Artificial general intelligence (AGI) is a kind of artificial intelligence (AI) that matches or surpasses human cognitive abilities throughout a large range of cognitive jobs. This contrasts with narrow AI, which is limited to particular tasks. [1] Artificial superintelligence (ASI), on the other hand, refers to AGI that significantly goes beyond human cognitive abilities. AGI is thought about one of the definitions of strong AI.

Creating AGI is a primary objective of AI research study and of companies such as OpenAI [2] and Meta. [3] A 2020 survey identified 72 active AGI research and advancement projects across 37 nations. [4]

The timeline for accomplishing AGI stays a subject of ongoing debate amongst scientists and specialists. Since 2023, some argue that it might be possible in years or years; others preserve it might take a century or longer; a minority think it might never ever be accomplished; and another minority claims that it is currently here. [5] [6] Notable AI researcher Geoffrey Hinton has revealed issues about the rapid development towards AGI, recommending it might be achieved quicker than many anticipate. [7]

There is dispute on the precise definition of AGI and relating to whether modern-day large language designs (LLMs) such as GPT-4 are early kinds of AGI. [8] AGI is a typical topic in sci-fi and futures studies. [9] [10]

Contention exists over whether AGI represents an existential risk. [11] [12] [13] Many experts on AI have stated that alleviating the threat of human extinction postured by AGI ought to be a global priority. [14] [15] Others find the advancement of AGI to be too remote to provide such a risk. [16] [17]

Terminology

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

Some scholastic sources book the term "strong AI" for computer programs that experience sentience or awareness. [a] In contrast, weak AI (or narrow AI) has the ability to resolve one specific issue but lacks basic 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 very same sense as human beings. [a]

Related concepts consist of artificial superintelligence and transformative AI. An artificial superintelligence (ASI) is a hypothetical type of AGI that is far more normally intelligent than humans, [23] while the idea of transformative AI associates with AI having a big influence on society, for instance, comparable to the farming or commercial transformation. [24]

A structure for classifying AGI in levels was proposed in 2023 by Google DeepMind scientists. They define 5 levels of AGI: emerging, competent, specialist, virtuoso, and superhuman. For example, a competent AGI is specified as an AI that exceeds 50% of knowledgeable adults in a wide variety of non-physical jobs, and a superhuman AGI (i.e. an artificial superintelligence) is likewise specified however with a limit of 100%. They consider big language models like ChatGPT or LLaMA 2 to be circumstances of emerging AGI. [25]

Characteristics

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

Intelligence qualities

Researchers usually hold that intelligence is needed to do all of the following: [27]

factor, use technique, fix puzzles, and make judgments under uncertainty
represent understanding, consisting of sound judgment understanding
plan
find out
- interact in natural language
- if required, integrate these skills in conclusion of any offered objective


Many interdisciplinary approaches (e.g. cognitive science, computational intelligence, and choice making) think about additional characteristics such as creativity (the ability to form novel mental images and principles) [28] and autonomy. [29]

Computer-based systems that exhibit numerous of these abilities exist (e.g. see computational imagination, automated reasoning, choice support group, robotic, evolutionary calculation, smart representative). There is dispute about whether contemporary AI systems have them to an appropriate degree.

Physical characteristics

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

- the ability to sense (e.g. see, hear, etc), and
- the ability to act (e.g. move and manipulate items, modification place to explore, and so on).


This includes the capability to spot and respond to danger. [31]

Although the ability to sense (e.g. see, hear, and so on) and the ability to act (e.g. relocation and control objects, modification area to explore, and so on) can be preferable for some intelligent systems, [30] these physical abilities are not strictly required for an entity to certify as AGI-particularly under the thesis that big language designs (LLMs) may currently be or end up being AGI. Even from a less optimistic viewpoint on LLMs, there is no firm requirement for an AGI to have a human-like form; being a silicon-based computational system is sufficient, provided it can process input (language) from the external world in location of human senses. This interpretation lines up with the understanding that AGI has actually never been proscribed a specific physical personification and hence does not demand a capacity for mobility or standard "eyes and ears". [32]

Tests for human-level AGI

Several tests suggested to confirm human-level AGI have actually been thought about, including: [33] [34]

The concept of the test is that the machine needs to try and pretend to be a male, by addressing concerns put to it, and it will only pass if the pretence is reasonably convincing. A significant part of a jury, who must not be expert about machines, need to 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 resolve it, one would need to carry out AGI, because the service is beyond the abilities of a purpose-specific algorithm. [47]

There are many problems that have been conjectured to require general intelligence to solve as well as people. Examples include computer vision, natural language understanding, and dealing with unexpected scenarios while resolving any real-world problem. [48] Even a particular job like translation needs a device to read and write in both languages, follow the author's argument (reason), understand the context (understanding), and faithfully recreate the author's original intent (social intelligence). All of these problems require to be resolved concurrently in order to reach human-level maker efficiency.

