Artificial general intelligence (AGI) is a type of expert system (AI) that matches or surpasses human cognitive capabilities throughout a wide variety of cognitive tasks. This contrasts with narrow AI, which is limited to specific jobs. [1] Artificial superintelligence (ASI), on the other hand, describes AGI that greatly goes beyond human cognitive abilities. AGI is thought about among the definitions of strong AI.
Creating AGI is a primary goal of AI research and of companies such as OpenAI [2] and Meta. [3] A 2020 survey determined 72 active AGI research and development tasks throughout 37 countries. [4]
The timeline for accomplishing AGI remains a subject of ongoing dispute among scientists and experts. As of 2023, some argue that it may be possible in years or years; others preserve it may take a century or longer; a minority believe it may never ever be attained; and another minority claims that it is already here. [5] [6] Notable AI researcher Geoffrey Hinton has actually revealed concerns about the fast development towards AGI, suggesting it might be accomplished faster than lots of anticipate. [7]
There is debate on the precise definition of AGI and regarding whether modern-day big language models (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 risk. [11] [12] [13] Many professionals on AI have actually stated that mitigating the threat of human extinction presented by AGI must be a global priority. [14] [15] Others find the advancement of AGI to be too remote to provide such a risk. [16] [17]
Terminology
AGI is likewise understood as strong AI, [18] [19] complete AI, [20] human-level AI, [5] human-level intelligent AI, or basic smart action. [21]
Some academic sources book the term "strong AI" for computer programs that experience sentience or awareness. [a] In contrast, weak AI (or narrow AI) has the ability to solve one particular issue but lacks general cognitive capabilities. [22] [19] Some scholastic sources use "weak AI" to refer more broadly to any programs that neither experience awareness nor have a mind in the very same sense as human beings. [a]
Related concepts consist of artificial superintelligence and transformative AI. A synthetic superintelligence (ASI) is a hypothetical kind of AGI that is much more generally smart than people, [23] while the idea of transformative AI connects to AI having a big effect on society, for example, comparable to the farming or commercial transformation. [24]
A framework for classifying AGI in levels was proposed in 2023 by Google DeepMind scientists. They specify five levels of AGI: emerging, skilled, expert, virtuoso, and superhuman. For instance, a proficient AGI is defined as an AI that surpasses 50% of proficient grownups in a broad variety of non-physical tasks, and a superhuman AGI (i.e. a synthetic superintelligence) is similarly specified however with a limit of 100%. They think about large language designs like ChatGPT or LLaMA 2 to be instances of emerging AGI. [25]
Characteristics
Various popular definitions of intelligence have been proposed. Among the leading propositions is the Turing test. However, there are other widely known definitions, and some researchers disagree with the more popular techniques. [b]
Intelligence characteristics
Researchers generally hold that intelligence is needed to do all of the following: [27]
reason, usage technique, fix puzzles, and make judgments under unpredictability
represent understanding, consisting of good sense understanding
plan
discover
- communicate in natural language
- if required, incorporate these abilities in conclusion of any given goal
Many interdisciplinary techniques (e.g. cognitive science, computational intelligence, and choice making) think about additional traits such as imagination (the capability to form unique psychological images and concepts) [28] and autonomy. [29]
Computer-based systems that show a lot of these abilities exist (e.g. see computational imagination, automated reasoning, choice support group, robotic, evolutionary calculation, intelligent agent). There is argument about whether contemporary AI systems have them to a sufficient degree.
Physical characteristics
Other capabilities are considered desirable in intelligent systems, as they might affect intelligence or help in its expression. These consist of: [30]
- the capability to sense (e.g. see, hear, and so on), and
- the capability to act (e.g. move and manipulate items, modification location to check out, and so on).
This consists of the capability to identify and react to danger. [31]
Although the ability to sense (e.g. see, hear, and so on) and the ability to act (e.g. move and control objects, modification area to check out, etc) can be preferable 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 already be or end up being AGI. Even from a less optimistic perspective 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 place of human senses. This analysis lines up with the understanding that AGI has actually never been proscribed a specific physical embodiment and therefore does not require a capability for mobility or conventional "eyes and ears". [32]
Tests for human-level AGI
Several tests meant to validate 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 male, by addressing concerns put to it, and it will only pass if the pretence is reasonably convincing. A substantial part of a jury, who should not be expert about devices, must 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, since the service is beyond the abilities of a purpose-specific algorithm. [47]
There are numerous problems that have actually been conjectured to need basic intelligence to resolve in addition to human beings. Examples include computer vision, natural language understanding, and dealing with unanticipated situations while resolving any real-world problem. [48] Even a particular job like translation requires a device to check out and compose in both languages, follow the author's argument (reason), comprehend the context (understanding), and consistently replicate the author's initial intent (social intelligence). All of these issues need to be fixed all at once in order to reach human-level machine performance.
