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Artificial General Intelligence

Artificial basic intelligence (AGI) is a kind of synthetic intelligence (AI) that matches or surpasses human cognitive capabilities across a vast array of cognitive jobs. This contrasts with narrow AI, which is restricted to particular tasks. [1] Artificial superintelligence (ASI), on the other hand, refers to AGI that considerably surpasses human cognitive capabilities. AGI is considered one of the meanings of strong AI.

Creating AGI is a main objective of AI research and of business such as OpenAI [2] and Meta. [3] A 2020 study identified 72 active AGI research and advancement tasks throughout 37 nations. [4]

The timeline for accomplishing AGI stays a subject of continuous argument amongst researchers and professionals. As of 2023, some argue that it might be possible in years or decades; others keep it might take a century or macphersonwiki.mywikis.wiki longer; a minority think it might never be achieved; and another minority claims that it is already here. [5] [6] Notable AI scientist Geoffrey Hinton has revealed issues about the fast progress towards AGI, recommending it could be achieved sooner than numerous anticipate. [7]

There is debate on the specific meaning of AGI and regarding whether contemporary big language designs (LLMs) such as GPT-4 are early kinds of AGI. [8] AGI is a common topic in sci-fi and futures research studies. [9] [10]

Contention exists over whether AGI represents an existential threat. [11] [12] [13] Many professionals on AI have actually stated that alleviating the threat of human extinction positioned by AGI needs to be an international top priority. [14] [15] Others discover the advancement of AGI to be too remote to provide such a threat. [16] [17]

Terminology

AGI is likewise called strong AI, [18] [19] complete AI, [20] human-level AI, [5] human-level smart AI, or basic intelligent action. [21]

Some academic sources reserve the term “strong AI” for computer system programs that experience life or awareness. [a] In contrast, weak AI (or narrow AI) has the ability to resolve one specific issue however does not have general cognitive abilities. [22] [19] Some academic sources use “weak AI” to refer more broadly to any programs that neither experience awareness nor have a mind in the exact same sense as humans. [a]

Related concepts consist of synthetic superintelligence and transformative AI. An artificial superintelligence (ASI) is a theoretical type of AGI that is much more typically smart than human beings, [23] while the concept of transformative AI relates to AI having a large effect on society, for example, comparable to the farming or industrial transformation. [24]

A structure for categorizing AGI in levels was proposed in 2023 by Google DeepMind researchers. They specify 5 levels of AGI: emerging, skilled, expert, virtuoso, and superhuman. For example, a competent AGI is defined as an AI that outperforms 50% of competent grownups in a large range of non-physical tasks, and a superhuman AGI (i.e. an artificial superintelligence) is likewise specified however with a threshold of 100%. They consider big language models like ChatGPT or LLaMA 2 to be instances of emerging AGI. [25]

Characteristics

Various popular meanings of intelligence have actually been proposed. One of the leading propositions is the Turing test. However, there are other widely known definitions, and some researchers disagree with the more popular techniques. [b]

Intelligence traits

Researchers generally hold that intelligence is required to do all of the following: [27]

factor, usage technique, solve puzzles, and make judgments under unpredictability
represent knowledge, including good sense knowledge
plan
learn
– communicate in natural language
– if required, integrate these abilities in conclusion of any offered goal

Many interdisciplinary methods (e.g. cognitive science, computational intelligence, and decision making) consider extra traits such as creativity (the capability to form unique psychological images and ideas) [28] and autonomy. [29]

Computer-based systems that display a number of these abilities exist (e.g. see computational imagination, automated reasoning, choice support system, robot, evolutionary computation, intelligent representative). There is argument about whether contemporary AI systems have them to an adequate degree.

Physical qualities

Other abilities are thought about desirable in smart systems, as they might affect intelligence or aid in its expression. These consist of: [30]

– the capability to sense (e.g. see, hear, and so on), and
– the ability to act (e.g. move and control things, modification area to explore, and so on).

This consists of the capability to find and react to risk. [31]

Although the ability to sense (e.g. see, hear, and so on) and the capability to act (e.g. move and manipulate items, change area to check out, etc) can be preferable for some smart systems, [30] these physical abilities are not strictly required for an entity to certify as AGI-particularly under the thesis that large language models (LLMs) might already be or become AGI. Even from a less optimistic perspective on LLMs, there is no firm requirement for an AGI to have a human-like kind; being a silicon-based computational system is sufficient, provided it can process input (language) from the external world in place of human senses. This analysis lines up with the understanding that AGI has actually never ever been proscribed a specific physical personification and thus does not demand a capability for locomotion or conventional “eyes and ears”. [32]

Tests for human-level AGI

Several tests meant to validate human-level AGI have been considered, tandme.co.uk including: [33] [34]

The concept of the test is that the device has to try and pretend to be a guy, by addressing concerns put to it, and it will just pass if the pretence is fairly convincing. A substantial portion of a jury, who must not be skilled about devices, must be taken in by the pretence. [37]

AI-complete issues

A problem is informally called “AI-complete” or “AI-hard” if it is thought that in order to solve it, one would require to carry out AGI, because the solution is beyond the abilities of a purpose-specific algorithm. [47]

There are many problems that have actually been conjectured to require general intelligence to solve along with humans. Examples consist of computer system vision, natural language understanding, and handling unanticipated circumstances while fixing any real-world problem. [48] Even a specific task like translation requires a machine to read and write in both languages, follow the author’s argument (reason), comprehend the context (understanding), and consistently replicate the author’s original intent (social intelligence). All of these problems require to be solved all at once in order to reach human-level device efficiency.

