Artificial basic intelligence (AGI) is a type of artificial intelligence (AI) that matches or goes beyond human cognitive abilities throughout a wide variety of cognitive tasks. This contrasts with narrow AI, which is limited to specific jobs. [1] Artificial superintelligence (ASI), on the other hand, describes AGI that considerably goes beyond human cognitive abilities. AGI is thought about among the definitions of strong AI.

Creating AGI is a main goal of AI research study and of business such as OpenAI [2] and Meta. [3] A 2020 survey identified 72 active AGI research and development projects throughout 37 countries. [4]
The timeline for attaining AGI remains a subject of ongoing argument among scientists and specialists. Since 2023, some argue that it may be possible in years or decades; others maintain it might take a century or longer; a minority believe it might never ever be accomplished; and another minority claims that it is already here. [5] [6] Notable AI researcher Geoffrey Hinton has expressed issues about the rapid progress towards AGI, recommending it could be accomplished faster than numerous anticipate. [7]
There is dispute on the precise definition of AGI and relating to whether modern big language models (LLMs) such as GPT-4 are early forms of AGI. [8] AGI is a typical subject in science fiction and futures studies. [9] [10]
Contention exists over whether AGI represents an existential risk. [11] [12] [13] Many specialists on AI have mentioned that mitigating the danger of human termination positioned by AGI needs to be a worldwide top priority. [14] [15] Others discover the advancement of AGI to be too remote to provide such a danger. [16] [17]
Terminology

AGI is likewise referred to as strong AI, [18] [19] complete AI, [20] human-level AI, [5] human-level smart AI, or basic intelligent action. [21]
Some scholastic sources reserve the term "strong AI" for computer programs that experience life or awareness. [a] On the other hand, weak AI (or narrow AI) is able to fix one specific problem but does not have basic cognitive capabilities. [22] [19] Some scholastic sources use "weak AI" to refer more broadly to any programs that neither experience consciousness nor have a mind in the very same sense as humans. [a]
Related principles consist of artificial superintelligence and transformative AI. An artificial superintelligence (ASI) is a theoretical kind of AGI that is a lot more typically intelligent than humans, [23] while the concept of transformative AI relates to AI having a large influence on society, for instance, similar to the agricultural or industrial transformation. [24]
A framework for categorizing AGI in levels was proposed in 2023 by Google DeepMind scientists. They specify five levels of AGI: emerging, proficient, specialist, virtuoso, and superhuman. For example, a skilled AGI is specified as an AI that surpasses 50% of experienced grownups in a wide variety of non-physical tasks, and a superhuman AGI (i.e. a synthetic superintelligence) is similarly specified however with a limit of 100%. They consider big language models like ChatGPT or LLaMA 2 to be instances of emerging AGI. [25]
Characteristics

Various popular definitions of intelligence have been proposed. One of the leading propositions is the Turing test. However, there are other popular meanings, and some researchers disagree with the more popular approaches. [b]
Intelligence characteristics

Researchers generally hold that intelligence is needed to do all of the following: [27]
factor, usage technique, resolve puzzles, and make judgments under unpredictability represent knowledge, consisting of typical sense knowledge strategy find out

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

Many interdisciplinary approaches (e.g. cognitive science, computational intelligence, and decision making) think about extra characteristics such as creativity (the ability to form novel psychological images and ideas) [28] and autonomy. [29]
Computer-based systems that display a number of these capabilities exist (e.g. see computational creativity, automated thinking, decision support group, robotic, evolutionary computation, intelligent representative). There is argument about whether modern AI systems have them to an adequate degree.

Physical characteristics

Other capabilities are thought about preferable in intelligent systems, as they might impact 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. relocation and manipulate items, change place to check out, and so on).
This includes the ability to spot and respond to hazard. [31]
Although the capability to sense (e.g. see, hear, and so on) and the ability to act (e.g. relocation and control things, change area to check out, etc) can be preferable for some intelligent systems, [30] these physical capabilities are not strictly required for an entity to certify as AGI-particularly under the thesis that big language designs (LLMs) may currently be or end up being AGI. Even from a less optimistic perspective on LLMs, there is no firm requirement for an AGI to have a human-like type; being a silicon-based computational system is sufficient, 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 personification and thus does not demand a capability for locomotion or traditional "eyes and ears". [32]
Tests for human-level AGI

Several tests indicated to validate human-level AGI have actually been considered, including: [33] [34]
The concept of the test is that the maker needs to attempt and pretend to be a guy, by answering questions put to it, and it will just pass if the pretence is fairly persuading. A significant part of a jury, who ought to not be skilled about machines, need to be taken in by the pretence. [37]
AI-complete issues

An issue is informally called "AI-complete" or "AI-hard" if it is believed that in order to solve it, one would need to carry out AGI, due to the fact that the solution is beyond the capabilities of a purpose-specific algorithm. [47]
There are numerous issues that have been conjectured to require general intelligence to resolve along with people. Examples consist of computer vision, natural language understanding, and handling unexpected circumstances while resolving any real-world issue. [48] Even a particular job like translation needs a maker to check out and write in both languages, follow the author's argument (factor), comprehend the context (understanding), and consistently replicate the author's initial intent (social intelligence). All of these problems need to be solved all at once in order to reach human-level machine performance.

