Artificial General Intelligence

From LinuxReviews
Jump to navigationJump to search


Artificial basic intelligence (AGI) is a kind of artificial intelligence (AI) that matches or exceeds human cognitive capabilities throughout a large range of cognitive jobs. This contrasts with narrow AI, which is limited to specific tasks. [1] Artificial superintelligence (ASI), on the other hand, refers to AGI that significantly goes beyond human cognitive abilities. AGI is considered one of the meanings of strong AI.


Creating AGI is a main objective of AI research and of business such as OpenAI [2] and Meta. [3] A 2020 survey determined 72 active AGI research and advancement tasks across 37 nations. [4]

The timeline for achieving AGI remains a topic of continuous argument among scientists and experts. Since 2023, some argue that it may be possible in years or decades; others keep it may take a century or longer; a minority think it may never ever be attained; and another minority claims that it is currently here. [5] [6] Notable AI researcher Geoffrey Hinton has actually revealed issues about the rapid progress towards AGI, suggesting it might be accomplished quicker than many anticipate. [7]

There is debate on the specific meaning of AGI and relating to whether modern-day big language models (LLMs) such as GPT-4 are early forms of AGI. [8] AGI is a common subject in sci-fi and archmageriseswiki.com futures research studies. [9] [10]

Contention exists over whether AGI represents an existential threat. [11] [12] [13] Many professionals on AI have specified that mitigating the danger of human termination positioned by AGI ought to be an international concern. [14] [15] Others discover the advancement of AGI to be too remote to provide such a danger. [16] [17]

Terminology


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

Some scholastic sources reserve the term "strong AI" for computer programs that experience sentience or consciousness. [a] On the other hand, weak AI (or narrow AI) is able to resolve one particular problem however lacks general cognitive capabilities. [22] [19] Some scholastic sources utilize "weak AI" to refer more broadly to any programs that neither experience consciousness nor have a mind in the same sense as people. [a]

Related concepts include artificial superintelligence and transformative AI. A synthetic superintelligence (ASI) is a theoretical type of AGI that is far more generally intelligent than human beings, [23] while the concept of transformative AI associates with AI having a big influence on society, for instance, similar to the agricultural or commercial transformation. [24]

A framework for classifying AGI in levels was proposed in 2023 by Google DeepMind researchers. They specify 5 levels of AGI: emerging, qualified, specialist, virtuoso, and superhuman. For example, a qualified AGI is specified as an AI that outshines 50% of proficient grownups in a vast array of non-physical jobs, and a superhuman AGI (i.e. a synthetic superintelligence) is likewise defined but with a threshold of 100%. They think about big language designs like ChatGPT or LLaMA 2 to be circumstances of emerging AGI. [25]

Characteristics


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

Intelligence characteristics


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

reason, use method, solve puzzles, and make judgments under unpredictability
represent understanding, consisting of common sense understanding
plan
find out
- interact in natural language
- if necessary, incorporate these abilities in completion of any offered objective


Many interdisciplinary approaches (e.g. cognitive science, computational intelligence, and decision making) consider extra characteristics such as imagination (the ability to form unique mental images and ideas) [28] and autonomy. [29]

Computer-based systems that show much of these abilities exist (e.g. see computational creativity, automated thinking, choice assistance system, robot, evolutionary computation, smart representative). There is dispute about whether modern-day AI systems have them to an adequate degree.


Physical qualities


Other abilities are considered desirable in intelligent systems, as they might affect intelligence or aid in its expression. These include: [30]

- the capability to sense (e.g. see, hear, etc), and
- the capability to act (e.g. move and manipulate objects, modification area to check out, etc).


This consists of the capability to spot and react to hazard. [31]

Although the capability to sense (e.g. see, hear, and so on) and the capability to act (e.g. move and manipulate objects, change place to check out, etc) can be desirable for some intelligent systems, [30] these physical capabilities are not strictly needed for an entity to qualify as AGI-particularly under the thesis that large language models (LLMs) may already be or become AGI. Even from a less positive perspective on LLMs, there is no company requirement for an AGI to have a human-like type; being a silicon-based computational system suffices, supplied 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 locomotion or traditional "eyes and ears". [32]

Tests for human-level AGI


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

The concept of the test is that the device needs to try and pretend to be a man, by responding to questions put to it, and it will just pass if the pretence is reasonably persuading. A considerable portion of a jury, who must not be professional about devices, should 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 implement AGI, because the service is beyond the abilities of a purpose-specific algorithm. [47]

There are many problems that have been conjectured to require basic intelligence to resolve as well as human beings. Examples include computer system vision, natural language understanding, and handling unexpected scenarios while fixing any real-world issue. [48] Even a particular job like translation requires a device to check out and write in both languages, follow the author's argument (reason), comprehend the context (understanding), and faithfully reproduce the author's initial intent (social intelligence). All of these issues require to be fixed all at once in order to reach human-level machine performance.


However, a lot of these jobs can now be carried out by modern-day large language models. According to Stanford University's 2024 AI index, AI has reached human-level efficiency on numerous standards for reading comprehension and visual thinking. [49]

History


Classical AI


Modern AI research study started in the mid-1950s. [50] The first generation of AI researchers were persuaded that artificial general intelligence was possible which it would exist in simply a few years. [51] AI pioneer Herbert A. Simon composed in 1965: "makers will be capable, within twenty years, of doing any work a guy can do." [52]

Their forecasts were the motivation for Stanley Kubrick and Arthur C. Clarke's character HAL 9000, who embodied what AI scientists believed they could develop by the year 2001. AI leader Marvin Minsky was an expert [53] on the task of making HAL 9000 as practical as possible according to the agreement forecasts of the time. He stated in 1967, "Within a generation ... the issue of developing 'artificial intelligence' will significantly be solved". [54]

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


However, in the early 1970s, it ended up being obvious that scientists had actually grossly undervalued the difficulty of the job. Funding companies became hesitant of AGI and put scientists under increasing pressure to produce useful "applied 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 objectives like "continue a table talk". [58] In response to this and the success of specialist systems, both market and federal government pumped money into the field. [56] [59] However, confidence in AI stunningly collapsed in the late 1980s, and the objectives of the Fifth Generation Computer Project were never ever satisfied. [60] For the second time in 20 years, AI researchers who predicted the imminent achievement of AGI had been mistaken. By the 1990s, AI scientists had a reputation for making vain guarantees. They ended up being reluctant to make forecasts at all [d] and prevented mention of "human level" artificial intelligence for worry of being labeled "wild-eyed dreamer [s]. [62]

Narrow AI research


In the 1990s and early 21st century, mainstream AI achieved commercial success and academic respectability by concentrating on particular sub-problems where AI can produce proven outcomes and industrial applications, such as speech recognition and recommendation algorithms. [63] These "applied AI" systems are now utilized thoroughly throughout the innovation industry, and research in this vein is heavily funded in both academia and industry. Since 2018 [update], development in this field was considered an emerging pattern, and a mature stage was anticipated to be reached in more than 10 years. [64]

