Artificial general intelligence (AGI) is a kind of synthetic intelligence (AI) that matches or goes beyond human cognitive abilities across a large range of cognitive jobs. This contrasts with narrow AI, which is limited to particular jobs. [1] Artificial superintelligence (ASI), on the other hand, describes AGI that considerably goes beyond human cognitive capabilities. AGI is thought about among the definitions 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 and development jobs across 37 nations. [4]
The timeline for accomplishing AGI remains a subject of ongoing argument among scientists and specialists. Since 2023, some argue that it might be possible in years or decades; others keep it may take a century or longer; a minority believe it might never be accomplished; and another minority declares that it is already here. [5] [6] Notable AI scientist Geoffrey Hinton has actually expressed concerns about the fast progress towards AGI, mariskamast.net suggesting it might be accomplished faster than lots of expect. [7]
There is debate on the specific definition of AGI and concerning whether contemporary large language designs (LLMs) such as GPT-4 are early kinds of AGI. [8] AGI is a typical subject in science fiction and futures studies. [9] [10]
Contention exists over whether AGI represents an existential danger. [11] [12] [13] Many specialists on AI have mentioned that mitigating the danger of human termination positioned by AGI ought to be a global concern. [14] [15] Others find the advancement of AGI to be too remote to present such a threat. [16] [17]
Terminology

AGI is also referred to as strong AI, [18] [19] full AI, [20] human-level AI, [5] human-level smart AI, or general intelligent action. [21]
Some academic sources book the term "strong AI" for computer programs that experience life or consciousness. [a] In contrast, weak AI (or narrow AI) is able to fix one specific issue but does not have general cognitive abilities. [22] [19] Some scholastic sources use "weak AI" to refer more broadly to any programs that neither experience consciousness nor have a mind in the very same sense as humans. [a]
Related ideas include synthetic superintelligence and transformative AI. An artificial superintelligence (ASI) is a theoretical type of AGI that is much more usually intelligent than human beings, [23] while the notion of transformative AI connects to AI having a large influence on society, for instance, comparable to the agricultural or industrial revolution. [24]
A structure for categorizing AGI in levels was proposed in 2023 by Google DeepMind researchers. They specify five levels of AGI: emerging, proficient, expert, virtuoso, and superhuman. For example, a competent AGI is defined as an AI that surpasses 50% of experienced grownups in a vast array of non-physical tasks, and a superhuman AGI (i.e. an artificial superintelligence) is likewise defined but with a threshold 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 widely known meanings, and some researchers 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, fix puzzles, and make judgments under uncertainty represent understanding, including sound judgment understanding strategy discover

• communicate in natural language
• if essential, integrate these skills in completion of any provided goal

Many interdisciplinary approaches (e.g. cognitive science, computational intelligence, and decision making) consider extra traits such as imagination (the capability to form unique psychological images and concepts) [28] and autonomy. [29]
Computer-based systems that display much of these capabilities exist (e.g. see computational imagination, automated reasoning, choice support group, robotic, evolutionary calculation, intelligent agent). There is dispute about whether modern AI systems have them to an adequate degree.

Physical characteristics

Other abilities are thought about desirable in intelligent systems, as they might impact intelligence or aid in its expression. These include: [30]
- the capability to sense (e.g. see, hear, and so on), and - the capability to act (e.g. relocation and manipulate things, change location to explore, etc).
This includes the capability to find and react to threat. [31]
Although the ability to sense (e.g. see, hear, etc) and the ability to act (e.g. move and manipulate things, change place to explore, etc) can be desirable for some intelligent systems, [30] these physical capabilities are not strictly required for an entity to qualify as AGI-particularly under the thesis that big language models (LLMs) may currently be or end up being AGI. Even from a less positive viewpoint on LLMs, there is no company requirement for an AGI to have a human-like form; being a silicon-based computational system is enough, supplied it can process input (language) from the external world in place of human senses. This analysis lines up with the understanding that AGI has never ever been proscribed a specific physical embodiment and therefore does not require a capacity for mobility or conventional "eyes and ears". [32]
Tests for human-level AGI

Several tests implied to confirm human-level AGI have been thought about, including: [33] [34]
The idea of the test is that the device has to attempt and pretend to be a male, by responding to concerns put to it, and it will just pass if the pretence is reasonably persuading. A considerable portion of a jury, who should not be expert about machines, need to be taken in by the pretence. [37]
AI-complete problems

A problem is informally called "AI-complete" or "AI-hard" if it is believed that in order to fix it, one would need to carry out AGI, due to the fact that the service is beyond the capabilities of a purpose-specific algorithm. [47]
There are numerous problems that have been conjectured to require basic intelligence to fix along with people. Examples consist of computer system vision, natural language understanding, and handling unforeseen circumstances while resolving any real-world problem. [48] Even a particular task like translation needs a machine to check out and write in both languages, follow the author's argument (factor), understand the context (understanding), and faithfully replicate the author's initial intent (social intelligence). All of these issues need to be resolved concurrently in order to reach human-level maker performance.

However, a number of these tasks can now be carried out by modern-day big language designs. According to Stanford University's 2024 AI index, AI has reached human-level performance on lots of criteria for reading understanding and visual reasoning. [49]
History

Classical AI

Modern AI research study started in the mid-1950s. [50] The very first generation of AI scientists were encouraged that artificial basic intelligence was possible which it would exist in simply a few decades. [51] AI pioneer Herbert A. Simon wrote in 1965: "makers will be capable, within twenty years, of doing any work a guy can do." [52]
Their forecasts were the motivation for Stanley Kubrick and Arthur C. Clarke's character HAL 9000, who embodied what AI scientists thought they might produce by the year 2001. AI leader Marvin Minsky was an expert [53] on the project of making HAL 9000 as sensible as possible according to the agreement forecasts of the time. He said in 1967, "Within a generation ... the issue of producing 'expert system' will substantially be solved". [54]
Several classical AI tasks, such as Doug Lenat's Cyc job (that began 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 ignored the difficulty of the job. Funding agencies ended up being hesitant of AGI and put scientists under increasing pressure to produce helpful "used AI". [c] In the early 1980s, Japan's Fifth Generation Computer Project revived interest in AGI, setting out a ten-year timeline that consisted of AGI objectives like "bring on a table talk". [58] In response to this and the success of expert systems, both market and federal government pumped cash into the field. [56] [59] However, confidence in AI stunningly collapsed in the late 1980s, and the goals of the Fifth Generation Computer Project were never satisfied. [60] For the second time in 20 years, AI researchers who anticipated the impending achievement of AGI had been misinterpreted. By the 1990s, AI researchers had a reputation for making vain pledges. They became hesitant to make forecasts at all [d] and prevented mention of "human level" synthetic intelligence for fear of being labeled "wild-eyed dreamer [s]. [62]
Narrow AI research

