Artificial general intelligence (AGI) is a type of synthetic intelligence (AI) that matches or surpasses human cognitive capabilities throughout a large range of cognitive jobs. This contrasts with narrow AI, which is limited to specific tasks. [1] Artificial superintelligence (ASI), on the other hand, refers to AGI that greatly exceeds human cognitive abilities. AGI is considered among the definitions of strong AI.

Creating AGI is a primary goal of AI research study and of companies such as OpenAI [2] and Meta. [3] A 2020 survey identified 72 active AGI research study and development projects throughout 37 nations. [4]
The timeline for achieving AGI stays a subject of continuous debate amongst researchers and experts. As of 2023, some argue that it may be possible in years or years; others maintain it may take a century or longer; a minority believe it may never ever be achieved; and another minority declares that it is already here. [5] [6] Notable AI researcher Geoffrey Hinton has revealed concerns about the quick progress towards AGI, suggesting it could be attained quicker than lots of expect. [7]
There is argument on the specific meaning of AGI and regarding whether contemporary large language designs (LLMs) such as GPT-4 are early forms of AGI. [8] AGI is a common topic in science fiction and futures research studies. [9] [10]
Contention exists over whether AGI represents an existential threat. [11] [12] [13] Many experts on AI have actually stated that alleviating the risk of human extinction posed by AGI should be an international concern. [14] [15] Others find the advancement of AGI to be too remote to present such a threat. [16] [17]
Terminology

AGI is likewise referred to as strong AI, [18] [19] full AI, [20] human-level AI, [5] human-level smart AI, or general smart action. [21]
Some scholastic sources schedule the term "strong AI" for computer programs that experience sentience or consciousness. [a] On the other hand, weak AI (or narrow AI) is able to resolve one particular problem however lacks basic cognitive capabilities. [22] [19] Some scholastic sources utilize "weak AI" to refer more broadly to any programs that neither experience awareness nor have a mind in the very same sense as humans. [a]
Related principles include artificial superintelligence and transformative AI. An artificial superintelligence (ASI) is a hypothetical kind of AGI that is a lot more typically smart than human beings, [23] while the idea of transformative AI connects to AI having a large effect on society, for example, comparable to the farming or commercial transformation. [24]
A structure for categorizing AGI in levels was proposed in 2023 by Google DeepMind scientists. They define five levels of AGI: emerging, qualified, professional, virtuoso, and superhuman. For example, a qualified AGI is defined as an AI that surpasses 50% of knowledgeable grownups in a vast array of non-physical jobs, and a superhuman AGI (i.e. a synthetic superintelligence) is likewise specified but with a threshold of 100%. They think about big language designs 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 techniques. [b]
Intelligence traits

Researchers usually hold that intelligence is required to do all of the following: [27]
factor, use technique, fix puzzles, and make judgments under unpredictability represent understanding, consisting of typical sense knowledge strategy learn

• communicate in natural language
• if needed, incorporate these abilities in completion of any offered goal

Many interdisciplinary techniques (e.g. cognitive science, computational intelligence, and decision making) think about extra characteristics such as creativity (the capability to form novel mental images and ideas) [28] and autonomy. [29]
Computer-based systems that exhibit many of these capabilities exist (e.g. see computational creativity, automated reasoning, decision support group, robotic, evolutionary calculation, intelligent representative). There is debate about whether contemporary AI systems have them to an adequate degree.

Physical traits

Other capabilities are considered desirable in smart systems, as they may impact intelligence or help in its expression. These include: [30]
- the capability to sense (e.g. see, hear, etc), and - the capability to act (e.g. move and control objects, change area to check out, and so on).
This consists of the ability to detect and react to hazard. [31]
Although the ability to sense (e.g. see, hear, etc) and the ability to act (e.g. move and control items, change place to check out, and so on) can be preferable for some intelligent systems, [30] these physical capabilities are not strictly required for an entity to qualify as AGI-particularly under the thesis that big language designs (LLMs) might already be or become AGI. Even from a less optimistic perspective on LLMs, there is no firm requirement for an AGI to have a human-like kind; being a silicon-based computational system suffices, 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 been proscribed a specific physical embodiment and therefore does not demand a capacity for mobility or conventional "eyes and ears". [32]
Tests for human-level AGI

Several tests indicated to validate human-level AGI have been considered, consisting of: [33] [34]
The concept of the test is that the device needs to attempt and pretend to be a male, by answering concerns put to it, and it will only pass if the pretence is reasonably persuading. A considerable part of a jury, who should not be expert about devices, must be taken in by the pretence. [37]
AI-complete issues

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

However, numerous of these jobs can now be carried out by contemporary big language models. According to Stanford University's 2024 AI index, AI has actually reached human-level efficiency on numerous benchmarks for reading comprehension and visual thinking. [49]
History

Classical AI

Modern AI research started in the mid-1950s. [50] The first generation of AI researchers were persuaded that artificial basic intelligence was possible and that it would exist in just a few decades. [51] AI leader Herbert A. Simon composed in 1965: "devices will be capable, within twenty years, of doing any work a male can do." [52]
Their forecasts were the motivation for Stanley Kubrick and Arthur C. Clarke's character HAL 9000, who embodied what AI scientists believed they might develop by the year 2001. AI leader Marvin Minsky was a consultant [53] on the project of making HAL 9000 as practical as possible according to the agreement forecasts of the time. He said in 1967, "Within a generation ... the problem of developing 'artificial intelligence' will substantially be resolved". [54]
Several classical AI projects, such as Doug Lenat's Cyc task (that started in 1984), and Allen Newell's Soar project, were directed at AGI.

However, in the early 1970s, it ended up being apparent that scientists had actually grossly underestimated the trouble of the job. Funding agencies became hesitant of AGI and put scientists under increasing pressure to produce helpful "used AI". [c] In the early 1980s, Japan's Fifth Generation Computer Project revived interest in AGI, setting out a ten-year timeline that consisted of AGI goals like "continue a casual discussion". [58] In response to this and the success of professional systems, both market and federal government pumped cash into the field. [56] [59] However, self-confidence in AI marvelously collapsed in the late 1980s, and the goals of the Fifth Generation Computer Project were never fulfilled. [60] For the 2nd time in twenty years, AI researchers who forecasted the imminent achievement of AGI had actually been mistaken. By the 1990s, AI researchers had a reputation for making vain guarantees. They ended up being unwilling to make predictions at all [d] and avoided mention of "human level" synthetic intelligence for worry of being identified "wild-eyed dreamer [s]. [62]
Narrow AI research

In the 1990s and early 21st century, mainstream AI achieved commercial success and academic respectability by focusing on particular sub-problems where AI can produce proven results and industrial applications, such as speech acknowledgment and recommendation algorithms. [63] These "applied AI" systems are now utilized extensively throughout the technology market, and research in this vein is heavily moneyed in both academic community and industry. As of 2018 [update], development in this field was considered an emerging pattern, and a fully grown stage was expected to be reached in more than ten years. [64]
At the turn of the century, many traditional AI scientists [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 expert system will one day fulfill the traditional top-down path majority way, prepared to supply the real-world proficiency and the commonsense understanding that has actually been so frustratingly elusive in thinking programs. Fully intelligent devices will result when the metaphorical golden spike is driven joining the two efforts. [65]
However, even at the time, this was disputed. For example, Stevan Harnad of Princeton University concluded his 1990 paper on the sign grounding hypothesis by mentioning:

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

The term "artificial general intelligence" was used as early as 1997, by Mark Gubrud [67] in a conversation of the implications of completely automated military production and operations. A mathematical formalism of AGI was proposed by Marcus Hutter in 2000. Named AIXI, the proposed AGI representative increases "the capability to please goals in a vast array of environments". [68] This type of AGI, defined by the ability to increase a mathematical definition of intelligence instead of display human-like behaviour, [69] was likewise called universal expert system. [70]
The term AGI was re-introduced and promoted by Shane Legg and Ben Goertzel around 2002. [71] AGI research activity in 2006 was described by Pei Wang and Ben Goertzel [72] as "producing publications and initial outcomes". The very first summertime school in AGI was organized in Xiamen, China in 2009 [73] by the Xiamen university's Artificial Brain Laboratory and OpenCog. The very first university course was given up 2010 [74] and 2011 [75] at Plovdiv University, Bulgaria by Todor Arnaudov. MIT provided a course on AGI in 2018, organized by Lex Fridman and featuring a variety of guest speakers.