At the millenium, numerous mainstream AI scientists [65] hoped that strong AI might be established by combining programs that resolve numerous sub-problems. Hans Moravec wrote in 1988:


I am positive that this bottom-up path to expert system will one day fulfill the conventional top-down route more than half method, prepared to offer the real-world competence 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 unifying the 2 efforts. [65]

However, even at the time, this was disputed. For instance, Stevan Harnad of Princeton University concluded his 1990 paper on the symbol grounding hypothesis by specifying:


The expectation has actually frequently been voiced that "top-down" (symbolic) approaches to modeling cognition will in some way meet "bottom-up" (sensory) approaches somewhere in between. If the grounding factors to consider in this paper stand, then this expectation is hopelessly modular and there is really just one viable route from sense to signs: from the ground up. A free-floating symbolic level like the software level of a computer system will never be reached by this route (or vice versa) - nor is it clear why we should even try to reach such a level, considering that it appears arriving would just amount to uprooting our symbols from their intrinsic significances (thereby merely minimizing ourselves to the functional equivalent of a programmable computer). [66]

Modern artificial basic intelligence research


The term "synthetic basic intelligence" was used 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 increases "the capability to please objectives in a broad variety of environments". [68] This kind of AGI, identified by the ability to increase a mathematical meaning of intelligence instead of show human-like behaviour, [69] was likewise called universal artificial intelligence. [70]

The term AGI was re-introduced and popularized 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 summertime 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 given in 2010 [74] and 2011 [75] at Plovdiv University, Bulgaria by Todor Arnaudov. MIT provided a course on AGI in 2018, organized by Lex Fridman and featuring a variety of visitor speakers.


Since 2023 [upgrade], a little number of computer system scientists are active in AGI research study, and numerous contribute to a series of AGI conferences. However, significantly more researchers have an interest in open-ended learning, [76] [77] which is the concept of enabling AI to continuously discover and innovate like human beings do.


Feasibility


Since 2023, the development and potential accomplishment of AGI stays a topic of extreme debate within the AI community. While traditional consensus held that AGI was a remote objective, current improvements have actually led some researchers and industry figures to claim that early forms of AGI might currently exist. [78] AI leader Herbert A. Simon speculated in 1965 that "machines will be capable, within twenty years, of doing any work a man can do". This prediction stopped working to come real. Microsoft co-founder Paul Allen thought that such intelligence is unlikely in the 21st century due to the fact that it would need "unforeseeable and fundamentally unforeseeable developments" and a "clinically deep understanding of cognition". [79] Writing in The Guardian, roboticist Alan Winfield claimed the gulf between modern-day computing and human-level expert system is as broad as the gulf between current area flight and practical faster-than-light spaceflight. [80]

A more challenge is the absence of clearness in defining what intelligence involves. Does it require awareness? Must it show the ability to set objectives in addition to pursue them? Is it simply a matter of scale such that if design sizes increase adequately, intelligence will emerge? Are facilities such as preparation, reasoning, and causal understanding needed? Does intelligence require clearly reproducing the brain and its specific professors? Does it require feelings? [81]

Most AI scientists think strong AI can be accomplished in the future, but some thinkers, like Hubert Dreyfus and Roger Penrose, reject the possibility of accomplishing strong AI. [82] [83] John McCarthy is amongst those who think human-level AI will be accomplished, but that the present level of progress is such that a date can not precisely be anticipated. [84] AI specialists' views on the feasibility of AGI wax and wane. Four polls carried out in 2012 and 2013 suggested that the typical price quote amongst professionals for when they would be 50% positive AGI would arrive was 2040 to 2050, depending on the poll, with the mean being 2081. Of the professionals, 16.5% responded to with "never" when asked the very same concern but with a 90% confidence instead. [85] [86] Further existing AGI development considerations can be found above Tests for validating human-level AGI.


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

In 2023, Microsoft researchers published an in-depth evaluation of GPT-4. They concluded: "Given the breadth and depth of GPT-4's capabilities, our company believe that it could reasonably be deemed an early (yet still incomplete) version of a synthetic basic intelligence (AGI) system." [88] Another study in 2023 reported that GPT-4 outshines 99% of people on the Torrance tests of creativity. [89] [90]

Blaise Agüera y Arcas and Peter Norvig composed in 2023 that a significant level of basic intelligence has currently been accomplished with frontier models. They wrote that hesitation to this view originates from 4 primary factors: a "healthy suspicion about metrics for AGI", an "ideological dedication to alternative AI theories or techniques", a "commitment to human (or biological) exceptionalism", or a "issue about the financial ramifications of AGI". [91]

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

In 2024, OpenAI launched o1-preview, the first of a series of designs that "invest more time believing before they respond". According to Mira Murati, this ability to believe before responding represents a new, extra paradigm. It improves design outputs by investing more computing power when producing the answer, whereas the design scaling paradigm enhances outputs by increasing the model size, training data and training compute power. [93] [94]

An OpenAI worker, Vahid Kazemi, claimed in 2024 that the business had achieved AGI, stating, "In my opinion, we have actually currently attained AGI and it's a lot more clear with O1." Kazemi clarified that while the AI is not yet "much better than any human at any job", it is "much better than a lot of humans at many tasks." He likewise addressed criticisms that large language designs (LLMs) merely follow predefined patterns, comparing their knowing procedure to the scientific approach of observing, hypothesizing, and verifying. These statements have actually sparked dispute, as they count on a broad and unconventional definition of AGI-traditionally comprehended as AI that matches human intelligence across all domains. Critics argue that, while OpenAI's designs demonstrate impressive flexibility, they might not completely meet this requirement. Notably, Kazemi's comments came quickly after OpenAI removed "AGI" from the terms of its collaboration with Microsoft, prompting speculation about the business's strategic intents. [95]

Timescales


Progress in artificial intelligence has actually historically gone through durations of rapid development separated by periods when progress appeared to stop. [82] Ending each hiatus were basic advances in hardware, software or both to create area for additional development. [82] [98] [99] For example, the computer hardware available in the twentieth century was not sufficient to execute deep knowing, which needs big numbers of GPU-enabled CPUs. [100]

In the intro to his 2006 book, [101] Goertzel states that price quotes of the time needed before a genuinely versatile AGI is constructed vary from ten years to over a century. Since 2007 [upgrade], the agreement in the AGI research community seemed to be that the timeline gone over by Ray Kurzweil in 2005 in The Singularity is Near [102] (i.e. in between 2015 and 2045) was possible. [103] Mainstream AI researchers have offered a large range of opinions on whether progress will be this fast. A 2012 meta-analysis of 95 such opinions found a bias towards predicting that the onset of AGI would take place within 16-26 years for modern and historic forecasts alike. That paper has actually been slammed for how it classified viewpoints 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 mistake rate of 15.3%, substantially better than the second-best entry's rate of 26.3% (the standard technique used a weighted sum of scores from different pre-defined classifiers). [105] AlexNet was related to as the preliminary ground-breaker of the present deep learning wave. [105]