In the 1990s and early 21st century, mainstream AI accomplished industrial success and scholastic respectability by focusing on specific sub-problems where AI can produce verifiable results and commercial applications, such as speech acknowledgment and recommendation algorithms. [63] These "applied AI" systems are now used extensively throughout the technology industry, and research in this vein is heavily moneyed in both academic community and market. As of 2018 [upgrade], advancement in this field was thought about an emerging trend, and a mature stage was expected to be reached in more than 10 years. [64]
At the millenium, lots of traditional AI scientists [65] hoped that strong AI could be developed by combining programs that solve numerous sub-problems. Hans Moravec wrote in 1988:

I am positive that this bottom-up route to expert system will one day fulfill the traditional top-down path over half way, prepared to supply the real-world proficiency and the commonsense understanding that has actually been so frustratingly evasive in thinking programs. Fully intelligent machines will result when the metaphorical golden spike is driven unifying the 2 efforts. [65]
However, even at the time, this was contested. For instance, Stevan Harnad of Princeton University concluded his 1990 paper on the sign grounding hypothesis by mentioning:

The expectation has often 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 are legitimate, then this expectation is hopelessly modular and there is really only one viable route from sense to signs: from the ground up. A free-floating symbolic level like the software application level of a computer system will never be reached by this path (or vice versa) - nor is it clear why we must even try to reach such a level, since it looks as if getting there would simply amount to uprooting our symbols from their intrinsic meanings (consequently merely lowering ourselves to the practical equivalent of a programmable computer). [66]
Modern synthetic general intelligence research

The term "synthetic general intelligence" was used as early as 1997, by Mark Gubrud [67] in a conversation of the implications of totally automated military production and operations. A mathematical formalism of AGI was proposed by Marcus Hutter in 2000. Named AIXI, the proposed AGI agent increases "the capability to please objectives in a wide variety of environments". [68] This kind of AGI, identified by the ability to increase a mathematical definition of intelligence rather than exhibit human-like behaviour, [69] was likewise called universal synthetic intelligence. [70]
The term AGI was re-introduced and popularized by Shane Legg and Ben Goertzel around 2002. [71] AGI research activity in 2006 was described by Pei Wang and Ben Goertzel [72] as "producing publications and preliminary results". The first summer school in AGI was arranged in Xiamen, China in 2009 [73] by the Xiamen university's Artificial Brain Laboratory and OpenCog. The first university course was given up 2010 [74] and 2011 [75] at Plovdiv University, Bulgaria by Todor Arnaudov. MIT provided a course on AGI in 2018, arranged by Lex Fridman and featuring a variety of visitor speakers.

As of 2023 [upgrade], a little number of computer system scientists are active in AGI research study, and many contribute to a series of AGI conferences. However, progressively more researchers are interested in open-ended knowing, [76] [77] which is the idea of permitting AI to continuously learn and innovate like people do.

Feasibility

Since 2023, the advancement and prospective accomplishment of AGI stays a subject of extreme debate within the AI community. While standard agreement held that AGI was a remote goal, current improvements have led some scientists and industry figures to declare that early kinds of AGI might already exist. [78] AI pioneer Herbert A. Simon hypothesized in 1965 that "makers will be capable, within twenty years, of doing any work a male can do". This prediction stopped working to come real. Microsoft co-founder Paul Allen thought that such intelligence is unlikely in the 21st century due to the fact that it would require "unforeseeable and fundamentally unpredictable advancements" and a "clinically deep understanding of cognition". [79] Writing in The Guardian, roboticist Alan Winfield declared the gulf between contemporary computing and human-level expert system is as broad as the gulf between current area flight and useful faster-than-light spaceflight. [80]
A more obstacle is the lack of clarity in defining what intelligence requires. Does it need consciousness? Must it display the ability to set objectives in addition to pursue them? Is it purely a matter of scale such that if design sizes increase sufficiently, intelligence will emerge? Are centers such as planning, reasoning, and causal understanding required? Does intelligence require explicitly replicating the brain and its specific faculties? Does it need emotions? [81]
Most AI researchers 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 believe human-level AI will be accomplished, but that today level of progress is such that a date can not properly be forecasted. [84] AI experts' views on the expediency of AGI wax and subside. Four polls conducted in 2012 and 2013 suggested that the typical estimate amongst professionals for when they would be 50% positive AGI would get here was 2040 to 2050, depending upon the poll, with the mean being 2081. Of the experts, 16.5% answered with "never" when asked the very same question but with a 90% self-confidence instead. [85] [86] Further existing AGI development considerations can be discovered above Tests for verifying human-level AGI.

A report by Stuart Armstrong and Kaj Sotala of the Machine Intelligence Research Institute discovered that "over [a] 60-year amount of time there is a strong predisposition towards anticipating the arrival of human-level AI as between 15 and 25 years from the time the prediction was made". They evaluated 95 predictions made between 1950 and 2012 on when human-level AI will come about. [87]
In 2023, Microsoft scientists published an in-depth examination of GPT-4. They concluded: "Given the breadth and depth of GPT-4's capabilities, we believe that it could reasonably be viewed as an early (yet still insufficient) version of a synthetic general intelligence (AGI) system." [88] Another study in 2023 reported that GPT-4 outshines 99% of human beings on the Torrance tests of creativity. [89] [90]
Blaise Agüera y Arcas and Peter Norvig composed in 2023 that a considerable level of general intelligence has already been attained with frontier models. They wrote that unwillingness to this view comes from 4 primary reasons: a "healthy suspicion about metrics for AGI", an "ideological commitment to alternative AI theories or strategies", a "commitment to human (or biological) exceptionalism", or a "issue about the financial ramifications of AGI". [91]
2023 also marked the emergence of large multimodal designs (large language models efficient in processing or producing multiple methods such as text, audio, and images). [92]
In 2024, OpenAI launched o1-preview, the first of a series of designs that "spend more time believing before they react". According to Mira Murati, this ability to think before reacting represents a brand-new, additional paradigm. It improves design outputs by spending more computing power when producing the answer, whereas the design scaling paradigm improves outputs by increasing the model size, training information and training compute power. [93] [94]
An OpenAI worker, Vahid Kazemi, claimed in 2024 that the company had actually accomplished AGI, stating, "In my viewpoint, we have already accomplished AGI and it's even more clear with O1." Kazemi clarified that while the AI is not yet "better than any human at any task", it is "better than a lot of humans at most jobs." He also addressed criticisms that big language designs (LLMs) simply follow predefined patterns, comparing their knowing procedure to the clinical method of observing, assuming, and verifying. These declarations have triggered dispute, as they count on a broad and unconventional meaning of AGI-traditionally understood as AI that matches human intelligence throughout all domains. Critics argue that, while OpenAI's models show exceptional flexibility, they might not fully satisfy this standard. Notably, Kazemi's remarks came shortly after OpenAI removed "AGI" from the regards to its partnership with Microsoft, prompting speculation about the business's tactical objectives. [95]
Timescales

