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Artificial general intelligence (AGI) is a type of artificial intelligence (AI) that matches or goes beyond human cognitive abilities throughout a wide variety of cognitive tasks. This contrasts with narrow AI, which is restricted to specific jobs. [1] Artificial superintelligence (ASI), on the other hand, describes AGI that greatly goes beyond human cognitive capabilities. AGI is thought about one of the definitions of strong AI.

Creating AGI is a main goal of AI research and of business such as OpenAI [2] and Meta. [3] A 2020 study determined 72 active AGI research and advancement tasks throughout 37 nations. [4]
The timeline for accomplishing AGI stays a subject of ongoing debate amongst scientists and experts. Since 2023, some argue that it may be possible in years or years; others keep it may take a century or longer; a minority believe it might never ever be achieved; and another minority declares that it is already here. [5] [6] Notable AI scientist Geoffrey Hinton has revealed issues about the quick development towards AGI, recommending it might be attained earlier than lots of expect. [7]
There is argument on the precise 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 topic in science fiction and futures studies. [9] [10]
Contention exists over whether AGI represents an existential risk. [11] [12] [13] Many specialists on AI have actually specified that alleviating the threat of human termination presented by AGI should be a global top priority. [14] [15] Others discover the development of AGI to be too remote to present such a danger. [16] [17]
Terminology

AGI is likewise understood as strong AI, [18] [19] complete AI, [20] human-level AI, [5] human-level smart AI, or general intelligent action. [21]
Some academic sources schedule the term "strong AI" for computer programs that experience sentience or consciousness. [a] In contrast, weak AI (or narrow AI) has the ability to solve one specific problem but does not have general cognitive capabilities. [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 very same sense as human beings. [a]
Related concepts consist of artificial superintelligence and transformative AI. A synthetic superintelligence (ASI) is a theoretical type of AGI that is far more usually intelligent than human beings, [23] while the concept of transformative AI connects to AI having a large effect on society, for instance, similar to the agricultural or commercial revolution. [24]
A structure for classifying AGI in levels was proposed in 2023 by Google DeepMind researchers. They specify five levels of AGI: emerging, proficient, specialist, virtuoso, and superhuman. For example, a proficient AGI is defined as an AI that exceeds 50% of knowledgeable grownups in a vast array of non-physical jobs, and a superhuman AGI (i.e. an artificial superintelligence) is similarly specified but with a limit of 100%. They consider big language models like ChatGPT or LLaMA 2 to be instances of emerging AGI. [25]
Characteristics

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

Researchers normally hold that intelligence is required to do all of the following: [27]
reason, use technique, resolve puzzles, and make judgments under unpredictability represent understanding, including typical sense knowledge plan learn

• communicate in natural language
• if necessary, integrate these abilities in completion of any given objective

Many interdisciplinary methods (e.g. cognitive science, computational intelligence, and choice making) think about extra qualities such as imagination (the ability to form novel psychological images and ideas) [28] and autonomy. [29]
Computer-based systems that show numerous of these capabilities exist (e.g. see computational imagination, automated reasoning, decision support group, robot, evolutionary computation, intelligent representative). There is argument about whether contemporary AI systems possess them to an appropriate degree.

Physical traits

Other abilities are thought about preferable in smart systems, as they may impact intelligence or aid in its expression. These include: [30]
- the ability to sense (e.g. see, hear, and so on), and - the ability to act (e.g. relocation and control things, modification location to explore, etc).
This includes the capability to detect and react to risk. [31]
Although the capability to sense (e.g. see, hear, and so on) and the ability to act (e.g. move and control objects, change location to check out, and so on) can be preferable for yewiki.org some smart systems, [30] these physical abilities are not strictly needed for an entity to certify as AGI-particularly under the thesis that large language designs (LLMs) may currently be or become AGI. Even from a less optimistic viewpoint on LLMs, there is no company requirement for an AGI to have a human-like form; being a silicon-based computational system suffices, supplied it can process input (language) from the external world in place of human senses. This analysis lines up with the understanding that AGI has never been proscribed a specific physical personification and wiki.philo.at thus does not require a capability for mobility or standard "eyes and ears". [32]
Tests for human-level AGI

Several tests implied to confirm human-level AGI have been thought about, including: [33] [34]
The idea of the test is that the maker needs to try and pretend to be a guy, by addressing questions put to it, and it will only pass if the pretence is reasonably convincing. A considerable part of a jury, who should not be expert about machines, must be taken in by the pretence. [37]
AI-complete problems

An issue is informally called "AI-complete" or "AI-hard" if it is believed that in order to resolve it, one would need to implement AGI, due to the fact that the service is beyond the capabilities of a purpose-specific algorithm. [47]
There are numerous issues that have actually been conjectured to require general intelligence to solve along with people. Examples include computer vision, natural language understanding, and dealing with unanticipated circumstances while fixing any real-world issue. [48] Even a particular task like translation requires a maker to read and compose in both languages, follow the author's argument (reason), comprehend the context (knowledge), and faithfully replicate the author's initial intent (social intelligence). All of these issues need to be fixed concurrently in order to reach human-level maker efficiency.

However, a lot of these jobs can now be carried out by modern-day large language models. According to Stanford University's 2024 AI index, AI has reached human-level efficiency on numerous benchmarks for checking out understanding and visual thinking. [49]
History

Classical AI

Modern AI research study began in the mid-1950s. [50] The first generation of AI scientists were encouraged that synthetic basic intelligence was possible and that it would exist in simply a couple of years. [51] AI leader 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 Stanley Kubrick and Arthur C. Clarke's character HAL 9000, who embodied what AI researchers thought they might produce by the year 2001. AI pioneer Marvin Minsky was a consultant [53] on the job of making HAL 9000 as sensible as possible according to the consensus forecasts of the time. He said in 1967, "Within a generation ... the issue of producing 'synthetic intelligence' will significantly be fixed". [54]
Several classical AI jobs, such as Doug Lenat's Cyc job (that started in 1984), and Allen Newell's Soar job, were directed at AGI.

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

In the 1990s and early 21st century, mainstream AI achieved commercial success and academic respectability by focusing on specific sub-problems where AI can produce proven outcomes and business applications, such as speech acknowledgment and recommendation algorithms. [63] These "applied AI" systems are now used extensively throughout the innovation market, and research study in this vein is heavily funded in both academia and market. As of 2018 [update], advancement in this field was thought about an emerging trend, and a mature stage was anticipated to be reached in more than ten years. [64]
At the millenium, lots of traditional AI scientists [65] hoped that strong AI could be developed by integrating programs that fix various sub-problems. Hans Moravec wrote in 1988:

I am positive that this bottom-up route to artificial intelligence will one day fulfill the traditional top-down route majority method, all set to provide the real-world competence and the commonsense knowledge that has actually been so frustratingly evasive in reasoning programs. Fully smart devices will result when the metaphorical golden spike is driven uniting the 2 efforts. [65]
However, even at the time, this was contested. For instance, Stevan Harnad of Princeton University concluded his 1990 paper on the symbol grounding hypothesis by mentioning:

The expectation has often been voiced that "top-down" (symbolic) approaches to modeling cognition will in some way meet "bottom-up" (sensory) approaches someplace in between. If the grounding factors to consider in this paper are legitimate, then this expectation is hopelessly modular and there is really just one viable route from sense to symbols: 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 should even try to reach such a level, considering that it looks as if arriving would simply total up to uprooting our symbols from their intrinsic significances (thereby simply decreasing ourselves to the practical equivalent of a programmable computer system). [66]
Modern artificial general 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 increases "the ability to please objectives in a vast array of environments". [68] This type of AGI, characterized by the ability to increase a mathematical meaning of intelligence rather than show human-like behaviour, [69] was likewise called universal artificial intelligence. [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 first summer 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 offered in 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 number of guest speakers.

