GitLab

Artificial basic intelligence (AGI) is a type of expert system (AI) that matches or exceeds human cognitive abilities throughout a large range of cognitive tasks. This contrasts with narrow AI, which is limited to specific tasks. [1] Artificial superintelligence (ASI), on the other hand, describes AGI that greatly goes beyond human cognitive capabilities. AGI is thought about among the meanings of strong AI.

Creating AGI is a primary goal of AI research and of business such as OpenAI [2] and Meta. [3] A 2020 survey identified 72 active AGI research and development tasks throughout 37 nations. [4]
The timeline for accomplishing AGI stays a topic of continuous dispute amongst researchers and experts. As of 2023, some argue that it may be possible in years or decades; others keep it may take a century or longer; a minority think it might never be accomplished; and another minority declares that it is already here. [5] [6] Notable AI researcher Geoffrey Hinton has expressed issues about the quick development towards AGI, recommending it could be achieved earlier than numerous anticipate. [7]
There is argument on the precise definition of AGI and concerning whether contemporary large language models (LLMs) such as GPT-4 are early kinds of AGI. [8] AGI is a common 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 actually mentioned that alleviating the danger of human extinction postured by AGI must be a global concern. [14] [15] Others find the development of AGI to be too remote to provide such a risk. [16] [17]
Terminology

AGI is also known as strong AI, [18] [19] complete AI, [20] human-level AI, [5] human-level smart AI, or basic smart action. [21]
Some scholastic sources schedule the term "strong AI" for computer system programs that experience sentience or consciousness. [a] In contrast, weak AI (or narrow AI) has the ability to fix one specific problem 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 consciousness nor have a mind in the same sense as humans. [a]
Related ideas include artificial superintelligence and transformative AI. An artificial superintelligence (ASI) is a hypothetical type of AGI that is far more usually smart than people, [23] while the concept of transformative AI connects to AI having a big influence on society, for instance, similar to the farming or commercial transformation. [24]
A framework for categorizing AGI in levels was proposed in 2023 by Google DeepMind scientists. They specify five levels of AGI: emerging, proficient, specialist, virtuoso, and superhuman. For example, a qualified AGI is specified as an AI that exceeds 50% of skilled grownups in a wide variety of non-physical jobs, and a superhuman AGI (i.e. an artificial superintelligence) is likewise defined however with a limit of 100%. They think about big language designs like ChatGPT or LLaMA 2 to be instances of emerging AGI. [25]
Characteristics

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

Researchers typically hold that intelligence is needed to do all of the following: [27]
reason, usage strategy, fix puzzles, and make judgments under uncertainty represent understanding, consisting of good sense understanding plan find out

• communicate in natural language
• if necessary, integrate these skills in completion of any provided goal

Many interdisciplinary techniques (e.g. cognitive science, computational intelligence, and choice making) think about extra characteristics such as imagination (the ability to form novel mental images and concepts) [28] and autonomy. [29]
Computer-based systems that exhibit numerous of these capabilities exist (e.g. see computational imagination, automated reasoning, decision assistance system, robot, evolutionary computation, intelligent representative). There is debate about whether modern-day AI systems possess them to an appropriate degree.

Physical traits

Other capabilities are considered preferable in intelligent systems, as they may affect intelligence or help in its expression. These consist of: [30]
- the capability to sense (e.g. see, hear, etc), and - the ability to act (e.g. relocation and control objects, modification location to explore, etc).
This includes the ability to detect and react to threat. [31]
Although the capability to sense (e.g. see, hear, etc) and the capability to act (e.g. relocation and control items, change location to explore, and so on) can be preferable for some smart systems, [30] these physical capabilities are not strictly needed for an entity to qualify as AGI-particularly under the thesis that big language designs (LLMs) may currently be or end up being AGI. Even from a less optimistic perspective on LLMs, there is no firm requirement for an AGI to have a human-like type; being a silicon-based computational system suffices, provided it can process input (language) from the external world in place of human senses. This analysis aligns with the understanding that AGI has actually never ever been proscribed a specific physical personification and hence does not demand a capacity for mobility or traditional "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 attempt and pretend to be a man, by addressing questions put to it, and it will only pass if the pretence is reasonably persuading. A significant part of a jury, who should not be professional about machines, should be taken in by the pretence. [37]
AI-complete problems

A problem is informally called "AI-complete" or "AI-hard" if it is thought that in order to resolve it, one would need to implement AGI, due to the fact that the service is beyond the abilities of a purpose-specific algorithm. [47]
There are numerous problems that have actually been conjectured to need basic intelligence to resolve as well as human beings. Examples consist of computer vision, natural language understanding, and dealing with unexpected situations while resolving any real-world problem. [48] Even a specific task like translation requires a maker to read and compose in both languages, follow the author's argument (factor), comprehend the context (knowledge), and faithfully reproduce the author's original intent (social intelligence). All of these issues require to be solved simultaneously in order to reach human-level maker efficiency.

However, numerous of these tasks can now be performed by modern large language models. According to Stanford University's 2024 AI index, AI has reached human-level performance on many benchmarks for reading comprehension 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 artificial basic intelligence was possible and that it would exist in just a few years. [51] AI leader Herbert A. Simon wrote in 1965: "makers will be capable, within twenty years, of doing any work a man can do." [52]
Their predictions were the inspiration for Stanley Kubrick and Arthur C. Clarke's character HAL 9000, who embodied what AI researchers thought they could produce by the year 2001. AI leader Marvin Minsky was a consultant [53] on the task of making HAL 9000 as realistic as possible according to the consensus forecasts of the time. He stated in 1967, "Within a generation ... the issue of creating 'artificial intelligence' will considerably be solved". [54]
Several classical AI jobs, such as Doug Lenat's Cyc job (that started in 1984), and Allen Newell's Soar task, were directed at AGI.

However, in the early 1970s, it ended up being apparent that scientists had grossly ignored the problem of the task. Funding firms ended up being doubtful of AGI and put scientists under increasing pressure to produce useful "applied AI". [c] In the early 1980s, Japan's Fifth Generation Computer Project restored 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 expert systems, both market and government pumped money into the field. [56] [59] However, confidence in AI spectacularly collapsed in the late 1980s, and the goals of the Fifth Generation Computer Project were never fulfilled. [60] For the 2nd time in twenty years, AI researchers who predicted the imminent accomplishment of AGI had actually been mistaken. By the 1990s, AI scientists had a track record for making vain promises. They became hesitant to make predictions at all [d] and avoided reference of "human level" synthetic intelligence for fear of being labeled "wild-eyed dreamer [s]. [62]
Narrow AI research

In the 1990s and early 21st century, mainstream AI attained commercial success and scholastic respectability by focusing on particular sub-problems where AI can produce proven results and commercial applications, such as speech recognition and recommendation algorithms. [63] These "applied AI" systems are now utilized thoroughly throughout the innovation industry, and research in this vein is heavily moneyed in both academic community and industry. As of 2018 [update], advancement in this field was thought about an emerging trend, and a mature phase was anticipated to be reached in more than ten years. [64]
At the millenium, lots of mainstream AI researchers [65] hoped that strong AI might be established by integrating programs that solve different sub-problems. Hans Moravec wrote in 1988:

I am confident that this bottom-up route to expert system will one day satisfy the conventional top-down path majority method, all set to supply the real-world competence and the commonsense understanding that has been so frustratingly evasive in reasoning programs. Fully smart machines will result when the metaphorical golden spike is driven uniting the two efforts. [65]
However, even at the time, this was disputed. For instance, Stevan Harnad of Princeton University concluded his 1990 paper on the symbol grounding hypothesis by specifying:

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

The term "artificial general intelligence" was utilized as early as 1997, by Mark Gubrud [67] in a discussion of the implications of fully automated military production and operations. A mathematical formalism of AGI was proposed by Marcus Hutter in 2000. Named AIXI, the proposed AGI agent maximises "the capability to please goals in a vast array of environments". [68] This type of AGI, identified by the ability to increase a mathematical definition of intelligence rather than show human-like behaviour, [69] was likewise called universal synthetic intelligence. [70]
The term AGI was re-introduced and popularized by Shane Legg and Ben Goertzel around 2002. [71] AGI research activity in 2006 was explained by Pei Wang and Ben Goertzel [72] as "producing publications and initial results". The first summertime school in AGI was organized in Xiamen, China in 2009 [73] by the Xiamen university's Artificial Brain Laboratory and OpenCog. The 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 visitor lecturers.

Since 2023 [update], a small number of computer researchers are active in AGI research, and lots of contribute to a series of AGI conferences. However, increasingly more researchers have an interest in open-ended knowing, [76] [77] which is the idea of enabling AI to continuously find out and innovate like humans do.

Feasibility

Since 2023, the development and prospective achievement of AGI remains a subject of extreme dispute within the AI neighborhood. While conventional consensus held that AGI was a distant goal, current advancements have led some scientists and market figures to declare that early types 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 stopped working to come true. Microsoft co-founder Paul Allen thought that such intelligence is unlikely in the 21st century due to the fact that it would require "unforeseeable and fundamentally unpredictable developments" and a "clinically deep understanding of cognition". [79] Writing in The Guardian, roboticist Alan Winfield claimed the gulf in between modern computing and human-level synthetic intelligence is as large as the gulf between existing area flight and useful faster-than-light spaceflight. [80]
A more challenge is the lack of clearness in defining what intelligence entails. Does it need consciousness? Must it show the ability to set objectives along with pursue them? Is it simply a matter of scale such that if design sizes increase adequately, intelligence will emerge? Are facilities such as preparation, reasoning, and causal understanding required? Does intelligence require explicitly duplicating the brain and its particular professors? Does it require feelings? [81]
Most AI researchers believe strong AI can be achieved in the future, however some thinkers, like Hubert Dreyfus and Roger Penrose, reject the possibility of achieving strong AI. [82] [83] John McCarthy is amongst those who think human-level AI will be achieved, however that the present level of development is such that a date can not precisely be anticipated. [84] AI experts' views on the expediency of AGI wax and wane. Four polls performed in 2012 and 2013 suggested that the average price quote among experts for when they would be 50% confident AGI would arrive was 2040 to 2050, depending on the poll, with the mean being 2081. Of the experts, 16.5% responded to with "never" when asked the same question however with a 90% self-confidence instead. [85] [86] Further current AGI progress considerations can be discovered above Tests for confirming human-level AGI.

A report by Stuart Armstrong and Kaj Sotala of the Machine Intelligence Research Institute found that "over [a] 60-year amount of time there is a strong bias towards forecasting the arrival of human-level AI as in between 15 and 25 years from the time the prediction was made". They evaluated 95 forecasts made between 1950 and 2012 on when human-level AI will come about. [87]
In 2023, Microsoft researchers published an in-depth evaluation of GPT-4. They concluded: "Given the breadth and depth of GPT-4's abilities, we believe that it might fairly be considered as an early (yet still incomplete) version of a synthetic basic intelligence (AGI) system." [88] Another research study in 2023 reported that GPT-4 outperforms 99% of human beings on the Torrance tests of imaginative thinking. [89] [90]
Blaise Agüera y Arcas and Peter Norvig composed in 2023 that a significant level of basic intelligence has already been achieved with frontier designs. They wrote that reluctance to this view comes from four primary factors: a "healthy apprehension about metrics for AGI", an "ideological dedication to alternative AI theories or strategies", a "devotion to human (or biological) exceptionalism", or a "concern about the economic ramifications of AGI". [91]
2023 also marked the emergence of big multimodal designs (big language designs efficient in processing or creating several techniques such as text, audio, and images). [92]
In 2024, OpenAI launched o1-preview, the first of a series of models that "invest more time believing before they respond". According to Mira Murati, this capability to think before responding represents a new, additional paradigm. It improves design outputs by spending more computing power when creating the response, whereas the design scaling paradigm improves outputs by increasing the design size, training information and training calculate power. [93] [94]
An OpenAI staff member, Vahid Kazemi, claimed in 2024 that the business had actually achieved AGI, mentioning, "In my viewpoint, we have actually currently accomplished AGI and it's even more clear with O1." Kazemi clarified that while the AI is not yet "better than any human at any job", it is "much better than the majority of people at many tasks." He also attended to criticisms that big language designs (LLMs) simply follow predefined patterns, comparing their learning procedure to the scientific technique of observing, hypothesizing, and confirming. These declarations have sparked dispute, as they depend on a broad and non-traditional meaning of AGI-traditionally comprehended as AI that matches human intelligence throughout all domains. Critics argue that, while OpenAI's designs demonstrate exceptional flexibility, they may not fully fulfill this requirement. Notably, Kazemi's remarks came shortly after OpenAI got rid of "AGI" from the terms of its partnership with Microsoft, triggering speculation about the business's tactical objectives. [95]
Timescales

Progress in expert system has historically gone through durations of rapid progress separated by durations when progress appeared to stop. [82] Ending each hiatus were basic advances in hardware, software or both to create space for additional development. [82] [98] [99] For example, the computer hardware offered in the twentieth century was not sufficient to execute deep learning, which requires large numbers of GPU-enabled CPUs. [100]
In the intro to his 2006 book, [101] Goertzel says that price quotes of the time needed before a genuinely flexible AGI is constructed vary from ten years to over a century. Since 2007 [upgrade], the consensus in the AGI research neighborhood appeared to be that the timeline talked about by Ray Kurzweil in 2005 in The Singularity is Near [102] (i.e. between 2015 and 2045) was possible. [103] Mainstream AI researchers have provided a large variety of opinions on whether development will be this quick. A 2012 meta-analysis of 95 such opinions discovered a bias towards anticipating that the onset of AGI would occur within 16-26 years for modern-day and historic forecasts alike. That paper has been slammed for how it categorized opinions as professional 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%, considerably better than the second-best entry's rate of 26.3% (the conventional technique used a weighted sum of scores from different pre-defined classifiers). [105] AlexNet was considered the preliminary ground-breaker of the existing deep knowing wave. [105]
In 2017, scientists Feng Liu, Yong Shi, and Ying Liu carried out intelligence tests on publicly readily available and easily accessible weak AI such as Google AI, Apple's Siri, and others. At the maximum, these AIs reached an IQ worth of about 47, which corresponds roughly to a six-year-old child in first grade. An adult comes to about 100 typically. Similar tests were performed in 2014, with the IQ rating reaching a maximum worth of 27. [106] [107]
In 2020, OpenAI established GPT-3, a language model capable of performing numerous diverse jobs without particular training. According to Gary Grossman in a VentureBeat post, while there is consensus that GPT-3 is not an example of AGI, it is thought about by some to be too advanced to be categorized as a narrow AI system. [108]
In the same year, Jason Rohrer utilized his GPT-3 account to establish a chatbot, and provided a chatbot-developing platform called "Project December". OpenAI requested for changes to the chatbot to abide by their safety standards; Rohrer disconnected Project December from the GPT-3 API. [109]
In 2022, DeepMind developed Gato, a "general-purpose" system efficient in performing more than 600 different tasks. [110]
In 2023, Microsoft Research published a research study on an early version of OpenAI's GPT-4, contending that it displayed more basic intelligence than previous AI models and showed human-level efficiency in jobs covering numerous domains, such as mathematics, coding, and law. This research triggered an argument on whether GPT-4 might be considered an early, incomplete version of synthetic basic intelligence, emphasizing the requirement for additional expedition and assessment of such systems. [111]
In 2023, the AI scientist Geoffrey Hinton mentioned that: [112]
The idea that this things could in fact get smarter than individuals - a couple of individuals thought that, [...] But most individuals thought it was way off. And I thought it was method off. I believed it was 30 to 50 years or even longer away. Obviously, I no longer believe that.