However, a number of these jobs can now be carried out by modern big language models. According to Stanford University's 2024 AI index, AI has actually reached human-level performance on numerous standards for reading comprehension and visual reasoning. [49]
History
Classical AI
Modern AI research study began in the mid-1950s. [50] The first generation of AI researchers were persuaded that synthetic general intelligence was possible which it would exist in just a few decades. [51] AI leader Herbert A. Simon wrote in 1965: "machines will be capable, within twenty years, of doing any work a man can do." [52]
Their predictions were the motivation for Stanley Kubrick and Arthur C. Clarke's character HAL 9000, who embodied what AI researchers believed they might create by the year 2001. AI leader Marvin Minsky was an expert [53] on the project of making HAL 9000 as sensible as possible according to the consensus predictions of the time. He said in 1967, "Within a generation ... the issue of developing 'synthetic intelligence' will significantly be resolved". [54]
Several classical AI tasks, such as Doug Lenat's Cyc task (that began in 1984), and Allen Newell's Soar job, were directed at AGI.
However, in the early 1970s, it became apparent that scientists had grossly underestimated the difficulty of the project. Funding agencies ended up being hesitant of AGI and put researchers under increasing pressure to produce helpful "applied AI". [c] In the early 1980s, Japan's Fifth Generation Computer Project restored interest in AGI, setting out a ten-year timeline that consisted of AGI goals like "continue a casual conversation". [58] In reaction to this and the success of professional systems, both industry and government pumped cash into the field. [56] [59] However, confidence in AI spectacularly 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 imminent accomplishment of AGI had been misinterpreted. By the 1990s, AI scientists had a track record for making vain pledges. They became unwilling to make predictions at all [d] and avoided reference of "human level" expert system for worry of being labeled "wild-eyed dreamer [s]. [62]
Narrow AI research
In the 1990s and early 21st century, mainstream AI accomplished business success and scholastic respectability by focusing on specific sub-problems where AI can produce verifiable outcomes and commercial applications, such as speech recognition and recommendation algorithms. [63] These "applied AI" systems are now used thoroughly throughout the innovation market, and research in this vein is heavily moneyed in both academic community and industry. As of 2018 [update], development in this field was considered an emerging trend, and a fully grown stage was anticipated to be reached in more than 10 years. [64]
At the turn of the century, lots of traditional AI scientists [65] hoped that strong AI might be established by integrating programs that solve various sub-problems. Hans Moravec wrote in 1988:
I am confident that this bottom-up path to artificial intelligence will one day meet the traditional top-down route majority way, ready to supply the real-world proficiency and the commonsense knowledge that has been so frustratingly evasive in reasoning programs. Fully intelligent makers will result when the metaphorical golden spike is driven uniting the two efforts. [65]
However, even at the time, this was contested. For example, Stevan Harnad of Princeton University concluded his 1990 paper on the symbol grounding hypothesis by stating:
The expectation has actually typically been voiced that "top-down" (symbolic) approaches to modeling cognition will in some way satisfy "bottom-up" (sensory) approaches someplace in between. If the grounding factors to consider in this paper stand, then this expectation is hopelessly modular and there is truly only one practical path from sense to signs: from the ground up. A free-floating symbolic level like the software level of a computer 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, given that it looks as if getting there would just total up to uprooting our signs from their intrinsic significances (thereby merely decreasing ourselves to the practical equivalent of a programmable computer). [66]
Modern synthetic general intelligence research
The term "artificial basic intelligence" was utilized as early as 1997, by Mark Gubrud [67] in a discussion of the ramifications of completely 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 ability to please goals in a large range of environments". [68] This kind of AGI, defined by the ability to increase a mathematical meaning of intelligence rather than exhibit human-like behaviour, [69] was also called universal artificial intelligence. [70]
The term AGI was re-introduced and promoted by Shane Legg and Ben Goertzel around 2002. [71] AGI research activity in 2006 was explained by Pei Wang and Ben Goertzel [72] as "producing publications and preliminary results". The very first summer season school in AGI was arranged in Xiamen, China in 2009 [73] by the Xiamen university's Artificial Brain Laboratory and OpenCog. The very first university course was provided in 2010 [74] and 2011 [75] at Plovdiv University, Bulgaria by Todor Arnaudov. MIT presented a course on AGI in 2018, arranged by Lex Fridman and including a variety of guest speakers.
Since 2023 [update], a little number of computer system scientists are active in AGI research study, and lots of contribute to a series of AGI conferences. However, progressively more researchers are interested in open-ended learning, [76] [77] which is the idea of allowing AI to continuously find out and innovate like humans do.
Feasibility
Since 2023, the advancement and prospective achievement of AGI remains a topic of intense dispute within the AI community. While conventional agreement held that AGI was a far-off goal, recent improvements have actually led some researchers and market figures to claim that early types of AGI might already exist. [78] AI leader Herbert A. Simon speculated in 1965 that "machines will be capable, within twenty years, of doing any work a man can do". This prediction failed to come real. Microsoft co-founder Paul Allen thought that such intelligence is unlikely in the 21st century due to the fact that it would require "unforeseeable and fundamentally unpredictable developments" and a "scientifically deep understanding of cognition". [79] Writing in The Guardian, roboticist Alan Winfield claimed the gulf in between contemporary computing and human-level artificial intelligence is as broad as the gulf between current area flight and practical faster-than-light spaceflight. [80]
A further obstacle is the lack of clarity in specifying what intelligence requires. Does it require awareness? Must it display the capability to set objectives in addition to pursue them? Is it simply a matter of scale such that if model sizes increase adequately, intelligence will emerge? Are centers such as preparation, reasoning, and causal understanding required? Does intelligence require clearly reproducing the brain and its specific faculties? Does it need feelings? [81]
Most AI researchers believe strong AI can be attained in the future, however some thinkers, like Hubert Dreyfus and Roger Penrose, reject the possibility of attaining strong AI. [82] [83] John McCarthy is among those who believe human-level AI will be achieved, but that today level of development is such that a date can not precisely be predicted. [84] AI specialists' views on the feasibility of AGI wax and wane. Four surveys carried out in 2012 and 2013 suggested that the average quote amongst experts for when they would be 50% confident AGI would show up was 2040 to 2050, depending upon the poll, with the mean being 2081. Of the specialists, 16.5% addressed with "never ever" when asked the exact same question but with a 90% self-confidence instead. [85] [86] Further current AGI development factors to consider can be discovered above Tests for confirming human-level AGI.
