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

Creating AGI is a main goal of AI research and of business such as OpenAI [2] and Meta. [3] A 2020 study determined 72 active AGI research study and advancement projects across 37 nations. [4]
The timeline for achieving AGI remains a topic of ongoing argument among scientists and experts. Since 2023, some argue that it may be possible in years or decades; others preserve it may take a century or longer; a minority believe it might never ever be accomplished; and another minority claims that it is currently here. [5] [6] Notable AI scientist Geoffrey Hinton has revealed issues about the rapid development towards AGI, recommending it might be achieved faster than many expect. [7]
There is debate on the precise definition of AGI and regarding whether modern-day big language designs (LLMs) such as GPT-4 are early forms of AGI. [8] AGI is a common subject in sci-fi and futures studies. [9] [10]
Contention exists over whether AGI represents an existential threat. [11] [12] [13] Many specialists on AI have stated that alleviating the danger of human termination posed by AGI must be an international top priority. [14] [15] Others discover the advancement of AGI to be too remote to provide such a risk. [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 basic smart action. [21]
Some scholastic sources reserve 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 resolve one specific issue but does not have general cognitive capabilities. [22] [19] Some scholastic sources utilize "weak AI" to refer more broadly to any programs that neither experience awareness nor have a mind in the exact same sense as human beings. [a]
Related principles consist of synthetic superintelligence and transformative AI. An artificial superintelligence (ASI) is a theoretical type of AGI that is much more normally intelligent than people, [23] while the concept of transformative AI relates to AI having a large influence on society, for example, similar to the farming or industrial revolution. [24]
A structure for classifying AGI in levels was proposed in 2023 by Google DeepMind scientists. They specify five levels of AGI: emerging, competent, expert, virtuoso, and superhuman. For instance, a proficient AGI is specified as an AI that outperforms 50% of proficient adults in a vast array of non-physical tasks, and a superhuman AGI (i.e. a synthetic superintelligence) is likewise specified however with a limit of 100%. They consider big language models like ChatGPT or LLaMA 2 to be circumstances of emerging AGI. [25]
Characteristics

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

Researchers usually hold that intelligence is needed to do all of the following: [27]
factor, use strategy, fix puzzles, and make judgments under unpredictability represent understanding, including sound judgment understanding strategy learn

• interact in natural language
• if required, integrate these abilities in completion of any offered objective

Many interdisciplinary techniques (e.g. cognitive science, computational intelligence, and choice making) think about extra qualities such as creativity (the ability to form novel psychological images and principles) [28] and autonomy. [29]
Computer-based systems that show many of these abilities exist (e.g. see computational creativity, automated reasoning, choice support group, robot, evolutionary calculation, intelligent representative). There is dispute about whether contemporary AI systems possess them to an appropriate degree.

Physical qualities

Other abilities are thought about desirable in intelligent systems, as they may affect intelligence or aid in its expression. These include: [30]
- the capability to sense (e.g. see, hear, etc), and - the ability to act (e.g. move and manipulate things, modification location to check out, etc).
This includes the capability to discover and respond to hazard. [31]
Although the capability to sense (e.g. see, hear, and so on) and the capability to act (e.g. relocation and manipulate things, modification place to explore, etc) can be preferable for some intelligent systems, [30] these physical abilities are not strictly required for an entity to qualify as AGI-particularly under the thesis that big language models (LLMs) may already be or become AGI. Even from a less positive point of view on LLMs, there is no company requirement for an AGI to have a human-like kind; 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 actually never been proscribed a specific physical embodiment and hence does not require a capability for locomotion or traditional "eyes and ears". [32]
Tests for human-level AGI

Several tests suggested to confirm human-level AGI have been thought about, consisting of: [33] [34]
The concept of the test is that the machine needs to attempt and pretend to be a male, by responding to questions put to it, and it will only pass if the pretence is reasonably convincing. A considerable part of a jury, who need to not be expert about machines, should be taken in by the pretence. [37]
AI-complete problems

A problem is informally called "AI-complete" or "AI-hard" if it is believed that in order to solve it, one would need to implement AGI, since the solution is beyond the capabilities of a purpose-specific algorithm. [47]
There are numerous issues that have actually been conjectured to require general intelligence to resolve in addition to human beings. Examples include computer system vision, natural language understanding, and dealing with unanticipated circumstances while solving any real-world issue. [48] Even a particular task like translation requires a device to check out and compose in both languages, follow the author's argument (reason), understand the context (understanding), and consistently replicate the author's initial intent (social intelligence). All of these problems require to be fixed simultaneously in order to reach human-level maker efficiency.

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

Classical AI

Modern AI research began in the mid-1950s. [50] The very first generation of AI scientists were persuaded that synthetic general intelligence was possible which it would exist in simply a few years. [51] AI leader Herbert A. Simon wrote in 1965: "makers will be capable, within twenty years, of doing any work a guy can do." [52]
Their predictions were the inspiration for Stanley Kubrick and Arthur C. Clarke's character HAL 9000, who embodied what AI scientists thought they could develop by the year 2001. AI pioneer Marvin Minsky was a consultant [53] on the task 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 creating 'expert system' will significantly be resolved". [54]
Several classical AI jobs, such as Doug Lenat's Cyc job (that began in 1984), and Allen Newell's Soar task, were directed at AGI.

However, in the early 1970s, it ended up being apparent that researchers had grossly undervalued the difficulty of the project. Funding firms became skeptical of AGI and put researchers under increasing pressure to produce helpful "applied 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 "bring on a table talk". [58] In reaction to this and the success of specialist systems, both industry and federal government pumped cash into the field. [56] [59] However, confidence in AI stunningly collapsed in the late 1980s, and the objectives of the Fifth Generation Computer Project were never satisfied. [60] For the second time in twenty years, AI researchers who forecasted the imminent accomplishment of AGI had been misinterpreted. By the 1990s, AI researchers had a reputation for making vain guarantees. They ended up being hesitant to make predictions at all [d] and prevented reference of "human level" artificial intelligence for worry of being labeled "wild-eyed dreamer [s]. [62]
Narrow AI research

In the 1990s and early 21st century, mainstream AI achieved business success and scholastic respectability by focusing on specific sub-problems where AI can produce proven results and industrial applications, such as speech recognition and recommendation algorithms. [63] These "applied AI" systems are now used thoroughly throughout the innovation industry, and research study in this vein is heavily moneyed in both academic community and market. Since 2018 [update], advancement in this field was thought about an emerging trend, and a mature phase was anticipated to be reached in more than ten years. [64]
At the turn of the century, numerous traditional AI researchers [65] hoped that strong AI might be established by combining programs that resolve various sub-problems. Hans Moravec wrote in 1988:

I am positive that this bottom-up path to expert system will one day fulfill the conventional top-down route majority way, all set to offer the real-world proficiency and the commonsense understanding that has actually been so frustratingly elusive in thinking programs. Fully smart machines will result when the metaphorical golden spike is driven unifying the two efforts. [65]
However, even at the time, this was challenged. For instance, Stevan Harnad of Princeton University concluded his 1990 paper on the symbol grounding hypothesis by mentioning:

The expectation has typically been voiced that "top-down" (symbolic) approaches to modeling cognition will somehow satisfy "bottom-up" (sensory) approaches someplace in between. If the grounding factors to consider in this paper are valid, then this expectation is hopelessly modular and there is really just one feasible 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 ought to even try to reach such a level, because it appears arriving would just amount to uprooting our signs from their intrinsic meanings (consequently simply lowering ourselves to the functional equivalent of a programmable computer system). [66]
Modern synthetic general intelligence research study

The term "artificial basic intelligence" was utilized as early as 1997, by Mark Gubrud [67] in a conversation 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 representative maximises "the capability to please objectives in a wide variety of environments". [68] This kind of AGI, defined by the capability to increase a mathematical definition of intelligence instead of display human-like behaviour, [69] was likewise called universal expert system. [70]
The term AGI was re-introduced and popularized by Shane Legg and Ben Goertzel around 2002. [71] AGI research study activity in 2006 was explained by Pei Wang and Ben Goertzel [72] as "producing publications and initial results". The first summer season 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 provided a course on AGI in 2018, arranged by Lex Fridman and featuring a variety of guest speakers.

