Artificial basic intelligence (AGI) is a type of expert system (AI) that matches or surpasses human cognitive abilities across a large range of cognitive jobs. This contrasts with narrow AI, which is restricted to particular jobs. [1] Artificial superintelligence (ASI), on the other hand, describes AGI that significantly goes beyond human cognitive abilities. AGI is considered one of the meanings of strong AI.
Creating AGI is a primary objective of AI research study and of business such as OpenAI [2] and Meta. [3] A 2020 survey determined 72 active AGI research study and advancement jobs across 37 countries. [4]
The timeline for accomplishing AGI remains a topic of ongoing argument amongst researchers and experts. Since 2023, some argue that it might be possible in years or years; others maintain it may take a century or longer; a minority think it may never ever be attained; and another minority declares that it is currently here. [5] [6] Notable AI researcher Geoffrey Hinton has revealed concerns about the fast progress towards AGI, recommending it could be attained sooner than many anticipate. [7]
There is argument on the specific meaning of AGI and relating to whether modern-day big language designs (LLMs) such as GPT-4 are early types of AGI. [8] AGI is a common subject in sci-fi and futures studies. [9] [10]
Contention exists over whether AGI represents an existential threat. [11] [12] [13] Many experts on AI have actually stated that reducing the threat of human extinction posed by AGI should be a global top priority. [14] [15] Others discover the advancement of AGI to be too remote to provide such a risk. [16] [17]
Terminology
AGI is also referred to as strong AI, [18] [19] complete AI, [20] human-level AI, [5] human-level smart AI, or general smart action. [21]
Some academic sources book the term "strong AI" for computer programs that experience life or consciousness. [a] In contrast, weak AI (or narrow AI) has the ability to fix one specific issue however lacks basic cognitive capabilities. [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 people. [a]
Related concepts include synthetic superintelligence and transformative AI. A synthetic superintelligence (ASI) is a theoretical kind of AGI that is much more usually smart than humans, [23] while the idea of transformative AI associates with AI having a big effect on society, for instance, similar to the farming or industrial revolution. [24]
A structure for classifying AGI in levels was proposed in 2023 by Google DeepMind researchers. They specify 5 levels of AGI: emerging, proficient, specialist, virtuoso, and superhuman. For example, a competent AGI is defined as an AI that outperforms 50% of competent grownups in a large range of non-physical jobs, and a superhuman AGI (i.e. a synthetic superintelligence) is similarly specified but with a limit of 100%. They think about big language designs like ChatGPT or LLaMA 2 to be instances of emerging AGI. [25]
Characteristics
Various popular meanings of intelligence have actually been proposed. Among the leading proposals is the Turing test. However, there are other widely known definitions, and some scientists disagree with the more popular methods. [b]
Intelligence characteristics
Researchers normally hold that intelligence is required to do all of the following: [27]
factor, usage method, solve puzzles, and make judgments under unpredictability
represent understanding, consisting of common sense understanding
strategy
find out
- interact in natural language
- if needed, integrate these abilities in completion of any offered goal
Many interdisciplinary methods (e.g. cognitive science, computational intelligence, and decision making) consider extra characteristics such as imagination (the capability to form unique psychological images and concepts) [28] and autonomy. [29]
Computer-based systems that display much of these capabilities exist (e.g. see computational creativity, e.bike.free.fr automated reasoning, decision support group, robot, evolutionary calculation, intelligent representative). There is debate about whether modern AI systems have them to a sufficient degree.
Physical characteristics
Other capabilities are considered desirable in smart systems, as they may impact intelligence or help in its expression. These include: [30]
- the capability to sense (e.g. see, hear, and so on), and
- the capability to act (e.g. move and control things, change location to explore, etc).
This consists of the capability to spot and react to danger. [31]
Although the ability to sense (e.g. see, hear, and so on) and the ability to act (e.g. relocation and manipulate objects, modification location to explore, and so on) can be preferable for some smart systems, [30] these physical abilities are not strictly required for an entity to certify as AGI-particularly under the thesis that large language models (LLMs) might currently be or become AGI. Even from a less optimistic point of view on LLMs, there is no firm requirement for an AGI to have a human-like type; being a silicon-based computational system suffices, offered it can process input (language) from the external world in location of human senses. This interpretation lines up with the understanding that AGI has never been proscribed a specific physical personification and therefore does not require a capacity for locomotion or online-learning-initiative.org traditional "eyes and ears". [32]
Tests for human-level AGI
Several tests indicated to verify human-level AGI have been considered, including: [33] [34]
The concept of the test is that the maker has to try and pretend to be a man, by responding to questions put to it, and it will just pass if the pretence is fairly persuading. A considerable portion of a jury, who must not be expert about devices, should be taken in by the pretence. [37]
AI-complete problems
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 capabilities of a purpose-specific algorithm. [47]
There are numerous issues that have been conjectured to need basic intelligence to solve along with people. Examples include computer vision, natural language understanding, and forum.batman.gainedge.org handling unanticipated situations while solving any real-world issue. [48] Even a particular job like translation needs a device to read and compose in both languages, follow the author's argument (factor), comprehend the context (understanding), and faithfully reproduce the author's initial intent (social intelligence). All of these problems need to be fixed concurrently in order to reach human-level machine efficiency.