In May 2023, Demis Hassabis similarly said that "The progress in the last couple of years has actually been quite extraordinary", which he sees no reason that it would slow down, anticipating AGI within a years and even a few years. [113] In March 2024, Nvidia's CEO, Jensen Huang, mentioned his expectation that within 5 years, AI would can passing any test at least as well as humans. [114] In June 2024, the AI researcher Leopold Aschenbrenner, a previous OpenAI employee, estimated AGI by 2027 to be "noticeably plausible". [115]
Whole brain emulation

While the development of transformer designs like in ChatGPT is thought about the most promising course to AGI, [116] [117] entire brain emulation can act as an alternative method. With entire brain simulation, a brain design is built by scanning and mapping a biological brain in information, and then copying and simulating it on a computer system or another computational gadget. The simulation design should be adequately devoted to the original, so that it acts in practically the same way as the initial brain. [118] Whole brain emulation is a kind of brain simulation that is talked about in computational neuroscience and neuroinformatics, and for medical research study purposes. It has actually been talked about in artificial intelligence research [103] as a method to strong AI. Neuroimaging innovations that could provide the required in-depth understanding are enhancing rapidly, and futurist Ray Kurzweil in the book The Singularity Is Near [102] forecasts that a map of enough quality will appear on a comparable timescale to the computing power needed to imitate it.

Early approximates

For low-level brain simulation, an extremely powerful cluster of computer systems or GPUs would be needed, given the massive quantity of synapses within the human brain. Each of the 1011 (one hundred billion) neurons has on average 7,000 synaptic connections (synapses) to other nerve cells. The brain of a three-year-old kid has about 1015 synapses (1 quadrillion). This number decreases with age, supporting by their adult years. Estimates vary for an adult, varying from 1014 to 5 × 1014 synapses (100 to 500 trillion). [120] An estimate of the brain's processing power, based on a simple switch design for nerve cell activity, is around 1014 (100 trillion) synaptic updates per second (SUPS). [121]
In 1997, Kurzweil took a look at different price quotes for the hardware required to equal the human brain and embraced a figure of 1016 computations per 2nd (cps). [e] (For comparison, if a "computation" was equivalent to one "floating-point operation" - a procedure utilized to rate existing supercomputers - then 1016 "computations" would be comparable to 10 petaFLOPS, achieved in 2011, while 1018 was accomplished in 2022.) He utilized this figure to forecast the necessary hardware would be offered at some point between 2015 and 2025, if the exponential growth 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 established an especially in-depth and publicly accessible atlas of the human brain. [124] In 2023, researchers from Duke University carried out a high-resolution scan of a mouse brain.

Criticisms of simulation-based techniques

The synthetic nerve cell model presumed by Kurzweil and utilized in lots of present artificial neural network applications is easy compared to biological nerve cells. A brain simulation would likely have to record the detailed cellular behaviour of biological neurons, currently comprehended only in broad outline. The overhead introduced by full modeling of the biological, chemical, and physical details of neural behaviour (especially on a molecular scale) would require computational powers numerous orders of magnitude larger than Kurzweil's estimate. In addition, the estimates do not represent glial cells, which are understood to play a role in cognitive processes. [125]
An essential criticism of the simulated brain technique originates from embodied cognition theory which asserts that human personification is a vital aspect of human intelligence and is necessary to ground significance. [126] [127] If this theory is right, any totally practical brain design will need to incorporate more than simply 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 viewpoint

In 1980, thinker John Searle coined the term "strong AI" as part of his Chinese room argument. [128] He proposed a distinction in between 2 hypotheses about expert system: [f]
Strong AI hypothesis: An artificial intelligence system can have "a mind" and "awareness". Weak AI hypothesis: An expert system system can (only) act like it thinks and has a mind and awareness.
The very first one he called "strong" due to the fact that it makes a more powerful statement: it presumes something special has occurred to the device that surpasses those abilities that we can evaluate. The behaviour of a "weak AI" maker would be specifically identical to a "strong AI" machine, however the latter would likewise have subjective mindful experience. This use is likewise 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 general intelligence". [102] This is not the like Searle's strong AI, unless it is assumed that awareness is needed for human-level AGI. Academic thinkers such as Searle do not believe that is the case, and to most synthetic intelligence scientists the question is out-of-scope. [130]
Mainstream AI is most interested in how a program acts. [131] According to Russell and Norvig, "as long as the program works, they don't 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 requirement to know if it in fact has mind - certainly, there would be no chance to inform. For AI research, Searle's "weak AI hypothesis" is equivalent to the declaration "artificial general intelligence is possible". Thus, according to Russell and Norvig, "most AI researchers take the weak AI hypothesis for granted, and do not care about the strong AI hypothesis." [130] Thus, for academic AI research, "Strong AI" and "AGI" are two different things.

Consciousness

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

Sentience (or "sensational consciousness"): The ability to "feel" understandings or feelings subjectively, as opposed to the capability to reason about perceptions. Some theorists, such as David Chalmers, utilize the term "awareness" to refer solely to sensational awareness, which is approximately comparable to life. [132] Determining why and how subjective experience occurs is called the hard problem of awareness. [133] Thomas Nagel described in 1974 that it "seems like" something to be conscious. If we are not conscious, then it doesn't seem like anything. Nagel uses the example of a bat: we can smartly ask "what does it seem 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 appears to be mindful (i.e., has consciousness) but a toaster does not. [134] In 2022, a Google engineer declared that the company's AI chatbot, LaMDA, had actually accomplished sentience, though this claim was extensively disputed by other professionals. [135]
Self-awareness: To have conscious awareness of oneself as a different person, particularly to be purposely familiar with one's own ideas. This is opposed to just being the "topic of one's believed"-an os or debugger has the ability to be "aware of itself" (that is, to represent itself in the very same method it represents whatever else)-but this is not what people generally indicate when they use the term "self-awareness". [g]
These characteristics have a moral dimension. AI life would provide increase to concerns of well-being and legal security, similarly to animals. [136] Other elements of consciousness associated to cognitive capabilities are also appropriate to the idea of AI rights. [137] Finding out how to integrate innovative AI with existing legal and social frameworks is an emergent concern. [138]
Benefits

AGI could have a wide range of applications. If oriented towards such objectives, AGI might help alleviate numerous issues worldwide such as hunger, poverty and health issue. [139]
AGI could enhance performance and efficiency in many jobs. For instance, in public health, AGI might speed up medical research study, especially versus cancer. [140] It might take care of the elderly, [141] and democratize access to quick, premium medical diagnostics. It might use enjoyable, low-cost and personalized education. [141] The requirement to work to subsist could end up being outdated if the wealth produced is correctly rearranged. [141] [142] This likewise raises the question of the place of people in a radically automated society.

