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

Creating AGI is a primary objective of AI research study and of companies such as OpenAI [2] and Meta. [3] A 2020 study recognized 72 active AGI research study and development jobs throughout 37 nations. [4]
The timeline for attaining AGI stays a subject of continuous argument amongst researchers and professionals. As of 2023, some argue that it might be possible in years or years; others maintain it might take a century or longer; a minority believe it might never be attained; and another minority claims that it is currently here. [5] [6] Notable AI scientist Geoffrey Hinton has revealed issues about the fast progress towards AGI, recommending it could be achieved sooner than lots of anticipate. [7]
There is dispute on the exact meaning of AGI and concerning whether modern-day large language models (LLMs) such as GPT-4 are early forms of AGI. [8] AGI is a typical topic in science fiction and futures research studies. [9] [10]
Contention exists over whether AGI represents an existential risk. [11] [12] [13] Many professionals on AI have mentioned that mitigating the danger of human extinction presented by AGI needs to be a global concern. [14] [15] Others find the advancement of AGI to be too remote to present such a risk. [16] [17]
Terminology

AGI is also known as strong AI, [18] [19] complete AI, [20] human-level AI, [5] human-level smart AI, or basic smart action. [21]
Some academic sources reserve the term "strong AI" for computer programs that experience life or awareness. [a] In contrast, weak AI (or narrow AI) has the ability to solve one particular problem however does not have general cognitive abilities. [22] [19] Some scholastic sources utilize "weak AI" to refer more broadly to any programs that neither experience consciousness nor have a mind in the same sense as human beings. [a]
Related concepts consist of synthetic superintelligence and transformative AI. A synthetic superintelligence (ASI) is a hypothetical kind of AGI that is a lot more usually smart than human beings, [23] while the notion of transformative AI associates with AI having a large influence on society, for instance, similar to the agricultural or industrial revolution. [24]
A structure for classifying AGI in levels was proposed in 2023 by Google DeepMind researchers. They specify five levels of AGI: emerging, proficient, specialist, virtuoso, and superhuman. For example, a skilled AGI is defined as an AI that surpasses 50% of competent grownups in a wide variety of non-physical tasks, and a superhuman AGI (i.e. an artificial superintelligence) is likewise specified however with a threshold of 100%. They consider large language designs like ChatGPT or LLaMA 2 to be instances of emerging AGI. [25]
Characteristics

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

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

• interact in natural language
• if necessary, integrate these skills in completion of any given objective

Many interdisciplinary techniques (e.g. cognitive science, computational intelligence, and historydb.date choice making) consider extra qualities such as creativity (the ability to form unique mental images and concepts) [28] and autonomy. [29]
Computer-based systems that display much of these abilities exist (e.g. see computational imagination, automated thinking, decision support system, robotic, evolutionary computation, smart agent). There is debate about whether contemporary AI systems possess them to an adequate degree.

Physical traits

Other capabilities are considered desirable in smart systems, as they might impact intelligence or help in its expression. These include: [30]
- the ability to sense (e.g. see, hear, etc), and - the ability to act (e.g. relocation and manipulate items, change area to explore, and so on).
This includes the capability to spot and react to risk. [31]
Although the capability to sense (e.g. see, hear, etc) and the ability to act (e.g. move and manipulate things, modification place to explore, etc) can be desirable for some smart systems, [30] these physical capabilities are not strictly required for an entity to qualify as AGI-particularly under the thesis that large language designs (LLMs) might already be or end up being AGI. Even from a less positive viewpoint on LLMs, there is no firm requirement for an AGI to have a human-like form; being a silicon-based computational system is adequate, supplied it can process input (language) from the external world in location of human senses. This interpretation aligns with the understanding that AGI has actually never ever been proscribed a particular physical embodiment and therefore does not require a capability for locomotion or traditional "eyes and ears". [32]
Tests for human-level AGI

Several tests indicated to confirm human-level AGI have actually been thought about, consisting of: [33] [34]
The idea of the test is that the device needs to try and pretend to be a guy, by responding to questions put to it, and it will just pass if the pretence is fairly persuading. A substantial part of a jury, who should not be expert about devices, should be taken in by the pretence. [37]
AI-complete issues

A problem is informally called "AI-complete" or "AI-hard" if it is believed that in order to solve it, one would require to execute AGI, due to the fact that the solution is beyond the abilities of a purpose-specific algorithm. [47]
There are numerous issues that have actually been conjectured to need general intelligence to fix along with people. Examples include computer vision, natural language understanding, and handling unanticipated scenarios while resolving any real-world problem. [48] Even a specific job like translation needs a machine to check out and write in both languages, follow the author's argument (reason), comprehend the context (knowledge), and consistently reproduce the author's original intent (social intelligence). All of these issues need to be resolved all at once in order to reach human-level maker performance.

However, a lot of these tasks can now be performed by modern-day big language models. According to Stanford University's 2024 AI index, AI has actually reached human-level performance on numerous criteria for checking out understanding and visual thinking. [49]
History

Classical AI

Modern AI research started in the mid-1950s. [50] The first generation of AI scientists were convinced that synthetic general intelligence was possible and that it would exist in just a couple of decades. [51] AI leader Herbert A. Simon composed in 1965: "devices 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 believed they could produce by the year 2001. AI leader Marvin Minsky was a specialist [53] on the task of making HAL 9000 as practical as possible according to the consensus forecasts of the time. He said in 1967, "Within a generation ... the issue of producing 'expert system' will significantly be solved". [54]
Several classical AI projects, such as Doug Lenat's Cyc project (that began in 1984), and Allen Newell's Soar job, were directed at AGI.

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

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

I am positive that this bottom-up route to expert system will one day meet the conventional top-down path over half way, ready to supply the real-world competence and the commonsense knowledge that has actually been so frustratingly evasive in thinking programs. Fully intelligent 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 sign grounding hypothesis by stating:

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

The term "synthetic basic intelligence" was used 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 agent increases "the ability to satisfy goals in a wide variety of environments". [68] This kind of AGI, characterized by the capability to increase a mathematical definition of intelligence rather than exhibit human-like behaviour, [69] was also called universal expert system. [70]
The term AGI was re-introduced and promoted by Shane Legg and Ben Goertzel around 2002. [71] AGI research activity in 2006 was explained by Pei Wang and Ben Goertzel [72] as "producing publications and preliminary outcomes". The very first summertime school in AGI was organized in Xiamen, China in 2009 [73] by the Xiamen university's Artificial Brain Laboratory and OpenCog. The very first university course was given up 2010 [74] and 2011 [75] at Plovdiv University, Bulgaria by Todor Arnaudov. MIT presented a course on AGI in 2018, arranged by Lex Fridman and featuring a number of visitor speakers.

Since 2023 [upgrade], a small number of computer system researchers are active in AGI research study, and many contribute to a series of AGI conferences. However, progressively more researchers have an interest in open-ended learning, [76] [77] which is the idea of permitting AI to constantly learn and innovate like people do.