As of 2023 [upgrade], a little number of computer scientists are active in AGI research, and lots of add to a series of AGI conferences. However, progressively more scientists are interested in open-ended knowing, [76] [77] which is the concept of permitting AI to continually find out and innovate like human beings do.

Feasibility

As of 2023, the advancement and possible achievement of AGI stays a topic of intense dispute within the AI community. While conventional consensus held that AGI was a remote objective, current improvements have actually led some scientists and market figures to declare that early forms 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 man can do". This forecast stopped working to come real. Microsoft co-founder Paul Allen believed that such intelligence is unlikely in the 21st century since it would need "unforeseeable and fundamentally unforeseeable breakthroughs" and a "scientifically deep understanding of cognition". [79] Writing in The Guardian, roboticist Alan Winfield declared the gulf between modern computing and human-level artificial intelligence is as wide as the gulf in between present space flight and practical faster-than-light spaceflight. [80]
A more difficulty is the lack of clarity in defining what intelligence entails. Does it require consciousness? Must it display the ability to set goals as well as 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 needed? Does intelligence need clearly reproducing the brain and its specific professors? Does it require feelings? [81]
Most AI scientists believe 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 amongst those who think human-level AI will be achieved, but that today level of progress is such that a date can not accurately be forecasted. [84] AI specialists' views on the expediency of AGI wax and subside. Four polls carried out in 2012 and 2013 recommended that the mean estimate among specialists for when they would be 50% confident AGI would get here was 2040 to 2050, depending upon the survey, with the mean being 2081. Of the professionals, 16.5% answered with "never ever" when asked the very same concern however with a 90% confidence rather. [85] [86] Further current AGI development considerations can be found above Tests for verifying human-level AGI.

A report by Stuart Armstrong and Kaj Sotala of the Machine Intelligence Research Institute found that "over [a] 60-year time frame there is a strong bias towards anticipating the arrival of human-level AI as between 15 and 25 years from the time the forecast was made". They examined 95 forecasts made in between 1950 and 2012 on when human-level AI will come about. [87]
In 2023, Microsoft researchers published a detailed evaluation of GPT-4. They concluded: "Given the breadth and depth of GPT-4's capabilities, our company believe that it could fairly be considered as an early (yet still incomplete) variation of an artificial general intelligence (AGI) system." [88] Another research study in 2023 reported that GPT-4 exceeds 99% of human beings on the Torrance tests of creativity. [89] [90]
Blaise Agüera y Arcas and Peter Norvig wrote in 2023 that a substantial level of basic intelligence has already been achieved with frontier models. They composed that unwillingness to this view originates from four main reasons: a "healthy suspicion about metrics for AGI", an "ideological dedication to alternative AI theories or techniques", a "devotion to human (or biological) exceptionalism", or a "issue about the financial implications of AGI". [91]
2023 also marked the emergence of big multimodal designs (large language designs efficient in processing or generating numerous methods such as text, audio, and images). [92]
In 2024, OpenAI released o1-preview, the first of a series of models that "invest more time thinking before they react". According to Mira Murati, this ability to believe before responding represents a new, additional paradigm. It enhances design outputs by spending more computing power when producing the answer, whereas the model scaling paradigm improves outputs by increasing the model size, training data and training calculate power. [93] [94]
An OpenAI worker, Vahid Kazemi, claimed in 2024 that the company had actually accomplished AGI, specifying, "In my viewpoint, we have currently attained AGI and it's a lot more clear with O1." Kazemi clarified that while the AI is not yet "better than any human at any job", it is "much better than many people at most tasks." He likewise dealt with criticisms that big language models (LLMs) merely follow predefined patterns, comparing their knowing process to the scientific technique of observing, hypothesizing, and verifying. These statements have sparked debate, as they count on a broad and unconventional meaning of AGI-traditionally comprehended as AI that matches human intelligence across all domains. Critics argue that, while OpenAI's models show exceptional flexibility, they might not fully meet this requirement. Notably, Kazemi's comments came soon after OpenAI removed "AGI" from the terms of its partnership with Microsoft, triggering speculation about the company's strategic intents. [95]
Timescales

Progress in artificial intelligence has traditionally gone through durations of rapid development separated by periods when progress appeared to stop. [82] Ending each hiatus were basic advances in hardware, software or both to develop area for additional development. [82] [98] [99] For instance, the computer hardware readily available in the twentieth century was not sufficient to carry out deep knowing, which requires great deals of GPU-enabled CPUs. [100]
In the intro to his 2006 book, [101] Goertzel states that quotes of the time required before a truly versatile AGI is constructed differ from 10 years to over a century. As of 2007 [update], the consensus in the AGI research study community seemed 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 possible. [103] Mainstream AI researchers have given a vast array of opinions on whether progress will be this rapid. A 2012 meta-analysis of 95 such viewpoints discovered a predisposition towards anticipating that the onset of AGI would happen within 16-26 years for contemporary and historic forecasts alike. That paper has actually been criticized for how it classified opinions as professional or non-expert. [104]
In 2012, Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton developed a neural network called AlexNet, which won the ImageNet competition with a top-5 test error rate of 15.3%, significantly much better than the second-best entry's rate of 26.3% (the conventional technique used a weighted amount of ratings from various pre-defined classifiers). [105] AlexNet was considered as the preliminary ground-breaker of the existing deep knowing wave. [105]
In 2017, scientists Feng Liu, Yong Shi, and Ying Liu performed intelligence tests on publicly readily available and easily accessible weak AI such as Google AI, Apple's Siri, and others. At the maximum, these AIs reached an IQ worth of about 47, which corresponds roughly to a six-year-old kid in very first grade. A grownup pertains to about 100 typically. Similar tests were brought out in 2014, with the IQ rating reaching an optimum value of 27. [106] [107]
In 2020, OpenAI established GPT-3, a language design capable of carrying out numerous varied jobs without specific training. According to Gary Grossman in a VentureBeat short article, while there is agreement that GPT-3 is not an example of AGI, it is considered by some to be too advanced to be categorized as a narrow AI system. [108]
In the very same year, Jason Rohrer used his GPT-3 account to develop a chatbot, and offered a chatbot-developing platform called "Project December". OpenAI asked for modifications to the chatbot to comply with their security guidelines; Rohrer detached Project December from the GPT-3 API. [109]
In 2022, DeepMind developed Gato, a "general-purpose" system efficient in performing more than 600 different jobs. [110]
In 2023, Microsoft Research released a research study on an early variation of OpenAI's GPT-4, competing that it displayed more general intelligence than previous AI models and showed human-level performance in jobs covering multiple domains, such as mathematics, coding, and law. This research triggered an argument on whether GPT-4 could be considered an early, incomplete version of artificial general intelligence, emphasizing the need for additional expedition and assessment of such systems. [111]
In 2023, the AI scientist Geoffrey Hinton stated that: [112]
The concept that this stuff could actually get smarter than individuals - a few people thought that, [...] But many people believed it was method off. And I believed it was way off. I thought it was 30 to 50 years or perhaps longer away. Obviously, I no longer believe that.