Progress in artificial intelligence has actually historically gone through durations of rapid progress separated by durations when development appeared to stop. [82] Ending each hiatus were essential advances in hardware, software or both to develop space for more development. [82] [98] [99] For example, the hardware offered in the twentieth century was not enough to execute deep learning, which requires great deals of GPU-enabled CPUs. [100]
In the intro to his 2006 book, [101] Goertzel says that estimates of the time required before a truly flexible AGI is built vary from ten years to over a century. Since 2007 [upgrade], the agreement in the AGI research study neighborhood appeared to be that the timeline gone over by Ray Kurzweil in 2005 in The Singularity is Near [102] (i.e. between 2015 and 2045) was plausible. [103] Mainstream AI researchers have offered a wide variety of viewpoints on whether progress will be this fast. A 2012 meta-analysis of 95 such viewpoints found a bias towards predicting that the onset of AGI would happen within 16-26 years for modern-day and historical predictions alike. That paper has been criticized for how it categorized opinions as expert or non-expert. [104]
In 2012, Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton developed a neural network called AlexNet, which won the ImageNet competition with a top-5 test mistake rate of 15.3%, substantially better than the second-best entry's rate of 26.3% (the standard method utilized a weighted amount of ratings from different pre-defined classifiers). [105] AlexNet was considered as the initial ground-breaker of the current deep knowing 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 optimum, these AIs reached an IQ worth of about 47, which corresponds around to a six-year-old kid in first grade. A grownup pertains to about 100 on average. Similar tests were performed in 2014, with the IQ score reaching an optimum value of 27. [106] [107]
In 2020, OpenAI established GPT-3, a language design capable of carrying out lots of 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 exact same year, Jason Rohrer used his GPT-3 account to develop a chatbot, and provided a chatbot-developing platform called "Project December". OpenAI asked for changes to the chatbot to adhere to 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 various tasks. [110]
In 2023, Microsoft Research published a study on an early version of OpenAI's GPT-4, competing that it displayed more basic intelligence than previous AI models and showed human-level efficiency in jobs spanning multiple domains, such as mathematics, coding, and law. This research stimulated an argument on whether GPT-4 might be considered an early, insufficient version of synthetic general intelligence, emphasizing the need for more exploration and assessment of such systems. [111]
In 2023, the AI researcher Geoffrey Hinton stated that: [112]
The concept that this stuff might really get smarter than individuals - a couple of people believed that, [...] But many people believed it was method off. And I believed it was way off. I believed it was 30 to 50 years and even longer away. Obviously, I no longer think that.

In May 2023, Demis Hassabis likewise said that "The progress in the last few years has been pretty unbelievable", which he sees no reason why it would decrease, anticipating AGI within a decade and even a couple of years. [113] In March 2024, Nvidia's CEO, Jensen Huang, stated his expectation that within 5 years, AI would be capable of passing any test a minimum of in addition to humans. [114] In June 2024, the AI scientist Leopold Aschenbrenner, a previous OpenAI worker, estimated AGI by 2027 to be "strikingly possible". [115]
Whole brain emulation

While the development of transformer models like in ChatGPT is thought about the most promising course to AGI, [116] [117] whole brain emulation can work as an alternative technique. With whole brain simulation, a brain model is constructed by scanning and mapping a biological brain in information, and then copying and simulating it on a computer system or another computational gadget. The simulation model must be adequately loyal to the original, so that it acts in almost the same method as the initial brain. [118] Whole brain emulation is a type of brain simulation that is discussed in computational neuroscience and neuroinformatics, and for medical research purposes. It has been discussed in artificial intelligence research [103] as a technique to strong AI. Neuroimaging technologies that could provide the needed comprehensive understanding are improving quickly, and futurist Ray Kurzweil in the book The Singularity Is Near [102] predicts that a map of sufficient quality will end up being readily available on a similar timescale to the computing power needed to emulate it.

Early approximates

For low-level brain simulation, a very powerful cluster of computers or GPUs would be needed, provided the enormous 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 declines with age, stabilizing by adulthood. Estimates differ for an adult, ranging from 1014 to 5 × 1014 synapses (100 to 500 trillion). [120] An estimate of the brain's processing power, based on a basic switch design for nerve cell activity, is around 1014 (100 trillion) synaptic updates per second (SUPS). [121]
In 1997, Kurzweil took a look at various quotes for the hardware needed to equal the human brain and embraced a figure of 1016 calculations per second (cps). [e] (For contrast, if a "calculation" was comparable to one "floating-point operation" - a procedure used to rate existing supercomputers - then 1016 "computations" would be comparable to 10 petaFLOPS, accomplished in 2011, while 1018 was accomplished in 2022.) He used this figure to forecast the required hardware would be offered sometime in between 2015 and 2025, if the exponential development in computer system power at the time of composing continued.

Current research

The Human Brain Project, an EU-funded effort active from 2013 to 2023, has actually established an especially comprehensive and publicly available atlas of the human brain. [124] In 2023, scientists from Duke University carried out a high-resolution scan of a mouse brain.

Criticisms of simulation-based techniques

The artificial neuron design assumed by Kurzweil and used in lots of existing artificial neural network executions is basic compared with biological neurons. A brain simulation would likely need to record the comprehensive cellular behaviour of biological nerve cells, presently comprehended only in broad summary. The overhead presented by full modeling of the biological, chemical, and physical information of neural behaviour (especially on a molecular scale) would require computational powers several orders of magnitude larger than Kurzweil's estimate. In addition, the quotes do not represent glial cells, which are understood to contribute in cognitive procedures. [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 required to ground meaning. [126] [127] If this theory is right, any completely functional brain model will require to include more than just the nerve cells (e.g., a robotic body). Goertzel [103] proposes virtual personification (like in metaverses like Second Life) as an option, but it is unknown whether this would be sufficient.

Philosophical perspective

"Strong AI" as defined in approach

In 1980, thinker John Searle created the term "strong AI" as part of his Chinese space argument. [128] He proposed a difference in between 2 hypotheses about expert system: [f]
Strong AI hypothesis: An artificial intelligence system can have "a mind" and "consciousness". Weak AI hypothesis: An expert system system can (only) act like it believes and has a mind and consciousness.
The very first one he called "strong" because it makes a stronger declaration: it presumes something special has taken place to the maker that exceeds those abilities that we can evaluate. The behaviour of a "weak AI" device would be exactly similar to a "strong AI" maker, however the latter would also have subjective mindful experience. This usage is also typical in academic AI research study and textbooks. [129]
In contrast to Searle and mainstream AI, some futurists such as Ray Kurzweil use the term "strong AI" to suggest "human level synthetic general intelligence". [102] This is not the like Searle's strong AI, unless it is assumed that consciousness is necessary for human-level AGI. Academic theorists such as Searle do not think that holds true, and to most synthetic intelligence scientists the question is out-of-scope. [130]
Mainstream AI is most interested in how a program behaves. [131] According to Russell and Norvig, "as long as the program works, they do not 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 need to understand if it really has mind - undoubtedly, there would be no method to inform. For AI research, Searle's "weak AI hypothesis" is equivalent to the statement "synthetic general intelligence is possible". Thus, according to Russell and Norvig, "most AI researchers take the weak AI hypothesis for given, and do not care about the strong AI hypothesis." [130] Thus, for scholastic AI research study, "Strong AI" and "AGI" are two various things.

