Artificial general intelligence (AGI) is a kind of expert system (AI) that matches or exceeds human cognitive abilities throughout a large range of cognitive tasks. This contrasts with narrow AI, which is restricted to particular tasks. [1] Artificial superintelligence (ASI), on the other hand, describes AGI that considerably exceeds human cognitive abilities. AGI is thought about one of the meanings of strong AI.

Creating AGI is a primary goal of AI research study and of companies such as OpenAI [2] and Meta. [3] A 2020 survey recognized 72 active AGI research study and development projects throughout 37 countries. [4]

The timeline for attaining AGI remains a subject of continuous debate amongst scientists and professionals. Since 2023, some argue that it might be possible in years or decades; others keep it may take a century or longer; a minority think it might never be attained; and another minority claims that it is currently here. [5] [6] Notable AI scientist Geoffrey Hinton has revealed concerns about the fast development towards AGI, recommending it could be attained faster than numerous expect. [7]

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

Contention exists over whether AGI represents an existential threat. [11] [12] [13] Many experts on AI have actually mentioned that reducing the danger of human extinction presented by AGI must be a global top priority. [14] [15] Others discover the development of AGI to be too remote to present such a threat. [16] [17]

Terminology

AGI is also called strong AI, [18] [19] full 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 life or awareness. [a] In contrast, weak AI (or narrow AI) is able to fix one particular issue however does not have general cognitive capabilities. [22] [19] Some academic sources use "weak AI" to refer more broadly to any programs that neither experience consciousness nor have a mind in the exact same sense as human beings. [a]

Related concepts consist of artificial superintelligence and transformative AI. A synthetic superintelligence (ASI) is a theoretical kind of AGI that is much more normally intelligent than people, [23] while the concept of transformative AI associates with AI having a large impact on society, for instance, similar to the agricultural or commercial revolution. [24]

A framework for categorizing AGI in levels was proposed in 2023 by Google DeepMind researchers. They define 5 levels of AGI: emerging, qualified, specialist, virtuoso, and superhuman. For instance, a qualified AGI is defined as an AI that outperforms 50% of skilled grownups in a vast array of non-physical tasks, and a superhuman AGI (i.e. a synthetic superintelligence) is similarly specified however with a threshold of 100%. They think about large language models like ChatGPT or LLaMA 2 to be circumstances of emerging AGI. [25]

Characteristics

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

Intelligence characteristics

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

reason, use strategy, resolve puzzles, and make judgments under unpredictability
represent knowledge, including common sense knowledge
plan
find out
- communicate in natural language
- if needed, integrate these abilities in conclusion of any provided goal


Many interdisciplinary approaches (e.g. cognitive science, computational intelligence, and decision making) consider extra qualities such as imagination (the capability to form novel psychological images and ideas) [28] and autonomy. [29]

Computer-based systems that display a number of these abilities exist (e.g. see computational creativity, automated reasoning, choice support group, robot, evolutionary computation, intelligent representative). There is debate about whether modern-day AI systems have them to a sufficient degree.

Physical traits

Other capabilities are considered desirable in smart systems, as they may impact intelligence or aid in its expression. These consist of: [30]

- the ability to sense (e.g. see, hear, and so on), and
- the ability to act (e.g. move and control things, modification place to check out, and so on).


This consists of the ability to detect and react to hazard. [31]

Although the ability to sense (e.g. see, hear, etc) and the capability to act (e.g. move and manipulate things, modification area to check out, and so on) can be preferable for some intelligent systems, [30] these physical abilities are not strictly needed 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 viewpoint on LLMs, there is no firm requirement for an AGI to have a human-like form; being a silicon-based computational system suffices, provided it can process input (language) from the external world in location of human senses. This analysis aligns with the understanding that AGI has never ever been proscribed a particular physical embodiment and thus does not demand a capability for locomotion or conventional "eyes and ears". [32]

Tests for human-level AGI

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

The idea of the test is that the machine has to try and pretend to be a guy, by addressing questions put to it, and it will only pass if the pretence is fairly convincing. A substantial part of a jury, who should not be professional about devices, need to be taken in by the pretence. [37]

AI-complete issues

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

There are many issues that have been conjectured to require general intelligence to resolve as well as humans. Examples include computer system vision, natural language understanding, and handling unanticipated circumstances while fixing any real-world issue. [48] Even a particular task like translation requires a machine to read and write in both languages, follow the author's argument (factor), comprehend the context (knowledge), and faithfully replicate the author's original intent (social intelligence). All of these problems need to be resolved all at once in order to reach human-level device performance.