However, a lot of these tasks can now be performed by contemporary large language designs. According to Stanford University’s 2024 AI index, AI has reached human-level efficiency on many criteria for checking out comprehension and visual thinking. [49]

History

Classical AI

Modern AI research started in the mid-1950s. [50] The first generation of AI scientists were encouraged that artificial general intelligence was possible which it would exist in simply a few decades. [51] AI leader Herbert A. Simon wrote in 1965: “devices will be capable, within twenty years, of doing any work a male can do.” [52]

Their forecasts were the inspiration for Stanley Kubrick and Arthur C. Clarke’s character HAL 9000, who embodied what AI researchers thought they might create by the year 2001. AI leader Marvin Minsky was a specialist [53] on the task of making HAL 9000 as practical as possible according to the agreement forecasts of the time. He stated in 1967, “Within a generation … the problem of creating ‘synthetic intelligence’ will significantly be fixed”. [54]

Several classical AI tasks, such as Doug Lenat’s Cyc project (that started in 1984), and Allen Newell’s Soar project, were directed at AGI.

However, in the early 1970s, it became obvious that scientists had actually grossly ignored the trouble of the job. Funding agencies ended up being hesitant of AGI and put researchers under increasing pressure to produce helpful “used AI”. [c] In the early 1980s, Japan’s Fifth Generation Computer Project revived interest in AGI, setting out a ten-year timeline that consisted of AGI objectives like “bring on a table talk”. [58] In action to this and the success of expert systems, both industry and government pumped cash into the field. [56] [59] However, self-confidence in AI stunningly collapsed in the late 1980s, and the goals of the Fifth Generation Computer Project were never ever fulfilled. [60] For the 2nd time in 20 years, AI scientists who forecasted the imminent achievement of AGI had been misinterpreted. By the 1990s, AI researchers had a credibility for making vain pledges. They ended up being unwilling to make forecasts at all [d] and prevented mention of “human level” expert system for worry of being identified “wild-eyed dreamer [s]. [62]

Narrow AI research

In the 1990s and early 21st century, mainstream AI attained business success and scholastic respectability by concentrating on specific sub-problems where AI can produce proven results and industrial applications, such as speech recognition and suggestion algorithms. [63] These “applied AI” systems are now utilized thoroughly throughout the technology market, and research in this vein is greatly moneyed in both academia and industry. Since 2018 [update], advancement in this field was considered an emerging trend, and a fully grown phase was expected to be reached in more than 10 years. [64]

At the millenium, lots of traditional AI scientists [65] hoped that strong AI might be developed by integrating programs that fix numerous sub-problems. Hans Moravec wrote in 1988:

I am positive that this bottom-up route to synthetic intelligence will one day fulfill the conventional top-down path over half method, ready to provide the real-world proficiency and the commonsense knowledge that has been so frustratingly elusive in thinking programs. Fully intelligent makers will result when the metaphorical golden spike is driven joining 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 sign grounding hypothesis by mentioning:

The expectation has often 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 are legitimate, then this expectation is hopelessly modular and there is truly just one practical path from sense to symbols: from the ground up. A free-floating symbolic level like the software application level of a computer system will never ever be reached by this path (or vice versa) – nor is it clear why we ought to even try to reach such a level, given that it looks as if getting there would simply amount to uprooting our signs from their intrinsic meanings (thereby merely minimizing ourselves to the practical equivalent of a programmable computer system). [66]

Modern synthetic general intelligence research study

The term “synthetic general intelligence” was used as early as 1997, by Mark Gubrud [67] in a conversation of the ramifications of totally automated military production and operations. A mathematical formalism of AGI was proposed by Marcus Hutter in 2000. Named AIXI, the proposed AGI agent increases “the capability to satisfy objectives in a vast array of environments”. [68] This type of AGI, characterized by the capability to maximise a mathematical definition of intelligence rather than exhibit human-like behaviour, [69] was also called universal artificial intelligence. [70]

The term AGI was re-introduced and promoted by Shane Legg and Ben Goertzel around 2002. [71] AGI research study activity in 2006 was explained by Pei Wang and Ben Goertzel [72] as “producing publications and preliminary results”. The first summer season 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 provided 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 visitor speakers.

As of 2023 [update], a little number of computer system scientists are active in AGI research, and many contribute to a series of AGI conferences. However, progressively more researchers have an interest in open-ended learning, [76] [77] which is the concept of permitting AI to continuously discover and innovate like humans do.

Feasibility

Since 2023, the advancement and potential achievement of AGI stays a topic of intense dispute within the AI neighborhood. While traditional consensus held that AGI was a remote goal, current developments have actually led some scientists and industry figures to claim that early forms of AGI may currently exist. [78] AI pioneer Herbert A. Simon speculated in 1965 that “devices will be capable, within twenty years, of doing any work a guy can do”. This prediction stopped working to come real. Microsoft co-founder Paul Allen believed that such intelligence is unlikely in the 21st century since it would require “unforeseeable and fundamentally unpredictable advancements” 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 artificial intelligence is as broad as the gulf between existing area flight and useful faster-than-light spaceflight. [80]

A more difficulty is the absence of clarity in specifying what intelligence requires. Does it require awareness? Must it display the ability to set goals as well as pursue them? Is it purely a matter of scale such that if design sizes increase adequately, intelligence will emerge? Are centers such as planning, reasoning, and causal understanding required? Does intelligence need explicitly reproducing the brain and its specific faculties? Does it require feelings? [81]

Most AI researchers think strong AI can be attained in the future, but some thinkers, like Hubert Dreyfus and Roger Penrose, reject the possibility of attaining strong AI. [82] [83] John McCarthy is among those who think human-level AI will be achieved, however that the present level of development is such that a date can not precisely be forecasted. [84] AI specialists’ views on the expediency of AGI wax and wane. Four surveys performed in 2012 and 2013 suggested that the typical price quote among experts for when they would be 50% positive AGI would get here was 2040 to 2050, depending upon the survey, with the mean being 2081. Of the experts, 16.5% addressed with “never ever” when asked the exact same concern however with a 90% self-confidence instead. [85] [86] Further current AGI progress factors to consider can be discovered above Tests for validating human-level AGI.