Consciousness

Consciousness can have numerous significances, and some elements play considerable functions in sci-fi and the principles of expert system:

Sentience (or "remarkable awareness"): The ability to "feel" perceptions or emotions subjectively, instead of the ability to factor about perceptions. Some philosophers, such as David Chalmers, utilize the term "consciousness" to refer exclusively to incredible awareness, which is approximately comparable to sentience. [132] Determining why and how subjective experience emerges is called the tough issue of awareness. [133] Thomas Nagel discussed in 1974 that it "feels like" something to be mindful. If we are not mindful, then it does not feel like anything. Nagel utilizes the example of a bat: we can smartly ask "what does it seem like to be a bat?" However, we are not likely to ask "what does it feel like to be a toaster?" Nagel concludes that a bat appears to be mindful (i.e., has consciousness) but a toaster does not. [134] In 2022, a Google engineer declared that the company's AI chatbot, LaMDA, had actually achieved sentience, though this claim was commonly challenged by other specialists. [135]
Self-awareness: To have mindful awareness of oneself as a different individual, particularly to be purposely familiar with one's own thoughts. This is opposed to simply being the "subject of one's believed"-an os or debugger is able to be "familiar with itself" (that is, to represent itself in the very same method it represents whatever else)-but this is not what people generally imply when they use the term "self-awareness". [g]
These qualities have a moral dimension. AI sentience would provide rise to concerns of welfare and legal defense, likewise to animals. [136] Other aspects of consciousness related to cognitive abilities are likewise appropriate to the idea of AI rights. [137] Figuring out how to incorporate sophisticated AI with existing legal and social structures is an emergent problem. [138]
Benefits

AGI might have a wide variety of applications. If oriented towards such objectives, AGI could assist reduce numerous problems worldwide such as hunger, poverty and illness. [139]
AGI could improve productivity and performance in most jobs. For example, in public health, AGI might speed up medical research, especially against cancer. [140] It could look after the senior, [141] and democratize access to rapid, premium medical diagnostics. It might provide enjoyable, inexpensive and tailored education. [141] The requirement to work to subsist might become obsolete if the wealth produced is appropriately redistributed. [141] [142] This also raises the question of the place of human beings in a radically automated society.

AGI might also help to make reasonable choices, and to anticipate and avoid disasters. It might also help to reap the advantages of possibly devastating innovations such as nanotechnology or climate engineering, while preventing the associated threats. [143] If an AGI's main goal is to avoid existential catastrophes such as human extinction (which could be difficult if the Vulnerable World Hypothesis ends up being real), [144] it might take procedures to considerably decrease the dangers [143] while minimizing the effect of these measures on our quality of life.

Risks

Existential risks

AGI may represent numerous kinds of existential danger, which are risks that threaten "the early extinction of Earth-originating smart life or the irreversible and drastic destruction of its potential for preferable future advancement". [145] The danger 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 result in a permanently problematic future. Notably, it could be utilized to spread and protect the set of values of whoever establishes it. If mankind still has moral blind spots comparable to slavery in the past, AGI might irreversibly entrench it, avoiding ethical progress. [146] Furthermore, AGI could help with mass surveillance and indoctrination, which might be used to develop a steady repressive worldwide totalitarian program. [147] [148] There is likewise a threat for the makers themselves. If makers that are sentient or otherwise worthwhile of moral consideration are mass produced in the future, taking part in a civilizational course that forever ignores their welfare and interests could be an existential disaster. [149] [150] Considering just how much AGI might enhance mankind's future and help in reducing other existential threats, Toby Ord calls these existential threats "an argument for continuing with due care", not for "abandoning AI". [147]
Risk of loss of control and human extinction

The thesis that AI poses an existential threat for people, which this risk requires more attention, is questionable however has actually been endorsed in 2023 by numerous public figures, AI scientists and CEOs of AI business such as Elon Musk, Bill Gates, Geoffrey Hinton, Yoshua Bengio, Demis Hassabis and Sam Altman. [151] [152]
In 2014, Stephen Hawking slammed prevalent indifference:

So, dealing with possible futures of incalculable advantages and dangers, the specialists are certainly doing whatever possible to ensure the very best outcome, right? Wrong. If a remarkable alien civilisation sent us a message stating, 'We'll arrive in a couple of decades,' would we just respond, 'OK, call us when you get here-we'll leave the lights on?' Probably not-but this is basically what is taking place with AI. [153]
The possible fate of humankind has actually in some cases been compared to the fate of gorillas threatened by human activities. The comparison specifies that greater intelligence enabled humankind to control gorillas, which are now susceptible in manner ins which they could not have expected. As an outcome, the gorilla has become an endangered species, not out of malice, but merely as a collateral damage from human activities. [154]
The skeptic Yann LeCun thinks about that AGIs will have no desire to dominate humanity and that we need to take care not to anthropomorphize them and translate their intents as we would for people. He stated that individuals won't be "wise adequate to design super-intelligent devices, yet extremely foolish to the point of offering it moronic objectives without any safeguards". [155] On the other side, the idea of important convergence suggests that practically whatever their goals, intelligent agents will have reasons to attempt to survive and get more power as intermediary actions to achieving these goals. And that this does not need having emotions. [156]
Many scholars who are worried about existential danger advocate for more research study into fixing the "control issue" to address the question: what kinds of safeguards, algorithms, or architectures can programmers carry out to increase the probability that their recursively-improving AI would continue to behave 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 lead to a race to the bottom of safety preventative measures in order to launch products before rivals), [159] and using AI in weapon systems. [160]
The thesis that AI can posture existential risk also has detractors. Skeptics generally say that AGI is not likely in the short-term, or that issues about AGI distract from other concerns related to existing AI. [161] Former Google fraud czar Shuman Ghosemajumder considers that for many individuals outside of the innovation market, existing chatbots and LLMs are currently perceived as though they were AGI, resulting in more misconception and worry. [162]
Skeptics in some cases charge that the thesis is crypto-religious, with an illogical belief in the possibility of superintelligence changing an illogical belief in an omnipotent God. [163] Some scientists believe that the communication projects on AI existential danger by certain AI groups (such as OpenAI, Anthropic, DeepMind, and Conjecture) might be an at effort at regulative capture and to pump up interest in their products. [164] [165]
In 2023, the CEOs of Google DeepMind, OpenAI and Anthropic, along with other market leaders and scientists, issued a joint declaration asserting that "Mitigating the danger of extinction from AI ought to be a global priority alongside other societal-scale risks such as pandemics and nuclear war." [152]
Mass joblessness

Researchers from OpenAI approximated that "80% of the U.S. labor force could have at least 10% of their work jobs affected by the introduction of LLMs, while around 19% of workers might see a minimum of 50% of their jobs impacted". [166] [167] They think about office employees to be the most exposed, for example mathematicians, accounting professionals or web designers. [167] AGI could 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 redistributed: [142]
Everyone can delight in a life of glamorous leisure if the machine-produced wealth is shared, or many people can end up miserably poor if the machine-owners successfully lobby against wealth redistribution. So far, the pattern appears to be toward the 2nd choice, with technology driving ever-increasing inequality

