GitLab

Artificial general intelligence (AGI) is a type of expert system (AI) that matches or goes beyond human cognitive capabilities throughout a wide variety of cognitive jobs. This contrasts with narrow AI, which is restricted to particular jobs. [1] Artificial superintelligence (ASI), on the other hand, refers to AGI that significantly goes beyond human cognitive capabilities. AGI is thought about among the meanings 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 study recognized 72 active AGI research study and development projects throughout 37 nations. [4]
The timeline for achieving AGI stays a topic of ongoing argument amongst researchers and experts. Since 2023, some argue that it might be possible in years or years; others preserve it may take a century or longer; a minority believe it might never ever be attained; and another minority claims that it is currently here. [5] [6] Notable AI researcher Geoffrey Hinton has actually revealed concerns about the fast development towards AGI, recommending it might be accomplished quicker than lots of expect. [7]
There is debate on the exact meaning of AGI and regarding whether modern-day large language designs (LLMs) such as GPT-4 are early kinds of AGI. [8] AGI is a typical subject in science fiction and futures research studies. [9] [10]
Contention exists over whether AGI represents an existential danger. [11] [12] [13] Many professionals on AI have specified that mitigating the risk of human termination postured by AGI needs to be a worldwide top priority. [14] [15] Others discover the advancement of AGI to be too remote to provide such a threat. [16] [17]
Terminology

AGI is also known as strong AI, [18] [19] full AI, [20] human-level AI, [5] human-level intelligent AI, or general intelligent action. [21]
Some scholastic sources schedule the term "strong AI" for computer programs that experience sentience or awareness. [a] On the other hand, weak AI (or narrow AI) is able to solve one specific issue however does not have general cognitive capabilities. [22] [19] Some academic sources utilize "weak AI" to refer more broadly to any programs that neither experience awareness nor have a mind in the exact same sense as human beings. [a]
Related ideas consist of artificial superintelligence and transformative AI. A synthetic superintelligence (ASI) is a hypothetical kind of AGI that is far more generally intelligent than human beings, [23] while the concept of transformative AI relates to AI having a big influence on society, for instance, comparable to the agricultural or commercial revolution. [24]
A structure for categorizing AGI in levels was proposed in 2023 by Google DeepMind researchers. They specify five levels of AGI: emerging, competent, professional, virtuoso, and superhuman. For instance, a proficient AGI is specified as an AI that outperforms 50% of knowledgeable grownups in a large range of non-physical jobs, and a superhuman AGI (i.e. a synthetic superintelligence) is likewise specified but with a threshold of 100%. They consider large language models like ChatGPT or LLaMA 2 to be instances of emerging AGI. [25]
Characteristics

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

Researchers typically hold that intelligence is needed to do all of the following: [27]
factor, usage technique, resolve puzzles, and make judgments under unpredictability represent understanding, consisting of good sense understanding strategy discover

• interact in natural language
• if necessary, integrate these abilities in completion of any offered goal

Many interdisciplinary approaches (e.g. cognitive science, computational intelligence, and decision making) think about additional qualities such as creativity (the capability to form novel psychological images and principles) [28] and autonomy. [29]
Computer-based systems that display much of these abilities exist (e.g. see computational imagination, automated reasoning, decision support system, robot, evolutionary calculation, smart agent). There is debate about whether contemporary AI systems have them to an adequate degree.

Physical characteristics

Other capabilities are considered desirable in intelligent systems, as they may affect intelligence or aid in its expression. These consist of: [30]
- the capability to sense (e.g. see, hear, and so on), and - the ability to act (e.g. relocation and manipulate items, change place to check out, and so on).
This consists of the capability to detect and react to risk. [31]
Although the capability to sense (e.g. see, hear, etc) and the capability to act (e.g. move and control things, modification place to check out, etc) can be desirable for some smart systems, [30] these physical capabilities are not strictly required for an entity to certify as AGI-particularly under the thesis that big language models (LLMs) may already be or become AGI. Even from a less optimistic viewpoint on LLMs, there is no company requirement for an AGI to have a human-like kind; being a silicon-based computational system is sufficient, 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 been proscribed a specific physical personification and coastalplainplants.org thus does not demand a capability for mobility or conventional "eyes and ears". [32]
Tests for human-level AGI

Several tests suggested to validate human-level AGI have been considered, consisting of: [33] [34]
The idea of the test is that the maker needs to attempt and pretend to be a male, by responding to questions put to it, and it will just pass if the pretence is fairly convincing. A substantial portion of a jury, who should not be expert about machines, should be taken in by the pretence. [37]
AI-complete issues

A problem is informally called "AI-complete" or "AI-hard" if it is believed that in order to resolve it, one would need to implement AGI, due to the fact that the solution is beyond the capabilities of a purpose-specific algorithm. [47]
There are lots of problems that have actually been conjectured to require general intelligence to fix along with people. Examples include computer vision, natural language understanding, and handling unexpected situations while solving any real-world problem. [48] Even a particular task like translation requires a maker to read and write in both languages, follow the author's argument (reason), comprehend the context (knowledge), and consistently reproduce the author's initial intent (social intelligence). All of these issues need to be fixed concurrently in order to reach human-level device performance.

However, a number of these tasks can now be carried out by contemporary big language designs. According to Stanford University's 2024 AI index, AI has actually reached human-level efficiency on many benchmarks for reading comprehension and systemcheck-wiki.de visual reasoning. [49]
History

Classical AI

Modern AI research began in the mid-1950s. [50] The first generation of AI scientists were encouraged that synthetic basic intelligence was possible which it would exist in simply a couple of decades. [51] AI pioneer Herbert A. Simon wrote in 1965: "devices will be capable, within twenty years, of doing any work a male can do." [52]
Their predictions were the inspiration for Stanley Kubrick and Arthur C. Clarke's character HAL 9000, who embodied what AI scientists believed they could produce by the year 2001. AI leader Marvin Minsky was an expert [53] on the job of making HAL 9000 as reasonable as possible according to the agreement predictions of the time. He said in 1967, "Within a generation ... the issue of creating 'expert system' will significantly be resolved". [54]
Several classical AI tasks, such as Doug Lenat's Cyc job (that began in 1984), and Allen Newell's Soar task, were directed at AGI.

However, in the early 1970s, it became apparent that scientists had grossly underestimated the difficulty of the job. Funding companies ended up being doubtful of AGI and put researchers under increasing pressure to produce beneficial "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 objectives like "continue a casual conversation". [58] In response to this and the success of professional systems, both industry and government pumped money into the field. [56] [59] However, self-confidence in AI amazingly collapsed in the late 1980s, and the goals of the Fifth Generation Computer Project were never fulfilled. [60] For the second time in twenty years, AI researchers who anticipated the imminent accomplishment of AGI had been mistaken. By the 1990s, AI researchers had a reputation for making vain promises. They became hesitant to make forecasts at all [d] and prevented mention of "human level" expert system for worry of being identified "wild-eyed dreamer [s]. [62]
Narrow AI research

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

I am confident that this bottom-up path to artificial intelligence will one day fulfill the traditional top-down route over half method, ready to supply the real-world competence and the commonsense knowledge that has actually been so frustratingly evasive in thinking programs. Fully smart devices will result when the metaphorical golden spike is driven uniting the 2 efforts. [65]
However, even at the time, this was challenged. For example, Stevan Harnad of Princeton University concluded his 1990 paper on the symbol grounding hypothesis by specifying:

