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

Creating AGI is a main goal of AI research study and of business such as OpenAI [2] and Meta. [3] A 2020 study determined 72 active AGI research and advancement projects throughout 37 countries. [4]
The timeline for attaining AGI stays a subject of ongoing argument among scientists and professionals. Since 2023, some argue that it might be possible in years or decades; others maintain it might take a century or longer; a minority think it may never ever be accomplished; and another minority declares that it is currently here. [5] [6] Notable AI scientist Geoffrey Hinton has actually expressed concerns about the fast development towards AGI, suggesting it could be achieved earlier than numerous anticipate. [7]
There is argument on the precise meaning of AGI and concerning whether contemporary big language models (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 danger. [11] [12] [13] Many experts on AI have mentioned that reducing the danger of human extinction posed by AGI ought to be an international priority. [14] [15] Others find the advancement of AGI to be too remote to present such a threat. [16] [17]
Terminology

AGI is also understood as strong AI, [18] [19] full AI, [20] human-level AI, [5] human-level smart AI, or general smart action. [21]
Some scholastic sources book the term "strong AI" for computer system programs that experience life or awareness. [a] In contrast, weak AI (or narrow AI) has the ability to resolve one specific issue however does not have general cognitive abilities. [22] [19] Some academic sources use "weak AI" to refer more broadly to any programs that neither experience awareness nor have a mind in the exact same sense as humans. [a]
Related ideas consist of artificial superintelligence and transformative AI. A synthetic superintelligence (ASI) is a hypothetical kind of AGI that is much more normally smart than people, [23] while the notion of transformative AI connects to AI having a big effect on society, for example, comparable to the farming or industrial revolution. [24]
A framework for categorizing AGI in levels was proposed in 2023 by Google DeepMind scientists. They specify 5 levels of AGI: emerging, proficient, specialist, virtuoso, and superhuman. For instance, a proficient AGI is specified as an AI that exceeds 50% of experienced adults in a wide variety of non-physical jobs, and a superhuman AGI (i.e. a synthetic superintelligence) is similarly defined but with a limit of 100%. They consider big language models like ChatGPT or LLaMA 2 to be instances of emerging AGI. [25]
Characteristics

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

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

• communicate in natural language
• if essential, incorporate these skills in completion of any offered objective

Many interdisciplinary techniques (e.g. cognitive science, computational intelligence, and decision making) think about additional characteristics such as imagination (the ability to form unique mental images and ideas) [28] and autonomy. [29]
Computer-based systems that display numerous of these capabilities exist (e.g. see computational imagination, automated reasoning, choice support group, robotic, evolutionary computation, intelligent agent). There is dispute about whether modern-day AI systems possess them to an appropriate degree.

Physical characteristics

Other capabilities are thought about preferable in intelligent systems, as they might affect intelligence or aid in its expression. These include: [30]
- the capability to sense (e.g. see, hear, etc), and - the ability to act (e.g. relocation and manipulate objects, modification location to check out, and so on).
This consists of the ability to discover and respond to hazard. [31]
Although the capability to sense (e.g. see, hear, etc) and the capability to act (e.g. relocation and control objects, modification location to explore, etc) can be preferable for some intelligent systems, [30] these physical abilities are not strictly required for an entity to certify as AGI-particularly under the thesis that large language designs (LLMs) may already be or become AGI. Even from a less optimistic viewpoint on LLMs, there is no firm requirement for an AGI to have a human-like type; being a silicon-based computational system suffices, supplied it can process input (language) from the external world in location of human senses. This analysis lines up with the understanding that AGI has actually never ever been proscribed a particular physical embodiment and hence does not demand a capacity for mobility or conventional "eyes and ears". [32]
Tests for human-level AGI

Several tests indicated to confirm human-level AGI have been considered, including: [33] [34]
The idea of the test is that the machine has to try and pretend to be a man, by addressing concerns put to it, and it will only pass if the pretence is reasonably persuading. A considerable portion of a jury, who ought to not be skilled about devices, should be taken in by the pretence. [37]
AI-complete problems

A problem is informally called "AI-complete" or "AI-hard" if it is believed that in order to fix it, one would need to carry out AGI, due to the fact that the service is beyond the capabilities of a purpose-specific algorithm. [47]
There are many issues that have been conjectured to need general intelligence to resolve along with humans. Examples consist of computer vision, natural language understanding, and dealing with unanticipated scenarios while resolving any real-world issue. [48] Even a particular job like translation requires a device to check out and write in both languages, follow the author's argument (reason), timeoftheworld.date comprehend the context (knowledge), and faithfully reproduce the author's initial intent (social intelligence). All of these problems need to be resolved all at once in order to reach human-level device performance.

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

Classical AI

Modern AI research study began in the mid-1950s. [50] The first generation of AI scientists were convinced that synthetic general intelligence was possible and that it would exist in just a couple of years. [51] AI pioneer Herbert A. Simon composed in 1965: "makers will be capable, within twenty years, of doing any work a man 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 create by the year 2001. AI leader Marvin Minsky was an expert [53] on the task of making HAL 9000 as reasonable as possible according to the agreement forecasts of the time. He stated in 1967, "Within a generation ... the problem of developing 'artificial intelligence' will considerably be resolved". [54]
Several classical AI projects, such as Doug Lenat's Cyc job (that began 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 underestimated the trouble of the job. Funding firms ended up being skeptical of AGI and put scientists under increasing pressure to produce beneficial "used AI". [c] In the early 1980s, Japan's Fifth Generation Computer Project restored interest in AGI, setting out a ten-year timeline that included AGI objectives like "carry on a casual conversation". [58] In reaction to this and the success of professional systems, both market and government pumped cash into the field. [56] [59] However, confidence in AI stunningly collapsed in the late 1980s, and the goals of the Fifth Generation Computer Project were never satisfied. [60] For the 2nd time in 20 years, AI researchers who anticipated the imminent accomplishment of AGI had actually been misinterpreted. By the 1990s, AI scientists had a credibility for making vain pledges. They became unwilling to make forecasts at all [d] and prevented mention of "human level" synthetic intelligence for fear of being identified "wild-eyed dreamer [s]. [62]
Narrow AI research study

In the 1990s and early 21st century, mainstream AI accomplished industrial success and academic respectability by focusing on particular sub-problems where AI can produce proven results and business applications, such as speech acknowledgment and suggestion algorithms. [63] These "applied AI" systems are now used extensively throughout the technology industry, and research study in this vein is greatly moneyed in both academia and industry. As of 2018 [upgrade], development in this field was considered an emerging pattern, and a fully grown stage was anticipated to be reached in more than ten years. [64]
At the turn of the century, lots of traditional AI scientists [65] hoped that strong AI could be developed by combining programs that fix various sub-problems. Hans Moravec wrote in 1988:

I am confident that this bottom-up path to synthetic intelligence will one day satisfy the standard top-down path more than half method, prepared to provide the real-world skills and the commonsense knowledge that has been so frustratingly elusive in thinking programs. Fully smart makers will result when the metaphorical golden spike is driven joining the 2 efforts. [65]
However, even at the time, this was contested. For example, Stevan Harnad of Princeton University concluded his 1990 paper on the sign grounding hypothesis by specifying:

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

The term "synthetic basic intelligence" was utilized as early as 1997, by Mark Gubrud [67] in a discussion of the implications of totally automated military production and operations. A mathematical formalism of AGI was proposed by Marcus Hutter in 2000. Named AIXI, the proposed AGI agent maximises "the capability to please objectives in a wide variety of environments". [68] This type of AGI, characterized by the capability to increase a mathematical definition of intelligence instead of exhibit human-like behaviour, [69] was likewise called universal expert system. [70]
The term AGI was re-introduced and popularized by Shane Legg and Ben Goertzel around 2002. [71] AGI research activity in 2006 was explained by Pei Wang and Ben Goertzel [72] as "producing publications and initial outcomes". The first summer school in AGI was arranged in Xiamen, China in 2009 [73] by the Xiamen university's Artificial Brain Laboratory and OpenCog. The very first university course was provided in 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 number of visitor speakers.