Feasibility

Since 2023, the advancement and prospective accomplishment of AGI remains a topic of intense debate within the AI community. While conventional consensus held that AGI was a distant goal, current developments have actually led some researchers and market figures to declare that early types of AGI might currently exist. [78] AI leader 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 not likely in the 21st century because it would require "unforeseeable and essentially unpredictable developments" and a "scientifically deep understanding of cognition". [79] Writing in The Guardian, roboticist Alan Winfield claimed the gulf in between modern-day computing and human-level artificial intelligence is as large as the gulf in between current space flight and practical faster-than-light spaceflight. [80]
A more challenge is the absence of clarity in defining what intelligence requires. Does it require awareness? Must it show the capability to set objectives along with pursue them? Is it purely a matter of scale such that if design sizes increase sufficiently, intelligence will emerge? Are centers such as planning, reasoning, and causal understanding needed? Does intelligence require clearly reproducing the brain and its specific professors? Does it require feelings? [81]
Most AI scientists believe strong AI can be attained in the future, however some thinkers, like Hubert Dreyfus and Roger Penrose, deny the possibility of accomplishing strong AI. [82] [83] John McCarthy is amongst those who believe human-level AI will be accomplished, but that the present level of development is such that a date can not accurately be anticipated. [84] AI specialists' views on the feasibility of AGI wax and wane. Four surveys carried out in 2012 and 2013 recommended that the typical estimate amongst specialists for when they would be 50% positive AGI would show up was 2040 to 2050, depending upon the poll, with the mean being 2081. Of the specialists, 16.5% answered with "never ever" when asked the very same question but with a 90% self-confidence rather. [85] [86] Further existing 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 discovered that "over [a] 60-year amount of time there is a strong bias towards forecasting the arrival of human-level AI as in between 15 and 25 years from the time the prediction was made". They evaluated 95 forecasts made between 1950 and 2012 on when human-level AI will come about. [87]
In 2023, Microsoft scientists published a detailed assessment of GPT-4. They concluded: "Given the breadth and depth of GPT-4's capabilities, our company believe that it could fairly be viewed as an early (yet still incomplete) version of an artificial basic intelligence (AGI) system." [88] Another study in 2023 reported that GPT-4 outshines 99% of human beings on the Torrance tests of innovative thinking. [89] [90]
Blaise Agüera y Arcas and Peter Norvig wrote in 2023 that a considerable level of basic intelligence has currently been accomplished with frontier models. They composed that reluctance to this view originates from four primary reasons: a "healthy uncertainty about metrics for AGI", an "ideological dedication to alternative AI theories or methods", a "devotion to human (or biological) exceptionalism", or a "concern about the financial implications of AGI". [91]
2023 also marked the introduction of large multimodal designs (big language models efficient in processing or creating multiple techniques such as text, audio, and images). [92]
In 2024, OpenAI released o1-preview, the very first of a series of designs that "spend more time believing before they respond". According to Mira Murati, this ability to believe before reacting represents a brand-new, extra paradigm. It improves model outputs by spending more computing power when generating the response, whereas the model scaling paradigm improves outputs by increasing the design size, training information and training compute power. [93] [94]
An OpenAI staff member, Vahid Kazemi, declared in 2024 that the business had actually accomplished AGI, stating, "In my opinion, we have currently attained AGI and it's even more clear with O1." Kazemi clarified that while the AI is not yet "much better than any human at any job", it is "much better than many people at many jobs." He likewise resolved criticisms that large language models (LLMs) simply follow predefined patterns, comparing their learning procedure to the scientific method of observing, assuming, and verifying. These declarations have actually stimulated argument, as they count on a broad and non-traditional definition of AGI-traditionally comprehended as AI that matches human intelligence across all domains. Critics argue that, while OpenAI's designs show remarkable versatility, they may not fully meet this standard. Notably, Kazemi's remarks came shortly after OpenAI got rid of "AGI" from the terms of its partnership with Microsoft, triggering speculation about the company's tactical intentions. [95]
Timescales

Progress in synthetic intelligence has traditionally gone through durations of fast progress separated by periods when development appeared to stop. [82] Ending each hiatus were fundamental advances in hardware, software or both to create area for additional development. [82] [98] [99] For example, the hardware offered in the twentieth century was not enough to implement deep knowing, which needs large numbers of GPU-enabled CPUs. [100]
In the introduction to his 2006 book, [101] Goertzel states that estimates of the time required before a truly versatile AGI is built vary from 10 years to over a century. As of 2007 [update], the consensus in the AGI research community seemed to be that the timeline gone over by Ray Kurzweil in 2005 in The Singularity is Near [102] (i.e. in between 2015 and 2045) was possible. [103] Mainstream AI scientists have actually provided a wide variety of opinions on whether development will be this rapid. A 2012 meta-analysis of 95 such opinions discovered a predisposition towards anticipating that the start of AGI would occur within 16-26 years for modern-day and historical predictions alike. That paper has been criticized for how it categorized 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 competitors with a top-5 test error rate of 15.3%, substantially better than the second-best entry's rate of 26.3% (the conventional method utilized a weighted sum of scores from different pre-defined classifiers). [105] AlexNet was considered the initial ground-breaker of the existing deep knowing wave. [105]
In 2017, researchers Feng Liu, Yong Shi, and Ying Liu conducted intelligence tests on openly offered and easily accessible weak AI such as Google AI, Apple's Siri, and others. At the optimum, these AIs reached an IQ value of about 47, which corresponds around to a six-year-old child in first grade. An adult pertains to about 100 on average. Similar tests were performed in 2014, with the IQ rating reaching a maximum value of 27. [106] [107]
In 2020, OpenAI established GPT-3, a language design capable of performing many varied tasks 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 classified as a narrow AI system. [108]
In the very same year, Jason Rohrer used his GPT-3 account to establish a chatbot, and offered a chatbot-developing platform called "Project December". OpenAI requested modifications to the chatbot to abide by their security guidelines; Rohrer disconnected Project December from the GPT-3 API. [109]
In 2022, DeepMind established Gato, a "general-purpose" system capable of performing more than 600 different tasks. [110]
In 2023, Microsoft Research published a study on an early variation of OpenAI's GPT-4, competing that it displayed more general intelligence than previous AI models and demonstrated human-level performance in jobs covering multiple domains, such as mathematics, coding, and law. This research study stimulated an argument on whether GPT-4 could be considered an early, incomplete version of synthetic basic intelligence, highlighting the requirement for more expedition and evaluation of such systems. [111]
In 2023, the AI researcher Geoffrey Hinton specified that: [112]
The concept that this things could really get smarter than individuals - a couple of individuals thought that, [...] But most people believed it was way off. And I thought it was method off. I thought it was 30 to 50 years or perhaps longer away. Obviously, I no longer think that.