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

Artificial brain - Software and hardware with cognitive capabilities similar to those of the animal or human brain AI effect AI safety - Research location on making AI safe and useful AI alignment - AI conformance to the intended objective A.I. Rising - 2018 film directed by Lazar Bodroža Expert system Automated artificial intelligence - Process of automating the application of device learning BRAIN Initiative - Collaborative public-private research study effort announced by the Obama administration China Brain Project Future of Humanity Institute - Defunct Oxford interdisciplinary research study centre General game playing - Ability of expert system to play various games Generative expert system - AI system efficient in generating material in reaction to triggers Human Brain Project - Scientific research job Intelligence amplification - Use of details technology to enhance human intelligence (IA). Machine principles - Moral behaviours of manufactured makers. Moravec's paradox. Multi-task knowing - Solving several device finding out tasks at the same time. Neural scaling law - Statistical law in device knowing. Outline of artificial intelligence - Overview of and topical guide to synthetic intelligence. Transhumanism - Philosophical motion. Synthetic intelligence - Alternate term for or form of synthetic intelligence. Transfer learning - Machine learning technique. Loebner Prize - Annual AI competition. Hardware for expert system - Hardware specifically created and optimized for synthetic intelligence. Weak artificial intelligence - Form of artificial intelligence.
Notes

^ a b See below for the origin of the term "strong AI", and see the scholastic meaning of "strong AI" and weak AI in the post Chinese room. ^ AI creator John McCarthy composes: "we can not yet characterize in general what kinds of computational treatments we want to call smart. " [26] (For a discussion of some definitions of intelligence utilized by artificial intelligence scientists, see viewpoint of artificial intelligence.). ^ The Lighthill report particularly slammed 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 fund just "mission-oriented direct research, rather than basic undirected research". [56] [57] ^ As AI creator John McCarthy composes "it would be a fantastic relief to the rest of the employees in AI if the inventors of new basic formalisms would express their hopes in a more secured kind than has actually often been the case." [61] ^ In "Mind Children" [122] 1015 cps is used. More recently, in 1997, [123] Moravec argued for 108 MIPS which would roughly correspond to 1014 cps. Moravec talks in terms of MIPS, not "cps", which is a non-standard term Kurzweil introduced. ^ As defined in a basic AI textbook: "The assertion that machines might possibly act smartly (or, maybe better, act as if they were intelligent) is called the 'weak AI' hypothesis by thinkers, and the assertion that makers that do so are actually believing (as opposed to mimicing thinking) is called the 'strong AI' hypothesis." [121] ^ Alan Turing made this point in 1950. [36] References