Since 2023 [update], a little number of computer scientists are active in AGI research study, and lots of add to a series of AGI conferences. However, significantly more researchers are interested in open-ended knowing, [76] [77] which is the concept of permitting AI to continuously find out and innovate like people do.

Feasibility

As of 2023, the advancement and possible achievement of AGI remains a topic of extreme argument within the AI community. While standard agreement held that AGI was a remote objective, recent developments have actually led some scientists and industry figures to declare that early forms of AGI may currently exist. [78] AI leader 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 need "unforeseeable and essentially unforeseeable developments" and a "clinically deep understanding of cognition". [79] Writing in The Guardian, roboticist Alan Winfield claimed the gulf in between contemporary computing and human-level synthetic intelligence is as large as the gulf between existing area flight and useful faster-than-light spaceflight. [80]
A further challenge is the lack of clarity in specifying what intelligence entails. Does it require awareness? Must it show the capability to set goals along with pursue them? Is it simply a matter of scale such that if model sizes increase sufficiently, intelligence will emerge? Are centers such as preparation, reasoning, and causal understanding required? Does intelligence need explicitly reproducing the brain and its specific professors? Does it require feelings? [81]
Most AI researchers think strong AI can be accomplished in the future, but some thinkers, like Hubert Dreyfus and Roger Penrose, deny the possibility of accomplishing strong AI. [82] [83] John McCarthy is among those who think human-level AI will be achieved, but that today level of development is such that a date can not properly be anticipated. [84] AI professionals' views on the expediency of AGI wax and subside. Four surveys conducted in 2012 and 2013 suggested that the median price quote among professionals for when they would be 50% positive AGI would arrive was 2040 to 2050, depending upon the poll, with the mean being 2081. Of the experts, 16.5% responded to with "never" when asked the exact same concern however with a 90% confidence instead. [85] [86] Further existing AGI progress factors to consider can be found above Tests for validating human-level AGI.

A report by Stuart Armstrong and Kaj Sotala of the Machine Intelligence Research Institute discovered that "over [a] 60-year timespan there is a strong bias towards predicting the arrival of human-level AI as between 15 and 25 years from the time the forecast was made". They analyzed 95 forecasts made between 1950 and 2012 on when human-level AI will come about. [87]
In 2023, Microsoft researchers published a detailed assessment of GPT-4. They concluded: "Given the breadth and depth of GPT-4's abilities, our company believe that it could fairly be deemed an early (yet still incomplete) version of an artificial general intelligence (AGI) system." [88] Another study in 2023 reported that GPT-4 surpasses 99% of humans on the Torrance tests of creativity. [89] [90]
Blaise Agüera y Arcas and Peter Norvig wrote in 2023 that a significant level of basic intelligence has already been attained with frontier designs. They composed that reluctance to this view comes from 4 main reasons: a "healthy hesitation about metrics for AGI", an "ideological dedication to alternative AI theories or techniques", a "commitment to human (or biological) exceptionalism", or a "issue about the economic implications of AGI". [91]
2023 also marked the development of big multimodal models (large language models capable of processing or producing several methods such as text, audio, and images). [92]
In 2024, OpenAI released o1-preview, the very first of a series of designs that "invest more time believing before they respond". According to Mira Murati, this ability to believe before reacting represents a brand-new, extra paradigm. It enhances model outputs by spending more computing power when producing the answer, whereas the model scaling paradigm improves outputs by increasing the design size, training information and training calculate power. [93] [94]
An OpenAI staff member, Vahid Kazemi, declared in 2024 that the company had accomplished AGI, stating, "In my viewpoint, we have already attained AGI and it's a lot more clear with O1." Kazemi clarified that while the AI is not yet "much better than any human at any task", it is "much better than most human beings at a lot of tasks." He also attended to criticisms that big language designs (LLMs) simply follow predefined patterns, comparing their knowing process to the scientific method of observing, assuming, and confirming. These statements have stimulated dispute, as they depend on a broad and non-traditional 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 may not fully fulfill this requirement. Notably, Kazemi's comments came quickly after OpenAI eliminated "AGI" from the regards to its collaboration with Microsoft, prompting speculation about the business's strategic intentions. [95]
Timescales

Progress in expert system has actually historically gone through durations of fast progress separated by durations when development appeared to stop. [82] Ending each hiatus were basic advances in hardware, software or both to create area for further progress. [82] [98] [99] For example, the hardware offered in the twentieth century was not sufficient to carry out deep knowing, which requires 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 flexible AGI is developed differ from 10 years to over a century. As of 2007 [upgrade], the consensus in the AGI research community seemed to be that the timeline gone over by Ray Kurzweil in 2005 in The Singularity is Near [102] (i.e. in between 2015 and 2045) was plausible. [103] Mainstream AI researchers have given a broad variety of opinions on whether development will be this fast. A 2012 meta-analysis of 95 such opinions found a bias towards forecasting that the beginning of AGI would happen within 16-26 years for modern and historic forecasts alike. That paper has actually been criticized for how it classified viewpoints as expert or non-expert. [104]
In 2012, Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton established a neural network called AlexNet, which won the ImageNet competitors with a top-5 test error rate of 15.3%, considerably much better than the second-best entry's rate of 26.3% (the conventional technique utilized a weighted amount of scores from various pre-defined classifiers). [105] AlexNet was regarded as the preliminary ground-breaker of the current deep learning wave. [105]
In 2017, scientists Feng Liu, Yong Shi, and Ying Liu conducted intelligence tests on publicly available and freely accessible 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 very first grade. A grownup concerns about 100 typically. Similar tests were performed in 2014, with the IQ score reaching a maximum value of 27. [106] [107]
In 2020, OpenAI established GPT-3, a language design efficient in carrying out numerous varied jobs without specific training. According to Gary Grossman in a VentureBeat article, while there is agreement that GPT-3 is not an example of AGI, it is considered by some to be too advanced to be classified as a narrow AI system. [108]
In the same year, Jason Rohrer utilized his GPT-3 account to develop a chatbot, and supplied a chatbot-developing platform called "Project December". OpenAI asked for modifications to the chatbot to comply with their safety standards; 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 different tasks. [110]
In 2023, Microsoft Research released a research study on an early version of OpenAI's GPT-4, contending that it displayed more general intelligence than previous AI models and showed human-level efficiency in tasks spanning numerous domains, such as mathematics, coding, and law. This research study stimulated a debate on whether GPT-4 could be considered an early, insufficient variation of synthetic basic intelligence, highlighting the requirement for further expedition and evaluation of such systems. [111]
In 2023, the AI researcher Geoffrey Hinton specified that: [112]
The concept that this things might actually get smarter than individuals - a few 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 perhaps longer away. Obviously, I no longer believe that.