As of 2023 [update], a little number of computer system scientists are active in AGI research study, and lots of contribute to a series of AGI conferences. However, progressively more researchers are interested in open-ended learning, [76] [77] which is the concept of enabling AI to constantly find out and innovate like human beings do.

Feasibility

Since 2023, the advancement and prospective achievement of AGI stays a subject of extreme argument within the AI community. While traditional consensus held that AGI was a distant goal, current improvements have led some researchers and market figures to declare that early types of AGI may currently exist. [78] AI leader Herbert A. Simon hypothesized in 1965 that "machines will be capable, within twenty years, of doing any work a male can do". This prediction stopped working to come true. Microsoft co-founder Paul Allen thought that such intelligence is unlikely in the 21st century because it would need "unforeseeable and essentially unpredictable breakthroughs" and a "clinically deep understanding of cognition". [79] Writing in The Guardian, roboticist Alan Winfield claimed the gulf in between contemporary computing and human-level expert system is as wide as the gulf in between current space flight and practical faster-than-light spaceflight. [80]
A more obstacle is the lack of clarity in specifying what intelligence requires. Does it require consciousness? Must it display the capability to set objectives along with pursue them? Is it purely a matter of scale such that if model sizes increase sufficiently, intelligence will emerge? Are facilities such as planning, reasoning, and causal understanding required? Does intelligence require clearly duplicating the brain and its specific faculties? Does it need feelings? [81]
Most AI researchers believe strong AI can be achieved in the future, however some thinkers, like Hubert Dreyfus and Roger Penrose, deny the possibility of attaining strong AI. [82] [83] John McCarthy is among those who think human-level AI will be accomplished, however that the present level of development is such that a date can not properly be predicted. [84] AI experts' views on the feasibility of AGI wax and wane. Four polls performed in 2012 and 2013 suggested that the typical quote among experts for when they would be 50% positive AGI would arrive was 2040 to 2050, depending on the survey, with the mean being 2081. Of the professionals, 16.5% responded to with "never" when asked the same concern but with a 90% self-confidence instead. [85] [86] Further current AGI development considerations can be discovered above Tests for verifying human-level AGI.

A report by Stuart Armstrong and Kaj Sotala of the Machine Intelligence Research Institute discovered that "over [a] 60-year time frame there is a strong predisposition towards anticipating the arrival of human-level AI as between 15 and 25 years from the time the forecast was made". They examined 95 predictions made between 1950 and 2012 on when human-level AI will happen. [87]
In 2023, Microsoft researchers released a comprehensive evaluation of GPT-4. They concluded: "Given the breadth and depth of GPT-4's abilities, we think that it might reasonably be seen as an early (yet still incomplete) variation of an artificial basic intelligence (AGI) system." [88] Another research study in 2023 reported that GPT-4 exceeds 99% of people on the Torrance tests of creativity. [89] [90]
Blaise Agüera y Arcas and Peter Norvig wrote in 2023 that a significant level of basic intelligence has currently been accomplished with frontier models. They composed that reluctance to this view comes from four primary reasons: a "healthy uncertainty about metrics for AGI", an "ideological dedication to alternative AI theories or methods", a "devotion to human (or biological) exceptionalism", or a "concern about the economic implications of AGI". [91]
2023 also marked the development of big multimodal designs (large language designs efficient in processing or generating multiple techniques such as text, audio, and images). [92]
In 2024, OpenAI released o1-preview, the first of a series of models that "spend more time thinking before they react". 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 answer, whereas the model scaling paradigm improves outputs by increasing the model size, training data and training calculate power. [93] [94]
An OpenAI staff member, Vahid Kazemi, claimed in 2024 that the business had attained AGI, mentioning, "In my viewpoint, we have currently attained AGI and it's a lot more clear with O1." Kazemi clarified that while the AI is not yet "much better than any human at any job", it is "much better than many humans at the majority of jobs." He also attended to criticisms that big language models (LLMs) simply follow predefined patterns, comparing their knowing procedure to the scientific method of observing, assuming, and confirming. These statements have sparked dispute, as they count on a broad and non-traditional definition of AGI-traditionally comprehended as AI that matches human intelligence throughout all domains. Critics argue that, while OpenAI's models show exceptional adaptability, they might not totally meet this standard. Notably, Kazemi's comments came shortly after OpenAI got rid of "AGI" from the terms of its partnership with Microsoft, triggering speculation about the company's strategic intents. [95]
Timescales

Progress in expert system has historically gone through periods of quick development separated by durations when progress appeared to stop. [82] Ending each hiatus were essential advances in hardware, software application or both to create space for additional progress. [82] [98] [99] For example, the hardware offered in the twentieth century was not enough to carry out deep knowing, which requires great deals of GPU-enabled CPUs. [100]
In the intro to his 2006 book, [101] Goertzel states that price quotes of the time needed before a truly flexible AGI is developed vary from ten years to over a century. Since 2007 [upgrade], the consensus in the AGI research neighborhood appeared to be that the timeline gone over by Ray Kurzweil in 2005 in The Singularity is Near [102] (i.e. between 2015 and 2045) was plausible. [103] Mainstream AI researchers have actually given a vast array of viewpoints on whether progress will be this fast. A 2012 meta-analysis of 95 such opinions discovered a bias towards anticipating that the start of AGI would take place within 16-26 years for modern-day and historic forecasts alike. That paper has actually been slammed for how it classified viewpoints as specialist or non-expert. [104]
In 2012, Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton developed a neural network called AlexNet, which won the ImageNet competitors with a top-5 test mistake rate of 15.3%, significantly much better than the second-best entry's rate of 26.3% (the conventional method used a weighted sum of ratings from various pre-defined classifiers). [105] AlexNet was considered the initial ground-breaker of the present deep knowing wave. [105]
In 2017, scientists Feng Liu, Yong Shi, and Ying Liu performed intelligence tests on openly offered and freely accessible weak AI such as Google AI, Apple's Siri, and others. At the optimum, these AIs reached an IQ value of about 47, which corresponds roughly to a six-year-old child in very first grade. An adult comes to about 100 on average. Similar tests were brought out in 2014, with the IQ score reaching a maximum value of 27. [106] [107]
In 2020, OpenAI developed GPT-3, a language model efficient in carrying out numerous diverse jobs without particular training. According to Gary Grossman in a VentureBeat article, while there is consensus that GPT-3 is not an example of AGI, it is considered by some to be too advanced to be categorized as a narrow AI system. [108]
In the very same year, Jason Rohrer used his GPT-3 account to develop a chatbot, and supplied a chatbot-developing platform called "Project December". OpenAI asked 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 capable of performing more than 600 different tasks. [110]
In 2023, Microsoft Research published a study on an early variation of OpenAI's GPT-4, contending that it exhibited more general intelligence than previous AI designs and showed human-level efficiency in jobs spanning numerous domains, such as mathematics, coding, and law. This research sparked a dispute on whether GPT-4 might be considered an early, incomplete variation of artificial basic intelligence, highlighting the requirement for further expedition and evaluation of such systems. [111]
In 2023, the AI researcher Geoffrey Hinton stated that: [112]
The concept that this things might in fact get smarter than people - a couple of individuals believed that, [...] But most individuals believed it was method off. And I believed it was way off. I believed it was 30 to 50 years and even longer away. Obviously, I no longer believe that.

