 development. [82] [98] [99] For instance, the computer system hardware available in the twentieth century was not enough to implement deep learning, 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 built vary from 10 years to over a century. Since 2007 [update], the agreement in the AGI research study neighborhood appeared to be that the timeline talked about by Ray Kurzweil in 2005 in The Singularity is Near [102] (i.e. between 2015 and 2045) was plausible. [103] Mainstream AI researchers have actually given a wide variety of viewpoints on whether development will be this fast. A 2012 meta-analysis of 95 such viewpoints found a bias towards forecasting that the start of AGI would occur within 16-26 years for modern-day and historical forecasts alike. That paper has been criticized for how it classified opinions as professional or non-expert. [104]
In 2012, Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton established a neural network called AlexNet, which won the ImageNet competition with a top-5 test mistake rate of 15.3%, considerably much better than the second-best entry's rate of 26.3% (the conventional approach used a weighted amount of scores from various pre-defined classifiers). [105] AlexNet was considered as the preliminary ground-breaker of the existing deep learning wave. [105]
In 2017, scientists Feng Liu, Yong Shi, and Ying Liu performed intelligence tests on publicly readily available and easily available weak AI such as Google AI, Apple's Siri, and others. At the optimum, these AIs reached an IQ value of about 47, which corresponds approximately to a six-year-old kid in first grade. A grownup comes to about 100 typically. Similar tests were carried out in 2014, with the IQ score reaching a maximum worth of 27. [106] [107]
In 2020, OpenAI developed GPT-3, a language design capable of carrying out lots of diverse jobs without specific training. According to Gary Grossman in a VentureBeat short article, while there is agreement that GPT-3 is not an example of AGI, it is considered by some to be too advanced to be classified as a narrow AI system. [108]
In the exact same year, Jason Rohrer used his GPT-3 account to establish a chatbot, and provided a chatbot-developing platform called "Project December". OpenAI requested changes to the chatbot to abide by their security guidelines; Rohrer detached Project December from the GPT-3 API. [109]
In 2022, DeepMind established Gato, a "general-purpose" system efficient in performing more than 600 various jobs. [110]
In 2023, Microsoft Research released a research study on an early variation of OpenAI's GPT-4, competing that it exhibited more general intelligence than previous AI designs and demonstrated human-level performance in jobs spanning numerous domains, such as mathematics, coding, and law. This research study sparked an argument on whether GPT-4 could be considered an early, insufficient variation of artificial basic intelligence, highlighting the need for more expedition and evaluation of such systems. [111]
In 2023, the AI researcher Geoffrey Hinton stated that: [112]
The idea that this stuff could in fact get smarter than people - a few individuals thought that, [...] But many people thought it was way off. And I thought it was way off. I believed it was 30 to 50 years or perhaps longer away. Obviously, I no longer think that.

In May 2023, Demis Hassabis similarly said that "The development in the last few years has been pretty incredible", and that he sees no reason it would slow down, anticipating AGI within a years or even a couple of years. [113] In March 2024, Nvidia's CEO, Jensen Huang, specified his expectation that within five years, AI would can passing any test a minimum of in addition to people. [114] In June 2024, the AI scientist Leopold Aschenbrenner, a former OpenAI staff member, approximated AGI by 2027 to be "strikingly plausible". [115]
Whole brain emulation

While the advancement of transformer models like in ChatGPT is considered the most promising course to AGI, [116] [117] whole brain emulation can act as an alternative technique. With entire brain simulation, a brain design is developed by scanning and mapping a biological brain in information, and after that copying and imitating it on a computer system or another computational device. The simulation design should be adequately loyal to the original, so that it behaves in practically the exact same method as the initial brain. [118] Whole brain emulation is a kind of brain simulation that is discussed in computational neuroscience and neuroinformatics, and for medical research purposes. It has actually been gone over in expert system research [103] as an approach to strong AI. Neuroimaging innovations that might deliver the needed detailed understanding are enhancing quickly, and futurist Ray Kurzweil in the book The Singularity Is Near [102] anticipates that a map of enough quality will appear on a similar timescale to the computing power required to emulate it.

Early estimates

For low-level brain simulation, a very effective cluster of computers or GPUs would be needed, given the massive quantity of synapses within the human brain. Each of the 1011 (one hundred billion) neurons has on average 7,000 synaptic connections (synapses) to other neurons. The brain of a three-year-old child has about 1015 synapses (1 quadrillion). This number decreases with age, stabilizing by the adult years. Estimates differ 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 upon 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 numerous price quotes for the hardware required to equal the human brain and adopted a figure of 1016 computations per 2nd (cps). [e] (For comparison, if a "calculation" was equivalent to one "floating-point operation" - a measure used to rate current supercomputers - then 1016 "calculations" would be equivalent to 10 petaFLOPS, achieved in 2011, while 1018 was accomplished in 2022.) He utilized this figure to anticipate the required hardware would be available sometime between 2015 and 2025, if the rapid development in computer power at the time of composing continued.

Current research study

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

Criticisms of simulation-based methods

The artificial neuron model presumed by Kurzweil and used in numerous existing synthetic neural network applications is simple compared to biological neurons. A brain simulation would likely have to capture the comprehensive cellular behaviour of biological neurons, presently understood only in broad summary. 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 a number of orders of magnitude bigger than Kurzweil's quote. In addition, the quotes do not represent glial cells, which are known to contribute in cognitive procedures. [125]
An essential criticism of the simulated brain method stems from embodied cognition theory which asserts that human personification is an essential element of human intelligence and is essential to ground meaning. [126] [127] If this theory is proper, any totally practical brain design will require 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 alternative, but it is unknown whether this would be sufficient.