In May 2023, Demis Hassabis likewise stated that "The progress in the last few years has been pretty amazing", which he sees no factor why it would slow down, anticipating AGI within a years or even a couple of years. [113] In March 2024, Nvidia's CEO, Jensen Huang, mentioned his expectation that within 5 years, AI would be capable of passing any test at least along with humans. [114] In June 2024, the AI scientist Leopold Aschenbrenner, a former OpenAI employee, approximated AGI by 2027 to be "noticeably plausible". [115]
Whole brain emulation

While the development of transformer models like in ChatGPT is considered the most promising path to AGI, [116] [117] entire brain emulation can function as an alternative technique. With whole brain simulation, a brain design is developed by scanning and mapping a biological brain in detail, and after that copying and replicating it on a computer system or another computational gadget. The simulation design should be sufficiently devoted to the original, so that it behaves in practically the exact same method 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 functions. It has been talked about in synthetic intelligence research [103] as an approach to strong AI. Neuroimaging innovations that could provide the necessary detailed understanding are enhancing rapidly, and futurist Ray Kurzweil in the book The Singularity Is Near [102] predicts that a map of enough quality will end up being offered on a comparable timescale to the computing power needed to replicate it.

Early estimates

For low-level brain simulation, a really powerful cluster of computers or GPUs would be needed, offered the enormous amount 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 nerve cells. The brain of a three-year-old kid has about 1015 synapses (1 quadrillion). This number declines with age, supporting by adulthood. 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 nerve cell activity, is around 1014 (100 trillion) synaptic updates per second (SUPS). [121]
In 1997, Kurzweil took a look at different quotes for the hardware required to equal the human brain and embraced a figure of 1016 calculations per second (cps). [e] (For comparison, if a "calculation" was comparable to one "floating-point operation" - a procedure utilized to rate current supercomputers - then 1016 "calculations" would be equivalent to 10 petaFLOPS, attained in 2011, while 1018 was accomplished in 2022.) He utilized this figure to predict the necessary hardware would be readily available at some point in between 2015 and 2025, if the exponential development in computer system power at the time of writing continued.

Current research study

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

Criticisms of simulation-based methods

The synthetic nerve cell model presumed by Kurzweil and utilized in numerous existing synthetic neural network executions is simple compared to biological nerve cells. A brain simulation would likely need to catch the detailed cellular behaviour of biological neurons, currently understood just in broad outline. The overhead introduced by full modeling of the biological, chemical, and physical information of neural behaviour (especially on a molecular scale) would require computational powers several orders of magnitude larger than Kurzweil's price quote. In addition, the quotes do not represent glial cells, which are known to play a role in cognitive procedures. [125]
An essential criticism of the simulated brain method obtains from embodied cognition theory which asserts that human personification is an important element of human intelligence and is essential to ground meaning. [126] [127] If this theory is proper, any totally practical brain design will need to encompass more than just the nerve cells (e.g., a robotic body). Goertzel [103] proposes virtual personification (like in metaverses like Second Life) as a choice, but it is unknown whether this would suffice.

Philosophical perspective

"Strong AI" as defined in philosophy

In 1980, thinker John Searle coined the term "strong AI" as part of his Chinese space argument. [128] He proposed a difference in between 2 hypotheses about expert system: [f]
Strong AI hypothesis: A synthetic intelligence system can have "a mind" and "consciousness". Weak AI hypothesis: An artificial intelligence system can (just) imitate it believes and has a mind and consciousness.
The first one he called "strong" because it makes a stronger declaration: it presumes something unique has occurred to the maker that surpasses those capabilities that we can evaluate. The behaviour of a "weak AI" machine would be precisely identical to a "strong AI" machine, however 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 mainstream AI, some futurists such as Ray Kurzweil use the term "strong AI" to suggest "human level synthetic basic 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 holds true, and to most expert system researchers the concern 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 real or a simulation." [130] If the program can act as if it has a mind, then there is no need to understand if it in fact has mind - undoubtedly, there would be no other way to tell. For AI research study, Searle's "weak AI hypothesis" is comparable to the declaration "artificial basic intelligence is possible". Thus, according to Russell and Norvig, "most AI scientists take the weak AI hypothesis for approved, and don't care about the strong AI hypothesis." [130] Thus, for academic AI research, "Strong AI" and "AGI" are two various things.

Consciousness

Consciousness can have various meanings, and some elements play substantial functions in sci-fi and the ethics of expert system:

Sentience (or "extraordinary awareness"): The ability to "feel" understandings or feelings subjectively, as opposed to the capability to factor about perceptions. Some philosophers, such as David Chalmers, utilize the term "awareness" to refer exclusively to phenomenal consciousness, which is roughly comparable to sentience. [132] Determining why and how subjective experience develops is referred to as the tough problem of awareness. [133] Thomas Nagel discussed in 1974 that it "feels like" something to be mindful. If we are not mindful, then it doesn't 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 seems mindful (i.e., has consciousness) but a toaster does not. [134] In 2022, a Google engineer claimed that the business's AI chatbot, LaMDA, had achieved sentience, though this claim was extensively challenged by other specialists. [135]
Self-awareness: To have mindful awareness of oneself as a different individual, especially to be consciously aware of one's own thoughts. This is opposed to just being the "topic of one's believed"-an operating system or debugger has the ability to be "mindful of itself" (that is, to represent itself in the very same way it represents whatever else)-but this is not what individuals generally indicate when they use the term "self-awareness". [g]
These traits have a moral dimension. AI sentience would generate issues of well-being and legal protection, similarly to animals. [136] Other aspects of consciousness associated to cognitive abilities are likewise appropriate to the concept of AI rights. [137] Figuring out how to incorporate innovative AI with existing legal and social structures is an emerging problem. [138]
Benefits

AGI might have a wide array of applications. If oriented towards such goals, AGI could assist mitigate various problems on the planet such as appetite, poverty and illness. [139]
AGI might improve efficiency and performance in most jobs. For instance, in public health, AGI could speed up medical research, especially versus cancer. [140] It might take care of the senior, [141] and democratize access to fast, high-quality medical diagnostics. It could use fun, inexpensive and customized education. [141] The need to work to subsist could become obsolete if the wealth produced is effectively rearranged. [141] [142] This also raises the question of the location of human beings in a radically automated society.

