Artificial general intelligence (AGI) is a type of synthetic intelligence (AI) that matches or exceeds human cognitive abilities across a vast array of cognitive jobs. This contrasts with narrow AI, which is restricted to specific jobs. [1] Artificial superintelligence (ASI), on the other hand, describes AGI that greatly exceeds human cognitive abilities. AGI is considered among the definitions of strong AI.
Creating AGI is a main goal of AI research and of business such as OpenAI [2] and Meta. [3] A 2020 survey determined 72 active AGI research and advancement tasks across 37 countries. [4]
The timeline for attaining AGI stays a subject of continuous debate among researchers and experts. As of 2023, some argue that it might be possible in years or decades; others keep it might take a century or longer; a minority believe it may never be achieved; and another minority claims that it is currently here. [5] [6] Notable AI scientist Geoffrey Hinton has actually revealed issues about the rapid development towards AGI, suggesting it might be attained sooner than many expect. [7]
There is argument on the specific meaning of AGI and relating to whether contemporary large language designs (LLMs) such as GPT-4 are early kinds of AGI. [8] AGI is a common topic in science fiction 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 mitigating the danger of human extinction positioned by AGI needs to be an international concern. [14] [15] Others discover the advancement of AGI to be too remote to present such a danger. [16] [17]
Terminology
AGI is likewise called strong AI, [18] [19] complete AI, [20] human-level AI, [5] human-level intelligent AI, or general smart action. [21]
Some scholastic sources book the term "strong AI" for computer programs that experience sentience or consciousness. [a] On the other hand, weak AI (or narrow AI) has the ability to solve one particular problem but does not have general cognitive abilities. [22] [19] Some academic sources use "weak AI" to refer more broadly to any programs that neither experience consciousness nor have a mind in the same sense as human beings. [a]
Related principles include artificial superintelligence and transformative AI. An artificial superintelligence (ASI) is a theoretical kind of AGI that is far more usually smart than people, [23] while the idea of transformative AI associates with AI having a large effect on society, for example, similar to the agricultural or industrial transformation. [24]
A structure for classifying AGI in levels was proposed in 2023 by Google DeepMind scientists. They define 5 levels of AGI: emerging, skilled, specialist, virtuoso, and superhuman. For example, a proficient AGI is defined as an AI that exceeds 50% of skilled adults in a large range of non-physical tasks, and a superhuman AGI (i.e. an artificial superintelligence) is likewise specified but with a limit of 100%. They consider big language models like ChatGPT or LLaMA 2 to be instances of emerging AGI. [25]
Characteristics
Various popular definitions of intelligence have actually been proposed. One of the leading propositions is the Turing test. However, there are other well-known definitions, and some scientists disagree with the more popular approaches. [b]
Intelligence characteristics
Researchers generally hold that intelligence is needed to do all of the following: [27]
factor, usage technique, resolve puzzles, and make judgments under unpredictability
represent understanding, including common sense understanding
strategy
discover
- communicate in natural language
- if necessary, incorporate these skills in completion of any given goal
Many interdisciplinary techniques (e.g. cognitive science, computational intelligence, and decision making) think about extra traits such as creativity (the capability to form novel mental images and concepts) [28] and autonomy. [29]
Computer-based systems that display numerous of these abilities exist (e.g. see computational creativity, automated thinking, choice support group, robot, evolutionary computation, intelligent agent). There is argument about whether modern AI systems have them to an appropriate degree.
Physical characteristics
Other capabilities are thought about preferable in smart systems, as they may impact intelligence or help in its expression. These consist of: [30]
- the capability to sense (e.g. see, hear, etc), and
- the ability to act (e.g. move and control items, change place to check out, etc).
This includes the ability to detect and react to threat. [31]
Although the capability to sense (e.g. see, hear, and so on) and the ability to act (e.g. move and control items, modification area to explore, etc) can be desirable for some smart systems, [30] these physical capabilities are not strictly required for an entity to certify as AGI-particularly under the thesis that large language designs (LLMs) may currently be or end up being AGI. Even from a less positive perspective on LLMs, there is no company requirement for an AGI to have a human-like form; being a silicon-based computational system is enough, offered it can process input (language) from the external world in place of human senses. This analysis lines up with the understanding that AGI has never ever been proscribed a specific physical embodiment and thus does not demand a capability for mobility or conventional "eyes and ears". [32]
Tests for human-level AGI
Several tests suggested to confirm human-level AGI have been considered, consisting of: [33] [34]
The concept of the test is that the machine needs to attempt and pretend to be a man, by answering questions put to it, and it will only pass if the pretence is reasonably persuading. A considerable portion of a jury, who need to not be skilled about devices, should be taken in by the pretence. [37]
AI-complete issues
An issue is informally called "AI-complete" or "AI-hard" if it is thought that in order to solve it, one would need to execute AGI, since the option is beyond the abilities of a purpose-specific algorithm. [47]
There are numerous problems that have actually been conjectured to require general intelligence to solve along with people. Examples consist of computer system vision, natural language understanding, and handling unanticipated situations while resolving any real-world problem. [48] Even a particular task like translation needs a maker 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 need to be resolved simultaneously in order to reach human-level device performance.
