Arrabidalegend

A PROPOS DE L'ENTREPRISE

Artificial General Intelligence

Artificial general intelligence (AGI) is a kind of expert system (AI) that matches or goes beyond human cognitive abilities across a vast array of cognitive tasks. This contrasts with narrow AI, which is limited to particular tasks. [1] Artificial superintelligence (ASI), on the other hand, refers to AGI that significantly surpasses human cognitive abilities. AGI is considered one of the meanings of strong AI.

Creating AGI is a main objective of AI research and of companies such as OpenAI [2] and Meta. [3] A 2020 study determined 72 active AGI research and development projects throughout 37 nations. [4]

The timeline for accomplishing AGI stays a topic of ongoing debate among scientists and experts. Since 2023, some argue that it may be possible in years or years; others preserve it might take a century or longer; a minority think it might never be achieved; and another minority declares that it is already here. [5] [6] Notable AI scientist Geoffrey Hinton has actually expressed issues about the fast progress towards AGI, recommending it could be attained earlier than lots of expect. [7]

There is debate on the exact definition of AGI and relating to whether modern-day large language models (LLMs) such as GPT-4 are early kinds of AGI. [8] AGI is a typical topic in science fiction and futures studies. [9] [10]

Contention exists over whether AGI represents an existential risk. [11] [12] [13] Many specialists on AI have actually mentioned that mitigating the risk of human termination postured by AGI needs to be a global top priority. [14] [15] Others find the development of AGI to be too remote to provide such a risk. [16] [17]

Terminology

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

Some scholastic sources schedule the term « strong AI » for computer programs that experience sentience or awareness. [a] In contrast, weak AI (or narrow AI) is able to resolve one particular problem however lacks basic cognitive abilities. [22] [19] Some academic sources use « weak AI » to refer more broadly to any programs that neither experience awareness nor have a mind in the very same sense as human beings. [a]

Related concepts consist of artificial superintelligence and transformative AI. A synthetic superintelligence (ASI) is a hypothetical kind of AGI that is far more generally smart than humans, [23] while the concept of transformative AI associates with AI having a large influence on society, for example, similar to the farming or commercial transformation. [24]

A structure for classifying AGI in levels was proposed in 2023 by Google DeepMind researchers. They define five levels of AGI: emerging, competent, professional, virtuoso, and superhuman. For example, a skilled AGI is specified as an AI that exceeds 50% of experienced grownups in a large range of non-physical tasks, and a superhuman AGI (i.e. an artificial superintelligence) is similarly defined however with a limit of 100%. They think about large language models like ChatGPT or LLaMA 2 to be instances of emerging AGI. [25]

Characteristics

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

Intelligence qualities

Researchers generally hold that intelligence is required to do all of the following: [27]

reason, use method, fix puzzles, and make judgments under unpredictability
represent knowledge, including typical sense knowledge
plan
discover
– interact in natural language
– if essential, incorporate these skills in completion of any given goal

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

Computer-based systems that exhibit much of these capabilities exist (e.g. see computational imagination, automated reasoning, choice support system, robotic, evolutionary calculation, smart agent). There is dispute about whether contemporary AI systems possess them to a sufficient degree.

Physical qualities

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

– the capability to sense (e.g. see, hear, and so on), and
– the ability to act (e.g. move and control objects, change place to explore, etc).

This includes the capability to detect and react to risk. [31]

Although the ability to sense (e.g. see, hear, etc) and the capability to act (e.g. move and control things, change location to explore, and so on) can be desirable for some smart systems, [30] these physical capabilities are not strictly required for an entity to qualify as AGI-particularly under the thesis that big language designs (LLMs) may currently be or become AGI. Even from a less positive viewpoint on LLMs, there is no company requirement for an AGI to have a human-like type; being a silicon-based computational system is enough, offered it can process input (language) from the external world in location of human senses. This analysis lines up with the understanding that AGI has never ever been proscribed a particular physical personification and therefore does not demand a capacity for mobility or traditional « eyes and ears ». [32]

Tests for human-level AGI

Several tests suggested to validate human-level AGI have actually 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 answering questions put to it, and it will only pass if the pretence is reasonably convincing. A substantial portion of a jury, who must not be professional about makers, must be taken in by the pretence. [37]

AI-complete issues

An issue is informally called « AI-complete » or « AI-hard » if it is believed that in order to solve it, one would require to implement AGI, larsaluarna.se since the option is beyond the abilities of a purpose-specific algorithm. [47]

There are numerous problems that have been conjectured to need general intelligence to fix as well as people. Examples consist of computer system vision, natural language understanding, and dealing with unanticipated scenarios while solving any real-world problem. [48] Even a specific job like translation requires a machine to check out and compose in both languages, follow the author’s argument (factor), understand the context (knowledge), and consistently reproduce the author’s initial intent (social intelligence). All of these issues need to be fixed at the same time in order to reach human-level maker efficiency.

However, a lot of these tasks can now be performed by modern large language models. According to Stanford University’s 2024 AI index, AI has actually reached human-level performance on numerous benchmarks for checking out understanding and visual reasoning. [49]

History

Classical AI

Modern AI research study started in the mid-1950s. [50] The very first generation of AI scientists were encouraged that synthetic basic intelligence was possible and that it would exist in simply a few decades. [51] AI leader Herbert A. Simon wrote in 1965: « machines will be capable, within twenty years, of doing any work a male can do. » [52]

Their predictions were the inspiration for Stanley Kubrick and Arthur C. Clarke’s character HAL 9000, who embodied what AI scientists thought they could produce by the year 2001. AI pioneer Marvin Minsky was an expert [53] on the project of making HAL 9000 as sensible as possible according to the agreement forecasts of the time. He stated in 1967, « Within a generation … the issue of creating ‘artificial intelligence’ will significantly be fixed ». [54]

Several classical AI projects, such as Doug Lenat’s Cyc job (that began in 1984), and Allen Newell’s Soar job, were directed at AGI.

However, in the early 1970s, it ended up being apparent that scientists had actually grossly undervalued the trouble of the job. Funding firms ended up being hesitant of AGI and put scientists under increasing pressure to produce helpful « used AI ». [c] In the early 1980s, Japan’s Fifth Generation Computer Project revived interest in AGI, setting out a ten-year timeline that consisted of AGI goals like « carry on a casual discussion ». [58] In action to this and the success of specialist systems, both market and government pumped money into the field. [56] [59] However, confidence in AI amazingly collapsed in the late 1980s, and the goals of the Fifth Generation Computer Project were never ever satisfied. [60] For the 2nd time in 20 years, AI scientists who anticipated the imminent accomplishment of AGI had been mistaken. By the 1990s, AI researchers had a credibility for making vain pledges. They became hesitant to make predictions at all [d] and avoided mention of « human level » synthetic intelligence for fear of being labeled « wild-eyed dreamer [s]. [62]

Narrow AI research

In the 1990s and early 21st century, mainstream AI accomplished industrial success and academic respectability by focusing on specific sub-problems where AI can produce proven results and commercial applications, such as speech recognition and recommendation algorithms. [63] These « applied AI » systems are now utilized extensively throughout the innovation industry, and research in this vein is heavily moneyed in both academic community and market. Since 2018 [update], advancement in this field was thought about an emerging pattern, and a fully grown phase was expected to be reached in more than 10 years. [64]

At the turn of the century, many mainstream AI scientists [65] hoped that strong AI could be established by combining programs that resolve numerous sub-problems. Hans Moravec composed in 1988:

I am positive that this bottom-up path to artificial intelligence will one day meet the traditional top-down route over half way, all set to provide the real-world proficiency and the commonsense understanding that has actually been so frustratingly elusive in thinking programs. Fully intelligent machines 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 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 considerations in this paper stand, then this expectation is hopelessly modular and there is really only one practical route from sense to signs: from the ground up. A free-floating symbolic level like the software application level of a computer will never ever be reached by this route (or vice versa) – nor is it clear why we must even try to reach such a level, considering that it looks as if arriving would simply total up to uprooting our symbols from their intrinsic significances (thus simply lowering ourselves to the practical equivalent of a programmable computer). [66]

Modern synthetic general intelligence research study

The term « synthetic general intelligence » was used as early as 1997, by Mark Gubrud [67] in a discussion of the implications of totally automated military production and operations. A mathematical formalism of AGI was proposed by Marcus Hutter in 2000. Named AIXI, the proposed AGI representative increases « the ability to please objectives in a wide variety of environments ». [68] This kind of AGI, defined by the ability to increase a mathematical meaning of intelligence rather than display human-like behaviour, [69] was likewise called universal expert system. [70]

The term AGI was re-introduced and promoted by Shane Legg and Ben Goertzel around 2002. [71] AGI research activity in 2006 was described by Pei Wang and Ben Goertzel [72] as « producing publications and preliminary outcomes ». The very first summer season school in AGI was organized in Xiamen, China in 2009 [73] by the Xiamen university’s Artificial Brain Laboratory and OpenCog. The first university course was offered in 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 [upgrade], a little number of computer researchers are active in AGI research, and numerous add to a series of AGI conferences. However, increasingly more researchers are interested in open-ended learning, [76] [77] which is the idea of allowing AI to constantly learn and innovate like people do.

Feasibility

Since 2023, the advancement and prospective accomplishment of AGI remains a topic of extreme dispute within the AI community. While traditional consensus held that AGI was a remote goal, current developments have actually led some scientists and industry figures to claim that early types of AGI might already exist. [78] AI leader Herbert A. Simon hypothesized in 1965 that « makers will be capable, within twenty years, of doing any work a male can do ». This forecast failed to come true. Microsoft co-founder Paul Allen believed that such intelligence is not likely in the 21st century because it would need « unforeseeable and fundamentally unpredictable breakthroughs » and a « clinically deep understanding of cognition ». [79] Writing in The Guardian, roboticist Alan Winfield claimed the gulf between modern computing and human-level artificial intelligence is as large as the gulf in between present space flight and useful faster-than-light spaceflight. [80]

A further challenge is the absence of clarity in specifying what intelligence requires. Does it require consciousness? Must it display the capability to set goals as well as pursue them? Is it simply a matter of scale such that if model sizes increase sufficiently, intelligence will emerge? Are facilities such as planning, reasoning, and causal understanding needed? Does intelligence require clearly duplicating the brain and its particular faculties? Does it need emotions? [81]

Most AI researchers think strong AI can be attained in the future, but 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 accomplished, however that the present level of progress is such that a date can not accurately be anticipated. [84] AI professionals’ views on the feasibility of AGI wax and wane. Four polls carried out in 2012 and 2013 suggested that the median price quote among experts for when they would be 50% confident AGI would show up was 2040 to 2050, depending upon the survey, with the mean being 2081. Of the specialists, 16.5% addressed with « never ever » when asked the very same question however with a 90% self-confidence rather. [85] [86] Further current AGI progress considerations can be discovered above Tests for verifying human-level AGI.

A report by Stuart Armstrong and Kaj Sotala of the Machine Intelligence Research Institute discovered that « over [a] 60-year amount of time there is a strong bias towards forecasting the arrival of human-level AI as in between 15 and 25 years from the time the prediction was made ». They analyzed 95 predictions made between 1950 and 2012 on when human-level AI will come about. [87]

In 2023, Microsoft researchers released a comprehensive evaluation of GPT-4. They concluded: « Given the breadth and depth of GPT-4’s abilities, we think that it might reasonably be considered as an early (yet still incomplete) version of a synthetic basic intelligence (AGI) system. » [88] Another research study in 2023 reported that GPT-4 exceeds 99% of human beings on the Torrance tests of innovative thinking. [89] [90]

Blaise Agüera y Arcas and Peter Norvig composed in 2023 that a significant level of basic intelligence has currently been attained with frontier designs. They composed that unwillingness to this view originates from 4 main reasons: a « healthy skepticism about metrics for AGI », an « ideological commitment to alternative AI theories or strategies », a « commitment to human (or biological) exceptionalism », or a « concern about the financial implications of AGI ». [91]

2023 likewise marked the development of large multimodal designs (large language models capable of processing or creating numerous methods such as text, audio, and images). [92]

In 2024, OpenAI released o1-preview, the very first of a series of models that « spend more time thinking before they react ». According to Mira Murati, this capability to believe before reacting represents a new, extra paradigm. It enhances model outputs by investing more computing power when producing the response, whereas the model scaling paradigm enhances outputs by increasing the model size, training data and training compute power. [93] [94]

An OpenAI staff member, Vahid Kazemi, declared in 2024 that the business had actually achieved AGI, mentioning, « In my opinion, we have already attained AGI and it’s even 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 people at a lot of jobs. » He likewise addressed criticisms that large language models (LLMs) simply follow predefined patterns, comparing their knowing process to the clinical technique of observing, assuming, and verifying. These declarations have sparked argument, as they depend on a broad and unconventional meaning of AGI-traditionally understood as AI that matches human intelligence throughout all domains. Critics argue that, while OpenAI’s designs show impressive flexibility, they might not fully meet this standard. Notably, Kazemi’s remarks came shortly after OpenAI eliminated « AGI » from the regards to its partnership with Microsoft, triggering speculation about the company’s tactical objectives. [95]

Timescales

Progress in expert system has traditionally gone through durations of rapid development separated by durations when development appeared to stop. [82] Ending each hiatus were fundamental advances in hardware, software application or both to develop space for additional development. [82] [98] [99] For example, the computer hardware available in the twentieth century was not sufficient to implement deep knowing, which requires big numbers of GPU-enabled CPUs. [100]

In the introduction to his 2006 book, [101] Goertzel says that estimates of the time needed before a genuinely versatile AGI is developed differ from 10 years to over a century. As of 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 plausible. [103] Mainstream AI scientists have given a wide variety of opinions on whether development will be this quick. A 2012 meta-analysis of 95 such opinions found a bias towards predicting that the beginning of AGI would take place within 16-26 years for modern-day and historical forecasts alike. That paper has been slammed for how it categorized 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 competition with a top-5 test error rate of 15.3%, considerably much better than the second-best entry’s rate of 26.3% (the conventional technique utilized a weighted sum of scores from different pre-defined classifiers). [105] AlexNet was related to as the preliminary ground-breaker of the existing deep knowing wave. [105]

In 2017, scientists Feng Liu, Yong Shi, and Ying Liu performed intelligence tests on openly readily available and freely accessible weak AI such as Google AI, Apple’s Siri, and others. At the optimum, these AIs reached an IQ worth of about 47, which corresponds roughly to a six-year-old kid in first grade. An adult 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 efficient in performing many diverse jobs without particular training. According to Gary Grossman in a VentureBeat post, 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 asked for modifications to the chatbot to abide by their security guidelines; Rohrer detached Project December from the GPT-3 API. [109]

In 2022, DeepMind developed Gato, a « general-purpose » system efficient in carrying out more than 600 various jobs. [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 demonstrated human-level efficiency in tasks spanning numerous domains, such as mathematics, coding, and law. This research study sparked an argument on whether GPT-4 could be thought about an early, insufficient variation of artificial general intelligence, stressing the requirement for further exploration and examination of such systems. [111]

In 2023, the AI scientist Geoffrey Hinton stated that: [112]

The idea that this stuff might actually get smarter than individuals – a couple of individuals believed that, […] But the majority of individuals believed it was method off. And I thought it was method off. I thought it was 30 to 50 years or even longer away. Obviously, I no longer think that.