However, many of these tasks can now be performed by modern big language designs. According to Stanford University's 2024 AI index, AI has reached human-level performance on numerous benchmarks for checking out comprehension and visual reasoning. [49]

History

Classical AI

Modern AI research began in the mid-1950s. [50] The very first generation of AI scientists were encouraged that artificial general intelligence was possible which it would exist in simply a few years. [51] AI leader Herbert A. Simon wrote in 1965: "machines will be capable, within twenty years, of doing any work a male 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 could produce by the year 2001. AI leader Marvin Minsky was a consultant [53] on the project of making HAL 9000 as practical as possible according to the consensus predictions of the time. He stated in 1967, "Within a generation ... the problem of producing 'expert system' will significantly be solved". [54]

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

However, in the early 1970s, it became apparent that researchers had grossly underestimated the difficulty of the task. Funding firms ended up being hesitant of AGI and put researchers under increasing pressure to produce beneficial "applied AI". [c] In the early 1980s, Japan's Fifth Generation Computer Project restored interest in AGI, setting out a ten-year timeline that consisted of AGI goals like "bring on a casual conversation". [58] In reaction to this and the success of professional systems, both industry and government pumped cash into the field. [56] [59] However, confidence in AI amazingly collapsed in the late 1980s, and the goals of the Fifth Generation Computer Project were never fulfilled. [60] For the 2nd time in twenty years, AI scientists who forecasted the imminent achievement of AGI had been mistaken. By the 1990s, AI scientists had a reputation for making vain guarantees. They became reluctant to make forecasts at all [d] and prevented reference 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 accomplished commercial success and academic respectability by concentrating on particular sub-problems where AI can produce verifiable results and industrial applications, such as speech recognition and recommendation algorithms. [63] These "applied AI" systems are now utilized extensively throughout the technology market, and research in this vein is greatly funded in both academia and industry. Since 2018 [upgrade], advancement in this field was considered an emerging pattern, and a mature phase was anticipated to be reached in more than 10 years. [64]

At the turn of the century, lots of mainstream AI scientists [65] hoped that strong AI could be developed by integrating programs that resolve various sub-problems. Hans Moravec wrote in 1988:

I am positive that this bottom-up route to synthetic intelligence will one day meet the conventional top-down path majority method, all set to offer the real-world skills and the commonsense knowledge that has actually been so frustratingly evasive in reasoning programs. Fully intelligent machines will result when the metaphorical golden spike is driven joining the 2 efforts. [65]

However, even at the time, this was challenged. For instance, Stevan Harnad of Princeton University concluded his 1990 paper on the symbol grounding hypothesis by specifying:

The expectation has actually 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 considerations in this paper are legitimate, then this expectation is hopelessly modular and there is actually just one feasible path from sense to signs: from the ground up. A free-floating symbolic level like the software application level of a computer will never ever be reached by this path (or vice versa) - nor is it clear why we ought to even attempt to reach such a level, since it looks as if arriving would just amount to uprooting our signs from their intrinsic meanings (therefore merely reducing ourselves to the functional equivalent of a programmable computer). [66]

Modern synthetic basic intelligence research

The term "artificial basic intelligence" was utilized as early as 1997, by Mark Gubrud [67] in a discussion of the ramifications of fully automated military production and operations. A mathematical formalism of AGI was proposed by Marcus Hutter in 2000. Named AIXI, the proposed AGI agent maximises "the ability to please objectives in a wide variety of environments". [68] This kind of AGI, defined by the ability to increase a mathematical meaning of intelligence instead of exhibit human-like behaviour, [69] was also called universal expert system. [70]

The term AGI was re-introduced and promoted by Shane Legg and Ben Goertzel around 2002. [71] AGI research activity in 2006 was described by Pei Wang and Ben Goertzel [72] as "producing publications and preliminary outcomes". The first summer season school in AGI was arranged in Xiamen, China in 2009 [73] by the Xiamen university's Artificial Brain Laboratory and OpenCog. The very first university course was given up 2010 [74] and 2011 [75] at Plovdiv University, Bulgaria by Todor Arnaudov. MIT presented a course on AGI in 2018, arranged by Lex Fridman and featuring a number of visitor speakers.

Since 2023 [update], a little number of computer system scientists are active in AGI research study, and lots of add to a series of AGI conferences. However, significantly more scientists have an interest in open-ended learning, [76] [77] which is the idea of enabling AI to continuously find out and innovate like human beings do.