In 2017, scientists Feng Liu, Yong Shi, and Ying Liu carried out intelligence tests on openly available and freely available weak AI such as Google AI, Apple's Siri, and others. At the optimum, these AIs reached an IQ worth of about 47, which corresponds roughly to a six-year-old kid in first grade. A grownup comes to about 100 typically. Similar tests were performed in 2014, with the IQ rating reaching a maximum worth of 27. [106] [107]

In 2020, OpenAI established GPT-3, a language design efficient in carrying out numerous varied tasks without specific training. According to Gary Grossman in a VentureBeat short article, while there is consensus that GPT-3 is not an example of AGI, it is thought about by some to be too advanced to be categorized as a narrow AI system. [108]

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

In 2022, DeepMind established Gato, a "general-purpose" system capable of carrying out more than 600 various tasks. [110]

In 2023, Microsoft Research released a research study on an early variation of OpenAI's GPT-4, competing that it showed more basic intelligence than previous AI designs and showed human-level efficiency in tasks spanning numerous domains, such as mathematics, coding, and law. This research stimulated a debate on whether GPT-4 might be thought about an early, incomplete version of synthetic basic intelligence, emphasizing the requirement for more expedition and evaluation of such systems. [111]

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

The idea that this things could in fact get smarter than people - a couple of individuals believed that, [...] But a lot of individuals thought it was way off. And I thought it was way off. I thought it was 30 to 50 years or even longer away. Obviously, I no longer think that.


In May 2023, Demis Hassabis similarly stated that "The development in the last few years has been quite incredible", which he sees no reason that it would slow down, expecting AGI within a years and even a few years. [113] In March 2024, Nvidia's CEO, Jensen Huang, specified his expectation that within five years, AI would be capable of passing any test at least along with people. [114] In June 2024, the AI researcher Leopold Aschenbrenner, a former OpenAI staff member, estimated AGI by 2027 to be "strikingly possible". [115]

Whole brain emulation


While the advancement of transformer designs like in ChatGPT is considered the most promising course to AGI, [116] [117] whole brain emulation can work as an alternative technique. With entire brain simulation, a brain model is built by scanning and mapping a biological brain in information, and after that copying and replicating it on a computer system or another computational gadget. The simulation model must be sufficiently faithful to the initial, so that it behaves in almost the very same way as the original brain. [118] Whole brain emulation is a type of brain simulation that is discussed in computational neuroscience and neuroinformatics, and for medical research study functions. It has been discussed in expert system research [103] as an approach to strong AI. Neuroimaging technologies that might deliver the essential in-depth understanding are improving rapidly, and futurist Ray Kurzweil in the book The Singularity Is Near [102] forecasts that a map of enough quality will appear on a similar timescale to the computing power needed to imitate it.


Early estimates


For low-level brain simulation, a really effective cluster of computer systems or GPUs would be needed, given the massive quantity 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 neurons. The brain of a three-year-old kid has about 1015 synapses (1 quadrillion). This number declines with age, stabilizing by their adult years. Estimates vary for an adult, varying from 1014 to 5 × 1014 synapses (100 to 500 trillion). [120] A price quote of the brain's processing power, based upon a basic switch model for neuron activity, is around 1014 (100 trillion) synaptic updates per second (SUPS). [121]

In 1997, Kurzweil looked at various quotes for the hardware required to equal the human brain and adopted a figure of 1016 computations per second (cps). [e] (For comparison, if a "calculation" was equivalent to one "floating-point operation" - a step used to rate present supercomputers - then 1016 "computations" would be comparable to 10 petaFLOPS, accomplished in 2011, while 1018 was attained in 2022.) He used this figure to anticipate the required hardware would be offered sometime between 2015 and 2025, if the rapid growth in computer system power at the time of writing continued.


Current research


The Human Brain Project, an EU-funded effort active from 2013 to 2023, has developed a particularly in-depth and openly available atlas of the human brain. [124] In 2023, researchers from Duke University performed a high-resolution scan of a mouse brain.


Criticisms of simulation-based methods


The artificial nerve cell model presumed by Kurzweil and used in many existing synthetic neural network implementations is easy compared to biological neurons. A brain simulation would likely have to catch the detailed cellular behaviour of biological neurons, currently comprehended only in broad summary. The overhead presented by complete modeling of the biological, chemical, and physical details of neural behaviour (especially on a molecular scale) would need computational powers numerous orders of magnitude larger than Kurzweil's quote. In addition, the price quotes do not account for glial cells, which are understood to contribute in cognitive procedures. [125]

A basic criticism of the simulated brain approach obtains from embodied cognition theory which asserts that human embodiment is an essential element of human intelligence and is needed to ground significance. [126] [127] If this theory is correct, any fully functional brain model will require to encompass more than just 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 be adequate.


Philosophical viewpoint


"Strong AI" as defined 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 expert system: [f]

Strong AI hypothesis: An expert system system can have "a mind" and "awareness".
Weak AI hypothesis: An expert system system can (only) act like it thinks and has a mind and awareness.


The very first one he called "strong" because it makes a stronger declaration: it presumes something special has actually occurred to the device that exceeds those capabilities that we can test. The behaviour of a "weak AI" machine would be specifically similar to a "strong AI" machine, however the latter would also have subjective conscious experience. This usage is likewise typical in academic AI research study and textbooks. [129]

In contrast to Searle and mainstream AI, some futurists such as Ray Kurzweil use the term "strong AI" to mean "human level artificial basic intelligence". [102] This is not the very same as Searle's strong AI, unless it is assumed that consciousness is needed for human-level AGI. Academic thinkers such as Searle do not think that is the case, and to most expert system scientists 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 need to know if it actually has mind - certainly, there would be no chance to tell. For AI research study, Searle's "weak AI hypothesis" is comparable to the declaration "artificial general intelligence is possible". Thus, according to Russell and Norvig, "most AI researchers take the weak AI hypothesis for approved, and do not care about the strong AI hypothesis." [130] Thus, for scholastic AI research, "Strong AI" and "AGI" are two different things.