A report by Stuart Armstrong and Kaj Sotala of the Machine Intelligence Research Institute discovered that "over [a] 60-year time frame there is a strong bias towards forecasting the arrival of human-level AI as between 15 and 25 years from the time the prediction was made". They evaluated 95 predictions made between 1950 and 2012 on when human-level AI will happen. [87]
In 2023, Microsoft researchers published an in-depth evaluation of GPT-4. They concluded: "Given the breadth and depth of GPT-4's abilities, we think that it might reasonably be deemed an early (yet still insufficient) variation of a synthetic general intelligence (AGI) system." [88] Another research study in 2023 reported that GPT-4 surpasses 99% of people on the Torrance tests of innovative thinking. [89] [90]
Blaise Agüera y Arcas and Peter Norvig wrote in 2023 that a considerable level of basic intelligence has actually already been attained with frontier models. They wrote that hesitation to this view comes from 4 primary reasons: a "healthy apprehension 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 financial ramifications of AGI". [91]
2023 also marked the introduction of large multimodal models (large language designs efficient in processing or producing several modalities such as text, audio, and images). [92]
In 2024, OpenAI launched o1-preview, the first of a series of models that "spend more time believing before they react". According to Mira Murati, this ability to believe before reacting represents a new, extra paradigm. It enhances model outputs by investing more computing power when generating the answer, whereas the model scaling paradigm enhances outputs by increasing the model size, training data and training compute power. [93] [94]
An OpenAI worker, Vahid Kazemi, claimed in 2024 that the business had achieved AGI, mentioning, "In my viewpoint, we have actually already attained AGI and it's a lot more clear with O1." Kazemi clarified that while the AI is not yet "better than any human at any job", it is "better than many human beings at a lot of jobs." He also dealt with criticisms that big language models (LLMs) simply follow predefined patterns, comparing their learning procedure to the scientific method of observing, hypothesizing, and validating. These statements have sparked dispute, as they depend on a broad and non-traditional definition of AGI-traditionally comprehended as AI that matches human intelligence across all domains. Critics argue that, while OpenAI's models show exceptional adaptability, they might not fully fulfill this requirement. Notably, Kazemi's comments came shortly after OpenAI eliminated "AGI" from the terms of its partnership with Microsoft, triggering speculation about the company's tactical intents. [95]
Timescales
Progress in synthetic intelligence has actually traditionally gone through durations of quick development separated by periods when development appeared to stop. [82] Ending each hiatus were fundamental advances in hardware, software application or both to create area for additional progress. [82] [98] [99] For example, the computer hardware offered in the twentieth century was not adequate to execute deep learning, which requires great deals of GPU-enabled CPUs. [100]
In the introduction to his 2006 book, [101] Goertzel says that price quotes of the time required before a truly versatile AGI is developed differ from 10 years to over a century. Since 2007 [update], the agreement in the AGI research community seemed to be that the timeline discussed by Ray Kurzweil in 2005 in The Singularity is Near [102] (i.e. in between 2015 and 2045) was possible. [103] Mainstream AI scientists have actually provided a wide variety of viewpoints on whether development will be this quick. A 2012 meta-analysis of 95 such viewpoints discovered a predisposition towards predicting that the beginning of AGI would take place within 16-26 years for modern-day and historic predictions alike. That paper has actually been criticized for how it classified opinions as professional or non-expert. [104]
In 2012, Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton developed a neural network called AlexNet, which won the ImageNet competitors with a top-5 test error rate of 15.3%, considerably much better than the second-best entry's rate of 26.3% (the traditional approach used a weighted amount of scores from different pre-defined classifiers). [105] AlexNet was considered as the initial ground-breaker of the current deep learning wave. [105]
In 2017, scientists Feng Liu, Yong Shi, and Ying Liu performed 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 worth of about 47, which corresponds approximately to a six-year-old kid in very first grade. An adult pertains to about 100 on average. Similar tests were performed in 2014, with the IQ score reaching a maximum value of 27. [106] [107]
In 2020, OpenAI established GPT-3, a language design capable of performing numerous varied jobs without specific training. According to Gary Grossman in a VentureBeat short article, while there is agreement that GPT-3 is not an example of AGI, it is thought about by some to be too advanced to be classified 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 for modifications to the chatbot to abide by their safety standards; Rohrer disconnected Project December from the GPT-3 API. [109]
In 2022, DeepMind established Gato, a "general-purpose" system efficient in 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 displayed more general intelligence than previous AI designs and showed human-level performance in tasks spanning multiple domains, such as mathematics, coding, and law. This research triggered an argument on whether GPT-4 might be thought about an early, incomplete version of synthetic general intelligence, emphasizing the need for more exploration and examination of such systems. [111]
In 2023, the AI researcher Geoffrey Hinton mentioned that: [112]
The idea that this things might actually get smarter than people - a few individuals thought that, [...] But the majority of people believed it was method off. And I thought it was way off. I believed it was 30 to 50 years or perhaps longer away. Obviously, I no longer think that.