However, a number of these tasks can now be carried out by modern-day big language models. According to Stanford University's 2024 AI index, AI has reached human-level efficiency on numerous standards 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 scientists were convinced that artificial general intelligence was possible and that it would exist in just a couple of decades. [51] AI leader Herbert A. Simon wrote in 1965: "makers 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 scientists believed they might develop by the year 2001. AI leader Marvin Minsky was a consultant [53] on the project of making HAL 9000 as realistic as possible according to the consensus predictions of the time. He stated in 1967, "Within a generation ... the problem of developing 'artificial intelligence' will significantly be resolved". [54]

Several classical AI jobs, such as Doug Lenat's Cyc job (that began in 1984), and wikitravel.org Allen Newell's Soar task, were directed at AGI.

However, in the early 1970s, it became obvious that scientists had grossly ignored the trouble of the job. Funding firms ended up being hesitant of AGI and put scientists under increasing pressure to produce helpful "applied AI". [c] In the early 1980s, Japan's Fifth Generation Computer Project revived interest in AGI, setting out a ten-year timeline that included AGI objectives like "bring on a table talk". [58] In action to this and the success of specialist systems, both market and 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 twenty years, AI researchers who predicted the imminent accomplishment of AGI had actually been mistaken. By the 1990s, AI researchers had a reputation for making vain guarantees. They ended up being hesitant to make forecasts at all [d] and avoided mention of "human level" synthetic 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 focusing on particular sub-problems where AI can produce verifiable outcomes and commercial applications, such as speech acknowledgment and recommendation algorithms. [63] These "applied AI" systems are now utilized thoroughly throughout the innovation market, and research in this vein is heavily moneyed in both academia and market. Since 2018 [update], advancement in this field was considered an emerging trend, and a mature phase was anticipated to be reached in more than 10 years. [64]

At the turn of the century, numerous mainstream AI researchers [65] hoped that strong AI might be established by combining programs that fix various sub-problems. Hans Moravec composed in 1988:

I am confident that this bottom-up route to synthetic intelligence will one day satisfy the standard top-down path over half way, prepared to offer the real-world skills and the commonsense knowledge that has been so frustratingly evasive in reasoning programs. Fully intelligent devices will result when the metaphorical golden spike is driven uniting the 2 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 mentioning:

The expectation has frequently been voiced that "top-down" (symbolic) approaches to modeling cognition will somehow meet "bottom-up" (sensory) approaches somewhere in between. If the grounding considerations in this paper are valid, then this expectation is hopelessly modular and there is really just one viable path from sense to signs: from the ground up. A free-floating symbolic level like the software application level of a computer will never be reached by this route (or vice versa) - nor is it clear why we need to even try to reach such a level, considering that it appears getting there would simply amount to uprooting our symbols from their intrinsic significances (consequently simply lowering ourselves to the functional equivalent of a programmable computer). [66]

Modern artificial general intelligence research study

The term "artificial general intelligence" was utilized as early as 1997, by Mark Gubrud [67] in a discussion of the ramifications of totally automated military production and operations. A mathematical formalism of AGI was proposed by Marcus Hutter in 2000. Named AIXI, the proposed AGI agent maximises "the capability to satisfy goals in a large range of environments". [68] This kind of AGI, identified by the capability to increase a mathematical meaning of intelligence instead of display human-like behaviour, [69] was also called universal synthetic intelligence. [70]

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

Since 2023 [update], a small number of computer researchers are active in AGI research, and lots of contribute to a series of AGI conferences. However, significantly more scientists have an interest in open-ended learning, [76] [77] which is the concept of enabling AI to continuously find out and innovate like humans do.

Feasibility

Since 2023, the advancement and potential achievement of AGI stays a subject of extreme argument within the AI neighborhood. While conventional agreement held that AGI was a distant objective, recent improvements have led some researchers and market figures to claim that early types of AGI may already exist. [78] AI pioneer Herbert A. Simon hypothesized 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 due to the fact that it would require "unforeseeable and basically 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 broad as the gulf between existing space flight and practical faster-than-light spaceflight. [80]

A more difficulty is the absence of clarity in specifying what intelligence entails. Does it need awareness? Must it display the ability to set goals 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 needed? Does intelligence need explicitly reproducing the brain and its particular professors? Does it require feelings? [81]