AGI might likewise help to make reasonable decisions, and to expect and avoid catastrophes. It might likewise help to profit of potentially disastrous innovations such as nanotechnology or environment engineering, while preventing the associated dangers. [143] If an AGI's primary objective is to prevent existential disasters such as human extinction (which might be difficult if the Vulnerable World Hypothesis ends up being true), [144] it could take steps to dramatically reduce the dangers [143] while minimizing the impact of these measures on our quality of life.

Risks

Existential risks

AGI may represent numerous types of existential danger, which are dangers that threaten "the premature extinction of Earth-originating intelligent life or the long-term and extreme destruction of its capacity for desirable future advancement". [145] The threat of human termination from AGI has been the subject of many debates, but there is likewise the possibility that the development of AGI would cause a completely flawed future. Notably, it could be used to spread out and protect the set of worths of whoever establishes it. If mankind still has moral blind spots comparable to slavery in the past, AGI may irreversibly entrench it, preventing ethical development. [146] Furthermore, AGI might assist in mass surveillance and brainwashing, which might be used to develop a stable repressive around the world totalitarian regime. [147] [148] There is likewise a threat for the machines themselves. If devices that are sentient or otherwise deserving of moral consideration are mass produced in the future, engaging in a civilizational course that indefinitely disregards their well-being and interests could be an existential catastrophe. [149] [150] Considering how much AGI could enhance humanity's future and assistance decrease other existential dangers, Toby Ord calls these existential dangers "an argument for proceeding with due caution", not for "deserting AI". [147]
Risk of loss of control and human extinction

The thesis that AI positions an existential risk for people, which this danger needs more attention, is questionable however has been endorsed in 2023 by numerous public figures, AI researchers 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 benefits and threats, the experts are surely doing everything possible to ensure the best result, right? Wrong. If an exceptional alien civilisation sent us a message stating, 'We'll show up in a couple of years,' would we simply reply, 'OK, call us when you get here-we'll leave the lights on?' Probably not-but this is basically what is occurring with AI. [153]
The potential fate of mankind has actually in some cases been compared to the fate of gorillas threatened by human activities. The comparison specifies that greater intelligence enabled humanity to control gorillas, which are now susceptible in methods that they might not have actually prepared for. As an outcome, the gorilla has actually ended up being an endangered species, not out of malice, but just as a civilian casualties from human activities. [154]
The skeptic Yann LeCun thinks about that AGIs will have no desire to dominate humanity and that we should take care not to anthropomorphize them and interpret their intents as we would for humans. He said that people will not be "clever enough to design super-intelligent devices, yet unbelievably silly to the point of offering it moronic objectives with no safeguards". [155] On the other side, the idea of important convergence recommends that practically whatever their goals, smart representatives will have reasons to try to make it through and get more power as intermediary actions to accomplishing these objectives. Which this does not require having emotions. [156]
Many scholars who are concerned about existential risk supporter for more research into resolving the "control issue" to respond to the concern: what types of safeguards, algorithms, or architectures can developers execute to maximise the possibility that their recursively-improving AI would continue to act in a friendly, rather than destructive, manner after it reaches superintelligence? [157] [158] Solving the control issue is complicated by the AI arms race (which might result in a race to the bottom of safety preventative measures in order to release products before competitors), [159] and using AI in weapon systems. [160]
The thesis that AI can present existential threat likewise has critics. Skeptics usually state that AGI is unlikely in the short-term, or that concerns about AGI distract from other problems associated with current AI. [161] Former Google fraud czar Shuman Ghosemajumder thinks about that for many individuals outside of the technology market, existing chatbots and LLMs are already perceived as though they were AGI, resulting in more misconception and fear. [162]
Skeptics sometimes charge that the thesis is crypto-religious, with an irrational belief in the possibility of superintelligence changing an illogical belief in a supreme God. [163] Some researchers believe that the interaction campaigns on AI existential risk by specific AI groups (such as OpenAI, Anthropic, DeepMind, and Conjecture) may be an at effort at regulative capture and to pump up interest in their products. [164] [165]
In 2023, the CEOs of Google DeepMind, OpenAI and Anthropic, in addition to other market leaders and researchers, provided a joint statement asserting that "Mitigating the threat of extinction from AI must be a worldwide concern together with other societal-scale dangers such as pandemics and 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 tasks affected by the introduction of LLMs, smfsimple.com while around 19% of workers might see at least 50% of their jobs impacted". [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, capability to make choices, to interface with other computer system tools, however likewise to manage robotized bodies.

According to Stephen Hawking, the outcome of automation on the lifestyle will depend on how the wealth will be rearranged: [142]
Everyone can enjoy a life of elegant leisure if the machine-produced wealth is shared, or many people can wind up badly bad if the machine-owners successfully lobby versus wealth redistribution. Up until now, the trend seems to be toward the second alternative, with technology driving ever-increasing inequality

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

Artificial brain - Software and hardware with cognitive capabilities comparable to those of the animal or human brain AI effect AI safety - Research location on making AI safe and advantageous AI positioning - AI conformance to the intended objective A.I. Rising - 2018 film directed by Lazar Bodroža Expert system Automated artificial intelligence - Process of automating the application of artificial intelligence BRAIN Initiative - Collaborative public-private research study initiative announced 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 games Generative expert system - AI system capable of generating content in response to triggers Human Brain Project - Scientific research task Intelligence amplification - Use of infotech to augment human intelligence (IA). Machine principles - Moral behaviours of man-made machines. Moravec's paradox. Multi-task learning - Solving numerous machine learning jobs at the same time. Neural scaling law - Statistical law in device learning. Outline of expert system - Overview of and topical guide to expert system. Transhumanism - Philosophical motion. Synthetic intelligence - Alternate term for or kind of artificial intelligence. Transfer learning - Machine knowing technique. Loebner Prize - Annual AI competitors. Hardware for synthetic intelligence - Hardware specially designed and optimized for synthetic intelligence. Weak synthetic intelligence - Form of artificial intelligence.
Notes