However, many of these tasks can now be carried out by modern-day big language models. According to Stanford University's 2024 AI index, AI has actually reached human-level efficiency on numerous criteria for reading comprehension and visual thinking. [49]
History

Classical AI

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

However, in the early 1970s, it became apparent that researchers had actually grossly undervalued the trouble of the job. Funding companies became skeptical of AGI and put scientists under increasing pressure to produce beneficial "used AI". [c] In the early 1980s, Japan's Fifth Generation Computer Project revived interest in AGI, setting out a ten-year timeline that included AGI objectives like "continue a casual discussion". [58] In reaction to this and the success of expert systems, macphersonwiki.mywikis.wiki both industry and 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 fulfilled. [60] For the 2nd time in 20 years, AI researchers who anticipated the impending accomplishment of AGI had actually been misinterpreted. By the 1990s, AI researchers had a credibility for making vain pledges. They became reluctant to make forecasts at all [d] and avoided reference of "human level" synthetic intelligence for fear of being labeled "wild-eyed dreamer [s]. [62]
Narrow AI research study

In the 1990s and early 21st century, mainstream AI achieved industrial success and scholastic respectability by concentrating on specific sub-problems where AI can produce proven results and commercial applications, such as speech recognition and suggestion algorithms. [63] These "applied AI" systems are now used thoroughly throughout the technology market, and research study in this vein is heavily moneyed in both academia and market. As of 2018 [upgrade], development in this field was thought about an emerging pattern, and a mature stage was expected to be reached in more than ten years. [64]
At the millenium, numerous traditional AI scientists [65] hoped that strong AI could be established by combining programs that solve various sub-problems. Hans Moravec composed in 1988:

I am confident that this bottom-up route to synthetic intelligence will one day fulfill the standard top-down route more than half method, all set to offer the real-world skills and the commonsense knowledge that has 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 disputed. 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 somewhere in between. If the grounding factors to consider in this paper stand, then this expectation is hopelessly modular and there is really only one practical 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 need to even attempt to reach such a level, because it appears getting there would simply amount to uprooting our symbols from their intrinsic meanings (thus merely minimizing ourselves to the functional equivalent of a programmable computer system). [66]
Modern artificial basic intelligence research

The term "synthetic basic intelligence" was utilized as early as 1997, by Mark Gubrud [67] in a conversation of the implications of totally automated military production and operations. A mathematical formalism of AGI was proposed by Marcus Hutter in 2000. Named AIXI, the proposed AGI representative increases "the capability to please goals 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 also called universal artificial intelligence. [70]
The term AGI was re-introduced and popularized by Shane Legg and Ben Goertzel around 2002. [71] AGI research activity in 2006 was explained by Pei Wang and Ben Goertzel [72] as "producing publications and initial results". The first summertime school in AGI was organized in Xiamen, China in 2009 [73] by the Xiamen university's Artificial Brain Laboratory and OpenCog. The first university course was given up 2010 [74] and 2011 [75] at Plovdiv University, Bulgaria by Todor Arnaudov. MIT presented a course on AGI in 2018, arranged by Lex Fridman and including a number of guest speakers.

As of 2023 [update], a little number of computer system researchers are active in AGI research, and numerous 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 constantly learn and innovate like humans do.

Feasibility

Since 2023, the development and prospective accomplishment of AGI stays a topic of intense debate within the AI community. While traditional consensus held that AGI was a remote goal, current developments have led some scientists and market figures to declare that early forms of AGI may currently exist. [78] AI leader Herbert A. Simon hypothesized in 1965 that "devices will be capable, within twenty years, of doing any work a male can do". This prediction failed to come real. Microsoft co-founder Paul Allen thought that such intelligence is not likely in the 21st century due to the fact that it would need "unforeseeable and fundamentally unforeseeable developments" and a "clinically deep understanding of cognition". [79] Writing in The Guardian, roboticist Alan Winfield claimed the gulf between modern-day computing and human-level expert system is as broad as the gulf in between existing space flight and useful faster-than-light spaceflight. [80]
An additional challenge is the lack of clarity in defining what intelligence entails. Does it need awareness? Must it display the ability to set goals as well as pursue them? Is it simply a matter of scale such that if design sizes increase adequately, intelligence will emerge? Are facilities such as preparation, reasoning, and causal understanding needed? Does intelligence need explicitly replicating the brain and its particular professors? Does it require emotions? [81]
Most AI scientists believe strong AI can be accomplished in the future, however some thinkers, like Hubert Dreyfus and Roger Penrose, deny the possibility of achieving strong AI. [82] [83] John McCarthy is among those who think human-level AI will be achieved, however that the present level of progress is such that a date can not precisely be forecasted. [84] AI experts' views on the expediency of AGI wax and wane. Four polls carried out in 2012 and 2013 recommended that the median estimate among experts for when they would be 50% positive AGI would get here was 2040 to 2050, depending upon the survey, with the mean being 2081. Of the experts, 16.5% addressed with "never ever" when asked the exact same concern however with a 90% self-confidence rather. [85] [86] Further current AGI development factors to consider 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 predisposition towards predicting the arrival of human-level AI as in between 15 and 25 years from the time the prediction was made". They examined 95 forecasts made in between 1950 and 2012 on when human-level AI will come about. [87]
In 2023, Microsoft researchers released an in-depth evaluation of GPT-4. They concluded: "Given the breadth and depth of GPT-4's capabilities, our company believe that it might fairly be considered as an early (yet still insufficient) version of a synthetic basic intelligence (AGI) system." [88] Another study in 2023 reported that GPT-4 surpasses 99% of humans on the Torrance tests of creative thinking. [89] [90]
Blaise Agüera y Arcas and Peter Norvig composed in 2023 that a substantial level of general intelligence has actually already been achieved with frontier models. They wrote that hesitation to this view comes from four main factors: a "healthy uncertainty about metrics for AGI", an "ideological commitment to alternative AI theories or strategies", 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 models (big language models capable of processing or creating several techniques such as text, audio, and images). [92]
In 2024, OpenAI released o1-preview, the first of a series of designs that "invest more time thinking before they react". According to Mira Murati, this ability to believe before reacting represents a new, extra paradigm. It enhances design outputs by investing more computing power when generating the response, whereas the model scaling paradigm improves outputs by increasing the model size, training data and training calculate power. [93] [94]
An OpenAI employee, Vahid Kazemi, declared in 2024 that the business had actually accomplished AGI, specifying, "In my opinion, we have actually already achieved AGI and it's much more clear with O1." Kazemi clarified that while the AI is not yet "better than any human at any task", it is "better than many humans at most tasks." He likewise addressed criticisms that large language models (LLMs) simply follow predefined patterns, comparing their learning procedure to the scientific approach of observing, assuming, and validating. These statements have sparked debate, as they rely on a broad and non-traditional definition of AGI-traditionally comprehended as AI that matches human intelligence throughout all domains. Critics argue that, while OpenAI's models show impressive flexibility, they might not completely fulfill this standard. Notably, Kazemi's comments came shortly after OpenAI eliminated "AGI" from the terms of its collaboration with Microsoft, prompting speculation about the business's strategic intentions. [95]
Timescales