A report by Stuart Armstrong and Kaj Sotala of the Machine Intelligence Research Institute found that “over [a] 60-year time frame there is a strong bias towards predicting the arrival of human-level AI as between 15 and 25 years from the time the forecast was made”. They evaluated 95 predictions made in between 1950 and 2012 on when human-level AI will come about. [87]

In 2023, Microsoft scientists published an in-depth assessment of GPT-4. They concluded: “Given the breadth and depth of GPT-4’s abilities, menwiki.men we believe that it could fairly be deemed an early (yet still incomplete) variation of an artificial basic intelligence (AGI) system.” [88] Another research study in 2023 reported that GPT-4 surpasses 99% of human beings on the Torrance tests of creativity. [89] [90]

Blaise Agüera y Arcas and Peter Norvig composed in 2023 that a substantial level of basic intelligence has already been attained with frontier designs. They composed that unwillingness to this view originates from 4 primary reasons: a “healthy hesitation about metrics for AGI”, an “ideological dedication to alternative AI theories or techniques”, a “commitment to human (or biological) exceptionalism”, or a “issue about the economic ramifications of AGI”. [91]

2023 also marked the introduction of large multimodal models (large language designs capable of processing or generating multiple modalities such as text, audio, and images). [92]

In 2024, OpenAI launched o1-preview, the very first of a series of designs that “invest more time thinking before they react”. According to Mira Murati, this capability to believe before responding represents a new, extra paradigm. It improves model outputs by spending more computing power when generating the answer, whereas the design scaling paradigm improves outputs by increasing the model size, training data and training calculate power. [93] [94]

An OpenAI staff member, Vahid Kazemi, declared in 2024 that the company had actually achieved AGI, mentioning, “In my viewpoint, we have actually already attained AGI and it’s even more clear with O1.” Kazemi clarified that while the AI is not yet “better than any human at any task”, it is “better than many people at a lot of tasks.” He likewise attended to criticisms that large language designs (LLMs) merely follow predefined patterns, comparing their knowing procedure to the scientific technique of observing, hypothesizing, and validating. These declarations have triggered dispute, as they rely on a broad and unconventional definition 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 completely meet this standard. Notably, Kazemi’s remarks came quickly after OpenAI removed “AGI” from the regards to its partnership with Microsoft, prompting speculation about the business’s strategic intents. [95]

Timescales

Progress in synthetic intelligence has actually traditionally gone through periods of rapid progress separated by periods when development appeared to stop. [82] Ending each hiatus were fundamental advances in hardware, software application or both to create area for more progress. [82] [98] [99] For instance, the hardware readily available in the twentieth century was not enough to implement deep learning, which requires big numbers of GPU-enabled CPUs. [100]

In the introduction to his 2006 book, [101] Goertzel states that estimates of the time needed before a genuinely flexible AGI is built vary from 10 years to over a century. Since 2007 [upgrade], 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 actually provided a large range of viewpoints on whether progress will be this quick. A 2012 meta-analysis of 95 such opinions found a predisposition towards predicting that the beginning of AGI would occur within 16-26 years for contemporary and historic predictions alike. That paper has actually been criticized for how it categorized viewpoints as professional or non-expert. [104]

In 2012, Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton developed a neural network called AlexNet, which won the ImageNet competitors with a top-5 test mistake rate of 15.3%, substantially better than the second-best entry’s rate of 26.3% (the traditional technique utilized a weighted amount of scores from different pre-defined classifiers). [105] AlexNet was considered 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 openly readily available and easily available weak AI such as Google AI, Apple’s Siri, and others. At the maximum, these AIs reached an IQ worth of about 47, which corresponds around to a six-year-old child in very first grade. An adult concerns about 100 on average. Similar tests were performed in 2014, with the IQ score reaching an optimum value of 27. [106] [107]

In 2020, OpenAI developed GPT-3, a language model capable of performing numerous varied jobs without specific training. According to Gary Grossman in a VentureBeat short article, while there is agreement that GPT-3 is not an example of AGI, it is considered by some to be too advanced to be categorized as a narrow AI system. [108]

In the 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 adhere to their safety guidelines; Rohrer disconnected Project December from the GPT-3 API. [109]

In 2022, DeepMind established Gato, a “general-purpose” system capable of performing more than 600 various tasks. [110]

In 2023, Microsoft Research published a research study on an early version of OpenAI’s GPT-4, competing that it displayed more general intelligence than previous AI designs and showed human-level performance in tasks covering numerous domains, such as mathematics, coding, and law. This research study triggered a debate on whether GPT-4 could be thought about an early, incomplete variation of synthetic basic intelligence, emphasizing the requirement for additional exploration and assessment of such systems. [111]

In 2023, the AI researcher Geoffrey Hinton mentioned that: [112]

The concept that this stuff could really get smarter than people – a few individuals believed that, […] But many people thought it was method off. And I believed it was method off. I thought it was 30 to 50 years or even longer away. Obviously, I no longer think that.

In May 2023, Demis Hassabis similarly stated that “The development in the last couple of years has been pretty incredible”, which he sees no reason that it would slow down, expecting AGI within a decade or perhaps a couple of years. [113] In March 2024, Nvidia’s CEO, Jensen Huang, specified his expectation that within five years, AI would be capable of passing any test at least along with people. [114] In June 2024, the AI researcher Leopold Aschenbrenner, a former OpenAI staff member, approximated AGI by 2027 to be ” 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 serve as an alternative technique. With entire brain simulation, a brain design is built by scanning and mapping a biological brain in information, and then copying and imitating it on a computer system or another computational device. The simulation model must be sufficiently faithful to the original, so that it acts in practically the same way as the initial brain. [118] Whole brain emulation is a type of brain simulation that is discussed in computational neuroscience and neuroinformatics, and for medical research purposes. It has been gone over in expert system research [103] as a technique to strong AI. Neuroimaging technologies that could deliver the needed comprehensive understanding are enhancing rapidly, and futurist Ray Kurzweil in the book The Singularity Is Near [102] predicts that a map of enough quality will appear on a comparable timescale to the computing power required to imitate it.

