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Artificial basic intelligence (AGI) is a kind of synthetic intelligence (AI) that matches or exceeds human cognitive capabilities across a large range of cognitive tasks. This contrasts with narrow AI, which is restricted to specific tasks. [1] Artificial superintelligence (ASI), on the other hand, refers to AGI that greatly goes beyond human cognitive abilities. AGI is thought about among the definitions of strong AI.

Creating AGI is a primary objective of AI research and of companies such as OpenAI [2] and Meta. [3] A 2020 study recognized 72 active AGI research and advancement jobs across 37 nations. [4]
The timeline for achieving AGI stays a subject of ongoing dispute among scientists and experts. Since 2023, some argue that it might be possible in years or years; others maintain it may take a century or longer; a minority think it might never be accomplished; and another minority claims that it is already here. [5] [6] Notable AI researcher Geoffrey Hinton has expressed concerns about the fast progress towards AGI, suggesting it could be attained earlier than lots of anticipate. [7]
There is argument on the exact definition of AGI and regarding whether modern-day large language designs (LLMs) such as GPT-4 are early types 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 actually specified that alleviating the risk of human termination positioned by AGI must be a worldwide concern. [14] [15] Others find the development of AGI to be too remote to present such a danger. [16] [17]
Terminology

AGI is likewise referred to as strong AI, [18] [19] complete AI, [20] human-level AI, [5] human-level intelligent AI, or general intelligent action. [21]
Some academic sources reserve the term "strong AI" for computer programs that experience sentience or awareness. [a] On the other hand, weak AI (or narrow AI) has the ability to resolve one specific issue but lacks general cognitive abilities. [22] [19] Some academic sources utilize "weak AI" to refer more broadly to any programs that neither experience consciousness nor have a mind in the same sense as people. [a]
Related principles consist of artificial superintelligence and transformative AI. An artificial superintelligence (ASI) is a theoretical kind of AGI that is a lot more usually intelligent than human beings, [23] while the idea of transformative AI relates to AI having a large influence on society, for instance, similar to the agricultural or commercial transformation. [24]
A structure for classifying AGI in levels was proposed in 2023 by Google DeepMind researchers. They specify 5 levels of AGI: emerging, competent, specialist, virtuoso, and superhuman. For instance, a competent AGI is specified as an AI that surpasses 50% of proficient adults in a vast array of non-physical tasks, and a superhuman AGI (i.e. an artificial superintelligence) is likewise defined however with a threshold of 100%. They think about big language models like ChatGPT or LLaMA 2 to be circumstances of emerging AGI. [25]
Characteristics

Various popular definitions of intelligence have been proposed. Among 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 usually hold that intelligence is required to do all of the following: [27]
reason, use strategy, resolve puzzles, and make judgments under unpredictability represent knowledge, including sound judgment understanding plan learn

• interact in natural language
• if necessary, incorporate these abilities in conclusion of any given objective

Many interdisciplinary techniques (e.g. cognitive science, computational intelligence, and decision making) consider additional characteristics such as creativity (the ability to form novel psychological images and concepts) [28] and autonomy. [29]
Computer-based systems that show much of these abilities exist (e.g. see computational creativity, automated reasoning, choice support system, robotic, evolutionary calculation, smart agent). There is dispute about whether modern AI systems have them to an adequate degree.

Physical traits

Other capabilities are thought about desirable in intelligent systems, as they may affect intelligence or aid in its expression. These include: [30]
- the ability to sense (e.g. see, hear, and so on), and - the capability to act (e.g. relocation and control things, modification location to check out, and so on).
This consists of the capability to spot and react to risk. [31]
Although the ability to sense (e.g. see, hear, and so on) and the ability to act (e.g. relocation and control things, change place to explore, and so on) can be desirable for some intelligent systems, [30] these physical capabilities are not strictly needed for an entity to qualify as AGI-particularly under the thesis that big language models (LLMs) might currently be or become AGI. Even from a less optimistic point of view on LLMs, there is no firm requirement for morphomics.science an AGI to have a human-like kind; being a silicon-based computational system suffices, supplied it can process input (language) from the external world in location of human senses. This interpretation lines up with the understanding that AGI has never ever been proscribed a specific physical personification and therefore does not require a capability for mobility or standard "eyes and ears". [32]
Tests for human-level AGI

Several tests meant to verify human-level AGI have been thought about, consisting of: [33] [34]
The concept of the test is that the device has to attempt and pretend to be a guy, by answering questions put to it, and it will just pass if the pretence is reasonably persuading. A considerable part of a jury, who need to not be skilled about makers, need to be taken in by the pretence. [37]
AI-complete issues

An issue is informally called "AI-complete" or "AI-hard" if it is thought that in order to resolve it, one would need to carry out AGI, since the option is beyond the abilities of a purpose-specific algorithm. [47]
There are many issues that have been conjectured to need basic intelligence to resolve as well as humans. Examples include computer system vision, natural language understanding, and dealing with unanticipated scenarios while solving any real-world problem. [48] Even a particular job like translation requires a machine to check out and write in both languages, follow the author's argument (factor), comprehend the context (knowledge), and faithfully recreate the author's initial intent (social intelligence). All of these problems need to be resolved at the same time in order to reach human-level device performance.

However, numerous of these jobs can now be carried out by modern big language models. According to Stanford University's 2024 AI index, AI has reached human-level performance on many standards for checking out understanding and visual reasoning. [49]
History

Classical AI

Modern AI research began in the mid-1950s. [50] The first generation of AI researchers were encouraged that synthetic general intelligence was possible which it would exist in just a few decades. [51] AI pioneer Herbert A. Simon composed in 1965: "machines will be capable, within twenty years, of doing any work a man can do." [52]
Their forecasts were the motivation for vmeste-so-vsemi.ru Stanley Kubrick and Arthur C. Clarke's character HAL 9000, who embodied what AI scientists thought they could develop by the year 2001. AI pioneer Marvin Minsky was an expert [53] on the task of making HAL 9000 as sensible as possible according to the agreement forecasts of the time. He stated in 1967, "Within a generation ... the issue of creating 'artificial intelligence' will substantially be solved". [54]
Several classical AI jobs, such as Doug Lenat's Cyc project (that began in 1984), and Allen Newell's Soar task, were directed at AGI.