However, a lot of these jobs can now be carried out by modern large language models. According to Stanford University's 2024 AI index, AI has reached human-level performance on numerous benchmarks for checking out understanding and visual reasoning. [49]
History
Classical AI
Modern AI research study started in the mid-1950s. [50] The first generation of AI researchers were convinced that synthetic general intelligence was possible and that it would exist in just a few years. [51] AI leader Herbert A. Simon wrote in 1965: "devices will be capable, within twenty years, of doing any work a man can do." [52]
Their predictions were the inspiration for Stanley Kubrick and Arthur C. Clarke's character HAL 9000, who embodied what AI researchers believed they might develop by the year 2001. AI pioneer Marvin Minsky was a consultant [53] on the task of making HAL 9000 as practical as possible according to the consensus predictions of the time. He stated in 1967, "Within a generation ... the issue of creating 'expert system' will significantly be resolved". [54]
Several classical AI tasks, such as Doug Lenat's Cyc task (that started in 1984), and archmageriseswiki.com Allen Newell's Soar job, were directed at AGI.
However, in the early 1970s, it ended up being obvious that scientists had grossly underestimated the trouble of the project. Funding agencies became hesitant 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 "continue a table talk". [58] In reaction to this and the success of specialist systems, both market and federal government pumped cash into the field. [56] [59] However, confidence in AI marvelously collapsed in the late 1980s, and the goals of the Fifth Generation Computer Project were never ever fulfilled. [60] For the 2nd time in 20 years, AI scientists who predicted the impending achievement of AGI had been misinterpreted. By the 1990s, AI scientists had a credibility for making vain pledges. They became reluctant to make forecasts 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 achieved industrial success and scholastic respectability by concentrating on specific sub-problems where AI can produce proven results and industrial applications, such as speech acknowledgment and recommendation algorithms. [63] These "applied AI" systems are now utilized extensively throughout the technology market, and research in this vein is heavily funded in both academia and market. As of 2018 [upgrade], advancement in this field was considered an emerging trend, and a fully grown stage was expected to be reached in more than 10 years. [64]
At the millenium, lots of mainstream AI researchers [65] hoped that strong AI could be established by combining programs that fix different sub-problems. Hans Moravec wrote in 1988:
I am confident that this bottom-up route to synthetic intelligence will one day satisfy the conventional top-down path majority way, prepared to offer the real-world competence and the commonsense understanding 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 disputed. For example, 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 somehow fulfill "bottom-up" (sensory) approaches someplace in between. If the grounding considerations in this paper are valid, then this expectation is hopelessly modular and there is truly just one viable route from sense to symbols: from the ground up. A free-floating symbolic level like the software application level of a computer will never ever be reached by this path (or vice versa) - nor is it clear why we must even try to reach such a level, because it looks as if getting there would just total up to uprooting our signs from their intrinsic meanings (thereby merely minimizing ourselves to the practical equivalent of a programmable computer). [66]
Modern artificial general intelligence research
The term "artificial basic intelligence" was used as early as 1997, by Mark Gubrud [67] in a discussion of the implications 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 maximises "the capability to satisfy goals in a vast array of environments". [68] This type of AGI, defined by the ability to increase a mathematical meaning of intelligence rather than display human-like behaviour, [69] was likewise called universal synthetic intelligence. [70]
The term AGI was re-introduced and popularized by Shane Legg and Ben Goertzel around 2002. [71] AGI research study activity in 2006 was described by Pei Wang and Ben Goertzel [72] as "producing publications and initial outcomes". The first summer school in AGI was organized in Xiamen, China in 2009 [73] by the Xiamen university's Artificial Brain Laboratory and OpenCog. The first university course was offered in 2010 [74] and 2011 [75] at Plovdiv University, Bulgaria by Todor Arnaudov. MIT provided a course on AGI in 2018, organized by Lex Fridman and including a variety of visitor lecturers.
Since 2023 [update], a little number of computer researchers are active in AGI research study, and numerous add to a series of AGI conferences. However, increasingly more researchers are interested in open-ended knowing, [76] [77] which is the concept of allowing AI to continuously find out and innovate like humans do.
Feasibility
Since 2023, the development and potential achievement of AGI stays a topic of intense argument within the AI community. While conventional consensus held that AGI was a far-off goal, recent developments have actually led some researchers and market figures to declare that early forms of AGI may currently exist. [78] AI pioneer Herbert A. Simon speculated in 1965 that "machines will be capable, within twenty years, of doing any work a male can do". This prediction stopped working to come true. Microsoft co-founder Paul Allen thought that such intelligence is not likely in the 21st century since it would need "unforeseeable and essentially unpredictable breakthroughs" and a "scientifically 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 large as the gulf in between current area flight and useful faster-than-light spaceflight. [80]
An additional challenge is the absence of clarity in defining what intelligence involves. Does it need consciousness? Must it display the ability to set goals in addition to 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 explicitly duplicating the brain and its particular professors? Does it require feelings? [81]
Most AI scientists think strong AI can be achieved in the future, but some thinkers, like Hubert Dreyfus and Roger Penrose, reject the possibility of attaining strong AI. [82] [83] John McCarthy is amongst those who believe human-level AI will be achieved, but that today level of development is such that a date can not accurately be predicted. [84] AI specialists' views on the expediency of AGI wax and subside. Four surveys performed in 2012 and 2013 suggested that the mean estimate among professionals for when they would be 50% confident AGI would get here was 2040 to 2050, depending on the survey, with the mean being 2081. Of the specialists, 16.5% responded to with "never ever" when asked the same question but with a 90% confidence rather. [85] [86] Further present AGI progress considerations can be found above Tests for validating human-level AGI.