In May 2023, Demis Hassabis similarly stated that "The development in the last couple of years has been quite incredible", and that he sees no reason it would decrease, anticipating AGI within a decade or even a couple of years. [113] In March 2024, Nvidia's CEO, Jensen Huang, stated his expectation that within five years, AI would be capable of passing any test at least as well as human beings. [114] In June 2024, the AI researcher Leopold Aschenbrenner, a former OpenAI employee, approximated AGI by 2027 to be "strikingly plausible". [115]
Whole brain emulation

While the advancement of transformer models like in ChatGPT is considered the most promising path to AGI, [116] [117] whole brain emulation can act as an alternative method. With whole brain simulation, a brain design is developed by scanning and mapping a biological brain in detail, and after that copying and mimicing it on a computer system or another computational device. The simulation model need to be adequately loyal to the original, so that it behaves in practically the very same way as the original brain. [118] Whole brain emulation is a kind of brain simulation that is gone over in computational neuroscience and neuroinformatics, and for medical research study functions. It has been gone over in expert system research [103] as an approach to strong AI. Neuroimaging technologies that might provide the required in-depth understanding are improving rapidly, and futurist Ray Kurzweil in the book The Singularity Is Near [102] forecasts that a map of sufficient quality will become offered on a comparable timescale to the computing power required to emulate it.

Early approximates

For low-level brain simulation, an extremely effective cluster of computers or GPUs would be required, offered the enormous amount of synapses within the human brain. Each of the 1011 (one hundred billion) neurons has on typical 7,000 synaptic connections (synapses) to other nerve cells. The brain of a three-year-old child has about 1015 synapses (1 quadrillion). This number declines with age, stabilizing by their adult years. Estimates differ for an adult, varying from 1014 to 5 × 1014 synapses (100 to 500 trillion). [120] A quote of the brain's processing power, based on an easy switch design for nerve cell activity, is around 1014 (100 trillion) synaptic updates per second (SUPS). [121]
In 1997, Kurzweil took a look at numerous quotes for the hardware needed to equal the human brain and embraced a figure of 1016 computations per second (cps). [e] (For contrast, if a "computation" was equivalent to one "floating-point operation" - a procedure used to rate current supercomputers - then 1016 "computations" would be equivalent to 10 petaFLOPS, accomplished in 2011, while 1018 was attained in 2022.) He utilized this figure to anticipate the necessary hardware would be readily available sometime between 2015 and 2025, if the rapid development in computer system power at the time of composing continued.

Current research

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

Criticisms of simulation-based methods

The synthetic neuron model presumed by Kurzweil and used in lots of current synthetic neural network executions is simple compared with biological nerve cells. A brain simulation would likely need to catch the comprehensive cellular behaviour of biological nerve cells, presently comprehended only in broad outline. The overhead introduced by complete modeling of the biological, chemical, and physical details of neural behaviour (particularly on a molecular scale) would need computational powers numerous orders of magnitude bigger than Kurzweil's estimate. In addition, the price quotes do not represent glial cells, which are understood to play a role in cognitive procedures. [125]
A basic criticism of the simulated brain method obtains from embodied cognition theory which asserts that human personification is a vital aspect of human intelligence and is required to ground significance. [126] [127] If this theory is appropriate, any completely practical brain design will require to encompass more than just the nerve cells (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 enough.

Philosophical point of view

"Strong AI" as specified in philosophy

In 1980, philosopher John Searle coined the term "strong AI" as part of his Chinese room argument. [128] He proposed a distinction in between 2 hypotheses about artificial intelligence: [f]
Strong AI hypothesis: An expert system system can have "a mind" and "awareness". Weak AI hypothesis: An artificial intelligence system can (only) act like it believes and has a mind and consciousness.
The very first one he called "strong" since it makes a stronger statement: it assumes something special has taken place to the maker that surpasses those capabilities that we can test. The behaviour of a "weak AI" maker would be specifically identical to a "strong AI" machine, but the latter would likewise have subjective conscious experience. This use is likewise common in scholastic AI research study and textbooks. [129]
In contrast to Searle and traditional AI, some futurists such as Ray Kurzweil utilize the term "strong AI" to mean "human level artificial general intelligence". [102] This is not the exact same as Searle's strong AI, unless it is assumed that consciousness is needed for human-level AGI. Academic theorists such as Searle do not think that is the case, 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 real or a simulation." [130] If the program can act as if it has a mind, then there is no requirement to understand if it really has mind - certainly, there would be no chance to tell. For AI research, Searle's "weak AI hypothesis" is equivalent to the statement "artificial general intelligence is possible". Thus, according to Russell and Norvig, "most AI researchers take the weak AI hypothesis for granted, and don't care about the strong AI hypothesis." [130] Thus, for academic AI research study, "Strong AI" and "AGI" are two different things.

Consciousness

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

Sentience (or "sensational awareness"): The ability to "feel" understandings or feelings subjectively, instead of the capability to reason about understandings. Some philosophers, such as David Chalmers, utilize the term "consciousness" to refer exclusively to phenomenal awareness, which is roughly comparable to life. [132] Determining why and how subjective experience emerges is called the tough issue of consciousness. [133] Thomas Nagel discussed in 1974 that it "seems like" something to be mindful. If we are not conscious, then it doesn't seem like anything. Nagel uses the example of a bat: we can sensibly 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 conscious (i.e., has awareness) however a toaster does not. [134] In 2022, a Google engineer declared that the company's AI chatbot, LaMDA, had actually accomplished sentience, though this claim was widely challenged by other experts. [135]
Self-awareness: To have mindful awareness of oneself as a different person, specifically to be knowingly conscious of one's own thoughts. This is opposed to simply being the "subject of one's believed"-an os or debugger has the ability to be "mindful of itself" (that is, to represent itself in the same way it represents whatever else)-however this is not what people typically mean when they utilize the term "self-awareness". [g]
These characteristics have an ethical dimension. AI sentience would generate issues of well-being and legal protection, likewise to animals. [136] Other aspects of awareness related to cognitive abilities are likewise pertinent to the idea of AI rights. [137] Finding out how to integrate advanced AI with existing legal and social structures is an emerging concern. [138]
Benefits

AGI might have a wide range of applications. If oriented towards such goals, AGI might assist reduce various problems worldwide such as cravings, hardship and illness. [139]
AGI might improve productivity and efficiency in many tasks. For example, in public health, AGI could accelerate medical research study, significantly against cancer. [140] It could take care of the elderly, [141] and democratize access to quick, top quality medical diagnostics. It could offer enjoyable, inexpensive and tailored education. [141] The requirement to work to subsist could end up being obsolete if the wealth produced is correctly rearranged. [141] [142] This also raises the concern of the location of human beings in a radically automated society.