Most AI researchers believe strong AI can be accomplished in the future, however some thinkers, like Hubert Dreyfus and Roger Penrose, deny the possibility of accomplishing strong AI. [82] [83] John McCarthy is among those who believe human-level AI will be achieved, however that today level of progress is such that a date can not accurately be predicted. [84] AI professionals' views on the expediency of AGI wax and subside. Four surveys conducted in 2012 and 2013 recommended that the median quote among experts for when they would be 50% confident AGI would arrive was 2040 to 2050, depending on the poll, with the mean being 2081. Of the professionals, 16.5% addressed with "never" when asked the same concern but with a 90% self-confidence rather. [85] [86] Further current AGI progress 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 found that "over [a] 60-year amount of time there is a strong predisposition towards forecasting the arrival of human-level AI as between 15 and 25 years from the time the prediction was made". They analyzed 95 predictions made in between 1950 and 2012 on when human-level AI will come about. [87]

In 2023, Microsoft researchers published an in-depth examination of GPT-4. They concluded: "Given the breadth and depth of GPT-4's capabilities, we think that it might fairly be seen as an early (yet still incomplete) variation of a synthetic basic intelligence (AGI) system." [88] Another study in 2023 reported that GPT-4 outperforms 99% of human beings on the Torrance tests of creative thinking. [89] [90]

Blaise Agüera y Arcas and Peter Norvig wrote in 2023 that a significant level of general intelligence has actually already been attained with frontier models. They composed that hesitation to this view originates from four main reasons: a "healthy suspicion about metrics for AGI", an "ideological dedication to alternative AI theories or strategies", a "dedication to human (or biological) exceptionalism", or a "issue about the financial implications of AGI". [91]

2023 also marked the introduction of large multimodal designs (big language designs capable of processing or generating multiple modalities 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 react". According to Mira Murati, this capability to think before responding represents a brand-new, additional paradigm. It enhances design outputs by spending more computing power when producing the answer, whereas the design 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 attained AGI, mentioning, "In my opinion, we have currently accomplished AGI and it's much more clear with O1." Kazemi clarified that while the AI is not yet "better than any human at any task", it is "better than most human beings at most jobs." He likewise dealt with criticisms that large language designs (LLMs) merely follow predefined patterns, comparing their knowing process to the clinical approach of observing, assuming, and confirming. These statements have actually triggered debate, as they rely on a broad and unconventional definition of AGI-traditionally understood as AI that matches human intelligence across all domains. Critics argue that, while OpenAI's models show amazing versatility, they might not fully fulfill this requirement. Notably, Kazemi's remarks came soon after OpenAI eliminated "AGI" from the regards to its collaboration with Microsoft, triggering speculation about the company's strategic intents. [95]

Timescales

Progress in artificial intelligence has traditionally gone through durations of fast progress separated by durations when development appeared to stop. [82] Ending each hiatus were fundamental advances in hardware, software application or both to create area for more progress. [82] [98] [99] For instance, the computer hardware available in the twentieth century was not sufficient to implement deep learning, which requires great deals of GPU-enabled CPUs. [100]

In the introduction to his 2006 book, [101] Goertzel states that price quotes of the time required before a genuinely flexible AGI is built vary from 10 years to over a century. Since 2007 [update], the consensus in the AGI research neighborhood seemed to be that the timeline discussed by Ray Kurzweil in 2005 in The Singularity is Near [102] (i.e. in between 2015 and 2045) was plausible. [103] Mainstream AI researchers have actually given a large range of viewpoints on whether development will be this quick. A 2012 meta-analysis of 95 such viewpoints found a bias towards predicting that the start of AGI would happen within 16-26 years for modern and historical predictions alike. That paper has been criticized for how it classified viewpoints as expert or non-expert. [104]

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

In 2017, researchers Feng Liu, Yong Shi, and Ying Liu performed intelligence tests on publicly offered and freely available weak AI such as Google AI, Apple's Siri, and others. At the maximum, these AIs reached an IQ worth of about 47, which corresponds around to a six-year-old kid in very first grade. A grownup pertains to about 100 on average. Similar tests were performed in 2014, with the IQ score reaching a maximum worth of 27. [106] [107]

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

In the same year, Jason Rohrer used his GPT-3 account to develop a chatbot, and supplied a chatbot-developing platform called "Project December". OpenAI requested for changes to the chatbot to comply with their safety guidelines; Rohrer disconnected Project December from the GPT-3 API. [109]

In 2022, DeepMind developed Gato, a "general-purpose" system efficient in carrying out more than 600 different tasks. [110]

In 2023, Microsoft Research published a research study on an early variation of OpenAI's GPT-4, competing that it showed more general intelligence than previous AI designs and showed human-level performance in tasks spanning numerous domains, such as mathematics, coding, and law. This research stimulated a dispute on whether GPT-4 might be considered an early, incomplete variation of synthetic basic intelligence, highlighting the need for more exploration and examination of such systems. [111]

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

The concept that this stuff might actually get smarter than people - a couple of individuals believed that, [...] But many people thought it was method off. And I thought it was way off. I thought it was 30 to 50 years or perhaps longer away. Obviously, I no longer think that.