In May 2023, Demis Hassabis similarly said that "The development in the last couple of years has been pretty incredible", and that he sees no reason that it would slow down, expecting AGI within a years or perhaps a few 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 in addition to humans. [114] In June 2024, the AI researcher Leopold Aschenbrenner, a former OpenAI employee, estimated AGI by 2027 to be "strikingly possible". [115]
Whole brain emulation

While the development of transformer designs like in ChatGPT is thought about the most appealing path to AGI, [116] [117] whole brain emulation can function as an alternative technique. With entire brain simulation, a brain design is constructed by scanning and mapping a biological brain in information, and after that copying and simulating it on a computer system or another computational gadget. The simulation model should be sufficiently devoted 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 study functions. It has been discussed in artificial intelligence research study [103] as an approach to strong AI. Neuroimaging innovations that could provide the necessary detailed understanding are enhancing rapidly, and futurist Ray Kurzweil in the book The Singularity Is Near [102] anticipates that a map of sufficient quality will appear on a comparable timescale to the computing power needed to replicate it.

Early approximates

For low-level brain simulation, an extremely effective cluster of computer systems or GPUs would be required, given the enormous quantity of synapses within the human brain. Each of the 1011 (one hundred billion) neurons has on typical 7,000 synaptic connections (synapses) to other nerve cells. The brain of a three-year-old child has about 1015 synapses (1 quadrillion). This number declines with age, supporting by their adult years. Estimates differ for an adult, ranging from 1014 to 5 × 1014 synapses (100 to 500 trillion). [120] A quote of the brain's processing power, based upon 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 required to equate to the human brain and embraced a figure of 1016 computations per second (cps). [e] (For contrast, if a "calculation" was equivalent to one "floating-point operation" - a measure used to rate present supercomputers - then 1016 "computations" would be comparable to 10 petaFLOPS, achieved in 2011, while 1018 was achieved in 2022.) He used this figure to anticipate the required hardware would be offered sometime in between 2015 and 2025, if the exponential development in computer system power at the time of writing continued.

Current research study

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

Criticisms of simulation-based techniques

The artificial nerve cell design presumed by Kurzweil and utilized in numerous present artificial neural network executions is easy compared to biological nerve cells. A brain simulation would likely have to capture the detailed cellular behaviour of biological nerve cells, presently understood just in broad summary. The overhead introduced by full modeling of the biological, chemical, and physical information of neural behaviour (especially on a molecular scale) would require computational powers numerous orders of magnitude larger than Kurzweil's price quote. In addition, the price quotes do not account for glial cells, which are understood to play a role in cognitive processes. [125]
An essential criticism of the simulated brain technique obtains from embodied cognition theory which asserts that human personification is an important aspect of human intelligence and is essential to ground meaning. [126] [127] If this theory is proper, any fully practical brain model will need to incorporate more than simply the neurons (e.g., a robotic body). Goertzel [103] proposes virtual personification (like in metaverses like Second Life) as an option, however it is unknown whether this would suffice.

Philosophical viewpoint

"Strong AI" as specified in approach

In 1980, philosopher John Searle created the term "strong AI" as part of his Chinese space argument. [128] He proposed a difference in between 2 hypotheses about artificial intelligence: [f]
Strong AI hypothesis: An expert system system can have "a mind" and "consciousness". Weak AI hypothesis: An expert system system can (only) imitate it thinks and has a mind and awareness.
The very first one he called "strong" because it makes a more powerful statement: it assumes something special has taken place to the device that goes beyond those capabilities that we can evaluate. The behaviour of a "weak AI" device would be specifically similar to a "strong AI" device, however the latter would also have subjective conscious experience. This use is likewise typical in scholastic AI research study and textbooks. [129]
In contrast to Searle and mainstream AI, some futurists such as Ray Kurzweil utilize the term "strong AI" to imply "human level artificial general intelligence". [102] This is not the exact same as Searle's strong AI, unless it is assumed that consciousness is required for human-level AGI. Academic thinkers such as Searle do not think that holds true, and to most expert system researchers the concern is out-of-scope. [130]
Mainstream AI is most thinking about how a program acts. [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 need to understand if it really has mind - indeed, there would be no method to tell. For AI research study, Searle's "weak AI hypothesis" is comparable to the declaration "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, "Strong AI" and "AGI" are 2 various things.

Consciousness

Consciousness can have different meanings, and some aspects play substantial roles in sci-fi and the ethics of synthetic intelligence:

Sentience (or "incredible awareness"): The ability to "feel" perceptions or feelings subjectively, rather than the capability to factor about perceptions. Some theorists, such as David Chalmers, utilize the term "consciousness" to refer exclusively to extraordinary consciousness, which is roughly comparable to life. [132] Determining why and how subjective experience occurs is called the difficult issue of awareness. [133] Thomas Nagel explained in 1974 that it "feels like" something to be conscious. If we are not conscious, then it doesn't 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 seem like to be a toaster?" Nagel concludes that a bat seems conscious (i.e., has consciousness) however a toaster does not. [134] In 2022, a Google engineer declared that the business's AI chatbot, LaMDA, had accomplished life, though this claim was extensively disputed by other experts. [135]
Self-awareness: To have conscious awareness of oneself as a separate individual, especially to be purposely familiar with one's own ideas. This is opposed to simply being the "topic of one's thought"-an operating system or debugger has the ability to be "familiar with itself" (that is, to represent itself in the exact same way it represents whatever else)-however this is not what people usually imply when they use the term "self-awareness". [g]
These qualities have an ethical dimension. AI life would provide increase to concerns of well-being and legal protection, similarly to animals. [136] Other aspects of consciousness associated to cognitive abilities are likewise appropriate to the idea of AI rights. [137] Finding out how to integrate innovative AI with existing legal and social structures is an emerging issue. [138]
Benefits

AGI could have a wide array of applications. If oriented towards such objectives, AGI could assist mitigate various issues on the planet such as hunger, hardship and health issue. [139]
AGI could improve efficiency and effectiveness in many tasks. For example, in public health, AGI might accelerate medical research, significantly against cancer. [140] It could take care of the senior, [141] and democratize access to rapid, top quality medical diagnostics. It might use enjoyable, cheap and individualized education. [141] The requirement to work to subsist could end up being obsolete if the wealth produced is effectively redistributed. [141] [142] This also raises the concern of the place of human beings in a radically automated society.

AGI could also assist to make logical choices, and to anticipate and avoid catastrophes. It might likewise help to profit of possibly devastating technologies such as nanotechnology or climate engineering, while avoiding the associated risks. [143] If an AGI's main goal is to prevent existential disasters such as human extinction (which could be tough if the Vulnerable World Hypothesis ends up being true), [144] it could take steps to drastically lower the risks [143] while lessening the effect of these steps on our lifestyle.