A report by Stuart Armstrong and Kaj Sotala of the Machine Intelligence Research Institute found that "over [a] 60-year timespan there is a strong bias towards forecasting the arrival of human-level AI as between 15 and 25 years from the time the prediction was made". They examined 95 predictions made between 1950 and 2012 on when human-level AI will happen. [87]
In 2023, Microsoft researchers released a comprehensive evaluation of GPT-4. They concluded: "Given the breadth and depth of GPT-4's capabilities, we think that it might fairly be considered as an early (yet still incomplete) variation of an artificial general intelligence (AGI) system." [88] Another study in 2023 reported that GPT-4 outperforms 99% of people on the Torrance tests of imaginative thinking. [89] [90]
Blaise Agüera y Arcas and Peter Norvig wrote in 2023 that a substantial level of general intelligence has already been achieved with frontier models. They composed that unwillingness to this view originates from four main factors: a "healthy suspicion about metrics for AGI", an "ideological commitment to alternative AI theories or techniques", a "devotion to human (or biological) exceptionalism", or a "issue about the economic implications of AGI". [91]
2023 also marked the development of large multimodal designs (big language models capable of processing or producing several techniques such as text, audio, and images). [92]
In 2024, OpenAI launched o1-preview, the first of a series of designs that "spend more time believing before they respond". According to Mira Murati, this ability to think before responding represents a new, additional paradigm. It enhances design outputs by investing more computing power when creating the answer, whereas the model scaling paradigm enhances outputs by increasing the model size, training data and training calculate power. [93] [94]
An OpenAI employee, Vahid Kazemi, claimed in 2024 that the company had accomplished AGI, stating, "In my viewpoint, we have already achieved 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 task", it is "much better than a lot of human beings at a lot of tasks." He likewise dealt with criticisms that big language models (LLMs) simply follow predefined patterns, comparing their learning process to the clinical approach of observing, hypothesizing, and verifying. These statements have stimulated argument, as they depend on a broad and unconventional definition of AGI-traditionally comprehended as AI that matches human intelligence across all domains. Critics argue that, while OpenAI's designs show impressive versatility, they might not completely fulfill this standard. Notably, Kazemi's remarks came shortly after OpenAI got rid of "AGI" from the regards to its partnership with Microsoft, prompting speculation about the business's tactical objectives. [95]
Timescales
Progress in expert system has traditionally gone through durations of fast development separated by periods when progress appeared to stop. [82] Ending each hiatus were fundamental advances in hardware, software application or both to create area for more development. [82] [98] [99] For example, the computer hardware offered in the twentieth century was not sufficient to execute deep learning, which needs great deals of GPU-enabled CPUs. [100]
In the introduction to his 2006 book, [101] Goertzel states that quotes of the time required before a really flexible AGI is developed vary from 10 years to over a century. Since 2007 [upgrade], the agreement in the AGI research neighborhood seemed 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 possible. [103] Mainstream AI scientists have provided a large range of viewpoints on whether development will be this rapid. A 2012 meta-analysis of 95 such opinions found a predisposition towards forecasting that the start of AGI would happen within 16-26 years for contemporary and historical predictions alike. That paper has been slammed for how it categorized 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 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 method used a weighted sum of scores from different pre-defined classifiers). [105] AlexNet was considered as the initial ground-breaker of the existing deep knowing wave. [105]
In 2017, researchers Feng Liu, Yong Shi, and Ying Liu conducted intelligence tests on publicly available and easily available weak AI such as Google AI, Apple's Siri, and others. At the optimum, these AIs reached an IQ value of about 47, which corresponds roughly to a six-year-old kid in first grade. A grownup pertains to about 100 usually. Similar tests were brought out in 2014, with the IQ score reaching a maximum worth of 27. [106] [107]
In 2020, OpenAI established GPT-3, a language design efficient in performing many diverse tasks without specific training. According to Gary Grossman in a VentureBeat short article, while there is agreement that GPT-3 is not an example of AGI, it is thought about by some to be too advanced to be categorized as a narrow AI system. [108]
In the same year, Jason Rohrer utilized his GPT-3 account to establish a chatbot, and supplied a chatbot-developing platform called "Project December". OpenAI asked for changes to the chatbot to adhere to their safety standards; Rohrer disconnected Project December from the GPT-3 API. [109]
In 2022, DeepMind established Gato, a "general-purpose" system capable of performing more than 600 various tasks. [110]
In 2023, Microsoft Research released a research study on an early variation of OpenAI's GPT-4, contending that it showed more general intelligence than previous AI designs and demonstrated human-level efficiency in jobs covering numerous domains, such as mathematics, coding, and law. This research study sparked a debate on whether GPT-4 might be thought about an early, insufficient variation of artificial general intelligence, stressing the need for additional exploration and assessment of such systems. [111]
In 2023, the AI researcher Geoffrey Hinton mentioned that: [112]
The idea that this things could actually get smarter than individuals - a couple of people believed that, [...] But the majority of individuals believed it was way off. And I thought it was method off. I believed it was 30 to 50 years or even longer away. Obviously, I no longer think that.