However, a lot of these tasks can now be performed by modern-day large language designs. According to Stanford University's 2024 AI index, AI has actually reached human-level efficiency on many standards for reading understanding and visual thinking. [49]
History
Classical AI
Modern AI research began in the mid-1950s. [50] The first generation of AI scientists were persuaded that artificial basic intelligence was possible which it would exist in simply a couple of years. [51] AI leader Herbert A. Simon composed in 1965: "machines will be capable, within twenty years, of doing any work a man can do." [52]
Their forecasts were the motivation for Stanley Kubrick and Arthur C. Clarke's character HAL 9000, who embodied what AI scientists believed they could develop by the year 2001. AI pioneer Marvin Minsky was a specialist [53] on the job of making HAL 9000 as reasonable as possible according to the agreement forecasts of the time. He said in 1967, "Within a generation ... the issue of developing 'artificial intelligence' will substantially be fixed". [54]
Several classical AI projects, such as Doug Lenat's Cyc job (that began in 1984), and Allen Newell's Soar project, were directed at AGI.
However, in the early 1970s, it ended up being apparent that scientists had grossly undervalued the problem of the project. Funding agencies became skeptical of AGI and put researchers under increasing pressure to produce useful "used AI". [c] In the early 1980s, Japan's Fifth Generation Computer Project revived interest in AGI, setting out a ten-year timeline that included AGI objectives like "bring on a table talk". [58] In reaction to this and the success of specialist systems, both industry and government pumped cash into the field. [56] [59] However, self-confidence in AI amazingly collapsed in the late 1980s, and the objectives of the Fifth Generation Computer Project were never fulfilled. [60] For the 2nd time in twenty years, AI researchers who predicted the imminent achievement of AGI had actually been mistaken. By the 1990s, AI researchers had a credibility for making vain promises. They became hesitant to make predictions at all [d] and avoided mention of "human level" artificial intelligence for fear of being identified "wild-eyed dreamer [s]. [62]
Narrow AI research
In the 1990s and early 21st century, mainstream AI accomplished business success and academic respectability by focusing on specific sub-problems where AI can produce proven outcomes and commercial applications, such as speech recognition and suggestion algorithms. [63] These "applied AI" systems are now used thoroughly throughout the technology market, and research study in this vein is heavily funded in both academic community and market. As of 2018 [update], advancement in this field was considered an emerging trend, and a mature stage was expected to be reached in more than 10 years. [64]
At the millenium, many traditional AI researchers [65] hoped that strong AI could be developed by combining programs that solve numerous sub-problems. Hans Moravec composed in 1988:
I am confident that this bottom-up route to expert system will one day satisfy the standard top-down path more than half method, all set to supply the real-world competence and the commonsense knowledge that has been so frustratingly evasive in thinking programs. Fully intelligent machines will result when the metaphorical golden spike is driven joining the two 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 stating:
The expectation has actually frequently been voiced that "top-down" (symbolic) approaches to modeling cognition will in some way fulfill "bottom-up" (sensory) approaches somewhere in between. If the grounding considerations in this paper stand, then this expectation is hopelessly modular and there is actually only one practical path from sense to signs: from the ground up. A free-floating symbolic level like the software application level of a computer system will never ever be reached by this path (or vice versa) - nor is it clear why we need to even attempt to reach such a level, considering that it looks as if arriving would just amount to uprooting our signs from their intrinsic meanings (thus merely lowering ourselves to the functional equivalent of a programmable computer). [66]
Modern artificial basic intelligence research
The term "artificial basic intelligence" was utilized 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 goals in a large range of environments". [68] This kind of AGI, characterized by the ability to increase a mathematical definition of intelligence rather than exhibit human-like behaviour, [69] was also called universal expert system. [70]
The term AGI was re-introduced and popularized by Shane Legg and Ben Goertzel around 2002. [71] AGI research study activity in 2006 was explained by Pei Wang and Ben Goertzel [72] as "producing publications and initial outcomes". The very 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 given up 2010 [74] and 2011 [75] at Plovdiv University, Bulgaria by Todor Arnaudov. MIT presented a course on AGI in 2018, organized by Lex Fridman and featuring a number of guest speakers.
Since 2023 [upgrade], a small number of computer scientists are active in AGI research, and numerous add to a series of AGI conferences. However, increasingly more scientists have an interest in open-ended learning, [76] [77] which is the concept of allowing AI to continually learn and innovate like human beings do.