Risks

Existential dangers

AGI may represent multiple types of existential risk, which are dangers that threaten "the early termination of Earth-originating smart life or the long-term and drastic destruction of its capacity for desirable future advancement". [145] The danger of human extinction from AGI has been the topic of numerous disputes, but there is likewise the possibility that the advancement of AGI would lead to a permanently flawed future. Notably, it could be used to spread out and preserve the set of worths of whoever develops it. If mankind still has ethical blind areas similar to slavery in the past, AGI may irreversibly entrench it, avoiding ethical development. [146] Furthermore, AGI might facilitate mass surveillance and indoctrination, which could be utilized to create a steady repressive worldwide totalitarian regime. [147] [148] There is also a threat for the machines themselves. If makers that are sentient or otherwise worthwhile of ethical consideration are mass created in the future, taking part in a civilizational course that forever disregards their welfare and interests could be an existential disaster. [149] [150] Considering how much AGI could enhance humanity's future and aid reduce other existential threats, Toby Ord calls these existential dangers "an argument for continuing with due care", not for "deserting AI". [147]
Risk of loss of control and human extinction

The thesis that AI presents an existential threat for humans, and that this threat requires more attention, is questionable however has been endorsed in 2023 by numerous public figures, AI researchers and CEOs of AI companies such as Elon Musk, Bill Gates, Geoffrey Hinton, Yoshua Bengio, Demis Hassabis and Sam Altman. [151] [152]
In 2014, Stephen Hawking criticized extensive indifference:

So, dealing with possible futures of enormous advantages and risks, the experts are undoubtedly doing whatever possible to ensure the finest outcome, right? Wrong. If an exceptional alien civilisation sent us a message stating, 'We'll get here in a few decades,' would we simply reply, 'OK, call us when you get here-we'll leave the lights on?' Probably not-but this is basically what is happening with AI. [153]
The prospective fate of humanity has actually in some cases been compared to the fate of gorillas threatened by human activities. The contrast mentions that higher intelligence permitted humanity to dominate gorillas, which are now vulnerable in ways that they might not have prepared for. As a result, the gorilla has actually ended up being an endangered types, not out of malice, but simply as a civilian casualties from human activities. [154]
The skeptic Yann LeCun considers that AGIs will have no desire to dominate humankind which we should beware not to anthropomorphize them and translate their intents as we would for humans. He said that individuals won't be "wise enough to develop super-intelligent machines, yet extremely foolish to the point of providing it moronic objectives without any safeguards". [155] On the other side, the idea of important merging suggests that almost whatever their objectives, intelligent agents will have reasons to try to make it through and acquire more power as intermediary steps to attaining these goals. And that this does not require having emotions. [156]
Many scholars who are worried about existential threat supporter for more research into resolving the "control problem" to answer the concern: what kinds of safeguards, algorithms, or architectures can developers carry out to increase the likelihood that their recursively-improving AI would continue to act in a friendly, instead of devastating, way after it reaches superintelligence? [157] [158] Solving the control issue is made complex by the AI arms race (which might lead to a race to the bottom of safety preventative measures in order to release items before competitors), [159] and the use of AI in weapon systems. [160]
The thesis that AI can present existential danger also has detractors. Skeptics generally state that AGI is not likely in the short-term, or that concerns about AGI sidetrack from other problems connected to existing AI. [161] Former Google scams czar Shuman Ghosemajumder considers that for many individuals outside of the innovation market, existing chatbots and LLMs are currently perceived as though they were AGI, leading to more misunderstanding and fear. [162]
Skeptics sometimes charge that the thesis is crypto-religious, with an unreasonable belief in the possibility of superintelligence replacing an unreasonable belief in an omnipotent God. [163] Some researchers believe that the interaction projects on AI existential risk by specific AI groups (such as OpenAI, Anthropic, DeepMind, and Conjecture) may be an at attempt at regulatory capture and to pump up interest in their products. [164] [165]
In 2023, the CEOs of Google DeepMind, OpenAI and Anthropic, together with other market leaders and researchers, released a joint statement asserting that "Mitigating the danger of extinction from AI need to be a worldwide concern together with other societal-scale threats such as pandemics and nuclear war." [152]
Mass joblessness

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

According to Stephen Hawking, the result of automation on the quality of life will depend on how the wealth will be rearranged: [142]
Everyone can take pleasure in a life of elegant leisure if the machine-produced wealth is shared, or the majority of people can end up miserably bad if the machine-owners successfully lobby against wealth redistribution. Up until now, the trend seems to be towards the second option, with technology driving ever-increasing inequality