The expectation has typically been voiced that "top-down" (symbolic) approaches to modeling cognition will in some way satisfy "bottom-up" (sensory) approaches someplace in between. If the grounding factors to consider in this paper are valid, then this expectation is hopelessly modular and there is actually 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 be reached by this route (or vice versa) - nor is it clear why we should even attempt to reach such a level, since it looks as if getting there would just total up to uprooting our symbols from their intrinsic meanings (therefore simply lowering ourselves to the functional equivalent of a programmable computer system). [66]
Modern artificial general intelligence research

The term "artificial general intelligence" was utilized as early as 1997, by Mark Gubrud [67] in a conversation of the implications of fully automated military production and operations. A mathematical formalism of AGI was proposed by Marcus Hutter in 2000. Named AIXI, the proposed AGI agent maximises "the capability to please goals in a vast array of environments". [68] This kind of AGI, identified by the ability to maximise a mathematical meaning of intelligence rather than show human-like behaviour, [69] was also called universal artificial intelligence. [70]
The term AGI was re-introduced and promoted by Shane Legg and Ben Goertzel around 2002. [71] AGI research activity in 2006 was explained by Pei Wang and Ben Goertzel [72] as "producing publications and preliminary outcomes". The very first summer season school in AGI was arranged in Xiamen, China in 2009 [73] by the Xiamen university's Artificial Brain Laboratory and OpenCog. The 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.

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

Feasibility

As of 2023, the advancement and possible achievement of AGI stays a subject of intense debate within the AI neighborhood. While conventional consensus held that AGI was a distant goal, recent improvements have actually led some researchers and industry figures to declare that early forms of AGI might already exist. [78] AI pioneer Herbert A. Simon speculated in 1965 that "makers will be capable, within twenty years, of doing any work a male can do". This forecast failed to come true. Microsoft co-founder Paul Allen believed that such intelligence is not likely in the 21st century due to the fact that it would require "unforeseeable and fundamentally unpredictable breakthroughs" and a "scientifically deep understanding of cognition". [79] Writing in The Guardian, roboticist Alan Winfield declared the gulf between modern-day computing and human-level expert system is as broad as the gulf in between current area flight and useful faster-than-light spaceflight. [80]
A more difficulty is the lack of clarity in specifying what intelligence requires. Does it require awareness? Must it show the capability to set objectives along with pursue them? Is it simply a matter of scale such that if model sizes increase adequately, intelligence will emerge? Are centers such as planning, thinking, and causal understanding needed? Does intelligence need explicitly replicating the brain and its specific faculties? Does it need emotions? [81]
Most AI scientists think strong AI can be accomplished in the future, however some thinkers, like Hubert Dreyfus and Roger Penrose, deny the possibility of accomplishing strong AI. [82] [83] John McCarthy is among those who believe human-level AI will be achieved, however that the present level of development is such that a date can not accurately be anticipated. [84] AI professionals' views on the feasibility of AGI wax and wane. Four surveys performed in 2012 and 2013 suggested that the average price quote among professionals for when they would be 50% confident AGI would get here was 2040 to 2050, depending upon the poll, with the mean being 2081. Of the professionals, 16.5% addressed with "never ever" when asked the exact same question however with a 90% self-confidence rather. [85] [86] Further current AGI development factors to consider can be discovered above Tests for validating human-level AGI.

A report by Stuart Armstrong and Kaj Sotala of the Machine Intelligence Research Institute discovered that "over [a] 60-year time frame there is a strong bias towards anticipating the arrival of human-level AI as in between 15 and 25 years from the time the prediction was made". They examined 95 predictions made between 1950 and 2012 on when human-level AI will come about. [87]
In 2023, Microsoft scientists released an in-depth assessment of GPT-4. They concluded: "Given the breadth and depth of GPT-4's capabilities, our company believe that it could reasonably be deemed an early (yet still insufficient) variation of a synthetic general intelligence (AGI) system." [88] Another study in 2023 reported that GPT-4 outperforms 99% of human beings on the Torrance tests of innovative thinking. [89] [90]
Blaise Agüera y Arcas and Peter Norvig composed in 2023 that a considerable level of basic intelligence has actually already been achieved with frontier models. They composed that hesitation to this view comes from four primary factors: a "healthy suspicion about metrics for AGI", an "ideological dedication to alternative AI theories or techniques", a "devotion to human (or biological) exceptionalism", or a "issue about the financial implications of AGI". [91]
2023 likewise marked the introduction of large multimodal designs (large language models efficient in processing or producing multiple methods such as text, audio, and images). [92]
In 2024, OpenAI launched o1-preview, the very first of a series of designs that "spend more time thinking before they respond". According to Mira Murati, this capability to think before responding represents a brand-new, extra paradigm. It enhances model outputs by spending more computing power when generating the answer, whereas the model scaling paradigm enhances outputs by increasing the design size, training information and training compute power. [93] [94]
An OpenAI worker, Vahid Kazemi, declared in 2024 that the company had actually accomplished AGI, specifying, "In my viewpoint, we have actually already accomplished 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 job", it is "much better than the majority of human beings at a lot of jobs." He also attended to criticisms that large language models (LLMs) simply follow predefined patterns, comparing their knowing process to the scientific technique of observing, assuming, and confirming. These statements have stimulated dispute, as they depend on a broad and unconventional meaning of AGI-traditionally understood as AI that matches human intelligence throughout all domains. Critics argue that, while OpenAI's models demonstrate exceptional adaptability, they may not completely satisfy this standard. Notably, Kazemi's comments came shortly after OpenAI got rid of "AGI" from the terms of its partnership with Microsoft, prompting speculation about the business's tactical intentions. [95]
Timescales

Progress in artificial intelligence has actually historically gone through periods of rapid progress separated by durations when development appeared to stop. [82] Ending each hiatus were fundamental advances in hardware, software application or both to create area for additional progress. [82] [98] [99] For example, the computer system hardware available in the twentieth century was not adequate to execute deep learning, which needs great deals of GPU-enabled CPUs. [100]
In the introduction to his 2006 book, [101] Goertzel states that price quotes of the time needed before a truly flexible AGI is constructed differ from ten years to over a century. As of 2007 [update], 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 possible. [103] Mainstream AI scientists have given a wide variety of viewpoints on whether progress will be this fast. A 2012 meta-analysis of 95 such opinions discovered a predisposition towards predicting that the onset of AGI would take place within 16-26 years for modern and historic predictions alike. That paper has actually been criticized for how it categorized viewpoints as specialist 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 conventional method utilized a weighted amount of scores from different pre-defined classifiers). [105] AlexNet was considered the initial ground-breaker of the current deep learning wave. [105]
In 2017, scientists Feng Liu, Yong Shi, and Ying Liu performed intelligence tests on openly readily available and easily available weak AI such as Google AI, Apple's Siri, and others. At the optimum, these AIs reached an IQ worth of about 47, which corresponds around to a six-year-old kid in very first grade. An adult pertains to about 100 on average. Similar tests were carried out in 2014, with the IQ score reaching a maximum worth of 27. [106] [107]
In 2020, OpenAI established GPT-3, a language design capable of performing lots of varied jobs without specific training. According to Gary Grossman in a VentureBeat short article, while there is agreement that GPT-3 is not an example of AGI, it is considered by some to be too advanced to be categorized as a narrow AI system. [108]
In the very same year, Jason Rohrer utilized his GPT-3 account to establish a chatbot, and offered a chatbot-developing platform called "Project December". OpenAI requested changes to the chatbot to abide by their safety guidelines; Rohrer disconnected Project December from the GPT-3 API. [109]
In 2022, DeepMind established Gato, a "general-purpose" system efficient in carrying out more than 600 different jobs. [110]
In 2023, Microsoft Research published a research study on an early version of OpenAI's GPT-4, competing that it exhibited more general intelligence than previous AI designs and showed 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 version of synthetic general intelligence, stressing the requirement for additional expedition and examination of such systems. [111]
In 2023, the AI researcher Geoffrey Hinton mentioned that: [112]
The idea that this stuff could actually get smarter than individuals - a few individuals believed that, [...] But many people thought it was way off. And I thought it was way off. I believed it was 30 to 50 years and even longer away. Obviously, I no longer believe that.