Philosophical viewpoint

"Strong AI" as defined in philosophy

In 1980, philosopher John Searle coined the term "strong AI" as part of his Chinese room argument. [128] He proposed a difference between two hypotheses about synthetic intelligence: [f]
Strong AI hypothesis: An artificial intelligence 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 awareness.
The first one he called "strong" due to the fact that it makes a more powerful statement: it assumes something special has taken place to the maker that surpasses those abilities that we can test. The behaviour of a "weak AI" device would be specifically identical to a "strong AI" machine, but the latter would likewise have subjective mindful experience. This usage is also typical in academic AI research study and books. [129]
In contrast to Searle and traditional AI, some futurists such as Ray Kurzweil utilize the term "strong AI" to mean "human level artificial basic intelligence". [102] This is not the same as Searle's strong AI, unless it is assumed that awareness is necessary for human-level AGI. Academic thinkers such as Searle do not believe that holds true, and to most synthetic intelligence researchers the question is out-of-scope. [130]
Mainstream AI is most thinking about 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 requirement to understand if it in fact has mind - undoubtedly, there would be no chance to tell. For AI research, Searle's "weak AI hypothesis" is equivalent to the statement "synthetic general intelligence is possible". Thus, according to Russell and Norvig, "most AI researchers take the weak AI hypothesis for approved, and don't care about the strong AI hypothesis." [130] Thus, for scholastic AI research study, "Strong AI" and "AGI" are 2 different things.

Consciousness

Consciousness can have different meanings, and some elements play substantial roles in sci-fi and the principles of synthetic intelligence:

Sentience (or "extraordinary consciousness"): The capability to "feel" understandings or emotions subjectively, as opposed to the capability to reason about understandings. Some thinkers, such as David Chalmers, utilize the term "awareness" to refer exclusively to incredible consciousness, which is roughly comparable to life. [132] Determining why and how subjective experience arises is known as the difficult issue of consciousness. [133] Thomas Nagel discussed in 1974 that it "seems like" something to be conscious. If we are not mindful, then it does not feel like anything. Nagel uses the example of a bat: we can smartly ask "what does it seem like to be a bat?" However, we are not likely to ask "what does it 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 claimed that the business's AI chatbot, LaMDA, had accomplished sentience, though this claim was extensively challenged by other experts. [135]
Self-awareness: To have mindful awareness of oneself as a different individual, especially to be knowingly conscious of one's own ideas. This is opposed to simply being the "subject of one's believed"-an operating system or debugger is able to be "mindful of itself" (that is, to represent itself in the exact same method it represents everything else)-however this is not what individuals usually imply when they use the term "self-awareness". [g]
These characteristics have an ethical measurement. AI life would offer rise to issues of welfare and legal defense, likewise to animals. [136] Other aspects of awareness associated to cognitive capabilities are also appropriate to the idea of AI rights. [137] Figuring out how to integrate innovative AI with existing legal and social structures is an emergent problem. [138]
Benefits

AGI might have a wide range of applications. If oriented towards such goals, AGI could help alleviate different issues in the world such as hunger, poverty and illness. [139]
AGI could enhance performance and efficiency in many jobs. For instance, in public health, AGI might accelerate medical research study, significantly versus cancer. [140] It might look after the elderly, [141] and democratize access to quick, top quality medical diagnostics. It could use enjoyable, cheap and customized education. [141] The requirement to work to subsist might end up being outdated if the wealth produced is properly redistributed. [141] [142] This also raises the question of the location of humans in a radically automated society.

AGI might likewise assist to make rational decisions, and to anticipate and prevent disasters. It might also assist to profit of possibly disastrous technologies such as nanotechnology or climate engineering, while avoiding the associated risks. [143] If an AGI's primary goal is to avoid existential disasters such as human extinction (which could be difficult if the Vulnerable World Hypothesis ends up being true), [144] it might take procedures to considerably decrease the risks [143] while lessening the effect of these measures on our quality of life.

Risks

Existential threats

AGI may represent multiple kinds of existential danger, which are risks that threaten "the early extinction of Earth-originating smart life or the long-term and extreme damage of its potential for desirable future advancement". [145] The danger of human extinction from AGI has actually been the subject of lots of arguments, but there is likewise the possibility that the advancement of AGI would lead to a completely problematic future. Notably, it might be used to spread and preserve the set of values of whoever develops it. If mankind still has moral blind areas similar to slavery in the past, AGI may irreversibly entrench it, preventing moral progress. [146] Furthermore, AGI could facilitate mass surveillance and brainwashing, which could be used to create a stable repressive worldwide totalitarian program. [147] [148] There is likewise a threat for the makers themselves. If machines that are sentient or otherwise worthy of moral factor to consider are mass created in the future, engaging in a civilizational course that indefinitely disregards their welfare and interests might be an existential catastrophe. [149] [150] Considering how much AGI might enhance mankind's future and assistance reduce other existential risks, Toby Ord calls these existential risks "an argument for continuing with due caution", not for "deserting AI". [147]
Risk of loss of control and human termination