In May 2023, Demis Hassabis likewise said that « The progress in the last couple of years has been quite amazing », which he sees no factor why it would decrease, expecting AGI within a years or even a few years. [113] In March 2024, Nvidia’s CEO, Jensen Huang, mentioned his expectation that within 5 years, AI would can passing any test at least in addition to humans. [114] In June 2024, the AI scientist Leopold Aschenbrenner, a previous OpenAI employee, approximated AGI by 2027 to be « strikingly possible ». [115]

Whole brain emulation

While the development of transformer designs like in ChatGPT is considered the most appealing course to AGI, [116] [117] entire brain emulation can function as an alternative approach. With whole brain simulation, a brain model is built by scanning and mapping a biological brain in detail, and then copying and replicating it on a computer system or another computational device. The simulation model must be adequately faithful to the initial, so that it acts in virtually the same way 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 purposes. It has been gone over in synthetic intelligence research study [103] as a method to strong AI. Neuroimaging technologies that could provide the necessary comprehensive understanding are enhancing rapidly, and futurist Ray Kurzweil in the book The Singularity Is Near [102] forecasts that a map of adequate quality will become readily available on a comparable timescale to the computing power needed to replicate it.

Early approximates

For low-level brain simulation, a really effective cluster of computer systems or GPUs would be required, provided the enormous amount of synapses within the human brain. Each of the 1011 (one hundred billion) nerve cells has on typical 7,000 synaptic connections (synapses) to other nerve cells. The brain of a three-year-old kid has about 1015 synapses (1 quadrillion). This number decreases with age, stabilizing by their adult years. Estimates differ for an adult, ranging from 1014 to 5 × 1014 synapses (100 to 500 trillion). [120] A price quote of the brain’s processing power, based on a basic switch model for nerve cell activity, is around 1014 (100 trillion) synaptic updates per second (SUPS). [121]

In 1997, Kurzweil took a look at various price quotes for the hardware needed to equal the human brain and adopted a figure of 1016 computations per second (cps). [e] (For contrast, if a « computation » was comparable to one « floating-point operation » – a procedure utilized to rate existing supercomputers – then 1016 « calculations » would be comparable to 10 petaFLOPS, accomplished in 2011, while 1018 was attained in 2022.) He utilized this figure to predict the required hardware would be available sometime in between 2015 and 2025, if the exponential development in computer system power at the time of writing continued.

Current research study

The Human Brain Project, an EU-funded initiative active from 2013 to 2023, has actually developed an especially in-depth and publicly accessible atlas of the human brain. [124] In 2023, researchers from Duke University carried out a high-resolution scan of a mouse brain.

Criticisms of simulation-based approaches

The artificial neuron design assumed by Kurzweil and used in numerous present synthetic neural network implementations is easy compared to biological neurons. A brain simulation would likely need to capture the in-depth cellular behaviour of biological nerve cells, currently understood only in broad outline. The overhead introduced by full modeling of the biological, chemical, and physical details of neural behaviour (specifically on a molecular scale) would require computational powers a number of orders of magnitude bigger than Kurzweil’s estimate. In addition, the quotes do not represent glial cells, which are understood to contribute in cognitive processes. [125]

An essential criticism of the simulated brain approach stems from embodied cognition theory which asserts that human personification is an important element of human intelligence and is necessary to ground meaning. [126] [127] If this theory is right, any totally functional brain design will need to include more than simply the neurons (e.g., a robotic body). Goertzel [103] proposes virtual embodiment (like in metaverses like Second Life) as a choice, however it is unidentified whether this would suffice.

Philosophical perspective

« Strong AI » as specified in viewpoint

In 1980, thinker John Searle created the term « strong AI » as part of his Chinese room argument. [128] He proposed a difference between two hypotheses about artificial intelligence: [f]

Strong AI hypothesis: An expert system system can have « a mind » and « consciousness ».
Weak AI hypothesis: A synthetic intelligence system can (only) act like it believes and has a mind and consciousness.

The first one he called « strong » because it makes a stronger declaration: it assumes something special has happened to the device that exceeds those abilities that we can evaluate. The behaviour of a « weak AI » machine would be precisely similar to a « strong AI » maker, but the latter would likewise have subjective conscious experience. This use is likewise common in academic AI research study and textbooks. [129]

In contrast to Searle and traditional AI, some futurists such as Ray Kurzweil use the term « strong AI » to imply « human level synthetic general intelligence ». [102] This is not the exact same as Searle’s strong AI, unless it is assumed that awareness is essential for human-level AGI. Academic thinkers such as Searle do not think that holds true, and to most artificial intelligence scientists the concern is out-of-scope. [130]

Mainstream AI is most interested in how a program behaves. [131] According to Russell and Norvig, « as long as the program works, they do not care if you call it real or a simulation. » [130] If the program can behave as if it has a mind, then there is no requirement to understand if it in fact has mind – certainly, there would be no chance to tell. For AI research, Searle’s « weak AI hypothesis » is comparable to the declaration « synthetic general 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 scholastic AI research study, « Strong AI » and « AGI » are two different things.

Consciousness

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

Sentience (or « incredible awareness »): The capability to « feel » perceptions or feelings subjectively, as opposed to the capability to factor about understandings. Some thinkers, such as David Chalmers, use the term « awareness » to refer solely to sensational consciousness, which is roughly comparable to life. [132] Determining why and how subjective experience arises is called the difficult issue of awareness. [133] Thomas Nagel explained in 1974 that it « feels like » something to be conscious. If we are not mindful, then it does not seem like anything. Nagel utilizes the example of a bat: we can sensibly ask « what does it feel like to be a bat? » However, we are not likely to ask « what does it feel like to be a toaster? » Nagel concludes that a bat appears to be mindful (i.e., has consciousness) however a toaster does not. [134] In 2022, a Google engineer claimed that the company’s AI chatbot, LaMDA, had achieved sentience, though this claim was widely challenged by other professionals. [135]

Self-awareness: To have mindful awareness of oneself as a different individual, especially to be purposely knowledgeable about one’s own ideas. This is opposed to simply being the « subject of one’s believed »-an operating system or debugger is able to be « familiar with itself » (that is, to represent itself in the very same method it represents whatever else)-but this is not what individuals generally suggest when they use the term « self-awareness ». [g]

These qualities have a moral measurement. AI sentience would generate issues of well-being and legal defense, likewise to animals. [136] Other elements of consciousness associated to cognitive abilities are likewise relevant to the concept of AI rights. [137] Finding out how to integrate sophisticated AI with existing legal and social structures is an emerging problem. [138]

Benefits

AGI might have a wide array of applications. If oriented towards such goals, AGI might help reduce various issues worldwide such as cravings, hardship and health issue. [139]

AGI might improve performance and effectiveness in most tasks. For instance, in public health, AGI could speed up medical research, notably versus cancer. [140] It might look after the senior, [141] and equalize access to rapid, premium medical diagnostics. It could provide fun, low-cost and tailored education. [141] The requirement to work to subsist might become outdated if the wealth produced is correctly redistributed. [141] [142] This likewise raises the concern of the location of people in a radically automated society.