Progress in expert system has actually traditionally gone through durations of quick progress separated by periods when development appeared to stop. [82] Ending each hiatus were fundamental advances in hardware, software application or both to create area for additional development. [82] [98] [99] For example, the hardware offered in the twentieth century was not sufficient to execute deep knowing, which needs big numbers of GPU-enabled CPUs. [100]
In the intro to his 2006 book, [101] Goertzel states that price quotes of the time required before a truly versatile AGI is constructed vary from ten years to over a century. Since 2007 [upgrade], the agreement in the AGI research neighborhood appeared to be that the timeline talked about by Ray Kurzweil in 2005 in The Singularity is Near [102] (i.e. between 2015 and 2045) was possible. [103] Mainstream AI scientists have given a large range of opinions on whether development will be this quick. A 2012 meta-analysis of 95 such opinions found a bias towards anticipating that the beginning of AGI would happen within 16-26 years for modern-day and historic forecasts alike. That paper has been criticized for how it classified viewpoints as specialist or non-expert. [104]
In 2012, Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton established a neural network called AlexNet, which won the ImageNet competition with a top-5 test error rate of 15.3%, significantly better than the second-best entry's rate of 26.3% (the conventional technique used a weighted sum of scores from different pre-defined classifiers). [105] AlexNet was regarded as the initial ground-breaker of the current deep knowing wave. [105]
In 2017, scientists Feng Liu, Yong Shi, and Ying Liu performed intelligence tests on publicly readily available and freely available weak AI such as Google AI, Apple's Siri, and others. At the maximum, these AIs reached an IQ value of about 47, which corresponds around to a six-year-old child in very first grade. An adult concerns about 100 usually. Similar tests were carried out in 2014, with the IQ score reaching a maximum value of 27. [106] [107]
In 2020, OpenAI developed GPT-3, a language model capable of performing numerous diverse jobs without particular training. According to Gary Grossman in a VentureBeat article, while there is consensus that GPT-3 is not an example of AGI, it is thought about by some to be too advanced to be categorized as a narrow AI system. [108]
In the very same year, Jason Rohrer utilized his GPT-3 account to establish a chatbot, and supplied a chatbot-developing platform called "Project December". OpenAI requested modifications to the chatbot to adhere to their safety guidelines; Rohrer disconnected Project December from the GPT-3 API. [109]
In 2022, DeepMind developed Gato, a "general-purpose" system efficient in carrying out more than 600 various jobs. [110]
In 2023, Microsoft Research published a research study on an early variation of OpenAI's GPT-4, competing that it displayed more basic intelligence than previous AI designs and showed human-level efficiency in jobs spanning numerous domains, such as mathematics, coding, and law. This research stimulated an argument on whether GPT-4 might be thought about an early, insufficient variation of synthetic basic intelligence, highlighting the requirement for further exploration and examination of such systems. [111]
In 2023, the AI researcher Geoffrey Hinton mentioned that: [112]
The idea that this stuff could in fact get smarter than individuals - a couple of 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 and even longer away. Obviously, I no longer believe that.

In May 2023, Demis Hassabis likewise stated that "The progress in the last few years has been quite amazing", which he sees no reason it would decrease, anticipating AGI within a decade and even a couple of years. [113] In March 2024, Nvidia's CEO, Jensen Huang, stated his expectation that within five years, AI would can 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 possible". [115]
Whole brain emulation

While the advancement of transformer models like in ChatGPT is thought about the most appealing path to AGI, [116] [117] entire brain emulation can act as an alternative approach. 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 devoted to the original, so that it acts in practically the same method as the initial brain. [118] Whole brain emulation is a type of brain simulation that is talked about in computational neuroscience and neuroinformatics, and for medical research study functions. It has been talked about in artificial intelligence research [103] as a method to strong AI. Neuroimaging technologies that could provide the required detailed understanding are improving quickly, and futurist Ray Kurzweil in the book The Singularity Is Near [102] predicts that a map of enough quality will appear on a similar timescale to the computing power needed to emulate it.