The thesis that AI positions an existential threat for human beings, and that this danger requires more attention, is questionable but has been backed in 2023 by numerous public figures, AI scientists and CEOs of AI business such as Elon Musk, Bill Gates, Geoffrey Hinton, Yoshua Bengio, Demis Hassabis and Sam Altman. [151] [152]
In 2014, Stephen Hawking criticized extensive indifference:

So, facing possible futures of enormous benefits and risks, the specialists are certainly doing everything possible to guarantee the best result, right? Wrong. If a remarkable alien civilisation sent us a message stating, 'We'll arrive in a couple of years,' would we simply reply, '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 prospective fate of mankind has actually sometimes been compared to the fate of gorillas threatened by human activities. The contrast states that higher intelligence permitted mankind to dominate gorillas, which are now vulnerable in manner ins which they might not have prepared for. As an outcome, the gorilla has actually ended up being a threatened species, not out of malice, but just as a civilian casualties from human activities. [154]
The skeptic Yann LeCun considers that AGIs will have no desire to control mankind and that we must beware not to anthropomorphize them and interpret their intents as we would for people. He stated that people won't be "wise adequate to develop super-intelligent makers, yet extremely dumb to the point of offering it moronic goals without any safeguards". [155] On the other side, the principle of important convergence recommends that almost whatever their goals, smart representatives will have reasons to attempt to survive and acquire more power as intermediary steps to accomplishing these objectives. And that this does not need having feelings. [156]
Many scholars who are concerned about existential threat supporter for more research into solving the "control issue" to address the question: what kinds of safeguards, algorithms, or architectures can programmers execute to increase the likelihood that their recursively-improving AI would continue to behave in a friendly, instead of destructive, way after it reaches superintelligence? [157] [158] Solving the control issue is made complex by the AI arms race (which might cause a race to the bottom of security preventative measures in order to launch items before competitors), [159] and making use of AI in weapon systems. [160]
The thesis that AI can pose existential danger likewise has critics. Skeptics generally say that AGI is not likely in the short-term, or that issues about AGI sidetrack from other issues associated with existing AI. [161] Former Google scams czar Shuman Ghosemajumder thinks about that for lots of people beyond the innovation market, existing chatbots and LLMs are currently perceived as though they were AGI, causing additional misunderstanding and fear. [162]
Skeptics in some cases 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 scientists think that the communication campaigns on AI existential danger by specific AI groups (such as OpenAI, Anthropic, DeepMind, and Conjecture) might be an at effort at regulative capture and to pump up interest in their products. [164] [165]
In 2023, the CEOs of Google DeepMind, OpenAI and Anthropic, along with other industry leaders and researchers, released a joint statement asserting that "Mitigating the danger of termination from AI ought to be a worldwide concern along with other societal-scale dangers such as pandemics and nuclear war." [152]
Mass unemployment

Researchers from OpenAI estimated that "80% of the U.S. workforce might have at least 10% of their work tasks affected by the intro of LLMs, while around 19% of employees may see at least 50% of their tasks impacted". [166] [167] They consider office employees to be the most exposed, for instance mathematicians, accounting professionals or web designers. [167] AGI could have a much better autonomy, capability to make choices, to user interface with other computer tools, however likewise to control robotized bodies.

According to Stephen Hawking, the result of automation on the quality of life will depend upon how the wealth will be rearranged: [142]
Everyone can delight in a life of luxurious leisure if the machine-produced wealth is shared, or most individuals can wind up badly bad if the machine-owners effectively lobby against wealth redistribution. So far, the pattern seems to be towards the second alternative, with innovation driving ever-increasing inequality

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

Artificial brain - Software and hardware with cognitive abilities similar to those of the animal or human brain AI impact AI safety - Research area on making AI safe and helpful AI positioning - AI conformance to the desired goal A.I. Rising - 2018 film directed by Lazar Bodroža Expert system Automated artificial intelligence - Process of automating the application of maker learning BRAIN Initiative - Collaborative public-private research study effort revealed by the Obama administration China Brain Project Future of Humanity Institute - Defunct Oxford interdisciplinary research centre General video game playing - Ability of expert system to play different games Generative artificial intelligence - AI system efficient in generating material in response to triggers Human Brain Project - Scientific research task Intelligence amplification - Use of info innovation to augment human intelligence (IA). Machine principles - Moral behaviours of manufactured makers. Moravec's paradox. Multi-task learning - Solving several device discovering tasks at the exact same time. Neural scaling law - Statistical law in artificial intelligence. Outline of expert system - Overview of and topical guide to expert system. Transhumanism - Philosophical motion. Synthetic intelligence - Alternate term for or form of expert system. Transfer learning - Machine knowing strategy. Loebner Prize - Annual AI competitors. Hardware for expert system - Hardware specifically developed and optimized for artificial intelligence. Weak expert system - Form of expert system.
Notes