In May 2023, Demis Hassabis likewise said that "The development in the last couple of years has actually been pretty extraordinary", which he sees no reason it would slow down, expecting AGI within a years or perhaps a few years. [113] In March 2024, Nvidia's CEO, Jensen Huang, stated his expectation that within 5 years, AI would be capable of passing any test a minimum of in addition to human beings. [114] In June 2024, the AI scientist Leopold Aschenbrenner, a previous OpenAI staff member, approximated AGI by 2027 to be "noticeably plausible". [115]
Whole brain emulation

While the development of transformer designs like in ChatGPT is considered the most appealing path to AGI, [116] [117] whole brain emulation can work as an alternative technique. With entire brain simulation, a brain design is constructed 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 need to be sufficiently devoted to the original, so that it behaves in virtually the exact same way as the original brain. [118] Whole brain emulation is a kind of brain simulation that is discussed in computational neuroscience and neuroinformatics, and for medical research functions. It has actually been gone over in artificial intelligence research study [103] as an approach to strong AI. Neuroimaging innovations that could deliver the necessary detailed understanding are improving quickly, and futurist Ray Kurzweil in the book The Singularity Is Near [102] anticipates that a map of enough quality will appear on a similar timescale to the computing power needed to imitate it.

Early approximates

For low-level brain simulation, a really effective cluster of computer systems or GPUs would be required, given the enormous quantity of synapses within the human brain. Each of the 1011 (one hundred billion) neurons has on average 7,000 synaptic connections (synapses) to other neurons. The brain of a three-year-old child has about 1015 synapses (1 quadrillion). This number decreases with age, supporting by adulthood. 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 neuron activity, is around 1014 (100 trillion) synaptic updates per second (SUPS). [121]
In 1997, Kurzweil looked at different quotes for the hardware required to equate to the human brain and adopted a figure of 1016 computations per 2nd (cps). [e] (For contrast, if a "computation" was equivalent to one "floating-point operation" - a measure utilized to rate current supercomputers - then 1016 "calculations" would be comparable to 10 petaFLOPS, achieved in 2011, while 1018 was attained in 2022.) He utilized this figure to forecast the needed hardware would be offered sometime between 2015 and 2025, if the rapid growth in computer power at the time of writing continued.

Current research

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

Criticisms of simulation-based approaches

The synthetic neuron model assumed by Kurzweil and utilized in lots of present artificial neural network executions is easy compared with biological nerve cells. A brain simulation would likely have to capture the comprehensive cellular behaviour of biological neurons, currently comprehended only in broad outline. The overhead presented by full modeling of the biological, chemical, and physical details of neural behaviour (specifically on a molecular scale) would require computational powers a number of orders of magnitude bigger than Kurzweil's price quote. In addition, the price quotes do not account for glial cells, which are understood to play a function in cognitive procedures. [125]
A fundamental criticism of the simulated brain approach originates from embodied cognition theory which asserts that human embodiment is an essential aspect of human intelligence and is necessary to ground meaning. [126] [127] If this theory is proper, any totally practical brain model will need to include more than just the nerve cells (e.g., a robotic body). Goertzel [103] proposes virtual embodiment (like in metaverses like Second Life) as a choice, however it is unidentified whether this would suffice.

Philosophical viewpoint

"Strong AI" as defined in viewpoint

In 1980, thinker John Searle coined the term "strong AI" as part of his Chinese room argument. [128] He proposed a difference between two hypotheses about synthetic intelligence: [f]
Strong AI hypothesis: A synthetic intelligence system can have "a mind" and "consciousness". Weak AI hypothesis: An artificial intelligence system can (just) act like it believes and has a mind and consciousness.
The first one he called "strong" since it makes a more powerful statement: it assumes something unique has taken place to the maker that goes beyond those abilities that we can evaluate. The behaviour of a "weak AI" maker would be precisely identical to a "strong AI" machine, but the latter would also have subjective conscious experience. This use is likewise typical in scholastic AI research study and books. [129]
In contrast to Searle and traditional AI, some futurists such as Ray Kurzweil use the term "strong AI" to imply "human level synthetic basic intelligence". [102] This is not the exact same as Searle's strong AI, unless it is presumed that consciousness is required for human-level AGI. Academic thinkers such as Searle do not think that holds true, and to most expert system researchers the question is out-of-scope. [130]
Mainstream AI is most interested in how a program behaves. [131] According to Russell and Norvig, "as long as the program works, they don't 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 really has mind - certainly, there would be no chance to inform. For AI research, Searle's "weak AI hypothesis" is equivalent to the declaration "synthetic general intelligence is possible". Thus, according to Russell and Norvig, "most AI scientists take the weak AI hypothesis for approved, and don't care about the strong AI hypothesis." [130] Thus, for scholastic AI research, "Strong AI" and "AGI" are two different things.

Consciousness

Consciousness can have different meanings, and some elements play considerable functions in sci-fi and the ethics of artificial intelligence:

Sentience (or "sensational awareness"): The capability to "feel" understandings or emotions subjectively, as opposed to the ability to factor about perceptions. Some theorists, such as David Chalmers, use the term "awareness" to refer solely to phenomenal consciousness, which is approximately comparable to life. [132] Determining why and how subjective experience develops is called the difficult issue of consciousness. [133] Thomas Nagel described in 1974 that it "seems like" something to be conscious. If we are not mindful, then it doesn't feel like anything. Nagel uses the example of a bat: we can sensibly ask "what does it feel like to be a bat?" However, we are unlikely to ask "what does it feel like to be a toaster?" Nagel concludes that a bat seems conscious (i.e., has awareness) however a toaster does not. [134] In 2022, a Google engineer declared that the business's AI chatbot, LaMDA, had actually accomplished sentience, though this claim was widely disputed by other experts. [135]
Self-awareness: To have mindful awareness of oneself as a different person, specifically to be purposely familiar with one's own thoughts. This is opposed to simply being the "topic of one's thought"-an os or debugger is able to be "conscious of itself" (that is, to represent itself in the very same way it represents everything else)-however this is not what people generally mean when they utilize the term "self-awareness". [g]
These characteristics have an ethical dimension. AI life would generate concerns of well-being and legal security, similarly to animals. [136] Other aspects of consciousness associated to cognitive abilities are also appropriate to the idea of AI rights. [137] Figuring out how to incorporate sophisticated AI with existing legal and social frameworks is an emerging problem. [138]
Benefits

AGI could have a wide range of applications. If oriented towards such objectives, AGI might help reduce numerous problems on the planet such as appetite, poverty and health issue. [139]
AGI might enhance efficiency and efficiency in a lot of tasks. For instance, in public health, AGI might speed up medical research, especially versus cancer. [140] It could look after the elderly, [141] and equalize access to rapid, high-quality medical diagnostics. It might provide fun, low-cost and individualized education. [141] The need to work to subsist might end up being outdated if the wealth produced is appropriately redistributed. [141] [142] This likewise raises the question of the location of human beings in a radically automated society.