AGI might also help to make rational choices, and to prepare for and prevent catastrophes. It might also assist to reap the advantages of potentially disastrous technologies such as nanotechnology or climate engineering, while preventing the associated dangers. [143] If an AGI’s main goal is to prevent existential catastrophes such as human extinction (which might be difficult if the Vulnerable World Hypothesis ends up being true), [144] it might take measures to significantly decrease the risks [143] while decreasing the effect of these procedures on our quality of life.

Risks

Existential risks

AGI may represent numerous types of existential risk, which are threats that threaten « the early termination of Earth-originating intelligent life or the permanent and extreme damage of its potential for preferable future development ». [145] The danger of human termination from AGI has been the subject of many debates, however there is likewise the possibility that the advancement of AGI would result in a permanently flawed future. Notably, it could be utilized to spread out and preserve 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 moral development. [146] Furthermore, AGI might assist in mass monitoring and brainwashing, which might be utilized to develop a steady repressive worldwide totalitarian regime. [147] [148] There is likewise a danger for the machines themselves. If machines that are sentient or otherwise worthy of moral factor to consider are mass produced in the future, participating in a civilizational path that forever disregards their welfare and interests could be an existential disaster. [149] [150] Considering how much AGI could enhance humanity’s future and aid minimize other existential risks, Toby Ord calls these existential dangers « an argument for proceeding with due caution », not for « deserting AI ». [147]

Risk of loss of control and human extinction

The thesis that AI poses an existential threat for people, which this threat needs more attention, is controversial but has been backed in 2023 by numerous public figures, AI researchers and CEOs of AI business such as Elon Musk, Bill Gates, Geoffrey Hinton, Yoshua Bengio, Demis Hassabis and Sam Altman. [151] [152]

In 2014, Stephen Hawking slammed extensive indifference:

So, facing possible futures of incalculable advantages and threats, the specialists are surely doing everything possible to make sure the finest result, right? Wrong. If a remarkable alien civilisation sent us a message stating, ‘We’ll arrive in a couple of years,’ would we just respond, ‘OK, call us when you get here-we’ll leave the lights on?’ Probably not-but this is basically what is occurring with AI. [153]

The prospective fate of humankind has actually often been compared to the fate of gorillas threatened by human activities. The comparison specifies that greater intelligence allowed mankind to dominate gorillas, which are now vulnerable in manner ins which they might not have actually anticipated. As an outcome, the gorilla has actually ended up being a threatened types, not out of malice, however just as a security damage from human activities. [154]

The skeptic Yann LeCun thinks about that AGIs will have no desire to control humanity which we ought to be cautious not to anthropomorphize them and interpret their intents as we would for humans. He said that individuals will not be « clever enough to create super-intelligent makers, yet extremely dumb to the point of giving it moronic goals with no safeguards ». [155] On the other side, the idea of critical convergence recommends that practically whatever their objectives, smart representatives will have reasons to attempt to make it through and get more power as intermediary steps to attaining these goals. Which this does not need having feelings. [156]

Many scholars who are concerned about existential threat supporter for more research into solving the « control problem » to answer the concern: what types of safeguards, algorithms, or architectures can programmers execute to maximise the likelihood that their recursively-improving AI would continue to behave in a friendly, rather than harmful, way after it reaches superintelligence? [157] [158] Solving the control problem is complicated by the AI arms race (which could lead to a race to the bottom of safety preventative measures in order to launch items before competitors), [159] and the usage of AI in weapon systems. [160]

The thesis that AI can present existential risk also has detractors. Skeptics typically state that AGI is not likely in the short-term, or that issues about AGI sidetrack from other concerns associated with current AI. [161] Former Google scams czar Shuman Ghosemajumder considers that for lots of people outside of the technology market, existing chatbots and LLMs are currently perceived as though they were AGI, leading to further misconception and fear. [162]

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

In 2023, the CEOs of Google DeepMind, OpenAI and Anthropic, along with other market leaders and scientists, released a joint declaration asserting that « Mitigating the threat of extinction from AI must be a global top priority along with other societal-scale risks such as pandemics and nuclear war. » [152]

Mass joblessness

Researchers from OpenAI approximated that « 80% of the U.S. labor force could have at least 10% of their work jobs affected by the introduction of LLMs, while around 19% of employees may see at least 50% of their jobs impacted ». [166] [167] They consider workplace employees to be the most exposed, for instance mathematicians, accountants or web designers. [167] AGI could have a better autonomy, capability to make decisions, to user interface with other computer tools, but also 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 delight in a life of luxurious leisure if the machine-produced wealth is shared, or the majority of people can end up badly bad if the machine-owners successfully lobby versus wealth redistribution. So far, the trend appears to be towards the 2nd option, with technology driving ever-increasing inequality

Elon Musk considers that the automation of society will require governments to embrace a universal fundamental income. [168]

See also

Artificial brain – Software and hardware with cognitive capabilities similar to those of the animal or human brain
AI result
AI security – Research location on making AI safe and advantageous
AI alignment – AI conformance to the desired goal
A.I. Rising – 2018 movie directed by Lazar Bodroža
Artificial intelligence
Automated maker learning – Process of automating the application of device knowing
BRAIN Initiative – Collaborative public-private research effort 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 expert system – AI system capable of generating material in reaction to triggers
Human Brain Project – Scientific research task
Intelligence amplification – Use of infotech to enhance human intelligence (IA).
Machine ethics – Moral behaviours of man-made devices.
Moravec’s paradox.
Multi-task learning – Solving several 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 expert system.
Transhumanism – Philosophical motion.
Synthetic intelligence – Alternate term for or type of artificial intelligence.
Transfer knowing – Artificial intelligence technique.
Loebner Prize – Annual AI competition.
Hardware for expert system – Hardware specifically designed and enhanced for expert system.
Weak expert system – Form of artificial intelligence.

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 article Chinese room.
^ AI creator John McCarthy writes: « we can not yet define in basic what sort of computational procedures we desire to call intelligent. » [26] (For a conversation of some definitions of intelligence utilized by expert system scientists, see philosophy of artificial intelligence.).
^ The Lighthill report specifically criticized AI’s « grandiose objectives » and led the taking apart of AI research in England. [55] In the U.S., DARPA became identified to money just « mission-oriented direct research study, instead of basic undirected research study ». [56] [57] ^ As AI creator John McCarthy writes « it would be an excellent relief to the remainder of the workers in AI if the inventors of new basic formalisms would express their hopes in a more safeguarded kind than has often held true. » [61] ^ In « Mind Children » [122] 1015 cps is used. More just recently, in 1997, [123] Moravec argued for 108 MIPS which would roughly correspond to 1014 cps. Moravec talks in terms of MIPS, not « cps », which is a non-standard term Kurzweil presented.
^ As defined in a standard AI textbook: « The assertion that makers could possibly act intelligently (or, perhaps better, act as if they were smart) is called the ‘weak AI’ hypothesis by thinkers, and the assertion that makers that do so are actually thinking (as opposed to mimicing thinking) is called the ‘strong AI’ hypothesis. » [121] ^ Alan Turing made this point in 1950. [36] References