Feasibility

Since 2023, the advancement and potential achievement of AGI remains a subject of extreme dispute within the AI neighborhood. While traditional agreement held that AGI was a distant objective, current developments have actually led some scientists and industry figures to declare that early types of AGI might currently exist. [78] AI leader Herbert A. Simon speculated in 1965 that "machines will be capable, within twenty years, of doing any work a male can do". This prediction failed to come true. Microsoft co-founder Paul Allen thought that such intelligence is not likely in the 21st century since it would require "unforeseeable and basically unpredictable developments" and a "scientifically deep understanding of cognition". [79] Writing in The Guardian, roboticist Alan Winfield claimed the gulf between modern computing and human-level expert system is as large as the gulf in between current space flight and useful faster-than-light spaceflight. [80]

A more challenge is the absence of clarity in defining what intelligence requires. Does it require consciousness? Must it display the capability to set objectives along with pursue them? Is it simply a matter of scale such that if model sizes increase sufficiently, intelligence will emerge? Are centers such as preparation, thinking, and causal understanding required? Does intelligence need explicitly replicating the brain and its particular faculties? Does it require emotions? [81]

Most AI scientists think strong AI can be achieved in the future, but some thinkers, like Hubert Dreyfus and Roger Penrose, deny the possibility of achieving strong AI. [82] [83] John McCarthy is amongst those who believe human-level AI will be achieved, however that today level of progress is such that a date can not accurately be predicted. [84] AI experts' views on the feasibility of AGI wax and subside. Four polls conducted in 2012 and 2013 suggested that the mean price quote among specialists for when they would be 50% positive AGI would arrive was 2040 to 2050, depending upon the survey, with the mean being 2081. Of the specialists, 16.5% answered with "never ever" when asked the exact same concern however with a 90% self-confidence instead. [85] [86] Further present AGI progress considerations can be found 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 timespan there is a strong bias towards forecasting the arrival of human-level AI as in between 15 and 25 years from the time the prediction was made". They analyzed 95 forecasts made in between 1950 and 2012 on when human-level AI will come about. [87]

In 2023, Microsoft scientists released a comprehensive evaluation of GPT-4. They concluded: "Given the breadth and depth of GPT-4's capabilities, we believe that it might reasonably be seen as an early (yet still incomplete) version of an artificial general intelligence (AGI) system." [88] Another research study in 2023 reported that GPT-4 exceeds 99% of humans on the Torrance tests of creativity. [89] [90]

Blaise Agüera y Arcas and Peter Norvig composed in 2023 that a substantial level of general intelligence has currently been attained with frontier designs. They wrote that unwillingness to this view comes from four main reasons: a "healthy hesitation about metrics for AGI", an "ideological commitment to alternative AI theories or methods", a "commitment to human (or biological) exceptionalism", or a "issue about the financial ramifications of AGI". [91]

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

In 2024, OpenAI launched o1-preview, the very first of a series of models that "invest more time thinking before they respond". According to Mira Murati, this ability to believe before responding represents a brand-new, additional paradigm. It enhances design outputs by spending more computing power when producing the answer, whereas the model scaling paradigm improves outputs by increasing the model size, training information and training compute power. [93] [94]

An OpenAI employee, Vahid Kazemi, claimed in 2024 that the company had actually achieved AGI, mentioning, "In my opinion, we have actually currently attained AGI and it's much more clear with O1." Kazemi clarified that while the AI is not yet "much better than any human at any job", it is "better than a lot of human beings at many jobs." He also addressed criticisms that large language designs (LLMs) merely follow predefined patterns, comparing their learning procedure to the clinical method of observing, hypothesizing, and confirming. These declarations have triggered dispute, as they rely on a broad and non-traditional definition of AGI-traditionally understood as AI that matches human intelligence across all domains. Critics argue that, while OpenAI's models show impressive versatility, they may not completely fulfill this standard. Notably, Kazemi's remarks came shortly after OpenAI eliminated "AGI" from the terms of its collaboration with Microsoft, prompting speculation about the company'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 produce area for further progress. [82] [98] [99] For instance, the hardware readily available in the twentieth century was not sufficient to carry out deep learning, 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 really flexible AGI is constructed differ from ten years to over a century. Since 2007 [update], the agreement in the AGI research neighborhood seemed to be that the timeline discussed by Ray Kurzweil in 2005 in The Singularity is Near [102] (i.e. in between 2015 and 2045) was possible. [103] Mainstream AI researchers have actually provided a large range of opinions on whether progress will be this rapid. A 2012 meta-analysis of 95 such opinions discovered a bias towards predicting that the onset of AGI would occur within 16-26 years for modern and historical predictions alike. That paper has actually been slammed for how it categorized viewpoints as specialist or non-expert. [104]

In 2012, Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton developed a neural network called AlexNet, which won the ImageNet competitors with a top-5 test error rate of 15.3%, considerably better than the second-best entry's rate of 26.3% (the conventional technique used a weighted amount of ratings from different pre-defined classifiers). [105] AlexNet was considered the preliminary ground-breaker of the present deep knowing wave. [105]

In 2017, scientists Feng Liu, Yong Shi, and Ying Liu carried out intelligence tests on publicly available and freely available 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 kid in very first grade. An adult comes to about 100 on average. Similar tests were performed in 2014, with the IQ score reaching an optimum worth of 27. [106] [107]

In 2020, OpenAI established GPT-3, a language design capable of carrying out many varied jobs without particular training. According to Gary Grossman in a VentureBeat post, while there is consensus that GPT-3 is not an example of AGI, it is considered by some to be too advanced to be categorized as a narrow AI system. [108]