AGI might also assist to make reasonable decisions, and to anticipate and prevent catastrophes. It could likewise assist to profit of possibly disastrous technologies such as nanotechnology or environment engineering, while preventing the associated threats. [143] If an AGI's main goal is to avoid existential catastrophes such as human termination (which could be difficult if the Vulnerable World Hypothesis ends up being real), [144] it might take steps to significantly minimize the threats [143] while minimizing the effect of these steps on our lifestyle.

Risks

Existential risks

AGI might represent multiple types of existential danger, which are risks that threaten "the premature termination of Earth-originating smart life or the irreversible and drastic destruction of its potential for desirable future development". [145] The threat of human termination from AGI has actually been the subject of many debates, however there is likewise the possibility that the development of AGI would cause a permanently flawed future. Notably, it could be used to spread out and protect the set of values of whoever develops it. If humankind still has moral blind areas similar to slavery in the past, AGI might irreversibly entrench it, avoiding ethical progress. [146] Furthermore, AGI could facilitate mass surveillance and indoctrination, which might be utilized to produce a steady repressive around the world totalitarian regime. [147] [148] There is also a risk for the devices themselves. If machines that are sentient or otherwise worthwhile of ethical factor to consider are mass developed in the future, engaging in a civilizational path that forever disregards their welfare and interests might be an existential disaster. [149] [150] Considering how much AGI might improve mankind's future and help in reducing other existential dangers, Toby Ord calls these existential threats "an argument for continuing with due caution", not for "abandoning AI". [147]
Risk of loss of control and human extinction

The thesis that AI positions an existential risk for people, and that this risk needs more attention, is questionable however has been backed in 2023 by numerous public figures, AI scientists and CEOs of AI companies 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, facing possible futures of enormous advantages and dangers, the specialists are definitely doing everything possible to ensure the very best outcome, right? Wrong. If a remarkable alien civilisation sent us a message saying, 'We'll arrive in a few decades,' would we just reply, 'OK, call us when you get here-we'll leave the lights on?' Probably not-but this is basically what is happening with AI. [153]
The possible fate of humanity has in some cases been compared to the fate of gorillas threatened by human activities. The contrast mentions that greater intelligence permitted humanity to control gorillas, which are now vulnerable in methods that they might not have prepared for. As an outcome, the gorilla has ended up being a threatened types, not out of malice, however simply as a security damage from human activities. [154]
The skeptic Yann LeCun thinks about that AGIs will have no desire to control humankind which we must be cautious not to anthropomorphize them and interpret their intents as we would for human beings. He said that individuals will not be "wise enough to create super-intelligent makers, yet ridiculously stupid to the point of offering it moronic objectives without any safeguards". [155] On the other side, the principle of critical convergence suggests that nearly whatever their objectives, smart agents will have reasons to try to make it through and obtain more power as intermediary actions to attaining these goals. Which this does not need having feelings. [156]
Many scholars who are concerned about existential danger advocate for more research study into fixing the "control problem" to answer the concern: what types of safeguards, algorithms, or architectures can developers carry out to increase the probability that their recursively-improving AI would continue to act in a friendly, rather than damaging, way after it reaches superintelligence? [157] [158] Solving the control issue is complicated by the AI arms race (which might lead to a race to the bottom of safety precautions in order to launch items before rivals), [159] and using AI in weapon systems. [160]
The thesis that AI can position existential threat also has detractors. Skeptics generally state that AGI is unlikely in the short-term, or that concerns about AGI distract from other concerns connected to present AI. [161] Former Google fraud czar Shuman Ghosemajumder thinks about that for many individuals beyond the innovation industry, existing chatbots and LLMs are already viewed as though they were AGI, causing more misunderstanding and fear. [162]
Skeptics sometimes charge that the thesis is crypto-religious, with an irrational belief in the possibility of superintelligence changing an irrational belief in an omnipotent God. [163] Some researchers think that the interaction campaigns on AI existential threat by specific AI groups (such as OpenAI, Anthropic, DeepMind, and Conjecture) may be an at attempt at regulative capture and to inflate interest in their items. [164] [165]
In 2023, the CEOs of Google DeepMind, OpenAI and Anthropic, in addition to other industry leaders and scientists, provided a joint declaration asserting that "Mitigating the threat of extinction from AI should be a worldwide priority alongside other societal-scale threats such as pandemics and nuclear war." [152]
Mass joblessness

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

According to Stephen Hawking, the result of automation on the lifestyle will depend on how the wealth will be rearranged: [142]
Everyone can delight in a life of luxurious leisure if the machine-produced wealth is shared, or the majority of people can end up badly poor if the machine-owners effectively lobby versus wealth redistribution. So far, the pattern seems to be towards the second option, with technology driving ever-increasing inequality

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

Artificial brain - Software and hardware with cognitive abilities comparable to those of the animal or human brain AI result AI safety - Research location on making AI safe and advantageous AI alignment - AI conformance to the intended objective A.I. Rising - 2018 movie directed by Lazar Bodroža Artificial intelligence Automated device learning - Process of automating the application of artificial intelligence BRAIN Initiative - Collaborative public-private research initiative revealed by the Obama administration China Brain Project Future of Humanity Institute - Defunct Oxford interdisciplinary research study centre General game playing - Ability of artificial intelligence to play various video games Generative synthetic intelligence - AI system efficient in generating content in response to prompts Human Brain Project - Scientific research study task Intelligence amplification - Use of details innovation to augment human intelligence (IA). Machine principles - Moral behaviours of man-made devices. Moravec's paradox. Multi-task knowing - Solving multiple machine finding out tasks at the exact same time. Neural scaling law - Statistical law in artificial intelligence. Outline of synthetic intelligence - Overview of and topical guide to artificial intelligence. Transhumanism - Philosophical movement. Synthetic intelligence - Alternate term for or type of artificial intelligence. Transfer knowing - Machine knowing method. Loebner Prize - Annual AI competition. Hardware for expert system - Hardware specially developed and enhanced for synthetic intelligence. Weak synthetic intelligence - Form of expert system.
Notes

^ a b See listed below for the origin of the term "strong AI", and see the scholastic definition of "strong AI" and weak AI in the short article Chinese room. ^ AI creator John McCarthy composes: "we can not yet define in general what kinds of computational procedures we wish to call intelligent. " [26] (For a discussion of some meanings of intelligence utilized by expert system researchers, see approach of expert system.). ^ The Lighthill report particularly criticized AI's "grandiose objectives" and led the dismantling of AI research in England. [55] In the U.S., DARPA became determined to fund only "mission-oriented direct research study, rather than basic undirected research". [56] [57] ^ As AI creator John McCarthy composes "it would be a great relief to the remainder of the workers in AI if the developers of brand-new general formalisms would reveal their hopes in a more safeguarded form than has often 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 basic AI book: "The assertion that devices could perhaps act wisely (or, maybe better, act as if they were intelligent) is called the 'weak AI' hypothesis by thinkers, and the assertion that devices that do so are actually thinking (as opposed to imitating thinking) is called the 'strong AI' hypothesis." [121] ^ Alan Turing made this point in 1950. [36] References