^ Krishna, Sri (9 February 2023). « What is artificial narrow intelligence (ANI)? ». VentureBeat. Retrieved 1 March 2024. ANI is developed to perform a single job.
^ « OpenAI Charter ». OpenAI. Retrieved 6 April 2023. Our objective is to guarantee that artificial general intelligence benefits 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 build AI that is much better than human-level at all of the human senses.
^ Baum, Seth D. (2020 ). A Study of Artificial General Intelligence Projects for Ethics, Risk, and Policy (PDF) (Report). Global Catastrophic Risk Institute. Retrieved 28 November 2024. 72 AGI R&D projects were identified as being active in 2020.
^ a b c « AI timelines: What do specialists in synthetic intelligence expect for the future? ». Our World in Data. Retrieved 6 April 2023.
^ Metz, Cade (15 May 2023). « Some Researchers Say A.I. Is Already Here, Stirring Debate in Tech Circles ». The New York City Times. Retrieved 18 May 2023.
^ « AI leader Geoffrey Hinton stops Google and alerts of threat ahead ». The New York Times. 1 May 2023. Retrieved 2 May 2023. It is difficult to see how you can avoid the bad actors from using it for bad things.
^ Bubeck, Sébastien; Chandrasekaran, Varun; Eldan, Ronen; Gehrke, Johannes; Horvitz, Eric (2023 ). « Sparks of Artificial General Intelligence: Early explores GPT-4 ». arXiv preprint. arXiv:2303.12712. GPT-4 shows stimulates of AGI.
^ Butler, Octavia E. (1993 ). Parable of the Sower. Grand Central Publishing. ISBN 978-0-4466-7550-5. All that you touch you alter. All that you alter changes you.
^ Vinge, Vernor (1992 ). A Fire Upon the Deep. Tor Books. ISBN 978-0-8125-1528-2. The Singularity is coming.
^ Morozov, Evgeny (30 June 2023). « The True Threat of Expert System ». The New York City Times. The real risk is not AI itself however the method we deploy it.
^ « Impressed by synthetic intelligence? Experts say AGI is coming next, and it has ‘existential’ threats ». ABC News. 23 March 2023. Retrieved 6 April 2023. AGI could present existential dangers 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 invention that humankind requires to make.
^ Roose, Kevin (30 May 2023). « A.I. Poses ‘Risk of Extinction,’ Industry Leaders Warn ». The New York Times. Mitigating the risk of extinction from AI must be a global concern.
^ « Statement on AI Risk ». Center for AI Safety. Retrieved 1 March 2024. AI professionals warn of risk of termination from AI.
^ Mitchell, Melanie (30 May 2023). « Are AI’s Doomsday Scenarios Worth Taking Seriously? ». The New York City Times. We are far from producing machines that can outthink us in basic methods.
^ LeCun, Yann (June 2023). « AGI does not present an existential danger ». Medium. There is no reason to fear AI as an existential hazard.
^ Kurzweil 2005, p. 260.
^ a b Kurzweil, Ray (5 August 2005), « Long Live AI », Forbes, archived from the initial on 14 August 2005: Kurzweil explains strong AI as « maker intelligence with the full variety of human intelligence. ».
^ « The Age of Artificial Intelligence: George John at TEDxLondonBusinessSchool 2013 ». Archived from the initial on 26 February 2014. Retrieved 22 February 2014.
^ Newell & Simon 1976, This is the term they utilize for « human-level » intelligence in the physical 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.
^ « Expert system is changing our world – it is on all of us 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 accomplishing AGI, according to DeepMind ». VentureBeat.
^ McCarthy, John (2007a). « Basic Questions ». Stanford University. Archived from the initial on 26 October 2007. Retrieved 6 December 2007.
^ This list of intelligent qualities is based on the topics 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: Academic Press.
^ a b Pfeifer, R. and Bongard J. C., How the body shapes the way we believe: a brand-new view of intelligence (The MIT Press, 2007). ISBN 0-2621-6239-3.
^ White, R. W. (1959 ). « Motivation reassessed: 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 principle of competence ». Psychological Review. 66 (5 ): 297-333. doi:10.1037/ h0040934. PMID 13844397. S2CID 37385966.
^ Muehlhauser, Luke (11 August 2013). « What is AGI? ». Machine Intelligence Research Institute. Archived from the original on 25 April 2014. Retrieved 1 May 2014.
^ « What is Artificial General Intelligence (AGI)?|4 Tests For Ensuring Artificial General Intelligence ». Talky Blog. 13 July 2019. Archived from the 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 occurs when it does? ». The Conversation. Retrieved 22 September 2024.
^ a b Turing 1950.
^ Turing, Alan (1952 ). B. Jack Copeland (ed.). Can Automatic Calculating Machines Be Said To Think?. Oxford: Oxford University Press. pp. 487-506. ISBN 978-0-1982-5079-1.
^ « Eugene Goostman is a real young boy – the Turing Test says so ». The Guardian. 9 June 2014. ISSN 0261-3077. Retrieved 3 March 2024.
^ « Scientists dispute whether computer ‘Eugene Goostman’ passed Turing test ». BBC News. 9 June 2014. Retrieved 3 March 2024.
^ Jones, Cameron R.; Bergen, Benjamin K. (9 May 2024). « People can not differentiate GPT-4 from a human in a Turing test ». arXiv:2405.08007 [cs.HC]
^ Varanasi, Lakshmi (21 March 2023). « AI models like ChatGPT and GPT-4 are acing everything from the bar test to AP Biology. Here’s a list of difficult exams both AI versions have actually passed ». Business Insider. Retrieved 30 May 2023.
^ Naysmith, Caleb (7 February 2023). « 6 Jobs Expert System Is Already Replacing and How Investors Can Capitalize on It ». Retrieved 30 May 2023.
^ Turk, Victoria (28 January 2015). « The Plan to Replace the Turing Test with a ‘Turing Olympics' ». Vice. Retrieved 3 March 2024.
^ Gopani, Avi (25 May 2022). « Turing Test is unreliable. The Winograd Schema is outdated. Coffee is the answer ». Analytics India Magazine. Retrieved 3 March 2024.
^ Bhaimiya, Sawdah (20 June 2023). « DeepMind’s co-founder recommended evaluating 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 Expert System (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 Defining Feature of AI-Completeness » (PDF). Expert System, 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 priced quote in Crevier 1993, p. 109.
^ « Scientist on the Set: An Interview with Marvin Minsky ». Archived from the initial on 16 July 2012. Retrieved 5 April 2008.
^ Marvin Minsky to Darrach (1970 ), estimated in Crevier (1993, p. 109).
^ Lighthill 1973; Howe 1994.
^ a b NRC 1999, « Shift to Applied Research Increases Investment ».
^ Crevier 1993, pp. 115-117; Russell & Norvig 2003, pp. 21-22.
^ Crevier 1993, p. 211, Russell & Norvig 2003, p. 24 and see also Feigenbaum & McCorduck 1983.
^ Crevier 1993, pp. 161-162, 197-203, 240; Russell & Norvig 2003, p. 25.
^ Crevier 1993, pp. 209-212.
^ McCarthy, John (2000 ). « Reply to Lighthill ». Stanford University. Archived from the 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 scientists and software application engineers prevented the term synthetic 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 Artificial Intelligence: 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 coined the term « AGI »? ». goertzel.org. Archived from the original on 28 December 2018. Retrieved 28 December 2018., through Life 3.0: ‘The term « AGI » was promoted by … Shane Legg, Mark Gubrud and Ben Goertzel’
^ Wang & Goertzel 2007
^ « First International Summer School in Artificial General Intelligence, Main summer school: June 22 – July 3, 2009, OpenCog Lab: July 6-9, 2009 ». Archived from the 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 season trimester] Факултет по математика и информатика [Faculty of Mathematics and Informatics] (in Bulgarian). Archived from the initial on 26 July 2020. Retrieved 11 May 2020.