^ a b See below for the origin of the term "strong AI", and see the scholastic definition of "strong AI" and weak AI in the article Chinese space. ^ AI creator John McCarthy composes: "we can not yet identify in basic what kinds of computational procedures we wish to call smart. " [26] (For a conversation of some definitions of intelligence utilized by artificial intelligence scientists, see philosophy of expert system.). ^ The Lighthill report particularly criticized AI's "grandiose goals" and led the taking apart of AI research in England. [55] In the U.S., DARPA became identified to money only "mission-oriented direct research, rather than fundamental undirected research study". [56] [57] ^ As AI creator John McCarthy writes "it would be a terrific relief to the remainder of the employees in AI if the innovators of new general formalisms would express their hopes in a more safeguarded type than has actually in some cases been the case." [61] ^ In "Mind Children" [122] 1015 cps is used. 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 defined in a basic AI book: "The assertion that machines could perhaps act wisely (or, possibly much better, act as if they were intelligent) is called the 'weak AI' hypothesis by philosophers, and the assertion that devices that do so are in fact believing (instead of simulating 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 designed to carry out a single task. ^ "OpenAI Charter". OpenAI. Retrieved 6 April 2023. Our mission is to make sure that artificial basic intelligence benefits all of humanity. ^ Heath, Alex (18 January 2024). "Mark Zuckerberg's new goal is creating synthetic basic intelligence". The Verge. Retrieved 13 June 2024. Our vision is to build AI that is much better than human-level at all of the human senses. ^ Baum, Seth D. (2020 ). A Study of Artificial General Intelligence Projects for Ethics, Risk, and Policy (PDF) (Report). Global Catastrophic Risk Institute. Retrieved 28 November 2024. 72 AGI R&D projects were determined as being active in 2020. ^ a b c "AI timelines: What do experts in artificial intelligence anticipate for the future?". Our World in Data. Retrieved 6 April 2023. ^ Metz, Cade (15 May 2023). "Some Researchers Say A.I. Is Already Here, Stirring Debate in Tech Circles". The New York Times. Retrieved 18 May 2023. ^ "AI pioneer Geoffrey Hinton stops Google and cautions of risk ahead". The New York 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 experiments with GPT-4". arXiv preprint. arXiv:2303.12712. GPT-4 shows sparks 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 change modifications you. ^ Vinge, Vernor (1992 ). A Fire Upon the Deep. Tor Books. ISBN 978-0-8125-1528-2. The Singularity is coming. ^ Morozov, Evgeny (30 June 2023). "The True Threat of Artificial Intelligence". The New York City Times. The real risk is not AI itself however the method we deploy it. ^ "Impressed by artificial intelligence? Experts say AGI is following, and it has 'existential' risks". ABC News. 23 March 2023. Retrieved 6 April 2023. AGI might position existential risks to humankind. ^ Bostrom, Nick (2014 ). Superintelligence: Paths, Dangers, Strategies. Oxford University Press. ISBN 978-0-1996-7811-2. The first superintelligence will be the last invention that humanity requires to make. ^ Roose, Kevin (30 May 2023). "A.I. Poses 'Risk of Extinction,' Industry Leaders Warn". The New York City Times. Mitigating the threat of termination from AI should be an international top priority. ^ "Statement on AI Risk". Center for AI Safety. Retrieved 1 March 2024. AI specialists alert of danger of termination from AI. ^ Mitchell, Melanie (30 May 2023). "Are AI's Doomsday Scenarios Worth Taking Seriously?". The New York Times. We are far from creating makers that can outthink us in general ways. ^ LeCun, Yann (June 2023). "AGI does not present an existential risk". Medium. There is no reason 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 describes strong AI as "machine intelligence with the complete variety of human intelligence.". ^ "The Age of Expert System: George John at TEDxLondonBusinessSchool 2013". Archived from the initial on 26 February 2014. Retrieved 22 February 2014. ^ Newell & Simon 1976, This is the term they use for "human-level" intelligence in the physical sign system hypothesis. ^ "The Open University on Strong and Weak AI". Archived from the initial on 25 September 2009. Retrieved 8 October 2007. ^ "What is artificial superintelligence (ASI)?|Definition from TechTarget". Enterprise AI. Retrieved 8 October 2023. ^ "Artificial intelligence is transforming our world - it is on 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 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 intelligent 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 way we believe: a new view of intelligence (The MIT Press, 2007). ISBN 0-2621-6239-3. ^ White, R. W. (1959 ). "Motivation reconsidered: The idea of competence". Psychological Review. 66 (5 ): 297-333. doi:10.1037/ h0040934. PMID 13844397. S2CID 37385966. ^ White, R. W. (1959 ). "Motivation reconsidered: 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 initial on 25 April 2014. Retrieved 1 May 2014. ^ "What is Artificial General Intelligence (AGI)?|4 Tests For Ensuring Artificial General Intelligence". Talky Blog. 13 July 2019. Archived from the original on 17 July 2019. Retrieved 17 July 2019. ^ Kirk-Giannini, Cameron Domenico; Goldstein, Simon (16 October 2023). "AI is closer than ever to passing the Turing test for 'intelligence'. What takes place when it does?". The Conversation. Retrieved 22 September 2024. ^ a b Turing 1950. ^ Turing, Alan (1952 ). B. Jack Copeland (ed.). Can Automatic Calculating Machines Be Said To Think?. Oxford: Oxford University Press. pp. 487-506. ISBN 978-0-1982-5079-1. ^ "Eugene Goostman is a real young boy - the Turing Test states so". The Guardian. 9 June 2014. ISSN 0261-3077. Retrieved 3 March 2024. ^ "Scientists dispute 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 distinguish 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 whatever from the bar exam to AP Biology. Here's a list of difficult examinations both AI versions have passed". Business Insider. Retrieved 30 May 2023. ^ Naysmith, Caleb (7 February 2023). "6 Jobs Artificial Intelligence Is Already Replacing and How Investors Can Capitalize on It". Retrieved 30 May 2023. ^ Turk, Victoria (28 January 2015). "The Plan to Replace the Turing Test with a 'Turing Olympics'". Vice. Retrieved 3 March 2024. ^ Gopani, Avi (25 May 2022). "Turing Test is undependable. The Winograd Schema is outdated. Coffee is the response". Analytics India Magazine. Retrieved 3 March 2024. ^ Bhaimiya, Sawdah (20 June 2023). "DeepMind's co-founder recommended checking an AI chatbot's 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 ). "Expert System" (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). 