Consciousness


Consciousness can have different significances, and some aspects play significant roles in science fiction and the principles of synthetic intelligence:


Sentience (or "phenomenal consciousness"): The ability to "feel" understandings or feelings subjectively, rather than the capability to reason about perceptions. Some thinkers, such as David Chalmers, use the term "consciousness" to refer exclusively to sensational awareness, which is roughly equivalent to sentience. [132] Determining why and how subjective experience arises is referred to as the difficult issue of awareness. [133] Thomas Nagel explained in 1974 that it "feels like" something to be conscious. If we are not conscious, then it doesn't seem like anything. Nagel uses the example of a bat: we can smartly ask "what does it seem like to be a bat?" However, we are unlikely to ask "what does it seem like to be a toaster?" Nagel concludes that a bat appears to be mindful (i.e., has awareness) however a toaster does not. [134] In 2022, a Google engineer declared that the business's AI chatbot, LaMDA, had attained life, though this claim was commonly contested by other specialists. [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 just being the "topic of one's thought"-an os or debugger has the ability to be "familiar with itself" (that is, to represent itself in the same way it represents whatever else)-however this is not what people usually indicate when they utilize the term "self-awareness". [g]

These qualities have an ethical measurement. AI sentience would provide increase to concerns of welfare and legal security, likewise to animals. [136] Other aspects of awareness related to cognitive capabilities are also pertinent to the concept of AI rights. [137] Determining how to incorporate sophisticated AI with existing legal and social frameworks is an emergent issue. [138]

Benefits


AGI could have a wide array of applications. If oriented towards such objectives, AGI might help mitigate different problems worldwide such as cravings, poverty and health issues. [139]

AGI might enhance efficiency and effectiveness in a lot of tasks. For instance, in public health, AGI could accelerate medical research, notably versus cancer. [140] It might look after the elderly, [141] and equalize access to fast, top quality medical diagnostics. It might use fun, cheap and individualized education. [141] The need to work to subsist might end up being outdated if the wealth produced is appropriately rearranged. [141] [142] This also raises the question of the place of humans in a radically automated society.


AGI might also help to make logical decisions, and to expect and prevent disasters. It might also assist to profit of potentially disastrous innovations such as nanotechnology or environment engineering, while avoiding the associated dangers. [143] If an AGI's primary goal is to avoid existential disasters such as human extinction (which might be difficult if the Vulnerable World Hypothesis turns out to be real), [144] it could take steps to significantly reduce the dangers [143] while minimizing the impact of these procedures on our quality of life.


Risks


Existential risks


AGI may represent several types of existential risk, which are threats that threaten "the early termination of Earth-originating smart life or the permanent and extreme damage of its capacity for desirable future advancement". [145] The threat of human extinction from AGI has been the subject of numerous debates, however there is likewise the possibility that the advancement of AGI would cause a permanently problematic future. Notably, it might be used to spread out and preserve the set of worths of whoever develops it. If mankind still has ethical blind areas similar to slavery in the past, AGI might irreversibly entrench it, preventing moral progress. [146] Furthermore, AGI might assist in mass monitoring and indoctrination, which might be used to produce a steady repressive around the world totalitarian routine. [147] [148] There is also a danger for the makers themselves. If devices that are sentient or otherwise worthy of ethical consideration are mass developed in the future, engaging in a civilizational course that indefinitely ignores their welfare and interests could be an existential disaster. [149] [150] Considering how much AGI could improve mankind's future and help in reducing other existential threats, Toby Ord calls these existential risks "an argument for continuing with due care", not for "abandoning AI". [147]

Risk of loss of control and human termination


The thesis that AI postures an existential risk for humans, which this risk requires more attention, is questionable however has been backed in 2023 by numerous public figures, AI scientists and CEOs of AI companies such as Elon Musk, Bill Gates, Geoffrey Hinton, Yoshua Bengio, Demis Hassabis and Sam Altman. [151] [152]

In 2014, Stephen Hawking slammed widespread indifference:


So, facing possible futures of incalculable benefits and threats, the experts are undoubtedly doing everything possible to guarantee the very best result, right? Wrong. If a superior alien civilisation sent us a message stating, 'We'll get here in a couple of decades,' would we just reply, 'OK, call us when you get here-we'll leave the lights on?' Probably not-but this is more or less what is taking place with AI. [153]

The prospective fate of mankind has actually in some cases been compared to the fate of gorillas threatened by human activities. The contrast states that greater intelligence permitted mankind to control gorillas, which are now vulnerable in ways that they might not have actually prepared for. As a result, the gorilla has become a threatened types, not out of malice, however just as a security damage from human activities. [154]

The skeptic Yann LeCun considers that AGIs will have no desire to dominate humanity which we must be mindful not to anthropomorphize them and analyze their intents as we would for humans. He said that people will not be "smart sufficient to create super-intelligent machines, yet ridiculously silly to the point of giving it moronic objectives without any safeguards". [155] On the other side, the idea of crucial merging suggests that almost whatever their goals, smart representatives will have factors to try to endure and get more power as intermediary steps to attaining these objectives. Which this does not need having feelings. [156]

Many scholars who are worried about existential danger supporter for more research into fixing the "control issue" to address the concern: what kinds of safeguards, algorithms, or architectures can developers implement 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 issue is complicated by the AI arms race (which could result in a race to the bottom of security precautions in order to launch items before competitors), [159] and making use of AI in weapon systems. [160]

The thesis that AI can pose existential risk also has detractors. Skeptics generally state that AGI is unlikely in the short-term, or that issues about AGI sidetrack from other issues associated with existing AI. [161] Former Google fraud czar Shuman Ghosemajumder thinks about that for numerous people outside of the innovation industry, existing chatbots and LLMs are already perceived as though they were AGI, leading to further misconception and fear. [162]

Skeptics often charge that the thesis is crypto-religious, with an illogical belief in the possibility of superintelligence changing an irrational belief in a supreme God. [163] Some researchers believe that the interaction projects on AI existential threat by particular AI groups (such as OpenAI, Anthropic, DeepMind, and Conjecture) may be an at effort at regulatory capture and to inflate interest in their items. [164] [165]

In 2023, the CEOs of Google DeepMind, OpenAI and Anthropic, in addition to other market leaders and scientists, issued a joint statement asserting that "Mitigating the threat of extinction from AI must be a global concern alongside other societal-scale threats such as pandemics and nuclear war." [152]

Mass joblessness


Researchers from OpenAI approximated that "80% of the U.S. labor force might have at least 10% of their work tasks affected by the introduction of LLMs, while around 19% of workers may see a minimum of 50% of their tasks affected". [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 choices, to interface with other computer tools, but also to manage robotized bodies.


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

Everyone can delight in a life of elegant leisure if the machine-produced wealth is shared, or the majority of individuals can end up miserably bad if the machine-owners successfully lobby versus wealth redistribution. So far, the pattern appears to be towards the second alternative, with innovation driving ever-increasing inequality


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

See also


Artificial brain - Software and hardware with cognitive capabilities comparable to those of the animal or human brain
AI impact
AI security - Research location on making AI safe and helpful
AI positioning - AI conformance to the designated goal
A.I. Rising - 2018 film directed by Lazar Bodroža
Expert system
Automated device learning - Process of automating the application of device learning
BRAIN Initiative - Collaborative public-private research study initiative revealed by the Obama administration
China Brain Project
Future of Humanity Institute - Defunct Oxford interdisciplinary research study centre
General game playing - Ability of expert system to play various games
Generative artificial intelligence - AI system capable of generating material in response to triggers
Human Brain Project - Scientific research study job
Intelligence amplification - Use of info technology to augment human intelligence (IA).
Machine principles - Moral behaviours of manufactured machines.
Moravec's paradox.
Multi-task learning - Solving multiple machine finding out jobs at the very same time.
Neural scaling law - Statistical law in maker learning.
Outline of artificial intelligence - Overview of and topical guide to artificial intelligence.
Transhumanism - Philosophical motion.
Synthetic intelligence - Alternate term for or type of expert system.
Transfer learning - Machine knowing strategy.
Loebner Prize - Annual AI competition.
Hardware for synthetic intelligence - Hardware specifically developed and enhanced for artificial intelligence.
Weak expert system - Form of synthetic intelligence.