^ Krishna, Sri (9 February 2023). "What is synthetic narrow intelligence (ANI)?". VentureBeat. Retrieved 1 March 2024. ANI is created to carry out a single task. ^ "OpenAI Charter". OpenAI. Retrieved 6 April 2023. Our objective is to ensure that artificial basic intelligence benefits all of humankind. ^ Heath, Alex (18 January 2024). "Mark Zuckerberg's new objective is creating artificial basic 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 projects were recognized as being active in 2020. ^ a b c "AI timelines: What do specialists in synthetic intelligence expect for the future?". Our World in Data. Retrieved 6 April 2023. ^ Metz, Cade (15 May 2023). "Some Researchers Say A.I. Is Already Here, Stirring Debate in Tech Circles". The New York Times. Retrieved 18 May 2023. ^ "AI leader Geoffrey Hinton quits Google and cautions of danger ahead". The New York Times. 1 May 2023. Retrieved 2 May 2023. It is hard to see how you can prevent the bad stars from utilizing it for bad things. ^ Bubeck, Sébastien; Chandrasekaran, Varun; Eldan, Ronen; Gehrke, Johannes; Horvitz, Eric (2023 ). "Sparks of Artificial General Intelligence: Early experiments with GPT-4". arXiv preprint. arXiv:2303.12712. GPT-4 shows stimulates of AGI. ^ Butler, Octavia E. (1993 ). Parable of the Sower. Grand Central Publishing. ISBN 978-0-4466-7550-5. All that you touch you change. All that you alter changes you. ^ Vinge, Vernor (1992 ). A Fire Upon the Deep. Tor Books. ISBN 978-0-8125-1528-2. The Singularity is coming. ^ Morozov, Evgeny (30 June 2023). "The True Threat of Expert System". The New York City Times. The real risk is not AI itself however the method we deploy it. ^ "Impressed by artificial intelligence? Experts state AGI is following, and it has 'existential' threats". ABC News. 23 March 2023. Retrieved 6 April 2023. AGI might posture existential dangers to humankind. ^ Bostrom, Nick (2014 ). Superintelligence: Paths, Dangers, Strategies. Oxford University Press. ISBN 978-0-1996-7811-2. The first superintelligence will be the last creation that humanity requires to make. ^ Roose, Kevin (30 May 2023). "A.I. Poses 'Risk of Extinction,' Industry Leaders Warn". The New York City Times. Mitigating the threat of extinction from AI should be a global priority. ^ "Statement on AI Risk". Center for AI Safety. Retrieved 1 March 2024. AI specialists alert of threat of extinction from AI. ^ Mitchell, Melanie (30 May 2023). "Are AI's Doomsday Scenarios Worth Taking Seriously?". The New York City Times. We are far from creating machines that can outthink us in basic ways. ^ LeCun, Yann (June 2023). "AGI does not present an existential threat". Medium. There is no factor to fear AI as an existential hazard. ^ Kurzweil 2005, p. 260. ^ a b Kurzweil, Ray (5 August 2005), "Long Live AI", Forbes, archived from the original on 14 August 2005: Kurzweil describes strong AI as "machine intelligence with the complete series of human intelligence.". ^ "The Age of Artificial Intelligence: George John at TEDxLondonBusinessSchool 2013". Archived from the initial on 26 February 2014. Retrieved 22 February 2014. ^ Newell & Simon 1976, This is the term they use for "human-level" intelligence in the physical sign system hypothesis. ^ "The Open University on Strong and Weak AI". Archived from the initial 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 transforming our world - it is on all of us to make certain that it works out". Our World in Data. Retrieved 8 October 2023. ^ Dickson, Ben (16 November 2023). "Here is how far we are to accomplishing AGI, according to DeepMind". VentureBeat. ^ McCarthy, John (2007a). "Basic Questions". Stanford University. Archived from the initial on 26 October 2007. Retrieved 6 December 2007. ^ This list of smart traits is based on the topics covered by major AI textbooks, consisting of: Russell & Norvig 2003, Luger & Stubblefield 2004, Poole, Mackworth & Goebel 1998 and Nilsson 1998. ^ Johnson 1987. ^ de Charms, R. (1968 ). Personal causation. New York: Academic Press. ^ a b Pfeifer, R. and Bongard J. C., How the body 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 principle of proficiency". Psychological Review. 66 (5 ): 297-333. doi:10.1037/ h0040934. PMID 13844397. S2CID 37385966. ^ White, R. W. (1959 ). "Motivation reassessed: The principle 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 original on 17 July 2019. Retrieved 17 July 2019. ^ Kirk-Giannini, Cameron Domenico; Goldstein, Simon (16 October 2023). "AI is closer than ever to passing the Turing test for 'intelligence'. What takes place when it does?". The Conversation. Retrieved 22 September 2024. ^ a b Turing 1950. ^ Turing, Alan (1952 ). B. Jack Copeland (ed.). Can Automatic Calculating Machines Be Said To Think?. Oxford: Oxford University Press. pp. 487-506. ISBN 978-0-1982-5079-1. ^ "Eugene Goostman is a genuine kid - the Turing Test says 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 distinguish GPT-4 from a human in a Turing test". arXiv:2405.08007 [cs.HC] ^ Varanasi, Lakshmi (21 March 2023). "AI designs like ChatGPT and GPT-4 are acing whatever from the bar exam to AP Biology. Here's a list of difficult tests both AI versions have passed". Business Insider. Retrieved 30 May 2023. ^ Naysmith, Caleb (7 February 2023). "6 Jobs Artificial Intelligence Is Already Replacing and How Investors Can Capitalize on It". Retrieved 30 May 2023. ^ Turk, Victoria (28 January 2015). "The Plan to Replace the Turing Test with a 'Turing Olympics'". Vice. Retrieved 3 March 2024. ^ Gopani, Avi (25 May 2022). "Turing Test is unreliable. The Winograd Schema is outdated. Coffee is the answer". 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 ). "Artificial Intelligence" (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). Expert System, Evolutionary Computation and Metaheuristics (AIECM): 3-17. Archived (PDF) from the original on 22 May 2013. ^ "AI Index: State of AI in 13 Charts". Stanford University Human-Centered 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 initial on 6 May 2022. Retrieved 12 March 2022. ^ Simon 1965, p. 96 estimated in Crevier 1993, p. 109. ^ "Scientist on the Set: An Interview with Marvin Minsky". Archived from the initial on 16 July 2012. Retrieved 5 April 2008. ^ Marvin Minsky to Darrach (1970 ), quoted in Crevier (1993, p. 109). ^ Lighthill 1973; Howe 1994. ^ a b NRC 1999, "Shift to Applied Research Increases Investment". ^ Crevier 1993, pp. 115-117; Russell & Norvig 2003, pp. 21-22. ^ Crevier 1993, p. 211, Russell & Norvig 2003, p. 24 and see also Feigenbaum & McCorduck 1983. ^ Crevier 1993, pp. 161-162, 197-203, 240; Russell & Norvig 2003, p. 25. ^ Crevier 1993, pp. 209-212. ^ McCarthy, John (2000 ). "Reply to Lighthill". Stanford University. Archived from the 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 scientists and software application engineers prevented the term expert system for fear of being viewed 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 on Algorithmic Probability. Texts in Theoretical Computer Technology an EATCS Series. Springer. doi:10.1007/ b138233. ISBN 978-3-5402-6877-2. S2CID 33352850. Archived from the original on 19 July 2022. Retrieved 19 July 2022. ^ Legg, Shane (2008 ). Machine Super Intelligence (PDF) (Thesis). University of Lugano. Archived (PDF) from the original on 15 June 2022. Retrieved 19 July 2022. ^ Goertzel, Ben (2014 ). Artificial General Intelligence. Lecture Notes in Computer 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 created the term "AGI"?". goertzel.org. Archived from the initial on 28 December 2018. Retrieved 28 December 2018., through Life 3.0: 'The term "AGI" was promoted by ... Shane Legg, Mark Gubrud and Ben Goertzel' ^ Wang & Goertzel 2007 ^ "First International Summer School in Artificial General Intelligence, Main summer school: June 22 - July 3, 2009, OpenCog Lab: July 6-9, 2009". Archived from the original on 28 September 2020. Retrieved 11 May 2020. ^ "Избираеми дисциплини 2009/2010 - пролетен триместър" [Elective courses 2009/2010 - spring trimester] Факултет по математика и информатика [Faculty of Mathematics and Informatics] (in Bulgarian). Archived from the initial on 26 July 2020. Retrieved 11 May 2020. ^ "Избираеми дисциплини 2010/2011 - зимен триместър" [Elective courses 2010/2011 - winter season trimester] Факултет по математика и информатика [Faculty of Mathematics and Informatics] (in Bulgarian). Archived from the initial on 26 July 2020. Retrieved 11 May 2020. ^ Shevlin, Henry; Vold, Karina; Crosby, Matthew; Halina, Marta (4 October 2019). "The limitations of maker intelligence: Despite progress in maker intelligence, synthetic general intelligence is still a major obstacle". EMBO Reports. 