^ a b See below for the origin of the term "strong AI", and see the scholastic meaning of "strong AI" and weak AI in the short article Chinese space. ^ AI founder John McCarthy writes: "we can not yet define in basic what kinds of computational treatments we wish to call smart. " [26] (For a conversation of some definitions of intelligence used by expert system scientists, see viewpoint of expert system.). ^ The Lighthill report specifically criticized AI's "grandiose goals" and led the taking apart of AI research in England. [55] In the U.S., DARPA ended up being figured out to money only "mission-oriented direct research study, instead of fundamental undirected research". [56] [57] ^ As AI founder John McCarthy writes "it would be an excellent relief to the remainder of the employees in AI if the inventors of new general formalisms would reveal their hopes in a more secured type than has actually sometimes been the case." [61] ^ In "Mind Children" [122] 1015 cps is used. More just recently, in 1997, [123] Moravec argued for 108 MIPS which would roughly correspond to 1014 cps. Moravec talks in regards to MIPS, not "cps", which is a non-standard term Kurzweil introduced. ^ As specified in a basic AI book: "The assertion that devices could perhaps act smartly (or, perhaps better, act as if they were intelligent) is called the 'weak AI' hypothesis by theorists, and the assertion that machines that do so are really believing (rather than replicating thinking) is called the 'strong AI' hypothesis." [121] ^ Alan Turing made this point in 1950. [36] References