Since 2023 [upgrade], a little number of computer system researchers are active in AGI research, and numerous contribute to a series of AGI conferences. However, increasingly more researchers are interested in open-ended learning, [76] [77] which is the concept of permitting AI to continually learn and innovate like human beings do.

Feasibility

Since 2023, the advancement and prospective accomplishment of AGI remains a subject of extreme debate within the AI neighborhood. While conventional agreement held that AGI was a far-off goal, recent advancements have actually led some researchers and market figures to declare that early kinds of AGI may already exist. [78] AI leader Herbert A. Simon hypothesized in 1965 that "devices will be capable, within twenty years, of doing any work a guy can do". This prediction failed to come true. Microsoft co-founder Paul Allen thought that such intelligence is unlikely in the 21st century since it would require "unforeseeable and basically unpredictable advancements" and a "clinically 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 broad as the gulf in between current space flight and practical faster-than-light spaceflight. [80]
An additional difficulty is the lack of clarity in defining what intelligence requires. Does it need consciousness? Must it show the capability to set goals in addition to pursue them? Is it simply a matter of scale such that if design sizes increase sufficiently, intelligence will emerge? Are centers such as preparation, reasoning, and causal understanding needed? Does intelligence require clearly replicating the brain and its particular faculties? Does it require feelings? [81]
Most AI researchers think strong AI can be achieved in the future, however some thinkers, like Hubert Dreyfus and Roger Penrose, deny the possibility of attaining strong AI. [82] [83] John McCarthy is among those who believe human-level AI will be achieved, but that today level of development is such that a date can not accurately be predicted. [84] AI specialists' views on the expediency of AGI wax and wane. Four polls conducted in 2012 and 2013 suggested that the median quote amongst professionals for when they would be 50% positive AGI would get here was 2040 to 2050, depending upon the survey, with the mean being 2081. Of the specialists, 16.5% addressed with "never" when asked the same question but with a 90% self-confidence rather. [85] [86] Further current AGI progress 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 found that "over [a] 60-year amount of time there is a strong predisposition towards predicting the arrival of human-level AI as between 15 and 25 years from the time the forecast was made". They analyzed 95 predictions made between 1950 and 2012 on when human-level AI will come about. [87]
In 2023, Microsoft researchers published a detailed evaluation of GPT-4. They concluded: "Given the breadth and depth of GPT-4's capabilities, we believe that it could reasonably be considered as an early (yet still insufficient) version of an artificial basic intelligence (AGI) system." [88] Another study in 2023 reported that GPT-4 surpasses 99% of human beings on the Torrance tests of creativity. [89] [90]
Blaise Agüera y Arcas and Peter Norvig wrote in 2023 that a considerable level of basic intelligence has actually already been attained with frontier designs. They wrote that reluctance to this view originates from four primary factors: a "healthy apprehension about metrics for AGI", an "ideological commitment to alternative AI theories or techniques", a "commitment to human (or biological) exceptionalism", or a "issue about the economic implications of AGI". [91]
2023 likewise marked the emergence of big multimodal designs (large language designs capable of processing or generating multiple methods such as text, audio, and images). [92]
In 2024, OpenAI released o1-preview, the first of a series of designs that "spend more time believing before they react". According to Mira Murati, this ability to think before reacting represents a brand-new, extra paradigm. It enhances design outputs by investing more computing power when producing the response, whereas the design scaling paradigm enhances outputs by increasing the design size, training information and training compute power. [93] [94]
An OpenAI staff member, Vahid Kazemi, claimed in 2024 that the company had actually attained AGI, specifying, "In my viewpoint, we have actually already attained AGI and it's much more clear with O1." Kazemi clarified that while the AI is not yet "better than any human at any task", it is "better than the majority of human beings at many jobs." He likewise dealt with criticisms that big language designs (LLMs) simply follow predefined patterns, comparing their knowing process to the scientific method of observing, hypothesizing, and verifying. These statements have sparked dispute, as they count on a broad and non-traditional definition of AGI-traditionally comprehended as AI that matches human intelligence throughout all domains. Critics argue that, while OpenAI's designs demonstrate amazing flexibility, they might not fully fulfill this standard. Notably, Kazemi's comments came soon after OpenAI eliminated "AGI" from the regards to its partnership with Microsoft, triggering speculation about the business's strategic intents. [95]
Timescales

Progress in synthetic intelligence has actually traditionally gone through durations of fast development separated by durations when development appeared to stop. [82] Ending each hiatus were fundamental advances in hardware, software or both to develop area for further development. [82] [98] [99] For example, the hardware available in the twentieth century was not adequate to carry out deep learning, which needs great deals of GPU-enabled CPUs. [100]
In the intro to his 2006 book, [101] Goertzel says that quotes of the time required before a truly versatile AGI is developed differ from ten years to over a century. Since 2007 [update], the consensus in the AGI research neighborhood seemed to be that the timeline talked about by Ray Kurzweil in 2005 in The Singularity is Near [102] (i.e. between 2015 and 2045) was plausible. [103] Mainstream AI researchers have actually given a large range of viewpoints on whether progress will be this quick. A 2012 meta-analysis of 95 such opinions found a bias towards forecasting that the start of AGI would take place within 16-26 years for modern-day and historic predictions alike. That paper has been slammed for how it classified opinions as expert or non-expert. [104]
In 2012, Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton developed a neural network called AlexNet, which won the ImageNet competition with a top-5 test mistake rate of 15.3%, substantially better than the second-best entry's rate of 26.3% (the standard technique used a weighted amount of scores from different pre-defined classifiers). [105] AlexNet was regarded as the initial ground-breaker of the present deep knowing wave. [105]
In 2017, scientists Feng Liu, Yong Shi, and Ying Liu performed intelligence tests on openly offered and easily available weak AI such as Google AI, Apple's Siri, and others. At the maximum, these AIs reached an IQ value of about 47, which corresponds roughly to a six-year-old kid in first grade. An adult pertains to about 100 typically. Similar tests were performed in 2014, with the IQ score reaching a maximum worth of 27. [106] [107]
In 2020, OpenAI established GPT-3, a language model efficient in performing numerous diverse jobs without specific training. According to Gary Grossman in a VentureBeat short 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 categorized as a narrow AI system. [108]
In the very same year, Jason Rohrer used his GPT-3 account to establish a chatbot, and supplied a chatbot-developing platform called "Project December". OpenAI requested changes to the chatbot to adhere to their security guidelines; Rohrer disconnected Project December from the GPT-3 API. [109]
In 2022, DeepMind established Gato, a "general-purpose" system efficient in performing more than 600 different tasks. [110]
In 2023, Microsoft Research published a study on an early variation of OpenAI's GPT-4, competing that it displayed more general intelligence than previous AI models and demonstrated human-level efficiency in jobs covering several domains, such as mathematics, coding, and law. This research study sparked an argument on whether GPT-4 might be thought about an early, incomplete variation of artificial basic intelligence, emphasizing the requirement for more exploration and assessment of such systems. [111]
In 2023, the AI scientist Geoffrey Hinton specified that: [112]
The idea that this stuff might actually get smarter than individuals - a couple of people thought that, [...] But the majority of people thought it was way off. And I thought it was method off. I thought it was 30 to 50 years or even longer away. Obviously, I no longer think that.