Early estimates

For low-level brain simulation, a very powerful cluster of computer systems or GPUs would be needed, given the massive quantity of synapses within the human brain. Each of the 1011 (one hundred billion) neurons has on average 7,000 synaptic connections (synapses) to other neurons. The brain of a three-year-old child has about 1015 synapses (1 quadrillion). This number decreases with age, stabilizing by the adult years. Estimates vary for an adult, varying from 1014 to 5 × 1014 synapses (100 to 500 trillion). [120] A price quote of the brain's processing power, based on an easy switch model for nerve cell activity, is around 1014 (100 trillion) synaptic updates per second (SUPS). [121]
In 1997, Kurzweil looked at various estimates for the hardware required to equal the human brain and adopted a figure of 1016 computations per second (cps). [e] (For comparison, if a "computation" was equivalent to one "floating-point operation" - a procedure used to rate present supercomputers - then 1016 "calculations" would be equivalent to 10 petaFLOPS, accomplished in 2011, while 1018 was accomplished in 2022.) He utilized this figure to forecast the necessary hardware would be available at some point in between 2015 and 2025, if the exponential growth in computer system power at the time of writing continued.

Current research study

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

Criticisms of simulation-based approaches

The artificial neuron model assumed by Kurzweil and used in numerous current artificial neural network executions is easy compared with biological neurons. A brain simulation would likely have to capture the detailed cellular behaviour of biological neurons, currently understood just in broad outline. The overhead introduced by full modeling of the biological, chemical, and physical details of neural behaviour (specifically on a molecular scale) would need computational powers a number of orders of magnitude bigger than Kurzweil's quote. In addition, the estimates do not account for glial cells, which are understood to play a function in cognitive processes. [125]
An essential criticism of the simulated brain technique stems from embodied cognition theory which asserts that human personification is an essential element of human intelligence and is needed to ground meaning. [126] [127] If this theory is appropriate, any totally practical brain design will need to include more than simply the neurons (e.g., a robotic body). Goertzel [103] proposes virtual embodiment (like in metaverses like Second Life) as a choice, however it is unidentified whether this would suffice.

Philosophical perspective

"Strong AI" as defined in viewpoint

In 1980, theorist John Searle created the term "strong AI" as part of his Chinese space argument. [128] He proposed a distinction between two hypotheses about expert system: [f]
Strong AI hypothesis: A synthetic intelligence system can have "a mind" and "consciousness". Weak AI hypothesis: A synthetic intelligence system can (just) imitate it thinks and has a mind and awareness.
The first one he called "strong" because it makes a more powerful statement: it presumes something special has taken place to the device that goes beyond those abilities that we can check. The behaviour of a "weak AI" device would be specifically identical to a "strong AI" machine, but the latter would likewise have subjective conscious experience. This usage is likewise typical in academic AI research 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 synthetic general intelligence". [102] This is not the like Searle's strong AI, unless it is assumed that consciousness is necessary for human-level AGI. Academic theorists such as Searle do not think that is the case, and to most synthetic intelligence researchers the question 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 actually has mind - undoubtedly, there would be no chance to tell. For AI research study, Searle's "weak AI hypothesis" is equivalent to the declaration "artificial general intelligence is possible". Thus, according to Russell and Norvig, "most AI researchers take the weak AI hypothesis for given, and don't care about the strong AI hypothesis." [130] Thus, for academic AI research, "Strong AI" and "AGI" are two various things.

Consciousness

Consciousness can have different significances, and some aspects play significant functions in sci-fi and the principles of expert system:

Sentience (or "phenomenal awareness"): The capability to "feel" perceptions or feelings subjectively, rather than the ability to factor about perceptions. Some philosophers, such as David Chalmers, use the term "consciousness" to refer solely to incredible consciousness, which is approximately equivalent to life. [132] Determining why and how subjective experience emerges is referred to as the tough issue of consciousness. [133] Thomas Nagel described 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 smartly ask "what does it seem like to be a bat?" However, we are not likely to ask "what does it feel like to be a toaster?" Nagel concludes that a bat seems conscious (i.e., has consciousness) however a toaster does not. [134] In 2022, a Google engineer claimed that the business's AI chatbot, LaMDA, had attained sentience, though this claim was widely challenged by other experts. [135]
Self-awareness: To have mindful awareness of oneself as a separate individual, especially to be purposely familiar with one's own thoughts. This is opposed to simply being the "subject of one's thought"-an os or debugger has the ability to be "familiar with itself" (that is, to represent itself in the very same way it represents whatever else)-but this is not what people normally suggest when they utilize the term "self-awareness". [g]
These traits have an ethical dimension. AI life would generate issues of welfare and legal protection, similarly to animals. [136] Other elements of consciousness related to cognitive capabilities are likewise pertinent to the concept of AI rights. [137] Determining how to incorporate advanced AI with existing legal and social frameworks is an emerging problem. [138]
Benefits

AGI might have a wide array of applications. If oriented towards such goals, AGI might help reduce different issues worldwide such as hunger, hardship and illness. [139]
AGI might improve productivity and performance in the majority of tasks. For example, in public health, AGI might speed up medical research study, especially versus cancer. [140] It might take care of the senior, [141] and democratize access to rapid, high-quality medical diagnostics. It could use enjoyable, cheap and individualized education. [141] The need to work to subsist might end up being outdated if the wealth produced is appropriately redistributed. [141] [142] This likewise raises the question of the place of humans in a significantly automated society.

