Artificial General Intelligence

From Shiapedia

Artificial general intelligence (AGI) is a kind of expert system (AI) that matches or goes beyond human cognitive abilities across a wide variety of cognitive jobs. This contrasts with narrow AI, which is restricted to particular jobs. [1] Artificial superintelligence (ASI), on the other hand, refers to AGI that greatly goes beyond human cognitive capabilities. AGI is thought about among the meanings of strong AI.


Creating AGI is a primary objective of AI research and of companies such as OpenAI [2] and Meta. [3] A 2020 study determined 72 active AGI research study and advancement jobs throughout 37 countries. [4]

The timeline for attaining AGI remains a topic of ongoing dispute among scientists and professionals. Since 2023, some argue that it might be possible in years or decades; others preserve it may take a century or longer; a minority think it might never ever be accomplished; and another minority declares that it is already here. [5] [6] Notable AI scientist Geoffrey Hinton has expressed concerns about the fast development towards AGI, suggesting it might be attained sooner than lots of anticipate. [7]

There is debate on the exact meaning of AGI and relating to whether modern large language designs (LLMs) such as GPT-4 are early kinds of AGI. [8] AGI is a common topic in sci-fi and futures research studies. [9] [10]

Contention exists over whether AGI represents an existential risk. [11] [12] [13] Many experts on AI have mentioned that mitigating the risk of human termination posed by AGI ought to be a global priority. [14] [15] Others discover the advancement of AGI to be too remote to present such a danger. [16] [17]

Terminology


AGI is also known as strong AI, [18] [19] full AI, [20] human-level AI, [5] human-level intelligent AI, or basic smart action. [21]

Some scholastic sources book the term "strong AI" for computer system programs that experience sentience or awareness. [a] In contrast, weak AI (or narrow AI) has the ability to fix one particular issue however lacks general cognitive abilities. [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 ideas include artificial superintelligence and transformative AI. An artificial superintelligence (ASI) is a hypothetical type of AGI that is far more usually smart than people, [23] while the notion of transformative AI connects to AI having a big influence on society, for example, similar to the farming or industrial revolution. [24]

A structure for categorizing AGI in levels was proposed in 2023 by Google DeepMind researchers. They specify 5 levels of AGI: emerging, skilled, expert, virtuoso, and superhuman. For example, a qualified AGI is defined as an AI that exceeds 50% of competent grownups in a large range of non-physical jobs, and a superhuman AGI (i.e. a synthetic superintelligence) is likewise specified 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 actually been proposed. One of the leading proposals is the Turing test. However, there are other popular meanings, and some scientists disagree with the more popular techniques. [b]

Intelligence characteristics


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

factor, use method, resolve puzzles, and make judgments under uncertainty
represent knowledge, including good sense knowledge
plan
find out
- interact in natural language
- if needed, incorporate these abilities in conclusion of any given goal


Many interdisciplinary methods (e.g. cognitive science, computational intelligence, and shiapedia.1god.org decision making) consider extra characteristics such as imagination (the capability to form unique psychological images and principles) [28] and autonomy. [29]

Computer-based systems that display numerous of these abilities exist (e.g. see computational imagination, automated reasoning, decision assistance system, robotic, evolutionary computation, smart agent). There is debate about whether modern AI systems possess them to an adequate degree.


Physical qualities


Other abilities are thought about desirable in intelligent systems, as they may affect intelligence or aid 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. relocation and manipulate items, modification location to check out, etc).


This includes the capability to identify and react to threat. [31]

Although the ability to sense (e.g. see, hear, etc) and the ability to act (e.g. relocation and manipulate objects, modification place to explore, and so on) can be preferable for some smart systems, [30] these physical capabilities are not strictly needed for an entity to certify as AGI-particularly under the thesis that large language designs (LLMs) might already be or end up being AGI. Even from a less optimistic perspective on LLMs, there is no firm requirement for an AGI to have a human-like kind; being a silicon-based computational system suffices, provided 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 been proscribed a specific physical personification and hence does not require a capability for locomotion or traditional "eyes and ears". [32]

Tests for human-level AGI


Several tests indicated to confirm human-level AGI have actually been considered, consisting of: [33] [34]

The idea of the test is that the device needs to attempt and pretend to be a man, by responding to questions put to it, and it will only pass if the pretence is reasonably persuading. A substantial portion of a jury, who need to not be professional about machines, should be taken in by the pretence. [37]

AI-complete problems


A problem is informally called "AI-complete" or "AI-hard" if it is thought that in order to resolve it, one would require to implement AGI, since the service is beyond the capabilities of a purpose-specific algorithm. [47]

There are numerous issues that have actually been conjectured to require basic intelligence to resolve in addition to humans. Examples include computer system vision, natural language understanding, and dealing with unforeseen scenarios while solving any real-world issue. [48] Even a specific job like translation requires a maker to read and write in both languages, follow the author's argument (factor), understand the context (understanding), and faithfully reproduce the author's initial intent (social intelligence). All of these issues need to be fixed all at once in order to reach human-level machine efficiency.