Feasibility
Since 2023, the development and prospective achievement of AGI stays a subject of intense debate within the AI neighborhood. While standard consensus held that AGI was a far-off goal, recent advancements have actually led some scientists and market figures to declare that early forms of AGI might already exist. [78] AI pioneer Herbert A. Simon hypothesized in 1965 that "devices will be capable, within twenty years, of doing any work a guy can do". This prediction failed to come true. Microsoft co-founder Paul Allen thought that such intelligence is not likely in the 21st century because it would require "unforeseeable and fundamentally unpredictable breakthroughs" and a "clinically deep understanding of cognition". [79] Writing in The Guardian, roboticist Alan Winfield declared the gulf between contemporary computing and human-level expert system is as wide as the gulf between present space flight and practical faster-than-light spaceflight. [80]
A more obstacle is the lack of clearness in specifying what intelligence involves. Does it need awareness? Must it show the capability to set objectives as well as pursue them? Is it purely a matter of scale such that if model sizes increase adequately, intelligence will emerge? Are facilities such as planning, thinking, and causal understanding needed? Does intelligence require clearly replicating the brain and its specific faculties? Does it need emotions? [81]
Most AI researchers believe strong AI can be accomplished in the future, however some thinkers, like Hubert Dreyfus and Roger Penrose, deny the possibility of accomplishing strong AI. [82] [83] John McCarthy is amongst those who believe human-level AI will be accomplished, however that the present level of progress is such that a date can not accurately be forecasted. [84] AI specialists' views on the expediency of AGI wax and subside. Four surveys conducted in 2012 and 2013 suggested that the average price quote among professionals for when they would be 50% confident AGI would get here was 2040 to 2050, depending upon the survey, with the mean being 2081. Of the professionals, 16.5% answered with "never ever" when asked the same question but with a 90% self-confidence rather. [85] [86] Further existing 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 discovered that "over [a] 60-year timespan there is a strong bias towards anticipating the arrival of human-level AI as between 15 and 25 years from the time the prediction was made". They examined 95 predictions made in between 1950 and 2012 on when human-level AI will come about. [87]
In 2023, Microsoft researchers released an in-depth evaluation of GPT-4. They concluded: "Given the breadth and depth of GPT-4's capabilities, we think that it might fairly be seen as an early (yet still incomplete) variation of a synthetic basic intelligence (AGI) system." [88] Another research study in 2023 reported that GPT-4 surpasses 99% of people on the Torrance tests of creativity. [89] [90]
Blaise Agüera y Arcas and Peter Norvig composed in 2023 that a considerable level of general intelligence has actually already been attained with frontier models. They wrote that hesitation to this view originates from 4 main factors: a "healthy suspicion about metrics for AGI", an "ideological dedication to alternative AI theories or methods", a "dedication to human (or biological) exceptionalism", or a "concern about the financial implications of AGI". [91]
2023 also marked the development of big multimodal models (big language designs efficient in processing or generating several modalities such as text, audio, and images). [92]
In 2024, OpenAI launched o1-preview, the very first of a series of models that "spend more time thinking before they respond". According to Mira Murati, this ability to believe before responding represents a new, extra paradigm. It improves model outputs by investing more computing power when generating the answer, whereas the design scaling paradigm improves outputs by increasing the model size, training information and training compute power. [93] [94]
An OpenAI worker, Vahid Kazemi, declared in 2024 that the company had accomplished AGI, stating, "In my opinion, we have currently accomplished AGI and it's much more clear with O1." Kazemi clarified that while the AI is not yet "better than any human at any task", it is "much better than many people at the majority of jobs." He likewise attended to criticisms that big language models (LLMs) merely follow predefined patterns, comparing their knowing process to the scientific technique of observing, assuming, and validating. These statements have actually stimulated argument, as they count on a broad and unconventional definition of AGI-traditionally comprehended as AI that matches human intelligence throughout all domains. Critics argue that, while OpenAI's models show impressive versatility, they may not fully satisfy this standard. Notably, Kazemi's remarks came quickly after OpenAI got rid of "AGI" from the terms of its collaboration with Microsoft, triggering speculation about the business's tactical intents. [95]
Timescales
Progress in synthetic intelligence has actually historically gone through periods of rapid progress separated by durations when progress appeared to stop. [82] Ending each hiatus were essential advances in hardware, software or both to develop space for further development. [82] [98] [99] For example, the hardware readily available in the twentieth century was not sufficient to execute deep learning, which needs big numbers of GPU-enabled CPUs. [100]
In the introduction to his 2006 book, [101] Goertzel says that estimates of the time needed before a truly versatile AGI is built differ from ten years to over a century. As of 2007 [update], the consensus in the AGI research study neighborhood appeared to be that the timeline gone over by Ray Kurzweil in 2005 in The Singularity is Near [102] (i.e. in between 2015 and 2045) was plausible. [103] Mainstream AI researchers have actually offered a broad range of viewpoints on whether development will be this fast. A 2012 meta-analysis of 95 such opinions discovered a bias towards anticipating that the start of AGI would happen within 16-26 years for modern-day and historical forecasts alike. That paper has actually been slammed for how it categorized viewpoints as professional or non-expert. [104]
In 2012, Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton established a neural network called AlexNet, which won the ImageNet competition with a top-5 test error rate of 15.3%, considerably better than the second-best entry's rate of 26.3% (the traditional method used a weighted sum of ratings from different pre-defined classifiers). [105] AlexNet was considered the initial ground-breaker of the current deep learning wave. [105]
In 2017, researchers Feng Liu, Yong Shi, and Ying Liu carried out intelligence tests on openly available and easily accessible weak AI such as Google AI, Apple's Siri, and others. At the optimum, these AIs reached an IQ worth of about 47, which corresponds roughly to a six-year-old child in very first grade. A grownup pertains to about 100 typically. Similar tests were performed in 2014, with the IQ score reaching a maximum value of 27. [106] [107]
In 2020, OpenAI developed GPT-3, a language design efficient in carrying out lots of 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 used his GPT-3 account to develop a chatbot, and offered a chatbot-developing platform called "Project December". OpenAI requested modifications to the chatbot to comply with their safety 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 tasks. [110]
In 2023, Microsoft Research released a research study on an early variation of OpenAI's GPT-4, contending that it exhibited more general intelligence than previous AI designs and demonstrated human-level performance in jobs spanning numerous domains, such as mathematics, coding, and law. This research triggered an argument on whether GPT-4 could be thought about an early, incomplete version of artificial basic intelligence, emphasizing the requirement for additional expedition and evaluation of such systems. [111]
In 2023, the AI scientist Geoffrey Hinton stated that: [112]
The concept that this stuff could in fact get smarter than people - a few individuals believed that, [...] But the majority of people thought it was method off. And I believed it was way off. I thought it was 30 to 50 years or perhaps longer away. Obviously, I no longer think that.
