Artificial general intelligence (AGI) is a type of synthetic intelligence (AI) that matches or goes beyond human cognitive capabilities throughout a wide variety of cognitive jobs. This contrasts with narrow AI, which is limited to particular tasks. [1] Artificial superintelligence (ASI), on the other hand, refers to AGI that significantly exceeds human cognitive abilities. AGI is considered among the meanings of strong AI.

Creating AGI is a primary goal of AI research and of companies such as OpenAI [2] and Meta. [3] A 2020 study recognized 72 active AGI research study and development tasks throughout 37 countries. [4]
The timeline for accomplishing AGI stays a subject of continuous argument amongst scientists and professionals. Since 2023, some argue that it might be possible in years or decades; others keep it might take a century or longer; a minority believe it may never ever be accomplished; and another minority claims that it is already here. [5] [6] Notable AI scientist Geoffrey Hinton has revealed concerns about the rapid progress towards AGI, recommending it could be accomplished faster than many anticipate. [7]
There is debate on the exact meaning of AGI and concerning whether modern-day big language models (LLMs) such as GPT-4 are early types of AGI. [8] AGI is a typical subject in sci-fi and futures research studies. [9] [10]
Contention exists over whether AGI represents an existential threat. [11] [12] [13] Many experts on AI have actually mentioned that alleviating the danger of human extinction presented by AGI ought to be an international priority. [14] [15] Others find the development of AGI to be too remote to provide such a danger. [16] [17]
Terminology

AGI is likewise known as strong AI, [18] [19] complete AI, [20] human-level AI, [5] human-level smart AI, or basic smart action. [21]
Some academic sources schedule the term "strong AI" for computer system programs that experience life or consciousness. [a] On the other hand, weak AI (or narrow AI) has the ability to solve one specific problem however lacks general cognitive capabilities. [22] [19] Some scholastic sources use "weak AI" to refer more broadly to any programs that neither experience awareness nor have a mind in the very same sense as people. [a]
Related concepts consist of synthetic superintelligence and transformative AI. An artificial superintelligence (ASI) is a theoretical type of AGI that is far more typically smart than people, [23] while the notion of transformative AI relates to AI having a big effect on society, for example, similar to the farming or industrial transformation. [24]
A framework for classifying AGI in levels was proposed in 2023 by Google DeepMind researchers. They specify 5 levels of AGI: emerging, skilled, specialist, virtuoso, and superhuman. For example, a skilled AGI is defined as an AI that exceeds 50% of skilled grownups in a large range of non-physical jobs, and a superhuman AGI (i.e. an artificial superintelligence) is similarly defined but with a limit of 100%. They consider large language designs like ChatGPT or LLaMA 2 to be circumstances of emerging AGI. [25]
Characteristics

Various popular definitions of intelligence have actually been proposed. One of the leading proposals is the Turing test. However, there are other widely known definitions, and some scientists disagree with the more popular techniques. [b]
Intelligence traits

Researchers normally hold that intelligence is needed to do all of the following: [27]
reason, use strategy, solve puzzles, and make judgments under unpredictability represent knowledge, consisting of sound judgment knowledge strategy find out

• interact in natural language
• if needed, incorporate these skills in completion of any offered goal

Many interdisciplinary techniques (e.g. cognitive science, computational intelligence, and decision making) consider extra qualities such as creativity (the ability to form unique psychological images and principles) [28] and autonomy. [29]
Computer-based systems that display a lot of these abilities exist (e.g. see computational imagination, automated reasoning, choice support system, robotic, evolutionary calculation, intelligent representative). There is argument about whether modern AI systems have them to an adequate degree.

Physical qualities

Other abilities are considered desirable in smart systems, as they may affect intelligence or help in its expression. These consist of: [30]
- the ability to sense (e.g. see, hear, etc), and - the capability to act (e.g. move and control things, change location to check out, and so on).
This includes the ability to discover and respond to risk. [31]
Although the capability to sense (e.g. see, hear, etc) and the ability to act (e.g. move and control things, modification place to check out, etc) can be desirable for some intelligent systems, [30] these physical abilities are not strictly required for an entity to certify as AGI-particularly under the thesis that big language models (LLMs) may already be or end up being AGI. Even from a less positive viewpoint on LLMs, there is no firm requirement for an AGI to have a human-like type; being a silicon-based computational system suffices, offered it can process input (language) from the external world in place of human senses. This analysis aligns with the understanding that AGI has actually never ever been proscribed a specific physical personification and thus does not demand a capacity for locomotion or conventional "eyes and ears". [32]
Tests for human-level AGI

Several tests suggested to confirm human-level AGI have been considered, consisting of: [33] [34]
The concept of the test is that the maker needs to attempt and pretend to be a male, by addressing concerns put to it, and it will just pass if the pretence is reasonably convincing. A substantial portion of a jury, who must not be expert about devices, need to be taken in by the pretence. [37]
issues

A problem is informally called "AI-complete" or "AI-hard" if it is believed that in order to solve it, one would need to execute AGI, since the service is beyond the capabilities of a purpose-specific algorithm. [47]
There are numerous problems that have been conjectured to require general intelligence to fix along with people. Examples consist of computer system vision, natural language understanding, and dealing with unforeseen situations while resolving any real-world problem. [48] Even a specific job like translation requires a device to read and write in both languages, follow the author's argument (reason), understand the context (understanding), and consistently reproduce the author's original intent (social intelligence). All of these issues need to be resolved simultaneously in order to reach human-level device efficiency.

However, numerous of these tasks can now be performed by contemporary big language models. According to Stanford University's 2024 AI index, AI has reached human-level efficiency on lots of benchmarks for checking out understanding and visual reasoning. [49]
History

Classical AI

Modern AI research study began in the mid-1950s. [50] The first generation of AI researchers were persuaded that artificial general intelligence was possible which it would exist in just a couple of years. [51] AI leader Herbert A. Simon wrote in 1965: "machines will be capable, within twenty years, of doing any work a man can do." [52]
Their forecasts were the inspiration for Stanley Kubrick and Arthur C. Clarke's character HAL 9000, who embodied what AI scientists believed they might produce by the year 2001. AI leader Marvin Minsky was a consultant [53] on the job of making HAL 9000 as reasonable as possible according to the consensus predictions of the time. He said in 1967, "Within a generation ... the issue of creating 'artificial intelligence' will substantially be fixed". [54]
Several classical AI tasks, such as Doug Lenat's Cyc task (that started in 1984), and Allen Newell's Soar project, were directed at AGI.