AGI might also help to make logical choices, and to prepare for and avoid catastrophes. It could also help to profit of possibly devastating innovations such as nanotechnology or climate engineering, while avoiding the associated risks. [143] If an AGI's primary objective is to prevent existential catastrophes such as human extinction (which could be hard if the Vulnerable World Hypothesis ends up being true), [144] it could take measures to considerably lower the dangers [143] while reducing the effect of these steps on our quality of life.

Risks

Existential risks

AGI may represent multiple types of existential danger, which are threats that threaten "the early extinction of Earth-originating intelligent life or the long-term and extreme destruction of its potential for desirable future advancement". [145] The risk of human extinction from AGI has been the subject of numerous arguments, however there is likewise the possibility that the development of AGI would lead to a permanently problematic future. Notably, it might be used to spread and maintain the set of worths of whoever develops it. If mankind still has moral blind spots comparable to slavery in the past, AGI might irreversibly entrench it, preventing ethical development. [146] Furthermore, AGI might facilitate mass monitoring and brainwashing, which might be utilized to produce a steady repressive around the world totalitarian regime. [147] [148] There is also a threat for the makers themselves. If machines that are sentient or otherwise worthy of moral factor to consider are mass created in the future, participating in a civilizational path that forever overlooks their welfare and interests might be an existential disaster. [149] [150] Considering how much AGI might improve humankind's future and assistance decrease other existential risks, Toby Ord calls these existential threats "an argument for continuing with due care", not for "deserting AI". [147]
Risk of loss of control and human extinction

The thesis that AI poses an existential danger for human beings, and that this risk requires more attention, is controversial but has actually been backed in 2023 by many public figures, AI researchers and CEOs of AI companies such as Elon Musk, Bill Gates, Geoffrey Hinton, Yoshua Bengio, Demis Hassabis and Sam Altman. [151] [152]
In 2014, Stephen Hawking slammed widespread indifference:

So, facing possible futures of incalculable benefits and dangers, the specialists are surely doing everything possible to guarantee the very best outcome, right? Wrong. If a remarkable alien civilisation sent us a message saying, 'We'll arrive in a couple of decades,' would we just respond, 'OK, call us when you get here-we'll leave the lights on?' Probably not-but this is more or less what is occurring with AI. [153]
The potential fate of mankind has sometimes been compared to the fate of gorillas threatened by human activities. The comparison specifies that greater intelligence enabled mankind to control gorillas, which are now vulnerable in manner ins which they could not have expected. As an outcome, the gorilla has actually become an endangered types, not out of malice, but merely as a civilian casualties from human activities. [154]
The skeptic Yann LeCun thinks about that AGIs will have no desire to control humanity and that we must be careful not to anthropomorphize them and analyze their intents as we would for human beings. He said that people will not be "clever adequate to design super-intelligent devices, yet extremely dumb to the point of offering it moronic goals with no safeguards". [155] On the other side, the principle of instrumental convergence suggests that nearly whatever their objectives, smart agents will have factors to try to survive and get more power as intermediary steps to accomplishing these objectives. And that this does not need having feelings. [156]
Many scholars who are worried about existential danger supporter for more research study into resolving the "control problem" to respond to the concern: what kinds of safeguards, algorithms, or architectures can developers implement to maximise the possibility that their recursively-improving AI would continue to act in a friendly, instead of destructive, manner after it reaches superintelligence? [157] [158] Solving the control problem is made complex by the AI arms race (which could cause a race to the bottom of security preventative measures in order to launch products before competitors), [159] and making use of AI in weapon systems. [160]
The thesis that AI can position existential threat also has detractors. Skeptics usually state that AGI is not likely in the short-term, or that issues about AGI sidetrack from other concerns related to existing AI. [161] Former Google fraud czar Shuman Ghosemajumder thinks about that for many individuals outside of the innovation industry, existing chatbots and LLMs are already perceived as though they were AGI, resulting in more misconception and fear. [162]
Skeptics often charge that the thesis is crypto-religious, with an irrational belief in the possibility of superintelligence replacing an unreasonable belief in a supreme God. [163] Some scientists believe that the communication campaigns on AI existential risk 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 industry leaders and scientists, released a joint declaration asserting that "Mitigating the threat of termination from AI ought to be an international priority alongside other societal-scale threats such as pandemics and nuclear war." [152]
Mass unemployment

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

According to Stephen Hawking, the outcome 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 many individuals can wind up badly poor if the machine-owners successfully lobby against wealth redistribution. So far, the pattern seems to be towards the 2nd choice, with technology driving ever-increasing inequality

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

Artificial brain - Software and hardware with cognitive capabilities comparable to those of the animal or human brain AI result AI safety - Research location on making AI safe and beneficial AI positioning - AI conformance to the intended objective A.I. Rising - 2018 movie directed by Lazar Bodroža Artificial intelligence Automated device learning - Process of automating the application of artificial intelligence BRAIN Initiative - Collaborative public-private research initiative announced by the Obama administration China Brain Project Future of Humanity Institute - Defunct Oxford interdisciplinary research centre General video game playing - Ability of expert system to play different games Generative expert system - AI system capable of producing content in action to triggers Human Brain Project - Scientific research job Intelligence amplification - Use of infotech to enhance human intelligence (IA). Machine principles - Moral behaviours of man-made machines. Moravec's paradox. Multi-task learning - Solving numerous maker discovering jobs at the very same time. Neural scaling law - Statistical law in artificial intelligence. Outline of artificial intelligence - Overview of and topical guide to artificial intelligence. Transhumanism - Philosophical motion. Synthetic intelligence - Alternate term for or form of expert system. Transfer learning - Artificial intelligence method. Loebner Prize - Annual AI competitors. Hardware for expert system - Hardware specifically developed and optimized for synthetic intelligence. Weak artificial intelligence - Form of artificial intelligence.
Notes