^ Krishna, Sri (9 February 2023). "What is synthetic narrow intelligence (ANI)?". VentureBeat. Retrieved 1 March 2024. ANI is designed to carry out a single job. ^ "OpenAI Charter". OpenAI. Retrieved 6 April 2023. Our mission is to ensure that synthetic general intelligence advantages all of humankind. ^ Heath, Alex (18 January 2024). "Mark Zuckerberg's brand-new goal is producing artificial general intelligence". The Verge. Retrieved 13 June 2024. Our vision is to construct AI that is better than human-level at all of the human senses. ^ Baum, Seth D. (2020 ). A Study of Artificial General Intelligence Projects for Ethics, Risk, and Policy (PDF) (Report). Global Catastrophic Risk Institute. Retrieved 28 November 2024. 72 AGI R&D projects were recognized as being active in 2020. ^ a b c "AI timelines: What do experts in expert system expect for the future?". Our World in Data. Retrieved 6 April 2023. ^ Metz, Cade (15 May 2023). "Some Researchers Say A.I. Is Already Here, Stirring Debate in Tech Circles". The New York Times. Retrieved 18 May 2023. ^ "AI pioneer Geoffrey Hinton quits Google and warns of danger ahead". The New York Times. 1 May 2023. Retrieved 2 May 2023. It is difficult to see how you can avoid the bad actors 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 shows stimulates of AGI. ^ Butler, Octavia E. (1993 ). Parable of the Sower. Grand Central Publishing. ISBN 978-0-4466-7550-5. All that you touch you alter. All that you change 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 genuine threat is not AI itself but the way we release it. ^ "Impressed by synthetic intelligence? Experts say AGI is coming next, and it has 'existential' threats". ABC News. 23 March 2023. Retrieved 6 April 2023. AGI might posture existential threats to humanity. ^ Bostrom, Nick (2014 ). Superintelligence: Paths, Dangers, Strategies. Oxford University Press. ISBN 978-0-1996-7811-2. The very first superintelligence will be the last innovation that humanity requires to make. ^ Roose, Kevin (30 May 2023). "A.I. Poses 'Risk of Extinction,' Industry Leaders Warn". The New York Times. Mitigating the threat of extinction from AI ought to be an international concern. ^ "Statement on AI Risk". Center for AI Safety. Retrieved 1 March 2024. AI experts alert of danger of extinction from AI. ^ Mitchell, Melanie (30 May 2023). "Are AI's Doomsday Scenarios Worth Taking Seriously?". The New York City Times. We are far from developing devices that can outthink us in general ways. ^ LeCun, Yann (June 2023). "AGI does not provide an existential threat". 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 "device intelligence with the complete 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 transforming our world - it is on everybody to make sure that it works out". Our World in Data. Retrieved 8 October 2023. ^ Dickson, Ben (16 November 2023). "Here is how far we are to attaining AGI, according to DeepMind". VentureBeat. ^ McCarthy, John (2007a). "Basic Questions". Stanford University. Archived from the initial on 26 October 2007. Retrieved 6 December 2007. ^ This list of intelligent traits is based upon the topics covered by significant AI textbooks, consisting of: Russell & Norvig 2003, Luger & Stubblefield 2004, Poole, Mackworth & Goebel 1998 and Nilsson 1998. ^ Johnson 1987. ^ de Charms, R. (1968 ). Personal causation. New York: Academic Press. ^ a b Pfeifer, R. and Bongard J. C., How the body forms the method we believe: a brand-new view of intelligence (The MIT Press, 2007). ISBN 0-2621-6239-3. ^ White, R. W. (1959 ). "Motivation reevaluated: The concept of skills". Psychological Review. 66 (5 ): 297-333. doi:10.1037/ h0040934. PMID 13844397. S2CID 37385966. ^ White, R. W. (1959 ). "Motivation reevaluated: The idea of skills". Psychological Review. 66 (5 ): 297-333. doi:10.1037/ h0040934. PMID 13844397. S2CID 37385966. ^ Muehlhauser, Luke (11 August 2013). "What is AGI?". Machine Intelligence Research Institute. Archived from the original on 25 April 2014. Retrieved 1 May 2014. ^ "What is Artificial General Intelligence (AGI)?|4 Tests For Ensuring Artificial General Intelligence". Talky Blog. 13 July 2019. Archived from the original on 17 July 2019. Retrieved 17 July 2019. ^ Kirk-Giannini, Cameron Domenico; Goldstein, Simon (16 October 2023). "AI is closer than ever to passing the Turing test for 'intelligence'. What occurs when it does?". The Conversation. Retrieved 22 September 2024. ^ a b Turing 1950. ^ Turing, Alan (1952 ). B. Jack Copeland (ed.). Can Automatic Calculating Machines Be Said To Think?. Oxford: Oxford University Press. pp. 487-506. ISBN 978-0-1982-5079-1. ^ "Eugene Goostman is a real boy - the Turing Test says so". The Guardian. 9 June 2014. ISSN 0261-3077. Retrieved 3 March 2024. ^ "Scientists contest 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 whatever from the bar examination to AP Biology. Here's a list of tough exams 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 suggested checking an AI chatbot's ability to turn $100,000 into $1 million to measure human-like intelligence". Business Insider. Retrieved 3 March 2024. ^ Suleyman, Mustafa (14 July 2023). "Mustafa Suleyman: My 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 City: John Wiley. pp. 54-57. Archived (PDF) from the initial on 1 February 2016. (Section 4 is on "AI-Complete Tasks".). ^ Yampolskiy, Roman V. (2012 ). Xin-She Yang (ed.). "Turing Test as a Defining Feature of AI-Completeness" (PDF). Artificial Intelligence, Evolutionary Computation and Metaheuristics (AIECM): 3-17. Archived (PDF) from the initial on 22 May 2013. ^ "AI Index: State of AI in 13 Charts". Stanford University Human-Centered Artificial Intelligence. 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 original on 16 July 2012. Retrieved 5 April 2008. ^ Marvin Minsky to Darrach (1970 ), priced estimate in Crevier (1993, p. 109). ^ Lighthill 1973; Howe 1994. ^ a b NRC 1999, "Shift to Applied Research Increases Investment". ^ Crevier 1993, pp. 115-117; Russell & Norvig 2003, pp. 21-22. ^ Crevier 1993, p. 211, Russell & Norvig 2003, p. 24 and see also Feigenbaum & McCorduck 1983. ^ Crevier 1993, pp. 161-162, 197-203, 240; Russell & Norvig 2003, p. 25. ^ Crevier 1993, pp. 209-212. ^ McCarthy, John (2000 ). "Reply to Lighthill". Stanford University. Archived from the initial on 30 September 2008. Retrieved 29 September 2007. ^ Markoff, John (14 October 2005). "Behind Artificial Intelligence, a Squadron of Bright Real People". The New York City Times. Archived from the initial on 2 February 2023. Retrieved 18 February 2017. At its low point, some computer scientists and software application engineers avoided the term synthetic intelligence for fear 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 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 Artificial Intelligence: 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., through Life 3.0: 'The term "AGI" was popularized by ... Shane Legg, Mark Gubrud and Ben Goertzel' ^ Wang & Goertzel 2007 ^ "First International Summer School in Artificial General Intelligence, Main summertime school: June 22 - July 3, 2009, OpenCog Lab: July 6-9, 2009". Archived from the initial on 28 September 2020. Retrieved 11 May 2020. ^ "Избираеми дисциплини 2009/2010 - пролетен триместър" [Elective courses 2009/2010 - spring trimester] Факултет по математика и информатика [Faculty of Mathematics and Informatics] (in Bulgarian). Archived from the initial on 26 July 2020. Retrieved 11 May 2020. ^ "Избир 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 limitations of machine intelligence: Despite development in device intelligence, artificial general intelligence is still a significant obstacle". 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 explores GPT-4". arXiv:2303.12712 [cs.CL] ^ "Microsoft Researchers Claim GPT-4 Is Showing "Sparks" of AGI". Futurism. 