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

While the advancement of transformer designs like in ChatGPT is considered the most promising path to AGI, [116] [117] entire brain emulation can function as an alternative technique. With entire brain simulation, a brain design is developed by scanning and mapping a biological brain in detail, and then copying and replicating it on a computer system or another computational gadget. The simulation design should be sufficiently devoted to the initial, so that it acts in almost the same way as the original brain. [118] Whole brain emulation is a kind of brain simulation that is talked about in computational neuroscience and neuroinformatics, and for medical research purposes. It has been gone over in expert system research [103] as an approach to strong AI. Neuroimaging innovations that might deliver the essential comprehensive understanding are enhancing rapidly, and futurist Ray Kurzweil in the book The Singularity Is Near [102] predicts that a map of sufficient quality will become offered on a similar timescale to the computing power required to imitate it.

Early approximates

For low-level brain simulation, a very effective cluster of computer systems or GPUs would be needed, offered 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, stabilizing by adulthood. Estimates differ 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 an easy switch model for neuron activity, is around 1014 (100 trillion) synaptic updates per second (SUPS). [121]
In 1997, Kurzweil looked at numerous quotes for the hardware needed to equate to the human brain and adopted a figure of 1016 calculations per 2nd (cps). [e] (For contrast, if a "computation" was comparable to one "floating-point operation" - a step utilized to rate current 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 at some point in between 2015 and 2025, if the rapid development 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 detailed and publicly available atlas of the human brain. [124] In 2023, scientists from Duke University performed a high-resolution scan of a mouse brain.

Criticisms of simulation-based methods

The synthetic nerve cell design presumed by Kurzweil and used in lots of current synthetic neural network applications is easy compared to biological neurons. A brain simulation would likely have to catch the detailed cellular behaviour of biological nerve cells, currently understood only in broad summary. The overhead introduced by full modeling of the biological, chemical, and physical information of neural behaviour (especially on a molecular scale) would need computational powers numerous orders of magnitude bigger than Kurzweil's estimate. In addition, the quotes do not account for glial cells, which are known to contribute in cognitive procedures. [125]
A fundamental criticism of the simulated brain technique derives from embodied cognition theory which asserts that human embodiment is an important aspect of human intelligence and is necessary to ground significance. [126] [127] If this theory is correct, any fully functional brain model will need to encompass more than just the nerve cells (e.g., a robotic body). Goertzel [103] proposes virtual personification (like in metaverses like Second Life) as an alternative, but it is unidentified whether this would be sufficient.

Philosophical point of view

"Strong AI" as defined in viewpoint

In 1980, theorist John Searle created the term "strong AI" as part of his Chinese space argument. [128] He proposed a distinction between two hypotheses about artificial intelligence: [f]
Strong AI hypothesis: An artificial intelligence system can have "a mind" and "consciousness". Weak AI hypothesis: A synthetic intelligence system can (only) act like it believes and has a mind and awareness.
The first one he called "strong" since it makes a more powerful statement: it assumes something special has actually occurred to the machine that exceeds those capabilities that we can evaluate. The behaviour of a "weak AI" maker would be precisely identical to a "strong AI" device, however the latter would also have subjective conscious experience. This use is likewise common in academic AI research and books. [129]
In contrast to Searle and traditional AI, some futurists such as Ray Kurzweil use the term "strong AI" to indicate "human level artificial basic intelligence". [102] This is not the like Searle's strong AI, unless it is presumed that consciousness is required for human-level AGI. Academic thinkers such as Searle do not think that holds true, and to most expert system researchers the question is out-of-scope. [130]
Mainstream AI is most thinking about how a program behaves. [131] According to Russell and Norvig, "as long as the program works, they do not care if you call it genuine or a simulation." [130] If the program can act as if it has a mind, then there is no need to know if it really has mind - indeed, there would be no way to tell. For AI research study, 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 approved, 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 different meanings, and some aspects play significant roles in sci-fi and the ethics of artificial intelligence:

Sentience (or "sensational consciousness"): The ability to "feel" perceptions or feelings subjectively, rather than the ability to reason about perceptions. Some philosophers, such as David Chalmers, use the term "consciousness" to refer solely to remarkable awareness, which is roughly equivalent to sentience. [132] Determining why and how subjective experience develops is called the hard issue of consciousness. [133] Thomas Nagel explained in 1974 that it "feels like" something to be mindful. If we are not mindful, then it doesn't feel like anything. Nagel uses the example of a bat: we can sensibly ask "what does it feel like to be a bat?" However, we are not likely to ask "what does it seem like to be a toaster?" Nagel concludes that a bat appears to be conscious (i.e., has consciousness) but a toaster does not. [134] In 2022, a Google engineer claimed that the company's AI chatbot, LaMDA, had actually accomplished life, though this claim was commonly 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 operating system or debugger is able to be "familiar with itself" (that is, to represent itself in the exact same way it represents everything else)-but this is not what people normally suggest when they utilize the term "self-awareness". [g]
These qualities have an ethical dimension. AI life would provide increase to concerns of welfare and legal protection, likewise to animals. [136] Other aspects of awareness associated to cognitive abilities are also appropriate to the idea of AI rights. [137] Figuring out how to incorporate advanced AI with existing legal and social structures is an emergent problem. [138]
Benefits

AGI could have a variety of applications. If oriented towards such goals, AGI could assist alleviate various problems on the planet such as appetite, hardship and illness. [139]
AGI could enhance performance and effectiveness in most jobs. For instance, in public health, AGI could accelerate medical research study, significantly against cancer. [140] It could take care of the elderly, [141] and equalize access to fast, top quality medical diagnostics. It might provide enjoyable, cheap and personalized education. [141] The need to work to subsist could end up being obsolete if the wealth produced is appropriately rearranged. [141] [142] This likewise raises the question of the place of human beings in a significantly automated society.

AGI might also assist to make reasonable choices, and to expect and avoid disasters. It could also help to reap the advantages of possibly catastrophic innovations such as nanotechnology or environment engineering, while avoiding the associated risks. [143] If an AGI's primary objective is to avoid existential catastrophes such as human termination (which might be difficult if the Vulnerable World Hypothesis turns out to be true), [144] it might take steps to dramatically decrease the threats [143] while reducing the effect of these measures on our lifestyle.