^ Shevlin, Henry; Vold, Karina; Crosby, Matthew; Halina, Marta (4 October 2019). « The limitations of device intelligence: Despite development in machine intelligence, synthetic basic intelligence is still a major 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. « Expert system will not become 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 general expert system will not be understood ». Humanities and Social Sciences Communications. 7 (1 ): 1-9. doi:10.1057/ s41599-020-0494-4. hdl:11250/ 2726984. ISSN 2662-9992. S2CID 219710554.
^ McCarthy 2007b.
^ Khatchadourian, Raffi (23 November 2015). « The Doomsday Invention: Will expert system bring us paradise or damage? ». The New Yorker. Archived from the initial on 28 January 2016. Retrieved 7 February 2016.
^ Müller, V. C., & Bostrom, N. (2016 ). Future development in expert system: A study of professional viewpoint. In Fundamental issues of expert system (pp. 555-572). Springer, Cham.
^ Armstrong, Stuart, and Kaj Sotala. 2012. « How We’re Predicting AI-or Failing To. » In Beyond AI: Artificial Dreams, edited by Jan Romportl, Pavel Ircing, Eva Žáčková, Michal Polák and Radek Schuster, 52-75. Plzeň: University of West Bohemia
^ « Microsoft Now Claims GPT-4 Shows ‘Sparks’ of General Intelligence ». 24 March 2023.
^ Shimek, Cary (6 July 2023). « AI Outperforms Humans in Creativity Test ». Neuroscience News. Retrieved 20 October 2023.
^ Guzik, Erik E.; Byrge, Christian; Gilde, Christian (1 December 2023). « The creativity of machines: AI takes the Test ». Journal of Creativity. 33 (3 ): 100065. doi:10.1016/ j.yjoc.2023.100065. ISSN 2713-3745. S2CID 261087185.
^ Arcas, Blaise Agüera y (10 October 2023). « Artificial General Intelligence Is Already Here ». Noema.
^ Zia, Tehseen (8 January 2024). « Unveiling of Large Multimodal Models: Shaping the Landscape of Language Models in 2024 ». Unite.ai. Retrieved 26 May 2024.
^ « Introducing OpenAI o1-preview ». OpenAI. 12 September 2024.
^ Knight, Will. « OpenAI Announces a Brand-new AI Model, Code-Named Strawberry, That Solves Difficult Problems Step by Step ». Wired. ISSN 1059-1028. Retrieved 17 September 2024.
^ « OpenAI Employee Claims AGI Has Been Achieved ». Orbital Today. 13 December 2024. Retrieved 27 December 2024.
^ « AI Index: State of AI in 13 Charts ». hai.stanford.edu. 15 April 2024. Retrieved 7 June 2024.
^ « Next-Gen AI: OpenAI and Meta’s Leap Towards Reasoning Machines ». Unite.ai. 19 April 2024. Retrieved 7 June 2024.
^ James, Alex P. (2022 ). « The Why, What, and How of Artificial General Intelligence Chip Development ». IEEE Transactions on Cognitive and Developmental Systems. 14 (2 ): 333-347. arXiv:2012.06338. doi:10.1109/ TCDS.2021.3069871. ISSN 2379-8920. S2CID 228376556. Archived from the original on 28 August 2022. Retrieved 28 August 2022.
^ Pei, Jing; Deng, Lei; Song, Sen; Zhao, Mingguo; Zhang, Youhui; Wu, Shuang; Wang, Guanrui; Zou, Zhe; Wu, Zhenzhi; He, Wei; Chen, Feng; Deng, Ning; Wu, Si; Wang, Yu; Wu, Yujie (2019 ). « Towards synthetic basic intelligence with hybrid Tianjic chip architecture ». Nature. 572 (7767 ): 106-111. Bibcode:2019 Natur.572..106 P. doi:10.1038/ s41586-019-1424-8. ISSN 1476-4687. PMID 31367028. S2CID 199056116. Archived from the original on 29 August 2022. Retrieved 29 August 2022.
^ Pandey, Mohit; Fernandez, Michael; Gentile, Francesco; Isayev, Olexandr; Tropsha, Alexander; Stern, Abraham C.; Cherkasov, Artem (March 2022). « The transformational role of GPU computing and deep knowing in drug discovery ». Nature Machine Intelligence. 4 (3 ): 211-221. doi:10.1038/ s42256-022-00463-x. ISSN 2522-5839. S2CID 252081559.
^ Goertzel & Pennachin 2006.
^ a b c (Kurzweil 2005, p. 260).
^ a b c Goertzel 2007.
^ Grace, Katja (2016 ). « Error in Armstrong and Sotala 2012 ». AI Impacts (blog). Archived from the initial on 4 December 2020. Retrieved 24 August 2020.
^ a b Butz, Martin V. (1 March 2021). « Towards Strong AI ». KI – Künstliche Intelligenz. 35 (1 ): 91-101. doi:10.1007/ s13218-021-00705-x. ISSN 1610-1987. S2CID 256065190.
^ Liu, Feng; Shi, Yong; Liu, Ying (2017 ). « Intelligence Quotient and Intelligence Grade of Expert System ». Annals of Data Science. 4 (2 ): 179-191. arXiv:1709.10242. doi:10.1007/ s40745-017-0109-0. S2CID 37900130.
^ Brien, Jörn (5 October 2017). « Google-KI doppelt so schlau wie Siri » [Google AI is two times as clever as Siri – however a six-year-old beats both] (in German). Archived from the original on 3 January 2019. Retrieved 2 January 2019.
^ Grossman, Gary (3 September 2020). « We’re getting in the AI golden zone in between narrow and general AI ». VentureBeat. Archived from the original on 4 September 2020. Retrieved 5 September 2020. Certainly, too, there are those who declare we are already seeing an early example of an AGI system in the just recently announced GPT-3 natural language processing (NLP) neural network. … So is GPT-3 the first example of an AGI system? This is debatable, however the consensus is that it is not AGI. … If absolutely nothing else, GPT-3 tells us there is a happy medium in between narrow and basic AI.
^ Quach, Katyanna. « A designer built an AI chatbot using GPT-3 that assisted a guy speak again to his late fiancée. OpenAI shut it down ». The Register. Archived from the initial on 16 October 2021. Retrieved 16 October 2021.
^ Wiggers, Kyle (13 May 2022), « DeepMind’s brand-new AI can carry out over 600 tasks, from playing games to controlling robots », TechCrunch, archived from the initial on 16 June 2022, retrieved 12 June 2022.
^ 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 (22 March 2023). « Sparks of Artificial General Intelligence: Early experiments with GPT-4 ». arXiv:2303.12712 [cs.CL]
^ Metz, Cade (1 May 2023). « ‘ The Godfather of A.I.’ Leaves Google and Warns of Danger Ahead ». The New York City Times. ISSN 0362-4331. Retrieved 7 June 2023.
^ Bove, Tristan. « A.I. might measure up to human intelligence in ‘just a couple of years,’ states CEO of Google’s main A.I. research study laboratory ». Fortune. Retrieved 4 September 2024.
^ Nellis, Stephen (2 March 2024). « Nvidia CEO states AI could pass human tests in five years ». Reuters. ^ Aschenbrenner, Leopold. « SITUATIONAL AWARENESS, The Decade Ahead ».
^ Sullivan, Mark (18 October 2023). « Why everyone seems to disagree on how to define Artificial General Intelligence ». Fast Company.
^ Nosta, John (5 January 2024). « The Accelerating Path to Artificial General Intelligence ». Psychology Today. Retrieved 30 March 2024.
^ Hickey, Alex. « Whole Brain Emulation: A Huge Step for Neuroscience ». Tech Brew. Retrieved 8 November 2023.
^ Sandberg & Boström 2008.
^ Drachman 2005.
^ a b Russell & Norvig 2003.
^ Moravec 1988, p. 61.
^ Moravec 1998.
^ Holmgaard Mersh, Amalie (15 September 2023). « Decade-long European research project maps the human brain ». euractiv.
^ Swaminathan, Nikhil (January-February 2011). « Glia-the other brain cells ». Discover. Archived from the original on 8 February 2014. Retrieved 24 January 2014.
^ de Vega, Glenberg & Graesser 2008. A wide variety of views in present research, all of which need grounding to some degree
^ Thornton, Angela (26 June 2023). « How publishing our minds to a computer system may end up being possible ». The Conversation. Retrieved 8 November 2023.
^ Searle 1980
^ For example: Russell & Norvig 2003,
Oxford University Press Dictionary of Psychology Archived 3 December 2007 at the Wayback Machine (quoted in » Encyclopedia.com »),.
MIT Encyclopedia of Cognitive Science Archived 19 July 2008 at the Wayback Machine (quoted in « AITopics »),.
Will Biological Computers Enable Artificially Intelligent Machines to Become Persons? Archived 13 May 2008 at the Wayback Machine Anthony Tongen.