Notes


^ a b See below for the origin of the term "strong AI", and see the scholastic definition of "strong AI" and weak AI in the article Chinese room.
^ AI creator John McCarthy composes: "we can not yet define in general what kinds of computational treatments we wish to call smart. " [26] (For a discussion of some definitions of intelligence used by expert system researchers, see philosophy of synthetic intelligence.).
^ The Lighthill report specifically slammed AI's "grand goals" 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 founder John McCarthy composes "it would be a fantastic relief to the rest of the workers in AI if the innovators of new general formalisms would express their hopes in a more protected kind than has in some cases held true." [61] ^ In "Mind Children" [122] 1015 cps is utilized. More recently, in 1997, [123] Moravec argued for 108 MIPS which would roughly correspond to 1014 cps. Moravec talks in regards to MIPS, not "cps", which is a non-standard term Kurzweil introduced.
^ As specified in a basic AI book: "The assertion that machines might perhaps act smartly (or, possibly better, act as if they were smart) is called the 'weak AI' hypothesis by philosophers, and the assertion that devices that do so are really thinking (rather than replicating 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 designed to carry out a single task.
^ "OpenAI Charter". OpenAI. Retrieved 6 April 2023. Our mission is to make sure that artificial basic intelligence benefits all of mankind.
^ Heath, Alex (18 January 2024). "Mark Zuckerberg's new objective is developing artificial basic intelligence". The Verge. Retrieved 13 June 2024. Our vision is to construct AI that is much better than human-level at all of the human senses.
^ Baum, Seth D. (2020 ). A Study of Artificial General Intelligence Projects for Ethics, Risk, and Policy (PDF) (Report). Global Catastrophic Risk Institute. Retrieved 28 November 2024. 72 AGI R&D projects were recognized as being active in 2020.
^ a b c "AI timelines: What do professionals in expert system anticipate for the future?". Our World in Data. Retrieved 6 April 2023.
^ Metz, Cade (15 May 2023). "Some Researchers Say A.I. Is Already Here, Stirring Debate in Tech Circles". The New York City Times. Retrieved 18 May 2023.
^ "AI pioneer 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 prevent 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 explores GPT-4". arXiv preprint. arXiv:2303.12712. GPT-4 reveals triggers of AGI.
^ Butler, Octavia E. (1993 ). Parable of the Sower. Grand Central Publishing. ISBN 978-0-4466-7550-5. All that you touch you alter. All that you change modifications you.
^ Vinge, Vernor (1992 ). A Fire Upon the Deep. Tor Books. ISBN 978-0-8125-1528-2. The Singularity is coming.
^ Morozov, Evgeny (30 June 2023). "The True Threat of Expert System". The New York Times. The genuine hazard is not AI itself however the way we release it.
^ "Impressed by expert system? Experts say AGI is coming next, and it has 'existential' dangers". ABC News. 23 March 2023. Retrieved 6 April 2023. AGI might present existential dangers to humankind.
^ Bostrom, Nick (2014 ). Superintelligence: Paths, Dangers, Strategies. Oxford University Press. ISBN 978-0-1996-7811-2. The first superintelligence will be the last innovation that humanity requires to make.
^ Roose, Kevin (30 May 2023). "A.I. Poses 'Risk of Extinction,' Industry Leaders Warn". The New York Times. Mitigating the danger of termination from AI need to be a global priority.
^ "Statement on AI Risk". Center for AI Safety. Retrieved 1 March 2024. AI experts 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 creating makers that can outthink us in general ways.
^ LeCun, Yann (June 2023). "AGI does not provide an existential threat". 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 original on 14 August 2005: Kurzweil describes strong AI as "maker intelligence with the complete range of human intelligence.".
^ "The Age of Artificial Intelligence: George John at TEDxLondonBusinessSchool 2013". Archived from the original on 26 February 2014. Retrieved 22 February 2014.
^ Newell & Simon 1976, This is the term they utilize for "human-level" intelligence in the physical symbol system hypothesis.
^ "The Open University on Strong and Weak AI". Archived from the original on 25 September 2009. Retrieved 8 October 2007.
^ "What is synthetic superintelligence (ASI)?|Definition from TechTarget". Enterprise AI. Retrieved 8 October 2023.
^ "Artificial intelligence is transforming our world - it is on everybody to ensure that it works out". 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 original on 26 October 2007. Retrieved 6 December 2007.
^ This list of intelligent traits is based on the topics covered by major AI books, 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 forms the method we believe: a brand-new view of intelligence (The MIT Press, 2007). ISBN 0-2621-6239-3.
^ White, R. W. (1959 ). "Motivation reevaluated: The idea of skills". Psychological Review. 66 (5 ): 297-333. doi:10.1037/ h0040934. PMID 13844397. S2CID 37385966.
^ White, R. W. (1959 ). "Motivation reassessed: The idea of competence". Psychological Review. 66 (5 ): 297-333. doi:10.1037/ h0040934. PMID 13844397. S2CID 37385966.
^ Muehlhauser, Luke (11 August 2013). "What is AGI?". Machine Intelligence Research Institute. Archived from the 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 kid - the Turing Test states so". The Guardian. 9 June 2014. ISSN 0261-3077. Retrieved 3 March 2024.
^ "Scientists challenge whether computer system 'Eugene Goostman' passed Turing test". BBC News. 9 June 2014. Retrieved 3 March 2024.
^ Jones, Cameron R.; Bergen, Benjamin K. (9 May 2024). "People can not 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 tough examinations both AI versions have actually passed". Business Insider. Retrieved 30 May 2023.
^ Naysmith, Caleb (7 February 2023). "6 Jobs Artificial Intelligence Is Already Replacing and How Investors Can 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 obsolete. Coffee is the answer". Analytics India Magazine. Retrieved 3 March 2024.
^ Bhaimiya, Sawdah (20 June 2023). "DeepMind's co-founder suggested testing an AI chatbot's capability 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 ). "Artificial Intelligence" (PDF). In Stuart C. Shapiro (ed.). Encyclopedia of Artificial Intelligence (Second ed.). New York: John Wiley. pp. 54-57. Archived (PDF) from the original on 1 February 2016. (Section 4 is on "AI-Complete Tasks".).
^ Yampolskiy, Roman V. (2012 ). Xin-She Yang (ed.). "Turing Test as a Specifying Feature of AI-Completeness" (PDF). Artificial Intelligence, Evolutionary Computation and Metaheuristics (AIECM): 3-17. Archived (PDF) from the initial on 22 May 2013.