However, a number of these tasks can now be carried out by modern large language designs. According to Stanford University's 2024 AI index, AI has actually reached human-level performance on many benchmarks for checking out understanding and visual thinking. [49]

History


Classical AI


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

Their forecasts were the inspiration for Stanley Kubrick and Arthur C. Clarke's character HAL 9000, who embodied what AI researchers thought they could develop by the year 2001. AI pioneer Marvin Minsky was an expert [53] on the job of making HAL 9000 as reasonable as possible according to the consensus predictions of the time. He stated in 1967, "Within a generation ... the issue of producing 'artificial intelligence' will significantly be resolved". [54]

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


However, in the early 1970s, it ended up being apparent that researchers had actually grossly underestimated the problem of the job. Funding agencies became skeptical of AGI and put scientists under increasing pressure to produce helpful "applied AI". [c] In the early 1980s, Japan's Fifth Generation Computer Project revived interest in AGI, setting out a ten-year timeline that included AGI objectives like "continue a casual discussion". [58] In action to this and the success of professional systems, both industry and federal government pumped money into the field. [56] [59] However, self-confidence in AI marvelously collapsed in the late 1980s, and the objectives of the Fifth Generation Computer Project were never fulfilled. [60] For the second time in 20 years, AI scientists who predicted the impending achievement of AGI had been misinterpreted. By the 1990s, AI scientists had a track record for making vain pledges. They became unwilling to make predictions at all [d] and prevented reference of "human level" expert system for fear of being identified "wild-eyed dreamer [s]. [62]

Narrow AI research


In the 1990s and early 21st century, mainstream AI accomplished commercial success and academic respectability by concentrating on particular sub-problems where AI can produce proven outcomes and business applications, such as speech acknowledgment and recommendation algorithms. [63] These "applied AI" systems are now used thoroughly throughout the technology industry, and research study in this vein is greatly funded in both academic community and industry. Since 2018 [upgrade], advancement in this field was thought about an emerging pattern, and a mature phase was anticipated to be reached in more than ten years. [64]

At the millenium, many mainstream AI scientists [65] hoped that strong AI could be established by combining programs that solve various sub-problems. Hans Moravec composed in 1988:


I am positive that this bottom-up route to synthetic intelligence will one day satisfy the traditional top-down path majority way, all set to offer the real-world proficiency and the commonsense understanding that has been so frustratingly elusive in reasoning programs. Fully smart machines will result when the metaphorical golden spike is driven joining 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 sign grounding hypothesis by mentioning:


The expectation has frequently been voiced that "top-down" (symbolic) approaches to modeling cognition will somehow meet "bottom-up" (sensory) approaches somewhere in between. If the grounding factors to consider in this paper stand, then this expectation is hopelessly modular and there is truly only one practical route from sense to signs: from the ground up. A free-floating symbolic level like the software level of a computer system will never ever be reached by this path (or vice versa) - nor is it clear why we must even try to reach such a level, considering that it looks as if getting there would simply amount to uprooting our symbols from their intrinsic meanings (consequently simply lowering ourselves to the practical equivalent of a programmable computer system). [66]

Modern artificial basic intelligence research study


The term "synthetic general intelligence" was utilized as early as 1997, by Mark Gubrud [67] in a discussion of the implications of fully automated military production and operations. A mathematical formalism of AGI was proposed by Marcus Hutter in 2000. Named AIXI, the proposed AGI agent increases "the ability to please goals in a wide range of environments". [68] This type of AGI, defined by the capability to increase a mathematical definition of intelligence rather than display 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 study activity in 2006 was explained by Pei Wang and Ben Goertzel [72] as "producing publications and initial results". The 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 up 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 number of guest lecturers.


As of 2023 [upgrade], a small number of computer system scientists are active in AGI research study, and numerous contribute to a series of AGI conferences. However, significantly more scientists are interested in open-ended learning, [76] [77] which is the concept of enabling AI to constantly discover and innovate like humans do.


Feasibility


Since 2023, the development and potential achievement of AGI remains a topic of intense dispute within the AI community. While standard agreement held that AGI was a remote goal, recent advancements have actually led some researchers and industry figures to claim that early types of AGI may currently exist. [78] AI leader Herbert A. Simon hypothesized in 1965 that "machines will be capable, within twenty years, of doing any work a man can do". This prediction failed to come true. Microsoft co-founder Paul Allen believed that such intelligence is not likely in the 21st century because it would require "unforeseeable and fundamentally unforeseeable breakthroughs" and a "scientifically deep understanding of cognition". [79] Writing in The Guardian, roboticist Alan Winfield declared the gulf in between modern-day computing and human-level expert system is as wide as the gulf between existing space flight and practical faster-than-light spaceflight. [80]

A further obstacle is the lack of clarity in defining what intelligence involves. Does it need awareness? Must it show the capability to set objectives along with pursue them? Is it simply a matter of scale such that if model sizes increase adequately, intelligence will emerge? Are facilities such as planning, thinking, and causal understanding required? Does intelligence require explicitly replicating the brain and its specific professors? Does it require feelings? [81]

Most AI scientists think strong AI can be accomplished in the future, however some thinkers, like Hubert Dreyfus and Roger Penrose, deny the possibility of achieving strong AI. [82] [83] John McCarthy is among those who think human-level AI will be accomplished, but that the present level of development is such that a date can not precisely be forecasted. [84] AI specialists' views on the feasibility of AGI wax and wane. Four polls carried out in 2012 and 2013 suggested that the median price quote amongst professionals for when they would be 50% positive AGI would get here was 2040 to 2050, depending on the poll, with the mean being 2081. Of the specialists, 16.5% answered with "never" when asked the exact same concern however with a 90% self-confidence instead. [85] [86] Further present AGI progress considerations can be found 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 time frame there is a strong predisposition towards anticipating the arrival of human-level AI as in between 15 and 25 years from the time the prediction was made". They examined 95 predictions made between 1950 and 2012 on when human-level AI will happen. [87]