In May 2023, Demis Hassabis similarly stated that "The development in the last few years has actually been quite amazing", and that he sees no reason that it would decrease, anticipating AGI within a years and even a couple of years. [113] In March 2024, Nvidia's CEO, Jensen Huang, specified his expectation that within 5 years, AI would can passing any test at least as well as humans. [114] In June 2024, the AI scientist Leopold Aschenbrenner, a previous OpenAI worker, estimated AGI by 2027 to be "strikingly possible". [115]
Whole brain emulation

While the development of transformer designs like in ChatGPT is thought about the most appealing course to AGI, [116] [117] entire brain emulation can act as an alternative technique. With entire brain simulation, a brain model is built by scanning and mapping a biological brain in detail, and after that copying and simulating it on a computer system or another computational device. The simulation design must be sufficiently faithful to the initial, so that it behaves in almost the exact same way as the original brain. [118] Whole brain emulation is a kind of brain simulation that is gone over in computational neuroscience and neuroinformatics, and for medical research purposes. It has actually been discussed in synthetic intelligence research study [103] as an approach to strong AI. Neuroimaging technologies that might provide the necessary comprehensive understanding are improving rapidly, and futurist Ray Kurzweil in the book The Singularity Is Near [102] forecasts that a map of adequate quality will appear on a comparable timescale to the computing power required to replicate it.

Early approximates

For low-level brain simulation, a very powerful cluster of computer systems or GPUs would be needed, offered the huge quantity of synapses within the human brain. Each of the 1011 (one hundred billion) nerve cells has on typical 7,000 synaptic connections (synapses) to other neurons. The brain of a three-year-old child has about 1015 synapses (1 quadrillion). This number decreases with age, stabilizing by their adult years. Estimates vary for an adult, varying from 1014 to 5 × 1014 synapses (100 to 500 trillion). [120] An estimate of the brain's processing power, based upon a basic switch design for nerve cell 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 needed to equate to the human brain and adopted a figure of 1016 computations per second (cps). [e] (For contrast, if a "calculation" was comparable to one "floating-point operation" - a step used to rate existing supercomputers - then 1016 "calculations" would be equivalent to 10 petaFLOPS, attained in 2011, while 1018 was attained in 2022.) He utilized this figure to anticipate the required hardware would be offered sometime between 2015 and 2025, if the rapid growth in computer power at the time of composing continued.

Current research

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

Criticisms of simulation-based approaches

The artificial nerve cell design presumed by Kurzweil and used in lots of present synthetic neural network executions is basic compared with biological nerve cells. A brain simulation would likely have to record the detailed cellular behaviour of biological nerve cells, currently understood only in broad outline. The overhead presented by complete modeling of the biological, chemical, and physical information of neural behaviour (specifically on a molecular scale) would need computational powers a number of orders of magnitude larger than Kurzweil's price quote. In addition, the price quotes do not represent glial cells, which are known to play a function in cognitive processes. [125]
A fundamental criticism of the simulated brain technique originates from embodied cognition theory which asserts that human personification is a vital aspect of human intelligence and is essential to ground meaning. [126] [127] If this theory is right, any totally functional brain design will require to encompass more than just the neurons (e.g., a robotic body). Goertzel [103] proposes virtual personification (like in metaverses like Second Life) as a choice, however it is unknown whether this would be sufficient.

Philosophical viewpoint

"Strong AI" as defined in viewpoint

In 1980, thinker John Searle created the term "strong AI" as part of his Chinese space argument. [128] He proposed a distinction in between 2 hypotheses about synthetic intelligence: [f]
Strong AI hypothesis: A synthetic intelligence system can have "a mind" and "consciousness". Weak AI hypothesis: An expert system system can (only) imitate it thinks and has a mind and awareness.
The first one he called "strong" since it makes a stronger declaration: it presumes something special has occurred to the device that surpasses those abilities that we can evaluate. The behaviour of a "weak AI" device would be precisely identical to a "strong AI" maker, however the latter would likewise have subjective conscious experience. This usage is likewise common in scholastic AI research and books. [129]
In contrast to Searle and mainstream AI, some futurists such as Ray Kurzweil utilize the term "strong AI" to mean "human level artificial general intelligence". [102] This is not the like Searle's strong AI, unless it is assumed that consciousness is required for human-level AGI. Academic theorists such as Searle do not think that is the case, and to most synthetic intelligence scientists the question 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 actually has mind - undoubtedly, there would be no chance to tell. For AI research, Searle's "weak AI hypothesis" is comparable to the declaration "artificial general intelligence is possible". Thus, according to Russell and Norvig, "most AI researchers take the weak AI hypothesis for approved, and don't care about the strong AI hypothesis." [130] Thus, for academic AI research, "Strong AI" and "AGI" are 2 various things.

Consciousness

Consciousness can have numerous meanings, and some elements play considerable functions in science fiction and the ethics of artificial intelligence:

Sentience (or "phenomenal consciousness"): The capability to "feel" perceptions or emotions subjectively, instead of the ability to factor about perceptions. Some philosophers, such as David Chalmers, utilize the term "awareness" to refer solely to incredible consciousness, which is approximately equivalent to life. [132] Determining why and how subjective experience occurs is known as the tough issue of awareness. [133] Thomas Nagel explained in 1974 that it "feels like" something to be mindful. If we are not mindful, then it does not seem 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 seems conscious (i.e., has awareness) but a toaster does not. [134] In 2022, a Google engineer claimed that the company's AI chatbot, LaMDA, had accomplished sentience, though this claim was commonly challenged by other specialists. [135]
Self-awareness: To have mindful awareness of oneself as a different individual, particularly to be purposely knowledgeable about one's own ideas. This is opposed to merely being the "subject of one's thought"-an operating system or debugger has the ability to be "familiar with itself" (that is, to represent itself in the exact same method it represents whatever else)-however this is not what individuals usually imply when they use the term "self-awareness". [g]
These characteristics have an ethical measurement. AI sentience would trigger issues of well-being and legal defense, similarly to animals. [136] Other elements of awareness associated to cognitive capabilities are also relevant to the concept of AI rights. [137] Finding out how to integrate innovative AI with existing legal and social frameworks is an emerging issue. [138]
Benefits

AGI could have a variety of applications. If oriented towards such goals, AGI could assist alleviate different issues in the world such as appetite, poverty and illness. [139]
AGI might improve productivity and effectiveness in the majority of jobs. For example, in public health, AGI could speed up medical research study, notably against cancer. [140] It could look after the senior, [141] and equalize access to rapid, high-quality medical diagnostics. It might offer fun, low-cost and tailored education. [141] The requirement to work to subsist could end up being obsolete if the wealth produced is effectively rearranged. [141] [142] This likewise raises the question of the place of human beings in a radically automated society.