20 (10 ): e49177. doi:10.15252/ embr.201949177. ISSN 1469-221X. PMC 6776890. PMID 31531926. ^ Bubeck, Sébastien; Chandrasekaran, Varun; Eldan, Ronen; Gehrke, Johannes; Horvitz, Eric; Kamar, Ece; Lee, Peter; Lee, Yin Tat; Li, Yuanzhi; Lundberg, Scott; Nori, Harsha; Palangi, Hamid; Ribeiro, Marco Tulio; Zhang, Yi (27 March 2023). "Sparks of Artificial General Intelligence: Early try outs GPT-4". arXiv:2303.12712 [cs.CL] ^ "Microsoft Researchers Claim GPT-4 Is Showing "Sparks" of AGI". Futurism. 23 March 2023. Retrieved 13 December 2023. ^ Allen, Paul; Greaves, Mark (12 October 2011). "The Singularity Isn't Near". MIT Technology Review. Retrieved 17 September 2014. ^ Winfield, Alan. "Expert system will not turn into 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 basic artificial intelligence 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 synthetic intelligence bring us utopia 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 professional viewpoint. In Fundamental concerns of artificial intelligence (pp. 555-572). Springer, Cham. ^ Armstrong, Stuart, and Kaj Sotala. 2012. "How We're Predicting AI-or Failing To." In Beyond AI: Artificial Dreams, edited by Jan Romportl, Pavel Ircing, Eva Žáčková, Michal Polák and Radek Schuster, 52-75. Plzeň: University of West Bohemia ^ "Microsoft Now Claims GPT-4 Shows 'Sparks' of General Intelligence". 24 March 2023. ^ Shimek, Cary (6 July 2023). "AI Outperforms Humans in Creativity Test". Neuroscience News. Retrieved 20 October 2023. ^ Guzik, Erik E.; Byrge, Christian; Gilde, Christian (1 December 2023). "The originality of devices: AI takes the Torrance Test". Journal of Creativity. 33 (3 ): 100065. doi:10.1016/ j.yjoc.2023.100065. ISSN 2713-3745. S2CID 261087185. ^ Arcas, Blaise Agüera y (10 October 2023). "Artificial General Intelligence Is Already Here". Noema. ^ Zia, Tehseen (8 January 2024). "Unveiling of Large Multimodal Models: Shaping the Landscape of Language Models in 2024". Unite.ai. Retrieved 26 May 2024. ^ "Introducing OpenAI o1-preview". OpenAI. 12 September 2024. ^ Knight, Will. "OpenAI Announces a 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 original on 29 August 2022. Retrieved 29 August 2022. ^ Pandey, Mohit; Fernandez, Michael; Gentile, Francesco; Isayev, Olexandr; Tropsha, Alexander; Stern, Abraham C.; Cherkasov, Artem (March 2022). "The transformational role of GPU computing and deep knowing in drug discovery". Nature Machine Intelligence. 4 (3 ): 211-221. doi:10.1038/ s42256-022-00463-x. ISSN 2522-5839. S2CID 252081559. ^ Goertzel & Pennachin 2006. ^ a b c (Kurzweil 2005, p. 260). ^ a b c Goertzel 2007. ^ Grace, Katja (2016 ). "Error in Armstrong and Sotala 2012". AI Impacts (blog 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 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 smart as Siri - but 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 entering the AI twilight 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 declare 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 very first example of an AGI system? This is debatable, however the agreement is that it is not AGI. ... If absolutely nothing else, GPT-3 informs us there is a happy medium between narrow and general AI. ^ Quach, Katyanna. "A developer constructed an AI chatbot utilizing GPT-3 that helped a man speak once again to his late fiancée. OpenAI shut it down". The Register. Archived from the initial on 16 October 2021. Retrieved 16 October 2021. ^ Wiggers, Kyle (13 May 2022), "DeepMind's new AI can carry out over 600 tasks, from playing video games to managing robots", TechCrunch, archived from the initial on 16 June 2022, recovered 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 Times. ISSN 0362-4331. Retrieved 7 June 2023. ^ Bove, Tristan. "A.I. might rival human intelligence in 'just a few years,' states CEO of Google's primary A.I. research laboratory". Fortune. Retrieved 4 September 2024. ^ Nellis, Stephen (2 March 2024). "Nvidia CEO states AI might pass human tests in 5 years". Reuters. ^ Aschenbrenner, Leopold. "SITUATIONAL AWARENESS, The Decade Ahead". ^ Sullivan, Mark (18 October 2023). "Why everyone appears to disagree on how to define Artificial General Intelligence". Fast Company. ^ Nosta, John (5 January 2024). "The Accelerating Path to Artificial General Intelligence". Psychology Today. Retrieved 30 March 2024. ^ Hickey, Alex. "Whole Brain Emulation: A Giant Step for Neuroscience". Tech Brew. Retrieved 8 November 2023. ^ Sandberg & Boström 2008. ^ Drachman 2005. ^ a b Russell & Norvig 2003. ^ Moravec 1988, p. 61. ^ Moravec 1998. ^ Holmgaard Mersh, Amalie (15 September 2023). "Decade-long European research study task 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 vast array of views in current research study, all of which require grounding to some degree ^ Thornton, Angela (26 June 2023). "How submitting our minds to a computer system might end up being 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 estimate 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 expert system for curiosity by the field about why a program behaves the method it does. ^ Chalmers, David J. (9 August 2023). "Could a Big Language Model Be Conscious?". Boston Review. ^ Seth, Anil. "Consciousness". New Scientist. Retrieved 5 September 2024. ^ Nagel 1974. ^ "The Google engineer who believes the company's AI has actually come to life". The Washington Post. 11 June 2022. Retrieved 12 June 2023. ^ Kateman, Brian (24 July 2023). "AI Should Be Terrified of Humans". TIME. Retrieved 5 September 2024. ^ Nosta, John (18 December 2023). "Should Artificial Intelligence Have Rights?". Psychology Today. Retrieved 5 September 2024. ^ Akst, Daniel (10 April 2023). "Should Robots With Artificial Intelligence Have Moral or Legal Rights?". The Wall Street Journal. ^ "Artificial General Intelligence - Do [es] the cost surpass benefits?". 23 August 2021. Retrieved 7 June 2023. ^ "How we can Take advantage of 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: paths, risks, methods (Reprinted with corrections 2017 ed.). Oxford, United Kingdom; New York, New York, USA: Oxford University Press. ISBN 978-0-1996-7811-2. ^ Piper, Kelsey (19 November 2018). "How technological development is making it likelier than ever that humans will ruin ourselves". Vox. Retrieved 8 June 2023. ^ Doherty, Ben (17 May 2018). "Climate change an 'existential security danger' to Australia, Senate inquiry states". The Guardian. ISSN 0261-3077. Retrieved 16 July 2023. ^ MacAskill, William (2022 ). What we owe the future. New York City, 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 expert system - 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 fascinating Facebook argument between Yann LeCun, Stuart Russel and Yoshua Bengio about the threats of strong AI". The remarkable Facebook argument between Yann LeCun, Stuart Russel and Yoshua Bengio about the risks 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 disastrous 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 dangers spiralling out of control, report warns". Financial Times. Archived from the initial on 11 April 2022. Retrieved 24 December 2023. ^ Milmo, Dan; Stacey, Kiran (25 September 2023). "Experts disagree over danger positioned but expert system can not be disregarded". The Guardian. ISSN 0261-3077. Retrieved 24 December 2023. ^ "Humanity, Security & AI, Oh My! (with Ian Bremmer & Shuman Ghosemajumder)". CAFE. 20 July 2023. Retrieved 15 September 2023. ^ Hamblin, James (9 May 2014). "But What Would 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 stiring fears over AI, alert scientists". The Telegraph. Retrieved 7 December 2023. ^ Davidson, John (30 October 2023). "Google Brain founder states huge tech is lying about AI extinction threat". Australian Financial Review. Archived from the original on 7 December 2023. Retrieved 7 December 2023. ^ Eloundou, Tyna; Manning, Sam; Mishkin, Pamela; Rock, Daniel (17 March 2023). "GPTs are GPTs: An early look at the labor market impact potential of big language models". OpenAI. Retrieved 7 June 2023. ^ a b Hurst, Luke (23 March 2023). "OpenAI says 80% of workers 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 income since '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), "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: Artificial Intelligence and Japan's Computer Challenge to the World, Michael Joseph, ISBN 978-0-7181-2401-4. Goertzel, Ben; Pennachin, Cassio, eds. (2006 ), Artificial General Intelligence (PDF), Springer, ISBN 978-3-5402-3733-4, archived from the initial (PDF) on 20 March 2013. Goertzel, Ben (December 2007), "Human-level synthetic basic intelligence and the possibility of a technological singularity: a reaction to Ray Kurzweil's The Singularity Is Near, and McDermott's critique of Kurzweil", Expert system, 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, obtained 