In May 2023, Demis Hassabis likewise stated that "The development in the last few years has actually been quite incredible", which he sees no reason that it would slow down, anticipating AGI within a decade or perhaps a few 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 as well as humans. [114] In June 2024, the AI researcher Leopold Aschenbrenner, a previous OpenAI employee, approximated AGI by 2027 to be "noticeably plausible". [115]
Whole brain emulation
While the advancement of transformer models like in ChatGPT is thought about the most appealing path to AGI, [116] [117] entire brain emulation can serve as an alternative technique. With whole brain simulation, a brain design is developed by scanning and mapping a biological brain in information, and then copying and mimicing it on a computer system or another computational gadget. The simulation model should be sufficiently loyal to the original, so that it behaves in virtually the same way as the initial brain. [118] Whole brain emulation is a kind of brain simulation that is gone over in computational neuroscience and neuroinformatics, and for medical research study functions. It has been discussed in artificial intelligence research study [103] as a method to strong AI. Neuroimaging innovations that might provide the essential detailed understanding are enhancing rapidly, and futurist Ray Kurzweil in the book The Singularity Is Near [102] predicts that a map of adequate quality will become readily available on a similar timescale to the computing power needed to emulate it.
Early estimates
For low-level brain simulation, a really powerful cluster of computers or GPUs would be needed, provided the enormous quantity of synapses within the human brain. Each of the 1011 (one hundred billion) neurons has on average 7,000 synaptic connections (synapses) to other neurons. The brain of a three-year-old child has about 1015 synapses (1 quadrillion). This number declines with age, stabilizing by the adult years. Estimates vary for an adult, ranging from 1014 to 5 × 1014 synapses (100 to 500 trillion). [120] A quote of the brain's processing power, based on a basic switch design for neuron activity, is around 1014 (100 trillion) synaptic updates per second (SUPS). [121]
In 1997, Kurzweil took a look at numerous estimates for the hardware required to equal the human brain and adopted a figure of 1016 calculations per 2nd (cps). [e] (For comparison, if a "computation" was equivalent to one "floating-point operation" - a procedure used to rate present supercomputers - then 1016 "calculations" would be equivalent to 10 petaFLOPS, achieved in 2011, while 1018 was accomplished in 2022.) He used this figure to anticipate the essential hardware would be offered sometime in between 2015 and 2025, if the exponential growth in computer system power at the time of composing continued.
Current research
The Human Brain Project, an EU-funded initiative active from 2013 to 2023, has actually established an especially in-depth and openly available atlas of the human brain. [124] In 2023, scientists from Duke University carried out a high-resolution scan of a mouse brain.
Criticisms of simulation-based methods
The artificial neuron model presumed by Kurzweil and utilized in lots of current artificial neural network implementations is simple compared with biological neurons. A brain simulation would likely have to catch the in-depth cellular behaviour of biological neurons, currently understood just in broad summary. The overhead introduced by complete modeling of the biological, chemical, and physical details of neural behaviour (specifically on a molecular scale) would require computational powers numerous orders of magnitude bigger than Kurzweil's quote. In addition, the estimates do not account for glial cells, which are known to contribute in cognitive procedures. [125]
A fundamental criticism of the simulated brain approach stems from embodied cognition theory which asserts that human embodiment is a vital element of human intelligence and is required to ground meaning. [126] [127] If this theory is right, any completely practical brain design will require to encompass more than just the nerve cells (e.g., a robotic body). Goertzel [103] proposes virtual embodiment (like in metaverses like Second Life) as an alternative, but it is unidentified whether this would be sufficient.
Philosophical point of view
"Strong AI" as specified in viewpoint
In 1980, thinker John Searle coined the term "strong AI" as part of his Chinese room argument. [128] He proposed a difference between two hypotheses about artificial intelligence: [f]
Strong AI hypothesis: An artificial intelligence system can have "a mind" and "awareness".
Weak AI hypothesis: An artificial intelligence system can (only) act like it thinks and has a mind and awareness.
The first one he called "strong" because it makes a stronger declaration: it assumes something special has occurred to the device that exceeds those capabilities that we can evaluate. The behaviour of a "weak AI" device would be exactly similar to a "strong AI" machine, however the latter would also have subjective mindful experience. This use is also typical in academic AI research 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 synthetic general intelligence". [102] This is not the same as Searle's strong AI, unless it is assumed that awareness is necessary for human-level AGI. Academic theorists such as Searle do not think that holds true, and to most artificial intelligence scientists the concern is out-of-scope. [130]
Mainstream AI is most interested in how a program behaves. [131] According to Russell and Norvig, "as long as the program works, they don't care if you call it genuine or a simulation." [130] If the program can behave as if it has a mind, then there is no requirement to know if it really has mind - certainly, there would be no method to inform. For AI research study, Searle's "weak AI hypothesis" is comparable to the declaration "synthetic basic 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 academic AI research, "Strong AI" and "AGI" are two different things.