In May 2023, Demis Hassabis similarly stated that "The progress in the last couple of years has actually been quite unbelievable", and that he sees no factor why it would decrease, expecting AGI within a years and even a few years. [113] In March 2024, Nvidia's CEO, Jensen Huang, mentioned his expectation that within 5 years, AI would can passing any test a minimum of in addition to humans. [114] In June 2024, the AI scientist Leopold Aschenbrenner, a previous OpenAI employee, approximated AGI by 2027 to be "noticeably plausible". [115]

Whole brain emulation

While the advancement of transformer designs like in ChatGPT is thought about the most appealing path to AGI, [116] [117] whole brain emulation can work as an alternative approach. With entire brain simulation, a brain design is built by scanning and mapping a biological brain in information, and after that copying and mimicing it on a computer system or another computational device. The simulation model need to be sufficiently faithful to the original, so that it acts in practically the very same method as the original brain. [118] Whole brain emulation is a kind of brain simulation that is gone over in computational neuroscience and neuroinformatics, and for medical research study purposes. It has actually been gone over in expert system research [103] as an approach to strong AI. Neuroimaging innovations that might deliver the essential detailed understanding are enhancing rapidly, and futurist Ray Kurzweil in the book The Singularity Is Near [102] forecasts that a map of adequate quality will appear on a similar timescale to the computing power required to imitate it.

Early estimates

For low-level brain simulation, a very effective cluster of computer systems or GPUs would be needed, offered the huge amount of synapses within the human brain. Each of the 1011 (one hundred billion) neurons has on average 7,000 synaptic connections (synapses) to other neurons. The brain of a three-year-old kid has about 1015 synapses (1 quadrillion). This number decreases with age, supporting by their adult years. Estimates differ for an adult, ranging from 1014 to 5 × 1014 synapses (100 to 500 trillion). [120] An estimate of the brain's processing power, based on 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 numerous quotes for the hardware needed to equal the human brain and embraced a figure of 1016 calculations per second (cps). [e] (For comparison, if a "computation" was comparable to one "floating-point operation" - a measure used to rate existing supercomputers - then 1016 "computations" would be equivalent to 10 petaFLOPS, achieved in 2011, while 1018 was achieved in 2022.) He used this figure to predict the necessary hardware would be readily available at some point between 2015 and 2025, if the exponential development in computer system power at the time of composing continued.

Current research

The Human Brain Project, an EU-funded initiative active from 2013 to 2023, has developed a particularly 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 methods

The synthetic nerve cell model assumed by Kurzweil and used in numerous current synthetic neural network applications is simple compared to biological nerve cells. A brain simulation would likely need to capture the in-depth cellular behaviour of biological nerve cells, presently comprehended only in broad overview. The overhead introduced by full modeling of the biological, chemical, and physical information of neural behaviour (especially on a molecular scale) would require computational powers a number of orders of magnitude larger than Kurzweil's estimate. 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 stems from embodied cognition theory which asserts that human embodiment is a vital aspect of human intelligence and is essential to ground significance. [126] [127] If this theory is proper, any fully functional brain model will require to include more than simply the nerve cells (e.g., a robotic body). Goertzel [103] proposes virtual personification (like in metaverses like Second Life) as an option, but it is unknown whether this would suffice.

Philosophical perspective

"Strong AI" as specified in philosophy

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

Strong AI hypothesis: An expert system system can have "a mind" and "consciousness".
Weak AI hypothesis: A synthetic intelligence system can (only) imitate it believes and has a mind and awareness.


The very first one he called "strong" due to the fact that it makes a stronger statement: it presumes something special has taken place to the machine that surpasses those abilities that we can check. The behaviour of a "weak AI" machine would be specifically similar to a "strong AI" maker, however the latter would likewise have subjective conscious experience. This usage is also common in scholastic AI research 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 general intelligence". [102] This is not the like Searle's strong AI, unless it is presumed that awareness is necessary for human-level AGI. Academic philosophers such as Searle do not believe that holds true, and to most expert system researchers the question is out-of-scope. [130]

Mainstream AI is most interested in how a program behaves. [131] According to Russell and Norvig, "as long as the program works, they don't care if you call it real or a simulation." [130] If the program can behave as if it has a mind, then there is no requirement to understand if it in fact has mind - certainly, there would be no chance to inform. For AI research, Searle's "weak AI hypothesis" is comparable to the statement "artificial basic intelligence is possible". Thus, according to Russell and Norvig, "most AI scientists take the weak AI hypothesis for granted, and do not care about the strong AI hypothesis." [130] Thus, for academic AI research, "Strong AI" and "AGI" are two various things.