Risks

Existential threats

AGI might represent numerous types of existential danger, which are dangers that threaten "the early extinction of Earth-originating intelligent life or the irreversible and extreme damage of its potential for desirable future development". [145] The danger of human termination from AGI has actually been the subject of many disputes, however there is also the possibility that the development of AGI would result in a completely problematic future. Notably, it could be used to spread and protect the set of values of whoever establishes it. If mankind still has ethical blind areas similar to slavery in the past, AGI might irreversibly entrench it, avoiding moral development. [146] Furthermore, AGI might assist in mass security and indoctrination, which could be used to create a steady repressive worldwide totalitarian program. [147] [148] There is also a danger for the devices themselves. If machines that are sentient or otherwise deserving of moral factor to consider are mass produced in the future, participating in a civilizational path that forever disregards their welfare and interests could be an existential disaster. [149] [150] Considering just how much AGI might improve humanity's future and help in reducing other existential risks, Toby Ord calls these existential threats "an argument for proceeding with due caution", not for "abandoning AI". [147]
Risk of loss of control and human termination

The thesis that AI postures an existential danger for people, and that this risk needs more attention, is questionable however has actually been backed in 2023 by numerous public figures, AI scientists and CEOs of AI companies such as Elon Musk, Bill Gates, Geoffrey Hinton, Yoshua Bengio, Demis Hassabis and Sam Altman. [151] [152]
In 2014, Stephen Hawking slammed prevalent indifference:

So, dealing with possible futures of enormous benefits and threats, the experts are surely doing everything possible to guarantee the finest result, right? Wrong. If an exceptional alien civilisation sent us a message saying, 'We'll arrive in a few decades,' would we simply reply, 'OK, call us when you get here-we'll leave the lights on?' Probably not-but this is more or less what is occurring with AI. [153]
The prospective fate of humanity has sometimes been compared to the fate of gorillas threatened by human activities. The comparison specifies that higher intelligence enabled mankind to control gorillas, which are now susceptible in methods that they could not have anticipated. As an outcome, the gorilla has actually become a threatened types, not out of malice, but simply as a civilian casualties from human activities. [154]
The skeptic Yann LeCun thinks about that AGIs will have no desire to dominate mankind and that we should take care not to anthropomorphize them and interpret their intents as we would for human beings. He stated that people won't be "clever sufficient to develop super-intelligent devices, yet unbelievably foolish to the point of providing it moronic goals without any safeguards". [155] On the other side, the principle of critical convergence suggests that nearly whatever their objectives, intelligent agents will have factors to try to survive and acquire more power as intermediary actions to achieving these objectives. And that this does not require having feelings. [156]
Many scholars who are concerned about existential danger supporter for more research study into fixing the "control problem" to address the concern: what kinds of safeguards, algorithms, or architectures can programmers execute to maximise the likelihood that their recursively-improving AI would continue to behave in a friendly, rather than devastating, way after it reaches superintelligence? [157] [158] Solving the control problem is complicated by the AI arms race (which could result in a race to the bottom of security preventative measures in order to launch products before rivals), [159] and using AI in weapon systems. [160]
The thesis that AI can present existential danger likewise has detractors. Skeptics generally say that AGI is unlikely in the short-term, or that issues about AGI sidetrack from other concerns connected to present AI. [161] Former Google scams czar Shuman Ghosemajumder considers that for many individuals outside of the innovation market, existing chatbots and LLMs are currently viewed as though they were AGI, causing additional misunderstanding and worry. [162]
Skeptics often charge that the thesis is crypto-religious, with an unreasonable belief in the possibility of superintelligence changing an irrational belief in a supreme God. [163] Some researchers believe that the interaction projects on AI existential risk by particular AI groups (such as OpenAI, Anthropic, DeepMind, and Conjecture) may 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, together with other industry leaders and researchers, provided a joint statement asserting that "Mitigating the risk of termination from AI must be a worldwide top priority together with other societal-scale risks such as pandemics and nuclear war." [152]
Mass joblessness

Researchers from OpenAI approximated that "80% of the U.S. labor force could have at least 10% of their work jobs impacted by the intro of LLMs, while around 19% of employees may see a minimum of 50% of their jobs impacted". [166] [167] They think about workplace workers to be the most exposed, for example mathematicians, accounting professionals or web designers. [167] AGI could have a better autonomy, ability to make decisions, to interface with other computer tools, however also to manage robotized bodies.

According to Stephen Hawking, the outcome of automation on the lifestyle will depend upon how the wealth will be redistributed: [142]
Everyone can take pleasure in a life of luxurious leisure if the machine-produced wealth is shared, or the majority of individuals can end up miserably poor if the machine-owners successfully lobby against wealth redistribution. Up until now, the pattern seems to be towards the second choice, with innovation driving ever-increasing inequality

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

Artificial brain - Software and hardware with cognitive abilities similar to those of the animal or human brain AI effect AI security - Research location on making AI safe and advantageous AI positioning - AI conformance to the desired goal A.I. Rising - 2018 movie directed by Lazar Bodroža Expert system Automated maker knowing - Process of automating the application of artificial intelligence BRAIN Initiative - Collaborative public-private research 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 artificial intelligence to play various games Generative artificial intelligence - AI system capable of generating material in response to prompts Human Brain Project - Scientific research task Intelligence amplification - Use of information technology to augment human intelligence (IA). Machine principles - Moral behaviours of man-made devices. Moravec's paradox. Multi-task knowing - Solving multiple maker discovering tasks at the very same time. Neural scaling law - Statistical law in machine knowing. Outline of expert system - Overview of and topical guide to expert system. Transhumanism - Philosophical movement. Synthetic intelligence - Alternate term for or type of expert system. Transfer knowing - Machine knowing technique. Loebner Prize - Annual AI competition. Hardware for synthetic intelligence - Hardware specifically developed and enhanced for expert system. Weak artificial intelligence - Form of artificial intelligence.
Notes