Consciousness
Consciousness can have different significances, and some aspects play significant roles in sci-fi and the principles of artificial intelligence:
Sentience (or "incredible awareness"): The ability to "feel" perceptions or feelings subjectively, rather than the capability to reason about understandings. Some thinkers, such as David Chalmers, utilize the term "awareness" to refer solely to extraordinary awareness, which is roughly comparable to sentience. [132] Determining why and how subjective experience occurs is called the hard problem of consciousness. [133] Thomas Nagel described in 1974 that it "feels like" something to be mindful. If we are not mindful, then it doesn't seem like anything. Nagel utilizes the example of a bat: we can smartly ask "what does it feel like to be a bat?" However, we are not likely to ask "what does it feel like to be a toaster?" Nagel concludes that a bat seems conscious (i.e., has consciousness) however a toaster does not. [134] In 2022, a Google engineer declared that the business's AI chatbot, LaMDA, had attained sentience, though this claim was extensively challenged by other experts. [135]
Self-awareness: To have mindful awareness of oneself as a different person, specifically to be consciously knowledgeable about one's own ideas. This is opposed to merely being the "subject of one's thought"-an operating system or debugger is able to be "familiar with itself" (that is, to represent itself in the exact same method it represents everything else)-however this is not what people normally indicate when they utilize the term "self-awareness". [g]
These traits have a moral dimension. AI sentience would trigger issues of welfare and legal defense, similarly to animals. [136] Other elements of consciousness associated to cognitive capabilities are also relevant to the concept of AI rights. [137] Figuring out how to integrate innovative AI with existing legal and social structures is an emerging issue. [138]
Benefits
AGI could have a wide range of applications. If oriented towards such objectives, AGI could assist reduce different problems worldwide such as cravings, hardship and health issue. [139]
AGI might improve efficiency and efficiency in many tasks. For example, in public health, AGI might accelerate medical research study, especially against cancer. [140] It might take care of the senior, [141] and democratize access to quick, top quality medical diagnostics. It might offer fun, low-cost and tailored education. [141] The requirement to work to subsist could end up being outdated if the wealth produced is properly redistributed. [141] [142] This also raises the concern of the location of humans in a drastically automated society.
AGI could likewise help to make logical choices, and to anticipate and avoid catastrophes. It could also help to profit of possibly devastating innovations such as nanotechnology or climate engineering, while preventing the associated risks. [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 true), [144] it might take steps to significantly minimize the dangers [143] while lessening the effect of these procedures on our lifestyle.
Risks
Existential threats
AGI may represent several types of existential danger, which are risks that threaten "the early 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 lots of disputes, but there is likewise the possibility that the development of AGI would lead to a completely problematic future. Notably, it could be used to spread and protect the set of worths of whoever establishes it. If humankind still has moral blind areas comparable to slavery in the past, AGI might irreversibly entrench it, preventing moral progress. [146] Furthermore, AGI might assist in mass security and brainwashing, which could be utilized to produce a stable repressive around the world totalitarian routine. [147] [148] There is also a threat for the devices themselves. If devices that are sentient or otherwise worthy of moral factor to consider are mass produced in the future, participating in a civilizational path that forever neglects their well-being and interests might be an existential disaster. [149] [150] Considering how much AGI might improve humankind's future and help in reducing other existential threats, Toby Ord calls these existential threats "an argument for proceeding with due care", not for "abandoning AI". [147]
Risk of loss of control and human extinction
The thesis that AI poses an existential risk for people, and that this threat requires more attention, is controversial however 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 criticized prevalent indifference:
So, facing possible futures of enormous benefits and dangers, the specialists are certainly doing everything possible to make sure the very best result, right? Wrong. If an exceptional alien civilisation sent us a message stating, 'We'll arrive 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 occurring with AI. [153]
The possible 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 enabled mankind to dominate gorillas, which are now vulnerable in methods that they might not have prepared for. As an outcome, the gorilla has actually become an endangered types, not out of malice, but merely as a civilian casualties from human activities. [154]
The skeptic Yann LeCun considers that AGIs will have no desire to dominate humankind which we should be cautious not to anthropomorphize them and interpret their intents as we would for humans. He said that people won't be "smart adequate to develop super-intelligent machines, yet extremely dumb to the point of providing it moronic goals without any safeguards". [155] On the other side, the concept of important merging recommends that practically whatever their objectives, smart agents will have factors to attempt to make it through and obtain more power as intermediary actions to attaining these goals. And that this does not require having emotions. [156]
Many scholars who are worried about existential threat advocate for more research study into resolving the "control problem" to answer the concern: what types of safeguards, algorithms, or architectures can developers carry out to increase the possibility that their recursively-improving AI would continue to act in a friendly, rather than damaging, way after it reaches superintelligence? [157] [158] Solving the control problem is made complex by the AI arms race (which could result in a race to the bottom of security preventative measures in order to release products before competitors), [159] and using AI in weapon systems. [160]
The thesis that AI can posture existential threat also has detractors. Skeptics generally state that AGI is not likely in the short-term, or that concerns about AGI distract from other issues related to present AI. [161] Former Google fraud czar Shuman Ghosemajumder considers that for many individuals outside of the innovation market, existing chatbots and LLMs are currently perceived as though they were AGI, resulting in additional misconception and fear. [162]
Skeptics in some cases charge that the thesis is crypto-religious, with an unreasonable 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) may be an at effort at regulatory capture and to inflate interest in their items. [164] [165]
In 2023, the CEOs of Google DeepMind, OpenAI and Anthropic, in addition to other market leaders and researchers, issued a joint declaration asserting that "Mitigating the threat of termination from AI need to 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. labor force might have at least 10% of their work tasks affected by the intro of LLMs, annunciogratis.net while around 19% of workers may see a minimum of 50% of their tasks impacted". [166] [167] They think about workplace workers to be the most exposed, for instance mathematicians, accounting professionals or web designers. [167] AGI could have a better autonomy, capability to make choices, to user interface with other computer tools, however likewise to manage robotized bodies.