Consciousness

Consciousness can have different meanings, and some aspects play significant roles in science fiction and the ethics of expert system:

Sentience (or "extraordinary awareness"): The ability to "feel" perceptions or feelings subjectively, instead of the ability to factor about perceptions. Some thinkers, such as David Chalmers, use the term "consciousness" to refer solely to remarkable consciousness, which is approximately equivalent to life. [132] Determining why and how subjective experience occurs is understood as the tough problem of awareness. [133] Thomas Nagel discussed in 1974 that it "feels like" something to be conscious. If we are not mindful, then it does not feel like anything. Nagel utilizes the example of a bat: we can smartly ask "what does it feel like to be a bat?" However, we are not likely to ask "what does it 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 declared that the company's AI chatbot, LaMDA, had accomplished sentience, though this claim was widely disputed by other professionals. [135]

Self-awareness: To have conscious awareness of oneself as a separate person, especially to be knowingly familiar with one's own ideas. This is opposed to merely being the "topic of one's believed"-an operating system or debugger has the ability to be "mindful of itself" (that is, to represent itself in the very same method it represents everything else)-but this is not what people typically mean when they utilize the term "self-awareness". [g]

These qualities have a moral dimension. AI sentience would trigger issues of well-being and legal protection, similarly to animals. [136] Other elements of awareness associated to cognitive abilities are also appropriate to the concept of AI rights. [137] Finding out how to incorporate advanced AI with existing legal and social structures is an emergent concern. [138]

Benefits

AGI could have a wide variety of applications. If oriented towards such objectives, AGI could help mitigate different problems in the world such as hunger, poverty and health issue. [139]

AGI might improve performance and performance in most jobs. For instance, in public health, AGI might accelerate medical research study, significantly against cancer. [140] It could look after the elderly, [141] and equalize access to rapid, top quality medical diagnostics. It might use enjoyable, cheap and personalized education. [141] The need to work to subsist could end up being obsolete if the wealth produced is appropriately rearranged. [141] [142] This likewise raises the question of the location of people in a significantly automated society.

AGI might also help to make logical choices, and to prepare for and prevent disasters. It could also help to gain the advantages of potentially devastating innovations such as nanotechnology or environment engineering, while preventing the associated threats. [143] If an AGI's main goal is to prevent existential disasters such as human extinction (which could be tough if the Vulnerable World Hypothesis turns out to be real), [144] it could take measures to significantly reduce the risks [143] while minimizing the impact of these steps on our lifestyle.

Risks

Existential dangers

AGI might represent multiple types of existential risk, which are risks that threaten "the early extinction of Earth-originating intelligent life or the irreversible and drastic destruction of its potential for preferable future development". [145] The threat of human termination from AGI has actually been the topic of numerous arguments, but there is also the possibility that the advancement of AGI would result in a permanently problematic future. Notably, it could be used to spread and preserve the set of values of whoever develops it. If humanity still has moral blind areas comparable to slavery in the past, AGI may irreversibly entrench it, preventing moral progress. [146] Furthermore, AGI could facilitate mass security and indoctrination, which could be utilized to develop a steady repressive worldwide totalitarian regime. [147] [148] There is also a threat for the devices themselves. If devices that are sentient or otherwise worthwhile of moral consideration are mass produced in the future, participating in a civilizational path that forever neglects their welfare and interests might be an existential catastrophe. [149] [150] Considering how much AGI might improve mankind's future and aid reduce other existential risks, Toby Ord calls these existential threats "an argument for continuing with due care", not for "deserting AI". [147]

Risk of loss of control and human termination

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

In 2014, Stephen Hawking criticized prevalent indifference:

So, dealing with possible futures of enormous advantages and threats, the specialists are definitely doing everything possible to make sure the finest outcome, right? Wrong. If a superior alien civilisation sent us a message saying, 'We'll get here in a few years,' would we just reply, 'OK, call us when you get here-we'll leave the lights on?' Probably not-but this is more or less what is occurring with AI. [153]

The possible fate of humanity has in some cases been compared to the fate of gorillas threatened by human activities. The contrast mentions that greater intelligence enabled mankind to control gorillas, which are now susceptible in manner ins which they might not have expected. As a result, the gorilla has become a threatened species, not out of malice, but simply as a security damage from human activities. [154]