AGI might likewise help to make reasonable choices, and to prepare for and prevent disasters. It might also help to gain the benefits of possibly devastating innovations such as nanotechnology or environment engineering, while avoiding the associated risks. [143] If an AGI's main objective is to prevent existential disasters such as human termination (which could be challenging if the Vulnerable World Hypothesis turns out to be real), [144] it could take measures to considerably decrease the threats [143] while reducing the impact of these steps on our quality of life.

Risks

Existential dangers

AGI might represent several types of existential threat, which are risks that threaten "the early termination of Earth-originating intelligent life or the irreversible and extreme damage of its potential for preferable future development". [145] The threat of human termination from AGI has actually been the topic of lots of disputes, however there is also the possibility that the advancement of AGI would cause a completely flawed future. Notably, it could be utilized to spread and preserve the set of values of whoever establishes it. If humanity still has ethical blind spots similar to slavery in the past, AGI may irreversibly entrench it, avoiding ethical progress. [146] Furthermore, AGI might facilitate mass monitoring and brainwashing, which could be used to develop a stable repressive worldwide totalitarian program. [147] [148] There is also a threat for the makers themselves. If makers that are sentient or otherwise deserving of ethical consideration are mass developed in the future, engaging in a civilizational course that indefinitely disregards their well-being and interests could be an existential disaster. [149] [150] Considering just how much AGI could enhance mankind's future and help decrease other existential threats, Toby Ord calls these existential dangers "an argument for continuing with due care", not for "deserting AI". [147]
Risk of loss of control and human extinction

The thesis that AI presents an existential danger for people, and that this risk needs more attention, is controversial however has actually been backed in 2023 by lots of public figures, AI researchers and CEOs of AI business such as Elon Musk, Bill Gates, Geoffrey Hinton, Yoshua Bengio, Demis Hassabis and Sam Altman. [151] [152]
In 2014, Stephen Hawking criticized widespread indifference:

So, dealing with possible futures of enormous benefits and dangers, the experts are undoubtedly doing whatever possible to make sure the best outcome, right? Wrong. If an exceptional alien civilisation sent us a message stating, 'We'll get here in a couple of decades,' would we simply respond, 'OK, call us when you get here-we'll leave the lights on?' Probably not-but this is basically what is taking place with AI. [153]
The prospective fate of humanity has actually sometimes been compared to the fate of gorillas threatened by human activities. The contrast specifies that higher intelligence permitted mankind to control gorillas, which are now susceptible in manner ins which they could not have actually expected. As a result, the gorilla has become an endangered species, not out of malice, but merely as a security damage from human activities. [154]
The skeptic Yann LeCun thinks about that AGIs will have no desire to dominate humanity which we must beware not to anthropomorphize them and interpret their intents as we would for humans. He stated that individuals will not be "wise enough to create super-intelligent devices, yet extremely silly to the point of offering it moronic goals without any safeguards". [155] On the other side, the idea of critical merging recommends that practically whatever their goals, smart agents will have factors to attempt to make it through and acquire more power as intermediary steps to attaining these objectives. And that this does not require having emotions. [156]
Many scholars who are worried about existential threat supporter for more research into solving the "control issue" to answer the question: what kinds of safeguards, algorithms, or architectures can developers implement to maximise the possibility that their recursively-improving AI would continue to act in a friendly, instead of devastating, way after it reaches superintelligence? [157] [158] Solving the control issue is complicated by the AI arms race (which could lead to a race to the bottom of safety precautions in order to release items before competitors), [159] and making use of AI in weapon systems. [160]
The thesis that AI can position existential threat likewise has critics. Skeptics normally state that AGI is not likely in the short-term, or that issues about AGI distract from other problems related to current AI. [161] Former Google fraud czar Shuman Ghosemajumder thinks about that for many individuals beyond the innovation market, existing chatbots and LLMs are already perceived as though they were AGI, resulting in further misconception and worry. [162]
Skeptics often charge that the thesis is crypto-religious, with an illogical belief in the possibility of superintelligence changing an irrational belief in a supreme God. [163] Some scientists think that the interaction projects on AI existential threat by certain AI groups (such as OpenAI, Anthropic, DeepMind, and Conjecture) might be an at attempt at regulatory capture and to inflate interest in their items. [164] [165]
In 2023, the CEOs of Google DeepMind, OpenAI and Anthropic, in addition to other market leaders and researchers, released a joint statement asserting that "Mitigating the danger of termination from AI ought to be a global priority together 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 jobs impacted by the introduction of LLMs, while around 19% of employees may see a minimum of 50% of their tasks impacted". [166] [167] They consider workplace workers to be the most exposed, for example mathematicians, accountants or web designers. [167] AGI might have a much better autonomy, capability to make decisions, to user interface with other computer tools, but also to manage robotized bodies.

According to Stephen Hawking, the outcome of automation on the quality of life will depend on how the wealth will be rearranged: [142]
Everyone can take pleasure in a life of glamorous leisure if the machine-produced wealth is shared, or the majority of people can end up badly bad if the machine-owners successfully lobby against wealth redistribution. So far, the trend seems to be toward the 2nd choice, with innovation driving ever-increasing inequality

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

Artificial brain - Software and hardware with cognitive capabilities similar to those of the animal or human brain AI impact AI security - Research location on making AI safe and beneficial AI positioning - AI conformance to the designated goal A.I. Rising - 2018 movie directed by Lazar Bodroža Expert system Automated artificial intelligence - Process of automating the application of machine knowing BRAIN Initiative - Collaborative public-private research initiative revealed by the Obama administration China Brain Project Future of Humanity Institute - Defunct Oxford interdisciplinary research study centre General video game playing - Ability of expert system to play various games Generative synthetic intelligence - AI system efficient in producing material in reaction to triggers Human Brain Project - Scientific research study project Intelligence amplification - Use of infotech to augment human intelligence (IA). Machine ethics - Moral behaviours of man-made machines. Moravec's paradox. Multi-task knowing - Solving several device discovering tasks at the same time. Neural scaling law - Statistical law in machine learning. Outline of expert system - Overview of and topical guide to artificial intelligence. Transhumanism - Philosophical movement. Synthetic intelligence - Alternate term for or kind of expert system. Transfer knowing - Machine learning technique. Loebner Prize - Annual AI competition. Hardware for artificial intelligence - Hardware specially designed and enhanced for expert system. Weak artificial intelligence - Form of synthetic intelligence.
Notes

^ a b See below for the origin of the term "strong AI", and see the scholastic meaning of "strong AI" and weak AI in the post Chinese room. ^ AI creator John McCarthy composes: "we can not yet characterize in general what type of computational procedures we want to call smart. " [26] (For a conversation of some meanings of intelligence utilized by expert system scientists, see viewpoint of expert system.). ^ The Lighthill report specifically slammed AI's "grand objectives" and led the taking apart of AI research study in England. [55] In the U.S., DARPA ended up being determined to money just "mission-oriented direct research, rather than fundamental undirected research". [56] [57] ^ As AI creator John McCarthy composes "it would be a fantastic relief to the rest of the workers in AI if the creators of new general formalisms would reveal their hopes in a more secured kind than has actually in some cases held true." [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 presented. ^ As specified in a standard AI textbook: "The assertion that makers could perhaps act wisely (or, possibly better, act as if they were intelligent) is called the 'weak AI' hypothesis by philosophers, and the assertion that machines that do so are really thinking (instead of mimicing thinking) is called the 'strong AI' hypothesis." [121] ^ Alan Turing made this point in 1950. [36] References