According to Stephen Hawking, the outcome of automation on the lifestyle will depend on how the wealth will be rearranged: [142]
Everyone can take pleasure in a life of elegant leisure if the machine-produced wealth is shared, or many people can end up miserably bad if the machine-owners effectively lobby against wealth redistribution. So far, the trend appears to be toward the 2nd choice, with innovation driving ever-increasing inequality
Elon Musk considers that the automation of society will require governments to adopt a universal standard income. [168]
See likewise
Artificial brain - Software and hardware with cognitive abilities comparable to those of the animal or human brain
AI impact
AI safety - Research area on making AI safe and beneficial
AI positioning - AI conformance to the designated objective
A.I. Rising - 2018 movie directed by Lazar Bodroža
Expert system
Automated machine learning - Process of automating the application of artificial intelligence
BRAIN Initiative - Collaborative public-private research study initiative announced by the Obama administration
China Brain Project
Future of Humanity Institute - Defunct Oxford interdisciplinary research centre
General game playing - Ability of expert system to play various video games
Generative artificial intelligence - AI system capable of generating content in response to triggers
Human Brain Project - Scientific research study task
Intelligence amplification - Use of information innovation to enhance human intelligence (IA).
Machine ethics - Moral behaviours of man-made machines.
Moravec's paradox.
Multi-task learning - Solving multiple machine discovering tasks at the same time.
Neural scaling law - Statistical law in machine knowing.
Outline of artificial intelligence - Overview of and topical guide to synthetic intelligence.
Transhumanism - Philosophical movement.
Synthetic intelligence - Alternate term for or form of synthetic intelligence.
Transfer learning - Machine knowing strategy.
Loebner Prize - Annual AI competitors.
Hardware for expert system - Hardware specifically developed and optimized for expert system.
Weak artificial intelligence - Form of synthetic intelligence.
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 post Chinese room.
^ AI founder John McCarthy composes: "we can not yet identify in basic what sort of computational procedures we desire to call smart. " [26] (For a conversation of some definitions of intelligence used by synthetic intelligence scientists, see philosophy of synthetic intelligence.).
^ The Lighthill report particularly slammed AI's "grandiose goals" and led the dismantling of AI research in England. [55] In the U.S., DARPA became figured out to fund only "mission-oriented direct research, rather than standard undirected research". [56] [57] ^ As AI creator John McCarthy composes "it would be a great relief to the rest of the employees in AI if the inventors of brand-new basic formalisms would express their hopes in a more secured form than has actually sometimes 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 presented.
^ As specified in a basic AI textbook: "The assertion that devices might potentially act intelligently (or, perhaps better, act as if they were intelligent) is called the 'weak AI' hypothesis by philosophers, and the assertion that devices that do so are in fact believing (instead of simulating thinking) is called the 'strong AI' hypothesis." [121] ^ Alan Turing made this point in 1950. [36] References
^ Krishna, Sri (9 February 2023). "What is artificial narrow intelligence (ANI)?". VentureBeat. Retrieved 1 March 2024. ANI is created to perform a single task.
^ "OpenAI Charter". OpenAI. Retrieved 6 April 2023. Our objective is to ensure that artificial general intelligence benefits all of humanity.
^ Heath, Alex (18 January 2024). "Mark Zuckerberg's brand-new objective is producing synthetic basic intelligence". The Verge. Retrieved 13 June 2024. Our vision is to construct AI that is much better than human-level at all of the human senses.
^ Baum, Seth D. (2020 ). A Study of Artificial General Intelligence Projects for Ethics, Risk, and Policy (PDF) (Report). Global Catastrophic Risk Institute. Retrieved 28 November 2024. 72 AGI R&D projects were determined as being active in 2020.
^ a b c "AI timelines: What do specialists in synthetic intelligence expect for the future?". Our World in Data. Retrieved 6 April 2023.
^ Metz, Cade (15 May 2023). "Some Researchers Say A.I. Is Already Here, Stirring Debate in Tech Circles". The New York City Times. Retrieved 18 May 2023.
^ "AI leader Geoffrey Hinton gives up Google and warns of risk ahead". The New York City Times. 1 May 2023. Retrieved 2 May 2023. It is difficult to see how you can avoid the bad stars from utilizing it for bad things.
^ Bubeck, Sébastien; Chandrasekaran, Varun; Eldan, Ronen; Gehrke, Johannes; Horvitz, Eric (2023 ). "Sparks of Artificial General Intelligence: Early explores GPT-4". arXiv preprint. arXiv:2303.12712. GPT-4 reveals 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 alter changes you.
^ Vinge, Vernor (1992 ). A Fire Upon the Deep. Tor Books. ISBN 978-0-8125-1528-2. The Singularity is coming.
^ Morozov, Evgeny (30 June 2023). "The True Threat of Artificial Intelligence". The New York Times. The genuine risk is not AI itself however the way we deploy it.
^ "Impressed by synthetic intelligence? Experts say AGI is following, and it has 'existential' threats". ABC News. 23 March 2023. Retrieved 6 April 2023. AGI could present existential risks 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 innovation 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 danger of extinction from AI should be an international priority.
^ "Statement on AI Risk". Center for AI Safety. Retrieved 1 March 2024. AI specialists warn of risk of extinction from AI.
^ Mitchell, Melanie (30 May 2023). "Are AI's Doomsday Scenarios Worth Taking Seriously?". The New York City Times. We are far from developing machines that can outthink us in basic ways.
^ LeCun, Yann (June 2023). "AGI does not provide an existential threat". Medium. There is no factor 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 original on 14 August 2005: Kurzweil describes 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 initial 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.
^ "Expert system is changing our world - it is on all of us to ensure that it goes well". Our World in Data. Retrieved 8 October 2023.