However, in the early 1970s, it became apparent that researchers had actually grossly ignored the problem of the job. Funding agencies ended up being hesitant of AGI and put researchers under increasing pressure to produce helpful "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 reaction to this and the success of professional systems, both market and federal government pumped cash into the field. [56] [59] However, self-confidence in AI spectacularly collapsed in the late 1980s, and the goals of the Fifth Generation Computer Project were never ever fulfilled. [60] For the 2nd time in twenty years, AI scientists who anticipated the imminent achievement of AGI had actually been mistaken. By the 1990s, AI scientists had a credibility for making vain promises. They ended up being hesitant to make predictions at all [d] and prevented mention of "human level" synthetic intelligence for worry of being labeled "wild-eyed dreamer [s]. [62]
Narrow AI research

In the 1990s and early 21st century, mainstream AI accomplished industrial success and scholastic respectability by concentrating 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 used extensively throughout the innovation market, and research study in this vein is greatly funded in both academia and industry. Since 2018 [upgrade], advancement in this field was considered an emerging trend, and a mature phase was expected to be reached in more than 10 years. [64]
At the turn of the century, lots of traditional AI researchers [65] hoped that strong AI could be established by integrating programs that resolve various sub-problems. Hans Moravec wrote in 1988:

I am positive that this bottom-up path to expert system will one day meet the traditional top-down route over half way, ready to provide the real-world proficiency and the commonsense knowledge that has actually been so frustratingly elusive in thinking programs. Fully intelligent makers will result when the metaphorical golden spike is driven unifying the 2 efforts. [65]
However, even at the time, this was challenged. For instance, Stevan Harnad of Princeton University concluded his 1990 paper on the sign grounding hypothesis by mentioning:

The expectation has frequently been voiced that "top-down" (symbolic) approaches to modeling cognition will in some way satisfy "bottom-up" (sensory) approaches somewhere in between. If the grounding factors to consider in this paper stand, then this expectation is hopelessly modular and there is truly just one viable path from sense to signs: from the ground up. A free-floating symbolic level like the software application level of a computer system will never be reached by this route (or vice versa) - nor is it clear why we ought to even try to reach such a level, because it appears getting there would simply total up to uprooting our signs from their intrinsic meanings (thereby simply lowering ourselves to the practical equivalent of a programmable computer). [66]
Modern artificial basic intelligence research

The term "artificial basic intelligence" was used as early as 1997, by Mark Gubrud [67] in a conversation of the implications of completely automated military production and operations. A mathematical formalism of AGI was proposed by Marcus Hutter in 2000. Named AIXI, the proposed AGI representative maximises "the ability to satisfy goals in a wide variety of environments". [68] This type of AGI, identified by the capability to maximise a mathematical meaning of intelligence rather than display human-like behaviour, [69] was also called universal expert system. [70]
The term AGI was re-introduced and popularized by Shane Legg and Ben Goertzel around 2002. [71] AGI research study activity in 2006 was explained by Pei Wang and Ben Goertzel [72] as "producing publications and preliminary results". The very first summer school in AGI was arranged in Xiamen, China in 2009 [73] by the Xiamen university's Artificial Brain Laboratory and OpenCog. The first university course was offered in 2010 [74] and 2011 [75] at Plovdiv University, Bulgaria by Todor Arnaudov. MIT presented a course on AGI in 2018, arranged by Lex Fridman and including a variety of guest lecturers.

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

Feasibility

As of 2023, the advancement and prospective accomplishment of AGI stays a subject of extreme dispute within the AI community. While conventional consensus held that AGI was a far-off goal, current developments have led some researchers and industry figures to declare that early types of AGI might currently exist. [78] AI pioneer Herbert A. Simon hypothesized in 1965 that "devices will be capable, within twenty years, of doing any work a guy can do". This prediction failed to come real. Microsoft co-founder Paul Allen believed that such intelligence is unlikely in the 21st century due to the fact that it would require "unforeseeable and essentially unforeseeable advancements" and a "clinically deep understanding of cognition". [79] Writing in The Guardian, roboticist Alan Winfield claimed the gulf between contemporary computing and human-level expert system is as broad as the gulf in between existing area flight and practical faster-than-light spaceflight. [80]
An additional challenge is the lack of clearness in defining what intelligence entails. Does it need awareness? Must it show the capability to set objectives in addition to pursue them? Is it purely a matter of scale such that if model sizes increase sufficiently, intelligence will emerge? Are facilities such as preparation, reasoning, and causal understanding needed? Does intelligence need explicitly duplicating the brain and its particular professors? Does it require feelings? [81]
Most AI scientists believe strong AI can be achieved in the future, but some thinkers, like Hubert Dreyfus and Roger Penrose, deny the possibility of achieving strong AI. [82] [83] John McCarthy is amongst those who think human-level AI will be achieved, however that today level of progress is such that a date can not precisely be predicted. [84] AI specialists' views on the feasibility of AGI wax and subside. Four surveys performed in 2012 and 2013 recommended that the mean quote among specialists for when they would be 50% positive AGI would arrive was 2040 to 2050, depending upon the poll, with the mean being 2081. Of the professionals, 16.5% addressed with "never" when asked the same question however with a 90% self-confidence rather. [85] [86] Further existing AGI development factors to consider can be found above Tests for verifying human-level AGI.

A report by Stuart Armstrong and Kaj Sotala of the Machine Intelligence Research Institute found that "over [a] 60-year timespan there is a strong bias towards anticipating the arrival of human-level AI as between 15 and 25 years from the time the forecast was made". They analyzed 95 forecasts made in between 1950 and 2012 on when human-level AI will happen. [87]
In 2023, Microsoft researchers published a comprehensive evaluation of GPT-4. They concluded: "Given the breadth and depth of GPT-4's capabilities, our company believe that it could reasonably be considered as an early (yet still incomplete) variation of an artificial general intelligence (AGI) system." [88] Another study in 2023 reported that GPT-4 outperforms 99% of human beings on the Torrance tests of creativity. [89] [90]
Blaise Agüera y Arcas and Peter Norvig composed in 2023 that a significant level of general intelligence has actually currently been accomplished with frontier models. They wrote that reluctance to this view comes from four primary reasons: a "healthy apprehension about metrics for AGI", an "ideological commitment to alternative AI theories or strategies", a "dedication to human (or biological) exceptionalism", or a "concern about the economic ramifications of AGI". [91]
2023 likewise marked the introduction of large multimodal designs (big language models capable of processing or creating several techniques such as text, audio, and images). [92]
In 2024, OpenAI released o1-preview, the very first of a series of models that "invest more time thinking before they respond". According to Mira Murati, this ability to think before responding represents a new, extra paradigm. It improves model outputs by spending more computing power when producing the answer, whereas the model scaling paradigm improves outputs by increasing the design size, training information and training compute power. [93] [94]
An OpenAI employee, Vahid Kazemi, declared in 2024 that the company had actually accomplished AGI, specifying, "In my opinion, we have actually already achieved AGI and it's even more clear with O1." Kazemi clarified that while the AI is not yet "much better than any human at any task", it is "better than most human beings at the majority of tasks." He likewise addressed criticisms that large language designs (LLMs) merely follow predefined patterns, comparing their learning procedure to the clinical method of observing, assuming, and verifying. These declarations have triggered debate, as they count on a broad and unconventional meaning of AGI-traditionally understood as AI that matches human intelligence across all domains. Critics argue that, while OpenAI's designs demonstrate exceptional adaptability, they might not fully meet this standard. Notably, Kazemi's comments came quickly after OpenAI got rid of "AGI" from the terms of its collaboration with Microsoft, triggering speculation about the business's tactical objectives. [95]
Timescales