AGI might likewise help to make rational choices, and to expect and avoid disasters. It could likewise help to enjoy the advantages of possibly catastrophic innovations such as nanotechnology or climate engineering, while avoiding the associated risks. [143] If an AGI's primary objective is to prevent existential disasters such as human extinction (which could be challenging if the Vulnerable World Hypothesis turns out to be true), [144] it might take measures to significantly minimize the threats [143] while lessening the effect of these steps on our lifestyle.

Risks

Existential dangers

AGI may represent several types of existential danger, which are dangers that threaten "the early extinction of Earth-originating intelligent life or the irreversible and drastic damage of its potential for desirable future advancement". [145] The risk of human extinction from AGI has actually been the subject of lots of arguments, but there is also the possibility that the advancement of AGI would lead to a permanently problematic future. Notably, it could be utilized to spread and preserve the set of worths of whoever develops it. If humanity still has ethical blind spots comparable to slavery in the past, AGI might irreversibly entrench it, preventing ethical progress. [146] Furthermore, AGI might facilitate mass surveillance and indoctrination, which might be utilized to develop a stable repressive worldwide totalitarian routine. [147] [148] There is likewise a danger for the makers themselves. If machines that are sentient or otherwise deserving of moral consideration are mass developed in the future, participating in a civilizational path that forever neglects their well-being and interests could be an existential disaster. [149] [150] Considering just how much AGI could improve humanity's future and assistance decrease other existential risks, Toby Ord calls these existential threats "an argument for continuing with due caution", not for "deserting AI". [147]
Risk of loss of control and human extinction

The thesis that AI positions an existential threat for people, which this threat needs 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 extensive indifference:

So, facing possible futures of incalculable advantages and threats, the specialists are undoubtedly doing everything possible to guarantee the best result, right? Wrong. If a superior alien civilisation sent us a message saying, 'We'll arrive in a couple of years,' would we simply respond, 'OK, call us when you get here-we'll leave the lights on?' Probably not-but this is basically what is occurring with AI. [153]
The possible fate of humankind has often been compared to the fate of gorillas threatened by human activities. The contrast states that greater intelligence permitted humankind to dominate gorillas, which are now vulnerable in manner ins which they could not have actually expected. As an outcome, the gorilla has actually become a threatened species, not out of malice, however just as a civilian casualties from human activities. [154]
The skeptic Yann LeCun thinks about that AGIs will have no desire to dominate humankind which we must take care not to anthropomorphize them and interpret their intents as we would for human beings. He said that individuals will not be "smart sufficient to design super-intelligent machines, yet ridiculously stupid to the point of providing it moronic goals without any safeguards". [155] On the other side, the concept of crucial convergence suggests that nearly whatever their goals, smart representatives will have reasons to try to endure and obtain more power as intermediary steps to attaining these goals. Which this does not need having feelings. [156]
Many scholars who are concerned about existential danger supporter for more research into fixing the "control problem" to answer the concern: what types of safeguards, algorithms, or architectures can developers execute to maximise the likelihood that their recursively-improving AI would continue to behave in a friendly, rather than damaging, manner after it reaches superintelligence? [157] [158] Solving the control problem is complicated by the AI arms race (which might result in a race to the bottom of safety precautions in order to launch products before rivals), [159] and the use of AI in weapon systems. [160]
The thesis that AI can posture existential threat likewise has critics. Skeptics usually state that AGI is not likely in the short-term, or that issues about AGI distract from other problems associated with existing AI. [161] Former Google fraud czar Shuman Ghosemajumder considers that for lots of people outside of the innovation industry, existing chatbots and LLMs are already perceived as though they were AGI, causing more misunderstanding and worry. [162]
Skeptics often charge that the thesis is crypto-religious, with an illogical belief in the possibility of superintelligence replacing an unreasonable belief in a supreme God. [163] Some scientists think that the interaction campaigns on AI existential threat by particular AI groups (such as OpenAI, Anthropic, DeepMind, and Conjecture) may be an at effort at regulatory capture and to inflate interest in their items. [164] [165]
In 2023, the CEOs of Google DeepMind, OpenAI and Anthropic, in addition to other market leaders and scientists, issued a joint statement asserting that "Mitigating the danger of extinction from AI should be a global concern together with other societal-scale risks such as pandemics and nuclear war." [152]
Mass joblessness

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

According to Stephen Hawking, the outcome of automation on the lifestyle will depend on how the wealth will be redistributed: [142]
Everyone can enjoy a life of elegant leisure if the machine-produced wealth is shared, or many people can wind up miserably poor if the machine-owners effectively lobby against wealth redistribution. So far, the pattern appears to be towards the 2nd alternative, with technology driving ever-increasing inequality

Elon Musk thinks about that the automation of society will require federal governments to embrace a universal fundamental income. [168]
See also

Artificial brain - Software and hardware with cognitive abilities similar to those of the animal or human brain AI effect AI security - Research area on making AI safe and advantageous AI alignment - AI conformance to the designated objective A.I. Rising - 2018 movie directed by Lazar Bodroža Expert system Automated artificial intelligence - Process of automating the application of artificial intelligence BRAIN Initiative - Collaborative public-private research effort revealed by the Obama administration China Brain Project Future of Humanity Institute - Defunct Oxford interdisciplinary research centre General video game playing - Ability of artificial intelligence to play different video games Generative expert system - AI system efficient in creating content in response to triggers Human Brain Project - Scientific research study project Intelligence amplification - Use of infotech to enhance human intelligence (IA). Machine ethics - Moral behaviours of man-made machines. Moravec's paradox. Multi-task learning - Solving numerous maker learning tasks at the same time. Neural scaling law - Statistical law in device knowing. Outline of expert system - Overview of and topical guide to artificial intelligence. Transhumanism - Philosophical movement. Synthetic intelligence - Alternate term for or form of expert system. Transfer knowing - Artificial intelligence method. 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 listed below for the origin of the term "strong AI", and see the scholastic meaning of "strong AI" and weak AI in the short article Chinese room. ^ AI founder John McCarthy writes: "we can not yet identify in basic what type of computational treatments we want to call intelligent. " [26] (For a discussion of some definitions of intelligence utilized by expert system scientists, see viewpoint of artificial intelligence.). ^ The Lighthill report specifically criticized AI's "grand objectives" and led the dismantling of AI research in England. [55] In the U.S., DARPA ended up being determined to money just "mission-oriented direct research study, rather than basic undirected research study". [56] [57] ^ As AI founder John McCarthy writes "it would be a great relief to the remainder of the workers in AI if the innovators of new basic formalisms would reveal their hopes in a more protected type than has actually often held true." [61] ^ In "Mind Children" [122] 1015 cps is used. More just recently, in 1997, [123] Moravec argued for 108 MIPS which would roughly represent 1014 cps. Moravec talks in terms of MIPS, not "cps", which is a non-standard term Kurzweil presented. ^ As specified in a standard AI book: "The assertion that machines could possibly act smartly (or, perhaps much better, act as if they were smart) is called the 'weak AI' hypothesis by thinkers, and the assertion that machines that do so are really believing (as opposed to simulating thinking) is called the 'strong AI' hypothesis." [121] ^ Alan Turing made this point in 1950. [36] References