In the very same year, Jason Rohrer utilized his GPT-3 account to establish a chatbot, and provided a chatbot-developing platform called "Project December". OpenAI asked for changes to the chatbot to adhere to their security standards; Rohrer detached Project December from the GPT-3 API. [109]

In 2022, DeepMind established Gato, a "general-purpose" system capable of carrying out more than 600 different jobs. [110]

In 2023, Microsoft Research published a research 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 several domains, such as mathematics, coding, and law. This research study triggered an argument on whether GPT-4 could be considered an early, incomplete variation of synthetic general intelligence, highlighting the requirement for further expedition and assessment of such systems. [111]

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

The concept that this things might really get smarter than people - a few people believed that, [...] But a lot of people thought it was method off. And I believed it was way off. I believed it was 30 to 50 years or even longer away. Obviously, I no longer think that.

In May 2023, Demis Hassabis likewise stated that "The development in the last couple of years has been pretty incredible", which he sees no reason why it would slow down, expecting AGI within a years or even 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 along with human beings. [114] In June 2024, the AI scientist Leopold Aschenbrenner, a former OpenAI worker, approximated AGI by 2027 to be "noticeably possible". [115]

Whole brain emulation

While the development of transformer models like in ChatGPT is thought about the most promising course to AGI, [116] [117] entire brain emulation can function as an alternative technique. With entire brain simulation, a brain model is built by scanning and mapping a biological brain in information, and after that copying and replicating it on a computer system or another computational device. The simulation design need to be sufficiently devoted to the initial, so that it behaves in almost the exact same way as the initial brain. [118] Whole brain emulation is a kind of brain simulation that is talked about in computational neuroscience and neuroinformatics, and for medical research study functions. It has been discussed in artificial intelligence research [103] as an approach to strong AI. Neuroimaging technologies that might deliver the necessary comprehensive understanding are improving quickly, and futurist Ray Kurzweil in the book The Singularity Is Near [102] predicts that a map of adequate quality will appear on a similar timescale to the computing power needed to emulate it.

Early estimates

For low-level brain simulation, a very powerful cluster of computers or GPUs would be needed, offered the huge amount of synapses within the human brain. Each of the 1011 (one hundred billion) neurons has on average 7,000 synaptic connections (synapses) to other nerve cells. The brain of a three-year-old 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] A price quote of the brain's processing power, based upon a simple switch design for nerve cell activity, is around 1014 (100 trillion) synaptic updates per second (SUPS). [121]

In 1997, Kurzweil took a look at numerous price quotes for the hardware needed to equal the human brain and embraced a figure of 1016 calculations per 2nd (cps). [e] (For comparison, if a "computation" was comparable to one "floating-point operation" - a procedure used 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 forecast the essential hardware would be offered sometime in between 2015 and 2025, if the exponential development in computer power at the time of writing continued.

Current research

The Human Brain Project, an EU-funded effort active from 2013 to 2023, has actually developed an especially in-depth and openly accessible 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 techniques

The synthetic neuron design presumed by Kurzweil and used in many existing artificial neural network applications is basic compared to biological neurons. A brain simulation would likely have to capture the comprehensive cellular behaviour of biological nerve cells, currently comprehended only in broad outline. The overhead presented by full modeling of the biological, chemical, and physical information of neural behaviour (particularly on a molecular scale) would need computational powers numerous orders of magnitude larger than Kurzweil's quote. In addition, the estimates do not account for glial cells, which are understood to contribute in cognitive procedures. [125]

An essential criticism of the simulated brain approach stems from embodied cognition theory which asserts that human embodiment is an important aspect of human intelligence and is required to ground meaning. [126] [127] If this theory is correct, any fully practical brain design will need to incorporate more than just the nerve cells (e.g., a robotic body). Goertzel [103] proposes virtual embodiment (like in metaverses like Second Life) as an option, but it is unidentified whether this would be adequate.

Philosophical viewpoint

"Strong AI" as defined in approach

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

Strong AI hypothesis: An expert system system can have "a mind" and "awareness".
Weak AI hypothesis: An expert system system can (just) act like it thinks and has a mind and consciousness.


The first one he called "strong" due to the fact that it makes a stronger statement: it assumes something unique has taken place to the maker that exceeds those abilities that we can evaluate. The behaviour of a "weak AI" maker would be specifically similar to a "strong AI" maker, however the latter would also have subjective mindful experience. This use is likewise typical in scholastic 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 general intelligence". [102] This is not the like Searle's strong AI, unless it is presumed that awareness is essential for human-level AGI. Academic thinkers such as Searle do not think that holds true, and to most expert system scientists the concern is out-of-scope. [130]

Mainstream AI is most interested in how a program acts. [131] According to Russell and Norvig, "as long as the program works, they do not care if you call it real or a simulation." [130] If the program can behave as if it has a mind, then there is no need to understand if it really has mind - undoubtedly, there would be no method to inform. For AI research study, Searle's "weak AI hypothesis" is equivalent to the declaration "artificial basic intelligence is possible". Thus, according to Russell and Norvig, "most AI scientists take the weak AI hypothesis for approved, and do not care about the strong AI hypothesis." [130] Thus, for academic AI research study, "Strong AI" and "AGI" are two various things.