Progress in expert system has actually historically gone through durations of fast development separated by periods when progress appeared to stop. [82] Ending each hiatus were fundamental advances in hardware, software application or both to produce space for further progress. [82] [98] [99] For example, the computer system hardware readily available in the twentieth century was not sufficient to execute deep learning, which needs large numbers of GPU-enabled CPUs. [100]
In the introduction to his 2006 book, [101] Goertzel says that quotes of the time required before a truly versatile AGI is built vary from ten years to over a century. As of 2007 [update], the consensus in the AGI research study community seemed to be that the timeline discussed by Ray Kurzweil in 2005 in The Singularity is Near [102] (i.e. between 2015 and 2045) was plausible. [103] Mainstream AI researchers have provided a large range of viewpoints on whether development will be this rapid. A 2012 meta-analysis of 95 such opinions discovered a predisposition towards forecasting that the start of AGI would happen within 16-26 years for contemporary and historical predictions 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 established a neural network called AlexNet, which won the ImageNet competition with a top-5 test error rate of 15.3%, significantly much better than the second-best entry's rate of 26.3% (the traditional method used a weighted sum of ratings from various pre-defined classifiers). [105] AlexNet was considered as the initial ground-breaker of the existing deep knowing wave. [105]
In 2017, researchers Feng Liu, Yong Shi, and Ying Liu conducted intelligence tests on publicly available and easily available weak AI such as Google AI, Apple's Siri, and others. At the maximum, these AIs reached an IQ value of about 47, which corresponds roughly to a six-year-old kid in very first grade. An adult pertains to about 100 usually. Similar tests were performed in 2014, with the IQ score reaching an optimum worth of 27. [106] [107]
In 2020, OpenAI established GPT-3, a language model efficient in carrying out many diverse tasks without specific training. According to Gary Grossman in a VentureBeat article, while there is agreement that GPT-3 is not an example of AGI, it is considered by some to be too advanced to be classified as a narrow AI system. [108]
In the same year, Jason Rohrer utilized his GPT-3 account to develop a chatbot, and provided a chatbot-developing platform called "Project December". OpenAI asked for changes to the chatbot to adhere to their safety guidelines; Rohrer detached Project December from the GPT-3 API. [109]
In 2022, DeepMind developed Gato, a "general-purpose" system capable of performing more than 600 different tasks. [110]
In 2023, Microsoft Research released a research study on an early variation of OpenAI's GPT-4, contending that it exhibited more basic intelligence than previous AI designs and showed human-level performance in jobs spanning multiple domains, such as mathematics, coding, and law. This research stimulated a debate on whether GPT-4 might be thought about an early, incomplete version of synthetic general intelligence, highlighting the need for more exploration and examination of such systems. [111]
In 2023, the AI scientist Geoffrey Hinton stated that: [112]
The concept that this stuff might actually get smarter than people - a few people believed that, [...] But a lot of individuals thought it was way off. And I believed it was way off. I thought it was 30 to 50 years or perhaps longer away. Obviously, I no longer believe that.

In May 2023, Demis Hassabis similarly said that "The development in the last couple of years has been pretty incredible", and that he sees no reason why it would decrease, expecting AGI within a years and even a couple of 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 along with human beings. [114] In June 2024, the AI researcher Leopold Aschenbrenner, a former OpenAI employee, estimated AGI by 2027 to be "strikingly possible". [115]
Whole brain emulation

While the development of transformer models like in ChatGPT is considered the most promising path to AGI, [116] [117] entire brain emulation can act as an alternative method. With entire brain simulation, a brain design is built by scanning and mapping a biological brain in detail, 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 virtually the very same way as the initial brain. [118] Whole brain emulation is a kind of brain simulation that is discussed in computational neuroscience and neuroinformatics, and for medical research functions. It has actually been discussed in expert system research [103] as a technique to strong AI. Neuroimaging technologies that could provide the required comprehensive understanding are improving quickly, and futurist Ray Kurzweil in the book The Singularity Is Near [102] forecasts that a map of enough quality will appear on a similar timescale to the computing power required to imitate it.

Early estimates

For low-level brain simulation, a really effective cluster of computer systems or GPUs would be needed, given the huge amount of synapses within the human brain. Each of the 1011 (one hundred billion) nerve cells 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 declines with age, supporting by their adult years. Estimates differ for an adult, varying from 1014 to 5 × 1014 synapses (100 to 500 trillion). [120] A quote of the brain's processing power, based on an easy switch design for nerve cell activity, is around 1014 (100 trillion) synaptic updates per second (SUPS). [121]
In 1997, Kurzweil looked at various quotes for the hardware needed to equate to the human brain and embraced a figure of 1016 calculations per 2nd (cps). [e] (For comparison, if a "computation" was comparable to one "floating-point operation" - a procedure utilized to rate existing supercomputers - then 1016 "computations" would be equivalent to 10 petaFLOPS, achieved in 2011, while 1018 was accomplished in 2022.) He used this figure to forecast the necessary hardware would be available sometime between 2015 and 2025, if the exponential growth in computer power at the time of writing continued.

Current research study

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

Criticisms of simulation-based techniques

The artificial neuron model presumed by Kurzweil and utilized in lots of present synthetic neural network applications is basic compared with biological nerve cells. A brain simulation would likely have to catch the detailed cellular behaviour of biological neurons, presently understood only in broad summary. The overhead introduced by complete modeling of the biological, chemical, and physical details of neural behaviour (particularly on a molecular scale) would require computational powers numerous orders of magnitude bigger than Kurzweil's estimate. In addition, the price quotes do not represent glial cells, which are known to play a role in cognitive processes. [125]
A basic criticism of the simulated brain technique stems from embodied cognition theory which asserts that human personification is a necessary aspect of human intelligence and is necessary to ground significance. [126] [127] If this theory is proper, any totally practical brain design will require 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 an option, however it is unknown whether this would be adequate.

Philosophical viewpoint

"Strong AI" as specified in approach

In 1980, theorist John Searle coined the term "strong AI" as part of his Chinese room argument. [128] He proposed a difference between two hypotheses about expert system: [f]
Strong AI hypothesis: An expert system system can have "a mind" and "awareness". Weak AI hypothesis: A synthetic intelligence system can (just) act like it thinks and has a mind and awareness.
The very first one he called "strong" because it makes a stronger statement: it assumes something special has happened to the device that exceeds those capabilities that we can test. The behaviour of a "weak AI" machine would be specifically similar to a "strong AI" device, however the latter would likewise have subjective conscious experience. This use is also common in academic AI research study and textbooks. [129]
In contrast to Searle and mainstream AI, some futurists such as Ray Kurzweil use the term "strong AI" to mean "human level synthetic general intelligence". [102] This is not the exact same as Searle's strong AI, unless it is presumed that consciousness is essential for human-level AGI. Academic thinkers such as Searle do not think that is the case, and to most artificial intelligence scientists the concern is out-of-scope. [130]
Mainstream AI is most interested in how a program acts. [131] According to Russell and Norvig, "as long as the program works, they do not care if you call it genuine or a simulation." [130] If the program can behave as if it has a mind, then there is no need to understand if it in fact has mind - undoubtedly, there would be no chance to inform. For AI research, Searle's "weak AI hypothesis" is equivalent to the statement "artificial general intelligence is possible". Thus, according to Russell and Norvig, "most AI scientists take the weak AI hypothesis for given, and don't care about the strong AI hypothesis." [130] Thus, for scholastic AI research study, "Strong AI" and "AGI" are 2 various things.