^ Krishna, Sri (9 February 2023). "What is artificial narrow intelligence (ANI)?". VentureBeat. Retrieved 1 March 2024. ANI is developed to perform a single task. ^ "OpenAI Charter". OpenAI. Retrieved 6 April 2023. Our mission is to guarantee that artificial basic intelligence advantages all of humankind. ^ Heath, Alex (18 January 2024). "Mark Zuckerberg's brand-new goal is producing artificial general intelligence". The Verge. Retrieved 13 June 2024. Our vision is to construct 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 tasks were identified as being active in 2020. ^ a b c "AI timelines: What do specialists in expert system expect for the future?". Our World in Data. Retrieved 6 April 2023. ^ Metz, Cade (15 May 2023). "Some Researchers Say A.I. Is Already Here, Stirring Debate in Tech Circles". The New York City Times. Retrieved 18 May 2023. ^ "AI pioneer Geoffrey Hinton gives up Google and warns of risk ahead". The New York City Times. 1 May 2023. Retrieved 2 May 2023. It is hard to see how you can avoid the bad actors from utilizing it for bad things. ^ Bubeck, Sébastien; Chandrasekaran, Varun; Eldan, Ronen; Gehrke, Johannes; Horvitz, Eric (2023 ). "Sparks of Artificial General Intelligence: Early 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 Times. The genuine risk is not AI itself but the way we deploy it. ^ "Impressed by expert system? Experts say AGI is following, and it has 'existential' risks". 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 very first superintelligence will be the last invention that humankind needs to make. ^ Roose, Kevin (30 May 2023). "A.I. Poses 'Risk of Extinction,' Industry Leaders Warn". The New York City Times. Mitigating the danger of termination from AI must be a global top priority. ^ "Statement on AI Risk". Center for AI Safety. Retrieved 1 March 2024. AI professionals alert of threat of extinction from AI. ^ Mitchell, Melanie (30 May 2023). "Are AI's Doomsday Scenarios Worth Taking Seriously?". The New York Times. We are far from developing machines that can outthink us in general methods. ^ LeCun, Yann (June 2023). "AGI does not present an existential threat". Medium. There is no reason to fear AI as an existential danger. ^ Kurzweil 2005, p. 260. ^ a b Kurzweil, Ray (5 August 2005), "Long Live AI", Forbes, archived from the initial on 14 August 2005: Kurzweil describes strong AI as "device intelligence with the full range 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 utilize for "human-level" intelligence in the physical sign system hypothesis. ^ "The Open University on Strong and Weak AI". Archived from the original on 25 September 2009. Retrieved 8 October 2007. ^ "What is artificial superintelligence (ASI)?|Definition from TechTarget". Enterprise AI. Retrieved 8 October 2023. ^ "Expert system is changing our world - it is on all of us to ensure that it works out". Our World in Data. Retrieved 8 October 2023. ^ Dickson, Ben (16 November 2023). "Here is how far we are to accomplishing AGI, according to DeepMind". VentureBeat. ^ McCarthy, John (2007a). "Basic Questions". Stanford University. Archived from the initial on 26 October 2007. Retrieved 6 December 2007. ^ This list of smart qualities is based upon the subjects covered by major AI books, consisting of: Russell & Norvig 2003, Luger & Stubblefield 2004, Poole, Mackworth & Goebel 1998 and Nilsson 1998. ^ Johnson 1987. ^ de Charms, R. (1968 ). Personal causation. New York: Academic Press. ^ a b Pfeifer, R. and Bongard J. C., How the body shapes the way we think: a new view of intelligence (The MIT Press, 2007). ISBN 0-2621-6239-3. ^ White, R. W. (1959 ). "Motivation reevaluated: The principle of skills". Psychological Review. 66 (5 ): 297-333. doi:10.1037/ h0040934. PMID 13844397. S2CID 37385966. ^ White, R. W. (1959 ). "Motivation reassessed: The idea of skills". Psychological Review. 66 (5 ): 297-333. doi:10.1037/ h0040934. PMID 13844397. S2CID 37385966. ^ Muehlhauser, Luke (11 August 2013). "What is AGI?". Machine Intelligence Research Institute. Archived from the original on 25 April 2014. Retrieved 1 May 2014. ^ "What is Artificial General Intelligence (AGI)?|4 Tests For Ensuring Artificial General Intelligence". Talky Blog. 13 July 2019. Archived from the 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 occurs when it does?". The Conversation. Retrieved 22 September 2024. ^ a b Turing 1950. ^ Turing, Alan (1952 ). B. Jack Copeland (ed.). Can Automatic Calculating Machines Be Said To Think?. Oxford: Oxford University Press. pp. 487-506. ISBN 978-0-1982-5079-1. ^ "Eugene Goostman is a real kid - the Turing Test states so". The Guardian. 9 June 2014. ISSN 0261-3077. Retrieved 3 March 2024. ^ "Scientists challenge whether computer system 'Eugene Goostman' passed Turing test". BBC News. 9 June 2014. Retrieved 3 March 2024. ^ Jones, Cameron R.; Bergen, Benjamin K. (9 May 2024). "People can not 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 everything from the bar test to AP Biology. Here's a list of hard tests both AI versions have actually passed". Business Insider. Retrieved 30 May 2023. ^ Naysmith, Caleb (7 February 2023). "6 Jobs Expert System Is Already Replacing and How Investors Can Take Advantage Of 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 undependable. 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 evaluating an AI chatbot's ability to turn $100,000 into $1 million to determine human-like intelligence". Business Insider. Retrieved 3 March 2024. ^ Suleyman, Mustafa (14 July 2023). "Mustafa Suleyman: My new Turing test would see if AI can make $1 million". MIT Technology Review. Retrieved 3 March 2024. ^ Shapiro, Stuart C. (1992 ). "Artificial Intelligence" (PDF). In Stuart C. Shapiro (ed.). Encyclopedia of Artificial Intelligence (Second ed.). New York: John Wiley. pp. 54-57. Archived (PDF) from the initial on 1 February 2016. (Section 4 is on "AI-Complete Tasks".). ^ Yampolskiy, Roman V. (2012 ). Xin-She Yang (ed.). "Turing Test as a Defining Feature of AI-Completeness" (PDF). Expert System, Evolutionary Computation and Metaheuristics (AIECM): 3-17. Archived (PDF) from the initial on 22 May 2013. ^ "AI Index: State of AI in 13 Charts". Stanford University Human-Centered Artificial Intelligence. 