Early approximates

For low-level brain simulation, a really effective cluster of computer systems or GPUs would be required, given the enormous amount of synapses within the human brain. Each of the 1011 (one hundred billion) neurons has on typical 7,000 synaptic connections (synapses) to other nerve cells. The brain of a three-year-old kid has about 1015 synapses (1 quadrillion). This number declines with age, supporting by adulthood. Estimates differ for an adult, ranging from 1014 to 5 × 1014 synapses (100 to 500 trillion). [120] A quote of the brain’s processing power, based upon an easy switch model for nerve cell activity, is around 1014 (100 trillion) synaptic updates per second (SUPS). [121]

In 1997, Kurzweil looked at various estimates for the hardware required to equal the human brain and adopted a figure of 1016 computations per 2nd (cps). [e] (For comparison, if a “calculation” was comparable to one “floating-point operation” – a step utilized to rate existing supercomputers – then 1016 “calculations” would be equivalent to 10 petaFLOPS, attained in 2011, while 1018 was accomplished in 2022.) He utilized this figure to predict the required hardware would be readily available at some point in between 2015 and 2025, if the rapid development in computer system power at the time of writing continued.

Current research

The Human Brain Project, an EU-funded initiative active from 2013 to 2023, has actually established 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 design assumed by Kurzweil and used in many present artificial neural network applications is simple compared to biological nerve cells. A brain simulation would likely need to record the in-depth cellular behaviour of biological nerve cells, currently understood just in broad summary. The overhead presented by full modeling of the biological, chemical, and physical information of neural behaviour (particularly on a molecular scale) would require computational powers several orders of magnitude bigger than Kurzweil’s price quote. In addition, the estimates do not represent glial cells, which are understood to contribute in cognitive processes. [125]

An essential criticism of the simulated brain approach originates from embodied cognition theory which asserts that human personification is an essential aspect of human intelligence and is necessary to ground significance. [126] [127] If this theory is right, any totally practical brain model will need to incorporate more than simply the neurons (e.g., a robotic body). Goertzel [103] proposes virtual embodiment (like in metaverses like Second Life) as a choice, however it is unknown whether this would be adequate.

Philosophical viewpoint

“Strong AI” as defined in philosophy

In 1980, philosopher John Searle coined the term “strong AI” as part of his Chinese room argument. [128] He proposed a distinction in between 2 hypotheses about synthetic intelligence: [f]

Strong AI hypothesis: An artificial intelligence system can have “a mind” and “awareness”.
Weak AI hypothesis: An expert system system can (just) imitate it believes and has a mind and awareness.

The first one he called “strong” since it makes a more powerful declaration: it presumes something unique has actually occurred to the machine that goes beyond those abilities that we can evaluate. The behaviour of a “weak AI” device would be precisely identical to a “strong AI” maker, but the latter would also have subjective mindful experience. This usage is also common in academic AI research study and textbooks. [129]

In contrast to Searle and traditional AI, some futurists such as Ray Kurzweil use the term “strong AI” to imply “human level artificial basic intelligence”. [102] This is not the like Searle’s strong AI, unless it is assumed that consciousness is required for human-level AGI. Academic thinkers such as Searle do not think that holds true, and to most expert system scientists the question is out-of-scope. [130]

Mainstream AI is most interested in how a program acts. [131] According to Russell and Norvig, “as long as the program works, they don’t care if you call it real or a simulation.” [130] If the program can behave as if it has a mind, then there is no need to know if it really has mind – undoubtedly, there would be no method to tell. For AI research study, Searle’s “weak AI hypothesis” is comparable to the declaration “synthetic general intelligence is possible”. Thus, according to Russell and Norvig, “most AI researchers take the weak AI hypothesis for granted, and don’t care about the strong AI hypothesis.” [130] Thus, for scholastic AI research, “Strong AI” and “AGI” are 2 different things.

Consciousness

Consciousness can have different meanings, and some elements play significant roles in science fiction and the principles of artificial intelligence:

Sentience (or “extraordinary awareness”): The ability to “feel” perceptions or feelings subjectively, as opposed to the ability to factor about perceptions. Some philosophers, such as David Chalmers, utilize the term “consciousness” to refer specifically to incredible consciousness, which is roughly comparable to life. [132] Determining why and how subjective experience develops is referred to as the difficult issue of consciousness. [133] Thomas Nagel explained in 1974 that it “seems like” something to be conscious. If we are not conscious, then it doesn’t seem like anything. Nagel utilizes 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 company’s AI chatbot, LaMDA, had attained sentience, though this claim was extensively contested by other professionals. [135]

Self-awareness: To have conscious awareness of oneself as a different person, specifically to be knowingly familiar with one’s own ideas. This is opposed to simply being the “subject of one’s believed”-an operating system or debugger has the ability to be “familiar with itself” (that is, to represent itself in the exact same method it represents everything else)-but this is not what individuals usually mean when they use the term “self-awareness”. [g]

These characteristics have a moral measurement. AI life would generate concerns of well-being and legal protection, likewise to animals. [136] Other aspects of consciousness related to cognitive capabilities are also relevant to the idea of AI rights. [137] Figuring out how to incorporate innovative AI with existing legal and social frameworks is an emerging problem. [138]

Benefits

AGI might have a broad range of applications. If oriented towards such objectives, AGI could help alleviate numerous problems on the planet such as appetite, hardship and illness. [139]

AGI could improve efficiency and effectiveness in most tasks. For instance, in public health, AGI could accelerate medical research study, notably against cancer. [140] It might take care of the senior, [141] and democratize access to fast, top quality medical diagnostics. It might offer enjoyable, low-cost and customized education. [141] The need to work to subsist could end up being outdated if the wealth produced is appropriately rearranged. [141] [142] This also raises the concern of the location of humans in a significantly automated society.

AGI might also assist to make reasonable decisions, and to prepare for and prevent catastrophes. It might also assist to profit of potentially devastating technologies such as nanotechnology or climate engineering, while preventing the associated threats. [143] If an AGI’s main goal is to prevent existential disasters such as human extinction (which could be challenging if the Vulnerable World Hypothesis ends up being real), [144] it might take procedures to dramatically minimize the risks [143] while lessening the impact of these measures on our lifestyle.