Consciousness

Consciousness can have numerous meanings, and some aspects play substantial roles in sci-fi and the principles of expert system:

Sentience (or "remarkable consciousness"): The ability to "feel" perceptions or emotions subjectively, as opposed to the capability to factor about understandings. Some theorists, such as David Chalmers, utilize the term "consciousness" to refer exclusively to incredible awareness, which is approximately equivalent to sentience. [132] Determining why and how subjective experience arises is called the hard issue of awareness. [133] Thomas Nagel explained in 1974 that it "feels 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 seems mindful (i.e., has awareness) but a toaster does not. [134] In 2022, a Google engineer claimed that the business's AI chatbot, LaMDA, had actually accomplished life, though this claim was commonly challenged by other professionals. [135]
Self-awareness: To have mindful awareness of oneself as a separate individual, especially to be knowingly knowledgeable about one's own ideas. This is opposed to simply being the "topic of one's believed"-an os or debugger has the ability to be "knowledgeable about itself" (that is, to represent itself in the same method it represents everything else)-but this is not what people normally suggest when they use the term "self-awareness". [g]
These traits have an ethical measurement. AI sentience would give rise to concerns of welfare and legal security, similarly to animals. [136] Other elements of awareness associated to cognitive capabilities are likewise pertinent to the idea of AI rights. [137] Finding out how to incorporate sophisticated AI with existing legal and social frameworks is an emergent issue. [138]
Benefits

AGI might have a large variety of applications. If oriented towards such goals, AGI might assist reduce different issues worldwide such as appetite, poverty and health issues. [139]
AGI might improve productivity and effectiveness in a lot of jobs. For example, in public health, AGI might accelerate medical research, especially versus cancer. [140] It might take care of the senior, [141] and equalize access to rapid, high-quality medical diagnostics. It might use enjoyable, cheap and personalized education. [141] The requirement to work to subsist might end up being obsolete if the wealth produced is appropriately redistributed. [141] [142] This likewise raises the question of the place of human beings in a significantly automated society.

AGI could also assist to make rational choices, and to prepare for and avoid catastrophes. It might also assist to profit of potentially catastrophic technologies such as nanotechnology or environment engineering, while preventing the associated dangers. [143] If an AGI's primary goal is to avoid existential disasters such as human termination (which could be tough if the Vulnerable World Hypothesis turns out to be true), [144] it might take steps to drastically reduce the threats [143] while decreasing the effect of these procedures on our quality of life.

Risks

Existential dangers

AGI might represent numerous types of existential danger, which are risks that threaten "the early termination of Earth-originating smart life or the permanent and drastic destruction of its potential for preferable future development". [145] The risk of human termination from AGI has actually been the subject of numerous arguments, but there is likewise the possibility that the development of AGI would lead to a permanently problematic future. Notably, it could be utilized to spread out and maintain the set of worths of whoever develops it. If humankind still has ethical blind spots comparable to slavery in the past, AGI may irreversibly entrench it, avoiding ethical progress. [146] Furthermore, AGI could assist in mass security and indoctrination, which could be utilized to create a stable repressive around the world totalitarian regime. [147] [148] There is also a risk for the machines themselves. If makers that are sentient or otherwise deserving of moral consideration are mass produced in the future, engaging in a civilizational course that indefinitely neglects their welfare and interests could be an existential disaster. [149] [150] Considering how much AGI could improve humanity's future and help lower other existential threats, Toby Ord calls these existential dangers "an argument for proceeding with due care", not for "deserting AI". [147]
Risk of loss of control and human extinction

The thesis that AI presents an existential danger for people, which this danger requires more attention, is questionable however has been endorsed in 2023 by many public figures, AI scientists and CEOs of AI business such as Elon Musk, Bill Gates, Geoffrey Hinton, Yoshua Bengio, Demis Hassabis and Sam Altman. [151] [152]
In 2014, Stephen Hawking criticized prevalent indifference:

So, facing possible futures of incalculable advantages and risks, the experts are surely doing whatever possible to make sure the very best outcome, right? Wrong. If a superior alien civilisation sent us a message stating, 'We'll arrive in a few decades,' would we just reply, 'OK, call us when you get here-we'll leave the lights on?' Probably not-but this is basically what is happening with AI. [153]
The prospective fate of humanity has actually sometimes been compared to the fate of gorillas threatened by human activities. The comparison states that higher intelligence permitted humankind to control gorillas, which are now susceptible in methods that they might not have expected. As a result, the gorilla has actually ended up being an endangered species, not out of malice, however just as a civilian casualties from human activities. [154]
The skeptic Yann LeCun thinks about that AGIs will have no desire to control humanity and that we should be careful not to anthropomorphize them and interpret their intents as we would for human beings. He said that individuals will not be "wise enough to design super-intelligent machines, yet ridiculously stupid to the point of giving it moronic goals without any safeguards". [155] On the other side, the idea of critical convergence suggests that nearly whatever their objectives, intelligent representatives will have factors to attempt to make it through and acquire more power as intermediary actions to accomplishing these objectives. Which this does not require having emotions. [156]
Many scholars who are worried about existential danger supporter for more research study into solving the "control issue" to address the concern: what kinds of safeguards, algorithms, or architectures can programmers carry out to increase the possibility that their recursively-improving AI would continue to behave in a friendly, instead of devastating, way after it reaches superintelligence? [157] [158] Solving the control issue is complicated by the AI arms race (which could cause a race to the bottom of security preventative measures in order to launch items before competitors), [159] and the use of AI in weapon systems. [160]
The thesis that AI can present existential threat also has critics. Skeptics typically state that AGI is not likely in the short-term, or that concerns about AGI distract from other concerns related to current AI. [161] Former Google fraud czar Shuman Ghosemajumder thinks about that for lots of individuals beyond the technology industry, existing chatbots and LLMs are already viewed as though they were AGI, causing further misconception and fear. [162]
Skeptics in some cases charge that the thesis is crypto-religious, with an illogical belief in the possibility of superintelligence replacing an irrational belief in a supreme God. [163] Some scientists believe that the communication projects on AI existential threat by specific AI groups (such as OpenAI, Anthropic, DeepMind, and Conjecture) might be an at effort at regulatory capture and to inflate interest in their products. [164] [165]
In 2023, the CEOs of Google DeepMind, OpenAI and Anthropic, along with other industry leaders and researchers, provided a joint declaration asserting that "Mitigating the threat of extinction from AI need to be an international top priority alongside other societal-scale risks such as pandemics and nuclear war." [152]
Mass unemployment

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

According to Stephen Hawking, the result of automation on the quality of life will depend upon how the wealth will be rearranged: [142]
Everyone can delight in a life of elegant leisure if the machine-produced wealth is shared, or the majority of people can end up badly bad if the machine-owners successfully lobby versus wealth redistribution. Up until now, the pattern seems to be toward the second option, with technology driving ever-increasing inequality