15 April 2024. Retrieved 27 May 2024. ^ Crevier 1993, pp. 48-50. ^ Kaplan, Andreas (2022 ). "Expert System, Business and Civilization - Our Fate Made in Machines". Archived from the original on 6 May 2022. Retrieved 12 March 2022. ^ Simon 1965, p. 96 priced estimate in Crevier 1993, p. 109. ^ "Scientist on the Set: An Interview with Marvin Minsky". Archived from the initial on 16 July 2012. Retrieved 5 April 2008. ^ Marvin Minsky to Darrach (1970 ), quoted in Crevier (1993, p. 109). ^ Lighthill 1973; Howe 1994. ^ a b NRC 1999, "Shift to Applied Research Increases Investment". ^ Crevier 1993, pp. 115-117; Russell & Norvig 2003, pp. 21-22. ^ Crevier 1993, p. 211, Russell & Norvig 2003, p. 24 and see likewise Feigenbaum & McCorduck 1983. ^ Crevier 1993, pp. 161-162, 197-203, 240; Russell & Norvig 2003, p. 25. ^ Crevier 1993, pp. 209-212. ^ McCarthy, John (2000 ). "Respond to Lighthill". Stanford University. Archived from the initial on 30 September 2008. Retrieved 29 September 2007. ^ Markoff, John (14 October 2005). "Behind Expert system, a Squadron of Bright Real People". The New York Times. Archived from the original on 2 February 2023. Retrieved 18 February 2017. At its low point, some computer researchers and software engineers avoided the term artificial intelligence for worry of being considered as wild-eyed dreamers. ^ Russell & Norvig 2003, pp. 25-26 ^ "Trends in the Emerging Tech Hype Cycle". Gartner Reports. Archived from the 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 initial on 19 July 2022. Retrieved 19 July 2022. ^ Legg, Shane (2008 ). Machine Super Intelligence (PDF) (Thesis). University of Lugano. Archived (PDF) from the initial on 15 June 2022. Retrieved 19 July 2022. ^ Goertzel, Ben (2014 ). Artificial General Intelligence. Lecture Notes in Computer Science. Vol. 8598. Journal of Artificial General Intelligence. doi:10.1007/ 978-3-319-09274-4. ISBN 978-3-3190-9273-7. S2CID 8387410. ^ "Who coined the term "AGI"?". goertzel.org. Archived from the initial on 28 December 2018. Retrieved 28 December 2018., 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 summertime school: June 22 - July 3, 2009, OpenCog Lab: July 6-9, 2009". Archived from the initial on 28 September 2020. Retrieved 11 May 2020. ^ "Избираеми дисциплини 2009/2010 - пролетен триместър" [Elective courses 2009/2010 - spring trimester] Факултет по математика и информатика [Faculty of Mathematics and Informatics] (in Bulgarian). Archived from the original on 26 July 2020. Retrieved 11 May 2020. ^ "Избираеми дисциплини 2010/2011 - зимен триместър" [Elective courses 2010/2011 - winter season trimester] Факултет по математика и информатика [Faculty of Mathematics and Informatics] (in Bulgarian). Archived from the initial on 26 July 2020. Retrieved 11 May 2020. ^ Shevlin, Henry; Vold, Karina; Crosby, Matthew; Halina, Marta (4 October 2019). "The limitations of device intelligence: Despite development in machine intelligence, artificial general intelligence is still a major difficulty". EMBO Reports. 20 (10 ): e49177. doi:10.15252/ embr.201949177. ISSN 1469-221X. PMC 6776890. PMID 31531926. ^ Bubeck, Sébastien; Chandrasekaran, Varun; Eldan, Ronen; Gehrke, Johannes; Horvitz, Eric; Kamar, Ece; Lee, Peter; Lee, Yin Tat; Li, Yuanzhi; Lundberg, Scott; Nori, Harsha; Palangi, Hamid; Ribeiro, Marco Tulio; Zhang, Yi (27 March 2023). "Sparks of Artificial General Intelligence: Early experiments with GPT-4". arXiv:2303.12712 [cs.CL] ^ "Microsoft Researchers Claim GPT-4 Is Showing "Sparks" of AGI". Futurism. 23 March 2023. Retrieved 13 December 2023. ^ Allen, Paul; Greaves, Mark (12 October 2011). "The Singularity Isn't Near". MIT Technology Review. Retrieved 17 September 2014. ^ Winfield, Alan. "Artificial intelligence will not develop into a Frankenstein's beast". The Guardian. Archived from the initial on 17 September 2014. Retrieved 17 September 2014. ^ Deane, George (2022 ). "Machines That Feel and Think: The Role of Affective Feelings and Mental Action in (Artificial) General Intelligence". Artificial Life. 28 (3 ): 289-309. doi:10.1162/ artl_a_00368. ISSN 1064-5462. PMID 35881678. S2CID 251069071. ^ a b c Clocksin 2003. ^ Fjelland, Ragnar (17 June 2020). "Why 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 artificial intelligence bring us paradise or damage?". The New Yorker. Archived from the initial on 28 January 2016. Retrieved 7 February 2016. ^ Müller, V. C., & Bostrom, N. (2016 ). Future development in synthetic intelligence: A survey of professional 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 Test". Neuroscience News. Retrieved 20 October 2023. ^ Guzik, Erik E.; Byrge, Christian; Gilde, Christian (1 December 2023). "The creativity of makers: AI takes the Torrance Test". Journal of Creativity. 33 (3 ): 100065. doi:10.1016/ j.yjoc.2023.100065. ISSN 2713-3745. S2CID 261087185. ^ Arcas, Blaise Agüera y (10 October 2023). "Artificial General Intelligence Is Already Here". Noema. ^ Zia, Tehseen (8 January 2024). "Unveiling of Large Multimodal Models: Shaping the Landscape of Language Models in 2024". Unite.ai. Retrieved 26 May 2024. ^ "Introducing OpenAI o1-preview". OpenAI. 12 September 2024. ^ Knight, Will. "OpenAI Announces a New AI Model, Code-Named Strawberry, That Solves Difficult Problems Step by Step". Wired. ISSN 1059-1028. Retrieved 17 September 2024. ^ "OpenAI Employee Claims AGI Has Been Achieved". Orbital Today. 13 December 2024. Retrieved 27 December 2024. ^ "AI Index: State of AI in 13 Charts". hai.stanford.edu. 15 April 2024. Retrieved 7 June 2024. ^ "Next-Gen AI: OpenAI and Meta's Leap Towards Reasoning Machines". Unite.ai. 19 April 2024. Retrieved 7 June 2024. ^ James, Alex P. (2022 ). "The Why, What, and How of Artificial General Intelligence Chip Development". IEEE Transactions on Cognitive and Developmental Systems. 14 (2 ): 333-347. arXiv:2012.06338. doi:10.1109/ TCDS.2021.3069871. ISSN 2379-8920. S2CID 228376556. Archived from the original on 28 August 2022. Retrieved 28 August 2022. ^ Pei, Jing; Deng, Lei; Song, Sen; Zhao, Mingguo; Zhang, Youhui; Wu, Shuang; Wang, Guanrui; Zou, Zhe; Wu, Zhenzhi; He, Wei; Chen, Feng; Deng, Ning; Wu, Si; Wang, Yu; Wu, Yujie (2019 ). "Towards synthetic basic intelligence with hybrid Tianjic chip architecture". Nature. 572 (7767 ): 106-111. Bibcode:2019 Natur.572..106 P. doi:10.1038/ s41586-019-1424-8. ISSN 1476-4687. PMID 31367028. S2CID 199056116. Archived from the initial on 29 August 2022. Retrieved 29 August 2022. ^ Pandey, Mohit; Fernandez, Michael; Gentile, Francesco; Isayev, Olexandr; Tropsha, Alexander; Stern, Abraham C.; Cherkasov, Artem (March 2022). "The transformational role of GPU computing and deep learning in drug discovery". Nature Machine Intelligence. 4 (3 ): 211-221. doi:10.1038/ s42256-022-00463-x. ISSN 2522-5839. S2CID 252081559. ^ Goertzel & Pennachin 2006. ^ a b c (Kurzweil 2005, p. 260). ^ a b c Goertzel 2007. ^ Grace, Katja (2016 ). "Error in Armstrong and Sotala 2012". AI Impacts (blog). Archived from the initial on 4 December 2020. Retrieved 24 August 2020. ^ a b Butz, Martin V. (1 March 2021). "Towards Strong AI". KI - Künstliche Intelligenz. 35 (1 ): 91-101. doi:10.1007/ s13218-021-00705-x. ISSN 1610-1987. S2CID 256065190. ^ Liu, Feng; Shi, Yong; Liu, Ying (2017 ). "Intelligence Quotient and Intelligence Grade of Expert System". Annals of Data Science. 