^ a b See listed below for the origin of the term "strong AI", and see the scholastic meaning of "strong AI" and weak AI in the article Chinese room. ^ AI founder John McCarthy writes: "we can not yet identify in basic what kinds of computational treatments we wish to call intelligent. " [26] (For a discussion of some definitions of intelligence utilized by expert system scientists, see viewpoint of synthetic intelligence.). ^ The Lighthill report specifically slammed AI's "grand objectives" and led the dismantling of AI research in England. [55] In the U.S., DARPA ended up being determined to money just "mission-oriented direct research, instead of basic undirected research". [56] [57] ^ As AI creator John McCarthy writes "it would be an excellent relief to the remainder of the employees in AI if the creators of brand-new basic formalisms would reveal their hopes in a more secured form than has often held true." [61] ^ In "Mind Children" [122] 1015 cps is utilized. More recently, in 1997, [123] Moravec argued for 108 MIPS which would roughly represent 1014 cps. Moravec talks in terms of MIPS, not "cps", which is a non-standard term Kurzweil presented. ^ As defined in a basic AI textbook: "The assertion that devices might perhaps act smartly (or, maybe better, act as if they were intelligent) is called the 'weak AI' hypothesis by thinkers, and the assertion that makers that do so are actually thinking (rather than 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 created to carry out a single job. ^ "OpenAI Charter". OpenAI. Retrieved 6 April 2023. Our objective is to guarantee that synthetic general intelligence benefits all of humanity. ^ Heath, Alex (18 January 2024). "Mark Zuckerberg's brand-new objective is creating synthetic basic intelligence". The Verge. Retrieved 13 June 2024. Our vision is to construct AI that is better than human-level at all of the human senses. ^ Baum, Seth D. (2020 ). A 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 determined as being active in 2020. ^ a b c "AI timelines: What do professionals in synthetic intelligence 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 pioneer Geoffrey Hinton stops Google and alerts of risk 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: hb9lc.org Early try outs GPT-4". arXiv preprint. arXiv:2303.12712. GPT-4 reveals stimulates of AGI. ^ Butler, Octavia E. (1993 ). Parable of the Sower. Grand Central Publishing. ISBN 978-0-4466-7550-5. All that you touch you change. 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 Artificial Intelligence". The New York Times. The real threat is not AI itself but the method we release it. ^ "Impressed by synthetic intelligence? Experts say AGI is following, and it has 'existential' dangers". ABC News. 23 March 2023. Retrieved 6 April 2023. AGI might position existential threats to humanity. ^ Bostrom, Nick (2014 ). Superintelligence: Paths, Dangers, Strategies. Oxford University Press. ISBN 978-0-1996-7811-2. The very first superintelligence will be the last creation that humanity 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 extinction from AI should be an international priority. ^ "Statement on AI Risk". Center for AI Safety. Retrieved 1 March 2024. AI professionals caution of risk of extinction from AI. ^ Mitchell, Melanie (30 May 2023). "Are AI's Doomsday Scenarios Worth Taking Seriously?". The New York City Times. We are far from creating devices that can outthink us in basic ways. ^ LeCun, Yann (June 2023). "AGI does not present an existential risk". Medium. There is no factor to fear AI as an existential threat. ^ 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 complete variety of human intelligence.". ^ "The Age of Expert System: George John at TEDxLondonBusinessSchool 2013". Archived from the initial on 26 February 2014. Retrieved 22 February 2014. ^ Newell & Simon 1976, This is the term they use for "human-level" intelligence in the physical sign system hypothesis. ^ "The Open University on Strong and Weak AI". Archived from the initial on 25 September 2009. Retrieved 8 October 2007. ^ "What is synthetic superintelligence (ASI)?|Definition from TechTarget". Enterprise AI. Retrieved 8 October 2023. ^ "Expert system is changing our world - it is on everyone to make sure 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 smart traits is based on the topics covered by major AI books, including: Russell & Norvig 2003, Luger & Stubblefield 2004, Poole, Mackworth & Goebel 1998 and Nilsson 1998. ^ Johnson 1987. ^ de Charms, R. (1968 ). Personal causation. New York: Academic Press. ^ a b Pfeifer, R. and Bongard J. C., How the body shapes the method we believe: a new view of intelligence (The MIT Press, 2007). ISBN 0-2621-6239-3. ^ White, R. W. (1959 ). "Motivation reevaluated: The concept of competence". 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 initial on 25 April 2014. Retrieved 1 May 2014. ^ "What is Artificial General Intelligence (AGI)?|4 Tests For Ensuring Artificial General Intelligence". Talky Blog. 13 July 2019. Archived from the 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 happens when it does?". The Conversation. Retrieved 22 September 2024. ^ a b Turing 1950. ^ Turing, Alan (1952 ). B. Jack Copeland (ed.). Can Automatic Calculating Machines Be Said To Think?. Oxford: Oxford University Press. pp. 487-506. ISBN 978-0-1982-5079-1. ^ "Eugene Goostman is a real boy - the Turing Test states so". The Guardian. 9 June 2014. ISSN 0261-3077. Retrieved 3 March 2024. ^ "Scientists contest 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 everything from the bar test to AP Biology. Here's a list of hard examinations both AI versions have actually passed". Business Insider. Retrieved 30 May 2023. ^ Naysmith, Caleb (7 February 2023). "6 Jobs Artificial Intelligence Is Already Replacing and How Investors Can Profit From It". Retrieved 30 May 2023. ^ Turk, Victoria (28 January 2015). "The Plan to Replace the Turing Test with a 'Turing Olympics'". Vice. Retrieved 3 March 2024. ^ Gopani, Avi (25 May 2022). "Turing Test is undependable. The Winograd Schema is obsolete. Coffee is the response". Analytics India Magazine. Retrieved 3 March 2024. ^ Bhaimiya, Sawdah (20 June 2023). "DeepMind's co-founder recommended testing an AI chatbot's capability to turn $100,000 into $1 million to measure human-like intelligence". Business Insider. Retrieved 3 March 2024. ^ Suleyman, Mustafa (14 July 2023). "Mustafa Suleyman: My new Turing test would see if AI can make $1 million". MIT Technology Review. Retrieved 3 March 2024. ^ Shapiro, Stuart C. (1992 ). "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 original 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 initial 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 initial on 16 July 2012. Retrieved 5 April 2008. ^ Marvin Minsky to Darrach (1970 ), quoted in Crevier (1993, p. 109). ^ Lighthill 1973; Howe 1994. ^ a b NRC 1999, "Shift to Applied Research Increases Investment". ^ Crevier 1993, pp. 115-117; Russell & Norvig 2003, pp. 21-22. ^ Crevier 1993, p. 211, Russell & Norvig 2003, p. 24 and see also Feigenbaum & McCorduck 1983. ^ Crevier 1993, pp. 161-162, 197-203, 240; Russell & Norvig 2003, p. 25. ^ Crevier 1993, pp. 209-212. ^ McCarthy, John (2000 ). "Reply to Lighthill". Stanford University. Archived from the initial on 30 September 2008. Retrieved 29 September 2007. ^ Markoff, John (14 October 2005). "Behind Artificial Intelligence, a Squadron