23 March 2023. Retrieved 13 December 2023. ^ Allen, Paul; Greaves, Mark (12 October 2011). "The Singularity Isn't Near". MIT Technology Review. Retrieved 17 September 2014. ^ Winfield, Alan. "Expert system will not become a Frankenstein's 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 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 synthetic intelligence bring us paradise or destruction?". The New Yorker. Archived from the initial on 28 January 2016. Retrieved 7 February 2016. ^ Müller, V. C., & Bostrom, N. (2016 ). Future development in synthetic intelligence: A survey of expert viewpoint. In Fundamental problems of expert system (pp. 555-572). Springer, Cham. ^ Armstrong, Stuart, and Kaj Sotala. 2012. "How We're Predicting AI-or Failing To." In Beyond AI: Artificial Dreams, edited by Jan Romportl, Pavel Ircing, Eva Žáčková, Michal Polák and Radek Schuster, 52-75. Plzeň: University of West Bohemia ^ "Microsoft Now Claims GPT-4 Shows 'Sparks' of General Intelligence". 24 March 2023. ^ Shimek, Cary (6 July 2023). "AI Outperforms Humans in Creativity Test". Neuroscience News. Retrieved 20 October 2023. ^ Guzik, Erik E.; Byrge, Christian; Gilde, Christian (1 December 2023). "The creativity of machines: AI takes the Torrance Test". Journal of Creativity. 33 (3 ): 100065. doi:10.1016/ j.yjoc.2023.100065. ISSN 2713-3745. S2CID 261087185. ^ Arcas, Blaise Agüera y (10 October 2023). "Artificial General Intelligence Is Already Here". Noema. ^ Zia, Tehseen (8 January 2024). "Unveiling of Large Multimodal Models: Shaping the Landscape of Language Models in 2024". Unite.ai. Retrieved 26 May 2024. ^ "Introducing OpenAI o1-preview". OpenAI. 12 September 2024. ^ Knight, Will. "OpenAI Announces a 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, utahsyardsale.com What, oke.zone and How of Artificial General Intelligence Chip Development". IEEE Transactions on Cognitive and Developmental Systems. 14 (2 ): 333-347. arXiv:2012.06338. doi:10.1109/ TCDS.2021.3069871. ISSN 2379-8920. S2CID 228376556. Archived from the initial on 28 August 2022. Retrieved 28 August 2022. ^ Pei, Jing; Deng, Lei; Song, Sen; Zhao, Mingguo; Zhang, Youhui; Wu, Shuang; Wang, Guanrui; Zou, Zhe; Wu, Zhenzhi; He, Wei; Chen, Feng; Deng, Ning; Wu, Si; Wang, Yu; Wu, Yujie (2019 ). "Towards synthetic basic intelligence with hybrid Tianjic chip architecture". Nature. 572 (7767 ): 106-111. Bibcode:2019 Natur.572..106 P. doi:10.1038/ s41586-019-1424-8. ISSN 1476-4687. PMID 31367028. S2CID 199056116. Archived from the 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 knowing 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 twice as smart as Siri - however a six-year-old beats both] (in German). Archived from the original on 3 January 2019. Retrieved 2 January 2019. ^ Grossman, Gary (3 September 2020). "We're entering the AI golden 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 declare 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 arguable, but the agreement is that it is not AGI. ... If absolutely nothing else, GPT-3 informs us there is a happy medium in between narrow and general AI. ^ Quach, Katyanna. "A developer constructed an AI chatbot utilizing GPT-3 that assisted a man speak once again to his late fiancée. OpenAI shut it down". The Register. Archived from the initial on 16 October 2021. Retrieved 16 October 2021. ^ Wiggers, Kyle (13 May 2022), "DeepMind's new AI can perform over 600 tasks, from playing video games to controlling 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 try outs GPT-4". arXiv:2303.12712 [cs.CL] ^ Metz, Cade (1 May 2023). "' The Godfather of A.I.' Leaves Google and Warns of Danger Ahead". The New York City Times. ISSN 0362-4331. Retrieved 7 June 2023. ^ Bove, Tristan. "A.I. might match human intelligence in 'simply a few years,' states CEO of Google's primary A.I. research lab". Fortune. Retrieved 4 September 2024. ^ Nellis, Stephen (2 March 2024). "Nvidia CEO says AI could pass human tests in 5 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 Giant Step for Neuroscience". Tech Brew. Retrieved 8 November 2023. ^ Sandberg & Boström 2008. ^ Drachman 2005. ^ a b Russell & Norvig 2003. ^ Moravec 1988, p. 61. ^ Moravec 1998. ^ Holmgaard Mersh, Amalie (15 September 2023). "Decade-long European research study job maps the human brain". euractiv. ^ Swaminathan, Nikhil (January-February 2011). "Glia-the other brain cells". Discover. Archived from the original on 8 February 2014. Retrieved 24 January 2014. ^ de Vega, Glenberg & Graesser 2008. A wide variety of views in current research, all of which require grounding to some degree ^ Thornton, Angela (26 June 2023). "How publishing our minds to a computer may end up being possible". The Conversation. Retrieved 8 November 2023. ^ Searle 1980 ^ For instance: Russell & Norvig 2003, Oxford University Press Dictionary of Psychology Archived 3 December 2007 at the Wayback Machine (estimated 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 expert system 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 believes 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 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 surpass advantages?". 23 August 2021. Retrieved 7 June 2023. ^ "How we can Benefit from Advancing Artificial General Intelligence (AGI) - Unite.AI". www.unite.ai. 7 April 2020. Retrieved 7 June 2023. ^ a b c Talty, Jules; Julien, Stephan. "What Will Our Society Appear Like When 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: courses, dangers, strategies (Reprinted with corrections 2017 ed.). Oxford, UK; New York, New York City, USA: Oxford University Press. ISBN 978-0-1996-7811-2. ^ Piper, Kelsey (19 November 2018). "How technological progress is making it likelier than ever that humans will damage ourselves". Vox. Retrieved 8 June 2023. ^ Doherty, Ben (17 May 2018). "Climate change an 'existential security danger' 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 City Times. ISSN 0362-4331. Retrieved 24 December 2023. ^ a b "Statement on AI Risk". Center for AI Safety. 30 May 2023. Retrieved 8 June 2023. ^ "Stephen Hawking: 'Transcendence looks at the ramifications of expert system - however are we taking AI seriously enough?'". The Independent (UK). Archived from the initial on 25 September 2015. Retrieved 3 December 2014. ^ Herger, Mario. "The Gorilla Problem - Enterprise Garage". Retrieved 7 June 2023. ^ "The interesting Facebook dispute in between Yann LeCun, Stuart Russel and Yoshua Bengio about the risks of strong AI". The interesting Facebook argument in 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 catastrophic AGI danger: a study". Physica Scripta. 90 (1 ): 018001. doi:10.1088/ 0031-8949/90/ 1/018001. ISSN 0031-8949. ^ Bostrom, Nick (2014 ). Superintelligence: Paths, Dangers, Strategies (First ed.). Oxford University Press. ISBN 978-0-1996-7811-2. ^ Chow, Andrew R.; Perrigo, Billy (16 February 2023). "The AI Arms Race Is On. Start Worrying". TIME. Retrieved 24 December 2023. ^ Tetlow, Gemma (12 January 2017). "AI arms race dangers spiralling out of control, report warns". Financial Times. Archived from the initial on 11 April 2022. Retrieved 24 December 2023. ^ Milmo, Dan; Stacey, Kiran (25 September 2023). "Experts disagree over danger posed but artificial intelligence can not be ignored". The Guardian. ISSN 0261-3077. Retrieved 24 December 2023. ^ "Humanity, Security & AI, Oh My! (with Ian Bremmer & Shuman Ghosemajumder)". CAFE. 20 July 2023. Retrieved 15 September 2023. ^ Hamblin, James (9 May 2014). "But What Would 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 stoking worries over AI, caution scientists". The Telegraph. Retrieved 7 December 2023. ^ Davidson, John (30 October 2023). "Google Brain founder states huge 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 take a look at the labor market impact capacity of large language models". OpenAI. Retrieved 7 June 2023. ^ a b Hurst, Luke (23 March 2023). "OpenAI says 80% of workers could see their jobs impacted by AI. These are the jobs most impacted". euronews. Retrieved 8 June 2023. ^ Sheffey, Ayelet (20 August 2021). "Elon Musk states we need universal basic income due to the fact that 'in the future, manual labor will be a choice'". Business Insider. Archived from the original on 9 July 2023. Retrieved 8 June 2023. Sources