Risks

Existential risks

AGI might represent several kinds of existential threat, which are dangers that threaten “the early extinction of Earth-originating intelligent life or the long-term and drastic damage of its potential for preferable future advancement”. [145] The danger of human termination from AGI has actually been the subject of lots of debates, but there is also the possibility that the development of AGI would result in a completely flawed future. Notably, it might be utilized to spread and protect the set of values of whoever establishes it. If humankind still has ethical blind spots comparable to slavery in the past, AGI may irreversibly entrench it, preventing moral progress. [146] Furthermore, AGI might facilitate mass monitoring and indoctrination, which might be used to create a stable repressive around the world totalitarian regime. [147] [148] There is likewise a danger for the makers themselves. If machines that are sentient or otherwise worthy of moral factor to consider are mass produced in the future, taking part in a civilizational path that forever ignores their well-being and interests might be an existential catastrophe. [149] [150] Considering just how much AGI might enhance humanity’s future and help lower 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 presents an existential danger for humans, which this danger needs more attention, is questionable however has been endorsed in 2023 by numerous public figures, AI researchers and CEOs of AI companies such as Elon Musk, Bill Gates, Geoffrey Hinton, Yoshua Bengio, Demis Hassabis and Sam Altman. [151] [152]

In 2014, Stephen Hawking criticized extensive indifference:

So, facing possible futures of incalculable advantages and threats, the specialists are undoubtedly doing whatever possible to guarantee the best outcome, right? Wrong. If a remarkable alien civilisation sent us a message stating, ‘We’ll get here in a couple of 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 mankind has actually in some cases been compared to the fate of gorillas threatened by human activities. The contrast states that greater intelligence allowed mankind to dominate gorillas, which are now susceptible in manner ins which they might not have actually expected. As an outcome, the gorilla has actually ended up being a threatened types, not out of malice, however simply as a collateral damage from human activities. [154]

The skeptic Yann LeCun thinks about that AGIs will have no desire to control humankind and that we must beware not to anthropomorphize them and interpret their intents as we would for humans. He stated that individuals will not be “wise adequate to design super-intelligent makers, yet unbelievably stupid to the point of offering it moronic goals with no safeguards”. [155] On the other side, the concept of crucial convergence suggests that nearly whatever their objectives, intelligent representatives will have factors to try to endure and obtain more power as intermediary actions to attaining these goals. Which this does not need having emotions. [156]

Many scholars who are worried about existential threat supporter for more research study into fixing the “control issue” to respond to the question: what types of safeguards, algorithms, or architectures can developers implement to maximise the likelihood that their recursively-improving AI would continue to behave in a friendly, rather than damaging, manner after it reaches superintelligence? [157] [158] Solving the control issue is complicated by the AI arms race (which might cause a race to the bottom of security precautions in order to launch items before competitors), [159] and using AI in weapon systems. [160]

The thesis that AI can pose existential risk likewise has critics. Skeptics typically state that AGI is unlikely in the short-term, or that concerns about AGI distract from other concerns related to current AI. [161] Former Google fraud czar Shuman Ghosemajumder considers that for many individuals outside of the innovation market, existing chatbots and LLMs are already perceived as though they were AGI, resulting in additional misconception and worry. [162]

Skeptics often charge that the thesis is crypto-religious, with an illogical belief in the possibility of superintelligence replacing an unreasonable belief in an omnipotent God. [163] Some scientists think that the communication campaigns on AI existential risk 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 scientists, issued a joint statement asserting that “Mitigating the risk of extinction from AI should be a global top priority along with other societal-scale threats such as pandemics and nuclear war.” [152]

Mass joblessness

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

According to Stephen Hawking, the outcome of automation on the quality of life will depend on how the wealth will be rearranged: [142]

Everyone can enjoy a life of glamorous leisure if the machine-produced wealth is shared, or many people can wind up badly poor if the machine-owners effectively lobby versus wealth redistribution. Up until now, the pattern seems to be towards the 2nd option, with technology driving ever-increasing inequality

Elon Musk thinks about 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 result
AI security – Research area on making AI safe and beneficial
AI alignment – AI conformance to the desired goal
A.I. Rising – 2018 film directed by Lazar Bodroža
Artificial intelligence
Automated artificial intelligence – Process of automating the application of maker knowing
BRAIN Initiative – Collaborative public-private research study effort revealed by the Obama administration
China Brain Project
Future of Humanity Institute – Defunct Oxford interdisciplinary research centre
General game playing – Ability of expert system to play various games
Generative expert system – AI system capable of producing content in action to triggers
Human Brain Project – Scientific research task
Intelligence amplification – Use of info innovation to enhance human intelligence (IA).
Machine principles – Moral behaviours of man-made devices.
Moravec’s paradox.
Multi-task knowing – Solving several maker discovering tasks at the exact same time.
Neural scaling law – Statistical law in maker knowing.
Outline of synthetic intelligence – Overview of and topical guide to synthetic intelligence.
Transhumanism – Philosophical motion.
Synthetic intelligence – Alternate term for or kind of artificial intelligence.
Transfer knowing – Machine learning strategy.
Loebner Prize – Annual AI competitors.
Hardware for expert system – Hardware specially designed and optimized for artificial intelligence.
Weak expert system – Form of expert system.

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 space.
^ AI creator John McCarthy writes: “we can not yet define in basic what type of computational treatments we want to call smart. ” [26] (For a discussion of some definitions of intelligence utilized by expert system researchers, see approach of artificial intelligence.).
^ The Lighthill report particularly criticized AI‘s “grandiose objectives” and led the taking apart of AI research study in England. [55] In the U.S., DARPA ended up being figured out to money only “mission-oriented direct research, instead of standard undirected research”. [56] [57] ^ As AI creator John McCarthy composes “it would be a great relief to the rest of the workers in AI if the inventors of new general formalisms would reveal their hopes in a more safeguarded form than has often been the case.” [61] ^ In “Mind Children” [122] 1015 cps is utilized. More recently, in 1997, [123] Moravec argued for 108 MIPS which would roughly represent 1014 cps. Moravec talks in regards to MIPS, not “cps”, which is a non-standard term Kurzweil introduced.
^ As defined in a basic AI book: “The assertion that makers could potentially act smartly (or, maybe much better, act as if they were smart) is called the ‘weak AI‘ hypothesis by philosophers, and the assertion that makers that do so are really thinking (rather than replicating thinking) is called the ‘strong AI’ hypothesis.” [121] ^ Alan Turing made this point in 1950. [36] References