In May 2023, Demis Hassabis likewise said that "The development in the last few years has been quite amazing", and that he sees no factor why it would decrease, anticipating AGI within a years or even a couple of years. [113] In March 2024, Nvidia's CEO, Jensen Huang, stated his expectation that within five years, AI would can passing any test a minimum of in addition to people. [114] In June 2024, the AI researcher Leopold Aschenbrenner, a previous OpenAI staff member, approximated AGI by 2027 to be "noticeably plausible". [115]
Whole brain emulation
While the advancement of transformer models like in ChatGPT is considered the most promising course to AGI, [116] [117] whole brain emulation can work as an alternative method. With entire brain simulation, a brain model is built by scanning and mapping a biological brain in information, and after that copying and replicating it on a computer system or another computational device. The simulation design must be adequately loyal to the initial, so that it behaves in practically the 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 functions. It has actually been gone over in expert system research study [103] as a method to strong AI. Neuroimaging innovations that could provide the essential in-depth understanding are improving quickly, and futurist Ray Kurzweil in the book The Singularity Is Near [102] forecasts that a map of adequate quality will become available on a comparable timescale to the computing power needed to replicate it.
Early approximates
For low-level brain simulation, a very effective cluster of computer systems or GPUs would be required, provided the huge quantity of synapses within the human brain. Each of the 1011 (one hundred billion) neurons has on typical 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, supporting by the adult years. Estimates differ for an adult, varying from 1014 to 5 × 1014 synapses (100 to 500 trillion). [120] A price quote of the brain's processing power, based upon a basic switch design for neuron activity, is around 1014 (100 trillion) synaptic updates per second (SUPS). [121]
In 1997, Kurzweil took a look at various quotes for the hardware required to equal the human brain and embraced a figure of 1016 computations per 2nd (cps). [e] (For comparison, if a "computation" was comparable 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 achieved in 2022.) He used this figure to anticipate the needed hardware would be readily available sometime in between 2015 and 2025, if the rapid development in computer system power at the time of composing continued.
Current research study
The Human Brain Project, an EU-funded initiative active from 2013 to 2023, has developed an especially in-depth and openly accessible atlas of the human brain. [124] In 2023, scientists from Duke University performed a high-resolution scan of a mouse brain.
Criticisms of simulation-based techniques
The synthetic nerve cell model assumed by Kurzweil and utilized in many existing synthetic neural network applications is basic compared with biological nerve cells. A brain simulation would likely need to record the detailed cellular behaviour of biological neurons, currently understood only in broad overview. The overhead presented by complete modeling of the biological, chemical, and physical information of neural behaviour (specifically on a molecular scale) would require computational powers several orders of magnitude bigger than Kurzweil's estimate. In addition, the estimates do not account for glial cells, which are known to play a function in cognitive processes. [125]
A basic criticism of the simulated brain approach originates from embodied cognition theory which asserts that human personification is an important aspect of human intelligence and is necessary to ground meaning. [126] [127] If this theory is appropriate, any completely functional brain model will need to encompass more than simply the neurons (e.g., a robotic body). Goertzel [103] proposes virtual embodiment (like in metaverses like Second Life) as a choice, but it is unknown whether this would suffice.
Philosophical viewpoint
"Strong AI" as specified in philosophy
In 1980, theorist John Searle created the term "strong AI" as part of his Chinese space argument. [128] He proposed a distinction between two hypotheses about synthetic intelligence: [f]
Strong AI hypothesis: An expert system system can have "a mind" and "consciousness".
Weak AI hypothesis: A synthetic intelligence system can (only) act like it thinks and has a mind and consciousness.
The very first one he called "strong" because it makes a more powerful declaration: it presumes something special has taken place to the maker that exceeds those capabilities that we can test. The behaviour of a "weak AI" maker would be specifically similar to a "strong AI" device, but the latter would likewise have subjective mindful experience. This use is likewise typical in academic AI research study and books. [129]
In contrast to Searle and mainstream AI, some futurists such as Ray Kurzweil utilize the term "strong AI" to suggest "human level artificial basic intelligence". [102] This is not the like Searle's strong AI, unless it is presumed that consciousness is essential for human-level AGI. Academic theorists such as Searle do not believe that holds true, and to most synthetic 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 do not 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 chance to inform. For AI research study, Searle's "weak AI hypothesis" is comparable to the statement "synthetic general intelligence is possible". Thus, according to Russell and Norvig, "most AI researchers take the weak AI hypothesis for granted, and don't care about the strong AI hypothesis." [130] Thus, for academic AI research study, "Strong AI" and "AGI" are 2 various things.