^ Krishna, Sri (9 February 2023). "What is synthetic narrow intelligence (ANI)?". VentureBeat. Retrieved 1 March 2024. ANI is designed to perform a single task. ^ "OpenAI Charter". OpenAI. Retrieved 6 April 2023. Our mission is to guarantee that artificial basic intelligence advantages all of humankind. ^ Heath, Alex (18 January 2024). "Mark Zuckerberg's new goal is developing artificial basic intelligence". The Verge. Retrieved 13 June 2024. Our vision is to construct AI that is much better than human-level at all of the human senses. ^ Baum, Seth D. (2020 ). A Study of Artificial General Intelligence Projects for Ethics, Risk, and Policy (PDF) (Report). Global Catastrophic Risk Institute. Retrieved 28 November 2024. 72 AGI R&D projects were identified as being active in 2020. ^ a b c "AI timelines: surgiteams.com What do professionals in expert system anticipate for the future?". Our World in Data. Retrieved 6 April 2023. ^ Metz, Cade (15 May 2023). "Some Researchers Say A.I. Is Already Here, Stirring Debate in Tech Circles". The New York City Times. Retrieved 18 May 2023. ^ "AI leader Geoffrey Hinton quits Google and cautions of danger ahead". The New York Times. 1 May 2023. Retrieved 2 May 2023. It is tough to see how you can prevent the bad stars from utilizing 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 reveals triggers of AGI. ^ Butler, Octavia E. (1993 ). Parable of the Sower. Grand Central Publishing. ISBN 978-0-4466-7550-5. All that you touch you alter. All that you alter changes you. ^ Vinge, Vernor (1992 ). A Fire Upon the Deep. Tor Books. ISBN 978-0-8125-1528-2. The Singularity is coming. ^ Morozov, Evgeny (30 June 2023). "The True Threat of Expert System". The New York Times. The real danger is not AI itself however the way we release it. ^ "Impressed by expert system? Experts state AGI is following, and it has 'existential' threats". ABC News. 23 March 2023. Retrieved 6 April 2023. AGI might posture existential threats to humankind. ^ Bostrom, Nick (2014 ). Superintelligence: Paths, Dangers, Strategies. Oxford University Press. ISBN 978-0-1996-7811-2. The very first superintelligence will be the last creation that mankind needs 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 must be a worldwide top priority. ^ "Statement on AI Risk". Center for AI Safety. Retrieved 1 March 2024. AI professionals alert of danger of termination from AI. ^ Mitchell, Melanie (30 May 2023). "Are AI's Doomsday Scenarios Worth Taking Seriously?". The New York City Times. We are far from producing makers that can outthink us in basic methods. ^ LeCun, Yann (June 2023). "AGI does not provide an existential threat". Medium. There is no factor 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 "device 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 utilize for "human-level" intelligence in the physical sign system hypothesis. ^ "The Open University on Strong and Weak AI". Archived from the original on 25 September 2009. Retrieved 8 October 2007. ^ "What is artificial superintelligence (ASI)?|Definition from TechTarget". Enterprise AI. Retrieved 8 October 2023. ^ "Artificial intelligence is changing our world - it is on all of us to make certain that it works out". Our World in Data. Retrieved 8 October 2023. ^ Dickson, Ben (16 November 2023). "Here is how far we are to accomplishing AGI, according to DeepMind". VentureBeat. ^ McCarthy, John (2007a). "Basic Questions". Stanford University. Archived from the original on 26 October 2007. Retrieved 6 December 2007. ^ This list of smart characteristics is based upon the topics covered by significant AI textbooks, including: 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 think: a new view of intelligence (The MIT Press, 2007). ISBN 0-2621-6239-3. ^ White, R. W. (1959 ). "Motivation reconsidered: timeoftheworld.date The idea of proficiency". Psychological Review. 66 (5 ): 297-333. doi:10.1037/ h0040934. PMID 13844397. S2CID 37385966. ^ White, R. W. (1959 ). "Motivation reassessed: The 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 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 occurs when it does?". The Conversation. Retrieved 22 September 2024. ^ a b Turing 1950. ^ Turing, Alan (1952 ). B. Jack Copeland (ed.). Can Automatic Calculating Machines Be Said To Think?. Oxford: Oxford University Press. pp. 487-506. ISBN 978-0-1982-5079-1. ^ "Eugene Goostman is a genuine kid - the Turing Test states so". The Guardian. 9 June 2014. ISSN 0261-3077. Retrieved 3 March 2024. ^ "Scientists challenge whether computer system 'Eugene Goostman' passed Turing test". BBC News. 9 June 2014. Retrieved 3 March 2024. ^ Jones, Cameron R.; Bergen, Benjamin K. (9 May 2024). "People can not differentiate GPT-4 from a human in a Turing test". arXiv:2405.08007 [cs.HC] ^ Varanasi, Lakshmi (21 March 2023). "AI designs like ChatGPT and GPT-4 are acing everything from the bar examination to AP Biology. Here's a list of hard examinations both AI versions have actually passed". Business Insider. Retrieved 30 May 2023. ^ Naysmith, Caleb (7 February 2023). "6 Jobs Artificial Intelligence Is Already Replacing and How Investors Can Profit From It". Retrieved 30 May 2023. ^ Turk, Victoria (28 January 2015). "The Plan to Replace the Turing Test with a 'Turing Olympics'". Vice. Retrieved 3 March 2024. ^ Gopani, Avi (25 May 2022). "Turing Test is unreliable. The Winograd Schema is obsolete. Coffee is the answer". Analytics India Magazine. Retrieved 3 March 2024. ^ Bhaimiya, Sawdah (20 June 2023). "DeepMind's co-founder recommended testing an AI chatbot's capability to turn $100,000 into $1 million to determine human-like intelligence". Business Insider. Retrieved 3 March 2024. ^ Suleyman, Mustafa (14 July 2023). "Mustafa Suleyman: My brand-new Turing test would see if AI can make $1 million". MIT Technology Review. Retrieved 3 March 2024. ^ Shapiro, Stuart C. (1992 ). "Artificial Intelligence" (PDF). In Stuart C. Shapiro (ed.). Encyclopedia of Artificial Intelligence (Second ed.). New York: John Wiley. pp. 54-57. Archived (PDF) from the original on 1 February 2016. (Section 4 is on "AI-Complete Tasks".). ^ Yampolskiy, Roman V. (2012 ). Xin-She Yang (ed.). "Turing Test as a Specifying Feature of AI-Completeness" (PDF). Expert System, Evolutionary Computation and Metaheuristics (AIECM): 3-17. Archived (PDF) from the initial on 22 May 2013. ^ "AI Index: State of AI in 13 Charts". Stanford University Human-Centered Artificial Intelligence. 15 April 2024. Retrieved 27 May 2024. ^ Crevier 1993, pp. 48-50. ^ Kaplan, Andreas (2022 ). "Expert System, Business and Civilization - Our Fate Made in Machines". Archived from the 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 quote in Crevier (1993, p. 109). ^ Lighthill 1973; Howe 1994. ^ a b NRC 1999, "Shift to Applied Research Increases Investment". ^ Crevier 1993, pp. 115-117; Russell & Norvig 2003, pp. 21-22. ^ Crevier 1993, p. 211, Russell & Norvig 2003, p. 24 and see likewise Feigenbaum & McCorduck 1983. ^ Crevier 1993, pp. 161-162, 197-203, 240; Russell & Norvig 2003, p. 25. ^ Crevier 1993, pp. 209-212. ^ McCarthy, John (2000 ). "Reply to Lighthill". Stanford University. Archived from the initial on 30 September 2008. Retrieved 29 September 2007. ^ Markoff, John (14 October 2005). "Behind Artificial Intelligence, a Squadron of Bright Real People". The New York City Times. Archived from the original on 2 February 2023. Retrieved 18 February 2017. At its low point, some computer system scientists and software application engineers avoided the term artificial intelligence for worry of being viewed as wild-eyed dreamers. ^ Russell & Norvig 2003, pp. 25-26 ^ "Trends in the Emerging Tech Hype Cycle". Gartner Reports. Archived from the original on 22 May 2019. Retrieved 7 May 2019. ^ a b Moravec 1988, p. 20 ^ Harnad, S. (1990 ). "The Symbol Grounding Problem". Physica D. 42 (1-3): 335-346. arXiv: cs/9906002. Bibcode:1990 PhyD ... 