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

Blaise Agüera y Arcas and Peter Norvig composed in 2023 that a considerable level of basic intelligence has already been accomplished with frontier designs. They wrote that hesitation to this view originates from 4 main factors: a "healthy uncertainty about metrics for AGI", an "ideological commitment 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 introduction of big multimodal designs (large language models efficient in processing or producing multiple 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 thinking before they react". According to Mira Murati, this ability to believe before reacting represents a brand-new, additional paradigm. It enhances model outputs by investing more computing power when producing the answer, whereas the design scaling paradigm enhances outputs by increasing the design size, training data and training compute power. [93] [94]

An OpenAI worker, Vahid Kazemi, claimed in 2024 that the company had actually achieved AGI, stating, "In my opinion, we have actually already attained AGI and it's much more clear with O1." Kazemi clarified that while the AI is not yet "better than any human at any task", it is "much better than a lot of humans at a lot of tasks." He also resolved criticisms that big language designs (LLMs) merely follow predefined patterns, comparing their knowing procedure to the clinical approach of observing, hypothesizing, and validating. These statements have actually sparked debate, as they depend on a broad and non-traditional definition of AGI-traditionally understood as AI that matches human intelligence throughout all domains. Critics argue that, while OpenAI's models show amazing adaptability, they might not completely fulfill this requirement. Notably, Kazemi's comments came soon after OpenAI eliminated "AGI" from the regards to its partnership with Microsoft, prompting speculation about the company's tactical objectives. [95]

Timescales


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

In the introduction to his 2006 book, [101] Goertzel says that quotes of the time needed before a really flexible AGI is built vary from ten years to over a century. As of 2007 [upgrade], the consensus in the AGI research study neighborhood appeared to be that the timeline talked about 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 actually provided a wide variety of opinions on whether development will be this fast. A 2012 meta-analysis of 95 such opinions found a predisposition towards forecasting that the onset of AGI would happen within 16-26 years for contemporary and historical forecasts alike. That paper has actually been slammed for how it categorized opinions as professional or non-expert. [104]

In 2012, Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton established a neural network called AlexNet, which won the ImageNet competition with a top-5 test mistake rate of 15.3%, substantially much better than the second-best entry's rate of 26.3% (the traditional technique used a weighted amount of scores from different pre-defined classifiers). [105] AlexNet was considered the initial ground-breaker of the existing deep knowing wave. [105]

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

In 2020, OpenAI developed GPT-3, a language model efficient in performing many varied tasks without particular training. According to Gary Grossman in a VentureBeat article, while there is agreement that GPT-3 is not an example of AGI, it is considered by some to be too advanced to be categorized as a narrow AI system. [108]

In the very same year, Jason Rohrer used his GPT-3 account to establish a chatbot, and provided a chatbot-developing platform called "Project December". OpenAI asked for modifications to the chatbot to comply with their safety standards; Rohrer detached Project December from the GPT-3 API. [109]

In 2022, DeepMind established Gato, a "general-purpose" system capable of performing more than 600 different jobs. [110]

In 2023, Microsoft Research released a study on an early variation of OpenAI's GPT-4, competing that it displayed more general intelligence than previous AI models and showed human-level performance in tasks covering multiple domains, such as mathematics, coding, and law. This research study triggered a dispute on whether GPT-4 might be considered an early, insufficient variation of synthetic basic intelligence, emphasizing the need for additional expedition and examination of such systems. [111]

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

The concept that this things could actually get smarter than individuals - a few people thought that, [...] But the majority of people thought it was way off. And I believed it was way off. I believed it was 30 to 50 years and even longer away. Obviously, I no longer believe that.


In May 2023, Demis Hassabis likewise stated that "The development in the last few years has been pretty unbelievable", and that he sees no reason it would slow down, anticipating AGI within a decade and even a couple of 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 as well as people. [114] In June 2024, the AI scientist Leopold Aschenbrenner, a former OpenAI staff member, approximated AGI by 2027 to be "strikingly plausible". [115]

Whole brain emulation


While the advancement of transformer designs like in ChatGPT is considered the most promising course to AGI, [116] [117] entire brain emulation can act as an alternative approach. With whole brain simulation, a brain design is built by scanning and mapping a biological brain in detail, and then copying and mimicing it on a computer system or another computational device. The simulation design should be adequately devoted to the initial, so that it acts in virtually the same method as the initial brain. [118] Whole brain emulation is a kind of brain simulation that is talked about in computational neuroscience and neuroinformatics, and for medical research study purposes. It has been discussed in expert system research [103] as a method to strong AI. Neuroimaging technologies that could deliver the needed in-depth understanding are enhancing rapidly, and futurist Ray Kurzweil in the book The Singularity Is Near [102] anticipates that a map of sufficient quality will appear on a similar timescale to the computing power required to replicate it.


Early approximates


For low-level brain simulation, a really powerful cluster of computers or GPUs would be needed, given the enormous quantity of synapses within the human brain. Each of the 1011 (one hundred billion) neurons has on typical 7,000 synaptic connections (synapses) to other nerve cells. The brain of a three-year-old kid has about 1015 synapses (1 quadrillion). This number decreases with age, supporting by adulthood. Estimates differ for an adult, varying from 1014 to 5 × 1014 synapses (100 to 500 trillion). [120] A price quote of the brain's processing power, based on a simple switch model for neuron activity, is around 1014 (100 trillion) synaptic updates per second (SUPS). [121]

In 1997, Kurzweil took a look at different price quotes for the hardware required to equal the human brain and embraced a figure of 1016 computations per 2nd (cps). [e] (For comparison, if a "calculation" was comparable to one "floating-point operation" - a procedure used to rate existing supercomputers - then 1016 "calculations" would be comparable to 10 petaFLOPS, accomplished in 2011, while 1018 was accomplished in 2022.) He utilized this figure to predict the required hardware would be available sometime between 2015 and 2025, if the rapid growth in computer power at the time of composing continued.