^ Krishna, Sri (9 February 2023). "What is synthetic narrow intelligence (ANI)?". VentureBeat. Retrieved 1 March 2024. ANI is developed to perform a single job. ^ "OpenAI Charter". OpenAI. Retrieved 6 April 2023. Our objective is to ensure that artificial general intelligence benefits all of humankind. ^ Heath, Alex (18 January 2024). "Mark Zuckerberg's brand-new goal is producing artificial basic intelligence". The Verge. Retrieved 13 June 2024. Our vision is to build AI that is much better than human-level at all of the human senses. ^ Baum, Seth D. (2020 ). A Study of Artificial General Intelligence Projects for Ethics, Risk, and Policy (PDF) (Report). Global Catastrophic Risk Institute. Retrieved 28 November 2024. 72 AGI R&D jobs were recognized as being active in 2020. ^ a b c "AI timelines: What do experts in expert system anticipate for the future?". Our World in Data. Retrieved 6 April 2023. ^ Metz, Cade (15 May 2023). "Some Researchers Say A.I. Is Already Here, Stirring Debate in Tech Circles". The New York City Times. Retrieved 18 May 2023. ^ "AI leader Geoffrey Hinton quits Google and warns of threat ahead". The New York City 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 sparks of AGI. ^ Butler, Octavia E. (1993 ). Parable of the Sower. Grand Central Publishing. ISBN 978-0-4466-7550-5. All that you touch you 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 hazard is not AI itself however the method we deploy it. ^ "Impressed by expert system? Experts state AGI is following, and it has 'existential' dangers". ABC News. 23 March 2023. Retrieved 6 April 2023. AGI might present existential dangers to humankind. ^ Bostrom, Nick (2014 ). Superintelligence: Paths, Dangers, Strategies. Oxford University Press. ISBN 978-0-1996-7811-2. The first superintelligence will be the last development that humankind requires to make. ^ Roose, Kevin (30 May 2023). "A.I. Poses 'Risk of Extinction,' Industry Leaders Warn". The New York City Times. Mitigating the threat of termination from AI ought to be a worldwide priority. ^ "Statement on AI Risk". Center for AI Safety. Retrieved 1 March 2024. AI specialists alert of threat of termination from AI. ^ Mitchell, Melanie (30 May 2023). "Are AI's Doomsday Scenarios Worth Taking Seriously?". The New York City Times. We are far from creating devices that can outthink us in basic methods. ^ LeCun, Yann (June 2023). "AGI does not provide an existential threat". Medium. There is no reason to fear AI as an existential hazard. ^ Kurzweil 2005, p. 260. ^ a b Kurzweil, Ray (5 August 2005), "Long Live AI", Forbes, archived from the initial on 14 August 2005: Kurzweil explains strong AI as "maker intelligence with the full series of human intelligence.". ^ "The Age of Expert System: George John at TEDxLondonBusinessSchool 2013". Archived from the original 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 transforming our world - it is on everybody to ensure that it goes well". Our World in Data. Retrieved 8 October 2023. ^ Dickson, Ben (16 November 2023). "Here is how far we are to accomplishing AGI, according to DeepMind". VentureBeat. ^ McCarthy, John (2007a). "Basic Questions". Stanford University. Archived from the original on 26 October 2007. Retrieved 6 December 2007. ^ This list of smart qualities is based upon the topics covered by significant AI books, 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 reconsidered: The idea of proficiency". Psychological Review. 66 (5 ): 297-333. doi:10.1037/ h0040934. PMID 13844397. S2CID 37385966. ^ White, R. W. (1959 ). "Motivation reconsidered: The idea of proficiency". Psychological Review. 66 (5 ): 297-333. doi:10.1037/ h0040934. PMID 13844397. S2CID 37385966. ^ Muehlhauser, Luke (11 August 2013). "What is AGI?". Machine Intelligence Research Institute. Archived from the original on 25 April 2014. Retrieved 1 May 2014. ^ "What is Artificial General Intelligence (AGI)?|4 Tests For Ensuring Artificial General Intelligence". Talky Blog. 13 July 2019. Archived from the original on 17 July 2019. Retrieved 17 July 2019. ^ Kirk-Giannini, Cameron Domenico; Goldstein, Simon (16 October 2023). "AI is closer than ever to passing the Turing test for 'intelligence'. What takes place when it does?". The Conversation. Retrieved 22 September 2024. ^ a b Turing 1950. ^ Turing, Alan (1952 ). B. Jack Copeland (ed.). Can Automatic Calculating Machines Be Said To Think?. Oxford: Oxford University Press. pp. 487-506. ISBN 978-0-1982-5079-1. ^ "Eugene Goostman is a genuine 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 identify 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 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 Take Advantage Of It". Retrieved 30 May 2023. ^ Turk, Victoria (28 January 2015). "The Plan to Replace the Turing Test with a 'Turing Olympics'". Vice. Retrieved 3 March 2024. ^ Gopani, Avi (25 May 2022). "Turing Test is unreliable. The Winograd Schema is outdated. Coffee is the answer". Analytics India Magazine. Retrieved 3 March 2024. ^ Bhaimiya, Sawdah (20 June 2023). "DeepMind's co-founder suggested evaluating an AI chatbot's capability to turn $100,000 into $1 million to determine human-like intelligence". Business Insider. Retrieved 3 March 2024. ^ Suleyman, Mustafa (14 July 2023). "Mustafa Suleyman: My 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). Artificial Intelligence, Evolutionary Computation and Metaheuristics (AIECM): 3-17. Archived (PDF) from the original on 22 May 2013. ^ "AI Index: State of AI in 13 Charts". Stanford University Human-Centered Expert System. 15 April 2024. Retrieved 27 May 2024. ^ Crevier 1993, pp. 48-50. ^ Kaplan, Andreas (2022 ). "Expert System, Business and Civilization - Our Fate Made in Machines". Archived from the original on 6 May 2022. Retrieved 12 March 2022. ^ Simon 1965, p. 96 priced quote 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 ), 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 Expert system, a Squadron of Bright Real People". The New York Times. Archived from the original on 2 February 2023. Retrieved 18 February 2017. At its low point, some computer system researchers and software engineers avoided the term artificial intelligence for worry of being seen as wild-eyed dreamers. ^ Russell & Norvig 2003, pp. 25-26 ^ "Trends in the Emerging Tech Hype Cycle". Gartner Reports. Archived from the original on 22 May 2019. Retrieved 7 May 2019. ^ a b Moravec 1988, p. 20 ^ Harnad, S. (1990 ). "The Symbol Grounding Problem". Physica D. 42 (1-3): 335-346. arXiv: cs/9906002. Bibcode:1990 PhyD ... 42..335 H. doi:10.1016/ 0167-2789( 90 )90087-6. S2CID 3204300. ^ Gubrud 1997 ^ Hutter, Marcus (2005 ). Universal Artificial Intelligence: Sequential Decisions Based on Algorithmic Probability. Texts in Theoretical Computer Science an EATCS Series. Springer. doi:10.1007/ b138233. ISBN 978-3-5402-6877-2. S2CID 33352850. Archived from the 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 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 initial on 26 July 2020. Retrieved 11 May 2020. ^ "Избираеми дисциплини 2010/2011 - зимен триместър" [Elective courses 2010/2011 - winter trimester] Факултет по математика и информатика [Faculty of Mathematics and Informatics] (in Bulgarian). Archived from the original on 26 July 2020. Retrieved 11 May 2020. ^ Shevlin, Henry; Vold, Karina; Crosby, Matthew; Halina, Marta (4 October 2019). "The limits of maker intelligence: Despite development in device intelligence, synthetic basic intelligence is still a major 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. "Artificial intelligence will not develop into a Frankenstein's beast". The Guardian. Archived from the original on 17 September 2014. Retrieved 17 September 2014. ^ Deane, George (2022 ). "Machines That Feel and Think: The Role of Affective Feelings and Mental Action in (Artificial) General Intelligence". Artificial Life. 28 (3 ): 289-309. doi:10.1162/ artl_a_00368. ISSN 1064-5462. PMID 35881678. S2CID 251069071. ^ a b c Clocksin 2003. ^ Fjelland, Ragnar (17 June 2020). "Why general artificial intelligence will not be 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: oke.zone Will expert system bring us paradise or damage?". The New Yorker. Archived from the initial on 28 January 2016. Retrieved 7 February 2016. ^ Müller, V. C., & Bostrom, N. (2016 ). Future progress in synthetic intelligence: A survey of skilled 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, 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 function of GPU computing and deep learning in drug discovery". Nature Machine Intelligence. 4 (3 ): 211-221. doi:10.1038/ s42256-022-00463-x. ISSN 2522-5839. S2CID 252081559. ^ Goertzel & Pennachin 2006. ^ a b c (Kurzweil 2005, p. 260). ^ a b c Goertzel 2007. ^ Grace, Katja (2016 ). "Error in Armstrong and Sotala 2012". AI Impacts (blog site). Archived from the initial on 4 December 2020. Retrieved 24 August 2020. ^ a b Butz, Martin V. (1 March 2021). "Towards Strong AI". KI - Künstliche Intelligenz. 35 (1 ): 91-101. doi:10.1007/ s13218-021-00705-x. ISSN 1610-1987. S2CID 256065190. ^ Liu, Feng; Shi, Yong; Liu, Ying (2017 ). "Intelligence Quotient and Intelligence Grade of Expert System". Annals of Data Science. 4 (2 ): 179-191. arXiv:1709.10242. doi:10.1007/ s40745-017-0109-0. S2CID 37900130. ^ Brien, Jörn (5 October 2017). "Google-KI doppelt so schlau wie Siri" [Google AI is two times as clever as Siri - but a six-year-old beats both] (in German). Archived from the initial on 3 January 2019. Retrieved 2 January 2019. ^ Grossman, Gary (3 September 2020). "We're entering the AI twilight zone in between narrow and basic AI". VentureBeat. Archived from the initial on 4 September 2020. Retrieved 5 September 2020. Certainly, too, there are those who declare we are currently seeing an early example of an AGI system in the just recently revealed GPT-3 natural language processing (NLP) neural network. ... So is GPT-3 the very first example of an AGI system? This is debatable, however the consensus is that it is not AGI. ... If absolutely nothing else, GPT-3 tells us there is a middle ground between narrow and general AI. ^ Quach, Katyanna. "A developer built an AI chatbot using GPT-3 that helped a man speak again to his late fiancée. OpenAI shut it down". The Register. Archived from the original on 16 October 2021. Retrieved 16 October 2021. ^ Wiggers, Kyle (13 May 2022), "DeepMind's brand-new AI can perform over 600 jobs, from playing video games to managing robots", TechCrunch, archived from the original on 16 June 2022, recovered 12 June 2022. ^ Bubeck, Sébastien; Chandrasekaran, Varun; Eldan, Ronen; Gehrke, Johannes; Horvitz, Eric; Kamar, Ece; Lee, Peter; Lee, Yin Tat; Li, Yuanzhi; Lundberg, Scott; Nori, Harsha; Palangi, Hamid; Ribeiro, Marco Tulio; Zhang, Yi (22 March 2023). "Sparks of Artificial General Intelligence: Early explores GPT-4". arXiv:2303.12712 [cs.CL] ^ Metz, Cade (1 May 2023). "' The Godfather of A.I.' Leaves Google and Warns of Danger Ahead". The New York Times. ISSN 0362-4331. Retrieved 7 June 2023. ^ Bove, Tristan. "A.I. might equal human intelligence in 'simply a couple of years,' states CEO of Google's main 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 everyone appears to disagree on how to define Artificial General Intelligence". Fast Company. ^ Nosta, John (5 January 2024). "The Accelerating Path to Artificial General Intelligence". Psychology Today. Retrieved 30 March 2024. ^ Hickey, Alex. "Whole Brain Emulation: A Giant Step for Neuroscience". Tech Brew. Retrieved 8 November 2023. ^ Sandberg & Boström 2008. ^ Drachman 2005. ^ a b Russell & Norvig 2003. ^ Moravec 1988, p. 61. ^ Moravec 1998. ^ Holmgaard Mersh, Amalie (15 September 2023). "Decade-long European research study task 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 large range of views in present 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 instance: Russell & Norvig 2003, Oxford University Press Dictionary of Psychology Archived 3 December 2007 at the Wayback Machine (priced quote in" Encyclopedia.com"),. MIT Encyclopedia of Cognitive Science Archived 19 July 2008 at the Wayback Machine (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 actually come to life". The Washington Post. 11 June 2022. Retrieved 12 June 2023. ^ Kateman, Brian (24 July 2023). "AI Should Be Terrified of Humans". TIME. Retrieved 5 September 2024. ^ Nosta, John (18 December 2023). "Should Artificial Intelligence Have Rights?". Psychology Today. Retrieved 5 September 2024. ^ Akst, Daniel (10 April 2023). "Should Robots With Expert System Have Moral or Legal Rights?". The Wall Street Journal. ^ "Artificial General Intelligence - Do [es] the cost outweigh advantages?". 23 August 2021. Retrieved 7 June 2023. ^ "How we can Gain from Advancing Artificial General Intelligence (AGI) - Unite.AI". www.unite.ai. 7 April 2020. Retrieved 7 June 2023. ^ a b c Talty, Jules; Julien, Stephan. "What Will Our Society Look Like When Artificial Intelligence Is Everywhere?". Smithsonian Magazine. Retrieved 7 June 2023. ^ a b Stevenson, Matt (8 October 2015). "Answers to Stephen Hawking's AMA are Here!". Wired. ISSN 1059-1028. Retrieved 8 June 2023. ^ a b Bostrom, Nick (2017 ). " § Preferred order of arrival". Superintelligence: paths, risks, strategies (Reprinted with corrections 2017 ed.). Oxford, UK; New York, New York, USA: Oxford University Press. ISBN 978-0-1996-7811-2. ^ Piper, Kelsey (19 November 2018). "How technological progress is making it likelier than ever that people will damage ourselves". Vox. Retrieved 8 June 2023. ^ Doherty, Ben (17 May 2018). "Climate change an 'existential security threat' to Australia, Senate questions says". The Guardian. ISSN 0261-3077. Retrieved 16 July 2023. ^ MacAskill, William (2022 ). What we owe the future. New York, NY: Basic Books. ISBN 978-1-5416-1862-6. ^ a b Ord, Toby (2020 ). "Chapter 5: Future Risks, Unaligned Expert System". The Precipice: Existential Risk and the Future of Humanity. Bloomsbury Publishing. ISBN 978-1-5266-0021-9. ^ Al-Sibai, Noor (13 February 2022). "OpenAI Chief Scientist Says Advanced AI May Already Be Conscious". Futurism. Retrieved 24 December 2023. ^ Samuelsson, Paul Conrad (2019 ). "Artificial Consciousness: Our Greatest Ethical Challenge". Philosophy Now. Retrieved 23 December 2023. ^ Kateman, Brian (24 July 2023). "AI Should Be Terrified of Humans". TIME. Retrieved 23 December 2023. ^ Roose, Kevin (30 May 2023). "A.I. Poses 'Risk of Extinction,' Industry Leaders Warn". The New York City Times. ISSN 0362-4331. Retrieved 24 December 2023. ^ a b "Statement on AI Risk". Center for AI Safety. 30 May 2023. Retrieved 8 June 2023. ^ "Stephen Hawking: 'Transcendence takes a look at the implications of synthetic intelligence - 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 fascinating Facebook dispute in between Yann LeCun, Stuart Russel and Yoshua Bengio about the threats of strong AI". The remarkable Facebook argument in between Yann LeCun, Stuart Russel and Yoshua Bengio about the dangers of strong AI (in French). Retrieved 8 June 2023. ^ "Will Artificial Intelligence Doom The Mankind Within The Next 100 Years?". HuffPost. 22 August 2014. Retrieved 8 June 2023. ^ Sotala, Kaj; Yampolskiy, Roman V. (19 December 2014). "Responses to devastating AGI threat: a study". Physica Scripta. 90 (1 ): 018001. doi:10.1088/ 0031-8949/90/ 1/018001. ISSN 0031-8949. ^ Bostrom, Nick (2014 ). Superintelligence: Paths, Dangers, Strategies (First ed.). Oxford University Press. ISBN 978-0-1996-7811-2. ^ Chow, Andrew R.; Perrigo, Billy (16 February 2023). "The AI Arms Race Is On. Start Worrying". TIME. Retrieved 24 December 2023. ^ Tetlow, Gemma (12 January 2017). "AI arms race risks spiralling out of control, report cautions". Financial Times. Archived from the original on 11 April 2022. Retrieved 24 December 2023. ^ Milmo, Dan; Stacey, Kiran (25 September 2023). "Experts disagree over threat postured but artificial intelligence can not be overlooked". 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 original on 4 June 2014. Retrieved 12 December 2015. ^ Titcomb, James (30 October 2023). "Big Tech is stoking worries over AI, alert researchers". The Telegraph. Retrieved 7 December 2023. ^ Davidson, John (30 October 2023). "Google Brain founder states huge tech is lying about AI extinction risk". Australian Financial Review. Archived from the initial on 7 December 2023. Retrieved 7 December 2023. ^ Eloundou, Tyna; Manning, Sam; Mishkin, Pamela; Rock, Daniel (17 March 2023). "GPTs are GPTs: An early take a look at the labor market effect capacity of big language designs". OpenAI. Retrieved 7 June 2023. ^ a b Hurst, Luke (23 March 2023). "OpenAI says 80% of employees could see their jobs impacted by AI. These are the tasks most affected". euronews. Retrieved 8 June 2023. ^ Sheffey, Ayelet (20 August 2021). "Elon Musk states we need universal basic income due to the fact that 'in the future, manual labor will be a choice'". Business Insider. Archived from the initial on 9 July 2023. Retrieved 8 June 2023. Sources