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 ). "Artificial Intelligence, Business and Civilization - Our Fate Made in Machines". Archived from the initial on 6 May 2022. Retrieved 12 March 2022. ^ Simon 1965, p. 96 priced estimate in Crevier 1993, p. 109. ^ "Scientist on the Set: An Interview with Marvin Minsky". Archived from the initial on 16 July 2012. Retrieved 5 April 2008. ^ Marvin Minsky to Darrach (1970 ), 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 likewise Feigenbaum & McCorduck 1983. ^ Crevier 1993, pp. 161-162, 197-203, 240; Russell & Norvig 2003, p. 25. ^ Crevier 1993, pp. 209-212. ^ McCarthy, John (2000 ). "Respond to Lighthill". Stanford University. Archived from the initial on 30 September 2008. Retrieved 29 September 2007. ^ Markoff, John (14 October 2005). "Behind Artificial Intelligence, a Squadron of Bright Real People". The New York Times. Archived from the original on 2 February 2023. Retrieved 18 February 2017. At its low point, some computer system researchers and software engineers avoided the term 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 initial on 22 May 2019. Retrieved 7 May 2019. ^ a b Moravec 1988, p. 20 ^ Harnad, S. (1990 ). "The Symbol Grounding Problem". Physica D. 42 (1-3): 335-346. arXiv: cs/9906002. Bibcode:1990 PhyD ... 42..335 H. doi:10.1016/ 0167-2789( 90 )90087-6. S2CID 3204300. ^ Gubrud 1997 ^ Hutter, Marcus (2005 ). Universal Expert System: Sequential Decisions Based Upon Algorithmic Probability. Texts in Theoretical Computer Technology an EATCS Series. Springer. doi:10.1007/ b138233. ISBN 978-3-5402-6877-2. S2CID 33352850. Archived from the 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 Science. Vol. 8598. Journal of Artificial General Intelligence. doi:10.1007/ 978-3-319-09274-4. ISBN 978-3-3190-9273-7. S2CID 8387410. ^ "Who created the term "AGI"?". goertzel.org. Archived from the initial on 28 December 2018. Retrieved 28 December 2018., 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 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 original on 26 July 2020. Retrieved 11 May 2020. ^ "Избираеми дисциплини 2010/2011 - зимен триместър" [Elective courses 2010/2011 - winter trimester] Факултет по математика и информатика [Faculty of Mathematics and Informatics] (in Bulgarian). Archived from the original on 26 July 2020. Retrieved 11 May 2020. ^ Shevlin, Henry; Vold, Karina; Crosby, Matthew; Halina, Marta (4 October 2019). "The limits of maker intelligence: Despite development in device intelligence, artificial general intelligence is still a major difficulty". EMBO Reports. 20 (10 ): e49177. doi:10.15252/ embr.201949177. ISSN 1469-221X. PMC 6776890. PMID 31531926. ^ Bubeck, Sébastien; Chandrasekaran, Varun; Eldan, Ronen; Gehrke, Johannes; Horvitz, Eric; Kamar, Ece; Lee, Peter; Lee, Yin Tat; Li, Yuanzhi; Lundberg, Scott; Nori, Harsha; Palangi, Hamid; Ribeiro, Marco Tulio; Zhang, Yi (27 March 2023). "Sparks of Artificial General Intelligence: Early try outs GPT-4". arXiv:2303.12712 [cs.CL] ^ "Microsoft Researchers Claim GPT-4 Is Showing "Sparks" of AGI". Futurism. 23 March 2023. Retrieved 13 December 2023. ^ Allen, Paul; Greaves, Mark (12 October 2011). "The Singularity Isn't Near". MIT Technology Review. Retrieved 17 September 2014. ^ Winfield, Alan. "Artificial intelligence will not turn into a Frankenstein's monster". 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 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 damage?". The New Yorker. Archived from the initial on 28 January 2016. Retrieved 7 February 2016. ^ Müller, V. C., & Bostrom, N. (2016 ). Future development in expert system: A study of skilled viewpoint. 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 originality of machines: AI takes the Torrance Test". Journal of Creativity. 33 (3 ): 100065. doi:10.1016/ j.yjoc.2023.100065. ISSN 2713-3745. S2CID 261087185. ^ Arcas, Blaise Agüera y (10 October 2023). "Artificial General Intelligence Is Already Here". Noema. ^ Zia, Tehseen (8 January 2024). "Unveiling of Large Multimodal Models: Shaping the Landscape of Language Models in 2024". Unite.ai. Retrieved 26 May 2024. ^ "Introducing OpenAI o1-preview". OpenAI. 12 September 2024. ^ Knight, Will. "OpenAI Announces a 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 synthetic general intelligence with hybrid Tianjic chip architecture". Nature. 572 (7767 ): 106-111. Bibcode:2019 Natur.572..106 P. doi:10.1038/ s41586-019-1424-8. ISSN 1476-4687. PMID 31367028. S2CID 199056116. Archived from the initial on 29 August 2022. Retrieved 29 August 2022. ^ Pandey, Mohit; Fernandez, Michael; Gentile, Francesco; Isayev, Olexandr; Tropsha, Alexander; Stern, Abraham C.; Cherkasov, Artem (March 2022). "The transformational 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). 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 Expert System". 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 initial 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 initial 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 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, however the consensus is that it is not AGI. ... If absolutely nothing else, GPT-3 informs us there is a happy medium between narrow and general AI. ^ Quach, Katyanna. "A developer built an AI chatbot utilizing GPT-3 that helped a guy speak again to his late fiancée. OpenAI shut it down". The Register. Archived from the original on 16 October 2021. Retrieved 16 October 2021. ^ Wiggers, Kyle (13 May 2022), "DeepMind's brand-new AI can carry out over 600 tasks, from playing games to managing robots", TechCrunch, archived from the original on 16 June 2022, obtained 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 measure up to human intelligence in 'simply a couple of years,' states CEO of Google's main 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 specify Artificial General Intelligence". Fast Company. ^ Nosta, John (5 January 2024). "The Accelerating Path to Artificial General Intelligence". Psychology Today. Retrieved 30 March 2024. ^ Hickey, Alex. "Whole Brain Emulation: A Huge Step for Neuroscience". Tech Brew. Retrieved 8 November 2023. ^ Sandberg & Boström 2008. ^ Drachman 2005. ^ a b Russell & Norvig 2003. ^ Moravec 1988, p. 61. ^ Moravec 1998. ^ Holmgaard Mersh, Amalie (15 September 2023). "Decade-long European research study project maps the human brain". euractiv. ^ Swaminathan, Nikhil (January-February 2011). "Glia-the other brain cells". Discover. Archived from the initial on 8 February 2014. Retrieved 24 January 2014. ^ de Vega, Glenberg & Graesser 2008. A vast array of views in existing research, all of which require grounding to some degree ^ Thornton, Angela (26 June 2023). "How uploading our minds to a computer system might end up being 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 (quoted in "AITopics"),. Will Biological Computers Enable Artificially Intelligent Machines to Become Persons? Archived 13 May 2008 at the Wayback Machine Anthony Tongen.