In May 2023, Demis Hassabis similarly said that "The development in the last few years has been quite unbelievable", and that he sees no reason that it would slow down, anticipating AGI within a decade or even a few years. [113] In March 2024, Nvidia's CEO, Jensen Huang, specified his expectation that within five years, AI would can passing any test at least as well as people. [114] In June 2024, the AI researcher Leopold Aschenbrenner, a former OpenAI staff member, approximated AGI by 2027 to be "noticeably plausible". [115]
Whole brain emulation
While the advancement of transformer designs like in ChatGPT is considered the most promising course to AGI, [116] [117] entire brain emulation can function as an alternative approach. With entire brain simulation, a brain model is built by scanning and mapping a biological brain in detail, and after that copying and replicating it on a computer system or another computational gadget. The simulation model should be adequately loyal to the original, so that it behaves in practically the exact same method as the initial brain. [118] Whole brain emulation is a kind of brain simulation that is discussed in computational neuroscience and neuroinformatics, and for medical research purposes. It has actually been talked about in expert system research study [103] as a technique to strong AI. Neuroimaging technologies that could deliver the needed detailed understanding are enhancing quickly, and futurist Ray Kurzweil in the book The Singularity Is Near [102] anticipates that a map of enough quality will appear on a similar timescale to the computing power required to emulate it.
Early approximates
For low-level brain simulation, an extremely powerful cluster of computer systems or GPUs would be needed, offered the massive 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 decreases with age, stabilizing by adulthood. Estimates differ for an adult, varying from 1014 to 5 × 1014 synapses (100 to 500 trillion). [120] A quote of the brain's processing power, based upon an easy switch design for neuron activity, is around 1014 (100 trillion) synaptic updates per second (SUPS). [121]
In 1997, Kurzweil took a look at different quotes for the hardware required to equal the human brain and adopted a figure of 1016 computations per second (cps). [e] (For comparison, if a "calculation" was equivalent to one "floating-point operation" - a procedure used to rate present supercomputers - then 1016 "computations" would be equivalent to 10 petaFLOPS, accomplished in 2011, while 1018 was accomplished in 2022.) He used this figure to anticipate the needed hardware would be readily available at some point between 2015 and 2025, if the exponential growth in computer system power at the time of writing continued.
Current research
The Human Brain Project, an EU-funded initiative active from 2013 to 2023, has actually established an especially comprehensive and publicly 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 artificial neuron design presumed by Kurzweil and utilized in lots of current artificial neural network executions is easy compared to biological neurons. A brain simulation would likely need to catch the in-depth cellular behaviour of biological neurons, presently comprehended only in broad summary. The overhead introduced by complete modeling of the biological, chemical, and physical details of neural behaviour (especially on a molecular scale) would need computational powers several orders of magnitude larger than Kurzweil's price 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 technique obtains from embodied cognition theory which asserts that human embodiment is a necessary aspect of human intelligence and is required to ground significance. [126] [127] If this theory is correct, any totally functional brain design will require to include more than just the nerve cells (e.g., a robotic body). Goertzel [103] proposes virtual embodiment (like in metaverses like Second Life) as a choice, however it is unidentified whether this would suffice.
Philosophical point of view
"Strong AI" as specified in approach
In 1980, philosopher John Searle created the term "strong AI" as part of his Chinese space argument. [128] He proposed a distinction between two hypotheses about expert system: [f]
Strong AI hypothesis: An expert system system can have "a mind" and "consciousness".
Weak AI hypothesis: An expert system system can (only) imitate it believes and has a mind and awareness.
The first one he called "strong" since it makes a more powerful declaration: it presumes something special has actually happened to the machine that goes beyond those capabilities that we can evaluate. The behaviour of a "weak AI" device would be precisely similar to a "strong AI" maker, but the latter would likewise have subjective conscious experience. This usage is likewise typical in academic AI research study and textbooks. [129]
In contrast to Searle and mainstream AI, some futurists such as Ray Kurzweil utilize the term "strong AI" to indicate "human level artificial general intelligence". [102] This is not the like Searle's strong AI, unless it is presumed that consciousness is essential for human-level AGI. Academic philosophers such as Searle do not think that holds true, and to most synthetic intelligence scientists the question is out-of-scope. [130]
Mainstream AI is most thinking about how a program behaves. [131] According to Russell and Norvig, "as long as the program works, they don't care if you call it real or a simulation." [130] If the program can act as if it has a mind, then there is no requirement to understand if it in fact has mind - undoubtedly, there would be no way to inform. For AI research, Searle's "weak AI hypothesis" is equivalent to the statement "artificial general intelligence is possible". Thus, according to Russell and Norvig, "most AI 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 2 various things.