UNESCO Science Report: the Race Against Time for Smarter Development. Paris: UNESCO. 11 June 2021. ISBN 978-9-2310-0450-6. Archived from the original on 18 June 2022. Retrieved 22 September 2021. Chalmers, David (1996 ), The Conscious Mind, Oxford University Press. Clocksin, William (August 2003), " and the future", Philosophical Transactions of the Royal Society A, vol. 361, no. 1809, pp. 1721-1748, Bibcode:2003 RSPTA.361.1721 C, doi:10.1098/ rsta.2003.1232, PMID 12952683, S2CID 31032007. Crevier, Daniel (1993 ). AI: The Tumultuous Search for 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 artificial basic intelligence and the possibility of a technological singularity: a reaction to Ray Kurzweil's The Singularity Is Near, and McDermott's critique of Kurzweil", Artificial Intelligence, vol. 171, no. 18, Special Review Issue, pp. 1161-1173, doi:10.1016/ j.artint.2007.10.011, archived from the original on 7 January 2016, retrieved 1 April 2009. Gubrud, Mark (November 1997), "Nanotechnology and International Security", Fifth Foresight Conference on Molecular Nanotechnology, archived from the initial on 29 May 2011, recovered 7 May 2011. Howe, J. (November 1994), Expert System at Edinburgh University: a Point of view, archived from the original on 17 August 2007, retrieved 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 Artificial Intelligence?. Stanford University. The ultimate effort is to make computer programs that can resolve issues and accomplish goals worldwide in addition to people. 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, recovered 23 June 2006 Nagel (1974 ), "What Is it Like to Be a Bat" (PDF), Philosophical Review, 83 (4 ): 435-50, doi:10.2307/ 2183914, JSTOR 2183914, archived (PDF) from the initial on 16 October 2011, 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 initial on 12 January 2008, obtained 29 September 2007 Poole, David; Mackworth, Alan; Goebel, Randy (1998 ), Computational Intelligence: A Rational Approach, New York: Oxford University Press, archived from the initial on 25 July 2009, recovered 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, obtained 3 September 2020 Simon, H. A. (1965 ), The Shape of Automation for Men and Management, New York City: Harper & Row Turing, Alan (October 1950). "Computing Machinery and Intelligence". Mind. 59 (236 ): 433-460. doi:10.1093/ mind/LIX.236.433. ISSN 1460-2113. JSTOR 2251299. S2CID 14636783.
de Vega, Manuel; Glenberg, Arthur; Graesser, Arthur, eds. (2008 ), Symbols and Embodiment: Debates on meaning and cognition, Oxford University Press, ISBN 978-0-1992-1727-4 Wang, Pei; Goertzel, Ben (2007 ). "Introduction: Aspects of Artificial General Intelligence". Advances in Artificial General Intelligence: Concepts, Architectures and Algorithms: Proceedings of the AGI Workshop 2006. IOS Press. pp. 1-16. ISBN 978-1-5860-3758-1. Archived from the original on 18 February 2021. Retrieved 13 December 2020 - through ResearchGate.
Further reading