Consciousness

Consciousness can have various significances, and some aspects play substantial functions in sci-fi and the ethics of expert system:

Sentience (or "incredible consciousness"): The capability to "feel" understandings or feelings subjectively, instead of the ability to factor about perceptions. Some thinkers, such as David Chalmers, use the term "awareness" to refer exclusively to remarkable consciousness, which is roughly comparable to life. [132] Determining why and how subjective experience occurs is called the hard problem of consciousness. [133] Thomas Nagel discussed in 1974 that it "feels like" something to be mindful. If we are not conscious, then it does not seem like anything. Nagel utilizes the example of a bat: we can sensibly ask "what does it seem like to be a bat?" However, we are unlikely to ask "what does it seem like to be a toaster?" Nagel concludes that a bat appears to be conscious (i.e., has awareness) however a toaster does not. [134] In 2022, a Google engineer claimed that the company's AI chatbot, LaMDA, had actually accomplished sentience, though this claim was commonly contested by other professionals. [135]

Self-awareness: To have mindful awareness of oneself as a different person, especially to be knowingly familiar with one's own thoughts. This is opposed to just being the "subject of one's believed"-an operating system or debugger is able to be "mindful of itself" (that is, to represent itself in the very same way it represents everything else)-however this is not what individuals typically mean when they use the term "self-awareness". [g]

These traits have an ethical measurement. AI sentience would offer increase to concerns of well-being and legal defense, similarly to animals. [136] Other aspects of awareness related to cognitive abilities are likewise pertinent to the principle of AI rights. [137] Determining how to integrate advanced AI with existing legal and social frameworks is an emerging issue. [138]

Benefits

AGI might have a variety of applications. If oriented towards such goals, AGI might assist mitigate numerous issues on the planet such as hunger, hardship and health problems. [139]

AGI might enhance productivity and effectiveness in a lot of jobs. For instance, in public health, AGI might accelerate medical research, significantly versus cancer. [140] It might take care of the elderly, [141] and democratize access to fast, high-quality medical diagnostics. It could use enjoyable, cheap and personalized education. [141] The requirement to work to subsist might end up being obsolete if the wealth produced is properly rearranged. [141] [142] This likewise raises the concern of the place of humans in a radically automated society.

AGI could likewise help to make reasonable choices, and to anticipate and avoid catastrophes. It could also help to profit of possibly disastrous technologies such as nanotechnology or environment engineering, while preventing the associated risks. [143] If an AGI's primary objective is to prevent existential disasters such as human extinction (which might be challenging if the Vulnerable World Hypothesis ends up being true), [144] it might take measures to considerably minimize the threats [143] while reducing the effect of these steps on our lifestyle.

Risks

Existential threats

AGI may represent several kinds of existential risk, which are dangers that threaten "the premature termination of Earth-originating smart life or the irreversible and extreme damage of its potential for desirable future development". [145] The risk of human extinction from AGI has actually been the topic of numerous disputes, however there is also the possibility that the development of AGI would result in a completely problematic future. Notably, it might be used to spread out and preserve the set of values of whoever establishes it. If humanity still has ethical blind areas similar to slavery in the past, AGI may irreversibly entrench it, preventing ethical progress. [146] Furthermore, AGI could help with mass surveillance and brainwashing, which could be used to develop a stable repressive worldwide totalitarian program. [147] [148] There is likewise a danger for the machines themselves. If devices that are sentient or otherwise worthy of moral factor to consider are mass developed in the future, taking part in a civilizational path that forever overlooks their well-being and interests could be an existential catastrophe. [149] [150] Considering just how much AGI could improve humankind's future and assistance reduce other existential dangers, Toby Ord calls these existential risks "an argument for continuing with due caution", not for "deserting AI". [147]

Risk of loss of control and human termination

The thesis that AI presents an existential risk for human beings, which this danger needs more attention, is questionable but has actually been backed in 2023 by many public figures, AI scientists and CEOs of AI business such as Elon Musk, Bill Gates, Geoffrey Hinton, Yoshua Bengio, Demis Hassabis and Sam Altman. [151] [152]

In 2014, Stephen Hawking slammed prevalent indifference:

So, dealing with possible futures of incalculable advantages and risks, the experts are undoubtedly doing everything possible to make sure the very best result, right? Wrong. If an exceptional alien civilisation sent us a message saying, 'We'll get here in a couple of decades,' would we just reply, 'OK, call us when you get here-we'll leave the lights on?' Probably not-but this is more or less what is taking place with AI. [153]

The possible fate of humankind has sometimes been compared to the fate of gorillas threatened by human activities. The comparison specifies that greater intelligence permitted humanity to dominate gorillas, which are now vulnerable in methods that they might not have actually prepared for. As a result, the gorilla has actually ended up being an endangered species, not out of malice, but merely as a collateral damage from human activities. [154]