^ a b c Russell & Norvig 2003, p. 947.
^ though see Explainable artificial intelligence for curiosity by the field about why a program behaves the way it does.
^ Chalmers, David J. (9 August 2023). « Could a Big Language Model Be Conscious? ». Boston Review.
^ Seth, Anil. « Consciousness ». New Scientist. Retrieved 5 September 2024.
^ Nagel 1974.
^ « The Google engineer who believes the company’s AI has actually come to life ». The Washington Post. 11 June 2022. Retrieved 12 June 2023.
^ Kateman, Brian (24 July 2023). « AI Should Be Terrified of Humans ». TIME. Retrieved 5 September 2024.
^ Nosta, John (18 December 2023). « Should Expert System Have Rights? ». Psychology Today. Retrieved 5 September 2024.
^ Akst, Daniel (10 April 2023). « Should Robots With Expert System Have Moral or Legal Rights? ». The Wall Street Journal.
^ « Artificial General Intelligence – Do [es] the expense exceed advantages? ». 23 August 2021. Retrieved 7 June 2023.
^ « How we can Benefit from Advancing Artificial General Intelligence (AGI) – Unite.AI ». www.unite.ai. 7 April 2020. Retrieved 7 June 2023.
^ a b c Talty, Jules; Julien, Stephan. « What Will Our Society Appear Like When Artificial Intelligence Is Everywhere? ». Smithsonian Magazine. Retrieved 7 June 2023.
^ a b Stevenson, Matt (8 October 2015). « Answers to Stephen Hawking’s AMA are Here! ». Wired. ISSN 1059-1028. Retrieved 8 June 2023.
^ a b Bostrom, Nick (2017 ). » § Preferred order of arrival ». Superintelligence: courses, risks, strategies (Reprinted with corrections 2017 ed.). Oxford, United Kingdom; New York, New York City, USA: Oxford University Press. ISBN 978-0-1996-7811-2.
^ Piper, Kelsey (19 November 2018). « How technological progress is making it likelier than ever that humans will destroy ourselves ». Vox. Retrieved 8 June 2023.
^ Doherty, Ben (17 May 2018). « Climate alter an ‘existential security risk’ to Australia, Senate query says ». The Guardian. ISSN 0261-3077. Retrieved 16 July 2023.
^ MacAskill, William (2022 ). What we owe the future. New York, NY: Basic Books. ISBN 978-1-5416-1862-6.
^ a b Ord, Toby (2020 ). « Chapter 5: Future Risks, Unaligned Expert System ». The Precipice: Existential Risk and the Future of Humanity. Bloomsbury Publishing. ISBN 978-1-5266-0021-9.
^ Al-Sibai, Noor (13 February 2022). « OpenAI Chief Scientist Says Advanced AI May Already Be Conscious ». Futurism. Retrieved 24 December 2023.
^ Samuelsson, Paul Conrad (2019 ). « Artificial Consciousness: Our Greatest Ethical Challenge ». Philosophy Now. Retrieved 23 December 2023.
^ Kateman, Brian (24 July 2023). « AI Should Be Terrified of Humans ». TIME. Retrieved 23 December 2023.
^ Roose, Kevin (30 May 2023). « A.I. Poses ‘Risk of Extinction,’ Industry Leaders Warn ». The New York Times. ISSN 0362-4331. Retrieved 24 December 2023.
^ a b « Statement on AI Risk ». Center for AI Safety. 30 May 2023. Retrieved 8 June 2023.
^ « Stephen Hawking: ‘Transcendence looks at the implications of artificial intelligence – but are we taking AI seriously enough?' ». The Independent (UK). Archived from the initial on 25 September 2015. Retrieved 3 December 2014.
^ Herger, Mario. « The Gorilla Problem – Enterprise Garage ». Retrieved 7 June 2023.
^ « The remarkable Facebook dispute in between Yann LeCun, Stuart Russel and Yoshua Bengio about the threats of strong AI ». The fascinating Facebook debate in between Yann LeCun, Stuart Russel and Yoshua Bengio about the threats of strong AI (in French). Retrieved 8 June 2023.
^ « Will Expert System Doom The Mankind Within The Next 100 Years? ». HuffPost. 22 August 2014. Retrieved 8 June 2023.
^ Sotala, Kaj; Yampolskiy, Roman V. (19 December 2014). « Responses to disastrous AGI threat: a study ». Physica Scripta. 90 (1 ): 018001. doi:10.1088/ 0031-8949/90/ 1/018001. ISSN 0031-8949.
^ Bostrom, Nick (2014 ). Superintelligence: Paths, Dangers, Strategies (First ed.). Oxford University Press. ISBN 978-0-1996-7811-2.
^ Chow, Andrew R.; Perrigo, Billy (16 February 2023). « The AI Arms Race Is On. Start Worrying ». TIME. Retrieved 24 December 2023.
^ Tetlow, Gemma (12 January 2017). « AI arms race dangers spiralling out of control, report warns ». Financial Times. Archived from the initial on 11 April 2022. Retrieved 24 December 2023.
^ Milmo, Dan; Stacey, Kiran (25 September 2023). « Experts disagree over threat positioned however artificial intelligence can not be disregarded ». The Guardian. ISSN 0261-3077. Retrieved 24 December 2023.
^ « Humanity, Security & AI, Oh My! (with Ian Bremmer & Shuman Ghosemajumder) ». CAFE. 20 July 2023. Retrieved 15 September 2023.
^ Hamblin, James (9 May 2014). « But What Would completion of Humanity Mean for Me? ». The Atlantic. Archived from the initial on 4 June 2014. Retrieved 12 December 2015.
^ Titcomb, James (30 October 2023). « Big Tech is stiring fears over AI, caution scientists ». The Telegraph. Retrieved 7 December 2023.
^ Davidson, John (30 October 2023). « Google Brain creator says big tech is lying about AI extinction threat ». Australian Financial Review. Archived from the initial on 7 December 2023. Retrieved 7 December 2023.
^ Eloundou, Tyna; Manning, Sam; Mishkin, Pamela; Rock, Daniel (17 March 2023). « GPTs are GPTs: An early appearance at the labor market impact capacity of large language models ». OpenAI. Retrieved 7 June 2023.
^ a b Hurst, Luke (23 March 2023). « OpenAI says 80% of employees could see their tasks affected by AI. These are the tasks most impacted ». euronews. Retrieved 8 June 2023.
^ Sheffey, Ayelet (20 August 2021). « Elon Musk says we need universal fundamental earnings because ‘in the future, physical work will be a choice' ». Business Insider. Archived from the original on 9 July 2023. Retrieved 8 June 2023.
Sources