^ "AI Index: State of AI in 13 Charts". Stanford University Human-Centered Artificial Intelligence. 15 April 2024. Retrieved 27 May 2024.
^ Crevier 1993, pp. 48-50.
^ Kaplan, Andreas (2022 ). "Artificial Intelligence, Business and Civilization - Our Fate Made in Machines". Archived from the original on 6 May 2022. Retrieved 12 March 2022.
^ Simon 1965, p. 96 estimated in Crevier 1993, p. 109.
^ "Scientist on the Set: An Interview with Marvin Minsky". Archived from the original on 16 July 2012. Retrieved 5 April 2008.
^ Marvin Minsky to Darrach (1970 ), priced quote in Crevier (1993, p. 109).
^ Lighthill 1973; Howe 1994.
^ a b NRC 1999, "Shift to Applied Research Increases Investment".
^ Crevier 1993, pp. 115-117; Russell & Norvig 2003, pp. 21-22.
^ Crevier 1993, p. 211, Russell & Norvig 2003, p. 24 and see also Feigenbaum & McCorduck 1983.
^ Crevier 1993, pp. 161-162, 197-203, 240; Russell & Norvig 2003, p. 25.
^ Crevier 1993, pp. 209-212.
^ McCarthy, John (2000 ). "Respond to Lighthill". Stanford University. Archived from the initial on 30 September 2008. Retrieved 29 September 2007.
^ Markoff, John (14 October 2005). "Behind Expert system, a Squadron of Bright Real People". The New York Times. Archived from the initial on 2 February 2023. Retrieved 18 February 2017. At its low point, some computer scientists and software engineers prevented the term expert system for worry of being considered as wild-eyed dreamers.
^ Russell & Norvig 2003, pp. 25-26
^ "Trends in the Emerging Tech Hype Cycle". Gartner Reports. Archived from the original on 22 May 2019. Retrieved 7 May 2019.
^ a b Moravec 1988, p. 20
^ Harnad, S. (1990 ). "The Symbol Grounding Problem". Physica D. 42 (1-3): 335-346. arXiv: cs/9906002. Bibcode:1990 PhyD ... 42..335 H. doi:10.1016/ 0167-2789( 90 )90087-6. S2CID 3204300.
^ Gubrud 1997
^ Hutter, Marcus (2005 ). Universal Expert System: Sequential Decisions Based on Algorithmic Probability. Texts in Theoretical Computer Science an EATCS Series. Springer. doi:10.1007/ b138233. ISBN 978-3-5402-6877-2. S2CID 33352850. Archived from the original on 19 July 2022. Retrieved 19 July 2022.
^ Legg, Shane (2008 ). Machine Super Intelligence (PDF) (Thesis). University of Lugano. Archived (PDF) from the initial 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., by means of Life 3.0: 'The term "AGI" was promoted by ... Shane Legg, Mark Gubrud and Ben Goertzel'
^ Wang & Goertzel 2007
^ "First International Summer School in Artificial General Intelligence, Main summer season 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 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 progress in device intelligence, synthetic general intelligence is still a major difficulty". EMBO Reports. 20 (10 ): e49177. doi:10.15252/ embr.201949177. ISSN 1469-221X. PMC 6776890. PMID 31531926.
^ Bubeck, Sébastien; Chandrasekaran, Varun; Eldan, Ronen; Gehrke, Johannes; Horvitz, Eric; Kamar, Ece; Lee, Peter; Lee, Yin Tat; Li, Yuanzhi; Lundberg, Scott; Nori, Harsha; Palangi, Hamid; Ribeiro, Marco Tulio; Zhang, Yi (27 March 2023). "Sparks of Artificial General Intelligence: Early try outs GPT-4". arXiv:2303.12712 [cs.CL]
^ "Microsoft Researchers Claim GPT-4 Is Showing "Sparks" of AGI". Futurism. 23 March 2023. Retrieved 13 December 2023.
^ Allen, Paul; Greaves, Mark (12 October 2011). "The Singularity Isn't Near". MIT Technology Review. Retrieved 17 September 2014.
^ Winfield, Alan. "Expert system will not become a Frankenstein's beast". The Guardian. Archived from the initial on 17 September 2014. Retrieved 17 September 2014.
^ Deane, George (2022 ). "Machines That Feel and Think: The Role of Affective Feelings and Mental Action in (Artificial) General Intelligence". Artificial Life. 28 (3 ): 289-309. doi:10.1162/ artl_a_00368. ISSN 1064-5462. PMID 35881678. S2CID 251069071.
^ a b c Clocksin 2003.
^ Fjelland, Ragnar (17 June 2020). "Why general 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 utopia or destruction?". The New Yorker. Archived from the original on 28 January 2016. Retrieved 7 February 2016.
^ Müller, V. C., & Bostrom, N. (2016 ). Future development in synthetic intelligence: A study of expert opinion. In Fundamental issues of synthetic intelligence (pp. 555-572). Springer, Cham.
^ Armstrong, Stuart, and Kaj Sotala. 2012. "How We're Predicting AI-or Failing To." In Beyond AI: Artificial Dreams, modified by Jan Romportl, Pavel Ircing, Eva Žáčková, Michal Polák and Radek Schuster, 52-75. Plzeň: University of West Bohemia
^ "Microsoft Now Claims GPT-4 Shows 'Sparks' of General Intelligence". 24 March 2023.
^ Shimek, Cary (6 July 2023). "AI Outperforms Humans in Creativity Test". Neuroscience News. Retrieved 20 October 2023.
^ Guzik, Erik E.; Byrge, Christian; Gilde, Christian (1 December 2023). "The originality of devices: AI takes the Torrance Test". Journal of Creativity. 33 (3 ): 100065. doi:10.1016/ j.yjoc.2023.100065. ISSN 2713-3745. S2CID 261087185.
^ Arcas, Blaise Agüera y (10 October 2023). "Artificial General Intelligence Is Already Here". Noema.
^ Zia, Tehseen (8 January 2024). "Unveiling of Large Multimodal Models: Shaping the Landscape of Language Models in 2024". Unite.ai. Retrieved 26 May 2024.
^ "Introducing OpenAI o1-preview". OpenAI. 12 September 2024.
^ Knight, Will. "OpenAI Announces a 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 initial 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 function 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 original 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 wise as Siri - however a six-year-old beats both] (in German). Archived from the initial 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 initial on 4 September 2020. Retrieved 5 September 2020. Certainly, too, there are those who claim we are already seeing an early example of an AGI system in the recently announced GPT-3 natural language processing (NLP) neural network. ... So is GPT-3 the first example of an AGI system? This is arguable, however the agreement is that it is not AGI. ... If absolutely nothing else, GPT-3 tells us there is a happy medium between narrow and general AI.
^ Quach, Katyanna. "A designer constructed an AI chatbot using GPT-3 that assisted a man speak again to his late fiancée. OpenAI shut it down". The Register. Archived from the original 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 managing robotics", TechCrunch, archived from the initial on 16 June 2022, obtained 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 try outs 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 few years,' states CEO of Google's primary A.I. research lab". 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 everybody seems to disagree on how to specify 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 Giant 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 study project maps the human brain". euractiv.