The skeptic Yann LeCun thinks about that AGIs will have no desire to dominate mankind and that we must be mindful not to anthropomorphize them and interpret their intents as we would for humans. He stated that people will not be "clever enough to design super-intelligent devices, yet unbelievably stupid to the point of giving it moronic goals with no safeguards". [155] On the other side, the principle of critical convergence suggests that practically whatever their objectives, smart representatives will have factors to attempt to survive and get more power as intermediary actions to achieving these goals. And that this does not need having feelings. [156]

Many scholars who are worried about existential risk supporter for more research study into fixing the "control problem" to respond to the concern: what kinds of safeguards, algorithms, or architectures can programmers execute to increase the probability that their recursively-improving AI would continue to behave in a friendly, instead of destructive, manner after it reaches superintelligence? [157] [158] Solving the control issue is made complex by the AI arms race (which might lead to a race to the bottom of safety precautions in order to release items before competitors), [159] and making use of AI in weapon systems. [160]

The thesis that AI can pose existential threat likewise has critics. Skeptics usually say that AGI is unlikely in the short-term, or that concerns about AGI distract from other problems related to current AI. [161] Former Google scams czar Shuman Ghosemajumder considers that for lots of people outside of 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 unreasonable belief in a supreme God. [163] Some scientists think that the communication campaigns on AI existential risk by particular AI groups (such as OpenAI, Anthropic, DeepMind, and Conjecture) may 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, issued a joint statement asserting that "Mitigating the threat of termination from AI should be a worldwide top priority together with other societal-scale risks such as pandemics and nuclear war." [152]

Mass unemployment

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

According to Stephen Hawking, the result of automation on the lifestyle will depend on how the wealth will be redistributed: [142]

Everyone can take pleasure in a life of glamorous leisure if the machine-produced wealth is shared, or many people can wind up badly poor if the machine-owners successfully lobby against wealth redistribution. So far, the pattern appears to be toward the second choice, with technology driving ever-increasing inequality