^ Krishna, Sri (9 February 2023). “What is synthetic narrow intelligence (ANI)?”. VentureBeat. Retrieved 1 March 2024. ANI is developed to perform a single task.
^ “OpenAI Charter”. OpenAI. Retrieved 6 April 2023. Our objective is to make sure that synthetic basic intelligence benefits all of mankind.
^ Heath, Alex (18 January 2024). “Mark Zuckerberg’s new goal is producing synthetic general intelligence”. The Verge. Retrieved 13 June 2024. Our vision is to construct AI that is better than human-level at all of the human senses.
^ Baum, Seth D. (2020 ). A Study of Artificial General Intelligence Projects for Ethics, Risk, and Policy (PDF) (Report). Global Catastrophic Risk Institute. Retrieved 28 November 2024. 72 AGI R&D jobs were determined as being active in 2020.
^ a b c “AI timelines: What do professionals in artificial 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 Times. Retrieved 18 May 2023.
^ “AI pioneer Geoffrey Hinton quits Google and alerts of danger ahead”. The New York Times. 1 May 2023. Retrieved 2 May 2023. It is difficult to see how you can prevent the bad stars from using it for bad things.
^ Bubeck, Sébastien; Chandrasekaran, Varun; Eldan, Ronen; Gehrke, Johannes; Horvitz, Eric (2023 ). “Sparks of Artificial General Intelligence: Early experiments with GPT-4”. arXiv preprint. arXiv:2303.12712. GPT-4 shows stimulates of AGI.
^ Butler, Octavia E. (1993 ). Parable of the Sower. Grand Central Publishing. ISBN 978-0-4466-7550-5. All that you touch you alter. All that you change changes you.
^ Vinge, Vernor (1992 ). A Fire Upon the Deep. Tor Books. ISBN 978-0-8125-1528-2. The Singularity is coming.
^ Morozov, Evgeny (30 June 2023). “The True Threat of Artificial Intelligence”. The New York City Times. The genuine risk is not AI itself however the way we deploy it.
^ “Impressed by synthetic intelligence? Experts state AGI is following, and it has ‘existential’ dangers”. ABC News. 23 March 2023. Retrieved 6 April 2023. AGI might present existential risks to mankind.
^ Bostrom, Nick (2014 ). Superintelligence: Paths, Dangers, Strategies. Oxford University Press. ISBN 978-0-1996-7811-2. The very first superintelligence will be the last invention that humankind requires to make.
^ Roose, Kevin (30 May 2023). “A.I. Poses ‘Risk of Extinction,’ Industry Leaders Warn”. The New York City Times. Mitigating the threat of termination from AI should be a global top priority.
^ “Statement on AI Risk”. Center for AI Safety. Retrieved 1 March 2024. AI experts alert of threat of termination from AI.
^ Mitchell, Melanie (30 May 2023). “Are AI‘s Doomsday Scenarios Worth Taking Seriously?”. The New York City Times. We are far from producing makers that can outthink us in basic methods.
^ LeCun, Yann (June 2023). “AGI does not provide an existential threat”. 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 Artificial Intelligence: 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 utilize for “human-level” intelligence in the physical sign system hypothesis.
^ “The Open University on Strong and Weak AI“. Archived from the original on 25 September 2009. Retrieved 8 October 2007.
^ “What is artificial superintelligence (ASI)?|Definition from TechTarget”. Enterprise AI. Retrieved 8 October 2023.
^ “Artificial intelligence is transforming our world – it is on everyone to ensure that it works out”. 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 characteristics is based on the topics 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 forms 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 reassessed: The concept 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 occurs when it does?”. The Conversation. Retrieved 22 September 2024.
^ a b Turing 1950.
^ Turing, Alan (1952 ). B. Jack Copeland (ed.). Can Automatic Calculating Machines Be Said To Think?. Oxford: Oxford University Press. pp. 487-506. ISBN 978-0-1982-5079-1.
^ “Eugene Goostman is a genuine young boy – the Turing Test says 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 distinguish GPT-4 from a human in a Turing test”. arXiv:2405.08007 [cs.HC]
^ Varanasi, Lakshmi (21 March 2023). “AI models like ChatGPT and GPT-4 are acing whatever from the bar test to AP Biology. Here’s a list of difficult exams both AI variations 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 Take Advantage Of It”. Retrieved 30 May 2023.
^ Turk, Victoria (28 January 2015). “The Plan to Replace the Turing Test with a ‘Turing Olympics'”. Vice. Retrieved 3 March 2024.
^ Gopani, Avi (25 May 2022). “Turing Test is undependable. 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: 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). Expert System, 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 Expert System. 15 April 2024. Retrieved 27 May 2024.
^ Crevier 1993, pp. 48-50.
^ Kaplan, Andreas (2022 ). “Artificial Intelligence, Business and Civilization – Our Fate Made in Machines”. Archived from the 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 ), priced estimate in Crevier (1993, p. 109).
^ Lighthill 1973; Howe 1994.
^ a b NRC 1999, “Shift to Applied Research Increases Investment”.
^ Crevier 1993, pp. 115-117; Russell & Norvig 2003, pp. 21-22.
^ Crevier 1993, p. 211, Russell & Norvig 2003, p. 24 and see likewise Feigenbaum & McCorduck 1983.
^ Crevier 1993, pp. 161-162, 197-203, 240; Russell & Norvig 2003, p. 25.
^ Crevier 1993, pp. 209-212.
^ McCarthy, John (2000 ). “Reply to Lighthill”. Stanford University. Archived from the original on 30 September 2008. Retrieved 29 September 2007.
^ Markoff, John (14 October 2005). “Behind Artificial Intelligence, a Squadron of Bright Real People”. The New York Times. Archived from the original on 2 February 2023. Retrieved 18 February 2017. At its low point, some computer system scientists and software application engineers avoided the term expert system for worry of being considered as wild-eyed dreamers.