Consciousness
Consciousness can have numerous significances, and some elements play significant roles in science fiction and the principles of expert system:
Sentience (or "sensational awareness"): The capability to "feel" understandings or feelings subjectively, instead of the capability to reason about perceptions. Some philosophers, such as David Chalmers, use the term "awareness" to refer solely to phenomenal consciousness, which is roughly equivalent to life. [132] Determining why and how subjective experience emerges is referred to as the difficult issue of consciousness. [133] Thomas Nagel discussed in 1974 that it "seems like" something to be mindful. If we are not mindful, then it does not feel like anything. Nagel uses 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 feel like to be a toaster?" Nagel concludes that a bat appears to be conscious (i.e., has awareness) however a toaster does not. [134] In 2022, a Google engineer declared that the business's AI chatbot, LaMDA, had actually achieved life, though this claim was extensively challenged by other experts. [135]
Self-awareness: To have conscious awareness of oneself as a different individual, particularly to be consciously aware of one's own thoughts. This is opposed to simply being the "subject of one's thought"-an operating system or debugger has the ability to be "familiar with itself" (that is, to represent itself in the same way it represents everything else)-however this is not what individuals normally mean when they use the term "self-awareness". [g]
These traits have a moral measurement. AI sentience would trigger issues of well-being and legal security, likewise to animals. [136] Other elements of awareness related to cognitive abilities are likewise appropriate to the idea of AI rights. [137] Determining how to integrate innovative AI with existing legal and social frameworks is an emerging concern. [138]
Benefits
AGI could have a wide array of applications. If oriented towards such goals, AGI might assist reduce various issues on the planet such as appetite, poverty and health issue. [139]
AGI might improve performance and performance in many jobs. For example, in public health, AGI might speed up medical research, notably against cancer. [140] It might look after the senior, [141] and democratize access to quick, top quality medical diagnostics. It might offer fun, cheap and customized education. [141] The requirement to work to subsist might end up being obsolete if the wealth produced is properly redistributed. [141] [142] This likewise raises the concern of the place of people in a significantly automated society.
AGI might likewise assist to make logical choices, and to expect and avoid disasters. It might likewise help to profit of potentially devastating technologies such as nanotechnology or climate engineering, while preventing the associated risks. [143] If an AGI's main goal is to avoid existential catastrophes such as human termination (which might be difficult if the Vulnerable World Hypothesis turns out to be real), [144] it might take procedures to considerably reduce the risks [143] while decreasing the effect of these steps on our quality of life.
Risks
Existential threats
AGI might represent multiple types of existential danger, which are risks that threaten "the early termination of Earth-originating smart life or the long-term and drastic destruction of its potential for preferable future advancement". [145] The threat of human termination from AGI has actually been the topic of lots of arguments, 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 out and protect the set of worths of whoever establishes it. If mankind still has ethical blind spots similar to slavery in the past, AGI might irreversibly entrench it, avoiding ethical progress. [146] Furthermore, AGI could assist in mass monitoring and indoctrination, which might be used to produce a steady repressive worldwide totalitarian program. [147] [148] There is likewise a danger for the machines themselves. If devices that are sentient or otherwise worthwhile of ethical consideration are mass developed in the future, engaging in a civilizational path that forever overlooks their welfare and interests could be an existential disaster. [149] [150] Considering just how much AGI could enhance humanity's future and help in reducing other existential threats, Toby Ord calls these existential risks "an argument for proceeding with due caution", not for "abandoning AI". [147]
Risk of loss of control and human extinction
The thesis that AI postures an existential risk for people, and that this threat needs more attention, is controversial however has actually been backed in 2023 by lots of 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 widespread indifference:
So, facing possible futures of incalculable benefits and threats, the professionals are undoubtedly doing everything possible to guarantee the finest result, right? Wrong. If a superior alien civilisation sent us a message saying, 'We'll arrive in a few decades,' would we just respond, 'OK, call us when you get here-we'll leave the lights on?' Probably not-but this is more or less what is taking place with AI. [153]
The possible fate of mankind has actually in some cases been compared to the fate of gorillas threatened by human activities. The comparison states that higher intelligence enabled humankind to dominate gorillas, which are now susceptible in manner ins which they might not have actually prepared for. As an outcome, the gorilla has ended up being an endangered types, not out of malice, but simply as a collateral damage from human activities. [154]
The skeptic Yann LeCun considers that AGIs will have no desire to control mankind which we ought to be cautious not to anthropomorphize them and translate their intents as we would for people. He stated that individuals will not be "clever enough to design super-intelligent devices, yet unbelievably stupid to the point of offering it moronic goals without any safeguards". [155] On the other side, the idea of important merging recommends that almost whatever their goals, intelligent agents will have reasons to try to endure and get more power as intermediary steps to attaining these objectives. Which this does not need having emotions. [156]
Many scholars who are concerned about existential danger supporter for more research into resolving the "control issue" to respond to the concern: what types of safeguards, algorithms, or architectures can programmers implement to maximise the likelihood that their recursively-improving AI would continue to act in a friendly, rather than devastating, manner after it reaches superintelligence? [157] [158] Solving the control issue is made complex by the AI arms race (which might result in a race to the bottom of safety preventative measures in order to launch items before rivals), [159] and the usage of AI in weapon systems. [160]
The thesis that AI can posture existential danger likewise has detractors. Skeptics usually say that AGI is unlikely in the short-term, or that issues about AGI sidetrack from other problems associated with current AI. [161] Former Google fraud czar Shuman Ghosemajumder thinks about that for many individuals beyond the technology industry, existing chatbots and LLMs are currently viewed as though they were AGI, resulting in further misunderstanding and worry. [162]
Skeptics in some cases charge that the thesis is crypto-religious, with an irrational belief in the possibility of superintelligence replacing an unreasonable belief in an omnipotent God. [163] Some researchers think that the interaction campaigns on AI existential risk by particular AI groups (such as OpenAI, Anthropic, DeepMind, and Conjecture) might be an at effort at regulative capture and to pump up interest in their items. [164] [165]
In 2023, the CEOs of Google DeepMind, OpenAI and Anthropic, together with other market leaders and researchers, released a joint statement asserting that "Mitigating the threat of extinction from AI ought to be an international concern alongside other societal-scale risks such as pandemics and nuclear war." [152]
Mass joblessness
Researchers from OpenAI estimated that "80% of the U.S. labor force might have at least 10% of their work tasks affected by the introduction of LLMs, while around 19% of employees might see a minimum of 50% of their tasks affected". [166] [167] They think about office workers to be the most exposed, for instance mathematicians, accountants or web designers. [167] AGI could have a better autonomy, capability to make decisions, to user interface with other computer system tools, however also to control robotized bodies.