AGI could also help to make rational decisions, and to prepare for and prevent catastrophes. It could likewise help to profit of possibly catastrophic innovations such as nanotechnology or climate engineering, while avoiding the associated threats. [143] If an AGI's main objective is to avoid existential disasters such as human termination (which might be hard if the Vulnerable World Hypothesis turns out to be true), [144] it could take steps to drastically minimize the dangers [143] while reducing the impact of these procedures on our quality of life.

Risks

Existential dangers

AGI might represent numerous kinds of existential danger, which are dangers that threaten "the premature extinction of Earth-originating smart life or the irreversible and extreme damage of its potential for desirable future development". [145] The threat of human extinction from AGI has actually been the subject of many disputes, however there is also the possibility that the development of AGI would cause a completely problematic future. Notably, it might be utilized to spread and preserve the set of worths of whoever establishes it. If humanity still has moral blind areas similar to slavery in the past, AGI may irreversibly entrench it, avoiding ethical progress. [146] Furthermore, AGI might help with mass monitoring and brainwashing, which could be used to create a stable repressive worldwide totalitarian regime. [147] [148] There is also a threat for the devices themselves. If makers that are sentient or otherwise worthy of ethical consideration are mass created in the future, engaging in a civilizational course that indefinitely disregards their welfare and interests might be an existential catastrophe. [149] [150] Considering how much AGI could improve humanity's future and help in reducing other existential dangers, Toby Ord calls these existential threats "an argument for continuing with due care", not for "abandoning AI". [147]
Risk of loss of control and human extinction

The thesis that AI positions an existential risk for human beings, and that this threat needs more attention, is controversial however has actually been endorsed in 2023 by lots of 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 prevalent indifference:

So, dealing with possible futures of incalculable benefits and threats, the professionals are certainly doing whatever possible to guarantee the best outcome, right? Wrong. If a superior alien civilisation sent us a message saying, 'We'll arrive in a few years,' would we just reply, 'OK, call us when you get here-we'll leave the lights on?' Probably not-but this is more or less what is happening with AI. [153]
The prospective fate of mankind has often been compared to the fate of gorillas threatened by human activities. The contrast specifies that higher intelligence enabled humankind to control gorillas, which are now susceptible in ways that they might not have anticipated. As an outcome, the gorilla has ended up being a threatened species, not out of malice, however merely as a civilian casualties from human activities. [154]
The skeptic Yann LeCun considers that AGIs will have no desire to dominate humankind and that we need to beware not to anthropomorphize them and interpret their intents as we would for humans. He stated that individuals will not be "wise enough to develop super-intelligent devices, yet ridiculously dumb to the point of giving it moronic objectives without any safeguards". [155] On the other side, the principle of instrumental merging suggests that almost whatever their goals, intelligent representatives will have reasons to attempt to endure and acquire more power as intermediary actions to attaining these goals. And that this does not need having emotions. [156]
Many scholars who are concerned about existential risk advocate for more research into solving the "control problem" to answer the question: what kinds of safeguards, algorithms, or architectures can developers execute to maximise the possibility that their recursively-improving AI would continue to act in a friendly, instead of damaging, manner after it reaches superintelligence? [157] [158] Solving the control issue is made complex by the AI arms race (which might result in a race to the bottom of security precautions in order to launch products before competitors), [159] and the usage of AI in weapon systems. [160]
The thesis that AI can posture existential danger also has detractors. Skeptics typically say that AGI is unlikely in the short-term, or that concerns about AGI sidetrack from other problems connected to current AI. [161] Former Google scams czar Shuman Ghosemajumder considers that for many individuals beyond the technology market, existing chatbots and LLMs are already perceived as though they were AGI, causing further misconception and worry. [162]
Skeptics in some cases charge that the thesis is crypto-religious, with an illogical belief in the possibility of superintelligence replacing an illogical belief in an omnipotent God. [163] Some researchers believe that the communication campaigns on AI existential threat by certain AI groups (such as OpenAI, Anthropic, DeepMind, and Conjecture) might 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 researchers, provided a joint statement asserting that "Mitigating the risk of termination from AI must be a global top priority alongside other societal-scale threats such as pandemics and bybio.co nuclear war." [152]
Mass joblessness

Researchers from OpenAI approximated that "80% of the U.S. labor force might have at least 10% of their work jobs impacted by the intro of LLMs, while around 19% of employees might see a minimum of 50% of their jobs impacted". [166] [167] They think about office workers to be the most exposed, for instance mathematicians, accountants or web designers. [167] AGI might have a much 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 outcome of automation on the quality of life will depend on how the wealth will be redistributed: [142]
Everyone can take pleasure in a life of glamorous leisure if the machine-produced wealth is shared, or the majority of people can end up miserably bad if the machine-owners successfully lobby against wealth redistribution. So far, the trend appears to be towards the second choice, with innovation driving ever-increasing inequality

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

Artificial brain - Software and hardware with cognitive capabilities similar to those of the animal or human brain AI result AI security - Research location on making AI safe and beneficial AI alignment - AI conformance to the designated objective A.I. Rising - 2018 film directed by Lazar Bodroža Artificial intelligence Automated artificial intelligence - Process of automating the application of artificial intelligence BRAIN Initiative - Collaborative public-private research study initiative revealed by the Obama administration China Brain Project Future of Humanity Institute - Defunct Oxford interdisciplinary research centre General game playing - Ability of expert system to play different video games Generative artificial intelligence - AI system efficient in generating content in reaction to prompts Human Brain Project - Scientific research study task Intelligence amplification - Use of infotech to augment human intelligence (IA). Machine principles - Moral behaviours of manufactured devices. Moravec's paradox. Multi-task learning - Solving multiple maker learning tasks at the very same time. Neural scaling law - Statistical law in maker knowing. Outline of expert system - Overview of and topical guide to artificial intelligence. Transhumanism - Philosophical motion. Synthetic intelligence - Alternate term for or type of expert system. Transfer knowing - Machine knowing strategy. Loebner Prize - Annual AI competition. Hardware for expert system - Hardware specially created and enhanced for synthetic intelligence. Weak synthetic intelligence - Form of synthetic intelligence.
Notes