Current research study


The Human Brain Project, an EU-funded effort active from 2013 to 2023, has actually established an especially 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 techniques


The synthetic nerve cell design assumed by Kurzweil and utilized in numerous present synthetic neural network executions is easy compared with biological nerve cells. A brain simulation would likely need to capture the comprehensive cellular behaviour of biological nerve cells, presently understood only in broad overview. The overhead presented by full modeling of the biological, chemical, and physical information of neural behaviour (specifically on a molecular scale) would require computational powers a number of orders of magnitude bigger than Kurzweil's quote. In addition, the estimates do not account for 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 embodiment is an essential element of human intelligence and is needed to ground significance. [126] [127] If this theory is correct, any completely practical brain model will need to incorporate more than simply the nerve cells (e.g., a robotic body). Goertzel [103] proposes virtual personification (like in metaverses like Second Life) as an alternative, however it is unknown whether this would be sufficient.


Philosophical viewpoint


"Strong AI" as defined in philosophy


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

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


The very first one he called "strong" since it makes a more powerful declaration: it presumes something special has occurred to the machine that goes beyond those abilities that we can check. The behaviour of a "weak AI" maker would be precisely identical to a "strong AI" machine, but the latter would likewise have subjective conscious experience. This usage is likewise common in scholastic AI research and textbooks. [129]

In contrast to Searle and mainstream AI, some futurists such as Ray Kurzweil use the term "strong AI" to indicate "human level synthetic general intelligence". [102] This is not the like Searle's strong AI, unless it is presumed that awareness is needed for human-level AGI. Academic thinkers such as Searle do not believe that holds true, and to most expert system scientists the question is out-of-scope. [130]

Mainstream AI is most thinking about how a program behaves. [131] According to Russell and Norvig, "as long as the program works, they 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 know if it actually has mind - indeed, there would be no chance to inform. For AI research, Searle's "weak AI hypothesis" is comparable to the statement "artificial general intelligence is possible". Thus, according to Russell and Norvig, "most AI scientists take the weak AI hypothesis for granted, and don't care about the strong AI hypothesis." [130] Thus, for academic AI research study, "Strong AI" and "AGI" are 2 various things.


Consciousness


Consciousness can have various significances, and some aspects play significant functions in science fiction and the ethics of artificial intelligence:


Sentience (or "phenomenal awareness"): The ability to "feel" perceptions or emotions subjectively, instead of the ability to factor about understandings. Some thinkers, such as David Chalmers, use the term "awareness" to refer solely to sensational awareness, which is approximately comparable to life. [132] Determining why and how subjective experience occurs is referred to as the hard problem of awareness. [133] Thomas Nagel discussed in 1974 that it "feels like" something to be mindful. If we are not mindful, then it doesn't feel like anything. Nagel uses the example of a bat: we can smartly ask "what does it feel like to be a bat?" However, we are unlikely to ask "what does it feel like to be a toaster?" Nagel concludes that a bat seems conscious (i.e., has awareness) but a toaster does not. [134] In 2022, a Google engineer declared that the business's AI chatbot, LaMDA, had actually accomplished life, though this claim was commonly disputed by other specialists. [135]

Self-awareness: To have mindful awareness of oneself as a separate individual, specifically to be purposely mindful of one's own ideas. This is opposed to just being the "subject of one's thought"-an os or debugger is able to be "mindful of itself" (that is, to represent itself in the same method it represents everything else)-but this is not what individuals typically suggest when they utilize the term "self-awareness". [g]

These characteristics have a moral dimension. AI sentience would generate concerns of welfare and legal defense, likewise to animals. [136] Other aspects of awareness associated to cognitive abilities are also appropriate to the concept of AI rights. [137] Finding out how to incorporate sophisticated AI with existing legal and social structures is an emerging issue. [138]

Benefits


AGI could have a wide variety of applications. If oriented towards such objectives, AGI could assist mitigate various issues on the planet such as cravings, poverty and health issue. [139]

AGI could enhance efficiency and effectiveness in many jobs. For instance, in public health, AGI might accelerate medical research, especially versus cancer. [140] It might take care of the senior, [141] and equalize access to fast, top quality medical diagnostics. It could use enjoyable, inexpensive and customized education. [141] The requirement to work to subsist might end up being obsolete if the wealth produced is effectively rearranged. [141] [142] This likewise raises the question of the place of humans in a significantly automated society.


AGI could also help to make logical choices, and to prepare for and avoid disasters. It might also help to enjoy the advantages of potentially devastating innovations such as nanotechnology or environment engineering, while preventing the associated dangers. [143] If an AGI's main objective is to prevent existential disasters such as human extinction (which could be hard if the Vulnerable World Hypothesis turns out to be true), [144] it could take steps to considerably lower the dangers [143] while lessening the effect of these procedures on our lifestyle.