The skeptic Yann LeCun thinks about that AGIs will have no desire to control mankind and that we should beware not to anthropomorphize them and interpret their intents as we would for human beings. He stated that people will not be "smart enough to create super-intelligent makers, yet extremely foolish to the point of offering it moronic objectives without any safeguards". [155] On the other side, the concept of critical convergence recommends that practically whatever their objectives, smart agents will have reasons to attempt to survive and acquire more power as intermediary actions to accomplishing these objectives. And that this does not need having emotions. [156]

Many scholars who are concerned about existential threat advocate for more research into resolving the "control issue" to address the question: what kinds of safeguards, algorithms, or architectures can programmers carry out to increase the probability that their recursively-improving AI would continue to act in a friendly, instead of destructive, way after it reaches superintelligence? [157] [158] Solving the control problem is made complex by the AI arms race (which might result in a race to the bottom of security preventative measures in order to release items before rivals), [159] and the use of AI in weapon systems. [160]

The thesis that AI can present existential danger likewise has critics. Skeptics typically say that AGI is not likely in the short-term, or that concerns about AGI sidetrack from other concerns associated with current AI. [161] Former Google fraud czar Shuman Ghosemajumder thinks about that for many individuals beyond the technology industry, existing chatbots and LLMs are already perceived as though they were AGI, causing further misunderstanding and fear. [162]

Skeptics often charge that the thesis is crypto-religious, with an irrational belief in the possibility of superintelligence changing an irrational belief in a supreme God. [163] Some scientists think that the interaction projects on AI existential danger by certain AI groups (such as OpenAI, Anthropic, DeepMind, and Conjecture) may be an at attempt at regulatory capture and to pump up interest in their products. [164] [165]

In 2023, the CEOs of Google DeepMind, OpenAI and Anthropic, in addition to other industry leaders and researchers, released a joint statement asserting that "Mitigating the risk of extinction from AI should be a global top priority together with other societal-scale dangers such as pandemics and nuclear war." [152]

Mass joblessness

Researchers from OpenAI approximated that "80% of the U.S. workforce could have at least 10% of their work jobs affected by the introduction of LLMs, while around 19% of workers may see at least 50% of their jobs impacted". [166] [167] They consider workplace employees to be the most exposed, for instance mathematicians, accountants or web designers. [167] AGI might have a much better autonomy, ability to make decisions, to interface with other computer tools, however also to control robotized bodies.

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

Everyone can delight in a life of elegant 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 second choice, with technology driving ever-increasing inequality

Elon Musk thinks about that the automation of society will need federal governments to adopt a universal basic earnings. [168]

See also

Artificial brain - Software and hardware with cognitive abilities comparable to those of the animal or human brain
AI effect
AI safety - Research area on making AI safe and advantageous
AI alignment - AI conformance to the desired objective
A.I. Rising - 2018 movie directed by Lazar Bodroža
Artificial intelligence
Automated maker knowing - Process of automating the application of artificial intelligence
BRAIN Initiative - Collaborative public-private research effort revealed by the Obama administration
China Brain Project
Future of Humanity Institute - Defunct Oxford interdisciplinary research study centre
General video game playing - Ability of expert system to play different video games
Generative synthetic intelligence - AI system efficient in generating content in action to prompts
Human Brain Project - Scientific research task
Intelligence amplification - Use of details innovation to enhance human intelligence (IA).
Machine ethics - Moral behaviours of manufactured makers.
Moravec's paradox.
Multi-task learning - Solving multiple maker learning tasks at the very same time.
Neural scaling law - Statistical law in artificial intelligence.
Outline of artificial intelligence - Overview of and topical guide to synthetic intelligence.
Transhumanism - Philosophical movement.
Synthetic intelligence - Alternate term for or kind of synthetic intelligence.
Transfer knowing - Machine learning technique.
Loebner Prize - Annual AI competition.
Hardware for expert system - Hardware specifically developed and enhanced for expert system.
Weak expert system - Form of expert system.


Notes

^ a b See below for the origin of the term "strong AI", and see the academic definition of "strong AI" and weak AI in the article Chinese room.
^ AI founder John McCarthy writes: "we can not yet identify in general what kinds of computational procedures we wish to call smart. " [26] (For a conversation of some meanings of intelligence used by synthetic intelligence researchers, see approach of artificial intelligence.).
^ The Lighthill report specifically criticized AI's "grand goals" and led the dismantling of AI research in England. [55] In the U.S., DARPA ended up being figured out to fund only "mission-oriented direct research, rather than basic undirected research". [56] [57] ^ As AI founder John McCarthy writes "it would be an excellent relief to the remainder of the workers in AI if the innovators of brand-new basic formalisms would express their hopes in a more protected form than has often been the case." [61] ^ In "Mind Children" [122] 1015 cps is used. 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 specified in a basic AI book: "The assertion that devices could perhaps act intelligently (or, perhaps better, act as if they were smart) is called the 'weak AI' hypothesis by thinkers, and the assertion that machines that do so are actually believing (as opposed to mimicing thinking) is called the 'strong AI' hypothesis." [121] ^ Alan Turing made this point in 1950. [36] References