^ a b See listed below for the origin of the term "strong AI", and see the scholastic meaning of "strong AI" and weak AI in the short article Chinese room. ^ AI founder John McCarthy composes: "we can not yet characterize in basic what type of computational procedures we desire to call intelligent. " [26] (For a discussion of some meanings of intelligence used by expert system scientists, see viewpoint of expert system.). ^ The Lighthill report particularly criticized AI's "grand goals" and led the taking apart of AI research study in England. [55] In the U.S., DARPA became identified to fund just "mission-oriented direct research, rather than standard undirected research study". [56] [57] ^ As AI founder John McCarthy writes "it would be a fantastic relief to the remainder of the employees in AI if the creators of new general formalisms would reveal their hopes in a more guarded type than has actually sometimes been the case." [61] ^ In "Mind Children" [122] 1015 cps is utilized. More recently, in 1997, [123] Moravec argued for 108 MIPS which would roughly represent 1014 cps. Moravec talks in regards to MIPS, not "cps", which is a non-standard term Kurzweil introduced. ^ As defined in a basic AI book: "The assertion that devices might perhaps act intelligently (or, perhaps much better, act as if they were smart) is called the 'weak AI' hypothesis by thinkers, and the assertion that makers that do so are really believing (instead of replicating thinking) is called the 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 created to carry out a single task. ^ "OpenAI Charter". OpenAI. Retrieved 6 April 2023. Our objective is to make sure that synthetic basic intelligence advantages all of humankind. ^ Heath, Alex (18 January 2024). "Mark Zuckerberg's new goal is developing synthetic basic 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 professionals in expert system expect for the future?". Our World in Data. Retrieved 6 April 2023. ^ Metz, Cade (15 May 2023). "Some Researchers Say A.I. Is Already Here, Stirring Debate in Tech Circles". The New York Times. Retrieved 18 May 2023. ^ "AI pioneer Geoffrey Hinton quits Google and warns of danger ahead". The New York Times. 1 May 2023. Retrieved 2 May 2023. It is hard to see how you can prevent 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 explores GPT-4". arXiv preprint. arXiv:2303.12712. GPT-4 shows 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 genuine risk is not AI itself but the way we deploy it. ^ "Impressed by synthetic intelligence? Experts say AGI is following, and it has 'existential' threats". ABC News. 23 March 2023. Retrieved 6 April 2023. AGI could position existential threats to humanity. ^ Bostrom, Nick (2014 ). Superintelligence: Paths, Dangers, Strategies. Oxford University Press. ISBN 978-0-1996-7811-2. The 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 risk of extinction from AI must be an international priority. ^ "Statement on AI Risk". Center for AI Safety. Retrieved 1 March 2024. AI professionals 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 creating machines that can outthink us in general methods. ^ LeCun, Yann (June 2023). "AGI does not provide an existential risk". Medium. There is no factor to fear AI as an existential danger. ^ Kurzweil 2005, p. 260. ^ a b Kurzweil, Ray (5 August 2005), "Long Live AI", Forbes, archived from the original on 14 August 2005: Kurzweil describes strong AI as "device intelligence with the complete variety of human intelligence.". ^ "The Age of Expert System: George John at TEDxLondonBusinessSchool 2013". Archived from the initial on 26 February 2014. Retrieved 22 February 2014. ^ Newell & Simon 1976, This is the term they use for "human-level" intelligence in the physical sign system hypothesis. ^ "The Open University on Strong and Weak AI". Archived from the 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 everybody to make certain that it goes well". Our World in Data. Retrieved 8 October 2023. ^ Dickson, Ben (16 November 2023). "Here is how far we are to achieving AGI, according to DeepMind". VentureBeat. ^ McCarthy, John (2007a). "Basic Questions". Stanford University. Archived from the original on 26 October 2007. Retrieved 6 December 2007. ^ This list of smart 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 shapes the method we believe: a new view of intelligence (The MIT Press, 2007). ISBN 0-2621-6239-3. ^ White, R. W. (1959 ). "Motivation reassessed: The idea of competence". Psychological Review. 66 (5 ): 297-333. doi:10.1037/ h0040934. PMID 13844397. S2CID 37385966. ^ White, R. W. (1959 ). "Motivation reconsidered: The concept of competence". Psychological Review. 66 (5 ): 297-333. doi:10.1037/ h0040934. PMID 13844397. S2CID 37385966. ^ Muehlhauser, Luke (11 August 2013). "What is AGI?". Machine Intelligence Research Institute. Archived from the initial on 25 April 2014. Retrieved 1 May 2014. ^ "What is Artificial General Intelligence (AGI)?|4 Tests For Ensuring Artificial General Intelligence". Talky Blog. 13 July 2019. Archived from the 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 real young boy - the Turing Test states so". The Guardian. 9 June 2014. ISSN 0261-3077. Retrieved 3 March 2024. ^ "Scientists challenge whether computer system 'Eugene Goostman' passed Turing test". BBC News. 9 June 2014. Retrieved 3 March 2024. ^ Jones, Cameron R.; Bergen, Benjamin K. (9 May 2024). "People can not distinguish GPT-4 from a human in a Turing test". arXiv:2405.08007 [cs.HC] ^ Varanasi, Lakshmi (21 March 2023). "AI models like ChatGPT and GPT-4 are acing whatever from the bar test to AP Biology. Here's a list of hard examinations both AI variations have actually passed". Business Insider. Retrieved 30 May 2023. ^ Naysmith, Caleb (7 February 2023). "6 Jobs Expert System Is Already Replacing and How Investors Can Take Advantage Of It". Retrieved 30 May 2023. ^ Turk, Victoria (28 January 2015). "The Plan to Replace the Turing Test with a 'Turing Olympics'". Vice. Retrieved 3 March 2024. ^ Gopani, Avi (25 May 2022). "Turing Test is unreliable. The Winograd Schema is obsolete. Coffee is the response". Analytics India Magazine. Retrieved 3 March 2024. ^ Bhaimiya, Sawdah (20 June 2023). "DeepMind's co-founder recommended evaluating an AI chatbot's ability 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 original on 22 May 2013. ^ "AI Index: State of AI in 13 Charts". Stanford University Human-Centered Artificial Intelligence. 15 April 2024. Retrieved 27 May 2024. ^ Crevier 1993, pp. 48-50. ^ Kaplan, Andreas (2022 ). "Expert System, Business and Civilization - Our Fate Made in Machines". Archived from the original on 6 May 2022. Retrieved 12 March 2022. ^ Simon 1965, p. 96 estimated in Crevier 1993, p. 109. ^ "Scientist on the Set: An Interview with Marvin Minsky". Archived from the initial on 16 July 2012. Retrieved 5 April 2008. ^ Marvin Minsky to Darrach (1970 ), estimated in Crevier (1993, p. 109). ^ Lighthill 1973; Howe 1994. ^ a b NRC 1999, "Shift to Applied Research Increases Investment". ^ Crevier 1993, pp. 115-117; Russell & Norvig 2003, pp. 21-22. ^ Crevier 1993, p. 211, Russell & Norvig 2003, p. 24 and see likewise Feigenbaum & McCorduck 1983. ^ Crevier 1993, pp. 161-162, 197-203, 240; Russell & Norvig 2003, p. 25. ^ Crevier 1993, pp. 209-212. ^ McCarthy, John (2000 ). "Reply to Lighthill". Stanford University. Archived from the original 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 researchers and software application engineers prevented the term synthetic intelligence for fear of being seen as wild-eyed dreamers. ^ Russell & Norvig 2003, pp. 25-26 ^ "Trends in the Emerging Tech Hype Cycle". Gartner Reports. Archived from the original on 22 May 2019. Retrieved 7 May 2019. ^ a b Moravec 1988, p. 20 ^ Harnad, S. (1990 ). "The Symbol Grounding Problem". Physica D. 42 (1-3): 335-346. arXiv: cs/9906002. Bibcode:1990 PhyD ... 