^ Russell & Norvig 2003, pp. 25-26
^ “Trends in the Emerging Tech Hype Cycle”. Gartner Reports. Archived from the original on 22 May 2019. Retrieved 7 May 2019.
^ a b Moravec 1988, p. 20
^ Harnad, S. (1990 ). “The Symbol Grounding Problem”. Physica D. 42 (1-3): 335-346. arXiv: cs/9906002. Bibcode:1990 PhyD … 42..335 H. doi:10.1016/ 0167-2789( 90 )90087-6. S2CID 3204300.
^ Gubrud 1997
^ Hutter, Marcus (2005 ). Universal Expert System: Sequential Decisions Based on Algorithmic Probability. Texts in Theoretical Computer Technology an EATCS Series. Springer. doi:10.1007/ b138233. ISBN 978-3-5402-6877-2. S2CID 33352850. Archived from the initial on 19 July 2022. Retrieved 19 July 2022.
^ Legg, Shane (2008 ). Machine Super Intelligence (PDF) (Thesis). University of Lugano. Archived (PDF) from the original on 15 June 2022. Retrieved 19 July 2022.
^ Goertzel, Ben (2014 ). Artificial General Intelligence. Lecture Notes in Computer Science. Vol. 8598. Journal of Artificial General Intelligence. doi:10.1007/ 978-3-319-09274-4. ISBN 978-3-3190-9273-7. S2CID 8387410.
^ “Who created the term “AGI”?”. goertzel.org. Archived from the 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 season school: June 22 – July 3, 2009, OpenCog Lab: July 6-9, 2009”. Archived from the initial on 28 September 2020. Retrieved 11 May 2020.
^ “Избираеми дисциплини 2009/2010 – пролетен триместър” [Elective courses 2009/2010 – spring trimester] Факултет по математика и информатика [Faculty of Mathematics and Informatics] (in Bulgarian). Archived from the original on 26 July 2020. Retrieved 11 May 2020.
^ “Избираеми дисциплини 2010/2011 – зимен триместър” [Elective courses 2010/2011 – winter 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 machine intelligence: Despite development in machine intelligence, synthetic general intelligence is still a major challenge”. EMBO Reports. 20 (10 ): e49177. doi:10.15252/ embr.201949177. ISSN 1469-221X. PMC 6776890. PMID 31531926.
^ Bubeck, Sébastien; Chandrasekaran, Varun; Eldan, Ronen; Gehrke, Johannes; Horvitz, Eric; Kamar, Ece; Lee, Peter; Lee, Yin Tat; Li, Yuanzhi; Lundberg, Scott; Nori, Harsha; Palangi, Hamid; Ribeiro, Marco Tulio; Zhang, Yi (27 March 2023). “Sparks of Artificial General Intelligence: Early explores GPT-4”. arXiv:2303.12712 [cs.CL]
^ “Microsoft Researchers Claim GPT-4 Is Showing “Sparks” of AGI”. Futurism. 23 March 2023. Retrieved 13 December 2023.
^ Allen, Paul; Greaves, Mark (12 October 2011). “The Singularity Isn’t Near”. MIT Technology Review. Retrieved 17 September 2014.
^ Winfield, Alan. “Expert system will not 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 expert system 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 artificial intelligence bring us utopia or damage?”. The New Yorker. Archived from the initial on 28 January 2016. Retrieved 7 February 2016.
^ Müller, V. C., & Bostrom, N. (2016 ). Future development in expert system: A study of skilled viewpoint. In Fundamental issues of synthetic 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 originality 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 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). 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 original on 3 January 2019. Retrieved 2 January 2019.
^ Grossman, Gary (3 September 2020). “We’re entering the AI twilight zone in between narrow and general AI”. VentureBeat. Archived from the initial on 4 September 2020. Retrieved 5 September 2020. Certainly, too, there are those who claim we are currently seeing an early example of an AGI system in the just recently revealed GPT-3 natural language processing (NLP) neural network. … So is GPT-3 the first example of an AGI system? This is arguable, but the agreement is that it is not AGI. … If nothing else, GPT-3 tells us there is a middle ground between narrow and general AI.
^ Quach, Katyanna. “A developer constructed an AI chatbot utilizing GPT-3 that assisted a man speak once again to his late fiancée. OpenAI shut it down”. The Register. Archived from the initial on 16 October 2021. Retrieved 16 October 2021.
^ Wiggers, Kyle (13 May 2022), “DeepMind’s new AI can perform over 600 jobs, from playing games to controlling robotics”, TechCrunch, archived from the initial on 16 June 2022, obtained 12 June 2022.
^ Bubeck, Sébastien; Chandrasekaran, Varun; Eldan, Ronen; Gehrke, Johannes; Horvitz, Eric; Kamar, Ece; Lee, Peter; Lee, Yin Tat; Li, Yuanzhi; Lundberg, Scott; Nori, Harsha; Palangi, Hamid; Ribeiro, Marco Tulio; Zhang, Yi (22 March 2023). “Sparks of Artificial General Intelligence: it-viking.ch 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 measure up to human intelligence in ‘just a few years,’ says CEO of Google’s primary A.I. research lab”. Fortune. Retrieved 4 September 2024.
^ Nellis, Stephen (2 March 2024). “Nvidia CEO says AI could pass human tests in five years”. Reuters. ^ Aschenbrenner, Leopold. “SITUATIONAL AWARENESS, The Decade Ahead”.
^ Sullivan, Mark (18 October 2023). “Why everyone 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 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 original on 8 February 2014. Retrieved 24 January 2014.
^ de Vega, Glenberg & Graesser 2008. A wide variety of views in existing research study, all of which require grounding to some degree
^ Thornton, Angela (26 June 2023). “How uploading our minds to a computer system might become possible”. The Conversation. Retrieved 8 November 2023.
^ Searle 1980
^ For instance: Russell & Norvig 2003,
Oxford University Press Dictionary of Psychology Archived 3 December 2007 at the Wayback Machine (priced estimate in” Encyclopedia.com”),.
MIT Encyclopedia of Cognitive Science Archived 19 July 2008 at the Wayback Machine (priced estimate in “AITopics”),.
Will Biological Computers Enable Artificially Intelligent Machines to Become Persons? Archived 13 May 2008 at the Wayback Machine Anthony Tongen.