42..335 H. doi:10.1016/ 0167-2789( 90 )90087-6. S2CID 3204300. ^ Gubrud 1997 ^ Hutter, Marcus (2005 ). Universal Expert System: Sequential Decisions Based on Algorithmic Probability. Texts in Theoretical Computer Technology an EATCS Series. Springer. doi:10.1007/ b138233. ISBN 978-3-5402-6877-2. S2CID 33352850. Archived from the original on 19 July 2022. Retrieved 19 July 2022. ^ Legg, Shane (2008 ). Machine Super Intelligence (PDF) (Thesis). University of Lugano. Archived (PDF) from the original on 15 June 2022. Retrieved 19 July 2022. ^ Goertzel, Ben (2014 ). Artificial General Intelligence. Lecture Notes in Computer Science. Vol. 8598. Journal of Artificial General Intelligence. doi:10.1007/ 978-3-319-09274-4. ISBN 978-3-3190-9273-7. S2CID 8387410. ^ "Who created the term "AGI"?". goertzel.org. Archived from the 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 summer 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 basic intelligence is still a major challenge". EMBO Reports. 20 (10 ): e49177. doi:10.15252/ embr.201949177. ISSN 1469-221X. PMC 6776890. PMID 31531926. ^ Bubeck, Sébastien; Chandrasekaran, Varun; Eldan, Ronen; Gehrke, Johannes; Horvitz, Eric; Kamar, Ece; Lee, Peter; Lee, Yin Tat; Li, Yuanzhi; Lundberg, Scott; Nori, Harsha; Palangi, Hamid; Ribeiro, Marco Tulio; Zhang, Yi (27 March 2023). "Sparks of Artificial General Intelligence: Early try outs GPT-4". arXiv:2303.12712 [cs.CL] ^ "Microsoft Researchers Claim GPT-4 Is Showing "Sparks" of AGI". Futurism. 23 March 2023. Retrieved 13 December 2023. ^ Allen, Paul; Greaves, Mark (12 October 2011). "The Singularity Isn't Near". MIT Technology Review. Retrieved 17 September 2014. ^ Winfield, Alan. "Artificial intelligence will not turn into a Frankenstein's beast". The Guardian. Archived from the original on 17 September 2014. Retrieved 17 September 2014. ^ Deane, George (2022 ). "Machines That Feel and Think: The Role of Affective Feelings and Mental Action in (Artificial) General Intelligence". Artificial Life. 28 (3 ): 289-309. doi:10.1162/ artl_a_00368. ISSN 1064-5462. PMID 35881678. S2CID 251069071. ^ a b c Clocksin 2003. ^ Fjelland, Ragnar (17 June 2020). "Why general artificial intelligence will not be understood". Humanities and Social Sciences Communications. 7 (1 ): 1-9. doi:10.1057/ s41599-020-0494-4. hdl:11250/ 2726984. ISSN 2662-9992. S2CID 219710554. ^ McCarthy 2007b. ^ Khatchadourian, Raffi (23 November 2015). "The Doomsday Invention: Will 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 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, modified by Jan Romportl, Pavel Ircing, Eva Žáčková, Michal Polák and Radek Schuster, 52-75. Plzeň: University of West Bohemia ^ "Microsoft Now Claims GPT-4 Shows 'Sparks' of General Intelligence". 24 March 2023. ^ Shimek, Cary (6 July 2023). "AI Outperforms Humans in Creativity Test". Neuroscience News. Retrieved 20 October 2023. ^ Guzik, Erik E.; Byrge, Christian; Gilde, Christian (1 December 2023). "The originality of machines: AI takes the Torrance Test". Journal of Creativity. 33 (3 ): 100065. doi:10.1016/ j.yjoc.2023.100065. ISSN 2713-3745. S2CID 261087185. ^ Arcas, Blaise Agüera y (10 October 2023). "Artificial General Intelligence Is Already Here". Noema. ^ Zia, Tehseen (8 January 2024). "Unveiling of Large Multimodal Models: Shaping the Landscape of Language Models in 2024". Unite.ai. Retrieved 26 May 2024. ^ "Introducing OpenAI o1-preview". OpenAI. 12 September 2024. ^ Knight, Will. "OpenAI Announces a 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 function of GPU computing and deep learning in drug discovery". Nature Machine Intelligence. 4 (3 ): 211-221. doi:10.1038/ s42256-022-00463-x. ISSN 2522-5839. S2CID 252081559. ^ Goertzel & Pennachin 2006. ^ a b c (Kurzweil 2005, p. 260). ^ a b c Goertzel 2007. ^ Grace, Katja (2016 ). "Error in Armstrong and Sotala 2012". AI Impacts (blog site). Archived from the initial on 4 December 2020. Retrieved 24 August 2020. ^ a b Butz, Martin V. (1 March 2021). "Towards Strong AI". KI - Künstliche Intelligenz. 35 (1 ): 91-101. doi:10.1007/ s13218-021-00705-x. ISSN 1610-1987. S2CID 256065190. ^ Liu, Feng; Shi, Yong; Liu, Ying (2017 ). "Intelligence Quotient and Intelligence Grade of Artificial Intelligence". Annals of Data Science. 4 (2 ): 179-191. arXiv:1709.10242. doi:10.1007/ s40745-017-0109-0. S2CID 37900130. ^ Brien, Jörn (5 October 2017). "Google-KI doppelt so schlau wie Siri" [Google AI is two times as clever as Siri - however 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 going into the AI twilight zone in between narrow and basic AI". VentureBeat. Archived from the initial on 4 September 2020. Retrieved 5 September 2020. Certainly, too, there are those who declare we are currently seeing an early example of an AGI system in the recently announced GPT-3 natural language processing (NLP) neural network. ... So is GPT-3 the very first example of an AGI system? This is arguable, but the consensus is that it is not AGI. ... If absolutely nothing else, GPT-3 tells us there is a happy medium between narrow and basic AI. ^ Quach, Katyanna. "A designer built an AI chatbot using GPT-3 that helped a male 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 new AI can perform over 600 tasks, from playing video games to managing robots", TechCrunch, archived from the initial on 16 June 2022, recovered 12 June 2022. ^ Bubeck, Sébastien; Chandrasekaran, Varun; Eldan, Ronen; Gehrke, Johannes; Horvitz, Eric; Kamar, Ece; Lee, Peter; Lee, Yin Tat; Li, Yuanzhi; Lundberg, Scott; Nori, Harsha; Palangi, Hamid; Ribeiro, Marco Tulio; Zhang, Yi (22 March 2023). "Sparks of Artificial General Intelligence: Early experiments with GPT-4". arXiv:2303.12712 [cs.CL] ^ Metz, Cade (1 May 2023). "' The Godfather of A.I.' Leaves Google and Warns of Danger Ahead". The New York City Times. ISSN 0362-4331. Retrieved 7 June 2023. ^ Bove, Tristan. "A.I. could rival human intelligence in 'just a few years,' says CEO of Google's main A.I. research laboratory". Fortune. Retrieved 4 September 2024. ^ Nellis, Stephen (2 March 2024). "Nvidia CEO says AI could pass human tests in five years". Reuters. ^ Aschenbrenner, Leopold. "SITUATIONAL AWARENESS, The Decade Ahead". ^ Sullivan, Mark (18 October 2023). "Why everybody seems 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 task 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 current research, all of which need grounding to some degree ^ Thornton, Angela (26 June 2023). "How uploading our minds to a computer 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 (priced quote in" Encyclopedia.com"),. MIT Encyclopedia of Cognitive Science Archived 19 July 2008 at the Wayback Machine (priced estimate in "AITopics"),. Will Biological Computers Enable Artificially Intelligent Machines to Become Persons? Archived 13 May 2008 at the Wayback Machine Anthony Tongen.