^ a b c Russell & Norvig 2003, p. 947. ^ though see Explainable artificial intelligence for interest by the field about why a program acts the method it does. ^ Chalmers, David J. (9 August 2023). "Could a Big Language Model Be Conscious?". Boston Review. ^ Seth, Anil. "Consciousness". New Scientist. Retrieved 5 September 2024. ^ Nagel 1974. ^ "The Google engineer who thinks the business's AI has actually come to life". The Washington Post. 11 June 2022. Retrieved 12 June 2023. ^ Kateman, Brian (24 July 2023). "AI Should Be Terrified of Humans". TIME. Retrieved 5 September 2024. ^ Nosta, John (18 December 2023). "Should Expert System Have Rights?". Psychology Today. Retrieved 5 September 2024. ^ Akst, Daniel (10 April 2023). "Should Robots With Expert System Have Moral or Legal Rights?". The Wall Street Journal. ^ "Artificial General Intelligence - Do [es] the cost outweigh benefits?". 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 Artificial Intelligence Is Everywhere?". Smithsonian Magazine. Retrieved 7 June 2023. ^ a b Stevenson, Matt (8 October 2015). "Answers to Stephen Hawking's AMA are Here!". Wired. ISSN 1059-1028. Retrieved 8 June 2023. ^ a b Bostrom, Nick (2017 ). " § Preferred order of arrival". Superintelligence: paths, threats, techniques (Reprinted with corrections 2017 ed.). Oxford, UK; New York, New York, USA: Oxford University Press. ISBN 978-0-1996-7811-2. ^ Piper, Kelsey (19 November 2018). "How technological development is making it likelier than ever that human beings will destroy ourselves". Vox. Retrieved 8 June 2023. ^ Doherty, Ben (17 May 2018). "Climate change an 'existential security risk' to Australia, Senate query states". The Guardian. ISSN 0261-3077. Retrieved 16 July 2023. ^ MacAskill, William (2022 ). What we owe the future. New York, NY: Basic Books. ISBN 978-1-5416-1862-6. ^ a b Ord, Toby (2020 ). "Chapter 5: akropolistravel.com 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 takes a look at the ramifications of expert system - however are we taking AI seriously enough?'". The Independent (UK). Archived from the original on 25 September 2015. Retrieved 3 December 2014. ^ Herger, Mario. "The Gorilla Problem - Enterprise Garage". Retrieved 7 June 2023. ^ "The interesting Facebook dispute between Yann LeCun, Stuart Russel and Yoshua Bengio about the dangers of strong AI". The remarkable 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 danger: a survey". 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 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 presented however artificial intelligence can not be neglected". The Guardian. ISSN 0261-3077. Retrieved 24 December 2023. ^ "Humanity, Security & AI, Oh My! (with Ian Bremmer & Shuman Ghosemajumder)". CAFE. 20 July 2023. Retrieved 15 September 2023. ^ Hamblin, James (9 May 2014). "But What Would the End of Humanity Mean for Me?". The Atlantic. Archived from the initial on 4 June 2014. Retrieved 12 December 2015. ^ Titcomb, James (30 October 2023). "Big Tech is stoking fears over AI, caution researchers". The Telegraph. Retrieved 7 December 2023. ^ Davidson, John (30 October 2023). "Google Brain creator states huge tech is lying about AI extinction risk". 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 big language models". OpenAI. Retrieved 7 June 2023. ^ a b Hurst, Luke (23 March 2023). "OpenAI states 80% of employees could see their jobs affected by AI. These are the jobs most impacted". euronews. Retrieved 8 June 2023. ^ Sheffey, Ayelet (20 August 2021). "Elon Musk states we require universal standard earnings since 'in the future, physical work 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 initial on 18 June 2022. Retrieved 22 September 2021. Chalmers, David (1996 ), The Conscious Mind, Oxford University Press. Clocksin, William (August 2003), "Expert system and the future", Philosophical Transactions of the Royal Society A, vol. 361, no. 1809, pp. 1721-1748, Bibcode:2003 