30 August 2007. Johnson, Mark (1987 ), The body in the mind, Chicago, ISBN 978-0-2264-0317-5. Kurzweil, Ray (2005 ), The Singularity is Near, Viking Press. Lighthill, Professor Sir James (1973 ), "Artificial Intelligence: A General Survey", Expert System: a paper seminar, 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 Artificial Intelligence?. Stanford University. The supreme effort is to make computer programs that can fix issues and accomplish goals on the planet along with people. Moravec, Hans (1988 ), Mind Children, Harvard University Press Moravec, Hans (1998 ), "When will computer hardware match the human brain?", Journal of Evolution and Technology, vol. 1, archived from the initial on 15 June 2006, obtained 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 Technology as Empirical Inquiry: Symbols and Search". Communications of the ACM. 19 (3 ): 113-126. doi:10.1145/ 360018.360022. Nilsson, Nils (1998 ), Expert System: A New Synthesis, Morgan Kaufmann Publishers, ISBN 978-1-5586-0467-4 NRC (1999 ), "Developments in Artificial Intelligence", Funding a Transformation: Government Support for Computing Research, National Academy Press, archived from the original on 12 January 2008, obtained 29 September 2007 Poole, David; Mackworth, Alan; Goebel, Randy (1998 ), Computational Intelligence: A Sensible Approach, New York: Oxford University Press, archived from the initial on 25 July 2009, obtained 6 December 2007 Russell, Stuart J.; Norvig, Peter (2003 ), Expert System: A Modern Approach (second ed.), Upper Saddle River, New Jersey: Prentice Hall, ISBN 0-13-790395-2 Sandberg, Anders; Boström, Nick (2008 ), Whole Brain Emulation: A Roadmap (PDF), Technical Report # 2008-3, Future of Humanity Institute, Oxford University, archived (PDF) from the initial on 25 March 2020, recovered 5 April 2009 Searle, John (1980 ), "Minds, Brains and Programs" (PDF), Behavioral and Brain Sciences, 3 (3 ): 417-457, doi:10.1017/ S0140525X00005756, S2CID 55303721, archived (PDF) from the initial on 17 March 2019, retrieved 3 September 2020 Simon, H. A. (1965 ), The Shape of Automation for Men and Management, New York: Harper & Row Turing, Alan (October 1950). "Computing Machinery and Intelligence". Mind. 59 (236 ): 433-460. doi:10.1093/ mind/LIX.236.433. ISSN 1460-2113. JSTOR 2251299. S2CID 14636783.
de Vega, Manuel; Glenberg, Arthur; Graesser, Arthur, eds. (2008 ), Symbols and Embodiment: Debates on 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 initial 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 varieties 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, obtained 4 September 2013 - via ResearchGate Berglas, Anthony (January 2012) [2008], Artificial Intelligence Will Kill Our Grandchildren (Singularity), archived from the original on 23 July 2014, recovered 31 August 2012 Cukier, Kenneth, "Ready for Robots? How to Think of the Future of AI", Foreign Affairs, vol. 98, no. 4 (July/August 2019), pp. 192-98. George Dyson, historian of computing, composes (in what may be called "Dyson's Law") that "Any system basic enough to be reasonable will not be complicated enough to behave intelligently, while any system complicated enough to behave smartly will be too complicated to comprehend." (p. 197.) Computer researcher Alex Pentland writes: "Current AI machine-learning algorithms are, at their core, dead basic silly. They work, but they work by strength." (p. 198.). Gelernter, David, Dream-logic, the Internet and Artificial Thought, Edge, archived from the initial on 26 July 2010, retrieved 25 July 2010. Gleick, James, "The Fate of Free Choice" (review of Kevin J. Mitchell, Free Agents: How Evolution Gave Us Free Choice, Princeton University Press, 2023, 333 pp.), The New York City 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 come from acting in the world and experiencing the effects. Expert systems - disembodied, complete strangers to blood, sweat, and tears - have no event 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: 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 anticipate that those who wish to get rich 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 finance contributions [from tech business] to press back.' ... Marcus details the demands that people must make from their federal governments and the tech business. They consist of transparency on how AI systems work; payment for individuals if their information [are] utilized to train LLMs (large language model) s and the right to approval to this use; and the capability to hold tech companies responsible for the harms they bring on by eliminating Section 230, enforcing money penalites, and passing stricter item liability laws ... Marcus likewise recommends ... that a new, AI-specific federal agency, similar to the FDA, the FCC, or the FTC, qoocle.com may supply the most robust oversight ... [T] he Fordham law teacher Chinmayi Sharma ... recommends ... develop [ing] an expert licensing routine for engineers that would operate in a comparable way to medical licenses, malpractice fits, and the Hippocratic oath in medication. 'What if, like doctors,' she asks ..., 'AI engineers likewise vowed to do no harm?'" (p. 46.). Holte, R. C.; Choueiry, B. Y. (2003 ), "Abstraction and reformulation in artificial 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 actually puzzled people for years, exposes the restrictions of natural-language-processing algorithms", Scientific American, vol. 329, no. 4 (November 2023), pp. 81-82. "This murder mystery competitors has revealed that although NLP (natural-language processing) designs are capable of incredible feats, their capabilities are really much restricted by the quantity of context they receive. This [...] might trigger [troubles] for scientists who hope to use them to do things such as evaluate ancient languages. In some cases, there are few historical records on long-gone civilizations to act as training data for such a function." (p. 82.). Immerwahr, Daniel, "Your Lying Eyes: People now use A.I. to generate fake videos identical from real ones. How much does it matter?", The New Yorker, 20 November 2023, pp. 54-59. "If by 'deepfakes' we suggest sensible videos produced utilizing synthetic intelligence that in fact deceive individuals, then they barely exist. The fakes aren't deep, and the deeps aren't fake. [...] A.I.-generated videos are not, in general, operating in our media as counterfeited evidence. Their role much better looks like that of animations, specifically smutty ones." (p. 59.). - Leffer, Lauren, "The Risks of Trusting AI: We need to avoid humanizing machine-learning models 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 discussion?", The New Yorker, 7 October 2024, pp. 12-16. Marcus, Gary, "Artificial Confidence: Even the most recent, buzziest systems of synthetic general intelligence are stymmied by the usual issues", Scientific American, vol. 327, no. 4 (October 2022), pp. 42-45. McCarthy, John (October 2007), "From here to human-level AI", Artificial Intelligence, 171 (18 ): 1174-1182, doi:10.1016/ j.artint.2007.10.009. McCorduck, Pamela (2004 ), Machines Who Think (second ed.), Natick, Massachusetts: A. K. Peters, ISBN 1-5688-1205-1. Moravec, Hans (1976 ), The Role of Raw Power in Intelligence, archived from the original on 3 March 2016, obtained 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, presented and dispersed at the 2007 Singularity Summit, San Francisco, California. Press, Eyal, "In Front of Their Faces: Does facial-recognition technology lead police to proof?", The New Yorker, 20 November 2023, pp. 20-26. Roivainen, Eka, "AI's IQ: ChatGPT aced a [basic intelligence] test but showed that intelligence can not be measured by IQ alone", Scientific American, vol. 329, no. 1 (July/August 2023), p. 7. "Despite its high IQ, ChatGPT stops working at tasks that require genuine humanlike reasoning or an understanding of the physical and social world ... ChatGPT seemed not able to reason realistically and tried to count on its large 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 innovations are effective however unreliable. Rules-based systems can not handle circumstances their programmers did not expect. Learning systems are restricted by the data on which they were trained. AI failures have already resulted in tragedy. Advanced autopilot functions in cars, although they perform well in some scenarios, have actually driven cars without warning into trucks, concrete barriers, and parked cars. In the wrong circumstance, AI systems go from supersmart to superdumb in an instant. When an opponent is trying to control and hack an AI system, the risks 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 but rely on the timelelss human propensity to anthropomorphise." (p. 29.). Williams, R. W.; Herrup, K.