Risks


Existential dangers


AGI might represent several kinds of existential danger, which are dangers that threaten "the premature termination of Earth-originating smart life or the irreversible and extreme damage of its potential for desirable future advancement". [145] The threat of human extinction from AGI has been the topic of lots of arguments, but there is likewise the possibility that the development of AGI would lead to a permanently problematic future. Notably, it could be used to spread out and protect the set of worths of whoever establishes it. If mankind still has moral blind areas comparable to slavery in the past, AGI may irreversibly entrench it, preventing ethical development. [146] Furthermore, AGI might assist in mass monitoring and brainwashing, which could be used to create a steady repressive around the world totalitarian regime. [147] [148] There is also a threat for the makers themselves. If devices that are sentient or otherwise worthy of moral factor to consider are mass developed in the future, participating in a civilizational path that indefinitely disregards their well-being and interests could be an existential catastrophe. [149] [150] Considering how much AGI could improve humankind's future and help in reducing other existential dangers, Toby Ord calls these existential threats "an argument for continuing with due care", not for "deserting AI". [147]

Risk of loss of control and human termination


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

In 2014, Stephen Hawking criticized extensive indifference:


So, dealing with possible futures of incalculable advantages and dangers, the professionals are surely doing everything possible to ensure the very best outcome, right? Wrong. If a remarkable alien civilisation sent us a message saying, 'We'll show up in a few decades,' would we simply reply, 'OK, call us when you get here-we'll leave the lights on?' Probably not-but this is basically what is occurring with AI. [153]

The potential fate of humankind has sometimes 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 susceptible in manner ins which they might not have actually anticipated. As a result, the gorilla has become an endangered species, not out of malice, but merely as a civilian casualties from human activities. [154]

The skeptic Yann LeCun thinks about that AGIs will have no desire to dominate humankind which we need to beware not to anthropomorphize them and analyze their intents as we would for people. He said that individuals won't be "smart adequate to create super-intelligent makers, yet unbelievably silly to the point of offering it moronic goals without any safeguards". [155] On the other side, the idea of crucial merging suggests that nearly whatever their goals, smart representatives will have reasons to try to endure and obtain more power as intermediary steps to achieving these goals. And that this does not need having feelings. [156]

Many scholars who are concerned about existential risk advocate for more research study into fixing the "control issue" to respond to the concern: what types of safeguards, algorithms, or architectures can programmers execute to increase the probability that their recursively-improving AI would continue to act in a friendly, instead of destructive, way after it reaches superintelligence? [157] [158] Solving the control issue is made complex by the AI arms race (which could result in a race to the bottom of security precautions in order to launch products before competitors), [159] and using AI in weapon systems. [160]

The thesis that AI can posture existential threat likewise has detractors. Skeptics typically say that AGI is unlikely in the short-term, or that concerns about AGI distract from other problems connected to existing AI. [161] Former Google scams czar Shuman Ghosemajumder considers that for many individuals outside of the innovation market, existing chatbots and LLMs are already viewed as though they were AGI, causing more misconception and worry. [162]

Skeptics in some cases charge that the thesis is crypto-religious, with an irrational belief in the possibility of superintelligence replacing an irrational belief in an omnipotent God. [163] Some researchers think that the communication projects on AI existential risk by particular AI groups (such as OpenAI, Anthropic, DeepMind, and Conjecture) might be an at effort at regulative capture and to inflate interest in their products. [164] [165]

In 2023, the CEOs of Google DeepMind, OpenAI and Anthropic, in addition to other market leaders and researchers, released a joint declaration asserting that "Mitigating the danger of termination from AI ought to be an international priority alongside other societal-scale threats such as pandemics and nuclear war." [152]

Mass unemployment


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


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

Everyone can delight in a life of glamorous leisure if the machine-produced wealth is shared, or the majority of people can wind up miserably bad if the machine-owners successfully lobby against wealth redistribution. So far, the trend appears to be toward the 2nd choice, with innovation driving ever-increasing inequality


Elon Musk thinks about that the automation of society will need federal governments to adopt 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 area on making AI safe and beneficial
AI positioning - AI conformance to the desired goal
A.I. Rising - 2018 film directed by Lazar Bodroža
Expert system
Automated device learning - Process of automating the application of artificial intelligence
BRAIN Initiative - Collaborative public-private research study effort revealed by the Obama administration
China Brain Project
Future of Humanity Institute - Defunct Oxford interdisciplinary research centre
General video game playing - Ability of expert system to play various games
Generative artificial intelligence - AI system efficient in generating content in action to prompts
Human Brain Project - Scientific research study job
Intelligence amplification - Use of infotech to augment human intelligence (IA).
Machine ethics - Moral behaviours of manufactured machines.
Moravec's paradox.
Multi-task knowing - Solving several machine learning jobs at the exact same time.
Neural scaling law - Statistical law in artificial intelligence.
Outline of artificial intelligence - Overview of and topical guide to expert system.
Transhumanism - Philosophical movement.
Synthetic intelligence - Alternate term for or type of expert system.
Transfer knowing - Machine learning strategy.
Loebner Prize - Annual AI competition.
Hardware for artificial intelligence - Hardware specifically created and optimized for artificial intelligence.
Weak artificial intelligence - Form of expert system.