Consciousness
Consciousness can have numerous significances, and some aspects play considerable roles in science fiction and the principles of synthetic intelligence:
Sentience (or "incredible consciousness"): The capability to "feel" perceptions or emotions subjectively, as opposed to the capability to factor about understandings. Some theorists, such as David Chalmers, use the term "awareness" to refer exclusively to phenomenal consciousness, which is approximately equivalent to sentience. [132] Determining why and how subjective experience develops is referred to as the difficult problem of consciousness. [133] Thomas Nagel discussed in 1974 that it "seems like" something to be mindful. If we are not conscious, then it does not feel 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 seems conscious (i.e., has consciousness) but a toaster does not. [134] In 2022, a Google engineer claimed that the company's AI chatbot, LaMDA, had actually achieved life, though this claim was widely contested by other experts. [135]
Self-awareness: To have mindful awareness of oneself as a different individual, especially to be consciously knowledgeable about one's own ideas. This is opposed to simply 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 way it represents everything else)-but this is not what people typically mean when they utilize the term "self-awareness". [g]
These characteristics have an ethical dimension. AI sentience would trigger concerns of well-being and legal protection, similarly to animals. [136] Other aspects of awareness associated to cognitive capabilities are also relevant to the concept of AI rights. [137] Finding out how to integrate sophisticated AI with existing legal and social structures is an emerging issue. [138]
Benefits
AGI might have a wide variety of applications. If oriented towards such goals, AGI might assist mitigate various issues on the planet such as hunger, poverty and health issue. [139]
AGI might improve performance and efficiency in most jobs. For example, in public health, AGI could speed up medical research study, significantly versus cancer. [140] It might look after the senior, [141] and equalize access to rapid, high-quality medical diagnostics. It might offer enjoyable, inexpensive and individualized education. [141] The requirement to work to subsist might end up being obsolete if the wealth produced is correctly redistributed. [141] [142] This also raises the concern of the location of people in a radically automated society.
AGI might also assist to make rational decisions, and to anticipate and avoid catastrophes. It could also help to profit of potentially disastrous innovations such as nanotechnology or climate engineering, while preventing the associated threats. [143] If an AGI's main objective is to prevent existential catastrophes such as human termination (which could be difficult if the Vulnerable World Hypothesis turns out to be real), [144] it could take measures to significantly minimize the threats [143] while minimizing the effect of these measures on our quality of life.
Risks
Existential threats
AGI might represent numerous types of existential danger, which are risks that threaten "the premature termination of Earth-originating intelligent life or the long-term and extreme destruction of its potential for desirable future development". [145] The threat of human extinction from AGI has actually been the subject of many debates, however there is likewise the possibility that the advancement of AGI would lead to a permanently flawed future. Notably, it could be used to spread out and preserve the set of values of whoever develops it. If humanity still has ethical blind spots comparable to slavery in the past, AGI might irreversibly entrench it, avoiding ethical development. [146] Furthermore, AGI could assist in mass monitoring and indoctrination, which might be utilized to produce a stable repressive worldwide totalitarian program. [147] [148] There is also a threat for the machines themselves. If devices that are sentient or otherwise worthy of moral factor to consider are mass developed in the future, participating in a civilizational course that indefinitely disregards their well-being and interests could be an existential catastrophe. [149] [150] Considering how much AGI could improve humanity's future and help in reducing other existential risks, Toby Ord calls these existential dangers "an argument for continuing with due caution", not for "deserting AI". [147]
Risk of loss of control and human extinction
The thesis that AI positions an existential risk for human beings, which this risk needs more attention, is questionable but has been backed in 2023 by many public figures, AI scientists and CEOs of AI companies such as Elon Musk, Bill Gates, Geoffrey Hinton, Yoshua Bengio, Demis Hassabis and Sam Altman. [151] [152]
In 2014, Stephen Hawking criticized prevalent indifference:
So, facing possible futures of enormous benefits and risks, the professionals are undoubtedly doing everything possible to make sure the best result, right? Wrong. If a superior 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 basically 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 mentions that higher intelligence allowed humanity to control gorillas, which are now vulnerable in manner ins which they might not have actually expected. As a result, the gorilla has actually ended up being a threatened 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 dominate humankind and that we should be careful not to anthropomorphize them and analyze their intents as we would for human beings. He said that people won't be "wise adequate to develop super-intelligent devices, yet ridiculously silly to the point of offering it moronic goals with no safeguards". [155] On the other side, the principle of critical convergence suggests that nearly whatever their objectives, intelligent agents will have reasons to attempt to endure and get more power as intermediary actions to attaining these goals. And that this does not require having feelings. [156]
Many scholars who are worried about existential risk supporter for more research study into resolving the "control issue" to respond to the concern: what kinds of safeguards, algorithms, or architectures can programmers carry out to increase the possibility that their recursively-improving AI would continue to act in a friendly, rather than destructive, manner 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 safety precautions in order to release items before rivals), [159] and making use of AI in weapon systems. [160]