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

Artificial brain - Software and hardware with cognitive abilities similar to those of the animal or human brain AI impact AI security - Research area on making AI safe and advantageous AI alignment - AI conformance to the intended goal A.I. Rising - 2018 movie directed by Lazar Bodroža Expert system Automated device learning - Process of automating the application of machine knowing BRAIN Initiative - Collaborative public-private research study effort revealed by the Obama administration China Brain Project Future of Humanity Institute - Defunct Oxford interdisciplinary research centre General game playing - Ability of synthetic intelligence to play various video games Generative expert system - AI system efficient in creating material in reaction to triggers Human Brain Project - Scientific research job Intelligence amplification - Use of infotech to augment human intelligence (IA). Machine ethics - Moral behaviours of manufactured makers. Moravec's paradox. Multi-task knowing - Solving several machine finding out tasks at the very same time. Neural scaling law - Statistical law in device knowing. Outline of expert system - Overview of and topical guide to expert system. Transhumanism - Philosophical movement. Synthetic intelligence - Alternate term for or type of synthetic intelligence. Transfer learning - Artificial intelligence method. Loebner Prize - Annual AI competition. Hardware for artificial intelligence - Hardware specifically developed and enhanced for artificial intelligence. Weak expert system - 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 post Chinese space. ^ AI founder John McCarthy writes: "we can not yet define in general what sort of computational treatments we want to call intelligent. " [26] (For a discussion of some meanings of intelligence used by expert system scientists, see viewpoint of synthetic intelligence.). ^ The Lighthill report specifically slammed AI's "grand objectives" and led the dismantling of AI research study in England. [55] In the U.S., DARPA became determined to fund just "mission-oriented direct research study, rather than standard undirected research study". [56] [57] ^ As AI creator John McCarthy composes "it would be a terrific relief to the rest of the workers in AI if the creators of new basic formalisms would express their hopes in a more secured type than has actually in some cases held true." [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 standard AI book: "The assertion that makers might possibly act wisely (or, maybe much better, act as if they were smart) is called the 'weak AI' hypothesis by philosophers, and the assertion that makers that do so are actually believing (rather than 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 artificial narrow intelligence (ANI)?". VentureBeat. Retrieved 1 March 2024. ANI is designed to carry out a single task. ^ "OpenAI Charter". OpenAI. Retrieved 6 April 2023. Our objective is to guarantee that synthetic basic intelligence benefits all of mankind. ^ Heath, Alex (18 January 2024). "Mark Zuckerberg's new goal is developing synthetic basic intelligence". The Verge. Retrieved 13 June 2024. Our vision is to develop AI that is much better than human-level at all of the human senses. ^ Baum, Seth D. (2020 ). A Survey of Artificial General Intelligence Projects for Ethics, Risk, and Policy (PDF) (Report). Global Catastrophic Risk Institute. Retrieved 28 November 2024. 72 AGI R&D projects were recognized as being active in 2020. ^ a b c "AI timelines: What do professionals in expert system anticipate for the future?". Our World in Data. Retrieved 6 April 2023. ^ Metz, Cade (15 May 2023). "Some Researchers Say A.I. Is Already Here, Stirring Debate in Tech Circles". The New York Times. Retrieved 18 May 2023. ^ "AI leader Geoffrey Hinton stops Google and cautions of threat ahead". The New York City Times. 1 May 2023. Retrieved 2 May 2023. It is tough to see how you can prevent the bad stars from using it for bad things. ^ Bubeck, Sébastien; Chandrasekaran, Varun; Eldan, Ronen; Gehrke, Johannes; Horvitz, Eric (2023 ). "Sparks of Artificial General Intelligence: Early try outs GPT-4". arXiv preprint. arXiv:2303.12712. GPT-4 shows triggers of AGI. ^ Butler, Octavia E. (1993 ). Parable of the Sower. Grand Central Publishing. ISBN 978-0-4466-7550-5. All that you touch you alter. All that you alter 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 genuine threat is not AI itself but the method we deploy it. ^ "Impressed by synthetic intelligence? Experts say AGI is following, and it has 'existential' threats". ABC News. 23 March 2023. Retrieved 6 April 2023. AGI might posture existential risks to humanity. ^ Bostrom, Nick (2014 ). Superintelligence: Paths, Dangers, Strategies. Oxford University Press. ISBN 978-0-1996-7811-2. The very first superintelligence will be the last invention that mankind needs to make. ^ Roose, Kevin (30 May 2023). "A.I. Poses 'Risk of Extinction,' Industry Leaders Warn". The New York Times. Mitigating the threat of termination from AI ought to be a worldwide top priority. ^ "Statement on AI Risk". Center for AI Safety. Retrieved 1 March 2024. AI professionals alert of danger 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 producing devices that can outthink us in general ways. ^ LeCun, Yann (June 2023). "AGI does not provide an existential threat". Medium. There is no reason to fear AI as an existential threat. ^ Kurzweil 2005, p. 260. ^ a b Kurzweil, Ray (5 August 2005), "Long Live AI", Forbes, archived from the original on 14 August 2005: Kurzweil describes strong AI as "machine intelligence with the full variety 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 utilize for "human-level" intelligence in the physical symbol system hypothesis. ^ "The Open University on Strong and Weak AI". Archived from the original on 25 September 2009. Retrieved 8 October 2007. ^ "What is 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 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 achieving AGI, according to DeepMind". VentureBeat. ^ McCarthy, John (2007a). "Basic Questions". Stanford University. Archived from the original on 26 October 2007. Retrieved 6 December 2007. ^ This list of smart qualities is based on the topics covered by significant 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 City: Academic Press. ^ a b Pfeifer, R. and Bongard J. C., How the body forms the method we think: a brand-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 reconsidered: The concept 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 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 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 happens when it does?". The Conversation. Retrieved 22 September 2024. ^ a b Turing 1950. ^ Turing, Alan (1952 ). B. Jack Copeland (ed.). Can Automatic Calculating Machines Be Said To Think?. Oxford: Oxford University Press. pp. 487-506. ISBN 978-0-1982-5079-1. ^ "Eugene Goostman is a real kid - the Turing Test states so". The Guardian. 9 June 2014. ISSN 0261-3077. Retrieved 3 March 2024. ^ "Scientists challenge whether computer 'Eugene Goostman' passed Turing test". BBC News. 9 June 2014. Retrieved 3 March 2024. ^ Jones, Cameron R.; Bergen, Benjamin K. (9 May 2024). "People can not distinguish GPT-4 from a human in a Turing test". arXiv:2405.08007 [cs.HC] ^ Varanasi, Lakshmi (21 March 2023). "AI models like ChatGPT and GPT-4 are acing everything from the bar exam to AP Biology. Here's a list of difficult exams both AI variations have passed". Business Insider. Retrieved 30 May 2023. ^ Naysmith, Caleb (7 February 2023). "6 Jobs Expert System Is Already Replacing and How Investors Can Profit From 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 outdated. Coffee is the response". Analytics India Magazine. Retrieved 3 March 2024. ^ Bhaimiya, Sawdah (20 June 2023). "DeepMind's co-founder recommended testing an AI 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 ). "Expert System" (PDF). In Stuart C. Shapiro (ed.). Encyclopedia of Expert System (Second ed.). New York City: John Wiley. pp. 54-57. Archived (PDF) from the original on 1 February 2016. (Section 4 is on "AI-Complete Tasks".). ^ Yampolskiy, Roman V. (2012 ). Xin-She Yang (ed.). "Turing Test as a Specifying Feature of AI-Completeness" (PDF). Expert System, Evolutionary Computation and Metaheuristics (AIECM): 3-17. Archived (PDF) from the original on 22 May 2013. ^ "AI Index: oke.zone State of AI in 13 Charts". Stanford University Human-Centered Artificial Intelligence. 15 April 2024. Retrieved 27 May 2024. ^ Crevier 1993, pp. 48-50. ^ Kaplan, Andreas (2022 ). "Artificial Intelligence, Business and wiki.dulovic.tech Civilization - Our Fate Made in Machines". Archived from the original on 6 May 2022. Retrieved 12 March 2022. ^ Simon 1965, p. 96 priced quote 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 ), estimated in Crevier (1993, p. 109). ^ Lighthill 1973; Howe 1994. ^ a b NRC 1999, "Shift to Applied Research Increases Investment". ^ Crevier 1993, pp. 115-117; Russell & Norvig 2003, pp. 21-22. ^ Crevier 1993, p. 211, Russell & Norvig 2003, p. 24 and see also Feigenbaum & McCorduck 1983. ^ Crevier 1993, pp. 161-162, 197-203, 240; Russell & Norvig 2003, p. 25. ^ Crevier 1993, pp. 209-212. ^ McCarthy, John (2000 ). "Respond to Lighthill". Stanford University. Archived from the original on 30 September 2008. Retrieved 29 September 2007. ^ Markoff, John (14 October 2005). "Behind Expert system, a Squadron of Bright Real People". The New York City Times. Archived from the initial on 2 February 2023. Retrieved 18 February 2017. At its low point, some computer system researchers and software engineers avoided the term artificial intelligence for worry of being deemed wild-eyed dreamers. ^ Russell & Norvig 2003, pp. 25-26 ^ "Trends in the Emerging Tech Hype Cycle". Gartner Reports. Archived from the original on 22 May 2019. Retrieved 7 May 2019. ^ a b Moravec 1988, p. 20 ^ Harnad, S. (1990 ). "The Symbol Grounding Problem". Physica D. 42 (1-3): 335-346. arXiv: cs/9906002. Bibcode:1990 PhyD ... 