RSPTA.361.1721 C, doi:10.1098/ rsta.2003.1232, PMID 12952683, S2CID 31032007. Crevier, Daniel (1993 ). AI: The Tumultuous Search for 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 synthetic general intelligence and the possibility of a technological singularity: a reaction to Ray Kurzweil's The Singularity Is Near, and McDermott's review of Kurzweil", Expert system, vol. 171, no. 18, Special Review Issue, pp. 1161-1173, doi:10.1016/ j.artint.2007.10.011, archived from the initial on 7 January 2016, recovered 1 April 2009. Gubrud, Mark (November 1997), "Nanotechnology and International Security", Fifth Foresight Conference on Molecular Nanotechnology, archived from the original on 29 May 2011, obtained 7 May 2011. Howe, J. (November 1994), Expert System at Edinburgh University: a Point of view, archived from the initial on 17 August 2007, obtained 30 August 2007. Johnson, Mark (1987 ), The body in the mind, Chicago, ISBN 978-0-2264-0317-5. Kurzweil, Ray (2005 ), The Singularity is Near, Viking Press. Lighthill, Professor Sir James (1973 ), "Artificial Intelligence: A General Survey", Expert System: a paper symposium, Science Research Council. Luger, George; Stubblefield, William (2004 ), Expert System: Structures and Strategies for Complex Problem Solving (fifth ed.), The Benjamin/Cummings Publishing Company, Inc., p. 720, ISBN 978-0-8053-4780-7. McCarthy, John (2007b). What is Artificial Intelligence?. Stanford University. The supreme effort is to make computer programs that can fix problems and accomplish goals on the planet in addition to people. Moravec, Hans (1988 ), Mind Children, Harvard University Press Moravec, Hans (1998 ), "When will hardware match the human brain?", Journal of Evolution and Technology, vol. 1, archived from the original on 15 June 2006, obtained 23 June 2006 Nagel (1974 ), "What Is it Like to Be a Bat" (PDF), Philosophical Review, 83 (4 ): 435-50, doi:10.2307/ 2183914, JSTOR 2183914, archived (PDF) from the initial on 16 October 2011, retrieved 7 November 2009 Newell, Allen; Simon, H. A. (1976 ). "Computer Technology as Empirical Inquiry: Symbols and Search". Communications of the ACM. 19 (3 ): 113-126. doi:10.1145/ 360018.360022. Nilsson, Nils (1998 ), Expert System: A New Synthesis, Morgan Kaufmann Publishers, ISBN 978-1-5586-0467-4 NRC (1999 ), "Developments in Artificial Intelligence", Funding a Transformation: Government Support for Computing Research, National Academy Press, archived from the initial on 12 January 2008, retrieved 29 September 2007 Poole, David; Mackworth, Alan; Goebel, Randy (1998 ), Computational Intelligence: A Sensible Approach, New York: Oxford University Press, archived from the original on 25 July 2009, obtained 6 December 2007 Russell, Stuart J.; Norvig, Peter (2003 ), Expert System: A Modern Approach (second ed.), Upper Saddle River, New Jersey: Prentice Hall, ISBN 0-13-790395-2 Sandberg, Anders; Boström, Nick (2008 ), Whole Brain Emulation: A Roadmap (PDF), Technical Report # 2008-3, Future of Humanity Institute, Oxford University, archived (PDF) from the 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, obtained 3 September 2020 Simon, H. A. (1965 ), The Shape of Automation for Men and Management, New York City: Harper & Row Turing, Alan (October 1950). "Computing Machinery and Intelligence". Mind. 59 (236 ): 433-460. doi:10.1093/ mind/LIX.236.433. ISSN 1460-2113. JSTOR 2251299. S2CID 14636783.
de Vega, Manuel; Glenberg, Arthur; Graesser, Arthur, eds. (2008 ), Symbols and Embodiment: Debates on significance and cognition, Oxford University Press, ISBN 978-0-1992-1727-4 Wang, Pei; Goertzel, Ben (2007 ). "Introduction: Aspects of Artificial General Intelligence". Advances in Artificial General Intelligence: Concepts, Architectures and Algorithms: Proceedings of the AGI Workshop 2006. IOS Press. pp. 1-16. ISBN 978-1-5860-3758-1. Archived from the initial on 18 February 2021. Retrieved 13 December 2020 - via ResearchGate.
Further reading