^ Dickson, Ben (16 November 2023). "Here is how far we are to accomplishing AGI, according to DeepMind". VentureBeat.
^ McCarthy, John (2007a). "Basic Questions". Stanford University. Archived from the initial on 26 October 2007. Retrieved 6 December 2007.
^ This list of smart characteristics is based on the topics covered by major AI books, including: Russell & Norvig 2003, Luger & Stubblefield 2004, Poole, Mackworth & Goebel 1998 and Nilsson 1998.
^ Johnson 1987.
^ de Charms, R. (1968 ). Personal causation. New York City: Academic Press.
^ a b Pfeifer, R. and Bongard J. C., How the body shapes the method we think: a new view of intelligence (The MIT Press, 2007). ISBN 0-2621-6239-3.
^ White, R. W. (1959 ). "Motivation reconsidered: The principle of skills". Psychological Review. 66 (5 ): 297-333. doi:10.1037/ h0040934. PMID 13844397. S2CID 37385966.
^ White, R. W. (1959 ). "Motivation reconsidered: The principle of proficiency". Psychological Review. 66 (5 ): 297-333. doi:10.1037/ h0040934. PMID 13844397. S2CID 37385966.
^ Muehlhauser, Luke (11 August 2013). "What is AGI?". Machine Intelligence Research Institute. Archived from the original on 25 April 2014. Retrieved 1 May 2014.
^ "What is Artificial General Intelligence (AGI)?|4 Tests For Ensuring Artificial General Intelligence". Talky Blog. 13 July 2019. Archived from the original on 17 July 2019. Retrieved 17 July 2019.
^ Kirk-Giannini, Cameron Domenico; Goldstein, Simon (16 October 2023). "AI is closer than ever to passing the Turing test for 'intelligence'. What takes place when it does?". The Conversation. Retrieved 22 September 2024.
^ a b Turing 1950.
^ Turing, Alan (1952 ). B. Jack Copeland (ed.). Can Automatic Calculating Machines Be Said To Think?. Oxford: Oxford University Press. pp. 487-506. ISBN 978-0-1982-5079-1.
^ "Eugene Goostman is a genuine young boy - the Turing Test states so". The Guardian. 9 June 2014. ISSN 0261-3077. Retrieved 3 March 2024.
^ "Scientists contest whether computer system 'Eugene Goostman' passed Turing test". BBC News. 9 June 2014. Retrieved 3 March 2024.
^ Jones, Cameron R.; Bergen, Benjamin K. (9 May 2024). "People can not identify GPT-4 from a human in a Turing test". arXiv:2405.08007 [cs.HC]
^ Varanasi, Lakshmi (21 March 2023). "AI models like ChatGPT and GPT-4 are acing everything from the bar test to AP Biology. Here's a list of challenging exams both AI versions have actually passed". Business Insider. Retrieved 30 May 2023.
^ Naysmith, Caleb (7 February 2023). "6 Jobs Expert System Is Already Replacing and How Investors Can Profit From It". Retrieved 30 May 2023.
^ Turk, Victoria (28 January 2015). "The Plan to Replace the Turing Test with a 'Turing Olympics'". Vice. Retrieved 3 March 2024.
^ Gopani, Avi (25 May 2022). "Turing Test is undependable. The Winograd Schema is outdated. Coffee is the response". Analytics India Magazine. Retrieved 3 March 2024.
^ Bhaimiya, Sawdah (20 June 2023). "DeepMind's co-founder recommended testing an AI chatbot's capability to turn $100,000 into $1 million to determine 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 ). "Artificial Intelligence" (PDF). In Stuart C. Shapiro (ed.). Encyclopedia of Artificial Intelligence (Second ed.). New York: John Wiley. pp. 54-57. Archived (PDF) from the initial 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 Expert System. 15 April 2024. Retrieved 27 May 2024.
^ Crevier 1993, pp. 48-50.
^ Kaplan, Andreas (2022 ). "Expert System, Business and Civilization - Our Fate Made in Machines". Archived from the original on 6 May 2022. Retrieved 12 March 2022.
^ Simon 1965, p. 96 priced quote in Crevier 1993, p. 109.
^ "Scientist on the Set: An Interview with Marvin Minsky". Archived from the original on 16 July 2012. Retrieved 5 April 2008.
^ Marvin Minsky to Darrach (1970 ), estimated in Crevier (1993, p. 109).
^ Lighthill 1973; Howe 1994.
^ a b NRC 1999, "Shift to Applied Research Increases Investment".
^ Crevier 1993, pp. 115-117; Russell & Norvig 2003, pp. 21-22.
^ Crevier 1993, p. 211, Russell & Norvig 2003, p. 24 and see likewise Feigenbaum & McCorduck 1983.
^ Crevier 1993, pp. 161-162, 197-203, 240; Russell & Norvig 2003, p. 25.
^ Crevier 1993, pp. 209-212.
^ McCarthy, John (2000 ). "Respond to Lighthill". Stanford University. Archived from the original on 30 September 2008. Retrieved 29 September 2007.
^ Markoff, John (14 October 2005). "Behind Expert system, a Squadron of Bright Real People". The New York City Times. Archived from the initial on 2 February 2023. Retrieved 18 February 2017. At its low point, some computer 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 Upon Algorithmic Probability. Texts in Theoretical Computer Science an EATCS Series. Springer. doi:10.1007/ b138233. ISBN 978-3-5402-6877-2. S2CID 33352850. Archived from the original on 19 July 2022. Retrieved 19 July 2022.