^ a b See below for the origin of the term "strong AI", and see the scholastic definition of "strong AI" and weak AI in the post Chinese room. ^ AI creator John McCarthy composes: "we can not yet identify in general what kinds of computational treatments we wish to call smart. " [26] (For a conversation of some definitions of intelligence used by synthetic intelligence researchers, see viewpoint of artificial intelligence.). ^ The Lighthill report specifically slammed AI's "grandiose goals" and led the dismantling of AI research in England. [55] In the U.S., DARPA became identified to money just "mission-oriented direct research, rather than basic undirected research". [56] [57] ^ As AI creator John McCarthy writes "it would be a great relief to the remainder of the employees in AI if the creators of new basic formalisms would express their hopes in a more secured type than has in some cases been the case." [61] ^ In "Mind Children" [122] 1015 cps is utilized. More recently, in 1997, [123] Moravec argued for 108 MIPS which would roughly correspond to 1014 cps. Moravec talks in terms of MIPS, not "cps", which is a non-standard term Kurzweil introduced. ^ As defined in a basic AI book: "The assertion that machines could perhaps act wisely (or, possibly much better, act as if they were intelligent) is called the 'weak AI' hypothesis by philosophers, and the assertion that makers that do so are actually believing (instead of imitating thinking) is called the 'strong AI' hypothesis." [121] ^ Alan Turing made this point in 1950. [36] References