^ a b c Russell & Norvig 2003, p. 947. ^ though see Explainable artificial intelligence for interest by the field about why a program behaves the method it does. ^ Chalmers, David J. (9 August 2023). "Could a Large Language Model Be Conscious?". Boston Review. ^ Seth, Anil. "Consciousness". New Scientist. Retrieved 5 September 2024. ^ Nagel 1974. ^ "The Google engineer who thinks the business's AI has 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 Expert System Have Moral or Legal Rights?". The Wall Street Journal. ^ "Artificial General Intelligence - Do [es] the cost outweigh advantages?". 23 August 2021. Retrieved 7 June 2023. ^ "How we can Gain from Advancing Artificial General Intelligence (AGI) - Unite.AI". www.unite.ai. 7 April 2020. Retrieved 7 June 2023. ^ a b c Talty, Jules; Julien, Stephan. "What Will Our Society Look Like When Expert System Is Everywhere?". Smithsonian Magazine. Retrieved 7 June 2023. ^ a b Stevenson, Matt (8 October 2015). "Answers to Stephen Hawking's AMA are Here!". Wired. ISSN 1059-1028. Retrieved 8 June 2023. ^ a b Bostrom, Nick (2017 ). " § Preferred order of arrival". Superintelligence: paths, risks, methods (Reprinted with corrections 2017 ed.). Oxford, United Kingdom; New York City, New York City, USA: Oxford University Press. ISBN 978-0-1996-7811-2. ^ Piper, Kelsey (19 November 2018). "How technological development is making it likelier than ever that humans will destroy ourselves". Vox. Retrieved 8 June 2023. ^ Doherty, Ben (17 May 2018). "Climate alter an 'existential security threat' to Australia, Senate inquiry says". The Guardian. ISSN 0261-3077. Retrieved 16 July 2023. ^ MacAskill, William (2022 ). What we owe the future. New York City, NY: Basic Books. ISBN 978-1-5416-1862-6. ^ a b Ord, Toby (2020 ). "Chapter 5: Future Risks, Unaligned Artificial Intelligence". The Precipice: Existential Risk and the Future of Humanity. Bloomsbury Publishing. ISBN 978-1-5266-0021-9. ^ Al-Sibai, Noor (13 February 2022). "OpenAI Chief Scientist Says Advanced AI May Already Be Conscious". Futurism. Retrieved 24 December 2023. ^ Samuelsson, Paul Conrad (2019 ). "Artificial Consciousness: Our Greatest Ethical Challenge". Philosophy Now. Retrieved 23 December 2023. ^ Kateman, Brian (24 July 2023). "AI Should Be Terrified of Humans". TIME. Retrieved 23 December 2023. ^ Roose, Kevin (30 May 2023). "A.I. Poses 'Risk of Extinction,' Industry Leaders Warn". The New York 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 - but 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 fascinating Facebook dispute in between Yann LeCun, Stuart Russel and Yoshua Bengio about the dangers of strong AI". The remarkable Facebook dispute in between Yann LeCun, Stuart Russel and Yoshua Bengio about the dangers 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 catastrophic AGI threat: 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 dangers 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 hazard presented however artificial intelligence can not be disregarded". 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 original 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 big tech is lying about AI termination threat". Australian Financial Review. Archived from the original on 7 December 2023. Retrieved 7 December 2023. ^ Eloundou, Tyna; Manning, Sam; Mishkin, Pamela; Rock, Daniel (17 March 2023). "GPTs are GPTs: An early look at the labor market impact capacity of big language designs". OpenAI. Retrieved 7 June 2023. ^ a b Hurst, Luke (23 March 2023). "OpenAI says 80% of workers could see their tasks impacted by AI. These are the jobs most affected". 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 original on 18 June 2022. Retrieved 22 September 2021. Chalmers, David (1996 ), The Conscious Mind, Oxford University Press. Clocksin, William (August 2003), "Artificial intelligence and the future", Philosophical Transactions of the Royal Society A, vol. 361, no. 1809, pp. 1721-1748, Bibcode:2003 RSPTA.361.1721 C, doi:10.1098/ rsta.2003.1232, PMID 12952683, S2CID 31032007. Crevier, Daniel (1993 ). AI: The Tumultuous Look For Artificial Intelligence. New York City, NY: BasicBooks. ISBN 0-465-02997-3. Darrach, Brad (20 November 1970), "Meet Shakey, the First Electronic Person", Life Magazine, pp. 58-68. Drachman, D. (2005 ), "Do we have brain to spare?", Neurology, 64 (12 ): 2004-2005, doi:10.1212/ 01. WNL.0000166914.38327. BB, PMID 15985565, S2CID 38482114. Feigenbaum, Edward A.; McCorduck, Pamela (1983 ), The Fifth Generation: Expert System and Japan's Computer Challenge to the World, Michael Joseph, ISBN 978-0-7181-2401-4. Goertzel, Ben; Pennachin, Cassio, eds. (2006 ), Artificial General Intelligence (PDF), Springer, ISBN 978-3-5402-3733-4, archived from the original (PDF) on 20 March 2013. Goertzel, Ben (December 2007), "Human-level artificial basic intelligence and the possibility of a technological singularity: a reaction to Ray Kurzweil's The Singularity Is Near, and McDermott's critique of Kurzweil", Artificial Intelligence, vol. 171, no. 18, Special Review Issue, pp. 1161-1173, doi:10.1016/ j.artint.2007.10.011, archived from the original on 7 January 2016, retrieved 1 April 2009. Gubrud, Mark (November 1997), "Nanotechnology and International Security", Fifth Foresight Conference on Molecular Nanotechnology, archived from the initial on 29 May 2011, obtained 7 May 2011. Howe, J. (November 1994), Expert System at Edinburgh University: a Perspective, archived from the initial on 17 August 2007, recovered 30 August 2007. Johnson, Mark (1987 ), The body in the mind, Chicago, ISBN 978-0-2264-0317-5. Kurzweil, Ray (2005 ), The Singularity is Near, Viking Press. Lighthill, Professor Sir James (1973 ), "Artificial Intelligence: A General Survey", Artificial Intelligence: 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 resolve issues and achieve objectives in the world as well as humans. Moravec, Hans (1988 ), Mind Children, Harvard University Press Moravec, Hans (1998 ), "When will computer system hardware match the human brain?", Journal of Evolution and Technology, vol. 1, archived from the initial on 15 June 2006, recovered 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 ), 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, retrieved 29 September 2007 Poole, David; Mackworth, Alan; Goebel, Randy (1998 ), Computational Intelligence: A Rational Approach, New York: Oxford University Press, archived from the initial on 25 July 2009, retrieved 6 December 2007 Russell, Stuart J.; Norvig, Peter (2003 ), Expert System: A Modern Approach (2nd ed.), Upper Saddle River, New Jersey: Prentice Hall, ISBN 0-13-790395-2 Sandberg, Anders; Boström, Nick (2008 ), Whole Brain Emulation: A Roadmap (PDF), Technical Report # 2008-3, Future of Humanity Institute, Oxford University, archived (PDF) from the initial on 25 March 2020, 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 original on 17 March 2019, retrieved 3 September 2020 Simon, H. A. (1965 ), The Shape of Automation for Men and Management, New York City: Harper & Row Turing, Alan (October 1950). "Computing Machinery and Intelligence". Mind. 59 (236 ): 433-460. doi:10.1093/ mind/LIX.236.433. ISSN 1460-2113. JSTOR 2251299. S2CID 14636783.
de Vega, Manuel; Glenberg, Arthur; Graesser, Arthur, eds. (2008 ), Symbols and Embodiment: Debates on meaning and cognition, Oxford University Press, ISBN 978-0-1992-1727-4 Wang, Pei; Goertzel, Ben (2007 ). "Introduction: Aspects of Artificial General Intelligence". Advances in Artificial General Intelligence: Concepts, Architectures and Algorithms: Proceedings of the AGI Workshop 2006. IOS Press. pp. 1-16. ISBN 978-1-5860-3758-1. Archived from the initial on 18 February 2021. Retrieved 13 December 2020 - by means of ResearchGate.
Further reading