42..335 H. doi:10.1016/ 0167-2789( 90 )90087-6. S2CID 3204300. ^ Gubrud 1997 ^ Hutter, Marcus (2005 ). Universal Expert System: Sequential Decisions Based Upon Algorithmic Probability. Texts in Theoretical Computer Technology an EATCS Series. Springer. doi:10.1007/ b138233. ISBN 978-3-5402-6877-2. S2CID 33352850. Archived from the initial on 19 July 2022. Retrieved 19 July 2022. ^ Legg, Shane (2008 ). Machine Super Intelligence (PDF) (Thesis). University of Lugano. Archived (PDF) from the initial on 15 June 2022. Retrieved 19 July 2022. ^ Goertzel, Ben (2014 ). Artificial General Intelligence. Lecture Notes in Computer Technology. Vol. 8598. Journal of Artificial General Intelligence. doi:10.1007/ 978-3-319-09274-4. ISBN 978-3-3190-9273-7. S2CID 8387410. ^ "Who created the term "AGI"?". goertzel.org. Archived from the original on 28 December 2018. Retrieved 28 December 2018., via Life 3.0: 'The term "AGI" was promoted by ... Shane Legg, Mark Gubrud and Ben Goertzel' ^ Wang & Goertzel 2007 ^ "First International Summer School in Artificial General Intelligence, Main summertime school: June 22 - July 3, 2009, OpenCog Lab: July 6-9, 2009". Archived from the 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 initial on 26 July 2020. Retrieved 11 May 2020. ^ Shevlin, Henry; Vold, Karina; Crosby, Matthew; Halina, Marta (4 October 2019). "The limitations of device intelligence: Despite development in device intelligence, artificial general intelligence is still a significant challenge". EMBO Reports. 20 (10 ): e49177. doi:10.15252/ embr.201949177. ISSN 1469-221X. PMC 6776890. PMID 31531926. ^ Bubeck, Sébastien; Chandrasekaran, Varun; Eldan, Ronen; Gehrke, Johannes; Horvitz, Eric; Kamar, Ece; Lee, Peter; Lee, Yin Tat; Li, Yuanzhi; Lundberg, Scott; Nori, Harsha; Palangi, Hamid; Ribeiro, Marco Tulio; Zhang, Yi (27 March 2023). "Sparks of Artificial General Intelligence: Early try outs GPT-4". arXiv:2303.12712 [cs.CL] ^ "Microsoft Researchers Claim GPT-4 Is Showing "Sparks" of AGI". Futurism. 23 March 2023. Retrieved 13 December 2023. ^ Allen, Paul; Greaves, Mark (12 October 2011). "The Singularity Isn't Near". MIT Technology Review. Retrieved 17 September 2014. ^ Winfield, Alan. "Expert system will not become a Frankenstein's beast". The Guardian. Archived from the original on 17 September 2014. Retrieved 17 September 2014. ^ Deane, George (2022 ). "Machines That Feel and Think: The Role of Affective Feelings and Mental Action in (Artificial) General Intelligence". Artificial Life. 28 (3 ): 289-309. doi:10.1162/ artl_a_00368. ISSN 1064-5462. PMID 35881678. S2CID 251069071. ^ a b c Clocksin 2003. ^ Fjelland, Ragnar (17 June 2020). "Why basic expert system will not be realized". Humanities and Social Sciences Communications. 7 (1 ): 1-9. doi:10.1057/ s41599-020-0494-4. hdl:11250/ 2726984. ISSN 2662-9992. S2CID 219710554. ^ McCarthy 2007b. ^ Khatchadourian, Raffi (23 November 2015). "The Doomsday Invention: Will artificial intelligence bring us paradise or destruction?". The New Yorker. Archived from the original on 28 January 2016. Retrieved 7 February 2016. ^ Müller, V. C., & Bostrom, N. (2016 ). Future development in expert system: A study of expert opinion. 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, 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 creativity of makers: AI takes the Torrance Test". Journal of Creativity. 33 (3 ): 100065. doi:10.1016/ j.yjoc.2023.100065. ISSN 2713-3745. S2CID 261087185. ^ Arcas, Blaise Agüera y (10 October 2023). "Artificial General Intelligence Is Already Here". Noema. ^ Zia, Tehseen (8 January 2024). "Unveiling of Large Multimodal Models: Shaping the Landscape of Language Models in 2024". Unite.ai. Retrieved 26 May 2024. ^ "Introducing OpenAI o1-preview". OpenAI. 12 September 2024. ^ Knight, Will. "OpenAI Announces a Brand-new AI Model, Code-Named Strawberry, That Solves Difficult Problems Step by Step". Wired. ISSN 1059-1028. Retrieved 17 September 2024. ^ "OpenAI Employee Claims AGI Has Been Achieved". Orbital Today. 13 December 2024. Retrieved 27 December 2024. ^ "AI Index: State of AI in 13 Charts". hai.stanford.edu. 15 April 2024. Retrieved 7 June 2024. ^ "Next-Gen AI: OpenAI and Meta's Leap Towards Reasoning Machines". Unite.ai. 19 April 2024. Retrieved 7 June 2024. ^ James, Alex P. (2022 ). "The Why, What, and How of Artificial General Intelligence Chip Development". IEEE Transactions on Cognitive and Developmental Systems. 14 (2 ): 333-347. arXiv:2012.06338. doi:10.1109/ TCDS.2021.3069871. ISSN 2379-8920. S2CID 228376556. Archived from the initial on 28 August 2022. Retrieved 28 August 2022. ^ Pei, Jing; Deng, Lei; Song, Sen; Zhao, Mingguo; Zhang, Youhui; Wu, Shuang; Wang, Guanrui; Zou, Zhe; Wu, Zhenzhi; He, Wei; Chen, Feng; Deng, Ning; Wu, Si; Wang, Yu; Wu, Yujie (2019 ). "Towards artificial basic intelligence with hybrid Tianjic chip architecture". Nature. 572 (7767 ): 106-111. Bibcode:2019 Natur.572..106 P. doi:10.1038/ s41586-019-1424-8. ISSN 1476-4687. PMID 31367028. S2CID 199056116. Archived from the initial on 29 August 2022. Retrieved 29 August 2022. ^ Pandey, Mohit; Fernandez, Michael; Gentile, Francesco; Isayev, Olexandr; Tropsha, Alexander; Stern, Abraham C.; Cherkasov, Artem (March 2022). "The transformational role of GPU computing and deep learning in drug discovery". Nature Machine Intelligence. 4 (3 ): 211-221. doi:10.1038/ s42256-022-00463-x. ISSN 2522-5839. S2CID 252081559. ^ Goertzel & Pennachin 2006. ^ a b c (Kurzweil 2005, p. 260). ^ a b c Goertzel 2007. ^ Grace, Katja (2016 ). "Error in Armstrong and Sotala 2012". AI Impacts (blog site). Archived from the original on 4 December 2020. Retrieved 24 August 2020. ^ a b Butz, Martin V. (1 March 2021). "Towards Strong AI". KI - Künstliche Intelligenz. 35 (1 ): 91-101. doi:10.1007/ s13218-021-00705-x. ISSN 1610-1987. S2CID 256065190. ^ Liu, Feng; Shi, Yong; Liu, Ying (2017 ). "Intelligence Quotient and Intelligence Grade of Expert System". Annals of Data Science. 4 (2 ): 179-191. arXiv:1709.10242. doi:10.1007/ s40745-017-0109-0. S2CID 37900130. ^ Brien, Jörn (5 October 2017). "Google-KI doppelt so schlau wie Siri" [Google AI is twice as wise as Siri - but a six-year-old beats both] (in German). Archived from the original on 3 January 2019. Retrieved 2 January 2019. ^ Grossman, Gary (3 September 2020). "We're getting in the AI twilight zone in between narrow and general AI". VentureBeat. Archived from the initial on 4 September 2020. Retrieved 5 September 2020. Certainly, too, there are those who claim we are currently seeing an early example of an AGI system in the recently revealed GPT-3 natural language processing (NLP) neural network. ... So is GPT-3 the first example of an AGI system? This is arguable, however the agreement is that it is not AGI. ... If absolutely nothing else, GPT-3 informs us there is a happy medium between narrow and basic AI. ^ Quach, Katyanna. "A developer developed an AI chatbot using GPT-3 that assisted a man speak again to his late fiancée. OpenAI shut it down". The Register. Archived from the initial on 16 October 2021. Retrieved 16 October 2021. ^ Wiggers, Kyle (13 May 2022), "DeepMind's brand-new AI can perform over 600 jobs, from playing video games to controlling robots", TechCrunch, archived from the initial 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 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. might equal human intelligence in 'simply a few years,' says CEO of Google's main A.I. research lab". Fortune. Retrieved 4 September 2024. ^ Nellis, Stephen (2 March 2024). "Nvidia CEO says AI might pass human tests in five years". Reuters. ^ Aschenbrenner, Leopold. "SITUATIONAL AWARENESS, The Decade Ahead". ^ Sullivan, Mark (18 October 2023). "Why everyone seems to disagree on how to define Artificial General Intelligence". Fast Company. ^ Nosta, John (5 January 2024). "The Accelerating Path to Artificial General Intelligence". Psychology Today. Retrieved 30 March 2024. ^ Hickey, Alex. "Whole Brain Emulation: A Giant Step for Neuroscience". Tech Brew. Retrieved 8 November 2023. ^ Sandberg & Boström 2008. ^ Drachman 2005. ^ a b Russell & Norvig 2003. ^ Moravec 1988, p. 61. ^ Moravec 1998. ^ Holmgaard Mersh, Amalie (15 September 2023). "Decade-long European research 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 broad variety of views in current research study, all of which need grounding to some degree ^ Thornton, Angela (26 June 2023). "How publishing 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 estimate in" Encyclopedia.com"),. MIT Encyclopedia of Cognitive Science Archived 19 July 2008 at the Wayback Machine (priced quote in "AITopics"),. Will Biological Computers Enable Artificially Intelligent Machines to Become Persons? Archived 13 May 2008 at the Wayback Machine Anthony Tongen.