Notes


^ a b See listed below for the origin of the term "strong AI", and see the scholastic meaning of "strong AI" and weak AI in the article Chinese room.
^ AI founder John McCarthy writes: "we can not yet identify in general what sort of computational treatments we desire to call smart. " [26] (For a conversation of some definitions of intelligence utilized by expert system scientists, see approach of synthetic intelligence.).
^ The Lighthill report specifically criticized AI's "grandiose goals" and led the taking apart of AI research study in England. [55] In the U.S., DARPA became figured out to money only "mission-oriented direct research study, instead of standard undirected research". [56] [57] ^ As AI creator John McCarthy writes "it would be an excellent relief to the rest of the employees in AI if the inventors of brand-new general formalisms would express their hopes in a more guarded type than has in some cases held true." [61] ^ In "Mind Children" [122] 1015 cps is used. More just recently, in 1997, [123] Moravec argued for 108 MIPS which would approximately correspond to 1014 cps. Moravec talks in terms of MIPS, not "cps", which is a non-standard term Kurzweil presented.
^ As defined in a basic AI book: "The assertion that machines might potentially act wisely (or, perhaps much better, act as if they were intelligent) is called the 'weak AI' hypothesis by theorists, and the assertion that machines that do so are really thinking (as opposed to simulating thinking) is called the 'strong AI' hypothesis." [121] ^ Alan Turing made this point in 1950. [36] References