42..335 H. doi:10.1016/ 0167-2789( 90 )90087-6. S2CID 3204300. ^ Gubrud 1997 ^ Hutter, Marcus (2005 ). Universal Expert System: Sequential Decisions Based Upon Algorithmic Probability. Texts in Theoretical Computer Science an EATCS Series. Springer. doi:10.1007/ b138233. ISBN 978-3-5402-6877-2. S2CID 33352850. Archived from the initial on 19 July 2022. Retrieved 19 July 2022. ^ Legg, Shane (2008 ). Machine Super Intelligence (PDF) (Thesis). University of Lugano. Archived (PDF) from the original on 15 June 2022. Retrieved 19 July 2022. ^ Goertzel, Ben (2014 ). Artificial General Intelligence. Lecture Notes in Computer 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 original on 28 December 2018. Retrieved 28 December 2018., through Life 3.0: 'The term "AGI" was popularized 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 original on 28 September 2020. Retrieved 11 May 2020. ^ "Избираеми дисциплини 2009/2010 - пролетен триместър" [Elective courses 2009/2010 - spring trimester] Факултет по математика и информатика [Faculty of Mathematics and Informatics] (in Bulgarian). Archived from the original on 26 July 2020. Retrieved 11 May 2020. ^ "Избираеми дисциплини 2010/2011 - зимен триместър" [Elective courses 2010/2011 - winter 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 limits of device intelligence: Despite development in device intelligence, synthetic general intelligence is still a significant 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 explores GPT-4". arXiv:2303.12712 [cs.CL] ^ "Microsoft Researchers Claim GPT-4 Is Showing "Sparks" of AGI". Futurism. 23 March 2023. Retrieved 13 December 2023. ^ Allen, Paul; Greaves, Mark (12 October 2011). "The Singularity Isn't Near". MIT Technology Review. Retrieved 17 September 2014. ^ Winfield, Alan. "Artificial intelligence will not develop into a Frankenstein's monster". 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 expert system will not be understood". Humanities and Social Sciences Communications. 7 (1 ): 1-9. doi:10.1057/ s41599-020-0494-4. hdl:11250/ 2726984. ISSN 2662-9992. S2CID 219710554. ^ McCarthy 2007b. ^ Khatchadourian, Raffi (23 November 2015). "The Doomsday Invention: Will expert system bring us utopia or 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 viewpoint. In Fundamental problems of expert system (pp. 555-572). Springer, Cham. ^ Armstrong, Stuart, and Kaj Sotala. 2012. "How We're Predicting AI-or Failing To." In Beyond AI: Artificial Dreams, modified by Jan Romportl, Pavel Ircing, Eva Žáčková, Michal Polák and Radek Schuster, 52-75. Plzeň: University of West Bohemia ^ "Microsoft Now Claims GPT-4 Shows 'Sparks' of General Intelligence". 24 March 2023. ^ Shimek, Cary (6 July 2023). "AI Outperforms Humans in Creativity Test". Neuroscience News. Retrieved 20 October 2023. ^ Guzik, Erik E.; Byrge, Christian; Gilde, Christian (1 December 2023). "The originality of devices: AI takes the Torrance Test". Journal of Creativity. 33 (3 ): 100065. doi:10.1016/ j.yjoc.2023.100065. ISSN 2713-3745. S2CID 261087185. ^ Arcas, Blaise Agüera y (10 October 2023). "Artificial General Intelligence Is Already Here". Noema. ^ Zia, Tehseen (8 January 2024). "Unveiling of Large Multimodal Models: Shaping the Landscape of Language Models in 2024". Unite.ai. Retrieved 26 May 2024. ^ "Introducing OpenAI o1-preview". OpenAI. 12 September 2024. ^ Knight, Will. "OpenAI Announces a Brand-new AI Model, Code-Named Strawberry, That Solves Difficult Problems Step by Step". Wired. ISSN 1059-1028. Retrieved 17 September 2024. ^ "OpenAI Employee Claims AGI Has Been Achieved". Orbital Today. 13 December 2024. Retrieved 27 December 2024. ^ "AI Index: State of AI in 13 Charts". hai.stanford.edu. 15 April 2024. Retrieved 7 June 2024. ^ "Next-Gen AI: OpenAI and Meta's Leap Towards Reasoning Machines". Unite.ai. 19 April 2024. Retrieved 7 June 2024. ^ James, Alex P. (2022 ). "The Why, What, and How of Artificial General Intelligence Chip Development". IEEE Transactions on Cognitive and Developmental Systems. 14 (2 ): 333-347. arXiv:2012.06338. doi:10.1109/ TCDS.2021.3069871. ISSN 2379-8920. S2CID 228376556. Archived from the initial on 28 August 2022. Retrieved 28 August 2022. ^ Pei, Jing; Deng, Lei; Song, Sen; Zhao, Mingguo; Zhang, Youhui; Wu, Shuang; Wang, Guanrui; Zou, Zhe; Wu, Zhenzhi; He, Wei; Chen, Feng; Deng, Ning; Wu, Si; Wang, Yu; Wu, Yujie (2019 ). "Towards synthetic general intelligence with hybrid Tianjic chip architecture". Nature. 572 (7767 ): 106-111. Bibcode:2019 Natur.572..106 P. doi:10.1038/ s41586-019-1424-8. ISSN 1476-4687. PMID 31367028. S2CID 199056116. Archived from the initial on 29 August 2022. Retrieved 29 August 2022. ^ Pandey, Mohit; Fernandez, Michael; Gentile, Francesco; Isayev, Olexandr; Tropsha, Alexander; Stern, Abraham C.; Cherkasov, Artem (March 2022). "The transformational function of GPU computing and lespoetesbizarres.free.fr 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 original on 4 December 2020. Retrieved 24 August 2020. ^ a b Butz, Martin V. (1 March 2021). "Towards Strong AI". KI - Künstliche Intelligenz. 35 (1 ): 91-101. doi:10.1007/ s13218-021-00705-x. ISSN 1610-1987. S2CID 256065190. ^ Liu, Feng; Shi, Yong; Liu, Ying (2017 ). "Intelligence Quotient and Intelligence Grade of Expert System". Annals of Data Science. 4 (2 ): 179-191. arXiv:1709.10242. doi:10.1007/ s40745-017-0109-0. S2CID 37900130. ^ Brien, Jörn (5 October 2017). "Google-KI doppelt so schlau wie Siri" [Google AI is two times as clever as Siri - however a six-year-old beats both] (in German). Archived from the initial on 3 January 2019. Retrieved 2 January 2019. ^ Grossman, Gary (3 September 2020). "We're entering the AI twilight zone between narrow and basic AI". VentureBeat. Archived from the initial on 4 September 2020. Retrieved 5 September 2020. Certainly, too, there are those who claim we are already seeing an early example of an AGI system in the just recently revealed GPT-3 natural language processing (NLP) neural network. ... So is GPT-3 the first example of an AGI system? This is arguable, but the agreement is that it is not AGI. ... If nothing else, GPT-3 tells us there is a happy medium in between narrow and basic AI. ^ Quach, Katyanna. "A designer constructed an AI chatbot utilizing GPT-3 that assisted 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 brand-new AI can perform over 600 tasks, from playing video games to managing robots", TechCrunch, archived from the initial on 16 June 2022, obtained 12 June 2022. ^ Bubeck, Sébastien; Chandrasekaran, Varun; Eldan, Ronen; Gehrke, Johannes; Horvitz, Eric; Kamar, Ece; Lee, Peter; Lee, Yin Tat; Li, Yuanzhi; Lundberg, Scott; Nori, Harsha; Palangi, Hamid; Ribeiro, Marco Tulio; Zhang, Yi (22 March 2023). "Sparks of Artificial General Intelligence: Early experiments with GPT-4". arXiv:2303.12712 [cs.CL] ^ Metz, Cade (1 May 2023). "' The Godfather of A.I.' Leaves Google and Warns of Danger Ahead". The New York City Times. ISSN 0362-4331. Retrieved 7 June 2023. ^ Bove, Tristan. "A.I. could match human intelligence in 'just a couple of years,' says CEO of Google's main A.I. research study laboratory". Fortune. Retrieved 4 September 2024. ^ Nellis, Stephen (2 March 2024). "Nvidia CEO states AI could 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 specify Artificial General Intelligence". Fast Company. ^ Nosta, John (5 January 2024). "The Accelerating Path to Artificial General Intelligence". Psychology Today. Retrieved 30 March 2024. ^ Hickey, Alex. "Whole Brain Emulation: A Giant Step for Neuroscience". Tech Brew. Retrieved 8 November 2023. ^ Sandberg & Boström 2008. ^ Drachman 2005. ^ a b Russell & Norvig 2003. ^ Moravec 1988, p. 61. ^ Moravec 1998. ^ Holmgaard Mersh, Amalie (15 September 2023). "Decade-long European research study project maps the human brain". euractiv. ^ Swaminathan, Nikhil (January-February 2011). "Glia-the other brain cells". Discover. Archived from the initial on 8 February 2014. Retrieved 24 January 2014. ^ de Vega, Glenberg & Graesser 2008. A broad range of views in current 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 (estimated 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 synthetic intelligence for curiosity by the field about why a program acts the way it does. ^ Chalmers, David J. (9 August 2023). "Could a Large Language Model Be Conscious?". Boston Review. ^ Seth, Anil. "Consciousness". New Scientist. Retrieved 5 September 2024. ^ Nagel 1974. ^ "The Google engineer who believes the company's AI has come to life". The Washington Post. 11 June 2022. Retrieved 12 June 2023. ^ Kateman, Brian (24 July 2023). "AI Should Be Terrified of Humans". TIME. Retrieved 5 September 2024. ^ Nosta, John (18 December 2023). "Should Artificial Intelligence Have Rights?". Psychology Today. Retrieved 5 September 2024. ^ Akst, Daniel (10 April 2023). "Should Robots With Expert System Have Moral or Legal Rights?". The Wall Street Journal. ^ "Artificial General Intelligence - Do [es] the expense 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 Look Like When Expert System 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, threats, techniques (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 progress is making it likelier than ever that people will ruin ourselves". Vox. Retrieved 8 June 2023. ^ Doherty, Ben (17 May 2018). "Climate alter an 'existential security risk' to Australia, Senate query 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 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 takes a look at the ramifications 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 risks of strong AI". The remarkable Facebook debate between Yann LeCun, Stuart Russel and Yoshua Bengio about the dangers of strong AI (in French). Retrieved 8 June 2023. ^ "Will Artificial Intelligence Doom The 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 danger: a survey". Physica Scripta. 90 (1 ): 018001. doi:10.1088/ 0031-8949/90/ 1/018001. ISSN 0031-8949. ^ Bostrom, Nick (2014 ). Superintelligence: Paths, Dangers, Strategies (First ed.). Oxford University Press. ISBN 978-0-1996-7811-2. ^ Chow, Andrew R.; Perrigo, Billy (16 February 2023). "The AI Arms Race Is On. Start Worrying". TIME. Retrieved 24 December 2023. ^ Tetlow, Gemma (12 January 2017). "AI arms race risks spiralling out of control, report cautions". Financial Times. Archived from the initial on 11 April 2022. Retrieved 24 December 2023. ^ Milmo, Dan; Stacey, Kiran (25 September 2023). "Experts disagree over threat posed however expert system can not be ignored". 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 original on 4 June 2014. Retrieved 12 December 2015. ^ Titcomb, James (30 October 2023). "Big Tech is stiring worries over AI, alert researchers". The Telegraph. Retrieved 7 December 2023. ^ Davidson, John (30 October 2023). "Google Brain creator states big tech is lying about AI extinction risk". Australian Financial Review. Archived from the original on 7 December 2023. Retrieved 7 December 2023. ^ Eloundou, Tyna; Manning, Sam; Mishkin, Pamela; Rock, Daniel (17 March 2023). "GPTs are GPTs: An early take a look at the labor market impact potential of big language designs". OpenAI. Retrieved 7 June 2023. ^ a b Hurst, Luke (23 March 2023). "OpenAI says 80% of employees might see their tasks impacted by AI. These are the tasks most impacted". euronews. Retrieved 8 June 2023. ^ Sheffey, Ayelet (20 August 2021). "Elon Musk says we require universal standard income because 'in the future, physical work will be an option'". Business Insider. Archived from the initial on 9 July 2023. Retrieved 8 June 2023. Sources