4 (2 ): 179-191. arXiv:1709.10242. doi:10.1007/ s40745-017-0109-0. S2CID 37900130. ^ Brien, Jörn (5 October 2017). "Google-KI doppelt so schlau wie Siri" [Google AI is twice as clever as Siri - however a six-year-old beats both] (in German). Archived from the original on 3 January 2019. Retrieved 2 January 2019. ^ Grossman, Gary (3 September 2020). "We're getting in the AI twilight zone in between narrow and general AI". VentureBeat. Archived from the original on 4 September 2020. Retrieved 5 September 2020. Certainly, too, there are those who declare we are currently 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 arguable, but the consensus is that it is not AGI. ... If nothing else, GPT-3 tells us there is a happy medium between narrow and general AI. ^ Quach, Katyanna. "A designer constructed an AI chatbot utilizing 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 brand-new AI can carry out over 600 tasks, from playing video games to managing robotics", TechCrunch, archived from the original 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 explores GPT-4". arXiv:2303.12712 [cs.CL] ^ Metz, Cade (1 May 2023). "' The Godfather of A.I.' Leaves Google and Warns of Danger Ahead". The New York City Times. ISSN 0362-4331. Retrieved 7 June 2023. ^ Bove, Tristan. "A.I. could measure up to human intelligence in 'just a couple of 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 five years". Reuters. ^ Aschenbrenner, Leopold. "SITUATIONAL AWARENESS, The Decade Ahead". ^ Sullivan, Mark (18 October 2023). "Why everyone seems to disagree on how to define Artificial General Intelligence". Fast Company. ^ Nosta, John (5 January 2024). "The Accelerating Path to Artificial General Intelligence". Psychology Today. Retrieved 30 March 2024. ^ Hickey, Alex. "Whole Brain Emulation: A Huge Step for Neuroscience". Tech Brew. Retrieved 8 November 2023. ^ Sandberg & Boström 2008. ^ Drachman 2005. ^ a b Russell & Norvig 2003. ^ Moravec 1988, p. 61. ^ Moravec 1998. ^ Holmgaard Mersh, Amalie (15 September 2023). "Decade-long European research study job maps the human brain". euractiv. ^ Swaminathan, Nikhil (January-February 2011). "Glia-the other brain cells". Discover. Archived from the original on 8 February 2014. Retrieved 24 January 2014. ^ de Vega, Glenberg & Graesser 2008. A wide variety of views in existing research study, all of which need grounding to some degree ^ Thornton, Angela (26 June 2023). "How uploading our minds to a computer may become possible". The Conversation. Retrieved 8 November 2023. ^ Searle 1980 ^ For example: 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 (estimated 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 interest by the field about why a program acts the way it does. ^ Chalmers, David J. (9 August 2023). "Could a Big Language Model Be Conscious?". Boston Review. ^ Seth, Anil. "Consciousness". New Scientist. Retrieved 5 September 2024. ^ Nagel 1974. ^ "The Google engineer who thinks 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: courses, 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 threat' 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 Expert System". The Precipice: Existential Risk and the Future of Humanity. Bloomsbury Publishing. ISBN 978-1-5266-0021-9. ^ Al-Sibai, Noor (13 February 2022). "OpenAI Chief Scientist Says Advanced AI May Already Be Conscious". Futurism. Retrieved 24 December 2023. ^ Samuelsson, Paul Conrad (2019 ). "Artificial Consciousness: Our Greatest Ethical Challenge". Philosophy Now. Retrieved 23 December 2023. ^ Kateman, Brian (24 July 2023). "AI Should Be Terrified of Humans". TIME. Retrieved 23 December 2023. ^ Roose, Kevin (30 May 2023). "A.I. Poses 'Risk of Extinction,' Industry Leaders Warn". The New York Times. ISSN 0362-4331. Retrieved 24 December 2023. ^ a b "Statement on AI Risk". Center for AI Safety. 30 May 2023. Retrieved 8 June 2023. ^ "Stephen Hawking: 'Transcendence looks at the ramifications of artificial intelligence - however are we taking AI seriously enough?'". The Independent (UK). Archived from the original on 25 September 2015. Retrieved 3 December 2014. ^ Herger, Mario. "The Gorilla Problem - Enterprise Garage". Retrieved 7 June 2023. ^ "The fascinating Facebook argument between Yann LeCun, Stuart Russel and Yoshua Bengio about the threats of strong AI". The fascinating Facebook dispute between Yann LeCun, Stuart Russel and Yoshua Bengio about the threats of strong AI (in French). Retrieved 8 June 2023. ^ "Will Expert System Doom The Mankind Within The Next 100 Years?". HuffPost. 22 August 2014. Retrieved 8 June 2023. ^ Sotala, Kaj; Yampolskiy, Roman V. (19 December 2014). "Responses to devastating AGI threat: a study". Physica Scripta. 90 (1 ): 018001. doi:10.1088/ 0031-8949/90/ 1/018001. ISSN 0031-8949. ^ Bostrom, Nick (2014 ). Superintelligence: Paths, Dangers, Strategies (First ed.). Oxford University Press. ISBN 978-0-1996-7811-2. ^ Chow, Andrew R.; Perrigo, Billy (16 February 2023). "The AI Arms Race Is On. Start Worrying". TIME. Retrieved 24 December 2023. ^ Tetlow, Gemma (12 January 2017). "AI arms race dangers spiralling out of control, report alerts". Financial Times. Archived from the original on 11 April 2022. Retrieved 24 December 2023. ^ Milmo, Dan; Stacey, Kiran (25 September 2023). "Experts disagree over hazard presented but expert system can not be neglected". The Guardian. ISSN 0261-3077. Retrieved 24 December 2023. ^ "Humanity, Security & AI, Oh My! (with Ian Bremmer & Shuman Ghosemajumder)". CAFE. 20 July 2023. Retrieved 15 September 2023. ^ Hamblin, James (9 May 2014). "But What Would completion of Humanity Mean for Me?". The Atlantic. Archived from the initial on 4 June 2014. Retrieved 12 December 2015. ^ Titcomb, James (30 October 2023). "Big Tech is stoking worries over AI, alert scientists". The Telegraph. Retrieved 7 December 2023. ^ Davidson, John (30 October 2023). "Google Brain creator says big tech is lying about AI termination danger". 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 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 might see their tasks impacted by AI. These are the tasks most affected". euronews. Retrieved 8 June 2023. ^ Sheffey, Ayelet (20 August 2021). "Elon Musk states we require universal standard earnings 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 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 Look For Artificial Intelligence. 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 general 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 initial on 7 January 2016, recovered 1 April 2009. Gubrud, Mark (November 1997), "Nanotechnology and International Security", Fifth Foresight Conference on Molecular Nanotechnology, archived from the original on 29 May 2011, obtained 7 May 2011. Howe, J. (November 1994), Expert System at Edinburgh University: a Point of view, archived from the initial on 17 August 2007, retrieved 30 August 2007. Johnson, Mark (1987 ), The body in the mind, Chicago, ISBN 978-0-2264-0317-5. Kurzweil, Ray (2005 ), The Singularity is Near, Viking Press. Lighthill, Professor Sir James (1973 ), "Artificial Intelligence: A General Survey", Artificial Intelligence: a paper symposium, Science Research Council. Luger, George; Stubblefield, William (2004 ), Expert System: Structures and Strategies for Complex Problem Solving (5th ed.), The Benjamin/Cummings Publishing Company, Inc., p. 720, ISBN 978-0-8053-4780-7. McCarthy, John (2007b). What is Expert system?. Stanford University. The supreme effort is to make computer system programs that can resolve problems and attain objectives in the world along with people. Moravec, Hans (1988 ), Mind Children, Harvard University Press Moravec, Hans (1998 ), "When will hardware match the human brain?", Journal of Evolution and Technology, vol. 1, archived from the original 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 initial on 16 October 2011, recovered 7 November 2009 Newell, Allen; Simon, H. A. (1976 ). "Computer Science as Empirical Inquiry: Symbols and Search". Communications of the ACM. 19 (3 ): 113-126. doi:10.1145/ 360018.360022. Nilsson, Nils (1998 ), Expert System: A New Synthesis, Morgan Kaufmann Publishers, ISBN 978-1-5586-0467-4 NRC (1999 ), "Developments in Expert System", Funding a Revolution: 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 Logical Approach, New York City: Oxford University Press, archived from the initial on 25 July 2009, retrieved 6 December 2007 Russell, Stuart J.; Norvig, Peter (2003 ), Expert System: A Modern Approach (second ed.), Upper Saddle River, New Jersey: Prentice Hall, ISBN 0-13-790395-2 Sandberg, Anders; Boström, Nick (2008 ), Whole Brain Emulation: A Roadmap (PDF), Technical Report # 2008-3, Future of Humanity Institute, Oxford University, archived (PDF) from the original on 25 March 2020, obtained 5 April 2009 Searle, John (1980 ), "Minds, Brains and Programs" (PDF), Behavioral and Brain Sciences, 3 (3 ): 417-457, doi:10.1017/ S0140525X00005756, S2CID 55303721, archived (PDF) from the initial on 17 March 2019, retrieved 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 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, retrieved 4 September 2013 - through ResearchGate Berglas, Anthony (January 2012) [2008], Expert System Will Kill Our Grandchildren (Singularity), archived from the initial on 23 July 2014, obtained 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, writes (in what may be called "Dyson's Law") that "Any system basic sufficient to be easy to understand will not be complicated enough to act intelligently, while any system made complex enough to behave wisely will be too complicated to comprehend." (p. 197.) Computer scientist Alex Pentland writes: "Current AI machine-learning algorithms are, at their core, dead simple silly. They work, however they work by brute force." (p. 198.). Gelernter, David, Dream-logic, the Internet and Artificial Thought, Edge, archived from the original on 26 July 2010, retrieved 25 July 2010. Gleick, James, "The Fate of Free Choice" (review of Kevin J. Mitchell, Free Agents: How Evolution Gave Us Free Will, 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 distinguishes us from devices. For biological animals, reason and function originate from acting in the world and experiencing the repercussions. Expert systems - disembodied, 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 initial (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 reasonably expect that those who wish to get abundant from AI are going to have the interests of the rest people close at heart,' ... writes [Gary Marcus] 'We can't rely on governments driven by campaign financing contributions [from tech companies] to push back.' ... Marcus details the demands that citizens need to make from their federal governments and the tech companies. They include transparency on how AI systems work; payment for individuals if their data [are] utilized to train LLMs (big language design) s and the right to approval to this usage; and the capability to hold tech business responsible for the harms they trigger by eliminating Section 230, enforcing cash penalites, and passing more stringent product liability laws ... Marcus also recommends ... that a brand-new, AI-specific federal company, similar to the FDA, the FCC, or the FTC, may offer the most robust oversight ... [T] he Fordham law professor Chinmayi Sharma ... recommends ... establish [ing] a professional licensing routine for engineers that would function in a similar method to medical licenses, malpractice fits, and the Hippocratic oath in medication. 'What if, like physicians,' she asks ..., 'AI engineers likewise pledged to do no harm?'" (p. 46.). Holte, R. C.; Choueiry, B. Y. (2003 ), "Abstraction and reformulation in expert system", Philosophical Transactions of the Royal Society B, vol. 358, no. 1435, pp. 1197-1204, doi:10.1098/ rstb.2003.1317, PMC 1693218, PMID 12903653. Hughes-Castleberry, Kenna, "A Murder Mystery Puzzle: The literary puzzle Cain's Jawbone, which has actually stymied people for years, reveals the constraints of natural-language-processing algorithms", Scientific American, vol. 329, no. 4 (November 2023), pp. 81-82. "This murder mystery competitors has actually revealed that although NLP (natural-language processing) models are capable of incredible tasks, their abilities are quite restricted by the amount of context they receive. This [...] might trigger [troubles] for researchers who want to use them to do things such as evaluate ancient languages. Sometimes, there are couple of historical records on long-gone civilizations to serve as training information for such a purpose." (p. 82.). Immerwahr, Daniel, "Your Lying Eyes: People now use A.I. to create fake videos identical from real ones. How much does it matter?", The New Yorker, 20 November 2023, pp. 54-59. "If by 'deepfakes' we imply reasonable videos produced using artificial intelligence that really trick individuals, then they barely exist. The fakes aren't deep, and the deeps aren't fake. [...] A.I.-generated videos are not, in general, running in our media as counterfeited proof. Their role much better looks like that of cartoons, especially smutty ones." (p. 59.). - Leffer, Lauren, "The Risks of Trusting AI: We should prevent humanizing machine-learning designs used in scientific research", Scientific American, vol. 330, no. 6 (June 2024), pp. 80-81. Lepore, Jill, "The Chit-Chatbot: Is talking with a machine a conversation?", The New Yorker, 7 October 2024, pp. 12-16. Marcus, Gary, "Artificial Confidence: Even the latest, buzziest systems of artificial basic intelligence are stymmied by the same old 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 initial 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, provided and dispersed at the 2007 Singularity Summit, San Francisco, California. Press, Eyal, "In Front of Their Faces: Does facial-recognition technology lead cops to overlook contradictory evidence?", The New Yorker, 20 November 2023, pp. 20-26. Roivainen, Eka, "AI's IQ: ChatGPT aced a [standard 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 tasks that need genuine humanlike thinking or an understanding of the physical and social world ... ChatGPT seemed not able to factor logically and tried to depend on its vast database of ... truths 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 technologies are powerful however unreliable. Rules-based systems can not deal with scenarios their developers did not expect. Learning systems are restricted by the data on which they were trained. AI failures have already led to catastrophe. Advanced autopilot functions in automobiles, clashofcryptos.trade although they carry out well in some situations, have driven automobiles without alerting into trucks, concrete barriers, and parked cars. In the incorrect situation, AI systems go from supersmart to superdumb in an immediate. When an opponent is trying to manipulate 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 made possible by brand-new innovations however rely on the timelelss human tendency to anthropomorphise." (p. 29.). Williams, R. W.; Herrup, K.