^ Krishna, Sri (9 February 2023). "What is 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 mission is to make sure that artificial general intelligence advantages all of humanity.
^ Heath, Alex (18 January 2024). "Mark Zuckerberg's new objective is developing synthetic general intelligence". The Verge. Retrieved 13 June 2024. Our vision is to build AI that is better than human-level at all of the human senses.
^ Baum, Seth D. (2020 ). A 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 jobs were determined as being active in 2020.
^ a b c "AI timelines: What do specialists in expert system expect for the future?". Our World in Data. Retrieved 6 April 2023.
^ Metz, Cade (15 May 2023). "Some Researchers Say A.I. Is Already Here, Stirring Debate in Tech Circles". The New York Times. Retrieved 18 May 2023.
^ "AI pioneer Geoffrey Hinton quits Google and warns of threat ahead". The New York City Times. 1 May 2023. Retrieved 2 May 2023. It is tough 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 change. All that you alter modifications you.
^ Vinge, Vernor (1992 ). A Fire Upon the Deep. Tor Books. ISBN 978-0-8125-1528-2. The Singularity is coming.
^ Morozov, Evgeny (30 June 2023). "The True Threat of Expert System". The New York City Times. The real risk is not AI itself however the method we deploy it.
^ "Impressed by artificial intelligence? Experts state AGI is following, and it has 'existential' risks". ABC News. 23 March 2023. Retrieved 6 April 2023. AGI could posture existential threats to humanity.
^ Bostrom, Nick (2014 ). Superintelligence: Paths, Dangers, Strategies. Oxford University Press. ISBN 978-0-1996-7811-2. The very first superintelligence will be the last 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 ought to be a global priority.
^ "Statement on AI Risk". Center for AI Safety. Retrieved 1 March 2024. AI specialists caution 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 devices that can outthink us in general ways.
^ LeCun, Yann (June 2023). "AGI does not present an existential threat". Medium. There is no reason to fear AI as an existential threat.
^ Kurzweil 2005, p. 260.
^ a b Kurzweil, Ray (5 August 2005), "Long Live AI", Forbes, archived from the original on 14 August 2005: Kurzweil describes strong AI as "maker intelligence with the full series 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 use for "human-level" intelligence in the physical symbol system hypothesis.
^ "The Open University on Strong and Weak AI". Archived from the original on 25 September 2009. Retrieved 8 October 2007.
^ "What is synthetic 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 attaining AGI, according to DeepMind". VentureBeat.
^ McCarthy, John (2007a). "Basic Questions". Stanford University. Archived from the initial on 26 October 2007. Retrieved 6 December 2007.
^ This list of smart qualities is based upon the topics covered by significant AI books, including: Russell & Norvig 2003, Luger & Stubblefield 2004, Poole, Mackworth & Goebel 1998 and Nilsson 1998.
^ Johnson 1987.
^ de Charms, R. (1968 ). Personal causation. New York City: Academic Press.
^ a b Pfeifer, R. and Bongard J. C., How the body forms the method we believe: a new view of intelligence (The MIT Press, 2007). ISBN 0-2621-6239-3.
^ White, R. W. (1959 ). "Motivation reassessed: The concept of proficiency". Psychological Review. 66 (5 ): 297-333. doi:10.1037/ h0040934. PMID 13844397. S2CID 37385966.
^ White, R. W. (1959 ). "Motivation reevaluated: The principle of skills". Psychological Review. 66 (5 ): 297-333. doi:10.1037/ h0040934. PMID 13844397. S2CID 37385966.
^ Muehlhauser, Luke (11 August 2013). "What is AGI?". Machine Intelligence Research Institute. Archived from the initial on 25 April 2014. Retrieved 1 May 2014.
^ "What is Artificial General Intelligence (AGI)?|4 Tests For Ensuring Artificial General Intelligence". Talky Blog. 13 July 2019. Archived from the initial on 17 July 2019. Retrieved 17 July 2019.
^ Kirk-Giannini, Cameron Domenico; Goldstein, Simon (16 October 2023). "AI is closer than ever to passing the Turing test for 'intelligence'. What happens when it does?". The Conversation. Retrieved 22 September 2024.
^ a b Turing 1950.
^ Turing, Alan (1952 ). B. Jack Copeland (ed.). Can Automatic Calculating Machines Be Said To Think?. Oxford: Oxford University Press. pp. 487-506. ISBN 978-0-1982-5079-1.
^ "Eugene Goostman is a genuine boy - the Turing Test states so". The Guardian. 9 June 2014. ISSN 0261-3077. Retrieved 3 March 2024.
^ "Scientists contest 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 distinguish 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 exam to AP Biology. Here's a list of tough tests both AI versions have 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 response". 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 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 ). "Expert System" (PDF). In Stuart C. Shapiro (ed.). Encyclopedia of Artificial Intelligence (Second ed.). New York City: John Wiley. pp. 54-57. Archived (PDF) from the initial on 1 February 2016. (Section 4 is on "AI-Complete Tasks".).
^ Yampolskiy, Roman V. (2012 ). Xin-She Yang (ed.). "Turing Test as a Specifying Feature of AI-Completeness" (PDF). Artificial Intelligence, Evolutionary Computation and Metaheuristics (AIECM): 3-17. Archived (PDF) from the original on 22 May 2013.
^ "AI Index: State of AI in 13 Charts". Stanford University Human-Centered Expert System. 15 April 2024. Retrieved 27 May 2024.
^ Crevier 1993, pp. 48-50.
^ Kaplan, Andreas (2022 ). "Expert System, Business and Civilization - Our Fate Made in Machines". Archived from the original on 6 May 2022. Retrieved 12 March 2022.
^ Simon 1965, p. 96 priced estimate in Crevier 1993, p. 109.
^ "Scientist on the Set: An Interview with Marvin Minsky". Archived from the initial on 16 July 2012. Retrieved 5 April 2008.
^ Marvin Minsky to Darrach (1970 ), quoted in Crevier (1993, p. 109).
^ Lighthill 1973; Howe 1994.
^ a b NRC 1999, "Shift to Applied Research Increases Investment".
^ Crevier 1993, pp. 115-117; Russell & Norvig 2003, pp. 21-22.
^ Crevier 1993, p. 211, Russell & Norvig 2003, p. 24 and see 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 original on 30 September 2008. Retrieved 29 September 2007.
^ Markoff, John (14 October 2005). "Behind Expert system, a Squadron of Bright Real People". The New York City Times. Archived from the original on 2 February 2023. Retrieved 18 February 2017. At its low point, some computer system scientists and software engineers prevented the term expert system for worry of being seen as wild-eyed dreamers.
^ Russell & Norvig 2003, pp. 25-26
^ "Trends in the Emerging Tech Hype Cycle". Gartner Reports. Archived from the original on 22 May 2019. Retrieved 7 May 2019.
^ a b Moravec 1988, p. 20
^ Harnad, S. (1990 ). "The Symbol Grounding Problem". Physica D. 42 (1-3): 335-346. arXiv: cs/9906002. Bibcode:1990 PhyD ... 42..335 H. doi:10.1016/ 0167-2789( 90 )90087-6. S2CID 3204300.
^ Gubrud 1997
^ Hutter, Marcus (2005 ). Universal Expert System: Sequential Decisions Based Upon Algorithmic Probability. Texts in Theoretical Computer Technology an EATCS Series. Springer. doi:10.1007/ b138233. ISBN 978-3-5402-6877-2. S2CID 33352850. Archived from the original on 19 July 2022. Retrieved 19 July 2022.
^ Legg, Shane (2008 ). Machine Super Intelligence (PDF) (Thesis). University of Lugano. Archived (PDF) from the initial on 15 June 2022. Retrieved 19 July 2022.
^ Goertzel, Ben (2014 ). Artificial General Intelligence. Lecture Notes in Computer Science. 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 initial on 28 December 2018. Retrieved 28 December 2018., via Life 3.0: 'The term "AGI" was promoted by ... Shane Legg, Mark Gubrud and Ben Goertzel'
^ Wang & Goertzel 2007
^ "First International Summer School in Artificial General Intelligence, Main 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 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 maker intelligence, artificial basic intelligence is still a significant 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. "Artificial intelligence will not become a Frankenstein's beast". The Guardian. Archived from the original 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 recognized". Humanities and Social Sciences Communications. 7 (1 ): 1-9. doi:10.1057/ s41599-020-0494-4. hdl:11250/ 2726984. ISSN 2662-9992. S2CID 219710554.
^ McCarthy 2007b.
^ Khatchadourian, Raffi (23 November 2015). "The Doomsday Invention: Will 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 progress in expert system: A survey of skilled 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, 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 creativity of makers: AI takes the Torrance Test". Journal of Creativity. 33 (3 ): 100065. doi:10.1016/ j.yjoc.2023.100065. ISSN 2713-3745. S2CID 261087185.
^ Arcas, Blaise Agüera y (10 October 2023). "Artificial General Intelligence Is Already Here". Noema.
^ Zia, Tehseen (8 January 2024). "Unveiling of Large Multimodal Models: Shaping the Landscape of Language Models in 2024". Unite.ai. Retrieved 26 May 2024.
^ "Introducing OpenAI o1-preview". OpenAI. 12 September 2024.
^ Knight, Will. "OpenAI Announces a 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 artificial 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 role of GPU computing and deep learning 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 site). 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 initial on 3 January 2019. Retrieved 2 January 2019.