According to Stephen Hawking, the result of automation on the lifestyle will depend upon how the wealth will be rearranged: [142]
Everyone can delight in a life of glamorous leisure if the machine-produced wealth is shared, or many people can wind up badly bad if the machine-owners effectively lobby against wealth redistribution. So far, the pattern seems to be toward the 2nd choice, with technology driving ever-increasing inequality
Elon Musk considers that the automation of society will need governments to adopt a universal fundamental income. [168]
See likewise
Artificial brain - Software and hardware with cognitive capabilities comparable to those of the animal or human brain
AI result
AI security - Research area on making AI safe and advantageous
AI alignment - AI conformance to the intended goal
A.I. Rising - 2018 film directed by Lazar Bodroža
Artificial intelligence
Automated machine learning - Process of automating the application of artificial intelligence
BRAIN Initiative - Collaborative public-private research effort revealed by the Obama administration
China Brain Project
Future of Humanity Institute - Defunct Oxford interdisciplinary research study centre
General game playing - Ability of expert system to play different games
Generative synthetic intelligence - AI system capable of creating material in reaction to prompts
Human Brain Project - Scientific research study project
Intelligence amplification - Use of infotech to enhance human intelligence (IA).
Machine ethics - Moral behaviours of manufactured machines.
Moravec's paradox.
Multi-task learning - Solving several machine learning tasks at the same time.
Neural scaling law - Statistical law in device knowing.
Outline of expert system - Overview of and topical guide to synthetic intelligence.
Transhumanism - Philosophical motion.
Synthetic intelligence - Alternate term for or type of synthetic intelligence.
Transfer learning - Artificial intelligence method.
Loebner Prize - Annual AI competitors.
Hardware for synthetic intelligence - Hardware specially developed and optimized for synthetic intelligence.
Weak expert system - Form of expert system.
Notes
^ a b See listed below for the origin of the term "strong AI", and see the scholastic meaning of "strong AI" and weak AI in the post Chinese room.
^ AI founder John McCarthy writes: "we can not yet characterize in general what type of computational procedures we wish to call intelligent. " [26] (For a conversation of some definitions of intelligence used by synthetic intelligence scientists, see approach 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 ended up being identified to money just "mission-oriented direct research, rather than fundamental undirected research". [56] [57] ^ As AI creator John McCarthy composes "it would be a terrific relief to the rest of the workers in AI if the creators of new general formalisms would express their hopes in a more secured form than has often held true." [61] ^ In "Mind Children" [122] 1015 cps is used. More recently, in 1997, [123] Moravec argued for 108 MIPS which would roughly correspond to 1014 cps. Moravec talks in regards to MIPS, not "cps", which is a non-standard term Kurzweil introduced.
^ As defined in a standard AI book: "The assertion that machines could possibly act wisely (or, maybe much better, act as if they were intelligent) is called the 'weak AI' hypothesis by theorists, and the assertion that makers that do so are in fact believing (rather than 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 synthetic narrow intelligence (ANI)?". VentureBeat. Retrieved 1 March 2024. ANI is created to carry out a single task.
^ "OpenAI Charter". OpenAI. Retrieved 6 April 2023. Our objective is to make sure that artificial general intelligence advantages all of humankind.
^ Heath, Alex (18 January 2024). "Mark Zuckerberg's brand-new goal is developing artificial general intelligence". The Verge. Retrieved 13 June 2024. Our vision is to construct AI that is better than human-level at all of the human senses.
^ Baum, Seth D. (2020 ). A 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 specialists in expert system 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 pioneer Geoffrey Hinton gives up Google and cautions of risk ahead". The New York City Times. 1 May 2023. Retrieved 2 May 2023. It is hard to see how you can avoid the bad actors from utilizing it for bad things.