However, in the early 1970s, it became obvious that researchers had actually grossly underestimated the difficulty of the job. Funding agencies ended up being hesitant of AGI and put scientists under increasing pressure to produce beneficial "used AI". [c] In the early 1980s, Japan's Fifth Generation Computer Project restored interest in AGI, setting out a ten-year timeline that consisted of AGI goals like "continue a casual discussion". [58] In action to this and the success of professional systems, both industry and government pumped money into the field. [56] [59] However, confidence in AI spectacularly collapsed in the late 1980s, and the objectives of the Fifth Generation Computer Project were never satisfied. [60] For the second time in 20 years, AI scientists who forecasted the impending achievement of AGI had actually been mistaken. By the 1990s, AI scientists had a credibility for making vain pledges. They ended up being hesitant to make predictions at all [d] and prevented mention of "human level" synthetic intelligence for fear of being labeled "wild-eyed dreamer [s]. [62]
Narrow AI research study

In the 1990s and early 21st century, mainstream AI accomplished business success and scholastic respectability by concentrating on specific sub-problems where AI can produce verifiable outcomes and commercial applications, such as speech acknowledgment and recommendation algorithms. [63] These "applied AI" systems are now utilized extensively throughout the technology market, and research in this vein is heavily funded in both academic community and market. As of 2018 [upgrade], advancement in this field was considered an emerging trend, and a fully grown stage was anticipated to be reached in more than ten years. [64]
At the millenium, many mainstream AI researchers [65] hoped that strong AI could be established by integrating programs that resolve different sub-problems. Hans Moravec wrote in 1988:

I am positive that this bottom-up route to expert system will one day meet the standard top-down path over half method, prepared to offer the real-world skills and the commonsense understanding that has actually been so frustratingly evasive in thinking programs. Fully smart machines will result when the metaphorical golden spike is driven uniting the two efforts. [65]
However, even at the time, this was disputed. For instance, Stevan Harnad of Princeton University concluded his 1990 paper on the sign grounding hypothesis by stating:

The expectation has 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 considerations in this paper are legitimate, then this expectation is hopelessly modular and there is really just one viable path from sense to symbols: from the ground up. A free-floating symbolic level like the software application level of a computer will never be reached by this path (or vice versa) - nor is it clear why we should even attempt to reach such a level, since it appears getting there would simply amount to uprooting our symbols from their intrinsic significances (thus simply minimizing ourselves to the practical 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 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 maximises "the capability to please goals in a large range of environments". [68] This kind of AGI, defined by the capability to maximise a mathematical definition of intelligence rather than display human-like behaviour, [69] was also called universal artificial intelligence. [70]
The term AGI was re-introduced and promoted by Shane Legg and Ben Goertzel around 2002. [71] AGI research activity in 2006 was described by Pei Wang and Ben Goertzel [72] as "producing publications and preliminary outcomes". The first summer school in AGI was organized in Xiamen, China in 2009 [73] by the Xiamen university's Artificial Brain Laboratory and OpenCog. The very first university course was given up 2010 [74] and 2011 [75] at Plovdiv University, Bulgaria by Todor Arnaudov. MIT presented a course on AGI in 2018, arranged by Lex Fridman and featuring a number of visitor speakers.

As of 2023 [update], a little number of computer researchers are active in AGI research, and numerous contribute to a series of AGI conferences. However, significantly more scientists have an interest in open-ended knowing, [76] [77] which is the idea of permitting AI to continuously find out and innovate like human beings do.

Feasibility

Since 2023, the development and possible accomplishment of AGI remains a subject of extreme argument within the AI neighborhood. While traditional consensus held that AGI was a far-off goal, current developments have actually led some scientists and market figures to declare that early forms of AGI may currently exist. [78] AI leader Herbert A. Simon hypothesized in 1965 that "makers will be capable, within twenty years, of doing any work a guy can do". This forecast failed to come real. Microsoft co-founder Paul Allen thought that such intelligence is unlikely in the 21st century since it would require "unforeseeable and basically unforeseeable breakthroughs" and a "scientifically deep understanding of cognition". [79] Writing in The Guardian, roboticist Alan Winfield claimed the gulf between contemporary computing and human-level artificial intelligence is as wide as the gulf between present area flight and useful faster-than-light spaceflight. [80]
An additional challenge is the lack of clearness in defining what intelligence entails. Does it require awareness? Must it show the capability to set objectives along with pursue them? Is it purely a matter of scale such that if design sizes increase adequately, intelligence will emerge? Are centers such as preparation, reasoning, and causal understanding required? Does intelligence require explicitly reproducing the brain and its specific professors? Does it need feelings? [81]
Most AI scientists think strong AI can be accomplished in the future, however some thinkers, like Hubert Dreyfus and Roger Penrose, reject the possibility of achieving strong AI. [82] [83] John McCarthy is among those who think human-level AI will be achieved, however that the present level of progress is such that a date can not accurately be predicted. [84] AI specialists' views on the expediency of AGI wax and wane. Four surveys performed in 2012 and 2013 suggested that the median estimate among specialists 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 experts, 16.5% addressed with "never" when asked the exact same question however with a 90% self-confidence rather. [85] [86] Further current AGI development factors to consider 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 time frame there is a strong bias towards predicting 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 released a detailed examination of GPT-4. They concluded: "Given the breadth and depth of GPT-4's abilities, our company believe that it could reasonably be viewed as an early (yet still incomplete) variation of a synthetic basic intelligence (AGI) system." [88] Another study in 2023 reported that GPT-4 outshines 99% of human beings on the Torrance tests of creative thinking. [89] [90]
Blaise Agüera y Arcas and Peter Norvig wrote in 2023 that a substantial level of general intelligence has currently been achieved with frontier designs. They composed that reluctance to this view originates from four main factors: a "healthy uncertainty about metrics for AGI", an "ideological dedication to alternative AI theories or techniques", a "dedication to human (or biological) exceptionalism", or a "concern about the financial ramifications of AGI". [91]
2023 also marked the introduction of large multimodal models (large language designs capable of processing or generating numerous modalities such as text, audio, and images). [92]
In 2024, OpenAI launched o1-preview, the first of a series of models that "invest more time thinking before they react". According to Mira Murati, this capability to believe before responding represents a new, additional paradigm. It improves design outputs by investing more computing power when creating the answer, whereas the design scaling paradigm enhances outputs by increasing the model size, training data and training calculate power. [93] [94]
An OpenAI worker, Vahid Kazemi, declared in 2024 that the company had attained AGI, stating, "In my opinion, we have actually already achieved AGI and it's much more clear with O1." Kazemi clarified that while the AI is not yet "better than any human at any job", it is "better than a lot of human beings at the majority of tasks." He likewise addressed criticisms that large language models (LLMs) simply follow predefined patterns, comparing their knowing procedure to the clinical approach of observing, hypothesizing, and verifying. These statements have stimulated debate, as they rely on a broad and unconventional definition of AGI-traditionally comprehended as AI that matches human intelligence throughout all domains. Critics argue that, while OpenAI's models show impressive flexibility, they might not completely satisfy this standard. Notably, Kazemi's remarks came soon after OpenAI got rid of "AGI" from the terms of its partnership with Microsoft, triggering speculation about the company's tactical intentions. [95]
Timescales