^ Krishna, Sri (9 February 2023). "What is synthetic narrow intelligence (ANI)?". VentureBeat. Retrieved 1 March 2024. ANI is designed to carry out a single task. ^ "OpenAI Charter". OpenAI. Retrieved 6 April 2023. Our objective is to ensure that synthetic basic intelligence advantages all of mankind. ^ Heath, Alex (18 January 2024). "Mark Zuckerberg's brand-new objective is creating synthetic basic intelligence". The Verge. Retrieved 13 June 2024. Our vision is to build AI that is much better than human-level at all of the human senses. ^ Baum, Seth D. (2020 ). A Survey of Artificial General Intelligence Projects for Ethics, Risk, and Policy (PDF) (Report). Global Catastrophic Risk Institute. Retrieved 28 November 2024. 72 AGI R&D tasks were determined as being active in 2020. ^ a b c "AI timelines: What do experts in artificial intelligence 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 City Times. Retrieved 18 May 2023. ^ "AI pioneer Geoffrey Hinton stops Google and alerts of threat ahead". The New York City Times. 1 May 2023. Retrieved 2 May 2023. It is hard to see how you can prevent the bad actors 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 reveals triggers of AGI. ^ Butler, Octavia E. (1993 ). Parable of the Sower. Grand Central Publishing. ISBN 978-0-4466-7550-5. All that you touch you change. All that you alter modifications you. ^ Vinge, Vernor (1992 ). A Fire Upon the Deep. Tor Books. ISBN 978-0-8125-1528-2. The Singularity is coming. ^ Morozov, Evgeny (30 June 2023). "The True Threat of Artificial Intelligence". The New York City Times. The genuine threat is not AI itself however the method we release it. ^ "Impressed by expert system? Experts state AGI is coming next, and it has 'existential' dangers". ABC News. 23 March 2023. Retrieved 6 April 2023. AGI might pose existential risks 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 creation that mankind requires to make. ^ Roose, Kevin (30 May 2023). "A.I. Poses 'Risk of Extinction,' Industry Leaders Warn". The New York Times. Mitigating the threat of extinction from AI need to be a global priority. ^ "Statement on AI Risk". Center for AI Safety. Retrieved 1 March 2024. AI experts warn of risk of termination from AI. ^ Mitchell, Melanie (30 May 2023). "Are AI's Doomsday Scenarios Worth Taking Seriously?". The New York City Times. We are far from creating devices that can outthink us in general ways. ^ LeCun, Yann (June 2023). "AGI does not provide 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 explains 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 original 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 synthetic superintelligence (ASI)?|Definition from TechTarget". Enterprise AI. Retrieved 8 October 2023. ^ "Expert system is transforming our world - it is on all of us to make sure that it goes well". Our World in Data. Retrieved 8 October 2023. ^ Dickson, Ben (16 November 2023). "Here is how far we are to attaining AGI, according to DeepMind". VentureBeat. ^ McCarthy, John (2007a). "Basic Questions". Stanford University. Archived from the original on 26 October 2007. Retrieved 6 December 2007. ^ This list of smart characteristics is based upon the topics covered by significant AI textbooks, consisting of: Russell & Norvig 2003, Luger & Stubblefield 2004, Poole, Mackworth & Goebel 1998 and Nilsson 1998. ^ Johnson 1987. ^ de Charms, R. (1968 ). Personal causation. New York: Academic Press. ^ a b Pfeifer, R. and Bongard J. C., How the body shapes the method we believe: a brand-new view of intelligence (The MIT Press, 2007). ISBN 0-2621-6239-3. ^ White, R. W. (1959 ). "Motivation reevaluated: The idea 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 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 says so". The Guardian. 9 June 2014. ISSN 0261-3077. Retrieved 3 March 2024. ^ "Scientists dispute 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 whatever from the bar examination to AP Biology. Here's a list of tough tests both AI variations have passed". Business Insider. Retrieved 30 May 2023. ^ Naysmith, Caleb (7 February 2023). "6 Jobs Artificial Intelligence Is Already Replacing and How Investors Can Capitalize on It". Retrieved 30 May 2023. ^ Turk, Victoria (28 January 2015). "The Plan to Replace the Turing Test with a 'Turing Olympics'". Vice. Retrieved 3 March 2024. ^ Gopani, Avi (25 May 2022). "Turing Test is unreliable. The Winograd Schema is obsolete. Coffee is the response". Analytics India Magazine. Retrieved 3 March 2024. ^ Bhaimiya, Sawdah (20 June 2023). "DeepMind's co-founder recommended testing an AI chatbot's capability to turn $100,000 into $1 million to determine human-like intelligence". Business Insider. Retrieved 3 March 2024. ^ Suleyman, Mustafa (14 July 2023). "Mustafa Suleyman: My 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 Expert System (Second ed.). New York: 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 Defining Feature of AI-Completeness" (PDF). Artificial Intelligence, Evolutionary Computation and Metaheuristics (AIECM): 3-17. Archived (PDF) from the initial on 22 May 2013. ^ "AI Index: State of AI in 13 Charts". Stanford University Human-Centered Expert System. 15 April 2024. Retrieved 27 May 2024. ^ Crevier 1993, pp. 48-50. ^ Kaplan, Andreas (2022 ). "Expert System, Business and Civilization - Our Fate Made in Machines". Archived from the initial on 6 May 2022. Retrieved 12 March 2022. ^ Simon 1965, p. 96 estimated in Crevier 1993, p. 109. ^ "Scientist on the Set: An Interview with Marvin Minsky". Archived from the original on 16 July 2012. Retrieved 5 April 2008. ^ Marvin Minsky to Darrach (1970 ), priced quote in Crevier (1993, p. 109). ^ Lighthill 1973; Howe 1994. ^ a b NRC 1999, "Shift to Applied Research Increases Investment". ^ Crevier 1993, pp. 115-117; Russell & Norvig 2003, pp. 21-22. ^ Crevier 1993, p. 211, Russell & Norvig 2003, p. 24 and see 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 ). "Reply to Lighthill". Stanford University. Archived from the original on 30 September 2008. Retrieved 29 September 2007. ^ Markoff, John (14 October 2005). "Behind Artificial Intelligence, a Squadron of Bright Real People". The New York Times. Archived from the initial on 2 February 2023. Retrieved 18 February 2017. At its low point, some computer system scientists and software engineers prevented the term expert system for fear of being seen as wild-eyed dreamers. ^ Russell & Norvig 2003, pp. 25-26 ^ "Trends in the Emerging Tech Hype Cycle". Gartner Reports. Archived from the 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 Artificial Intelligence: 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 summer season 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 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 major challenge". EMBO Reports. 