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

Artificial brain - Software and hardware with cognitive abilities similar to those of the animal or human brain AI effect AI safety - Research area on making AI safe and helpful AI positioning - AI conformance to the desired goal A.I. Rising - 2018 film directed by Lazar Bodroža Artificial intelligence Automated maker learning - Process of automating the application of device knowing BRAIN Initiative - Collaborative public-private research study effort announced by the Obama administration China Brain Project Future of Humanity Institute - Defunct Oxford interdisciplinary research study centre General video game playing - Ability of expert system to play various video games Generative expert system - AI system capable of generating content in reaction to prompts Human Brain Project - Scientific research study job Intelligence amplification - Use of infotech to enhance human intelligence (IA). Machine ethics - Moral behaviours of man-made machines. Moravec's paradox. Multi-task knowing - Solving several device learning tasks at the exact same time. Neural scaling law - Statistical law in artificial intelligence. Outline of expert system - Overview of and topical guide to expert system. Transhumanism - Philosophical motion. Synthetic intelligence - Alternate term for or kind of expert system. Transfer learning - Artificial intelligence technique. Loebner Prize - Annual AI competitors. Hardware for synthetic intelligence - Hardware specially created and optimized for expert system. Weak expert system - Form of expert system.
Notes

^ a b See below for the origin of the term "strong AI", and see the academic definition of "strong AI" and weak AI in the short article Chinese room. ^ AI creator John McCarthy writes: "we can not yet characterize in basic what kinds of computational procedures we wish to call intelligent. " [26] (For a discussion of some meanings of intelligence utilized by expert system researchers, see approach of artificial intelligence.). ^ The Lighthill report particularly slammed AI's "grand goals" and led the dismantling of AI research in England. [55] In the U.S., DARPA became identified to money just "mission-oriented direct research, rather than standard undirected research". [56] [57] ^ As AI creator John McCarthy composes "it would be a fantastic relief to the remainder of the employees in AI if the developers of brand-new basic formalisms would reveal their hopes in a more safeguarded type than has in some cases held true." [61] ^ In "Mind Children" [122] 1015 cps is utilized. More just recently, in 1997, [123] Moravec argued for 108 MIPS which would roughly represent 1014 cps. Moravec talks in terms of MIPS, not "cps", which is a non-standard term Kurzweil presented. ^ As specified in a standard AI textbook: "The assertion that makers could possibly act wisely (or, perhaps better, act as if they were smart) is called the 'weak AI' hypothesis by philosophers, and the assertion that machines that do so are really thinking (as opposed to mimicing thinking) is called the 'strong AI' hypothesis." [121] ^ Alan Turing made this point in 1950. [36] References