The thesis that AI can posture existential risk likewise has detractors. Skeptics normally say that AGI is unlikely in the short-term, or that concerns about AGI sidetrack from other problems connected to current AI. [161] Former Google fraud czar Shuman Ghosemajumder considers that for many individuals beyond the innovation industry, existing chatbots and LLMs are already viewed as though they were AGI, causing more misconception and fear. [162]
Skeptics in some cases charge that the thesis is crypto-religious, with an illogical belief in the possibility of superintelligence changing an illogical belief in a supreme God. [163] Some researchers think that the communication campaigns on AI existential risk by certain AI groups (such as OpenAI, Anthropic, DeepMind, and Conjecture) may be an at effort at regulatory capture and to inflate interest in their products. [164] [165]
In 2023, the CEOs of Google DeepMind, OpenAI and Anthropic, in addition to other industry leaders and researchers, issued a joint statement asserting that "Mitigating the danger of extinction from AI must be a global priority along with other societal-scale risks 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 intro of LLMs, while around 19% of employees may see at least 50% of their tasks affected". [166] [167] They consider workplace workers to be the most exposed, for instance mathematicians, accounting professionals or web designers. [167] AGI could have a much better autonomy, ability to make choices, to interface with other computer tools, but also 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 enjoy a life of luxurious leisure if the machine-produced wealth is shared, or many individuals can wind up miserably bad if the machine-owners successfully lobby versus wealth redistribution. Up until now, the pattern seems to be toward the 2nd option, with technology driving ever-increasing inequality
Elon Musk thinks about that the automation of society will require governments to embrace a universal basic earnings. [168]
See likewise
Artificial brain - Software and hardware with cognitive abilities similar to those of the animal or human brain
AI impact
AI safety - Research location on making AI safe and advantageous
AI positioning - AI conformance to the desired goal
A.I. Rising - 2018 film directed by Lazar Bodroža
Artificial intelligence
Automated artificial intelligence - Process of automating the application of 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 different video games
Generative artificial intelligence - AI system efficient in producing material in action to prompts
Human Brain Project - Scientific research study task
Intelligence amplification - Use of details technology to enhance human intelligence (IA).
Machine principles - Moral behaviours of manufactured machines.
Moravec's paradox.
Multi-task knowing - Solving several machine learning jobs at the exact same time.
Neural scaling law - Statistical law in artificial intelligence.
Outline of expert system - Overview of and topical guide to synthetic intelligence.
Transhumanism - Philosophical movement.
Synthetic intelligence - Alternate term for or type of expert system.
Transfer learning - Machine knowing strategy.
Loebner Prize - Annual AI competitors.
Hardware for artificial intelligence - Hardware specifically developed and optimized for artificial intelligence.
Weak synthetic intelligence - Form of expert system.
Notes
^ a b See listed 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 characterize in basic what sort of computational procedures we wish to call smart. " [26] (For a discussion of some definitions of intelligence used by synthetic intelligence scientists, see viewpoint of artificial intelligence.).
^ The Lighthill report particularly slammed AI's "grand goals" and led the taking apart of AI research in England. [55] In the U.S., DARPA became figured out to money just "mission-oriented direct research, instead of fundamental undirected research study". [56] [57] ^ As AI founder John McCarthy composes "it would be an excellent relief to the remainder of the workers in AI if the creators of brand-new basic formalisms would express their hopes in a more guarded kind than has actually in some cases 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 correspond to 1014 cps. Moravec talks in terms of MIPS, not "cps", which is a non-standard term Kurzweil introduced.
^ As specified in a basic AI book: "The assertion that devices could potentially act smartly (or, possibly much better, act as if they were intelligent) is called the 'weak AI' hypothesis by theorists, and the assertion that devices that do so are in fact thinking (instead of 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 carry out a single job.
^ "OpenAI Charter". OpenAI. Retrieved 6 April 2023. Our objective is to ensure that synthetic general intelligence benefits all of humanity.
^ Heath, Alex (18 January 2024). "Mark Zuckerberg's brand-new objective is producing artificial basic intelligence". The Verge. Retrieved 13 June 2024. Our vision is to build 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 tasks were recognized as being active in 2020.
^ a b c "AI timelines: What do professionals in artificial intelligence anticipate for the future?". Our World in Data. Retrieved 6 April 2023.
^ Metz, Cade (15 May 2023). "Some Researchers Say A.I. Is Already Here, Stirring Debate in Tech Circles". The New York City Times. Retrieved 18 May 2023.
^ "AI leader Geoffrey Hinton quits Google and alerts of threat ahead". The New York City Times. 1 May 2023. Retrieved 2 May 2023. It is hard to see how you can avoid the bad 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 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 modifications you.
^ Vinge, Vernor (1992 ). A Fire Upon the Deep. Tor Books. ISBN 978-0-8125-1528-2. The Singularity is coming.
^ Morozov, Evgeny (30 June 2023). "The True Threat of Expert System". The New York City Times. The genuine threat is not AI itself however the way we deploy it.
^ "Impressed by artificial intelligence? Experts say AGI is following, and it has 'existential' dangers". ABC News. 23 March 2023. Retrieved 6 April 2023. AGI could pose existential dangers to humanity.
^ Bostrom, Nick (2014 ). Superintelligence: Paths, Dangers, Strategies. Oxford University Press. ISBN 978-0-1996-7811-2. The very first superintelligence will be the last development that mankind needs to make.
^ Roose, Kevin (30 May 2023). "A.I. Poses 'Risk of Extinction,' Industry Leaders Warn". The New York Times. Mitigating the risk of termination from AI need to be an international priority.