Progress in artificial intelligence has actually traditionally gone through periods of fast progress separated by durations when progress appeared to stop. [82] Ending each hiatus were essential advances in hardware, software or both to produce space for additional progress. [82] [98] [99] For example, the hardware available in the twentieth century was not adequate to execute deep knowing, which requires big numbers of GPU-enabled CPUs. [100]
In the introduction to his 2006 book, [101] Goertzel says that estimates of the time required before a genuinely versatile AGI is developed vary from ten years to over a century. Since 2007 [update], the consensus in the AGI research study community seemed to be that the timeline talked about by Ray Kurzweil in 2005 in The Singularity is Near [102] (i.e. between 2015 and 2045) was plausible. [103] Mainstream AI researchers have provided a broad variety of viewpoints on whether development will be this quick. A 2012 meta-analysis of 95 such opinions found a predisposition towards predicting that the beginning of AGI would take place within 16-26 years for modern and historic predictions alike. That paper has actually been slammed for how it classified opinions as expert or non-expert. [104]
In 2012, Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton established a neural network called AlexNet, which won the ImageNet competitors with a top-5 test mistake rate of 15.3%, substantially much better than the second-best entry's rate of 26.3% (the conventional technique utilized a weighted sum of ratings from different pre-defined classifiers). [105] AlexNet was considered as the preliminary ground-breaker of the current deep learning wave. [105]
In 2017, researchers Feng Liu, Yong Shi, and Ying Liu carried out intelligence tests on publicly readily available and easily available weak AI such as Google AI, Apple's Siri, and others. At the maximum, these AIs reached an IQ value of about 47, which corresponds around to a six-year-old kid in first grade. A grownup pertains to about 100 typically. Similar tests were carried out in 2014, with the IQ rating reaching a maximum worth of 27. [106] [107]
In 2020, OpenAI established GPT-3, a language model efficient in carrying out many diverse jobs without specific training. According to Gary Grossman in a VentureBeat post, while there is agreement that GPT-3 is not an example of AGI, it is thought about by some to be too advanced to be classified as a narrow AI system. [108]
In the same year, Jason Rohrer used his GPT-3 account to establish a chatbot, and provided a chatbot-developing platform called "Project December". OpenAI requested changes to the chatbot to abide by their safety standards; Rohrer disconnected Project December from the GPT-3 API. [109]
In 2022, DeepMind established Gato, a "general-purpose" system efficient in performing more than 600 different jobs. [110]
In 2023, Microsoft Research published a research study on an early variation of OpenAI's GPT-4, contending that it displayed more basic intelligence than previous AI models and showed human-level performance in tasks covering multiple domains, such as mathematics, coding, and law. This research study stimulated a debate on whether GPT-4 might be thought about an early, insufficient version of synthetic basic intelligence, highlighting the requirement for additional exploration and assessment of such systems. [111]
In 2023, the AI scientist Geoffrey Hinton specified that: [112]
The idea that this stuff could actually get smarter than people - a few people thought that, [...] But the majority of people thought it was way off. And I thought it was method 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 said that "The development in the last couple of years has actually been pretty amazing", which he sees no reason it would decrease, anticipating AGI within a years or perhaps a few years. [113] In March 2024, Nvidia's CEO, Jensen Huang, mentioned his expectation that within 5 years, AI would can passing any test a minimum of along with human beings. [114] In June 2024, the AI researcher Leopold Aschenbrenner, a previous OpenAI employee, approximated AGI by 2027 to be "noticeably possible". [115]
Whole brain emulation

While the development of transformer models like in ChatGPT is considered the most appealing path 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 detail, and then copying and replicating it on a computer system or another computational device. The simulation design should be adequately devoted to the initial, so that it acts in practically the very same method 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 functions. It has been talked about in synthetic intelligence research study [103] as an approach to strong AI. Neuroimaging innovations that might provide the necessary in-depth understanding are improving quickly, and futurist Ray Kurzweil in the book The Singularity Is Near [102] predicts that a map of adequate quality will become available on a comparable timescale to the computing power needed to emulate it.

Early estimates

For low-level brain simulation, an extremely effective cluster of computer systems or GPUs would be required, given the huge 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 nerve cells. The brain of a three-year-old kid has about 1015 synapses (1 quadrillion). This number decreases with age, stabilizing by the adult years. Estimates vary for an adult, varying from 1014 to 5 × 1014 synapses (100 to 500 trillion). [120] An estimate of the brain's processing power, based upon a simple switch model for nerve cell activity, is around 1014 (100 trillion) synaptic updates per second (SUPS). [121]
In 1997, Kurzweil looked at various price quotes for the hardware needed to equate to the human brain and embraced a figure of 1016 computations per second (cps). [e] (For contrast, if a "calculation" was comparable to one "floating-point operation" - a measure utilized to rate current supercomputers - then 1016 "calculations" would be equivalent to 10 petaFLOPS, attained in 2011, while 1018 was attained in 2022.) He used this figure to predict the needed hardware would be readily available sometime in between 2015 and 2025, if the exponential growth in computer power at the time of composing continued.