of Bright Real People". The New York Times. Archived from the initial on 2 February 2023. Retrieved 18 February 2017. At its low point, some computer system researchers and software application engineers avoided the term expert system for fear of being deemed 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 Upon Algorithmic Probability. Texts in Theoretical Computer Technology an EATCS Series. Springer. doi:10.1007/ b138233. ISBN 978-3-5402-6877-2. S2CID 33352850. Archived from the initial on 19 July 2022. Retrieved 19 July 2022. ^ Legg, Shane (2008 ). Machine Super Intelligence (PDF) (Thesis). University of Lugano. Archived (PDF) from the initial on 15 June 2022. Retrieved 19 July 2022. ^ Goertzel, Ben (2014 ). Artificial General Intelligence. Lecture Notes in Computer Technology. Vol. 8598. Journal of Artificial General Intelligence. doi:10.1007/ 978-3-319-09274-4. ISBN 978-3-3190-9273-7. S2CID 8387410. ^ "Who 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 original on 26 July 2020. Retrieved 11 May 2020. ^ Shevlin, Henry; Vold, Karina; Crosby, Matthew; Halina, Marta (4 October 2019). "The limits of device intelligence: Despite progress in maker intelligence, artificial basic intelligence is still a significant difficulty". 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 experiments with GPT-4". arXiv:2303.12712 [cs.CL] ^ "Microsoft Researchers Claim GPT-4 Is Showing "Sparks" of AGI". Futurism. 23 March 2023. Retrieved 13 December 2023. ^ Allen, Paul; Greaves, Mark (12 October 2011). "The Singularity Isn't Near". MIT Technology Review. Retrieved 17 September 2014. ^ Winfield, Alan. "Expert system will not develop into a Frankenstein's beast". 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 expert system will not be realized". Humanities and Social Sciences Communications. 7 (1 ): 1-9. doi:10.1057/ s41599-020-0494-4. hdl:11250/ 2726984. ISSN 2662-9992. S2CID 219710554. ^ McCarthy 2007b. ^ Khatchadourian, Raffi (23 November 2015). "The Doomsday Invention: Will synthetic intelligence bring us utopia or damage?". The New Yorker. Archived from the original on 28 January 2016. Retrieved 7 February 2016. ^ Müller, V. C., & Bostrom, N. (2016 ). Future progress in synthetic intelligence: A survey of skilled opinion. In Fundamental issues of expert system (pp. 555-572). Springer, Cham. ^ Armstrong, Stuart, and Kaj Sotala. 2012. "How We're Predicting AI-or Failing To." In Beyond AI: Artificial Dreams, edited by Jan Romportl, Pavel Ircing, Eva Žáčková, Michal Polák and Radek Schuster, 52-75. Plzeň: University of West Bohemia ^ "Microsoft Now Claims GPT-4 Shows 'Sparks' of General Intelligence". 24 March 2023. ^ Shimek, Cary (6 July 2023). "AI Outperforms Humans in Creativity Test". Neuroscience News. Retrieved 20 October 2023. ^ Guzik, Erik E.; Byrge, Christian; Gilde, Christian (1 December 2023). "The creativity of machines: AI takes the Torrance Test". Journal of Creativity. 33 (3 ): 100065. doi:10.1016/ j.yjoc.2023.100065. ISSN 2713-3745. S2CID 261087185. ^ Arcas, Blaise Agüera y (10 October 2023). "Artificial General Intelligence Is Already Here". Noema. ^ Zia, Tehseen (8 January 2024). "Unveiling of Large Multimodal Models: Shaping the Landscape of Language Models in 2024". Unite.ai. Retrieved 26 May 2024. ^ "Introducing OpenAI o1-preview". OpenAI. 12 September 2024. ^ Knight, Will. "OpenAI Announces a 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 original on 28 August 2022. Retrieved 28 August 2022. ^ Pei, Jing; Deng, Lei; Song, Sen; Zhao, Mingguo; Zhang, Youhui; Wu, Shuang; Wang, Guanrui; Zou, Zhe; Wu, Zhenzhi; He, Wei; Chen, Feng; Deng, Ning; Wu, Si; Wang, Yu; Wu, Yujie (2019 ). "Towards artificial basic intelligence with hybrid Tianjic chip architecture". Nature. 572 (7767 ): 106-111. Bibcode:2019 Natur.572..106 P. doi:10.1038/ s41586-019-1424-8. ISSN 1476-4687. PMID 31367028. S2CID 199056116. Archived from the initial on 29 August 2022. Retrieved 29 August 2022. ^ Pandey, Mohit; Fernandez, Michael; Gentile, Francesco; Isayev, Olexandr; Tropsha, Alexander; Stern, Abraham C.; Cherkasov, Artem (March 2022). "The transformational role of GPU computing and deep learning in drug discovery". Nature Machine Intelligence. 4 (3 ): 211-221. doi:10.1038/ s42256-022-00463-x. ISSN 2522-5839. S2CID 252081559. ^ Goertzel & Pennachin 2006. ^ a b c (Kurzweil 2005, p. 260). ^ a b c Goertzel 2007. ^ Grace, Katja (2016 ). "Error in Armstrong and Sotala 2012". AI Impacts (blog site). Archived from the original on 4 December 2020. Retrieved 24 August 2020. ^ a b Butz, Martin V. (1 March 2021). "Towards Strong AI". KI - Künstliche Intelligenz. 35 (1 ): 91-101. doi:10.1007/ s13218-021-00705-x. ISSN 1610-1987. S2CID 256065190. ^ Liu, Feng; Shi, Yong; Liu, Ying (2017 ). "Intelligence Quotient and Intelligence Grade of Artificial Intelligence". Annals of Data Science. 4 (2 ): 179-191. arXiv:1709.10242. doi:10.1007/ s40745-017-0109-0. S2CID 37900130. ^ Brien, Jörn (5 October 2017). "Google-KI doppelt so schlau wie Siri" [Google AI is twice as wise 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 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 claim we are already seeing an early example of an AGI system in the recently announced GPT-3 natural language processing (NLP) neural network. ... So is GPT-3 the first example of an AGI system? This is debatable, however the consensus is that it is not AGI. ... If nothing else, GPT-3 tells us there is a happy medium in between narrow and general AI. ^ Quach, Katyanna. "A developer developed an AI chatbot using GPT-3 that helped a man 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 new AI can carry out over 600 jobs, from playing games to controlling robotics", TechCrunch, archived from the initial on 16 June 2022, recovered 12 June 2022. ^ Bubeck, Sébastien; Chandrasekaran, Varun; Eldan, Ronen; Gehrke, Johannes; Horvitz, Eric; Kamar, Ece; Lee, Peter; Lee, Yin Tat; Li, Yuanzhi; Lundberg, Scott; Nori, Harsha; Palangi, Hamid; Ribeiro, Marco Tulio; Zhang, Yi (22 March 2023). "Sparks of Artificial General Intelligence: Early experiments with GPT-4". arXiv:2303.12712 [cs.CL] ^ Metz, Cade (1 May 2023). "' The Godfather of A.I.' Leaves Google and Warns of Danger Ahead". The New York City Times. ISSN 0362-4331. Retrieved 7 June 2023. ^ Bove, Tristan. "A.I. could measure up to human intelligence in 'simply a couple of years,' states CEO of Google's primary A.I. research laboratory". Fortune. Retrieved 4 September 2024. ^ Nellis, Stephen (2 March 2024). "Nvidia CEO says AI could pass human tests in five years". Reuters. ^ Aschenbrenner, Leopold. "SITUATIONAL AWARENESS, The Decade Ahead". ^ Sullivan, Mark (18 October 2023). "Why everybody 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 project maps the human brain". euractiv. ^ Swaminathan, Nikhil (January-February 2011). "Glia-the other brain cells". Discover. Archived from the initial on 8 February 2014. Retrieved 24 January 2014. ^ de Vega, Glenberg & Graesser 2008. A vast array of views in current research study, all of which need grounding to some degree ^ Thornton, Angela (26 June 2023). "How submitting 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 (quoted in" Encyclopedia.com"),. MIT Encyclopedia of Cognitive Science Archived 19 July 2008 at the Wayback Machine (priced estimate in "AITopics"),. Will Biological Computers Enable Artificially Intelligent Machines to Become Persons? Archived 13 May 2008 at the Wayback Machine Anthony Tongen.