^ Krishna, Sri (9 February 2023). "What is synthetic narrow intelligence (ANI)?". VentureBeat. Retrieved 1 March 2024. ANI is developed to perform a single task.
^ "OpenAI Charter". OpenAI. Retrieved 6 April 2023. Our mission is to make sure that synthetic general intelligence advantages all of humanity.
^ Heath, Alex (18 January 2024). "Mark Zuckerberg's new goal is producing synthetic basic intelligence". The Verge. Retrieved 13 June 2024. Our vision is to develop 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 recognized as being active in 2020.
^ a b c "AI timelines: What do experts 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 warns of threat ahead". The New York City Times. 1 May 2023. Retrieved 2 May 2023. It is tough to see how you can avoid the bad actors from using it for bad things.
^ Bubeck, Sébastien; Chandrasekaran, Varun; Eldan, Ronen; Gehrke, Johannes; Horvitz, Eric (2023 ). "Sparks of Artificial General Intelligence: Early try outs GPT-4". arXiv preprint. arXiv:2303.12712. GPT-4 shows triggers of AGI.
^ Butler, Octavia E. (1993 ). Parable of the Sower. Grand Central Publishing. ISBN 978-0-4466-7550-5. All that you touch you 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 Expert System". The New York Times. The real hazard is not AI itself but the method we deploy it.
^ "Impressed by artificial intelligence? Experts state AGI is coming next, and it has 'existential' threats". ABC News. 23 March 2023. Retrieved 6 April 2023. AGI might present existential dangers to mankind.
^ Bostrom, Nick (2014 ). Superintelligence: Paths, Dangers, Strategies. Oxford University Press. ISBN 978-0-1996-7811-2. The first superintelligence will be the last creation that humanity needs to make.
^ Roose, Kevin (30 May 2023). "A.I. Poses 'Risk of Extinction,' Industry Leaders Warn". The New York City Times. Mitigating the risk of extinction from AI need to be a worldwide top priority.
^ "Statement on AI Risk". Center for AI Safety. Retrieved 1 March 2024. AI experts warn 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 devices that can outthink us in basic ways.
^ LeCun, Yann (June 2023). "AGI does not present an existential danger". Medium. There is no reason to fear AI as an existential hazard.
^ Kurzweil 2005, p. 260.
^ a b Kurzweil, Ray (5 August 2005), "Long Live AI", Forbes, archived from the initial on 14 August 2005: Kurzweil explains strong AI as "maker intelligence with the full series of human intelligence.".
^ "The Age of 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 symbol system hypothesis.
^ "The Open University on Strong and Weak AI". Archived from the original on 25 September 2009. Retrieved 8 October 2007.
^ "What is artificial superintelligence (ASI)?|Definition from TechTarget". Enterprise AI. Retrieved 8 October 2023.
^ "Artificial intelligence is changing our world - it is on all of us to make sure that it works out". Our World in Data. Retrieved 8 October 2023.
^ Dickson, Ben (16 November 2023). "Here is how far we are to attaining AGI, according to DeepMind". VentureBeat.
^ McCarthy, John (2007a). "Basic Questions". Stanford University. Archived from the original on 26 October 2007. Retrieved 6 December 2007.
^ This list of smart traits is based upon the topics covered by significant AI 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 City: Academic Press.
^ a b Pfeifer, R. and Bongard J. C., How the body shapes the way we believe: a new view of intelligence (The MIT Press, 2007). ISBN 0-2621-6239-3.
^ White, R. W. (1959 ). "Motivation reconsidered: The idea of competence". Psychological Review. 66 (5 ): 297-333. doi:10.1037/ h0040934. PMID 13844397. S2CID 37385966.
^ White, R. W. (1959 ). "Motivation reevaluated: The idea of skills". Psychological Review. 66 (5 ): 297-333. doi:10.1037/ h0040934. PMID 13844397. S2CID 37385966.
^ Muehlhauser, Luke (11 August 2013). "What is AGI?". Machine Intelligence Research Institute. Archived from the original on 25 April 2014. Retrieved 1 May 2014.
^ "What is Artificial General Intelligence (AGI)?|4 Tests For Ensuring Artificial General Intelligence". Talky Blog. 13 July 2019. Archived from the initial on 17 July 2019. Retrieved 17 July 2019.
^ Kirk-Giannini, Cameron Domenico; Goldstein, Simon (16 October 2023). "AI is closer than ever to passing the Turing test for 'intelligence'. What takes place when it does?". The Conversation. Retrieved 22 September 2024.
^ a b Turing 1950.
^ Turing, Alan (1952 ). B. Jack Copeland (ed.). Can Automatic Calculating Machines Be Said To Think?. Oxford: Oxford University Press. pp. 487-506. ISBN 978-0-1982-5079-1.