Current research study

The Human Brain Project, an EU-funded initiative active from 2013 to 2023, has developed a particularly in-depth and publicly 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 approaches

The synthetic nerve cell model assumed by Kurzweil and used in many existing synthetic neural network executions is basic compared with biological neurons. A brain simulation would likely need to record the detailed cellular behaviour of biological nerve cells, presently understood only in broad summary. The overhead introduced by complete modeling of the biological, chemical, and physical details of neural behaviour (specifically on a molecular scale) would require computational powers a number of orders of magnitude larger than Kurzweil's estimate. In addition, the estimates do not account for glial cells, which are known to play a role in cognitive procedures. [125]
An essential criticism of the simulated brain approach originates from embodied cognition theory which asserts that human embodiment is a vital aspect of human intelligence and is needed to ground meaning. [126] [127] If this theory is proper, any totally functional brain design will need to include more than simply the neurons (e.g., a robotic body). Goertzel [103] proposes virtual embodiment (like in metaverses like Second Life) as an option, however it is unidentified whether this would be adequate.

Philosophical perspective

"Strong AI" as specified in philosophy

In 1980, thinker John Searle coined the term "strong AI" as part of his Chinese room argument. [128] He proposed a distinction in between two hypotheses about synthetic intelligence: [f]
Strong AI hypothesis: A synthetic intelligence system can have "a mind" and "awareness". 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" due to the fact that it makes a stronger statement: it assumes something unique has occurred to the maker that exceeds those abilities that we can test. The behaviour of a "weak AI" maker would be exactly similar to a "strong AI" machine, but the latter would likewise have subjective conscious experience. This usage is likewise common in academic 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 artificial basic intelligence". [102] This is not the like Searle's strong AI, unless it is presumed that awareness is essential for human-level AGI. Academic philosophers 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 interested in how a program acts. [131] According to Russell and Norvig, "as long as the program works, they don't care if you call it genuine or a simulation." [130] If the program can behave as if it has a mind, then there is no requirement to understand if it in fact has mind - undoubtedly, there would be no other way to inform. For AI research study, Searle's "weak AI hypothesis" is comparable to the statement "synthetic general intelligence is possible". Thus, according to Russell and Norvig, "most AI researchers take the weak AI hypothesis for approved, and don't care about the strong AI hypothesis." [130] Thus, for academic AI research study, "Strong AI" and "AGI" are two various things.

Consciousness

Consciousness can have various meanings, and some aspects play considerable roles in science fiction and the ethics of synthetic intelligence:

Sentience (or "extraordinary awareness"): The ability to "feel" understandings or emotions subjectively, as opposed to the capability to factor about understandings. Some philosophers, such as David Chalmers, use the term "consciousness" to refer specifically to extraordinary consciousness, which is approximately equivalent to sentience. [132] Determining why and how subjective experience emerges is called the difficult problem of awareness. [133] Thomas Nagel described in 1974 that it "feels like" something to be conscious. If we are not mindful, then it does not seem like anything. Nagel utilizes the example of a bat: we can smartly ask "what does it feel like to be a bat?" However, we are not likely to ask "what does it seem like to be a toaster?" Nagel concludes that a bat appears to be conscious (i.e., has consciousness) however a toaster does not. [134] In 2022, a Google engineer declared that the company's AI chatbot, LaMDA, had achieved sentience, though this claim was widely disputed by other experts. [135]
Self-awareness: To have conscious awareness of oneself as a different individual, specifically to be consciously mindful of one's own thoughts. This is opposed to just being the "subject of one's thought"-an operating system or debugger has the ability to be "aware of itself" (that is, to represent itself in the exact same way it represents everything else)-however this is not what individuals normally imply when they use the term "self-awareness". [g]
These qualities have an ethical measurement. AI life would generate issues of well-being and legal protection, similarly to animals. [136] Other elements of awareness associated to cognitive capabilities are likewise relevant to the concept of AI rights. [137] Figuring out how to integrate innovative AI with existing legal and social structures is an emerging concern. [138]
Benefits

AGI might have a wide array of applications. If oriented towards such objectives, AGI could assist reduce numerous problems on the planet such as cravings, hardship and illness. [139]
AGI might enhance efficiency and efficiency in a lot of tasks. For example, in public health, AGI might accelerate medical research, notably against cancer. [140] It could take care of the elderly, [141] and equalize access to fast, premium medical diagnostics. It could use enjoyable, low-cost and customized education. [141] The requirement to work to subsist could become outdated if the wealth produced is properly redistributed. [141] [142] This also raises the question of the location of humans in a radically automated society.

AGI could likewise help to make reasonable choices, and to anticipate and avoid disasters. It might likewise help to gain the advantages of possibly devastating innovations such as nanotechnology or climate engineering, while avoiding the associated risks. [143] If an AGI's primary goal is to prevent existential disasters such as human termination (which might be difficult if the Vulnerable World Hypothesis ends up being real), [144] it might take steps to considerably reduce the threats [143] while lessening the effect of these steps on our quality of life.

Risks

Existential risks

AGI might represent multiple types of existential threat, which are threats that threaten "the premature termination of Earth-originating smart life or the irreversible and drastic destruction of its capacity for desirable future advancement". [145] The threat of human termination from AGI has actually been the subject of lots of debates, however there is also the possibility that the development of AGI would result in a completely problematic future. Notably, it could be utilized to spread and maintain the set of values of whoever develops it. If mankind still has ethical blind areas comparable to slavery in the past, AGI might irreversibly entrench it, preventing ethical development. [146] Furthermore, AGI could assist in mass monitoring and indoctrination, which might be utilized to develop a steady repressive worldwide totalitarian routine. [147] [148] There is also a threat for the machines themselves. If devices that are sentient or otherwise worthwhile of ethical consideration are mass developed in the future, taking part in a civilizational course that forever ignores their well-being and interests might be an existential disaster. [149] [150] Considering just how much AGI might enhance humankind's future and help in reducing other existential threats, Toby Ord calls these existential dangers "an argument for continuing with due caution", not for "deserting AI". [147]
Risk of loss of control and human termination

The thesis that AI presents an existential danger for people, which this threat requires more attention, is questionable however has actually been endorsed 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 slammed prevalent indifference:

So, dealing with possible futures of enormous advantages and dangers, the specialists are certainly doing whatever possible to make sure the very best result, right? Wrong. If an exceptional alien civilisation sent us a message saying, 'We'll arrive in a few years,' would we just respond, 'OK, call us when you get here-we'll leave the lights on?' Probably not-but this is more or less what is occurring with AI. [153]
The possible fate of mankind has actually in some cases been compared to the fate of gorillas threatened by human activities. The comparison mentions that greater intelligence allowed humanity to dominate gorillas, which are now vulnerable in ways that they might not have anticipated. As a result, the gorilla has actually become an endangered species, not out of malice, however simply as a security damage from human activities. [154]
The skeptic Yann LeCun considers that AGIs will have no desire to dominate humanity which we need to beware not to anthropomorphize them and interpret their intents as we would for human beings. He said that people won't be "clever adequate to create super-intelligent makers, yet ridiculously silly to the point of providing it moronic goals without any safeguards". [155] On the other side, the idea of instrumental merging recommends that almost whatever their goals, intelligent representatives will have factors to try to make it through and get more power as intermediary actions to accomplishing these goals. Which this does not require having emotions. [156]
Many scholars who are worried about existential danger advocate for more research into fixing the "control problem" to respond to the question: what types of safeguards, algorithms, or architectures can developers execute to maximise the likelihood that their recursively-improving AI would continue to act in a friendly, rather than devastating, manner after it reaches superintelligence? [157] [158] Solving the control problem is complicated by the AI arms race (which could cause a race to the bottom of security preventative measures in order to launch items before rivals), [159] and making use of AI in weapon systems. [160]
The thesis that AI can posture existential threat also has critics. Skeptics typically state that AGI is unlikely in the short-term, or that concerns about AGI sidetrack from other issues associated with present AI. [161] Former Google fraud czar Shuman Ghosemajumder considers that for lots of people outside of the technology industry, existing chatbots and LLMs are currently perceived as though they were AGI, leading to further misunderstanding and worry. [162]
Skeptics sometimes charge that the thesis is crypto-religious, with an irrational belief in the possibility of superintelligence replacing an unreasonable belief in an omnipotent God. [163] Some researchers think that the interaction projects on AI existential threat by specific AI groups (such as OpenAI, Anthropic, DeepMind, and Conjecture) may be an at attempt at regulatory capture and to pump up interest in their items. [164] [165]
In 2023, the CEOs of Google DeepMind, OpenAI and Anthropic, together with other market leaders and scientists, provided a joint declaration asserting that "Mitigating the threat of extinction from AI ought to be a worldwide concern along with other societal-scale threats such as pandemics and nuclear war." [152]
Mass unemployment

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

According to Stephen Hawking, the outcome of automation on the lifestyle will depend on how the wealth will be rearranged: [142]
Everyone can take pleasure in a life of elegant leisure if the machine-produced wealth is shared, or many people can wind up badly poor if the machine-owners effectively lobby versus wealth redistribution. Up until now, the pattern appears to be towards the second option, with technology driving ever-increasing inequality

Elon Musk considers that the automation of society will need governments to adopt a universal fundamental earnings. [168]
See likewise

Artificial brain - Software and hardware with cognitive abilities comparable to those of the animal or human brain AI effect AI safety - Research location on making AI safe and beneficial AI positioning - AI conformance to the intended goal A.I. Rising - 2018 film directed by Lazar Bodroža Artificial intelligence Automated machine knowing - Process of automating the application of artificial intelligence BRAIN Initiative - Collaborative public-private research initiative revealed by the Obama administration China Brain Project Future of Humanity Institute - Defunct Oxford interdisciplinary research centre General game playing - Ability of expert system to play different games Generative artificial intelligence - AI system efficient in creating material in response to triggers Human Brain Project - Scientific research project Intelligence amplification - Use of info technology to enhance human intelligence (IA). Machine principles - Moral behaviours of manufactured machines. Moravec's paradox. Multi-task learning - Solving several device discovering jobs at the exact same time. Neural scaling law - Statistical law in machine knowing. Outline of artificial intelligence - Overview of and topical guide to expert system. Transhumanism - Philosophical motion. Synthetic intelligence - Alternate term for or form of expert system. Transfer learning - Artificial intelligence technique. Loebner Prize - Annual AI competitors. Hardware for expert system - Hardware specially developed and enhanced for expert system. Weak expert system - Form of expert system.
Notes

^ a b See listed below for the origin of the term "strong AI", and see the academic meaning of "strong AI" and weak AI in the short article Chinese room. ^ AI founder John McCarthy writes: "we can not yet characterize in basic what sort of computational treatments we desire to call intelligent. " [26] (For a conversation of some meanings of intelligence used by expert system researchers, see viewpoint of expert system.). ^ The Lighthill report specifically criticized AI's "grand goals" and led the taking apart of AI research in England. [55] In the U.S., DARPA ended up being identified to fund only "mission-oriented direct research, instead of fundamental undirected research". [56] [57] ^ As AI creator John McCarthy writes "it would be a fantastic relief to the rest of the employees in AI if the inventors of brand-new basic formalisms would express their hopes in a more protected form than has actually in some cases been the case." [61] ^ In "Mind Children" [122] 1015 cps is utilized. More just recently, in 1997, [123] Moravec argued for 108 MIPS which would roughly represent 1014 cps. Moravec talks in regards to MIPS, not "cps", which is a non-standard term Kurzweil introduced. ^ As defined in a standard AI textbook: "The assertion that devices might potentially act smartly (or, perhaps much better, act as if they were intelligent) is called the 'weak AI' hypothesis by theorists, and the assertion that devices that do so are actually thinking (instead of simulating thinking) is called the 'strong AI' hypothesis." [121] ^ Alan Turing made this point in 1950. [36] References