^ Legg, Shane (2008 ). Machine Super Intelligence (PDF) (Thesis). University of Lugano. Archived (PDF) from the original on 15 June 2022. Retrieved 19 July 2022.
^ Goertzel, Ben (2014 ). Artificial General Intelligence. Lecture Notes in Computer 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 coined the term "AGI"?". goertzel.org. Archived from the original on 28 December 2018. Retrieved 28 December 2018., by means of Life 3.0: 'The term "AGI" was promoted by ... Shane Legg, Mark Gubrud and Ben Goertzel'
^ Wang & Goertzel 2007
^ "First International Summer School in Artificial General Intelligence, Main summer school: June 22 - July 3, 2009, OpenCog Lab: July 6-9, 2009". Archived from the original on 28 September 2020. Retrieved 11 May 2020.
^ "Избираеми дисциплини 2009/2010 - пролетен триместър" [Elective courses 2009/2010 - spring trimester] Факултет по математика и информатика [Faculty of Mathematics and Informatics] (in Bulgarian). Archived from the initial on 26 July 2020. Retrieved 11 May 2020.
^ "Избираеми дисциплини 2010/2011 - зимен триместър" [Elective courses 2010/2011 - winter season trimester] Факултет по математика и информатика [Faculty of Mathematics and Informatics] (in Bulgarian). Archived from the initial on 26 July 2020. Retrieved 11 May 2020.
^ Shevlin, Henry; Vold, Karina; Crosby, Matthew; Halina, Marta (4 October 2019). "The limitations of machine intelligence: Despite development in maker intelligence, synthetic general intelligence is still a major 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. "Artificial intelligence will not develop into a Frankenstein's beast". The Guardian. Archived from the initial on 17 September 2014. Retrieved 17 September 2014.
^ Deane, George (2022 ). "Machines That Feel and Think: The Role of Affective Feelings and Mental Action in (Artificial) General Intelligence". Artificial Life. 28 (3 ): 289-309. doi:10.1162/ artl_a_00368. ISSN 1064-5462. PMID 35881678. S2CID 251069071.
^ a b c Clocksin 2003.
^ Fjelland, Ragnar (17 June 2020). "Why general artificial intelligence will not be understood". Humanities and Social Sciences Communications. 7 (1 ): 1-9. doi:10.1057/ s41599-020-0494-4. hdl:11250/ 2726984. ISSN 2662-9992. S2CID 219710554.
^ McCarthy 2007b.
^ Khatchadourian, Raffi (23 November 2015). "The Doomsday Invention: Will expert system bring us utopia or destruction?". The New Yorker. Archived from the initial on 28 January 2016. Retrieved 7 February 2016.
^ Müller, V. C., & Bostrom, N. (2016 ). Future progress in synthetic intelligence: A survey of professional viewpoint. In Fundamental problems 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, modified by Jan Romportl, Pavel Ircing, Eva Žáčková, Michal Polák and Radek Schuster, 52-75. Plzeň: University of West Bohemia
^ "Microsoft Now Claims GPT-4 Shows 'Sparks' of General Intelligence". 24 March 2023.
^ Shimek, Cary (6 July 2023). "AI Outperforms Humans in Creativity Test". Neuroscience News. Retrieved 20 October 2023.
^ Guzik, Erik E.; Byrge, Christian; Gilde, Christian (1 December 2023). "The creativity of machines: AI takes the Torrance Test". Journal of Creativity. 33 (3 ): 100065. doi:10.1016/ j.yjoc.2023.100065. ISSN 2713-3745. S2CID 261087185.
^ Arcas, Blaise Agüera y (10 October 2023). "Artificial General Intelligence Is Already Here". Noema.
^ Zia, Tehseen (8 January 2024). "Unveiling of Large Multimodal Models: Shaping the Landscape of Language Models in 2024". Unite.ai. Retrieved 26 May 2024.
^ "Introducing OpenAI o1-preview". OpenAI. 12 September 2024.
^ Knight, Will. "OpenAI Announces a Brand-new AI Model, Code-Named Strawberry, That Solves Difficult Problems Step by Step". Wired. ISSN 1059-1028. Retrieved 17 September 2024.
^ "OpenAI Employee Claims AGI Has Been Achieved". Orbital Today. 13 December 2024. Retrieved 27 December 2024.
^ "AI Index: State of AI in 13 Charts". hai.stanford.edu. 15 April 2024. Retrieved 7 June 2024.
^ "Next-Gen AI: OpenAI and Meta's Leap Towards Reasoning Machines". Unite.ai. 19 April 2024. Retrieved 7 June 2024.
^ James, Alex P. (2022 ). "The Why, What, and How of Artificial General Intelligence Chip Development". IEEE Transactions on Cognitive and Developmental Systems. 14 (2 ): 333-347. arXiv:2012.06338. doi:10.1109/ TCDS.2021.3069871. ISSN 2379-8920. S2CID 228376556. Archived from the original on 28 August 2022. Retrieved 28 August 2022.
^ Pei, Jing; Deng, Lei; Song, Sen; Zhao, Mingguo; Zhang, Youhui; Wu, Shuang; Wang, Guanrui; Zou, Zhe; Wu, Zhenzhi; He, Wei; Chen, Feng; Deng, Ning; Wu, Si; Wang, Yu; Wu, Yujie (2019 ). "Towards synthetic general intelligence with hybrid Tianjic chip architecture". Nature. 572 (7767 ): 106-111. Bibcode:2019 Natur.572..106 P. doi:10.103