^ "Statement on AI Risk". Center for AI Safety. Retrieved 1 March 2024. AI professionals alert of risk of termination from AI.
^ Mitchell, Melanie (30 May 2023). "Are AI's Doomsday Scenarios Worth Taking Seriously?". The New York Times. We are far from creating machines that can outthink us in basic methods.
^ LeCun, Yann (June 2023). "AGI does not provide an existential danger". Medium. There is no factor to fear AI as an existential threat.
^ Kurzweil 2005, p. 260.
^ a b Kurzweil, Ray (5 August 2005), "Long Live AI", Forbes, archived from the original on 14 August 2005: Kurzweil explains strong AI as "machine intelligence with the full variety of human intelligence.".
^ "The Age of Expert System: George John at TEDxLondonBusinessSchool 2013". Archived from the initial on 26 February 2014. Retrieved 22 February 2014.
^ Newell & Simon 1976, This is the term they use for "human-level" intelligence in the physical 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 achieving AGI, according to DeepMind". VentureBeat.
^ McCarthy, John (2007a). "Basic Questions". Stanford University. Archived from the initial on 26 October 2007. Retrieved 6 December 2007.
^ This list of smart traits is based upon 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 forms the way we believe: a brand-new view of intelligence (The MIT Press, 2007). ISBN 0-2621-6239-3.
^ White, R. W. (1959 ). "Motivation reconsidered: The idea of skills". Psychological Review. 66 (5 ): 297-333. doi:10.1037/ h0040934. PMID 13844397. S2CID 37385966.
^ White, R. W. (1959 ). "Motivation reconsidered: The idea of competence". 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 happens when it does?". The Conversation. Retrieved 22 September 2024.
^ a b Turing 1950.
^ Turing, Alan (1952 ). B. Jack Copeland (ed.). Can Automatic Calculating Machines Be Said To Think?. Oxford: Oxford University Press. pp. 487-506. ISBN 978-0-1982-5079-1.
^ "Eugene Goostman is a real kid - the Turing Test states so". The Guardian. 9 June 2014. ISSN 0261-3077. Retrieved 3 March 2024.
^ "Scientists dispute whether computer 'Eugene Goostman' passed Turing test". BBC News. 9 June 2014. Retrieved 3 March 2024.
^ Jones, Cameron R.; Bergen, Benjamin K. (9 May 2024). "People can not differentiate 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 examination to AP Biology. Here's a list of challenging examinations both AI variations have actually passed". Business Insider. Retrieved 30 May 2023.
^ Naysmith, Caleb (7 February 2023). "6 Jobs Artificial Intelligence Is Already Replacing and How Investors Can 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 unreliable. The Winograd Schema is obsolete. Coffee is the answer". Analytics India Magazine. Retrieved 3 March 2024.
^ Bhaimiya, Sawdah (20 June 2023). "DeepMind's co-founder recommended checking 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 Artificial Intelligence (Second ed.). New York City: John Wiley. pp. 54-57. Archived (PDF) from the original on 1 February 2016. (Section 4 is on "AI-Complete Tasks".).
^ Yampolskiy, Roman V. (2012 ). Xin-She Yang (ed.). "Turing Test as a Defining Feature of AI-Completeness" (PDF). Artificial Intelligence, Evolutionary Computation and Metaheuristics (AIECM): 3-17. Archived (PDF) from the initial on 22 May 2013.
^ "AI Index: State of AI in 13 Charts". Stanford University Human-Centered Artificial Intelligence. 15 April 2024. Retrieved 27 May 2024.
^ Crevier 1993, pp. 48-50.
^ Kaplan, Andreas (2022 ). "Artificial Intelligence, Business and Civilization - Our Fate Made in Machines". Archived from the original on 6 May 2022. Retrieved 12 March 2022.
^ Simon 1965, p. 96 priced estimate in Crevier 1993, p. 109.
^ "Scientist on the Set: An Interview with Marvin Minsky". Archived from the original on 16 July 2012. Retrieved 5 April 2008.
^ Marvin Minsky to Darrach (1970 ), priced estimate in Crevier (1993, p. 109).
^ Lighthill 1973; Howe 1994.
^ a b NRC 1999, "Shift to Applied Research Increases Investment".
^ Crevier 1993, pp. 115-117; Russell & Norvig 2003, pp. 21-22.
^ Crevier 1993, p. 211, Russell & Norvig 2003, p. 24 and see likewise Feigenbaum & McCorduck 1983.
^ Crevier 1993, pp. 161-162, 197-203, 240; Russell & Norvig 2003, p. 25.
^ Crevier 1993, pp. 209-212.
^ McCarthy, John (2000 ). "Respond to Lighthill". Stanford University. Archived from the initial on 30 September 2008. Retrieved 29 September 2007.
^ Markoff, John (14 October 2005). "Behind Artificial Intelligence, a Squadron of Bright Real People". The New York Times. Archived from the original on 2 February 2023. Retrieved 18 February 2017. At its low point, some computer researchers and software application engineers avoided the term expert system for worry of being viewed as wild-eyed dreamers.