^ a b c Russell & Norvig 2003, p. 947.
^ though see Explainable expert system for 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 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 Artificial Intelligence Have Moral or Legal Rights?”. The Wall Street Journal.
^ “Artificial General Intelligence – Do [es] the expense surpass 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, dangers, techniques (Reprinted with corrections 2017 ed.). Oxford, United Kingdom; New York City, New York, USA: Oxford University Press. ISBN 978-0-1996-7811-2.
^ Piper, Kelsey (19 November 2018). “How technological progress is making it likelier than ever that humans will destroy ourselves”. Vox. Retrieved 8 June 2023.
^ Doherty, Ben (17 May 2018). “Climate alter an ‘existential security threat’ to Australia, Senate questions says”. The Guardian. ISSN 0261-3077. Retrieved 16 July 2023.
^ MacAskill, William (2022 ). What we owe the future. New York City, NY: Basic Books. ISBN 978-1-5416-1862-6.
^ a b Ord, Toby (2020 ). “Chapter 5: Future Risks, fraternityofshadows.com 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 implications 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 remarkable Facebook dispute in between Yann LeCun, Stuart Russel and Yoshua Bengio about the risks of strong AI”. The interesting Facebook dispute between Yann LeCun, Stuart Russel and Yoshua Bengio about the risks of strong AI (in French). Retrieved 8 June 2023.
^ “Will Expert System Doom The Human Race Within The Next 100 Years?”. HuffPost. 22 August 2014. Retrieved 8 June 2023.
^ Sotala, Kaj; Yampolskiy, Roman V. (19 December 2014). “Responses to catastrophic AGI risk: 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 threats spiralling out of control, report alerts”. Financial Times. Archived from the initial on 11 April 2022. Retrieved 24 December 2023.
^ Milmo, Dan; Stacey, Kiran (25 September 2023). “Experts disagree over threat positioned but artificial intelligence can not be disregarded”. 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 initial on 4 June 2014. Retrieved 12 December 2015.
^ Titcomb, James (30 October 2023). “Big Tech is stiring fears over AI, alert scientists”. The Telegraph. Retrieved 7 December 2023.
^ Davidson, John (30 October 2023). “Google Brain creator says big tech is lying about AI extinction threat”. 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 look at the labor market effect potential of big language designs”. OpenAI. Retrieved 7 June 2023.
^ a b Hurst, Luke (23 March 2023). “OpenAI states 80% of workers could see their tasks affected by AI. These are the jobs most impacted”. euronews. Retrieved 8 June 2023.
^ Sheffey, Ayelet (20 August 2021). “Elon Musk states we require universal standard earnings because ‘in the future, physical work 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 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 initial (PDF) on 20 March 2013.
Goertzel, Ben (December 2007), “Human-level artificial basic intelligence and the possibility of a technological singularity: a reaction to Ray Kurzweil’s The Singularity Is Near, and McDermott’s 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 original on 7 January 2016, recovered 1 April 2009.
Gubrud, Mark (November 1997), “Nanotechnology and International Security”, Fifth Foresight Conference on Molecular Nanotechnology, archived from the original on 29 May 2011, recovered 7 May 2011.
Howe, J. (November 1994), Expert System at Edinburgh University: a Point of view, archived from the initial on 17 August 2007, recovered 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 symposium, Science Research Council.
Luger, George; Stubblefield, William (2004 ), Expert System: Structures and Strategies for Complex Problem Solving (fifth ed.), The Benjamin/Cummings Publishing Company, Inc., p. 720, ISBN 978-0-8053-4780-7.
McCarthy, John (2007b). What is Expert system?. Stanford University. The supreme effort is to make computer programs that can resolve problems and attain goals worldwide as well as humans.
Moravec, Hans (1988 ), Mind Children, Harvard University Press
Moravec, Hans (1998 ), “When will hardware match the human brain?”, Journal of Evolution and Technology, vol. 1, archived from the initial on 15 June 2006, obtained 23 June 2006
Nagel (1974 ), “What Is it Like to Be a Bat” (PDF), Philosophical Review, 83 (4 ): 435-50, doi:10.2307/ 2183914, JSTOR 2183914, archived (PDF) from the 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 ), Artificial Intelligence: 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 original on 12 January 2008, obtained 29 September 2007
Poole, David; Mackworth, Alan; Goebel, Randy (1998 ), Computational Intelligence: A Rational Approach, New York: Oxford University Press, archived from the original on 25 July 2009, recovered 6 December 2007
Russell, Stuart J.; Norvig, Peter (2003 ), Expert System: A Modern Approach (second ed.), Upper Saddle River, New Jersey: Prentice Hall, ISBN 0-13-790395-2
Sandberg, Anders; Boström, Nick (2008 ), Whole Brain Emulation: A Roadmap (PDF), Technical Report # 2008-3, Future of Humanity Institute, Oxford University, archived (PDF) from the 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 initial on 17 March 2019, recovered 3 September 2020
Simon, H. A. (1965 ), The Shape of Automation for Men and Management, New York City: Harper & Row
Turing, Alan (October 1950). “Computing Machinery and Intelligence”. Mind. 59 (236 ): 433-460. doi:10.1093/ mind/LIX.236.433. ISSN 1460-2113. JSTOR 2251299. S2CID 14636783.

de Vega, Manuel; Glenberg, Arthur; Graesser, Arthur, eds. (2008 ), Symbols and Embodiment: Debates on significance and cognition, Oxford University Press, ISBN 978-0-1992-1727-4
Wang, Pei; Goertzel, Ben (2007 ). “Introduction: Aspects of Artificial General Intelligence”. Advances in Artificial General Intelligence: Concepts, Architectures and Algorithms: Proceedings of the AGI Workshop 2006. IOS Press. pp. 1-16. ISBN 978-1-5860-3758-1. Archived from the initial on 18 February 2021. Retrieved 13 December 2020 – by means of ResearchGate.

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