^ Krishna, Sri (9 February 2023). "What is synthetic narrow intelligence (ANI)?". VentureBeat. Retrieved 1 March 2024. ANI is designed to carry out a single job. ^ "OpenAI Charter". OpenAI. Retrieved 6 April 2023. Our mission is to ensure that synthetic general intelligence advantages all of mankind. ^ Heath, Alex (18 January 2024). "Mark Zuckerberg's new objective is creating synthetic general intelligence". The Verge. Retrieved 13 June 2024. Our vision is to develop AI that is much better than human-level at all of the human senses. ^ Baum, Seth D. (2020 ). A Survey 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 identified as being active in 2020. ^ a b c "AI timelines: What do professionals in expert system anticipate for the future?". Our World in Data. Retrieved 6 April 2023. ^ Metz, Cade (15 May 2023). "Some Researchers Say A.I. Is Already Here, Stirring Debate in Tech Circles". The New York Times. Retrieved 18 May 2023. ^ "AI leader Geoffrey Hinton stops Google and cautions of threat ahead". The New York City Times. 1 May 2023. Retrieved 2 May 2023. It is hard 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 explores GPT-4". arXiv preprint. arXiv:2303.12712. GPT-4 reveals 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 modifications you. ^ Vinge, Vernor (1992 ). A Fire Upon the Deep. Tor Books. ISBN 978-0-8125-1528-2. The Singularity is coming. ^ Morozov, Evgeny (30 June 2023). "The True Threat of Expert System". The New York Times. The real hazard is not AI itself however the method we deploy it. ^ "Impressed by synthetic intelligence? Experts say AGI is coming next, and it has 'existential' threats". ABC News. 23 March 2023. Retrieved 6 April 2023. AGI could present existential risks 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 development that mankind needs to make. ^ Roose, Kevin (30 May 2023). "A.I. Poses 'Risk of Extinction,' Industry Leaders Warn". The New York Times. Mitigating the danger of extinction from AI ought to be a worldwide concern. ^ "Statement on AI Risk". Center for AI Safety. Retrieved 1 March 2024. AI professionals warn of risk of extinction from AI. ^ Mitchell, Melanie (30 May 2023). "Are AI's Doomsday Scenarios Worth Taking Seriously?". The New York Times. We are far from creating machines that can outthink us in general 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 describes strong AI as "machine intelligence with the full variety 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 initial on 25 September 2009. Retrieved 8 October 2007. ^ "What is artificial superintelligence (ASI)?|Definition from TechTarget". Enterprise AI. Retrieved 8 October 2023. ^ "Artificial intelligence is 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 accomplishing AGI, according to DeepMind". VentureBeat. ^ McCarthy, John (2007a). "Basic Questions". Stanford University. Archived from the initial on 26 October 2007. Retrieved 6 December 2007. ^ This list of intelligent traits is based upon the subjects covered by major 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 shapes the way we believe: a new view of intelligence (The MIT Press, 2007). ISBN 0-2621-6239-3. ^ White, R. W. (1959 ). "Motivation reassessed: The concept of competence". Psychological Review. 66 (5 ): 297-333. doi:10.1037/ h0040934. PMID 13844397. S2CID 37385966. ^ White, R. W. (1959 ). "Motivation reassessed: The idea of skills". Psychological Review. 66 (5 ): 297-333. doi:10.1037/ h0040934. PMID 13844397. S2CID 37385966. ^ Muehlhauser, Luke (11 August 2013). "What is AGI?". Machine Intelligence Research Institute. Archived from the original on 25 April 2014. Retrieved 1 May 2014. ^ "What is Artificial General Intelligence (AGI)?|4 Tests For Ensuring Artificial General Intelligence". Talky Blog. 13 July 2019. Archived from the 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 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 young boy - the Turing Test states so". The Guardian. 9 June 2014. ISSN 0261-3077. Retrieved 3 March 2024. ^ "Scientists challenge whether computer '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 challenging examinations both AI variations have passed". Business Insider. Retrieved 30 May 2023. ^ Naysmith, Caleb (7 February 2023). "6 Jobs Expert System Is Already Replacing and How Investors Can Capitalize on It". Retrieved 30 May 2023. ^ Turk, Victoria (28 January 2015). "The Plan to Replace the Turing Test with a 'Turing Olympics'". Vice. Retrieved 3 March 2024. ^ Gopani, Avi (25 May 2022). "Turing Test is unreliable. The Winograd Schema is obsolete. Coffee is the answer". Analytics India Magazine. Retrieved 3 March 2024. ^ Bhaimiya, Sawdah (20 June 2023). "DeepMind's co-founder recommended testing an AI chatbot's ability to turn $100,000 into $1 million to measure human-like intelligence". Business Insider. Retrieved 3 March 2024. ^ Suleyman, Mustafa (14 July 2023). "Mustafa Suleyman: My brand-new Turing test would see if AI can make $1 million". MIT Technology Review. Retrieved 3 March 2024. ^ Shapiro, Stuart C. (1992 ). "Expert System" (PDF). In Stuart C. Shapiro (ed.). Encyclopedia of Artificial Intelligence (Second ed.). New York City: John Wiley. pp. 54-57. Archived (PDF) from the initial on 1 February 2016. (Section 4 is on "AI-Complete Tasks".). ^ Yampolskiy, Roman V. (2012 ). Xin-She Yang (ed.). "Turing Test as a Defining Feature of AI-Completeness" (PDF). Artificial Intelligence, Evolutionary Computation and Metaheuristics (AIECM): 3-17. Archived (PDF) from the initial on 22 May 2013. ^ "AI Index: State of AI in 13 Charts". Stanford University Human-Centered Artificial Intelligence. 15 April 2024. Retrieved 27 May 2024. ^ Crevier 1993, pp. 48-50. ^ Kaplan, Andreas (2022 ). "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 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 ). "Respond 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 City Times. Archived from the initial on 2 February 2023. Retrieved 18 February 2017. At its low point, some computer system researchers and software engineers prevented the term artificial intelligence for worry of being deemed wild-eyed dreamers. ^ Russell & Norvig 2003, pp. 25-26 ^ "Trends in the Emerging Tech Hype Cycle". Gartner Reports. Archived from the initial on 22 May 2019. Retrieved 7 May 2019. ^ a b Moravec 1988, p. 20 ^ Harnad, S. (1990 ). "The Symbol Grounding Problem". Physica D. 42 (1-3): 335-346. arXiv: cs/9906002. Bibcode:1990 PhyD ... 42..335 H. doi:10.1016/ 0167-2789( 90 )90087-6. S2CID 3204300. ^ Gubrud 1997 ^ Hutter, Marcus (2005 ). Universal Expert System: Sequential Decisions Based 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 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 coined the term "AGI"?". goertzel.org. Archived from the initial on 28 December 2018. Retrieved 28 December 2018., through Life 3.0: 'The term "AGI" was promoted by ... Shane Legg, Mark Gubrud and Ben Goertzel' ^ Wang & Goertzel 2007 ^ "First International Summer School in Artificial General Intelligence, Main summer school: June 22 - July 3, 2009, OpenCog Lab: July 6-9, 2009". Archived from the original 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 season 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 maker intelligence, artificial general 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 monster". The Guardian. Archived from the initial 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 artificial intelligence will not be recognized". Humanities and Social Sciences Communications. 7 (1 ): 1-9. doi:10.1057/ s41599-020-0494-4. hdl:11250/ 2726984. ISSN 2662-9992. S2CID 219710554. ^ McCarthy 2007b. ^ Khatchadourian, Raffi (23 November 2015). "The Doomsday Invention: Will expert system bring us utopia or destruction?". The New Yorker. Archived from the initial on 28 January 2016. Retrieved 7 February 2016. ^ Müller, V. C., & Bostrom, N. (2016 ). Future development in synthetic intelligence: A study of professional opinion. In Fundamental concerns of artificial intelligence (pp. 555-572). Springer, Cham. ^ Armstrong, Stuart, and Kaj Sotala. 2012. "How We're Predicting AI-or Failing To." In Beyond AI: Artificial Dreams, edited by Jan Romportl, Pavel Ircing, Eva Žáčková, Michal Polák and Radek Schuster, 52-75. Plzeň: University of West Bohemia ^ "Microsoft Now Claims GPT-4 Shows 'Sparks' of General Intelligence". 24 March 2023. ^ Shimek, Cary (6 July 2023). "AI Outperforms Humans in Creativity Test". Neuroscience News. Retrieved 20 October 2023. ^ Guzik, Erik E.; Byrge, Christian; Gilde, Christian (1 December 2023). "The creativity of machines: AI takes the Torrance Test". Journal of Creativity. 33 (3 ): 100065. doi:10.1016/ j.yjoc.2023.100065. ISSN 2713-3745. S2CID 261087185. ^ Arcas, Blaise Agüera y (10 October 2023). "Artificial General Intelligence Is Already Here". Noema. ^ Zia, Tehseen (8 January 2024). "Unveiling of Large Multimodal Models: Shaping the Landscape of Language Models in 2024". Unite.ai. Retrieved 26 May 2024. ^ "Introducing OpenAI o1-preview". OpenAI. 12 September 2024. ^ Knight, Will. "OpenAI Announces a New AI Model, Code-Named Strawberry, That Solves Difficult Problems Step by Step". Wired. ISSN 1059-1028. Retrieved 17 September 2024. ^ "OpenAI Employee Claims AGI Has Been Achieved". Orbital Today. 13 December 2024. Retrieved 27 December 2024. ^ "AI Index: State of AI in 13 Charts". hai.stanford.edu. 15 April 2024. Retrieved 7 June 2024. ^ "Next-Gen AI: OpenAI and Meta's Leap Towards Reasoning Machines". Unite.ai. 19 April 2024. Retrieved 7 June 2024. ^ James, Alex P. (2022 ). "The Why, 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 synthetic basic intelligence with hybrid Tianjic chip architecture". Nature. 572 (7767 ): 106-111. Bibcode:2019 Natur.572..106 P. doi:10.1038/ s41586-019-1424-8. ISSN 1476-4687. PMID 31367028. S2CID 199056116. Archived from the initial on 29 August 2022. Retrieved 29 August 2022. ^ Pandey, Mohit; Fernandez, Michael; Gentile, Francesco; Isayev, Olexandr; Tropsha, Alexander; Stern, Abraham C.; Cherkasov, Artem (March 2022). "The transformational role of GPU computing and deep 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 clever as Siri - however a six-year-old beats both] (in German). Archived from the original on 3 January 2019. Retrieved 2 January 2019. ^ Grossman, Gary (3 September 2020). "We're entering the AI golden zone between narrow and general AI". VentureBeat. Archived from the original on 4 September 2020. Retrieved 5 September 2020. Certainly, too, there are those who declare we are already seeing an early example of an AGI system in the just recently revealed GPT-3 natural language processing (NLP) neural network. ... So is GPT-3 the first example of an AGI system? This is debatable, however the agreement is that it is not AGI. ... If absolutely nothing else, GPT-3 informs us there is a happy medium in between narrow and basic AI. ^ Quach, Katyanna. "A designer developed an AI chatbot utilizing GPT-3 that helped a male speak once again to his late fiancée. OpenAI shut it down". The Register. Archived from the original on 16 October 2021. Retrieved 16 October 2021. ^ Wiggers, Kyle (13 May 2022), "DeepMind's brand-new AI can carry out over 600 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 City Times. ISSN 0362-4331. Retrieved 7 June 2023. ^ Bove, Tristan. "A.I. could equal human intelligence in 'simply a few years,' states CEO of Google's main A.I. research laboratory". Fortune. Retrieved 4 September 2024. ^ Nellis, Stephen (2 March 2024). "Nvidia CEO states AI might pass human tests in 5 years". Reuters. ^ Aschenbrenner, Leopold. "SITUATIONAL AWARENESS, The Decade Ahead". ^ Sullivan, Mark (18 October 2023). "Why everybody appears to disagree on how to specify Artificial General Intelligence". Fast Company. ^ Nosta, John (5 January 2024). "The Accelerating Path to Artificial General Intelligence". Psychology Today. Retrieved 30 March 2024. ^ Hickey, Alex. "Whole Brain Emulation: A Huge Step for Neuroscience". Tech Brew. Retrieved 8 November 2023. ^ Sandberg & Boström 2008. ^ Drachman 2005. ^ a b Russell & Norvig 2003. ^ Moravec 1988, p. 61. ^ Moravec 1998. ^ Holmgaard Mersh, Amalie (15 September 2023). "Decade-long European research study job 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 wide variety of views in present research study, all of which need grounding to some degree ^ Thornton, Angela (26 June 2023). "How uploading our minds to a computer might become possible". The Conversation. Retrieved 8 November 2023. ^ Searle 1980 ^ For example: Russell & Norvig 2003, Oxford University Press Dictionary of Psychology Archived 3 December 2007 at the Wayback Machine (priced quote in" Encyclopedia.com"),. MIT Encyclopedia of Cognitive Science Archived 19 July 2008 at the Wayback Machine (priced estimate in "AITopics"),. Will Biological Computers Enable Artificially Intelligent Machines to Become Persons? Archived 13 May 2008 at the Wayback Machine Anthony Tongen.