Aleksander, Igor (1996 ), Impossible Minds, World Scientific Publishing Company, ISBN 978-1-8609-4036-1 Azevedo FA, Carvalho LR, Grinberg LT, Farfel J, et al. (April 2009), "Equal varieties of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain", The Journal of Comparative Neurology, 513 (5 ): 532-541, doi:10.1002/ cne.21974, PMID 19226510, S2CID 5200449, archived from the original on 18 February 2021, obtained 4 September 2013 - via ResearchGate Berglas, Anthony (January 2012) [2008], Artificial Intelligence Will Kill Our Grandchildren (Singularity), archived from the initial on 23 July 2014, retrieved 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 simple sufficient to be reasonable will not be made complex enough to behave intelligently, while any system made complex enough to behave wisely will be too made complex to comprehend." (p. 197.) Computer scientist Alex Pentland composes: "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 initial on 26 July 2010, retrieved 25 July 2010. Gleick, James, "The Fate of Free Will" (review of Kevin J. Mitchell, Free Agents: How Evolution Gave Us Free Will, 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 identifies us from machines. For biological animals, factor and purpose originate from acting in the world and experiencing the repercussions. Artificial intelligences - disembodied, complete strangers to blood, sweat, and tears - have no occasion for asteroidsathome.net 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: Residing In the Shadow of AI, Henry Holt, 311 pp.), The New York Review of Books, vol. LXXI, no. 17 (7 November 2024), pp. 44-46. "' We can't realistically expect that those who intend to get abundant from AI are going to have the interests of the rest of us close at heart,' ... composes [Gary Marcus] 'We can't count on federal governments driven by campaign financing contributions [from tech business] to press back.' ... Marcus information the needs that people must make of their governments and the tech business. They consist of openness on how AI systems work; payment for individuals if their information [are] used to train LLMs (big language model) s and the right to grant this usage; and the ability to hold tech business accountable for the harms they bring on by removing Section 230, enforcing cash penalites, and passing stricter item ... Marcus also suggests ... that a brand-new, AI-specific federal agency, akin to the FDA, the FCC, or the FTC, might supply the most robust oversight ... [T] he Fordham law professor Chinmayi Sharma ... recommends ... develop [ing] a professional licensing program for engineers that would function in a comparable method to medical licenses, malpractice fits, and the Hippocratic oath in medicine. 'What if, like medical professionals,' she asks ..., 'AI engineers also 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 stumped people for decades, reveals the restrictions of natural-language-processing algorithms", Scientific American, vol. 329, no. 4 (November 2023), pp. 81-82. "This murder mystery competitors has revealed that although NLP (natural-language processing) models can extraordinary accomplishments, their abilities are really much limited by the quantity of context they receive. This [...] might trigger [problems] for researchers who want to utilize them to do things such as examine ancient languages. In some cases, there are couple of historic records on long-gone civilizations to serve as training information for such a function." (p. 82.). Immerwahr, Daniel, "Your Lying Eyes: People now utilize A.I. to create fake videos identical from genuine ones. Just how much does it matter?", The New Yorker, 20 November 2023, pp. 54-59. "If by 'deepfakes' we suggest realistic videos produced using synthetic intelligence that really deceive individuals, then they hardly exist. The phonies aren't deep, and the deeps aren't phony. [...] A.I.-generated videos are not, in general, running in our media as counterfeited evidence. Their role much better looks like that of cartoons, particularly smutty ones." (p. 59.). - Leffer, Lauren, "The Risks of Trusting AI: We should prevent humanizing machine-learning models used in scientific research", Scientific American, vol. 330, no. 6 (June 2024), pp. 80-81. Lepore, Jill, "The Chit-Chatbot: Is talking with a device a conversation?", The New Yorker, 7 October 2024, pp. 12-16. Marcus, Gary, "Artificial Confidence: Even the latest, buzziest systems of synthetic basic intelligence are stymmied by the usual problems", Scientific American, vol. 327, no. 4 (October 2022), pp. 42-45. McCarthy, John (October 2007), "From here to human-level AI", 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, retrieved 29 September 2007. Newell, Allen; Simon, H. A. (1963 ), "GPS: A Program that Simulates Human Thought", in Feigenbaum, E. A.; Feldman, J. (eds.), Computers and Thought, New York City: McGraw-Hill. Omohundro, Steve (2008 ), The Nature of Self-Improving Expert system, 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 revealed that intelligence can not be measured by IQ alone", Scientific American, vol. 329, no. 1 (July/August 2023), p. 7. "Despite its high IQ, ChatGPT stops working at jobs that require real humanlike reasoning or an understanding of the physical and social world ... ChatGPT appeared unable to factor rationally and tried to depend on its vast database of ... facts obtained from online texts. " - Scharre, Paul, "Killer Apps: The Real Dangers of an AI Arms Race", Foreign Affairs, vol. 98, no. 3 (May/June 2019), pp. 135-44. "Today's AI innovations are powerful however undependable. Rules-based systems can not deal with circumstances their developers did not expect. Learning systems are restricted by the information on which they were trained. AI failures have currently caused catastrophe. Advanced auto-pilot features in cars, although they carry out well in some circumstances, have actually driven cars and trucks without warning into trucks, concrete barriers, and parked automobiles. In the wrong situation, AI systems go from supersmart to superdumb in an instant. When an enemy is trying to manipulate 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 brand-new innovations however depend on the timelelss human tendency to anthropomorphise." (p. 29.). Williams, R. W.; Herrup, K.