Aleksander, Igor (1996 ), Impossible Minds, World Scientific Publishing Company, ISBN 978-1-8609-4036-1 Azevedo FA, Carvalho LR, Grinberg LT, Farfel J, et al. (April 2009), "Equal varieties of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain", The Journal of Comparative Neurology, 513 (5 ): 532-541, doi:10.1002/ cne.21974, PMID 19226510, S2CID 5200449, archived from the initial on 18 February 2021, recovered 4 September 2013 - via ResearchGate Berglas, Anthony (January 2012) [2008], Artificial Intelligence Will Kill Our Grandchildren (Singularity), archived from the initial on 23 July 2014, obtained 31 August 2012 Cukier, Kenneth, "Ready for Robots? How to Think about the Future of AI", Foreign Affairs, vol. 98, no. 4 (July/August 2019), pp. 192-98. George Dyson, historian of computing, writes (in what might be called "Dyson's Law") that "Any system simple adequate to be easy to understand will not be made complex enough to behave intelligently, while any system made complex enough to act smartly will be too complicated to comprehend." (p. 197.) Computer scientist Alex Pentland writes: "Current AI machine-learning algorithms are, at their core, dead simple foolish. They work, but they work by brute force." (p. 198.). Gelernter, David, Dream-logic, the Internet and Artificial Thought, Edge, archived from the initial on 26 July 2010, obtained 25 July 2010. Gleick, James, "The Fate of Free Choice" (review of Kevin J. Mitchell, Free Agents: How Evolution Gave Us Free Will, Princeton University Press, 2023, 333 pp.), The New York Review of Books, vol. LXXI, no. 1 (18 January 2024), pp. 27-28, 30. "Agency is what differentiates us from makers. For biological animals, factor and purpose come from acting on the planet and experiencing the consequences. Expert systems - disembodied, strangers to blood, sweat, and tears - have no celebration 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 City Review of Books, vol. LXXI, no. 17 (7 November 2024), pp. 44-46. "' We can't reasonably anticipate that those who hope to get abundant from AI are going to have the interests of the rest people close at heart,' ... writes [Gary Marcus] 'We can't depend on governments driven by campaign financing contributions [from tech business] to push back.' ... Marcus information the needs that people need to make from their governments and the tech companies. They consist of openness on how AI systems work; compensation for people if their data [are] used to train LLMs (large language model) s and the right to grant this use; and the capability to hold tech companies liable for the damages they trigger by eliminating Section 230, enforcing money penalites, and passing more stringent product liability laws ... Marcus likewise recommends ... that a new, AI-specific federal firm, akin to the FDA, the FCC, or the FTC, might offer the most robust oversight ... [T] he Fordham law teacher Chinmayi Sharma ... suggests ... develop [ing] an expert licensing program for engineers that would function in a similar method to medical licenses, photorum.eclat-mauve.fr malpractice fits, and the Hippocratic oath in medicine. 'What if, like doctors,' she asks ..., 'AI engineers also pledged to do no harm?'" (p. 46.). Holte, R. C.; Choueiry, B. Y. (2003 ), "Abstraction and reformulation in artificial intelligence", Philosophical Transactions of the Royal Society B, vol. 358, no. 1435, pp. 1197-1204, doi:10.1098/ rstb.2003.1317, PMC 1693218, PMID 12903653. Hughes-Castleberry, Kenna, "A Murder Mystery Puzzle: The literary puzzle Cain's Jawbone, which has actually stumped humans for years, exposes the constraints of natural-language-processing algorithms", Scientific American, vol. 329, no. 4 (November 2023), pp. 81-82. "This murder secret competition has actually revealed that although NLP (natural-language processing) designs are capable of amazing accomplishments, their abilities are quite restricted by the quantity of context they get. This [...] might cause [troubles] for scientists who hope to utilize them to do things such as analyze ancient languages. Sometimes, there are few historic records on long-gone civilizations to serve as training information for such a purpose." (p. 82.). Immerwahr, Daniel, "Your Lying Eyes: People now use A.I. to generate fake videos equivalent from real ones. How much does it matter?", The New Yorker, 20 November 2023, pp. 54-59. "If by 'deepfakes' we suggest reasonable videos produced using expert system 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, operating in our media as counterfeited proof. Their role much better resembles that of animations, specifically smutty ones." (p. 59.). - Leffer, Lauren, "The Risks of Trusting AI: We should prevent humanizing machine-learning models used in scientific research", Scientific American, vol. 330, no. 6 (June 2024), pp. 80-81. Lepore, Jill, "The Chit-Chatbot: Is talking with a maker 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 very same old issues", Scientific American, vol. 327, no. 4 (October 2022), pp. 42-45. McCarthy, John (October 2007), "From here to human-level AI", Artificial Intelligence, 171 (18 ): 1174-1182, doi:10.1016/ j.artint.2007.10.009. McCorduck, Pamela (2004 ), Machines Who Think (2nd ed.), Natick, Massachusetts: A. K. Peters, ISBN 1-5688-1205-1. Moravec, Hans (1976 ), The Role of Raw Power in Intelligence, archived from the original on 3 March 2016, 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, provided and distributed at the 2007 Singularity Summit, San Francisco, California. Press, Eyal, "In Front of Their Faces: Does facial-recognition technology lead authorities to disregard contradictory proof?", The New Yorker, 20 November 2023, pp. 20-26. Roivainen, Eka, "AI's IQ: ChatGPT aced a [standard intelligence] test but revealed that intelligence can not be measured by IQ alone", Scientific American, vol. 329, no. 1 (July/August 2023), p. 7. "Despite its high IQ, ChatGPT fails at tasks that require genuine humanlike reasoning or an understanding of the physical and social world ... ChatGPT appeared not able to factor rationally and attempted to rely on its large database of ... realities stemmed from online texts. " - Scharre, Paul, "Killer Apps: The Real Dangers of an AI Arms Race", Foreign Affairs, vol. 98, no. 3 (May/June 2019), pp. 135-44. "Today's AI technologies are powerful but unreliable. Rules-based systems can not handle situations their programmers did not anticipate. Learning systems are restricted by the data on which they were trained. AI failures have currently caused tragedy. Advanced auto-pilot functions in cars, although they carry out well in some scenarios, have actually driven vehicles without cautioning into trucks, concrete barriers, and parked vehicles. In the incorrect circumstance, AI systems go from supersmart to superdumb in an instant. When an enemy is attempting to manipulate and hack an AI system, the dangers are even greater." (p. 140.). Sutherland, J. G. (1990 ), "Holographic Model of Memory, Learning, and Expression", International Journal of Neural Systems, vol. 1-3, pp. 256-267. - Vincent, James, "Horny Robot Baby Voice: James Vincent on AI chatbots", London Review of Books, vol. 46, no. 19 (10 October 2024), pp. 29-32." [AI chatbot] programs are made possible by brand-new technologies but depend on the timelelss human tendency to anthropomorphise." (p. 29.). Williams, R. W.; Herrup, K.