^ Bubeck, Sébastien; Chandrasekaran, Varun; Eldan, Ronen; Gehrke, Johannes; Horvitz, Eric (2023 ). "Sparks of Artificial General Intelligence: Early experiments with GPT-4". arXiv preprint. arXiv:2303.12712. GPT-4 shows sparks 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 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 City Times. The genuine danger is not AI itself however the way we release it.
^ "Impressed by artificial intelligence? Experts state AGI is coming next, and it has 'existential' dangers". ABC News. 23 March 2023. Retrieved 6 April 2023. AGI might pose existential threats to humanity.
^ 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 Times. Mitigating the danger of extinction from AI should be a global top priority.
^ "Statement on AI Risk". Center for AI Safety. Retrieved 1 March 2024. AI specialists alert of risk of extinction from AI.
^ Mitchell, Melanie (30 May 2023). "Are AI's Doomsday Scenarios Worth Taking Seriously?". The New York Times. We are far from creating makers that can outthink us in general ways.
^ LeCun, Yann (June 2023). "AGI does not provide an existential risk". Medium. There is no factor to fear AI as an existential threat.
^ Kurzweil 2005, p. 260.
^ a b Kurzweil, Ray (5 August 2005), "Long Live AI", Forbes, archived from the original on 14 August 2005: Kurzweil describes strong AI as "device intelligence with the full variety of human intelligence.".
^ "The Age of Expert System: George John at TEDxLondonBusinessSchool 2013". Archived from the original on 26 February 2014. Retrieved 22 February 2014.
^ Newell & Simon 1976, This is the term they utilize for "human-level" intelligence in the physical sign system hypothesis.
^ "The Open University on Strong and Weak AI". Archived from the initial on 25 September 2009. Retrieved 8 October 2007.
^ "What is artificial superintelligence (ASI)?|Definition from TechTarget". Enterprise AI. Retrieved 8 October 2023.
^ "Artificial intelligence is changing our world - it is on everyone to make sure that it goes well". Our World in Data. Retrieved 8 October 2023.
^ Dickson, Ben (16 November 2023). "Here is how far we are to achieving AGI, according to DeepMind". VentureBeat.
^ McCarthy, John (2007a). "Basic Questions". Stanford University. Archived from the original on 26 October 2007. Retrieved 6 December 2007.
^ This list of smart qualities is based upon the subjects covered by major AI books, including: Russell & Norvig 2003, Luger & Stubblefield 2004, Poole, Mackworth & Goebel 1998 and Nilsson 1998.
^ Johnson 1987.
^ de Charms, R. (1968 ). Personal causation. New York: Academic Press.
^ a b Pfeifer, R. and Bongard J. C., How the body forms the way we think: a brand-new view of intelligence (The MIT Press, 2007). ISBN 0-2621-6239-3.
^ White, R. W. (1959 ). "Motivation reevaluated: The concept of proficiency". Psychological Review. 66 (5 ): 297-333. doi:10.1037/ h0040934. PMID 13844397. S2CID 37385966.
^ White, R. W. (1959 ). "Motivation reassessed: The concept 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 initial on 25 April 2014. Retrieved 1 May 2014.
^ "What is Artificial General Intelligence (AGI)?|4 Tests For Ensuring Artificial General Intelligence". Talky Blog. 13 July 2019. Archived from the initial on 17 July 2019. Retrieved 17 July 2019.
^ Kirk-Giannini, Cameron Domenico; Goldstein, Simon (16 October 2023). "AI is closer than ever to passing the Turing test for 'intelligence'. What takes place when it does?". The Conversation. Retrieved 22 September 2024.
^ a b Turing 1950.
^ Turing, Alan (1952 ). B. Jack Copeland (ed.). Can Automatic Calculating Machines Be Said To Think?. Oxford: Oxford University Press. pp. 487-506. ISBN 978-0-1982-5079-1.
^ "Eugene Goostman is a genuine kid - the Turing Test states so". The Guardian. 9 June 2014. ISSN 0261-3077. Retrieved 3 March 2024.
^ "Scientists challenge whether computer 'Eugene Goostman' passed Turing test". BBC News. 9 June 2014. Retrieved 3 March 2024.
^ Jones, Cameron R.; Bergen, Benjamin K. (9 May 2024). "People can not 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 whatever from the bar exam to AP Biology. Here's a list of tough tests both AI variations have passed". Business Insider. Retrieved 30 May 2023.
^ Naysmith, Caleb (7 February 2023). "6 Jobs Expert System Is Already Replacing and How Investors Can Take Advantage Of It". Retrieved 30 May 2023.
^ Turk, Victoria (28 January 2015). "The Plan to Replace the Turing Test with a 'Turing Olympics'". Vice. Retrieved 3 March 2024.
^ Gopani, Avi (25 May 2022). "Turing Test is undependable. The Winograd Schema is obsolete. Coffee is the response". Analytics India Magazine. Retrieved 3 March 2024.
^ Bhaimiya, Sawdah (20 June 2023). "DeepMind's co-founder suggested 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 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). 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 initial on 6 May 2022. Retrieved 12 March 2022.
^ Simon 1965, p. 96 estimated in Crevier 1993, p. 109.