^ "Eugene Goostman is a real young boy - the Turing Test says so". The Guardian. 9 June 2014. ISSN 0261-3077. Retrieved 3 March 2024.
^ "Scientists challenge whether computer system 'Eugene Goostman' passed Turing test". BBC News. 9 June 2014. Retrieved 3 March 2024.
^ Jones, Cameron R.; Bergen, Benjamin K. (9 May 2024). "People can not differentiate GPT-4 from a human in a Turing test". arXiv:2405.08007 [cs.HC]
^ Varanasi, Lakshmi (21 March 2023). "AI designs like ChatGPT and GPT-4 are acing whatever from the bar exam to AP Biology. Here's a list of tough exams both AI variations have passed". Business Insider. Retrieved 30 May 2023.
^ Naysmith, Caleb (7 February 2023). "6 Jobs Artificial Intelligence Is Already Replacing and How Investors Can Take Advantage Of It". Retrieved 30 May 2023.
^ Turk, Victoria (28 January 2015). "The Plan to Replace the Turing Test with a 'Turing Olympics'". Vice. Retrieved 3 March 2024.
^ Gopani, Avi (25 May 2022). "Turing Test is unreliable. The Winograd Schema is 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 ability to turn $100,000 into $1 million to determine human-like intelligence". Business Insider. Retrieved 3 March 2024.
^ Suleyman, Mustafa (14 July 2023). "Mustafa Suleyman: My 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 Expert System (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 Specifying Feature of AI-Completeness" (PDF). Expert System, Evolutionary Computation and Metaheuristics (AIECM): 3-17. Archived (PDF) from the original on 22 May 2013.
^ "AI Index: State of AI in 13 Charts". Stanford University Human-Centered Artificial Intelligence. 15 April 2024. Retrieved 27 May 2024.
^ Crevier 1993, pp. 48-50.
^ Kaplan, Andreas (2022 ). "Expert System, Business and Civilization - Our Fate Made in Machines". Archived from the original on 6 May 2022. Retrieved 12 March 2022.
^ Simon 1965, p. 96 priced estimate in Crevier 1993, p. 109.
^ "Scientist on the Set: An Interview with Marvin Minsky". Archived from the initial on 16 July 2012. Retrieved 5 April 2008.
^ Marvin Minsky to Darrach (1970 ), estimated in Crevier (1993, p. 109).
^ Lighthill 1973; Howe 1994.
^ a b NRC 1999, "Shift to Applied Research Increases Investment".
^ Crevier 1993, pp. 115-117; Russell & Norvig 2003, pp. 21-22.
^ Crevier 1993, p. 211, Russell & Norvig 2003, p. 24 and see 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 original on 30 September 2008. Retrieved 29 September 2007.
^ Markoff, John (14 October 2005). "Behind Artificial Intelligence, a Squadron of Bright Real People". The New York City Times. Archived from the initial on 2 February 2023. Retrieved 18 February 2017. At its low point, some computer researchers and software application engineers prevented the term synthetic intelligence for fear of being 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 on Algorithmic Probability. Texts in Theoretical Computer Science an EATCS Series. Springer. doi:10.1007/ b138233. ISBN 978-3-5402-6877-2. S2CID 33352850. Archived from the original on 19 July 2022. Retrieved 19 July 2022.
^ Legg, Shane (2008 ). Machine Super Intelligence (PDF) (Thesis). University of Lugano. Archived (PDF) from the 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., via Life 3.0: 'The term "AGI" was popularized 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 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 device intelligence, artificial general 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 try outs GPT-4". arXiv:2303.12712 [cs.CL]
^ "Microsoft Researchers Claim GPT-4 Is Showing "Sparks" of AGI". Futurism. 23 March 2023. Retrieved 13 December 2023.
^ Allen, Paul; Greaves, Mark (12 October 2011). "The Singularity Isn't Near". MIT Technology Review. Retrieved 17 September 2014.
^ Winfield, Alan. "Expert system will not turn 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 expert system 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 progress in expert system: A study of skilled viewpoint. 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 makers: AI takes the Torrance Test". Journal of Creativity. 33 (3 ): 100065. doi:10.1016/ j.yjoc.2023.100065. ISSN 2713-3745. S2CID 261087185.
^ Arcas, Blaise Agüera y (10 October 2023). "Artificial General Intelligence Is Already Here". Noema.
^ Zia, Tehseen (8 January 2024). "Unveiling of Large Multimodal Models: Shaping the Landscape of Language Models in 2024". Unite.ai. Retrieved 26 May 2024.
^ "Introducing OpenAI o1-preview". OpenAI. 12 September 2024.
^ Knight, Will. "OpenAI Announces a New AI Model, Code-Named Strawberry, That Solves Difficult Problems Step by Step". Wired. ISSN 1059-1028. Retrieved 17 September 2024.
^ "OpenAI E