^ a b c Russell & Norvig 2003, p. 947. ^ though see Explainable synthetic 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 believes the company's AI has come to life". The Washington Post. 11 June 2022. Retrieved 12 June 2023. ^ Kateman, Brian (24 July 2023). "AI Should Be Terrified of Humans". TIME. Retrieved 5 September 2024. ^ Nosta, John (18 December 2023). "Should Artificial Intelligence Have Rights?". Psychology Today. Retrieved 5 September 2024. ^ Akst, Daniel (10 April 2023). "Should Robots With Expert System Have Moral or Legal Rights?". The Wall Street Journal. ^ "Artificial General Intelligence - Do [es] the expense outweigh advantages?". 23 August 2021. Retrieved 7 June 2023. ^ "How we can Benefit from Advancing Artificial General Intelligence (AGI) - Unite.AI". www.unite.ai. 7 April 2020. Retrieved 7 June 2023. ^ a b c Talty, Jules; Julien, Stephan. "What Will Our Society Appear Like When Expert System Is Everywhere?". Smithsonian Magazine. Retrieved 7 June 2023. ^ a b Stevenson, Matt (8 October 2015). "Answers to Stephen Hawking's AMA are Here!". Wired. ISSN 1059-1028. Retrieved 8 June 2023. ^ a b Bostrom, Nick (2017 ). " § Preferred order of arrival". Superintelligence: paths, risks, techniques (Reprinted with corrections 2017 ed.). Oxford, United Kingdom; New York City, New York, USA: Oxford University Press. ISBN 978-0-1996-7811-2. ^ Piper, Kelsey (19 November 2018). "How technological development is making it likelier than ever that people will destroy ourselves". Vox. Retrieved 8 June 2023. ^ Doherty, Ben (17 May 2018). "Climate change an 'existential security threat' to Australia, Senate query 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 implications 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 debate between Yann LeCun, library.kemu.ac.ke Stuart Russel and Yoshua Bengio about the risks of strong AI". The remarkable Facebook dispute between Yann LeCun, Stuart Russel and Yoshua Bengio about the risks of strong AI (in French). Retrieved 8 June 2023. ^ "Will Expert System Doom The Human Race Within The Next 100 Years?". HuffPost. 22 August 2014. Retrieved 8 June 2023. ^ Sotala, Kaj; Yampolskiy, Roman V. (19 December 2014). "Responses to devastating AGI danger: a survey". Physica Scripta. 90 (1 ): 018001. doi:10.1088/ 0031-8949/90/ 1/018001. ISSN 0031-8949. ^ Bostrom, Nick (2014 ). Superintelligence: Paths, Dangers, Strategies (First ed.). Oxford University Press. ISBN 978-0-1996-7811-2. ^ Chow, Andrew R.; Perrigo, Billy (16 February 2023). "The AI Arms Race Is On. Start Worrying". TIME. Retrieved 24 December 2023. ^ Tetlow, Gemma (12 January 2017). "AI arms race 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 hazard posed however expert system can not be neglected". The Guardian. ISSN 0261-3077. Retrieved 24 December 2023. ^ "Humanity, Security & AI, Oh My! (with Ian Bremmer & Shuman Ghosemajumder)". CAFE. 20 July 2023. Retrieved 15 September 2023. ^ Hamblin, James (9 May 2014). "But What Would completion of Humanity Mean for Me?". The Atlantic. Archived from the initial on 4 June 2014. Retrieved 12 December 2015. ^ Titcomb, James (30 October 2023). "Big Tech is stiring fears over AI, alert scientists". The Telegraph. Retrieved 7 December 2023. ^ Davidson, John (30 October 2023). "Google Brain founder says 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 effect potential of big 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 affected". euronews. Retrieved 8 June 2023. ^ Sheffey, Ayelet (20 August 2021). "Elon Musk states we need universal standard income since 'in the future, physical work will be a choice'". Business Insider. Archived from the original on 9 July 2023. Retrieved 8 June 2023. Sources