UNESCO Science Report: the Race Against Time for Smarter Development. Paris: UNESCO. 11 June 2021. ISBN 978-9-2310-0450-6. Archived from the initial on 18 June 2022. Retrieved 22 September 2021. Chalmers, David (1996 ), The Conscious Mind, Oxford University Press. Clocksin, William (August 2003), "Expert system and the future", Philosophical Transactions of the Royal Society A, vol. 361, no. 1809, pp. 1721-1748, Bibcode:2003 RSPTA.361.1721 C, doi:10.1098/ rsta.2003.1232, PMID 12952683, S2CID 31032007. Crevier, Daniel (1993 ). AI: The Tumultuous Search for Artificial Intelligence. New York, NY: BasicBooks. ISBN 0-465-02997-3. Darrach, Brad (20 November 1970), "Meet Shakey, the First Electronic Person", Life Magazine, pp. 58-68. Drachman, D. (2005 ), "Do we have brain to spare?", Neurology, 64 (12 ): 2004-2005, doi:10.1212/ 01. WNL.0000166914.38327. BB, PMID 15985565, S2CID 38482114. Feigenbaum, Edward A.; McCorduck, Pamela (1983 ), The Fifth Generation: 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 original (PDF) on 20 March 2013. Goertzel, Ben (December 2007), "Human-level artificial 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", Artificial Intelligence, vol. 171, no. 18, Special Review Issue, pp. 1161-1173, doi:10.1016/ j.artint.2007.10.011, archived from the original on 7 January 2016, retrieved 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, recovered 7 May 2011. Howe, J. (November 1994), Artificial Intelligence at Edinburgh University: a Viewpoint, 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 ), "Expert System: A General Survey", Artificial Intelligence: a paper symposium, Science Research Council. Luger, George; Stubblefield, William (2004 ), Artificial Intelligence: Structures and Strategies for Complex Problem Solving (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 system programs that can fix problems and achieve goals in the world along with people. Moravec, Hans (1988 ), Mind Children, Harvard University Press Moravec, Hans (1998 ), "When will computer system hardware match the human brain?", Journal of Evolution and Technology, vol. 1, archived from the 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, retrieved 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 Revolution: Government Support for Computing Research, National Academy Press, archived from the initial on 12 January 2008, obtained 29 September 2007 Poole, David; Mackworth, Alan; Goebel, Randy (1998 ), Computational Intelligence: A Sensible Approach, New York: 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 (2nd ed.), Upper Saddle River, New Jersey: Prentice Hall, ISBN 0-13-790395-2 Sandberg, Anders; Boström, Nick (2008 ), Whole Brain Emulation: A Roadmap (PDF), Technical Report # 2008-3, Future of Humanity Institute, Oxford University, archived (PDF) from the initial on 25 March 2020, retrieved 5 April 2009 Searle, John (1980 ), "Minds, Brains and Programs" (PDF), Behavioral and Brain Sciences, 3 (3 ): 417-457, doi:10.1017/ S0140525X00005756, S2CID 55303721, archived (PDF) from the original on 17 March 2019, 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 original on 18 February 2021. Retrieved 13 December 2020 - via ResearchGate.
Further reading