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

See also

Artificial brain - Software and hardware with cognitive abilities comparable to those of the animal or human brain
AI impact
AI security - Research area on making AI safe and helpful
AI alignment - AI conformance to the intended goal
A.I. Rising - 2018 movie directed by Lazar Bodroža
Expert system
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 study centre
General video game playing - Ability of expert system to play different games
Generative synthetic intelligence - AI system capable of creating content in reaction to triggers
Human Brain Project - Scientific research study task
Intelligence amplification - Use of infotech to augment human intelligence (IA).
Machine ethics - Moral behaviours of man-made devices.
Moravec's paradox.
Multi-task knowing - Solving numerous maker discovering jobs at the very same time.
Neural scaling law - Statistical law in artificial intelligence.
Outline of expert system - Overview of and topical guide to synthetic intelligence.
Transhumanism - Philosophical motion.
Synthetic intelligence - Alternate term for or form of expert system.
Transfer learning - Artificial intelligence method.
Loebner Prize - Annual AI competition.
Hardware for artificial intelligence - Hardware specifically designed and enhanced 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 academic definition of "strong AI" and weak AI in the post Chinese space.
^ AI founder John McCarthy writes: "we can not yet identify in general what sort of computational treatments we want to call intelligent. " [26] (For a discussion of some meanings of intelligence utilized by synthetic intelligence scientists, see viewpoint of expert system.).
^ The Lighthill report specifically slammed AI's "grandiose goals" and led the taking apart of AI research study in England. [55] In the U.S., DARPA ended up being determined to money only "mission-oriented direct research, instead of fundamental undirected research". [56] [57] ^ As AI creator John McCarthy writes "it would be a great relief to the rest of the employees in AI if the developers of brand-new basic formalisms would reveal their hopes in a more secured type than has actually in some cases held true." [61] ^ In "Mind Children" [122] 1015 cps is used. More just recently, in 1997, [123] Moravec argued for 108 MIPS which would approximately correspond to 1014 cps. Moravec talks in terms of MIPS, not "cps", which is a non-standard term Kurzweil introduced.
^ As defined in a standard AI book: "The assertion that devices might potentially act intelligently (or, perhaps 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 (instead of imitating 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 job.
^ "OpenAI Charter". OpenAI. Retrieved 6 April 2023. Our objective is to ensure that artificial general intelligence advantages all of humankind.
^ Heath, Alex (18 January 2024). "Mark Zuckerberg's new objective is producing artificial basic intelligence". The Verge. Retrieved 13 June 2024. Our vision is to develop AI that is much better than human-level at all of the human senses.
^ Baum, Seth D. (2020 ). A Survey of Artificial General Intelligence Projects for Ethics, Risk, and Policy (PDF) (Report). Global Catastrophic Risk Institute. Retrieved 28 November 2024. 72 AGI R&D jobs were recognized as being active in 2020.
^ a b c "AI timelines: What do specialists in synthetic intelligence anticipate for the future?". Our World in Data. Retrieved 6 April 2023.
^ Metz, Cade (15 May 2023). "Some Researchers Say A.I. Is Already Here, Stirring Debate in Tech Circles". The New York Times. Retrieved 18 May 2023.
^ "AI leader Geoffrey Hinton stops Google and warns of danger ahead". The New York City Times. 1 May 2023. Retrieved 2 May 2023. It is difficult 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 try outs GPT-4". arXiv preprint. arXiv:2303.12712. GPT-4 reveals stimulates of AGI.
^ Butler, Octavia E. (1993 ). Parable of the Sower. Grand Central Publishing. ISBN 978-0-4466-7550-5. All that you touch you change. All that you change changes you.
^ Vinge, Vernor (1992 ). A Fire Upon the Deep. Tor Books. ISBN 978-0-8125-1528-2. The Singularity is coming.
^ Morozov, Evgeny (30 June 2023). "The True Threat of Artificial Intelligence". The New York Times. The real hazard is not AI itself however the way we deploy it.
^ "Impressed by artificial intelligence? Experts state AGI is coming next, and it has 'existential' threats". ABC News. 23 March 2023. Retrieved 6 April 2023. AGI could pose existential threats to mankind.
^ Bostrom, Nick (2014 ). Superintelligence: Paths, Dangers, Strategies. Oxford University Press. ISBN 978-0-1996-7811-2. The very first superintelligence will be the last innovation that mankind needs to make.
^ Roose, Kevin (30 May 2023). "A.I. Poses 'Risk of Extinction,' Industry Leaders Warn". The New York City Times. Mitigating the danger of termination from AI ought to be an international priority.
^ "Statement on AI Risk". Center for AI Safety. Retrieved 1 March 2024. AI experts caution of risk of extinction from AI.
^ Mitchell, Melanie (30 May 2023). "Are AI's Doomsday Scenarios Worth Taking Seriously?". The New York City Times. We are far from developing machines that can outthink us in basic ways.
^ LeCun, Yann (June 2023). "AGI does not present an existential risk". Medium. There is no reason to fear AI as an existential danger.
^ Kurzweil 2005, p. 260.
^ a b Kurzweil, Ray (5 August 2005), "Long Live AI", Forbes, archived from the initial on 14 August 2005: Kurzweil describes strong AI as "machine intelligence with the full range 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 utilize for "human-level" intelligence in the physical sign system hypothesis.
^ "The Open University on Strong and Weak AI". Archived from the original on 25 September 2009. Retrieved 8 October 2007.
^ "What is artificial superintelligence (ASI)?|Definition from TechTarget". Enterprise AI. Retrieved 8 October 2023.
^ "Artificial intelligence is changing our world - it is on everybody 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 attaining AGI, according to DeepMind". VentureBeat.