^ Krishna, Sri (9 February 2023). "What is synthetic narrow intelligence (ANI)?". VentureBeat. Retrieved 1 March 2024. ANI is developed to perform a single task. ^ "OpenAI Charter". OpenAI. Retrieved 6 April 2023. Our objective is to make sure that artificial general intelligence benefits all of mankind. ^ Heath, Alex (18 January 2024). "Mark Zuckerberg's brand-new goal is producing 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 Survey of Artificial General Intelligence Projects for Ethics, Risk, and Policy (PDF) (Report). Global Catastrophic Risk Institute. Retrieved 28 November 2024. 72 AGI R&D tasks were determined as being active in 2020. ^ a b c "AI timelines: What do professionals in artificial intelligence expect for the future?". Our World in Data. Retrieved 6 April 2023. ^ Metz, Cade (15 May 2023). "Some Researchers Say A.I. Is Already Here, Stirring Debate in Tech Circles". The New York City Times. Retrieved 18 May 2023. ^ "AI leader Geoffrey Hinton quits Google and cautions of threat ahead". The New York Times. 1 May 2023. Retrieved 2 May 2023. It is difficult to see how you can avoid the bad stars from using it for bad things. ^ Bubeck, Sébastien; Chandrasekaran, Varun; Eldan, Ronen; Gehrke, Johannes; Horvitz, Eric (2023 ). "Sparks of Artificial General Intelligence: Early explores GPT-4". arXiv preprint. arXiv:2303.12712. GPT-4 shows 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 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 Times. The real danger is not AI itself but the way we deploy it. ^ "Impressed by expert system? Experts say AGI is following, and it has 'existential' threats". ABC News. 23 March 2023. Retrieved 6 April 2023. AGI might posture existential dangers to mankind. ^ Bostrom, Nick (2014 ). Superintelligence: Paths, Dangers, Strategies. Oxford University Press. ISBN 978-0-1996-7811-2. The first superintelligence will be the last creation that humankind needs to make. ^ Roose, Kevin (30 May 2023). "A.I. Poses 'Risk of Extinction,' Industry Leaders Warn". The New York Times. Mitigating the threat of extinction from AI must be a global top priority. ^ "Statement on AI Risk". Center for AI Safety. Retrieved 1 March 2024. AI specialists alert of danger of termination from AI. ^ Mitchell, Melanie (30 May 2023). "Are AI's Doomsday Scenarios Worth Taking Seriously?". The New York Times. We are far from developing devices that can outthink us in basic ways. ^ LeCun, Yann (June 2023). "AGI does not provide an existential danger". 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 initial on 14 August 2005: Kurzweil explains strong AI as "machine intelligence with the full variety of human intelligence.". ^ "The Age of Expert System: George John at TEDxLondonBusinessSchool 2013". Archived from the initial on 26 February 2014. Retrieved 22 February 2014. ^ Newell & Simon 1976, This is the term they use for "human-level" intelligence in the physical sign system hypothesis. ^ "The Open University on Strong and Weak AI". Archived from the original on 25 September 2009. Retrieved 8 October 2007. ^ "What is artificial superintelligence (ASI)?|Definition from TechTarget". Enterprise AI. Retrieved 8 October 2023. ^ "Expert system is changing our world - it is on everyone 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 attaining AGI, according to DeepMind". VentureBeat. ^ McCarthy, John (2007a). "Basic Questions". Stanford University. Archived from the original on 26 October 2007. Retrieved 6 December 2007. ^ This list of intelligent characteristics 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: Academic Press. ^ a b Pfeifer, R. and Bongard J. C., How the body shapes the way we think: a brand-new view of intelligence (The MIT Press, 2007). ISBN 0-2621-6239-3. ^ White, R. W. (1959 ). "Motivation reassessed: The concept of competence". Psychological Review. 66 (5 ): 297-333. doi:10.1037/ h0040934. PMID 13844397. S2CID 37385966. ^ White, R. W. (1959 ). "Motivation reassessed: The idea of proficiency". Psychological Review. 66 (5 ): 297-333. doi:10.1037/ h0040934. PMID 13844397. S2CID 37385966. ^ Muehlhauser, Luke (11 August 2013). "What is AGI?". Machine Intelligence Research Institute. Archived from the initial on 25 April 2014. Retrieved 1 May 2014. ^ "What is Artificial General Intelligence (AGI)?|4 Tests For Ensuring Artificial General Intelligence". Talky Blog. 13 July 2019. Archived from the initial on 17 July 2019. Retrieved 17 July 2019. ^ Kirk-Giannini, Cameron Domenico; Goldstein, Simon (16 October 2023). "AI is closer than ever to passing the Turing test for 'intelligence'. What takes place when it does?". The Conversation. Retrieved 22 September 2024. ^ a b Turing 1950. ^ Turing, Alan (1952 ). B. Jack Copeland (ed.). Can Automatic Calculating Machines Be Said To Think?. Oxford: Oxford University Press. pp. 487-506. ISBN 978-0-1982-5079-1. ^ "Eugene Goostman is a genuine young boy - the Turing Test states so". The Guardian. 9 June 2014. ISSN 0261-3077. Retrieved 3 March 2024. ^ "Scientists dispute 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 identify GPT-4 from a human in a Turing test". arXiv:2405.08007 [cs.HC] ^ Varanasi, Lakshmi (21 March 2023). "AI models like ChatGPT and GPT-4 are acing whatever from the bar test to AP Biology. Here's a list of challenging exams 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 obsolete. Coffee is the response". Analytics India Magazine. Retrieved 3 March 2024. ^ Bhaimiya, Sawdah (20 June 2023). "DeepMind's co-founder suggested checking an AI chatbot's ability to turn $100,000 into $1 million to determine human-like intelligence". Business Insider. Retrieved 3 March 2024. ^ Suleyman, Mustafa (14 July 2023). "Mustafa Suleyman: My brand-new Turing test would see if AI can make $1 million". MIT Technology Review. Retrieved 3 March 2024. ^ Shapiro, Stuart C. (1992 ). "Expert System" (PDF). In Stuart C. Shapiro (ed.). Encyclopedia of Expert System (Second ed.). New York: John Wiley. pp. 54-57. Archived (PDF) from the initial on 1 February 2016. (Section 4 is on "AI-Complete Tasks".). ^ Yampolskiy, Roman V. (2012 ). Xin-She Yang (ed.). "Turing Test as a Defining Feature of AI-Completeness" (PDF). 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 ). "Expert System, Business and Civilization - Our Fate Made in Machines". Archived from the initial on 6 May 2022. Retrieved 12 March 2022. ^ Simon 1965, p. 96 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 ), 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 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 researchers and software application engineers prevented the term artificial intelligence for fear 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 initial on 22 May 2019. Retrieved 7 May 2019. ^ a b Moravec 1988, p. 20 ^ Harnad, S. (1990 ). "The Symbol Grounding Problem". Physica D. 42 (1-3): 335-346. arXiv: cs/9906002. Bibcode:1990 PhyD ... 