^ Russell & Norvig 2003, pp. 25-26
^ "Trends in the Emerging Tech Hype Cycle". Gartner Reports. Archived from the initial on 22 May 2019. Retrieved 7 May 2019.
^ a b Moravec 1988, p. 20
^ Harnad, S. (1990 ). "The Symbol Grounding Problem". Physica D. 42 (1-3): 335-346. arXiv: cs/9906002. Bibcode:1990 PhyD ... 42..335 H. doi:10.1016/ 0167-2789( 90 )90087-6. S2CID 3204300.
^ Gubrud 1997
^ Hutter, Marcus (2005 ). Universal 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 original 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 created the term "AGI"?". goertzel.org. Archived from the initial on 28 December 2018. Retrieved 28 December 2018., through Life 3.0: 'The term "AGI" was 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 maker intelligence: Despite development in maker intelligence, artificial general intelligence is still a significant challenge". EMBO Reports. 20 (10 ): e49177. doi:10.15252/ embr.201949177. ISSN 1469-221X. PMC 6776890. PMID 31531926.
^ Bubeck, Sébastien; Chandrasekaran, Varun; Eldan, Ronen; Gehrke, Johannes; Horvitz, Eric; Kamar, Ece; Lee, Peter; Lee, Yin Tat; Li, Yuanzhi; Lundberg, Scott; Nori, Harsha; Palangi, Hamid; Ribeiro, Marco Tulio; Zhang, Yi (27 March 2023). "Sparks of Artificial General Intelligence: Early experiments with GPT-4". arXiv:2303.12712 [cs.CL]
^ "Microsoft Researchers Claim GPT-4 Is Showing "Sparks" of AGI". Futurism. 23 March 2023. Retrieved 13 December 2023.
^ Allen, Paul; Greaves, Mark (12 October 2011). "The Singularity Isn't Near". MIT Technology Review. Retrieved 17 September 2014.
^ Winfield, Alan. "Expert system will not develop into a Frankenstein's beast". The Guardian. Archived from the initial on 17 September 2014. Retrieved 17 September 2014.
^ Deane, George (2022 ). "Machines That Feel and Think: The Role of Affective Feelings and Mental Action in (Artificial) General Intelligence". Artificial Life. 28 (3 ): 289-309. doi:10.1162/ artl_a_00368. ISSN 1064-5462. PMID 35881678. S2CID 251069071.
^ a b c Clocksin 2003.
^ Fjelland, Ragnar (17 June 2020). "Why basic artificial intelligence will not be realized". Humanities and Social Sciences Communications. 7 (1 ): 1-9. doi:10.1057/ s41599-020-0494-4. hdl:11250/ 2726984. ISSN 2662-9992. S2CID 219710554.
^ McCarthy 2007b.
^ Khatchadourian, Raffi (23 November 2015). "The Doomsday Invention: Will 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 development in synthetic intelligence: A survey of professional opinion. In Fundamental concerns of artificial intelligence (pp. 555-572). Springer, Cham.
^ Armstrong, Stuart, and Kaj Sotala. 2012. "How We're Predicting AI-or Failing To." In Beyond AI: Artificial Dreams, edited by Jan Romportl, Pavel Ircing, Eva Žáčková, Michal Polák and Radek Schuster, 52-75. Plzeň: University of West Bohemia
^ "Microsoft Now Claims GPT-4 Shows 'Sparks' of General Intelligence". 24 March 2023.
^ Shimek, Cary (6 July 2023). "AI Outperforms Humans in Creativity Test". Neuroscience News. Retrieved 20 October 2023.
^ Guzik, Erik E.; Byrge, Christian; Gilde, Christian (1 December 2023). "The originality of devices: AI takes the Torrance Test". Journal of Creativity. 33 (3 ): 100065. doi:10.1016/ j.yjoc.2023.100065. ISSN 2713-3745. S2CID 261087185.
^ Arcas, Blaise Agüera y (10 October 2023). "Artificial General Intelligence Is Already Here". Noema.
^ Zia, Tehseen (8 January 2024). "Unveiling of Large Multimodal Models: Shaping the Landscape of Language Models in 2024". Unite.ai. Retrieved 26 May 2024.
^ "Introducing OpenAI o1-preview". OpenAI. 12 September 2024.
^ Knight, Will. "OpenAI Announces a New AI Model, Code-Named Strawberry, That Solves Difficult Problems Step by Step". Wired. ISSN 1059-1028. Retrieved 17 September 2024.
^ "OpenAI Employee Claims AGI Has Been Achieved". Orbital Today. 13 December 2024. Retrieved 27 December 2024.
^ "AI Index: State of AI in 13 Charts". hai.stanford.edu. 15 April 2024. Retrieved 7 June 2024.
^ "Next-Gen AI: OpenAI and Meta's Leap Towards Reasoning Machines". Unite.ai. 19 April 2024. Retrieved 7 June 2024.
^ James, Alex P. (2022 ). "The Why, What, and How of Artificial General Intelligence Chip Development". IEEE Transactions on Cognitive and Developmental Systems. 14 (2 ): 333-347. arXiv:2012.06338. doi:10.1109/ TCDS.2021.3069871. ISSN 2379-8920. S2CID 228376556. Archived from the 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 s