^ a b c Russell & Norvig 2003, p. 947. ^ though see Explainable synthetic intelligence for curiosity 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 Artificial Intelligence Have Moral or Legal Rights?". The Wall Street Journal. ^ "Artificial General Intelligence - Do [es] the cost surpass advantages?". 23 August 2021. Retrieved 7 June 2023. ^ "How we can Take advantage of Advancing Artificial General Intelligence (AGI) - Unite.AI". www.unite.ai. 7 April 2020. Retrieved 7 June 2023. ^ a b c Talty, Jules; Julien, Stephan. "What Will Our Society Look Like When Expert System Is Everywhere?". Smithsonian Magazine. Retrieved 7 June 2023. ^ a b Stevenson, Matt (8 October 2015). "Answers to Stephen Hawking's AMA are Here!". Wired. ISSN 1059-1028. Retrieved 8 June 2023. ^ a b Bostrom, Nick (2017 ). " § Preferred order of arrival". Superintelligence: courses, risks, methods (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 ruin ourselves". Vox. Retrieved 8 June 2023. ^ Doherty, Ben (17 May 2018). "Climate change an 'existential security threat' to Australia, Senate query states". The Guardian. ISSN 0261-3077. Retrieved 16 July 2023. ^ MacAskill, William (2022 ). What we owe the future. New York, NY: Basic Books. ISBN 978-1-5416-1862-6. ^ a b Ord, Toby (2020 ). "Chapter 5: 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 Times. ISSN 0362-4331. Retrieved 24 December 2023. ^ a b "Statement on AI Risk". Center for AI Safety. 30 May 2023. Retrieved 8 June 2023. ^ "Stephen Hawking: 'Transcendence takes a look at the ramifications of expert system - however are we taking AI seriously enough?'". The Independent (UK). Archived from the initial on 25 September 2015. Retrieved 3 December 2014. ^ Herger, Mario. "The Gorilla Problem - Enterprise Garage". Retrieved 7 June 2023. ^ "The fascinating Facebook argument in between Yann LeCun, Stuart Russel and Yoshua Bengio about the threats of strong AI". The interesting Facebook debate 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 disastrous 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 dangers spiralling out of control, report warns". Financial Times. Archived from the original on 11 April 2022. Retrieved 24 December 2023. ^ Milmo, Dan; Stacey, Kiran (25 September 2023). "Experts disagree over hazard posed however synthetic intelligence can not be neglected". The Guardian. ISSN 0261-3077. Retrieved 24 December 2023. ^ "Humanity, Security & AI, Oh My! (with Ian Bremmer & Shuman Ghosemajumder)". CAFE. 20 July 2023. Retrieved 15 September 2023. ^ Hamblin, James (9 May 2014). "But What Would 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 stoking worries over AI, caution researchers". The Telegraph. Retrieved 7 December 2023. ^ Davidson, John (30 October 2023). " founder states huge tech is lying about AI termination 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 appearance at the labor market effect potential of big language designs". OpenAI. Retrieved 7 June 2023. ^ a b Hurst, Luke (23 March 2023). "OpenAI says 80% of employees might see their jobs affected by AI. These are the tasks most impacted". euronews. Retrieved 8 June 2023. ^ Sheffey, Ayelet (20 August 2021). "Elon Musk says we need universal fundamental 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), "Expert system and the future", Philosophical Transactions of the Royal Society A, vol. 361, no. 1809, pp. 1721-1748, Bibcode:2003 RSPTA.361.1721 C, doi:10.1098/ rsta.2003.1232, PMID 12952683, S2CID 31032007. Crevier, Daniel (1993 ). AI: The Tumultuous Search for 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 original (PDF) on 20 March 2013. Goertzel, Ben (December 2007), "Human-level artificial general intelligence and the possibility of a technological singularity: a reaction to Ray Kurzweil's The Singularity Is Near, and McDermott's review of Kurzweil", Expert system, vol. 171, no. 18, Special Review Issue, pp. 1161-1173, doi:10.1016/ j.artint.2007.10.011, archived from the initial on 7 January 2016, 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 Perspective, archived from the original on 17 August 2007, obtained 30 August 2007. Johnson, Mark (1987 ), The body in the mind, Chicago, ISBN 978-0-2264-0317-5. Kurzweil, Ray (2005 ), The Singularity is Near, Viking Press. Lighthill, Professor Sir James (1973 ), "Expert System: A General Survey", Artificial Intelligence: a paper seminar, Science Research Council. Luger, George; Stubblefield, William (2004 ), Artificial Intelligence: Structures and Strategies for Complex Problem Solving (5th ed.), The Benjamin/Cummings Publishing Company, Inc., p. 720, ISBN 978-0-8053-4780-7. McCarthy, John (2007b). What is Expert system?. Stanford University. The supreme effort is to make computer system programs that can solve issues and achieve goals worldwide along with human beings. 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 original on 16 October 2011, retrieved 7 November 2009 Newell, Allen; Simon, H. A. (1976 ). "Computer Technology as Empirical Inquiry: Symbols and Search". Communications of the ACM. 19 (3 ): 113-126. doi:10.1145/ 360018.360022. Nilsson, Nils (1998 ), Expert System: A New Synthesis, Morgan Kaufmann Publishers, ISBN 978-1-5586-0467-4 NRC (1999 ), "Developments in Expert System", Funding a Transformation: Government Support for Computing Research, National Academy Press, archived from the initial on 12 January 2008, recovered 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 ), Artificial Intelligence: A Modern Approach (2nd ed.), Upper Saddle River, New Jersey: Prentice Hall, ISBN 0-13-790395-2 Sandberg, Anders; Boström, Nick (2008 ), Whole Brain Emulation: A Roadmap (PDF), Technical Report # 2008-3, Future of Humanity Institute, Oxford University, archived (PDF) from the initial on 25 March 2020, obtained 5 April 2009 Searle, John (1980 ), "Minds, Brains and Programs" (PDF), Behavioral and Brain Sciences, 3 (3 ): 417-457, doi:10.1017/ S0140525X00005756, S2CID 55303721, archived (PDF) from the original on 17 March 2019, retrieved 3 September 2020 Simon, H. A. (1965 ), The Shape of Automation for Men and Management, New York: Harper & Row Turing, Alan (October 1950). "Computing Machinery and Intelligence". Mind. 59 (236 ): 433-460. doi:10.1093/ mind/LIX.236.433. ISSN 1460-2113. JSTOR 2251299. S2CID 14636783.
de Vega, Manuel; Glenberg, Arthur; Graesser, Arthur, eds. (2008 ), Symbols and Embodiment: Debates on meaning and cognition, Oxford University Press, ISBN 978-0-1992-1727-4 Wang, Pei; Goertzel, Ben (2007 ). "Introduction: Aspects of Artificial General Intelligence". Advances in Artificial General Intelligence: Concepts, Architectures and Algorithms: Proceedings of the AGI Workshop 2006. IOS Press. pp. 1-16. ISBN 978-1-5860-3758-1. Archived from the initial on 18 February 2021. Retrieved 13 December 2020 - via ResearchGate.
Further reading