42..335 H. doi:10.1016/ 0167-2789( 90 )90087-6. S2CID 3204300. ^ Gubrud 1997 ^ Hutter, Marcus (2005 ). Universal Expert System: Sequential Decisions Based Upon Algorithmic Probability. Texts in Theoretical Computer Science an EATCS Series. Springer. doi:10.1007/ b138233. ISBN 978-3-5402-6877-2. S2CID 33352850. Archived from the initial on 19 July 2022. Retrieved 19 July 2022. ^ Legg, Shane (2008 ). Machine Super Intelligence (PDF) (Thesis). University of Lugano. Archived (PDF) from the original on 15 June 2022. Retrieved 19 July 2022. ^ Goertzel, Ben (2014 ). Artificial General Intelligence. Lecture Notes in Computer Technology. Vol. 8598. Journal of Artificial General Intelligence. doi:10.1007/ 978-3-319-09274-4. ISBN 978-3-3190-9273-7. S2CID 8387410. ^ "Who created the term "AGI"?". goertzel.org. Archived from the original on 28 December 2018. Retrieved 28 December 2018., 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 original on 26 July 2020. Retrieved 11 May 2020. ^ "Избираеми дисциплини 2010/2011 - зимен триместър" [Elective courses 2010/2011 - winter trimester] Факултет по математика и информатика [Faculty of Mathematics and Informatics] (in Bulgarian). Archived from the original on 26 July 2020. Retrieved 11 May 2020. ^ Shevlin, Henry; Vold, Karina; Crosby, Matthew; Halina, Marta (4 October 2019). "The limitations of device intelligence: Despite progress in device intelligence, synthetic basic intelligence is still a major challenge". 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 explores 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 turn into a Frankenstein's monster". The Guardian. Archived from the initial on 17 September 2014. Retrieved 17 September 2014. ^ Deane, George (2022 ). "Machines That Feel and Think: The Role of Affective Feelings and Mental Action in (Artificial) General Intelligence". Artificial Life. 28 (3 ): 289-309. doi:10.1162/ artl_a_00368. ISSN 1064-5462. PMID 35881678. S2CID 251069071. ^ a b c Clocksin 2003. ^ Fjelland, Ragnar (17 June 2020). "Why basic artificial intelligence will not be realized". Humanities and Social Sciences Communications. 7 (1 ): 1-9. doi:10.1057/ s41599-020-0494-4. hdl:11250/ 2726984. ISSN 2662-9992. S2CID 219710554. ^ McCarthy 2007b. ^ Khatchadourian, Raffi (23 November 2015). "The Doomsday Invention: Will expert system bring us paradise or destruction?". The New Yorker. Archived from the initial on 28 January 2016. Retrieved 7 February 2016. ^ Müller, V. C., & Bostrom, N. (2016 ). Future development in synthetic intelligence: A survey of expert opinion. In Fundamental concerns of expert system (pp. 555-572). Springer, Cham. ^ Armstrong, Stuart, and Kaj Sotala. 2012. "How We're Predicting AI-or Failing To." In Beyond AI: Artificial Dreams, 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 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 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 general 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 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). 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 Artificial Intelligence". 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 - but a six-year-old beats both] (in German). Archived from the original on 3 January 2019. Retrieved 2 January 2019. ^ Grossman, Gary (3 September 2020). "We're getting in the AI golden zone in between narrow and basic AI". VentureBeat. Archived from the original 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 debatable, but the agreement is that it is not AGI. ... If nothing else, GPT-3 informs us there is a happy medium in between narrow and basic AI. ^ Quach, Katyanna. "A designer constructed an AI chatbot using GPT-3 that helped a guy 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 new AI can perform over 600 jobs, from playing video 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,' says CEO of Google's primary A.I. research study laboratory". Fortune. Retrieved 4 September 2024. ^ Nellis, Stephen (2 March 2024). "Nvidia CEO says AI could pass human tests in five years". Reuters. ^ Aschenbrenner, Leopold. "SITUATIONAL AWARENESS, The Decade Ahead". ^ Sullivan, Mark (18 October 2023). "Why everyone seems to disagree on how to 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 wide variety of views in current research, all of which require grounding to some degree ^ Thornton, Angela (26 June 2023). "How submitting our minds to a computer system 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 (priced quote in" Encyclopedia.com"),. MIT Encyclopedia of Cognitive Science Archived 19 July 2008 at the Wayback Machine (priced quote 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 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 Expert System Have Moral or Legal Rights?". The Wall Street Journal. ^ "Artificial General Intelligence - Do [es] the cost 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 Look 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: paths, risks, techniques (Reprinted with corrections 2017 ed.). Oxford, United Kingdom; New York City, New York City, 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 people will ruin ourselves". Vox. Retrieved 8 June 2023. ^ Doherty, Ben (17 May 2018). "Climate change an 'existential security risk' to Australia, Senate inquiry 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 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 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: thatswhathappened.wiki 'Transcendence looks at the implications of synthetic intelligence - but are we taking AI seriously enough?'". The Independent (UK). Archived from the initial on 25 September 2015. Retrieved 3 December 2014. ^ Herger, Mario. "The Gorilla Problem - Enterprise Garage". Retrieved 7 June 2023. ^ "The remarkable Facebook dispute between Yann LeCun, Stuart Russel and Yoshua Bengio about the risks of strong AI". The remarkable Facebook debate in between Yann LeCun, Stuart Russel and Yoshua Bengio about the dangers of strong AI (in French). Retrieved 8 June 2023. ^ "Will Expert System Doom The Mankind Within The Next 100 Years?". HuffPost. 22 August 2014. Retrieved 8 June 2023. ^ Sotala, Kaj; Yampolskiy, Roman V. (19 December 2014). "Responses to disastrous AGI risk: 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 original on 11 April 2022. Retrieved 24 December 2023. ^ Milmo, Dan; Stacey, Kiran (25 September 2023). "Experts disagree over threat postured but artificial intelligence can not be overlooked". 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 the End 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 founder says huge tech is lying about AI extinction risk". Australian Financial Review. Archived from the initial on 7 December 2023. Retrieved 7 December 2023. ^ Eloundou, Tyna; Manning, Sam; Mishkin, Pamela; Rock, Daniel (17 March 2023). "GPTs are GPTs: An early take a look at the labor market impact potential of large language designs". OpenAI. Retrieved 7 June 2023. ^ a b Hurst, Luke (23 March 2023). "OpenAI states 80% of employees could see their jobs affected by AI. These are the tasks most impacted". euronews. Retrieved 8 June 2023. ^ Sheffey, Ayelet (20 August 2021). "Elon Musk states we require universal standard earnings because 'in the future, physical work will be a choice'". Business Insider. Archived from the original on 9 July 2023. Retrieved 8 June 2023. Sources