^ Swaminathan, Nikhil (January-February 2011). "Glia-the other brain cells". Discover. Archived from the initial on 8 February 2014. Retrieved 24 January 2014.
^ de Vega, Glenberg & Graesser 2008. A large range of views in present research study, all of which need grounding to some degree
^ Thornton, Angela (26 June 2023). "How uploading our minds to a computer may become possible". The Conversation. Retrieved 8 November 2023.
^ Searle 1980
^ For instance: Russell & Norvig 2003,
Oxford University Press Dictionary of Psychology Archived 3 December 2007 at the Wayback Machine (estimated in" Encyclopedia.com"),.
MIT Encyclopedia of Cognitive Science Archived 19 July 2008 at the Wayback Machine (priced estimate 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 synthetic intelligence for interest by the field about why a program behaves the method it does.
^ Chalmers, David J. (9 August 2023). "Could a Large Language Model Be Conscious?". Boston Review.
^ Seth, Anil. "Consciousness". New Scientist. Retrieved 5 September 2024.
^ Nagel 1974.
^ "The Google engineer who believes the business'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 Artificial Intelligence Have Moral or Legal Rights?". The Wall Street Journal.
^ "Artificial General Intelligence - Do [es] the cost outweigh benefits?". 23 August 2021. Retrieved 7 June 2023.
^ "How we can Gain 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, dangers, strategies (Reprinted with corrections 2017 ed.). Oxford, UK; New York, New York, USA: Oxford University Press. ISBN 978-0-1996-7811-2.
^ Piper, Kelsey (19 November 2018). "How technological development is making it likelier than ever that humans will damage ourselves". Vox. Retrieved 8 June 2023.
^ Doherty, Ben (17 May 2018). "Climate change an 'existential security risk' to Australia, Senate questions 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 expert system - but are we taking AI seriously enough?'". The Independent (UK). Archived from the original on 25 September 2015. Retrieved 3 December 2014.
^ Herger, Mario. "The Gorilla Problem - Enterprise Garage". Retrieved 7 June 2023.
^ "The remarkable Facebook dispute between Yann LeCun, Stuart Russel and Yoshua Bengio about the threats of strong AI". The interesting Facebook argument in between Yann LeCun, Stuart Russel and Yoshua Bengio about the dangers of strong AI (in French). Retrieved 8 June 2023.
^ "Will Artificial Intelligence Doom The Human Race Within The Next 100 Years?". HuffPost. 22 August 2014. Retrieved 8 June 2023.
^ Sotala, Kaj; Yampolskiy, Roman V. (19 December 2014). "Responses to devastating AGI danger: a survey". 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 risks spiralling out of control, report cautions". Financial Times. Archived from the initial on 11 April 2022. Retrieved 24 December 2023.
^ Milmo, Dan; Stacey, Kiran (25 September 2023). "Experts disagree over risk presented however expert system 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 original on 4 June 2014. Retrieved 12 December 2015.
^ Titcomb, James (30 October 2023). "Big Tech is stoking worries over AI, alert scientists". The Telegraph. Retrieved 7 December 2023.
^ Davidson, John (30 October 2023). "Google Brain says big tech is lying about AI extinction risk". Australian Financial Review. Archived from the original on 7 December 2023. Retrieved 7 December 2023.
^ Eloundou, Tyna; Manning, Sam; Mishkin, Pamela; Rock, Daniel (17 March 2023). "GPTs are GPTs: An early look at the labor market impact potential of big language models". OpenAI. Retrieved 7 June 2023.
^ a b Hurst, Luke (23 March 2023). "OpenAI states 80% of employees could see their tasks impacted by AI. These are the tasks most impacted". euronews. Retrieved 8 June 2023.
^ Sheffey, Ayelet (20 August 2021). "Elon Musk says we require universal standard earnings because 'in the future, physical work will be an option'". 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 initial on 18 June 2022. Retrieved 22 September 2021.
Chalmers, David (1996 ), The Conscious Mind, Oxford University Press.
Clocksin, William (August 2003), "Expert system 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 Artificial Intelligence. New York, NY: BasicBooks. ISBN 0-465-02997-3.
Darrach, Brad (20 November 1970), "Meet Shakey, 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: Expert System 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 original (PDF) on 20 March 2013.
Goertzel, Ben (December 2007), "Human-level synthetic general intelligence and the possibility of a technological singularity: a response to Ray Kurzweil's The Singularity Is Near, and McDermott's critique 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), Expert System at Edinburgh University: a Perspective, archived from the original on 17 August 2007, recovered 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 ), "Artificial Intelligence: A General Survey", Artificial Intelligence: a paper symposium, Science Research Council.
Luger, George; Stubblefield, William (2004 ), Artificial Intelligence: Structures and Strategies for Complex Problem Solving (5th 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 resolve problems and accomplish goals in the world in addition to people.
Moravec, Hans (1988 ), Mind Children, Harvard University Press
Moravec, Hans (1998 ), "When will 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 ), Artificial Intelligence: A New Synthesis, Morgan Kaufmann Publishers, ISBN 978-1-5586-0467-4
NRC (1999 ), "Developments in Artificial Intelligence", Funding a Revolution: Government Support for Computing Research, National Academy Press, archived from the initial on 12 January 2008, retrieved 29 September 2007
Poole, David; Mackworth, Alan; Goebel, Randy (1998 ), Computational Intelligence: A Rational Approach, New York City: Oxford University Press, archived from the original on 25 July 2009, recovered 6 December 2007
Russell, Stuart J.; Norvig, Peter (2003 ), Expert System: A Modern Approach (2nd 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 original on 25 March 2020, obtained 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 original on 17 March 2019, obtained 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 significance 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 original on 18 February 2021. Retrieved 13 December 2020 - through ResearchGate.