^ a b c Russell & Norvig 2003, p. 947. ^ though see Explainable artificial intelligence for interest by the field about why a program behaves the way 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 benefits?". 23 August 2021. Retrieved 7 June 2023. ^ "How we can Benefit from Advancing Artificial General Intelligence (AGI) - Unite.AI". www.unite.ai. 7 April 2020. Retrieved 7 June 2023. ^ a b c Talty, Jules; Julien, Stephan. "What Will Our Society Appear Like When Artificial Intelligence Is Everywhere?". Smithsonian Magazine. Retrieved 7 June 2023. ^ a b Stevenson, Matt (8 October 2015). "Answers to Stephen Hawking's AMA are Here!". Wired. ISSN 1059-1028. Retrieved 8 June 2023. ^ a b Bostrom, Nick (2017 ). " § Preferred order of arrival". Superintelligence: paths, threats, strategies (Reprinted with corrections 2017 ed.). Oxford, UK; New York, New York City, USA: Oxford University Press. ISBN 978-0-1996-7811-2. ^ Piper, Kelsey (19 November 2018). "How technological progress is making it likelier than ever that human beings will ruin ourselves". Vox. Retrieved 8 June 2023. ^ Doherty, Ben (17 May 2018). "Climate change an 'existential security risk' to Australia, Senate questions says". The Guardian. ISSN 0261-3077. Retrieved 16 July 2023. ^ MacAskill, William (2022 ). What we owe the future. New York City, NY: Basic Books. ISBN 978-1-5416-1862-6. ^ a b Ord, Toby (2020 ). "Chapter 5: Future Risks, Unaligned Artificial Intelligence". The Precipice: Existential Risk and the Future of Humanity. Bloomsbury Publishing. ISBN 978-1-5266-0021-9. ^ Al-Sibai, Noor (13 February 2022). "OpenAI Chief Scientist Says Advanced AI May Already Be Conscious". Futurism. Retrieved 24 December 2023. ^ Samuelsson, Paul Conrad (2019 ). "Artificial Consciousness: Our Greatest Ethical Challenge". Philosophy Now. Retrieved 23 December 2023. ^ Kateman, Brian (24 July 2023). "AI Should Be Terrified of Humans". TIME. Retrieved 23 December 2023. ^ Roose, Kevin (30 May 2023). "A.I. Poses 'Risk of Extinction,' Industry Leaders Warn". The New York City Times. ISSN 0362-4331. Retrieved 24 December 2023. ^ a b "Statement on AI Risk". Center for AI Safety. 30 May 2023. Retrieved 8 June 2023. ^ "Stephen Hawking: 'Transcendence looks at the ramifications of artificial 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 interesting Facebook debate in between Yann LeCun, Stuart Russel and Yoshua Bengio about the risks of strong AI". The interesting Facebook debate between Yann LeCun, Stuart Russel and Yoshua Bengio about the threats of strong AI (in French). Retrieved 8 June 2023. ^ "Will Artificial Intelligence Doom The Human Race Within The Next 100 Years?". HuffPost. 22 August 2014. Retrieved 8 June 2023. ^ Sotala, Kaj; Yampolskiy, Roman V. (19 December 2014). "Responses to disastrous AGI danger: a survey". Physica Scripta. 90 (1 ): 018001. doi:10.1088/ 0031-8949/90/ 1/018001. ISSN 0031-8949. ^ Bostrom, Nick (2014 ). Superintelligence: Paths, Dangers, Strategies (First ed.). Oxford University Press. ISBN 978-0-1996-7811-2. ^ Chow, Andrew R.; Perrigo, Billy (16 February 2023). "The AI Arms Race Is On. Start Worrying". TIME. Retrieved 24 December 2023. ^ Tetlow, Gemma (12 January 2017). "AI arms race dangers spiralling out of control, report alerts". Financial Times. Archived from the original on 11 April 2022. Retrieved 24 December 2023. ^ Milmo, Dan; Stacey, Kiran (25 September 2023). "Experts disagree over risk posed but expert system 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 the End of Humanity Mean for Me?". The Atlantic. Archived from the original on 4 June 2014. Retrieved 12 December 2015. ^ Titcomb, James (30 October 2023). "Big Tech is stiring fears over AI, warn researchers". The Telegraph. Retrieved 7 December 2023. ^ Davidson, John (30 October 2023). "Google Brain creator states big 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 large language models". OpenAI. Retrieved 7 June 2023. ^ a b Hurst, Luke (23 March 2023). "OpenAI states 80% of employees might see their tasks impacted by AI. These are the jobs most impacted". euronews. Retrieved 8 June 2023. ^ Sheffey, Ayelet (20 August 2021). "Elon Musk says we require universal basic earnings because 'in the future, manual labor will be an option'". Business Insider. Archived from the original on 9 July 2023. Retrieved 8 June 2023. Sources