UNESCO Science Report: the Race Against Time for Smarter Development. Paris: UNESCO. 11 June 2021. ISBN 978-9-2310-0450-6. Archived from the original on 18 June 2022. Retrieved 22 September 2021. Chalmers, David (1996 ), The Conscious Mind, Oxford University Press. Clocksin, William (August 2003), "Expert system and the future", Philosophical Transactions of the Royal Society A, vol. 361, no. 1809, pp. 1721-1748, Bibcode:2003 RSPTA.361.1721 C, doi:10.1098/ rsta.2003.1232, PMID 12952683, S2CID 31032007. Crevier, Daniel (1993 ). AI: The Tumultuous Look For Expert System. New York, NY: BasicBooks. ISBN 0-465-02997-3. Darrach, Brad (20 November 1970), "Meet Shakey, the First Electronic Person", Life Magazine, pp. 58-68. Drachman, D. (2005 ), "Do we have brain to spare?", Neurology, 64 (12 ): 2004-2005, doi:10.1212/ 01. WNL.0000166914.38327. BB, PMID 15985565, S2CID 38482114. Feigenbaum, Edward A.; McCorduck, Pamela (1983 ), The Fifth Generation: Expert System and Japan's Computer Challenge to the World, Michael Joseph, ISBN 978-0-7181-2401-4. Goertzel, Ben; Pennachin, Cassio, eds. (2006 ), Artificial General Intelligence (PDF), Springer, ISBN 978-3-5402-3733-4, archived from the initial (PDF) on 20 March 2013. Goertzel, Ben (December 2007), "Human-level artificial basic intelligence and the possibility of a technological singularity: a response to Ray Kurzweil's The Singularity Is Near, photorum.eclat-mauve.fr 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 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, recovered 7 May 2011. Howe, J. (November 1994), Artificial Intelligence at Edinburgh University: a Perspective, archived from the original on 17 August 2007, obtained 30 August 2007. Johnson, Mark (1987 ), The body in the mind, Chicago, ISBN 978-0-2264-0317-5. Kurzweil, Ray (2005 ), The Singularity is Near, Viking Press. Lighthill, Professor Sir James (1973 ), "Expert System: A General Survey", Artificial Intelligence: a paper seminar, Science Research Council. Luger, George; Stubblefield, William (2004 ), Artificial Intelligence: Structures and Strategies for Complex Problem Solving (5th ed.), The Benjamin/Cummings Publishing Company, Inc., p. 720, ISBN 978-0-8053-4780-7. McCarthy, John (2007b). What is Artificial Intelligence?. Stanford University. The ultimate effort is to make computer system programs that can fix problems and attain objectives in the world along with 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 original on 15 June 2006, obtained 23 June 2006 Nagel (1974 ), "What Is it Like to Be a Bat" (PDF), Philosophical Review, 83 (4 ): 435-50, doi:10.2307/ 2183914, JSTOR 2183914, archived (PDF) from the original on 16 October 2011, obtained 7 November 2009 Newell, Allen; Simon, H. A. (1976 ). "Computer Science as Empirical Inquiry: Symbols and Search". Communications of the ACM. 19 (3 ): 113-126. doi:10.1145/ 360018.360022. Nilsson, Nils (1998 ), Artificial Intelligence: A New Synthesis, Morgan Kaufmann Publishers, ISBN 978-1-5586-0467-4 NRC (1999 ), "Developments in Expert System", Funding a Revolution: Government Support for Computing Research, National Academy Press, archived from the original on 12 January 2008, recovered 29 September 2007 Poole, David; Mackworth, Alan; Goebel, Randy (1998 ), Computational Intelligence: A Sensible Approach, New York: Oxford University Press, archived from the original on 25 July 2009, retrieved 6 December 2007 Russell, Stuart J.; Norvig, Peter (2003 ), Artificial Intelligence: A Modern Approach (2nd ed.), Upper Saddle River, New Jersey: Prentice Hall, ISBN 0-13-790395-2 Sandberg, Anders; Boström, Nick (2008 ), Whole Brain Emulation: A Roadmap (PDF), Technical Report # 2008-3, Future of Humanity Institute, Oxford University, archived (PDF) from the original on 25 March 2020, recovered 5 April 2009 Searle, John (1980 ), "Minds, Brains and Programs" (PDF), Behavioral and Brain Sciences, 3 (3 ): 417-457, doi:10.1017/ S0140525X00005756, S2CID 55303721, archived (PDF) from the initial on 17 March 2019, recovered 3 September 2020 Simon, H. A. (1965 ), The Shape of Automation for Men and Management, New York City: Harper & Row Turing, Alan (October 1950). "Computing Machinery and Intelligence". Mind. 59 (236 ): 433-460. doi:10.1093/ mind/LIX.236.433. ISSN 1460-2113. JSTOR 2251299. S2CID 14636783.
de Vega, Manuel; Glenberg, Arthur; Graesser, Arthur, eds. (2008 ), Symbols and Embodiment: Debates on significance and cognition, Oxford University Press, ISBN 978-0-1992-1727-4 Wang, Pei; Goertzel, Ben (2007 ). "Introduction: Aspects of Artificial General Intelligence". Advances in Artificial General Intelligence: Concepts, Architectures and Algorithms: Proceedings of the AGI Workshop 2006. IOS Press. pp. 1-16. ISBN 978-1-5860-3758-1. Archived from the initial on 18 February 2021. Retrieved 13 December 2020 - by means of ResearchGate.
Further reading