UNESCO Science Report: the Race Against Time for Smarter Development. Paris: UNESCO. 11 June 2021. ISBN 978-9-2310-0450-6. Archived from the original on 18 June 2022. Retrieved 22 September 2021. Chalmers, David (1996 ), The Conscious Mind, Oxford University Press. Clocksin, William (August 2003), "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: Artificial Intelligence and Japan's Computer Challenge to the World, Michael Joseph, ISBN 978-0-7181-2401-4. Goertzel, Ben; Pennachin, Cassio, eds. (2006 ), Artificial General Intelligence (PDF), Springer, ISBN 978-3-5402-3733-4, archived from the initial (PDF) on 20 March 2013. Goertzel, Ben (December 2007), "Human-level synthetic general intelligence and the possibility of a technological singularity: a response to Ray Kurzweil's The Singularity Is Near, and McDermott's 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, obtained 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, retrieved 7 May 2011. Howe, J. (November 1994), Artificial Intelligence at Edinburgh University: a Point of view, archived from the original on 17 August 2007, obtained 30 August 2007. Johnson, Mark (1987 ), The body in the mind, Chicago, ISBN 978-0-2264-0317-5. Kurzweil, Ray (2005 ), The Singularity is Near, Viking Press. Lighthill, Professor Sir James (1973 ), "Artificial Intelligence: A General Survey", Expert System: a paper symposium, Science Research Council. Luger, George; Stubblefield, William (2004 ), Artificial Intelligence: Structures and Strategies for Complex Problem Solving (fifth ed.), The Benjamin/Cummings Publishing Company, Inc., p. 720, ISBN 978-0-8053-4780-7. McCarthy, John (2007b). What is Expert system?. Stanford University. The ultimate effort is to make computer programs that can resolve issues and achieve goals in the world in addition to people. Moravec, Hans (1988 ), Mind Children, Harvard University Press Moravec, Hans (1998 ), "When will hardware match the human brain?", Journal of Evolution and Technology, vol. 1, archived from the initial on 15 June 2006, obtained 23 June 2006 Nagel (1974 ), "What Is it Like to Be a Bat" (PDF), Philosophical Review, 83 (4 ): 435-50, doi:10.2307/ 2183914, JSTOR 2183914, archived (PDF) from the initial on 16 October 2011, recovered 7 November 2009 Newell, Allen; Simon, H. A. (1976 ). "Computer Science as Empirical Inquiry: Symbols and Search". Communications of the ACM. 19 (3 ): 113-126. doi:10.1145/ 360018.360022. Nilsson, Nils (1998 ), Expert System: A New Synthesis, Morgan Kaufmann Publishers, ISBN 978-1-5586-0467-4 NRC (1999 ), "Developments in Expert System", Funding a Transformation: Government Support for Computing Research, National Academy Press, archived from the initial on 12 January 2008, retrieved 29 September 2007 Poole, David; Mackworth, Alan; Goebel, Randy (1998 ), Computational Intelligence: A Sensible Approach, New York City: Oxford University Press, archived from the original on 25 July 2009, obtained 6 December 2007 Russell, Stuart J.; Norvig, Peter (2003 ), Expert System: A Modern Approach (second ed.), Upper Saddle River, New Jersey: Prentice Hall, ISBN 0-13-790395-2 Sandberg, Anders; Boström, Nick (2008 ), Whole Brain Emulation: A Roadmap (PDF), Technical Report # 2008-3, Future of Humanity Institute, Oxford University, archived (PDF) from the original on 25 March 2020, 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 original 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 original on 18 February 2021, recovered 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, 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 may be called "Dyson's Law") that "Any system basic enough to be reasonable will not be made complex enough to behave smartly, mediawiki.hcah.in while any system made complex enough to act smartly will be too made complex to understand." (p. 197.) Computer researcher Alex Pentland composes: "Current AI machine-learning algorithms are, at their core, dead basic stupid. They work, however they work by brute force." (p. 198.). Gelernter, David, Dream-logic, the Internet and Artificial Thought, Edge, archived from the original on 26 July 2010, retrieved 25 July 2010. Gleick, James, "The Fate of Free Choice" (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 devices. For biological animals, reason and purpose originate from acting worldwide and experiencing the consequences. Artificial intelligences - disembodied, complete strangers to blood, sweat, and setiathome.berkeley.edu 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" (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 rich from AI are going to have the interests of the rest people close at heart,' ... composes [Gary Marcus] 'We can't depend on federal governments driven by campaign finance contributions [from tech business] to push back.' ... Marcus details the demands that residents need to make of their federal governments and the tech business. They include transparency on how AI systems work; compensation for people if their information [are] used to train LLMs (big language model) s and the right to grant this use; and the ability to hold tech companies responsible for the damages they bring on by removing Section 230, enforcing money penalites, and passing stricter product liability laws ... Marcus also recommends ... that a new, AI-specific federal firm, akin to the FDA, the FCC, or the FTC, might provide the most robust oversight ... [T] he Fordham law professor Chinmayi Sharma ... recommends ... develop [ing] a professional licensing regime for engineers that would operate in a similar method to medical licenses, malpractice matches, and the Hippocratic oath in medication. 'What if, like medical professionals,' she asks ..., 'AI engineers likewise promised to do no harm?'" (p. 46.). Holte, R. C.; Choueiry, B. Y. (2003 ), "Abstraction and reformulation in expert system", Philosophical Transactions of the Royal Society B, vol. 358, no. 1435, pp. 1197-1204, doi:10.1098/ rstb.2003.1317, PMC 1693218, PMID 12903653. Hughes-Castleberry, Kenna, "A Murder Mystery Puzzle: The literary puzzle Cain's Jawbone, which has stumped humans for decades, exposes the limitations of natural-language-processing algorithms", Scientific American, vol. 329, no. 4 (November 2023), pp. 81-82. "This murder mystery competition has exposed that although NLP (natural-language processing) designs can amazing feats, their capabilities are very much limited by the amount of context they receive. This [...] might trigger [problems] for scientists who wish to use them to do things such as analyze ancient languages. Sometimes, there are few historical 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 fake videos equivalent from real ones. Just how much does it matter?", The New Yorker, 20 November 2023, pp. 54-59. "If by 'deepfakes' we indicate practical videos produced using artificial intelligence that in fact trick people, then they hardly exist. The phonies aren't deep, and the deeps aren't fake. [...] A.I.-generated videos are not, in general, coastalplainplants.org running in our media as counterfeited proof. Their function much better resembles that of animations, specifically smutty ones." (p. 59.). - Leffer, Lauren, "The Risks of Trusting AI: We should avoid humanizing machine-learning models used in scientific research", Scientific American, vol. 330, no. 6 (June 2024), pp. 80-81. Lepore, Jill, "The Chit-Chatbot: Is talking with a machine a discussion?", The New Yorker, 7 October 2024, pp. 12-16. Marcus, Gary, "Artificial Confidence: Even the newest, buzziest systems of artificial basic intelligence are stymmied by the usual problems", Scientific American, vol. 327, no. 4 (October 2022), pp. 42-45. McCarthy, John (October 2007), "From here to human-level AI", Artificial Intelligence, 171 (18 ): 1174-1182, doi:10.1016/ j.artint.2007.10.009. McCorduck, Pamela (2004 ), Machines Who Think (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, recovered 29 September 2007. Newell, Allen; Simon, H. A. (1963 ), "GPS: A Program that Simulates Human Thought", in Feigenbaum, E. A.; Feldman, J. (eds.), Computers and Thought, New York City: McGraw-Hill. Omohundro, Steve (2008 ), The Nature of Self-Improving Artificial Intelligence, presented and dispersed at the 2007 Singularity Summit, San Francisco, California. Press, Eyal, "In Front of Their Faces: Does facial-recognition technology lead authorities to overlook contradictory evidence?", The New Yorker, 20 November 2023, pp. 20-26. Roivainen, Eka, "AI's IQ: ChatGPT aced a [basic intelligence] test however revealed 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 genuine humanlike reasoning or an understanding of the physical and social world ... ChatGPT seemed not able to reason logically and attempted to depend on its huge database of ... facts originated from online texts. " - Scharre, Paul, "Killer Apps: The Real Dangers of an AI Arms Race", Foreign Affairs, vol. 98, no. 3 (May/June 2019), pp. 135-44. "Today's AI innovations are powerful however unreliable. Rules-based systems can not handle scenarios their developers did not prepare for. Learning systems are restricted by the data on which they were trained. AI failures have currently caused catastrophe. Advanced auto-pilot features in cars and trucks, although they perform well in some circumstances, have driven automobiles without warning into trucks, concrete barriers, and parked cars. In the wrong circumstance, AI systems go from supersmart to superdumb in an instant. When an opponent is attempting to control and hack an AI system, the risks are even greater." (p. 140.). Sutherland, J. G. (1990 ), "Holographic Model of Memory, Learning, and Expression", International Journal of Neural Systems, vol. 1-3, pp. 256-267. - Vincent, James, "Horny Robot Baby Voice: James Vincent on AI chatbots", London Review of Books, vol. 46, no. 19 (10 October 2024), pp. 29-32." [AI chatbot] programs are enabled by brand-new technologies however count on the timelelss human propensity to anthropomorphise." (p. 29.). Williams, R. W.; Herrup, K.