Further reading


Aleksander, Igor (1996 ), Impossible Minds, World Scientific Publishing Company, ISBN 978-1-8609-4036-1
Azevedo FA, Carvalho LR, Grinberg LT, Farfel J, et al. (April 2009), "Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain", The Journal of Comparative Neurology, 513 (5 ): 532-541, doi:10.1002/ cne.21974, PMID 19226510, S2CID 5200449, archived from the initial on 18 February 2021, retrieved 4 September 2013 - via ResearchGate
Berglas, Anthony (January 2012) [2008], Artificial Intelligence Will Kill Our Grandchildren (Singularity), archived from the initial on 23 July 2014, obtained 31 August 2012
Cukier, Kenneth, "Ready for Robots? How to Consider the Future of AI", Foreign Affairs, vol. 98, no. 4 (July/August 2019), pp. 192-98. George Dyson, historian of computing, writes (in what may be called "Dyson's Law") that "Any system basic adequate to be reasonable will not be made complex enough to act intelligently, while any system made complex enough to behave intelligently will be too complicated to understand." (p. 197.) Computer scientist Alex Pentland composes: "Current AI machine-learning algorithms are, at their core, dead basic dumb. They work, however they work by strength." (p. 198.).
Gelernter, David, Dream-logic, the Internet and Artificial Thought, Edge, archived from the original on 26 July 2010, recovered 25 July 2010.
Gleick, James, "The Fate of Free Will" (evaluation of Kevin J. Mitchell, Free Agents: How Evolution Gave Us Free Will, Princeton University Press, 2023, 333 pp.), The New York Review of Books, vol. LXXI, no. 1 (18 January 2024), pp. 27-28, 30. "Agency is what identifies us from machines. For biological animals, factor and function come from acting in the world and experiencing the repercussions. Expert systems - disembodied, complete strangers to blood, sweat, and tears - have no celebration for that." (p. 30.).
Halal, William E. "TechCast Article Series: The Automation of Thought" (PDF). Archived from the original (PDF) on 6 June 2013.
- Halpern, Sue, "The Coming Tech Autocracy" (review of Verity Harding, AI Needs You: How We Can Change AI's Future and Save Our Own, Princeton University Press, 274 pp.; Gary Marcus, Taming Silicon Valley: How We Can Ensure That AI Works for Us, MIT Press, 235 pp.; Daniela Rus and Gregory Mone, The Mind's Mirror: Risk and Reward in the Age of AI, Norton, 280 pp.; Madhumita Murgia, Code Dependent: Residing In the Shadow of AI, Henry Holt, 311 pp.), The New York Review of Books, vol. LXXI, no. 17 (7 November 2024), pp. 44-46. "' We can't reasonably anticipate that those who want to get rich from AI are going to have the interests of the rest people close at heart,' ... writes [Gary Marcus] 'We can't count on federal governments driven by campaign financing contributions [from tech business] to press back.' ... Marcus details the demands that residents ought to make from their federal governments and the tech business. They include openness on how AI systems work; payment for individuals if their data [are] utilized to train LLMs (big language model) s and the right to grant this use; and the capability to hold tech companies responsible for the harms they trigger by getting rid of Section 230, imposing money penalites, and passing more stringent product liability laws ... Marcus also suggests ... that a brand-new, AI-specific federal firm, akin to the FDA, the FCC, or the FTC, may offer the most robust oversight ... [T] he Fordham law teacher Chinmayi Sharma ... recommends ... establish [ing] a professional licensing regime for engineers that would function in a comparable method to medical licenses, malpractice fits, and the Hippocratic oath in medicine. 'What if, like medical professionals,' she asks ..., 'AI engineers likewise vowed to do no harm?'" (p. 46.).
Holte, R. C.; Choueiry, B. Y. (2003 ), "Abstraction and reformulation in expert system", Philosophical Transactions of the Royal Society B, vol. 358, no. 1435, pp. 1197-1204, doi:10.1098/ rstb.2003.1317, PMC 1693218, PMID 12903653.
Hughes-Castleberry, Kenna, "A Murder Mystery Puzzle: The literary puzzle Cain's Jawbone, which has stymied humans for years, exposes the restrictions of natural-language-processing algorithms", Scientific American, vol. 329, no. 4 (November 2023), pp. 81-82. "This murder mystery competition has revealed that although NLP (natural-language processing) designs are capable of unbelievable tasks, their capabilities are quite limited by the amount of context they receive. This [...] could cause [problems] for scientists who want to use them to do things such as analyze ancient languages. Sometimes, there are few historical records on long-gone civilizations to act as training data for such a purpose." (p. 82.).
Immerwahr, Daniel, "Your Lying Eyes: People now utilize A.I. to produce phony videos indistinguishable from real ones. How much does it matter?", The New Yorker, 20 November 2023, pp. 54-59. "If by 'deepfakes' we indicate reasonable videos produced using expert system that really deceive people, then they barely exist. The fakes aren't deep, and the deeps aren't phony. [...] A.I.-generated videos are not, in general, running in our media as counterfeited evidence. Their function better looks like that of cartoons, especially smutty ones." (p. 59.).
- Leffer, Lauren, "The Risks of Trusting AI: We need to avoid humanizing machine-learning models used in clinical research", Scientific American, vol. 330, no. 6 (June 2024), pp. 80-81.
Lepore, Jill, "The Chit-Chatbot: Is talking with a machine a conversation?", The New Yorker, 7 October 2024, pp. 12-16.
Marcus, Gary, "Artificial Confidence: Even the latest, buzziest systems of synthetic general intelligence are stymmied by the usual problems", Scientific American, vol. 327, no. 4 (October 2022), pp. 42-45.
McCarthy, John (October 2007), "From here to human-level AI", Expert System, 171 (18 ): 1174-1182, doi:10.1016/ j.artint.2007.10.009.
McCorduck, Pamela (2004 ), Machines Who Think (second ed.), Natick, Massachusetts: A. K. Peters, ISBN 1-5688-1205-1.
Moravec, Hans (1976 ), The Role of Raw Power in Intelligence, archived from the original on 3 March 2016, retrieved 29 September 2007.
Newell, Allen; Simon, H. A. (1963 ), "GPS: A Program that Simulates Human Thought", in Feigenbaum, E. A.; Feldman, J. (eds.), Computers and Thought, New York City: McGraw-Hill.
Omohundro, Steve (2008 ), The Nature of Self-Improving Expert system, presented and dispersed at the 2007 Singularity Summit, San Francisco, California.
Press, Eyal, "In Front of Their Faces: Does facial-recognition technology lead cops to neglect inconsistent proof?", The New Yorker, 20 November 2023, pp. 20-26.
Roivainen, Eka, "AI's IQ: ChatGPT aced a [basic intelligence] test but revealed that intelligence can not be measured by IQ alone", Scientific American, vol. 329, no. 1 (July/August 2023), p. 7. "Despite its high IQ, ChatGPT fails at jobs that require genuine humanlike reasoning or an understanding of the physical and social world ... ChatGPT seemed unable to reason rationally and tried to count on its vast database of ... facts originated from online texts. "
- Scharre, Paul, "Killer Apps: The Real Dangers of an AI Arms Race", Foreign Affairs, vol. 98, no. 3 (May/June 2019), pp. 135-44. "Today's AI technologies are powerful however undependable. Rules-based systems can not handle circumstances their programmers did not anticipate. Learning systems are limited by the data on which they were trained. AI failures have actually already resulted in tragedy. Advanced auto-pilot features in cars and trucks, although they carry out well in some scenarios, have driven vehicles without cautioning into trucks, concrete barriers, and parked automobiles. In the wrong scenario, AI systems go from supersmart to superdumb in an instant. When an enemy is trying to control and hack an AI system, the dangers are even higher." (p. 140.).
Sutherland, J. G. (1990 ), "Holographic Model of Memory, Learning, and Expression", International Journal of Neural Systems, vol. 1-3, pp. 256-267.
- Vincent, James, "Horny Robot Baby Voice: James Vincent on AI chatbots", London Review of Books, vol. 46, no. 19 (10 October 2024), pp. 29-32." [AI chatbot] programs are made possible by new technologies however rely on the timelelss human tendency to anthropomorphise." (p. 29.).
Williams, R. W.; Herrup, K.