UNESCO Science Report: the Race Against Time for Smarter Development. Paris: UNESCO. 11 June 2021. ISBN 978-9-2310-0450-6. Archived from the original on 18 June 2022. Retrieved 22 September 2021.
Chalmers, David (1996 ), The Conscious Mind, Oxford University Press.
Clocksin, William (August 2003), « Artificial intelligence and the future », Philosophical Transactions of the Royal Society A, vol. 361, no. 1809, pp. 1721-1748, Bibcode:2003 RSPTA.361.1721 C, doi:10.1098/ rsta.2003.1232, PMID 12952683, S2CID 31032007.
Crevier, Daniel (1993 ). AI: The Tumultuous Search for Expert System. New York City, NY: BasicBooks. ISBN 0-465-02997-3.
Darrach, Brad (20 November 1970), « Meet Shakey, grandtribunal.org the First Electronic Person », Life Magazine, pp. 58-68.
Drachman, D. (2005 ), « Do we have brain to spare? », Neurology, 64 (12 ): 2004-2005, doi:10.1212/ 01. WNL.0000166914.38327. BB, PMID 15985565, S2CID 38482114.
Feigenbaum, Edward A.; McCorduck, Pamela (1983 ), The Fifth Generation: Artificial Intelligence and Japan’s Computer Challenge to the World, Michael Joseph, ISBN 978-0-7181-2401-4.
Goertzel, Ben; Pennachin, Cassio, eds. (2006 ), Artificial General Intelligence (PDF), Springer, ISBN 978-3-5402-3733-4, archived from the initial (PDF) on 20 March 2013.
Goertzel, Ben (December 2007), « Human-level synthetic general intelligence and the possibility of a technological singularity: a reaction to Ray Kurzweil’s The Singularity Is Near, and McDermott’s review of Kurzweil », Artificial Intelligence, vol. 171, no. 18, Special Review Issue, pp. 1161-1173, doi:10.1016/ j.artint.2007.10.011, archived from the initial on 7 January 2016, recovered 1 April 2009.
Gubrud, Mark (November 1997), « Nanotechnology and International Security », Fifth Foresight Conference on Molecular Nanotechnology, archived from the original on 29 May 2011, retrieved 7 May 2011.
Howe, J. (November 1994), Artificial Intelligence at Edinburgh University: a Point of view, archived from the initial on 17 August 2007, obtained 30 August 2007.
Johnson, Mark (1987 ), The body in the mind, Chicago, ISBN 978-0-2264-0317-5.
Kurzweil, Ray (2005 ), The Singularity is Near, Viking Press.
Lighthill, Professor Sir James (1973 ), « Expert System: A General Survey », Artificial Intelligence: a paper symposium, Science Research Council.
Luger, George; Stubblefield, William (2004 ), Expert System: Structures and Strategies for Complex Problem Solving (fifth ed.), The Benjamin/Cummings Publishing Company, Inc., p. 720, ISBN 978-0-8053-4780-7.
McCarthy, John (2007b). What is Expert system?. Stanford University. The supreme effort is to make computer programs that can solve issues and achieve goals in the world in addition to human beings.
Moravec, Hans (1988 ), Mind Children, Harvard University Press
Moravec, Hans (1998 ), « When will computer system hardware match the human brain? », Journal of Evolution and Technology, vol. 1, archived from the initial on 15 June 2006, recovered 23 June 2006
Nagel (1974 ), « What Is it Like to Be a Bat » (PDF), Philosophical Review, 83 (4 ): 435-50, doi:10.2307/ 2183914, JSTOR 2183914, archived (PDF) from the original on 16 October 2011, retrieved 7 November 2009
Newell, Allen; Simon, H. A. (1976 ). « Computer Science as Empirical Inquiry: Symbols and Search ». Communications of the ACM. 19 (3 ): 113-126. doi:10.1145/ 360018.360022.
Nilsson, Nils (1998 ), Expert System: A New Synthesis, Morgan Kaufmann Publishers, ISBN 978-1-5586-0467-4
NRC (1999 ), « Developments in Artificial Intelligence », Funding a Transformation: Government Support for Computing Research, National Academy Press, archived from the initial on 12 January 2008, recovered 29 September 2007
Poole, David; Mackworth, Alan; Goebel, Randy (1998 ), Computational Intelligence: A Logical Approach, New York: Oxford University Press, archived from the original on 25 July 2009, retrieved 6 December 2007
Russell, Stuart J.; Norvig, Peter (2003 ), Expert System: A Modern Approach (second ed.), Upper Saddle River, New Jersey: Prentice Hall, ISBN 0-13-790395-2
Sandberg, Anders; Boström, Nick (2008 ), Whole Brain Emulation: A Roadmap (PDF), Technical Report # 2008-3, Future of Humanity Institute, Oxford University, archived (PDF) from the initial on 25 March 2020, recovered 5 April 2009
Searle, John (1980 ), « Minds, Brains and Programs » (PDF), Behavioral and Brain Sciences, 3 (3 ): 417-457, doi:10.1017/ S0140525X00005756, S2CID 55303721, archived (PDF) from the initial on 17 March 2019, retrieved 3 September 2020
Simon, H. A. (1965 ), The Shape of Automation for Men and Management, New York: Harper & Row
Turing, Alan (October 1950). « Computing Machinery and Intelligence ». Mind. 59 (236 ): 433-460. doi:10.1093/ mind/LIX.236.433. ISSN 1460-2113. JSTOR 2251299. S2CID 14636783.

de Vega, Manuel; Glenberg, Arthur; Graesser, Arthur, eds. (2008 ), Symbols and Embodiment: Debates on meaning and cognition, Oxford University Press, ISBN 978-0-1992-1727-4
Wang, Pei; Goertzel, Ben (2007 ). « Introduction: Aspects of Artificial General Intelligence ». Advances in Artificial General Intelligence: Concepts, Architectures and Algorithms: Proceedings of the AGI Workshop 2006. IOS Press. pp. 1-16. ISBN 978-1-5860-3758-1. Archived from the initial on 18 February 2021. Retrieved 13 December 2020 – by means of ResearchGate.

Arrabidalegend 108 vues

Artificial General Intelligence

0 Avis

Noter cette Entreprise (Pas d'avis pour l'instant)

Cette entreprise n'a pas de postes à pourvoir

Artificial General Intelligence

0 Avis

Noter cette Entreprise (Pas d'avis pour l'instant)

Cette entreprise n'a pas de postes à pourvoir

Contactez le Recruteur

ESPACE CANDIDATS

ESPACE ENTREPRISES

NEWSLETTER

Contactez-Nous