^ "Scientist on the Set: An Interview with Marvin Minsky". Archived from the original on 16 July 2012. Retrieved 5 April 2008.
^ Marvin Minsky to Darrach (1970 ), quoted in Crevier (1993, p. 109).
^ Lighthill 1973; Howe 1994.
^ a b NRC 1999, "Shift to Applied Research Increases Investment".
^ Crevier 1993, pp. 115-117; Russell & Norvig 2003, pp. 21-22.
^ Crevier 1993, p. 211, Russell & Norvig 2003, p. 24 and see also Feigenbaum & McCorduck 1983.
^ Crevier 1993, pp. 161-162, 197-203, 240; Russell & Norvig 2003, p. 25.
^ Crevier 1993, pp. 209-212.
^ McCarthy, John (2000 ). "Respond to Lighthill". Stanford University. Archived from the initial 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 artificial intelligence 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 initial on 19 July 2022. Retrieved 19 July 2022.
^ Legg, Shane (2008 ). Machine Super Intelligence (PDF) (Thesis). University of Lugano. Archived (PDF) from the original on 15 June 2022. Retrieved 19 July 2022.
^ Goertzel, Ben (2014 ). Artificial General Intelligence. Lecture Notes in Computer 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 original on 28 December 2018. Retrieved 28 December 2018., via 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 summertime 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 limitations of machine intelligence: Despite development in machine intelligence, artificial general intelligence is still a significant obstacle". 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 become a Frankenstein's beast". The Guardian. Archived from the initial on 17 September 2014. Retrieved 17 September 2014.
^ Deane, George (2022 ). "Machines That Feel and Think: The Role of Affective Feelings and Mental Action in (Artificial) General Intelligence". Artificial Life. 28 (3 ): 289-309. doi:10.1162/ artl_a_00368. ISSN 1064-5462. PMID 35881678. S2CID 251069071.
^ a b c Clocksin 2003.
^ Fjelland, Ragnar (17 June 2020). "Why basic expert system will not be recognized". Humanities and Social Sciences Communications. 7 (1 ): 1-9. doi:10.1057/ s41599-020-0494-4. hdl:11250/ 2726984. ISSN 2662-9992. S2CID 219710554.
^ McCarthy 2007b.
^ Khatchadourian, Raffi (23 November 2015). "The Doomsday Invention: Will artificial intelligence bring us utopia or destruction?". The New Yorker. Archived from the original on 28 January 2016. Retrieved 7 February 2016.
^ Müller, V. C., & Bostrom, N. (2016 ). Future progress in expert system: A survey of expert opinion. In Fundamental concerns of expert system (pp. 555-572). Springer, Cham.
^ Armstrong, Stuart, and Kaj Sotala. 2012. "How We're Predicting AI-or Failing To." In Beyond AI: Artificial Dreams, edited by Jan Romportl, Pavel Ircing, Eva Žáčková, Michal Polák and Radek Schuster, 52-75. Plzeň: University of West Bohemia
^ "Microsoft Now Claims GPT-4 Shows 'Sparks' of General Intelligence". 24 March 2023.
^ Shimek, Cary (6 July 2023). "AI Outperforms Humans in Creativity Test". Neuroscience News. Retrieved 20 October 2023.
^ Guzik, Erik E.; Byrge, Christian; Gilde, Christian (1 December 2023). "The originality of makers: AI takes the Torrance Test". Journal of Creativity. 33 (3 ): 100065. doi:10.1016/ j.yjoc.2023.100065. ISSN 2713-3745. S2CID 261087185.
^ Arcas, Blaise Agüera y (10 October 2023). "Artificial General Intelligence Is Already Here". Noema.
^ Zia, Tehseen (8 January 2024). "Unveiling of Large Multimodal Models: Shaping the Landscape of Language Models in 2024". Unite.ai. Retrieved 26 May 2024.
^ "Introducing OpenAI o1-preview". OpenAI. 12 September 2024.
^ Knight, Will. "OpenAI Announces a New AI Model, Code-Named Strawberry, That Solves Difficult Problems Step by Step". Wired. ISSN 1059-1028. Retrieved 17 September 2024.
^ "OpenAI Employee Claims AGI Has Been Achieved". Orbital Today. 13 December 2024. Retrieved 27 December 2024.
^ "AI Index: State of AI in 13 Charts". hai.stanford.edu. 15 April 2024. Retrieved 7 June 2024.
^ "Next-Gen AI: OpenAI and Meta's Leap Towards Reasoning Machines". Unite.ai. 19 April 2024. Retrieved 7 June 2024.
^ James, Alex P. (2022 ). "The Why, What, and How of Artificial General Intelligence Chip Development". IEEE Transactions on Cognitive and Developmental Systems. 14 (2 ): 333-347. arXiv:2012.06338. doi:10.1109/ TCDS.2021.3069871. ISSN 2379-8920. S2CID 228376556. Archived from the initial on 28 August 2022. Retrieved 28 August 2022.
^ Pei, Jing; Deng, Lei; Song, Sen; Zhao, Mingguo; Zhang, Youhui; Wu, Shuang; Wang, Guanrui; Zou, Zhe; Wu, Zhenzhi; He, Wei; Chen, Feng; Deng, Ning; Wu, Si; Wang,