Aleksander, Igor (1996 ), Impossible Minds, World Scientific Publishing Company, ISBN 978-1-8609-4036-1 Azevedo FA, Carvalho LR, Grinberg LT, Farfel J, et al. (April 2009), "Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain", The Journal of Comparative Neurology, 513 (5 ): 532-541, doi:10.1002/ cne.21974, PMID 19226510, S2CID 5200449, archived from the initial 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 original on 23 July 2014, retrieved 31 August 2012 Cukier, Kenneth, "Ready for Robots? How to Think of 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 simple sufficient to be easy to understand will not be made complex enough to behave wisely, while any system complicated enough to behave intelligently will be too made complex to understand." (p. 197.) Computer researcher 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 initial on 26 July 2010, obtained 25 July 2010. Gleick, James, "The Fate of Free Will" (evaluation of Kevin J. Mitchell, Free Agents: How Evolution Gave Us Free Will, Princeton University Press, 2023, 333 pp.), The New York City Review of Books, vol. LXXI, no. 1 (18 January 2024), pp. 27-28, 30. "Agency is what distinguishes us from makers. For biological creatures, factor and function come from acting worldwide and experiencing the effects. Expert systems - disembodied, complete 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 original (PDF) on 6 June 2013. - Halpern, Sue, "The Coming Tech Autocracy" (review of Verity Harding, AI Needs You: How We Can Change AI's Future and Save Our Own, Princeton University Press, 274 pp.; Gary Marcus, Taming Silicon Valley: How We Can Ensure That AI Works for Us, MIT Press, 235 pp.; Daniela Rus and Gregory Mone, The Mind's Mirror: Risk and Reward in the Age of AI, Norton, 280 pp.; Madhumita Murgia, Code Dependent: Living in the Shadow of AI, Henry Holt, 311 pp.), The New York Review of Books, vol. LXXI, no. 17 (7 November 2024), pp. 44-46. "' We can't reasonably expect that those who want to get abundant from AI are going to have the interests of the rest people close at heart,' ... composes [Gary Marcus] 'We can't rely on federal governments driven by campaign financing contributions [from tech companies] to press back.' ... Marcus information the needs that citizens should make of their federal governments and the tech business. They consist of openness on how AI systems work; settlement for people if their information [are] used to train LLMs (big language design) s and the right to grant this use; and the ability to hold tech business responsible for the harms they bring on by getting rid of Section 230, enforcing money penalites, and passing more stringent product liability laws ... Marcus also recommends ... that a brand-new, AI-specific federal company, similar to the FDA, the FCC, or the FTC, might supply the most robust oversight ... [T] he Fordham law teacher Chinmayi Sharma ... recommends ... establish [ing] a professional licensing regime for engineers that would function in a similar method to medical licenses, malpractice suits, and the Hippocratic oath in medicine. 'What if, like medical professionals,' she asks ..., 'AI engineers likewise vowed to do no harm?'" (p. 46.). Holte, R. C.; Choueiry, B. Y. (2003 ), "Abstraction and reformulation in synthetic intelligence", Philosophical Transactions of the Royal Society B, vol. 358, no. 1435, pp. 1197-1204, doi:10.1098/ rstb.2003.1317, PMC 1693218, PMID 12903653. Hughes-Castleberry, Kenna, "A Murder Mystery Puzzle: The literary puzzle Cain's Jawbone, which has stumped human beings for years, reveals the restrictions of natural-language-processing algorithms", Scientific American, vol. 329, no. 4 (November 2023), pp. 81-82. "This murder secret competition has exposed that although NLP (natural-language processing) models can extraordinary accomplishments, their abilities are really much limited by the quantity of context they get. This [...] might cause [problems] for researchers who want to use them to do things such as analyze ancient languages. In many cases, there are few historic records on long-gone civilizations to function as training information for such a purpose." (p. 82.). Immerwahr, Daniel, "Your Lying Eyes: People now use A.I. to generate phony videos equivalent from genuine ones. How much does it matter?", The New Yorker, 20 November 2023, pp. 54-59. "If by 'deepfakes' we suggest practical videos produced using artificial intelligence that really deceive people, then they hardly exist. The fakes aren't deep, and the deeps aren't fake. [...] A.I.-generated videos are not, in basic, running in our media as counterfeited proof. Their function better looks like that of animations, specifically smutty ones." (p. 59.). - Leffer, Lauren, "The Risks of Trusting AI: We need to prevent humanizing machine-learning models utilized in scientific research", Scientific American, vol. 330, no. 6 (June 2024), pp. 80-81. Lepore, Jill, "The Chit-Chatbot: Is talking with a maker a discussion?", The New Yorker, 7 October 2024, pp. 12-16. Marcus, Gary, "Artificial Confidence: Even the latest, buzziest systems of synthetic general 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", 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 initial on 3 March 2016, obtained 29 September 2007. Newell, Allen; Simon, H. A. (1963 ), "GPS: A Program that Simulates Human Thought", in Feigenbaum, E. A.; Feldman, J. (eds.), Computers and Thought, New York: McGraw-Hill. Omohundro, Steve (2008 ), The Nature of Self-Improving Artificial Intelligence, provided and distributed at the 2007 Singularity Summit, San Francisco, California. Press, Eyal, "In Front of Their Faces: Does facial-recognition innovation lead authorities to disregard inconsistent proof?", The New Yorker, 20 November 2023, pp. 20-26. Roivainen, Eka, "AI's IQ: ChatGPT aced a [standard intelligence] test however 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, setiathome.berkeley.edu ChatGPT stops working at jobs that need real humanlike thinking or an understanding of the physical and social world ... ChatGPT appeared not able to factor rationally and tried to depend on its huge database of ... truths derived from online texts. " - Scharre, Paul, "Killer Apps: The Real Dangers of an AI Arms Race", Foreign Affairs, vol. 98, no. 3 (May/June 2019), pp. 135-44. "Today's AI innovations are powerful however 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 already resulted in catastrophe. Advanced autopilot features in vehicles, although they perform well in some circumstances, have actually driven cars without warning into trucks, concrete barriers, and parked cars. In the incorrect situation, AI systems go from supersmart to superdumb in an instant. 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 technologies however depend on the timelelss human propensity to anthropomorphise." (p. 29.). Williams, R. W.; Herrup, K.