UNESCO Science Report: the Race Against Time for Smarter Development. Paris: UNESCO. 11 June 2021. ISBN 978-9-2310-0450-6. Archived from the original on 18 June 2022. Retrieved 22 September 2021. Chalmers, David (1996 ), The Conscious Mind, Oxford University Press. Clocksin, William (August 2003), "Artificial intelligence and the future", Philosophical Transactions of the Royal Society A, vol. 361, no. 1809, pp. 1721-1748, Bibcode:2003 RSPTA.361.1721 C, doi:10.1098/ rsta.2003.1232, PMID 12952683, S2CID 31032007. Crevier, Daniel (1993 ). AI: The Tumultuous Search for Expert System. New York City, NY: BasicBooks. ISBN 0-465-02997-3. Darrach, Brad (20 November 1970), "Meet Shakey, 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 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 original 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 initial on 29 May 2011, recovered 7 May 2011. Howe, J. (November 1994), Expert System at Edinburgh University: a Viewpoint, archived from the initial 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 seminar, Science Research Council. Luger, George; Stubblefield, William (2004 ), Artificial Intelligence: 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 Artificial Intelligence?. Stanford University. The ultimate effort is to make computer system programs that can resolve problems and accomplish goals worldwide as well as human beings. 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, 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 initial on 16 October 2011, obtained 7 November 2009 Newell, Allen; Simon, H. A. (1976 ). "Computer Technology 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, obtained 29 September 2007 Poole, David; Mackworth, Alan; Goebel, Randy (1998 ), Computational Intelligence: A Sensible Approach, New York City: Oxford University Press, archived from the initial 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, retrieved 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 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 meaning and cognition, Oxford University Press, ISBN 978-0-1992-1727-4 Wang, Pei; Goertzel, Ben (2007 ). "Introduction: Aspects of Artificial General Intelligence". Advances in Artificial General Intelligence: Concepts, Architectures and Algorithms: Proceedings of the AGI Workshop 2006. IOS Press. pp. 1-16. ISBN 978-1-5860-3758-1. Archived from the 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], Expert System Will Kill Our Grandchildren (Singularity), archived from the original 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 might be called "Dyson's Law") that "Any system simple enough to be easy to understand will not be complicated enough to behave intelligently, while any system made complex enough to act smartly will be too made complex to comprehend." (p. 197.) Computer scientist Alex Pentland writes: "Current AI machine-learning algorithms are, at their core, dead basic stupid. They work, but they work by strength." (p. 198.). Gelernter, David, Dream-logic, the Internet and Artificial Thought, Edge, archived from the original on 26 July 2010, retrieved 25 July 2010. Gleick, James, "The Fate of Free Choice" (evaluation of Kevin J. Mitchell, Free Agents: How Evolution Gave Us Free Choice, 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 makers. For biological creatures, reason and function originate from acting on the planet and experiencing the effects. Artificial intelligences - 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" (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: Living in the Shadow of AI, Henry Holt, 311 pp.), The New York City Review of Books, vol. LXXI, no. 17 (7 November 2024), pp. 44-46. "' We can't realistically expect that those who want to get abundant from AI are going to have the interests of the rest of us close at heart,' ... writes [Gary Marcus] 'We can't count on governments driven by project financing contributions [from tech business] to push back.' ... Marcus details the needs that people need to make of their federal governments and the tech companies. They include openness on how AI systems work; settlement for people if their information [are] utilized to train LLMs (big language design) s and the right to grant this usage; and the ability to hold tech business responsible for the damages they trigger by removing Section 230, enforcing money penalites, and passing stricter product liability laws ... Marcus likewise suggests ... that a brand-new, AI-specific federal firm, asteroidsathome.net comparable 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 routine for engineers that would operate in a similar method to medical licenses, malpractice fits, and the Hippocratic oath in medication. '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 synthetic intelligence", 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 puzzled humans for decades, reveals the constraints of natural-language-processing algorithms", Scientific American, vol. 329, no. 4 (November 2023), pp. 81-82. "This murder secret competition has exposed that although NLP (natural-language processing) models can incredible tasks, their capabilities are quite limited by the quantity of context they get. This [...] could cause [problems] for scientists who wish to utilize 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 function." (p. 82.). Immerwahr, Daniel, "Your Lying Eyes: People now utilize A.I. to produce fake videos indistinguishable from genuine ones. Just how much does it matter?", The New Yorker, 20 November 2023, pp. 54-59. "If by 'deepfakes' we indicate practical videos produced utilizing expert system that actually trick 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 much better resembles that of animations, particularly smutty ones." (p. 59.). - Leffer, Lauren, "The Risks of Trusting AI: We need to prevent 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 maker a discussion?", The New Yorker, 7 October 2024, pp. 12-16. Marcus, Gary, "Artificial Confidence: Even the latest, buzziest systems of artificial basic 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, 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: McGraw-Hill. Omohundro, Steve (2008 ), The Nature of Self-Improving Artificial Intelligence, provided and dispersed at the 2007 Singularity Summit, San Francisco, California. Press, Eyal, "In Front of Their Faces: Does facial-recognition innovation lead police to ignore contradictory evidence?", The New Yorker, 20 November 2023, pp. 20-26. Roivainen, Eka, "AI's IQ: ChatGPT aced a [basic intelligence] test however showed 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 jobs that need real humanlike reasoning or an understanding of the physical and social world ... ChatGPT seemed unable to factor logically and attempted to rely on its huge database of ... facts obtained 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 innovations are powerful but unreliable. Rules-based systems can not handle circumstances their programmers did not prepare for. Learning systems are restricted by the data on which they were trained. AI failures have already caused tragedy. Advanced autopilot features in cars and trucks, although they perform well in some circumstances, have actually driven cars and trucks without alerting into trucks, concrete barriers, and parked automobiles. In the incorrect circumstance, AI systems go from supersmart to superdumb in an immediate. When an enemy is trying to manipulate 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 brand-new technologies however rely on the timelelss human tendency to anthropomorphise." (p. 29.). Williams, R. W.; Herrup, K.