Aleksander, Igor (1996 ), Impossible Minds, World Scientific Publishing Company, ISBN 978-1-8609-4036-1 Azevedo FA, Carvalho LR, Grinberg LT, Farfel J, et al. (April 2009), "Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain", The Journal of Comparative Neurology, 513 (5 ): 532-541, doi:10.1002/ cne.21974, PMID 19226510, S2CID 5200449, archived from the original 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, recovered 31 August 2012 Cukier, Kenneth, "Ready for Robots? How to Consider the Future of AI", Foreign Affairs, vol. 98, no. 4 (July/August 2019), pp. 192-98. George Dyson, historian of computing, composes (in what might be called "Dyson's Law") that "Any system easy sufficient to be reasonable will not be made complex enough to act wisely, while any system made complex enough to behave smartly will be too made complex to comprehend." (p. 197.) Computer researcher Alex Pentland composes: "Current AI machine-learning algorithms are, at their core, dead basic stupid. They work, but they work by brute force." (p. 198.). Gelernter, David, Dream-logic, the Internet and Artificial Thought, Edge, archived from the initial on 26 July 2010, retrieved 25 July 2010. Gleick, James, "The Fate of Free Will" (review of Kevin J. Mitchell, Free Agents: How Evolution Gave Us Free Will, Princeton University Press, 2023, 333 pp.), The New York City Review of Books, vol. LXXI, no. 1 (18 January 2024), pp. 27-28, 30. "Agency is what identifies us from machines. For biological animals, reason and purpose originate from acting worldwide and experiencing the repercussions. Artificial intelligences - 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 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 realistically anticipate that those who intend to get rich from AI are going to have the interests of the rest people close at heart,' ... composes [Gary Marcus] 'We can't rely on federal governments driven by project finance contributions [from tech companies] to press back.' ... Marcus details the needs that residents should make of their federal governments and the tech companies. They consist of transparency on how AI systems work; settlement for people if their information [are] used to train LLMs (big language model) s and the right to approval to this use; and the ability to hold tech companies responsible for the damages they bring on by removing Section 230, enforcing money penalites, and passing more stringent item liability laws ... Marcus likewise suggests ... that a brand-new, AI-specific federal company, similar to the FDA, the FCC, or the FTC, might supply the most robust oversight ... [T] he Fordham law professor Chinmayi Sharma ... recommends ... develop [ing] a professional licensing routine for engineers that would work in a comparable way to medical licenses, malpractice matches, and the Hippocratic oath in medication. 'What if, like medical professionals,' she asks ..., 'AI engineers also vowed to do no damage?'" (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 stumped human beings for years, exposes the limitations of natural-language-processing algorithms", Scientific American, vol. 329, no. 4 (November 2023), pp. 81-82. "This murder secret competitors has revealed that although NLP (natural-language processing) models are capable of incredible feats, their capabilities are quite limited by the amount of context they get. This [...] could cause [problems] for scientists who intend to use them to do things such as analyze ancient languages. Sometimes, there are few historical records on long-gone civilizations to serve as training data for such a purpose." (p. 82.). Immerwahr, Daniel, "Your Lying Eyes: People now use A.I. to produce phony videos indistinguishable from real ones. How much does it matter?", The New Yorker, 20 November 2023, pp. 54-59. "If by 'deepfakes' we imply practical videos produced using synthetic intelligence that really deceive individuals, then they hardly exist. The phonies aren't deep, and the deeps aren't fake. [...] A.I.-generated videos are not, in basic, operating in our media as counterfeited evidence. Their role much better looks like that of cartoons, particularly smutty ones." (p. 59.). - Leffer, Lauren, "The Risks of Trusting AI: We need to prevent humanizing machine-learning designs utilized in scientific research", Scientific American, vol. 330, no. 6 (June 2024), pp. 80-81. Lepore, Jill, "The Chit-Chatbot: Is talking with a device a conversation?", The New Yorker, 7 October 2024, pp. 12-16. Marcus, Gary, "Artificial Confidence: Even the latest, buzziest systems of synthetic general intelligence are stymmied by the very 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 (2nd ed.), Natick, Massachusetts: A. K. Peters, ISBN 1-5688-1205-1. Moravec, Hans (1976 ), The Role of Raw Power in Intelligence, archived from the original on 3 March 2016, 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 City: McGraw-Hill. Omohundro, Steve (2008 ), The Nature of Self-Improving Artificial Intelligence, provided and distributed at the 2007 Singularity Summit, San Francisco, California. Press, Eyal, "In Front of Their Faces: Does facial-recognition innovation lead police to overlook inconsistent evidence?", The New Yorker, 20 November 2023, pp. 20-26. Roivainen, Eka, "AI's IQ: ChatGPT aced a [standard intelligence] test but showed that intelligence can not be determined by IQ alone", Scientific American, vol. 329, no. 1 (July/August 2023), p. 7. "Despite its high IQ, ChatGPT stops working at jobs that require genuine humanlike reasoning or an understanding of the physical and social world ... ChatGPT seemed not able to reason logically and tried to count on its huge database of ... realities originated from online texts. " - Scharre, Paul, "Killer Apps: The Real Dangers of an AI Arms Race", Foreign Affairs, vol. 98, no. 3 (May/June 2019), pp. 135-44. "Today's AI technologies are effective but undependable. Rules-based systems can not handle circumstances their developers did not prepare for. Learning systems are limited by the data on which they were trained. AI failures have already led to disaster. Advanced auto-pilot features in cars and trucks, although they carry out well in some situations, have driven cars without warning into trucks, concrete barriers, and parked automobiles. In the incorrect circumstance, AI systems go from supersmart to superdumb in an immediate. When an opponent is attempting 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 made possible by brand-new innovations however depend on the timelelss human tendency to anthropomorphise." (p. 29.). Williams, R. W.; Herrup, K.