Aleksander, Igor (1996 ), Impossible Minds, World Scientific Publishing Company, ISBN 978-1-8609-4036-1 Azevedo FA, Carvalho LR, Grinberg LT, Farfel J, et al. (April 2009), "Equal varieties of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain", The Journal of Comparative Neurology, 513 (5 ): 532-541, doi:10.1002/ cne.21974, PMID 19226510, S2CID 5200449, archived from the 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 original on 23 July 2014, obtained 31 August 2012 Cukier, Kenneth, "Ready for Robots? How to Consider the Future of AI", Foreign Affairs, vol. 98, no. 4 (July/August 2019), pp. 192-98. George Dyson, historian of computing, writes (in what might be called "Dyson's Law") that "Any system easy adequate to be reasonable will not be made complex enough to behave smartly, while any system complicated enough to behave intelligently will be too made complex to understand." (p. 197.) Computer researcher Alex Pentland writes: "Current AI machine-learning algorithms are, at their core, dead easy foolish. They work, but 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, obtained 25 July 2010. Gleick, James, "The Fate of Free Choice" (evaluation of Kevin J. Mitchell, Free Agents: How Evolution Gave Us Free Will, Princeton University Press, 2023, 333 pp.), The New York Review of Books, vol. LXXI, no. 1 (18 January 2024), pp. 27-28, 30. "Agency is what identifies us from makers. For biological creatures, reason and purpose originate 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 initial (PDF) on 6 June 2013. - Halpern, Sue, "The Coming Tech Autocracy" (evaluation of Verity Harding, AI Needs You: How We Can Change AI's Future and Save Our Own, Princeton University Press, 274 pp.; Gary Marcus, Taming Silicon Valley: How We Can Ensure That AI Works for Us, MIT Press, 235 pp.; Daniela Rus and Gregory Mone, The Mind's Mirror: Risk and Reward in the Age of AI, Norton, 280 pp.; Madhumita Murgia, Code Dependent: 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 want to get abundant from AI are going to have the interests of the rest people close at heart,' ... writes [Gary Marcus] 'We can't count on governments driven by campaign finance contributions [from tech business] to push back.' ... Marcus details the demands that citizens should make of their federal governments and the tech companies. They include transparency on how AI systems work; payment for people if their data [are] utilized to train LLMs (large language model) s and the right to consent to this usage; and the ability to hold tech business liable for the damages they trigger by eliminating Section 230, imposing cash penalites, and passing stricter item liability laws ... Marcus likewise suggests ... that a brand-new, AI-specific federal company, comparable to the FDA, the FCC, or the FTC, might supply the most robust oversight ... [T] he Fordham law teacher Chinmayi Sharma ... recommends ... establish [ing] an expert licensing program for engineers that would work in a similar method to medical licenses, malpractice suits, and the Hippocratic oath in medicine. 'What if, like doctors,' she asks ..., 'AI engineers also swore to do no damage?'" (p. 46.). Holte, R. C.; Choueiry, B. Y. (2003 ), "Abstraction and reformulation in synthetic intelligence", Philosophical Transactions of the Royal Society B, vol. 358, no. 1435, pp. 1197-1204, doi:10.1098/ rstb.2003.1317, PMC 1693218, PMID 12903653. Hughes-Castleberry, Kenna, "A Murder Mystery Puzzle: The literary puzzle Cain's Jawbone, which has actually baffled people for years, reveals the constraints of natural-language-processing algorithms", Scientific American, vol. 329, no. 4 (November 2023), pp. 81-82. "This murder mystery competitors has actually exposed that although NLP (natural-language processing) models are capable of amazing accomplishments, their abilities are really much restricted by the quantity of context they get. This [...] might cause [difficulties] for scientists who hope to use them to do things such as evaluate ancient languages. In many cases, there are few historical records on long-gone civilizations to serve as training data for such a function." (p. 82.). Immerwahr, Daniel, "Your Lying Eyes: People now use A.I. to generate 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 mean reasonable videos produced utilizing artificial intelligence that actually deceive individuals, then they barely exist. The phonies aren't deep, and the deeps aren't fake. [...] A.I.-generated videos are not, in basic, running in our media as counterfeited evidence. Their role much better resembles that of cartoons, particularly smutty ones." (p. 59.). - Leffer, Lauren, "The Risks of Trusting AI: We must avoid humanizing machine-learning designs used in clinical research study", Scientific American, vol. 330, no. 6 (June 2024), pp. 80-81. Lepore, Jill, "The Chit-Chatbot: Is talking with a maker a conversation?", The New Yorker, 7 October 2024, pp. 12-16. Marcus, Gary, "Artificial Confidence: Even the most recent, buzziest systems of artificial general intelligence are stymmied by the usual issues", Scientific American, vol. 327, no. 4 (October 2022), pp. 42-45. McCarthy, John (October 2007), "From here to human-level AI", Expert System, 171 (18 ): 1174-1182, doi:10.1016/ j.artint.2007.10.009. McCorduck, Pamela (2004 ), Machines Who Think (second ed.), Natick, Massachusetts: A. K. Peters, ISBN 1-5688-1205-1. Moravec, Hans (1976 ), The Role of Raw Power in Intelligence, archived from the initial on 3 March 2016, obtained 29 September 2007. Newell, Allen; Simon, H. A. (1963 ), "GPS: A Program that Simulates Human Thought", in Feigenbaum, E. A.; Feldman, J. (eds.), Computers and Thought, New York 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 cops to ignore contradictory evidence?", The New Yorker, 20 November 2023, pp. 20-26. Roivainen, Eka, "AI's IQ: ChatGPT aced a [basic intelligence] test but showed that intelligence can not be measured by IQ alone", Scientific American, vol. 329, no. 1 (July/August 2023), p. 7. "Despite its high IQ, ChatGPT stops working at jobs that require real humanlike reasoning or an understanding of the physical and social world ... ChatGPT seemed unable to reason logically and attempted to count on its vast database of ... realities 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 technologies are effective however undependable. Rules-based systems can not deal with situations their developers did not anticipate. Learning systems are restricted by the information on which they were trained. AI failures have already led to catastrophe. Advanced autopilot features in automobiles, although they perform well in some circumstances, have actually driven cars and trucks without warning into trucks, concrete barriers, and parked cars. In the incorrect situation, AI systems go from supersmart to superdumb in an immediate. 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 new innovations however count on the timelelss human propensity to anthropomorphise." (p. 29.). Williams, R. W.; Herrup, K.