Aleksander, Igor (1996 ), Impossible Minds, World Scientific Publishing Company, ISBN 978-1-8609-4036-1 Azevedo FA, Carvalho LR, Grinberg LT, Farfel J, et al. (April 2009), "Equal 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 initial on 18 February 2021, retrieved 4 September 2013 - by means of ResearchGate Berglas, Anthony (January 2012) [2008], Artificial Intelligence Will Kill Our Grandchildren (Singularity), archived from the initial on 23 July 2014, obtained 31 August 2012 Cukier, Kenneth, "Ready for Robots? How to Think about the Future of AI", Foreign Affairs, vol. 98, no. 4 (July/August 2019), pp. 192-98. George Dyson, historian of computing, composes (in what might be called "Dyson's Law") that "Any system easy sufficient to be understandable will not be made complex enough to behave wisely, while any system complicated enough to act intelligently will be too complicated to comprehend." (p. 197.) Computer scientist Alex Pentland composes: "Current AI machine-learning algorithms are, at their core, dead simple foolish. They work, but they work by strength." (p. 198.). Gelernter, David, Dream-logic, the Internet and Artificial Thought, Edge, archived from the original 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 Choice, Princeton University Press, 2023, 333 pp.), The New York City Review of Books, vol. LXXI, no. 1 (18 January 2024), pp. 27-28, 30. "Agency is what differentiates us from makers. For creatures, factor and purpose originate from acting in the world and experiencing the consequences. Artificial intelligences - disembodied, strangers to blood, sweat, and tears - have no event 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 City Review of Books, vol. LXXI, no. 17 (7 November 2024), pp. 44-46. "' We can't realistically expect that those who wish to get abundant from AI are going to have the interests of the rest of us close at heart,' ... composes [Gary Marcus] 'We can't depend on governments driven by campaign finance contributions [from tech companies] to press back.' ... Marcus information the needs that residents must make of their governments and the tech companies. They include transparency on how AI systems work; compensation for people if their data [are] used to train LLMs (big language design) s and the right to authorization to this usage; and the capability to hold tech business accountable for the damages they bring on by removing Section 230, enforcing money penalites, and passing stricter item liability laws ... Marcus likewise recommends ... that a new, AI-specific federal company, comparable to the FDA, the FCC, or the FTC, may provide the most robust oversight ... [T] he Fordham law teacher Chinmayi Sharma ... suggests ... develop [ing] a professional licensing program for engineers that would function in a comparable way to medical licenses, malpractice fits, and the Hippocratic oath in medicine. 'What if, like physicians,' she asks ..., 'AI engineers likewise swore to do no harm?'" (p. 46.). Holte, R. C.; Choueiry, B. Y. (2003 ), "Abstraction and reformulation in artificial 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 puzzled human beings for decades, exposes the constraints of natural-language-processing algorithms", Scientific American, vol. 329, no. 4 (November 2023), pp. 81-82. "This murder mystery competition has actually exposed that although NLP (natural-language processing) designs are capable of amazing accomplishments, their capabilities are very much restricted by the amount of context they get. This [...] could cause [problems] for scientists who intend to utilize them to do things such as evaluate ancient languages. Sometimes, there are few historic records on long-gone civilizations to function as training data for such a function." (p. 82.). Immerwahr, Daniel, "Your Lying Eyes: People now utilize A.I. to create phony videos identical from real ones. How much does it matter?", The New Yorker, 20 November 2023, pp. 54-59. "If by 'deepfakes' we mean reasonable videos produced using artificial intelligence that actually deceive people, then they barely exist. The phonies aren't deep, and the deeps aren't fake. [...] A.I.-generated videos are not, in basic, 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 avoid humanizing machine-learning models utilized in clinical research", Scientific American, vol. 330, no. 6 (June 2024), pp. 80-81. Lepore, Jill, "The Chit-Chatbot: Is talking with a machine a conversation?", The New Yorker, 7 October 2024, pp. 12-16. Marcus, Gary, "Artificial Confidence: Even the newest, buzziest systems of artificial general intelligence are stymmied by the usual issues", Scientific American, vol. 327, no. 4 (October 2022), pp. 42-45. McCarthy, John (October 2007), "From here to human-level AI", Expert System, 171 (18 ): 1174-1182, doi:10.1016/ j.artint.2007.10.009. McCorduck, Pamela (2004 ), Machines Who Think (2nd ed.), Natick, Massachusetts: A. K. Peters, ISBN 1-5688-1205-1. Moravec, Hans (1976 ), The Role of Raw Power in Intelligence, archived from the original on 3 March 2016, retrieved 29 September 2007. Newell, Allen; Simon, H. A. (1963 ), "GPS: photorum.eclat-mauve.fr 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 Artificial Intelligence, provided and dispersed at the 2007 Singularity Summit, San Francisco, California. Press, Eyal, "In Front of Their Faces: forum.batman.gainedge.org Does facial-recognition innovation lead authorities to neglect inconsistent 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 stops working at jobs that require real humanlike reasoning or an understanding of the physical and social world ... ChatGPT seemed unable to factor rationally and tried to rely on its vast 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 effective but undependable. Rules-based systems can not handle scenarios their developers did not prepare for. Learning systems are limited by the information on which they were trained. AI failures have actually already resulted in disaster. Advanced autopilot features in vehicles, although they perform well in some situations, have driven cars and trucks without warning into trucks, concrete barriers, and parked cars and trucks. In the incorrect circumstance, AI systems go from supersmart to superdumb in an immediate. When an enemy is trying to manipulate and hack an AI system, the dangers 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 enabled by new innovations but depend on the timelelss human tendency to anthropomorphise." (p. 29.). Williams, R. W.; Herrup, K.