UNESCO Science Report: the Race Against Time for Smarter Development. Paris: UNESCO. 11 June 2021. ISBN 978-9-2310-0450-6. Archived from the initial on 18 June 2022. Retrieved 22 September 2021. Chalmers, David (1996 ), The Conscious Mind, Oxford University Press. Clocksin, William (August 2003), "Artificial intelligence and the future", Philosophical Transactions of the Royal Society A, vol. 361, no. 1809, pp. 1721-1748, Bibcode:2003 RSPTA.361.1721 C, doi:10.1098/ rsta.2003.1232, PMID 12952683, S2CID 31032007. Crevier, Daniel (1993 ). AI: The Tumultuous 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 basic intelligence and the possibility of a technological singularity: a response to Ray Kurzweil's The Singularity Is Near, and McDermott's critique 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, recovered 1 April 2009. Gubrud, Mark (November 1997), "Nanotechnology and International Security", Fifth Foresight Conference on Molecular Nanotechnology, archived from the original on 29 May 2011, retrieved 7 May 2011. Howe, J. (November 1994), Artificial Intelligence at Edinburgh University: a Perspective, archived from the initial 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", Artificial Intelligence: a paper seminar, Science Research Council. Luger, George; Stubblefield, William (2004 ), Artificial Intelligence: Structures and Strategies for Complex Problem Solving (5th ed.), The Benjamin/Cummings Publishing Company, Inc., p. 720, ISBN 978-0-8053-4780-7. McCarthy, John (2007b). What is Artificial Intelligence?. Stanford University. The supreme effort is to make computer programs that can solve problems and achieve objectives in the world along with human beings. Moravec, Hans (1988 ), Mind Children, Harvard University Press Moravec, Hans (1998 ), "When will hardware match the human brain?", Journal of Evolution and Technology, vol. 1, archived from the initial on 15 June 2006, recovered 23 June 2006 Nagel (1974 ), "What Is it Like to Be a Bat" (PDF), Philosophical Review, 83 (4 ): 435-50, doi:10.2307/ 2183914, JSTOR 2183914, archived (PDF) from the initial on 16 October 2011, retrieved 7 November 2009 Newell, Allen; Simon, H. A. (1976 ). "Computer Technology as Empirical Inquiry: Symbols and Search". Communications of the ACM. 19 (3 ): 113-126. doi:10.1145/ 360018.360022. Nilsson, Nils (1998 ), Expert System: A New Synthesis, Morgan Kaufmann Publishers, ISBN 978-1-5586-0467-4 NRC (1999 ), "Developments in Artificial Intelligence", Funding a Transformation: Government Support for Computing Research, National Academy Press, archived from the original on 12 January 2008, retrieved 29 September 2007 Poole, David; Mackworth, Alan; Goebel, Randy (1998 ), Computational Intelligence: A Sensible 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 (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, recovered 5 April 2009 Searle, John (1980 ), "Minds, Brains and Programs" (PDF), Behavioral and Brain Sciences, 3 (3 ): 417-457, doi:10.1017/ S0140525X00005756, S2CID 55303721, archived (PDF) from the initial on 17 March 2019, recovered 3 September 2020 Simon, H. A. (1965 ), The Shape of Automation for Men and Management, New York City: Harper & Row Turing, Alan (October 1950). "Computing Machinery and Intelligence". Mind. 59 (236 ): 433-460. doi:10.1093/ mind/LIX.236.433. ISSN 1460-2113. JSTOR 2251299. S2CID 14636783.
de Vega, Manuel; Glenberg, Arthur; Graesser, Arthur, eds. (2008 ), Symbols and Embodiment: Debates on significance and cognition, Oxford University Press, ISBN 978-0-1992-1727-4 Wang, Pei; Goertzel, Ben (2007 ). "Introduction: Aspects of Artificial General Intelligence". Advances in Artificial General Intelligence: Concepts, Architectures and Algorithms: Proceedings of the AGI Workshop 2006. IOS Press. pp. 1-16. ISBN 978-1-5860-3758-1. Archived from the initial on 18 February 2021. Retrieved 13 December 2020 - 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 original on 18 February 2021, obtained 4 September 2013 - through ResearchGate Berglas, Anthony (January 2012) [2008], Expert System Will Kill Our Grandchildren (Singularity), archived from the initial on 23 July 2014, retrieved 31 August 2012 Cukier, Kenneth, "Ready for Robots? How to Think of the Future of AI", Foreign Affairs, vol. 98, no. 4 (July/August 2019), pp. 192-98. George Dyson, historian of computing, writes (in what might be called "Dyson's Law") that "Any system simple adequate to be reasonable will not be complicated enough to act smartly, while any system made complex enough to behave intelligently will be too complicated to understand." (p. 197.) Computer researcher Alex Pentland composes: "Current AI machine-learning algorithms are, at their core, dead easy stupid. 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 Will" (evaluation of Kevin J. Mitchell, Free Agents: How Evolution Gave Us Free Choice, 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 devices. For biological animals, factor and purpose originate from acting in the world and experiencing the consequences. Expert systems - disembodied, strangers to blood, sweat, and tears - have no occasion for that." (p. 30.). Halal, William E. "TechCast Article Series: The Automation of Thought" (PDF). Archived from the original (PDF) on 6 June 2013. - Halpern, Sue, "The Coming Tech Autocracy" (review of Verity Harding, AI Needs You: How We Can Change AI's Future and Save Our Own, Princeton University Press, 274 pp.; Gary Marcus, Taming Silicon Valley: How We Can Ensure That AI Works for Us, MIT Press, 235 pp.; Daniela Rus and Gregory Mone, The Mind's Mirror: Risk and Reward in the Age of AI, Norton, 280 pp.; Madhumita Murgia, Code Dependent: Living in the Shadow of AI, Henry Holt, 311 pp.), The New York Review of Books, vol. LXXI, no. 17 (7 November 2024), pp. 44-46. "' We can't reasonably anticipate that those who wish to get abundant from AI are going to have the interests of the rest people close at heart,' ... writes [Gary Marcus] 'We can't rely on governments driven by project finance contributions [from tech companies] to push back.' ... Marcus details the needs that residents need to make of their federal governments and the tech companies. They include openness on how AI systems work; settlement for individuals if their information [are] utilized to train LLMs (big language design) s and the right to authorization to this usage; and the to hold tech companies liable for the damages they bring on by eliminating Section 230, enforcing cash penalites, and passing more stringent item liability laws ... Marcus also suggests ... that a brand-new, AI-specific federal company, similar to the FDA, the FCC, or the FTC, wolvesbaneuo.com may supply the most robust oversight ... [T] he Fordham law teacher Chinmayi Sharma ... recommends ... develop [ing] an expert licensing routine for engineers that would operate in a comparable method to medical licenses, malpractice matches, and the Hippocratic oath in medicine. 'What if, like medical professionals,' she asks ..., 'AI engineers likewise vowed to do no harm?'" (p. 46.). Holte, R. C.; Choueiry, B. Y. (2003 ), "Abstraction and reformulation in synthetic intelligence", Philosophical Transactions of the Royal Society B, vol. 358, no. 1435, pp. 1197-1204, doi:10.1098/ rstb.2003.1317, PMC 1693218, PMID 12903653. Hughes-Castleberry, Kenna, "A Murder Mystery Puzzle: The literary puzzle Cain's Jawbone, which has actually puzzled people for decades, exposes the restrictions of natural-language-processing algorithms", Scientific American, vol. 329, no. 4 (November 2023), pp. 81-82. "This murder secret competition has exposed that although NLP (natural-language processing) models are capable of incredible accomplishments, their capabilities are very much limited by the amount of context they get. This [...] could trigger [difficulties] for scientists who want to use them to do things such as analyze ancient languages. In many cases, there are couple of historical records on long-gone civilizations to work as training data for such a purpose." (p. 82.). Immerwahr, Daniel, "Your Lying Eyes: People now use A.I. to create phony videos equivalent from real ones. How much does it matter?", The New Yorker, 20 November 2023, pp. 54-59. "If by 'deepfakes' we mean reasonable videos produced using 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 evidence. Their role much better resembles that of animations, particularly smutty ones." (p. 59.). - Leffer, Lauren, "The Risks of Trusting AI: We need to prevent humanizing machine-learning models 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 device a conversation?", The New Yorker, 7 October 2024, pp. 12-16. Marcus, Gary, "Artificial Confidence: Even the most recent, buzziest systems of synthetic basic intelligence are stymmied by the 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 initial on 3 March 2016, recovered 29 September 2007. Newell, Allen; Simon, H. A. (1963 ), "GPS: A Program that Simulates Human Thought", in Feigenbaum, E. A.; Feldman, J. (eds.), Computers and Thought, New York: McGraw-Hill. Omohundro, Steve (2008 ), The Nature of Self-Improving Artificial Intelligence, presented and dispersed at the 2007 Singularity Summit, San Francisco, California. Press, Eyal, "In Front of Their Faces: Does facial-recognition technology lead cops to overlook contradictory evidence?", The New Yorker, 20 November 2023, pp. 20-26. Roivainen, Eka, "AI's IQ: ChatGPT aced a [basic intelligence] test however revealed that intelligence can not be determined by IQ alone", Scientific American, vol. 329, no. 1 (July/August 2023), p. 7. "Despite its high IQ, ChatGPT stops working at tasks that require genuine humanlike reasoning or an understanding of the physical and social world ... ChatGPT appeared unable to factor logically and attempted to depend on its large database of ... facts obtained from online texts. " - Scharre, Paul, "Killer Apps: The Real Dangers of an AI Arms Race", Foreign Affairs, vol. 98, no. 3 (May/June 2019), pp. 135-44. "Today's AI innovations are powerful but unreliable. Rules-based systems can not handle scenarios their programmers did not prepare for. Learning systems are restricted by the data on which they were trained. AI failures have currently led to tragedy. Advanced auto-pilot features in vehicles, although they perform well in some situations, have actually driven cars without cautioning into trucks, concrete barriers, and parked automobiles. In the wrong situation, AI systems go from supersmart to superdumb in an immediate. When an opponent is trying to control and hack an AI system, the risks are even higher." (p. 140.). Sutherland, J. G. (1990 ), "Holographic Model of Memory, Learning, and Expression", International Journal of Neural Systems, vol. 1-3, pp. 256-267. - Vincent, James, "Horny Robot Baby Voice: James Vincent on AI chatbots", London Review of Books, vol. 46, no. 19 (10 October 2024), pp. 29-32." [AI chatbot] programs are enabled by brand-new technologies but depend on the timelelss human propensity to anthropomorphise." (p. 29.). Williams, R. W.; Herrup, K.