^ McCarthy, John (2007a). "Basic Questions". Stanford University. Archived from the original on 26 October 2007. Retrieved 6 December 2007.
^ This list of smart characteristics is based on the subjects covered by significant AI textbooks, consisting of: Russell & Norvig 2003, Luger & Stubblefield 2004, Poole, Mackworth & Goebel 1998 and Nilsson 1998.
^ Johnson 1987.
^ de Charms, R. (1968 ). Personal causation. New York City: Academic Press.
^ a b Pfeifer, R. and Bongard J. C., How the body shapes the way we think: a 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 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 original on 17 July 2019. Retrieved 17 July 2019.
^ Kirk-Giannini, Cameron Domenico; Goldstein, Simon (16 October 2023). "AI is closer than ever to passing the Turing test for 'intelligence'. What takes place when it does?". The Conversation. Retrieved 22 September 2024.
^ a b Turing 1950.
^ Turing, Alan (1952 ). B. Jack Copeland (ed.). Can Automatic Calculating Machines Be Said To Think?. Oxford: Oxford University Press. pp. 487-506. ISBN 978-0-1982-5079-1.
^ "Eugene Goostman is a real boy - the Turing Test says so". The Guardian. 9 June 2014. ISSN 0261-3077. Retrieved 3 March 2024.
^ "Scientists contest 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 identify GPT-4 from a human in a Turing test". arXiv:2405.08007 [cs.HC]
^ Varanasi, Lakshmi (21 March 2023). "AI designs like ChatGPT and GPT-4 are acing whatever from the bar test to AP Biology. Here's a list of difficult examinations both AI versions have actually passed". Business Insider. Retrieved 30 May 2023.
^ Naysmith, Caleb (7 February 2023). "6 Jobs Expert System Is Already Replacing and How Investors Can 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 outdated. Coffee is the response". Analytics India Magazine. Retrieved 3 March 2024.
^ Bhaimiya, Sawdah (20 June 2023). "DeepMind's co-founder recommended testing an AI chatbot's ability to turn $100,000 into $1 million to measure human-like intelligence". Business Insider. Retrieved 3 March 2024.
^ Suleyman, Mustafa (14 July 2023). "Mustafa Suleyman: My brand-new Turing test would see if AI can make $1 million". MIT Technology Review. Retrieved 3 March 2024.
^ Shapiro, Stuart C. (1992 ). "Expert System" (PDF). In Stuart C. Shapiro (ed.). Encyclopedia of Artificial Intelligence (Second ed.). New York 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). Expert System, Evolutionary Computation and Metaheuristics (AIECM): 3-17. Archived (PDF) from the original on 22 May 2013.
^ "AI Index: State of AI in 13 Charts". Stanford University Human-Centered Artificial Intelligence. 15 April 2024. Retrieved 27 May 2024.
^ Crevier 1993, pp. 48-50.
^ Kaplan, Andreas (2022 ). "Expert System, Business and Civilization - Our Fate Made in Machines". Archived from the 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 initial 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 likewise Feigenbaum & McCorduck 1983.
^ Crevier 1993, pp. 161-162, 197-203, 240; Russell & Norvig 2003, p. 25.
^ Crevier 1993, pp. 209-212.
^ McCarthy, John (2000 ). "Reply to Lighthill". Stanford University. Archived from the 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 City Times. Archived from the original on 2 February 2023. Retrieved 18 February 2017. At its low point, some computer researchers and software engineers prevented the term synthetic intelligence for worry of being deemed 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 Upon Algorithmic Probability. Texts in Theoretical Computer Technology an EATCS Series. Springer. doi:10.1007/ b138233. ISBN 978-3-5402-6877-2. S2CID 33352850. Archived from the 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., via Life 3.0: 'The term "AGI" was popularized by ... Shane Legg, Mark Gubrud and Ben Goertzel'
^ Wang & Goertzel 2007
^ "First International Summer School in Artificial General Intelligence, Main summer school: June 22 - July 3, 2009, OpenCog Lab: July 6-9, 2009". Archived from the original on 28 September 2020. Retrieved 11 May 2020.
^ "Избираеми дисциплини 2009/2010 - пролетен триместър" [Elective courses 2009/2010 - spring trimester] Факултет по математика и информатика [Faculty of Mathematics and Informatics] (in Bulgarian). Archived from the initial on 26 July 2020. Retrieved 11 May 2020.
^ "Избираеми дисциплини 2010/2011 - зимен триместър" [Elective courses 2010/2011 - winter season trimester] Факултет по математика и информатика [Faculty of Mathematics and Informatics] (in Bulgarian). Archived from the 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, synthetic 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 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. "Artificial intelligence will not develop into a Frankenstein's monster". The Guardian. Archived from the initial on 17 September 2014. Retrieved 17 September 2014.
^ Deane, George (2022 ). "Machines That Feel and Think: The Role of Affective Feelings and Mental Action in (Artificial) General Intelligence". Artificial Life. 28 (3 ): 289-309. doi:10.1162/ artl_a_00368. ISSN 1064-5462. PMID 35881678. S2CID 251069071.
^ a b c Clocksin 2003.
^ Fjelland, Ragnar (17 June 2020). "Why basic expert system will not be 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 paradise or damage?". The New Yorker. Archived from the original on 28 January 2016. Retrieved 7 February 2016.
^ Müller, V. C., & Bostrom, N. (2016 ). Future development in synthetic intelligence: A study of skilled opinion. In Fundamental issues of expert system (pp. 555-572). Springer, Cham.
^ Armstrong, Stuart, and Kaj Sotala. 2012. "How We're Predicting AI-or Failing To." In Beyond AI: Artificial Dreams, modified by Jan Romportl, Pavel Ircing, Eva Žáčková, Michal Polák and Radek Schuster, 52-75. Plzeň: University of West Bohemia
^ "Microsoft Now Claims GPT-4 Shows 'Sparks' of General Intelligence". 24 March 2023.
^ Shimek, Cary (6 July 2023). "AI Outperforms Humans in Creativity Test". Neuroscience News. Retrieved 20 October 2023.
^ Guzik, Erik E.; Byrge, Christian; Gilde, Christian (1 December 2023). "The 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; Zo