^ Grossman, Gary (3 September 2020). "We're getting in the AI golden zone between narrow and basic AI". VentureBeat. Archived from the initial 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 arguable, but the agreement is that it is not AGI. ... If absolutely nothing else, GPT-3 tells us there is a happy medium between narrow and basic AI.
^ Quach, Katyanna. "A developer constructed an AI chatbot utilizing GPT-3 that assisted a male 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 controlling robotics", TechCrunch, archived from the original 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. could match human intelligence in 'simply a few years,' states CEO of Google's main A.I. research laboratory". Fortune. Retrieved 4 September 2024.
^ Nellis, Stephen (2 March 2024). "Nvidia CEO says AI could pass human tests in 5 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 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 study job 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 variety 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 system might 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 (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 synthetic intelligence for curiosity 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 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 Artificial Intelligence 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 outweigh 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, dangers, methods (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 progress is making it likelier than ever that humans will ruin ourselves". Vox. Retrieved 8 June 2023.
^ Doherty, Ben (17 May 2018). "Climate change an 'existential security risk' to Australia, Senate inquiry states". 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 Artificial Intelligence". 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 City 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 - however 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 interesting Facebook debate in between Yann LeCun, Stuart Russel and Yoshua Bengio about the dangers of strong AI". The interesting Facebook dispute 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 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 threats 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 danger postured but expert system can not be neglected". 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 stoking fears over AI, alert scientists". The Telegraph. Retrieved 7 December 2023.
^ Davidson, John (30 October 2023). "Google Brain creator states huge tech is lying about AI termination 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 appearance at the labor market impact potential of large language models". OpenAI. Retrieved 7 June 2023.
^ a b Hurst, Luke (23 March 2023). "OpenAI states 80% of workers 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 require universal basic earnings because 'in the future, manual labor 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 Expert System. New York City, 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 initial (PDF) on 20 March 2013.
Goertzel, Ben (December 2007), "Human-level artificial basic 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, obtained 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 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", Expert System: 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 fix problems and accomplish objectives in the world as well as people.
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 original on 15 June 2006, retrieved 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, recovered 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 Transformation: 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 Logical Approach, New York City: 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 original 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 original on 17 March 2019, recovered 3 September 2020
Simon, H. A. (1965 ), The Shape of Automation for Men and Management, New York City: 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 - by means of 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, recovered 4 September 2013 - by means of ResearchGate
Berglas, Anthony (January 2012) [2008], Expert System Will Kill Our Grandchildren (Singularity), archived from the initial on 23 July 2014, recovered 31 August 2012
Cukier, Kenneth, "Ready for Robots? How to Think about the Future of AI", Foreign Affairs, vol. 98, no. 4 (July/August 2019), pp. 192-98. George Dyson, historian of computing, composes (in what might be called "Dyson's Law") that "Any system easy enough to be will not be complicated enough to act intelligently, while any system complicated 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 stupid. They work, but they work by brute force." (p. 198.).
Gelernter, David, Dream-logic, the Internet and Artificial Thought, Edge, archived from the original on 26 July 2010, obtained 25 July 2010.
Gleick, James, "The Fate of Free Will" (review 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 differentiates us from devices. For biological animals, factor and purpose originate from acting worldwide and experiencing the consequences. Expert systems - disembodied, 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" (evaluation 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 hope to get abundant from AI are going to have the interests of the rest people close at heart,' ... writes [Gary Marcus] 'We can't count on governments driven by project finance contributions [from tech companies] to push back.' ... Marcus details the demands that residents must make of their federal governments and the tech companies. They include transparency on how AI systems work; settlement for individuals if their data [are] utilized to train LLMs (large language design) s and the right to approval to this usage; and the capability to hold tech business liable for the damages they cause by getting rid of Section 230, enforcing cash penalites, and passing stricter item liability laws ... Marcus likewise recommends ... that a new, AI-specific federal firm, akin to the FDA, the FCC, or the FTC, might supply the most robust oversight ... [T] he Fordham law teacher Chinmayi Sharma ... suggests ... develop [ing] a professional licensing routine for engineers that would operate in a similar method to medical licenses, malpractice matches, and the Hippocratic oath in medication. 'What if, like physicians,' she asks ..., 'AI engineers likewise promised 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 actually puzzled human beings for decades, exposes the constraints of natural-language-processing algorithms", Scientific American, vol. 329, no. 4 (November 2023), pp. 81-82. "This murder secret competitors has exposed that although NLP (natural-language processing) models are capable of amazing feats, their abilities are really much limited by the amount of context they get. This [...] might cause [difficulties] for scientists who want to use them to do things such as analyze ancient languages. In many cases, there are couple of historic records on long-gone civilizations to act as training data for such a purpose." (p. 82.).
Immerwahr, Daniel, "Your Lying Eyes: People now use A.I. to generate fake videos identical from genuine ones. How much does it matter?", The New Yorker, 20 November 2023, pp. 54-59. "If by 'deepfakes' we suggest reasonable videos produced utilizing expert system that in fact trick people, then they barely exist. The phonies aren't deep, and the deeps aren't fake. [...] A.I.-generated videos are not, in basic, operating in our media as counterfeited proof. Their role better looks like that of animations, particularly smutty ones." (p. 59.).
- Leffer, Lauren, "The Risks of Trusting AI: We must avoid humanizing machine-learning designs used in clinical research study", 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 newest, 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 (2nd 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, recovered 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 innovation lead authorities to neglect inconsistent evidence?", The New Yorker, 20 November 2023, pp. 20-26.
Roivainen, Eka, "AI's IQ: ChatGPT aced a [basic intelligence] test however revealed that intelligence can not be determined by IQ alone", Scientific American, vol. 329, no. 1 (July/August 2023), p. 7. "Despite its high IQ, ChatGPT stops working at tasks that need real humanlike thinking or an understanding of the physical and social world ... ChatGPT appeared unable to factor realistically and attempted to depend on its huge database of ... truths 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 situations their programmers did not expect. Learning systems are limited by the information on which they were trained. AI failures have actually currently caused tragedy. Advanced auto-pilot functions in cars and trucks, although they carry out well in some situations, have actually driven cars and trucks without alerting into trucks, concrete barriers, and parked cars. In the wrong scenario, AI systems go from supersmart to superdumb in an instant. When an enemy is attempting to control and hack an AI system, the threats 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 enabled by new innovations but depend on the timelelss human propensity to anthropomorphise." (p. 29.).
Williams, R. W.; Herrup, K.