20 (10 ): e49177. doi:10.15252/ embr.201949177. ISSN 1469-221X. PMC 6776890. PMID 31531926. ^ Bubeck, Sébastien; Chandrasekaran, Varun; Eldan, Ronen; Gehrke, Johannes; Horvitz, Eric; Kamar, Ece; Lee, Peter; Lee, Yin Tat; Li, Yuanzhi; Lundberg, Scott; Nori, Harsha; Palangi, Hamid; Ribeiro, Marco Tulio; Zhang, Yi (27 March 2023). "Sparks of Artificial General Intelligence: Early 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 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 general synthetic 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 utopia or destruction?". The New Yorker. Archived from the initial on 28 January 2016. Retrieved 7 February 2016. ^ Müller, V. C., & Bostrom, N. (2016 ). Future development in artificial intelligence: A survey of professional opinion. In Fundamental problems of synthetic intelligence (pp. 555-572). Springer, Cham. ^ Armstrong, Stuart, and Kaj Sotala. 2012. "How We're Predicting AI-or Failing To." In Beyond AI: Artificial Dreams, edited by Jan Romportl, Pavel Ircing, Eva Žáčková, Michal Polák and Radek Schuster, 52-75. Plzeň: University of West Bohemia ^ "Microsoft Now Claims GPT-4 Shows 'Sparks' of General Intelligence". 24 March 2023. ^ Shimek, Cary (6 July 2023). "AI Outperforms Humans in Creativity Test". Neuroscience News. Retrieved 20 October 2023. ^ Guzik, Erik E.; Byrge, Christian; Gilde, Christian (1 December 2023). "The originality of makers: AI takes the Torrance Test". Journal of Creativity. 33 (3 ): 100065. doi:10.1016/ j.yjoc.2023.100065. ISSN 2713-3745. S2CID 261087185. ^ Arcas, Blaise Agüera y (10 October 2023). "Artificial General Intelligence Is Already Here". Noema. ^ Zia, Tehseen (8 January 2024). "Unveiling of Large Multimodal Models: Shaping the Landscape of Language Models in 2024". Unite.ai. Retrieved 26 May 2024. ^ "Introducing OpenAI o1-preview". OpenAI. 12 September 2024. ^ Knight, Will. "OpenAI Announces a Brand-new AI Model, Code-Named Strawberry, That Solves Difficult Problems Step by Step". Wired. ISSN 1059-1028. Retrieved 17 September 2024. ^ "OpenAI Employee Claims AGI Has Been Achieved". Orbital Today. 13 December 2024. Retrieved 27 December 2024. ^ "AI Index: State of AI in 13 Charts". hai.stanford.edu. 15 April 2024. Retrieved 7 June 2024. ^ "Next-Gen AI: OpenAI and Meta's Leap Towards Reasoning Machines". Unite.ai. 19 April 2024. Retrieved 7 June 2024. ^ James, Alex P. (2022 ). "The Why, What, and How of Artificial General Intelligence Chip Development". IEEE Transactions on Cognitive and Developmental Systems. 14 (2 ): 333-347. arXiv:2012.06338. doi:10.1109/ TCDS.2021.3069871. ISSN 2379-8920. S2CID 228376556. Archived from the 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 basic intelligence with hybrid Tianjic chip architecture". Nature. 572 (7767 ): 106-111. Bibcode:2019 Natur.572..106 P. doi:10.1038/ s41586-019-1424-8. ISSN 1476-4687. PMID 31367028. S2CID 199056116. Archived from the original on 29 August 2022. Retrieved 29 August 2022. ^ Pandey, Mohit; Fernandez, Michael; Gentile, Francesco; Isayev, Olexandr; Tropsha, Alexander; Stern, Abraham C.; Cherkasov, Artem (March 2022). "The transformational role of GPU computing and deep learning in drug discovery". Nature Machine Intelligence. 4 (3 ): 211-221. doi:10.1038/ s42256-022-00463-x. ISSN 2522-5839. S2CID 252081559. ^ Goertzel & Pennachin 2006. ^ a b c (Kurzweil 2005, p. 260). ^ a b c Goertzel 2007. ^ Grace, Katja (2016 ). "Error in Armstrong and Sotala 2012". AI Impacts (blog site). Archived from the initial on 4 December 2020. Retrieved 24 August 2020. ^ a b Butz, Martin V. (1 March 2021). "Towards Strong AI". KI - Künstliche Intelligenz. 35 (1 ): 91-101. doi:10.1007/ s13218-021-00705-x. ISSN 1610-1987. S2CID 256065190. ^ Liu, Feng; Shi, Yong; Liu, Ying (2017 ). "Intelligence Quotient and Intelligence Grade of Artificial Intelligence". Annals of Data Science. 4 (2 ): 179-191. arXiv:1709.10242. doi:10.1007/ s40745-017-0109-0. S2CID 37900130. ^ Brien, Jörn (5 October 2017). "Google-KI doppelt so schlau wie Siri" [Google AI is two times as clever as Siri - but a six-year-old beats both] (in German). Archived from the initial on 3 January 2019. Retrieved 2 January 2019. ^ Grossman, Gary (3 September 2020). "We're going into the AI twilight zone between narrow and general AI". VentureBeat. Archived from the original on 4 September 2020. Retrieved 5 September 2020. Certainly, too, there are those who claim we are currently seeing an early example of an AGI system in the recently revealed GPT-3 natural language processing (NLP) neural network. ... So is GPT-3 the very first example of an AGI system? This is debatable, however the agreement is that it is not AGI. ... If absolutely nothing else, GPT-3 informs us there is a middle ground in between narrow and basic AI. ^ Quach, Katyanna. "A designer built an AI chatbot utilizing GPT-3 that assisted a male speak again to his late fiancée. OpenAI shut it down". The Register. Archived from the original on 16 October 2021. Retrieved 16 October 2021. ^ Wiggers, Kyle (13 May 2022), "DeepMind's brand-new AI can carry out over 600 tasks, from playing games to managing robotics", TechCrunch, archived from the original on 16 June 2022, recovered 12 June 2022. ^ Bubeck, Sébastien; Chandrasekaran, Varun; Eldan, Ronen; Gehrke, Johannes; Horvitz, Eric; Kamar, Ece; Lee, Peter; Lee, Yin Tat; Li, Yuanzhi; Lundberg, Scott; Nori, Harsha; Palangi, Hamid; Ribeiro, Marco Tulio; Zhang, Yi (22 March 2023). "Sparks of Artificial General Intelligence: Early try outs GPT-4". arXiv:2303.12712 [cs.CL] ^ Metz, Cade (1 May 2023). "' The Godfather of A.I.' Leaves Google and Warns of Danger Ahead". The New York Times. ISSN 0362-4331. Retrieved 7 June 2023. ^ Bove, Tristan. "A.I. could measure up to human intelligence in 'just a few years,' states CEO of Google's primary A.I. research lab". Fortune. Retrieved 4 September 2024. ^ Nellis, Stephen (2 March 2024). "Nvidia CEO says AI might pass human tests in 5 years". Reuters. ^ Aschenbrenner, Leopold. "SITUATIONAL AWARENESS, The Decade Ahead". ^ Sullivan, Mark (18 October 2023). "Why everyone appears to disagree on how to specify Artificial General Intelligence". Fast Company. ^ Nosta, John (5 January 2024). "The Accelerating Path to Artificial General Intelligence". Psychology Today. Retrieved 30 March 2024. ^ Hickey, Alex. "Whole Brain Emulation: A Huge Step for Neuroscience". Tech Brew. Retrieved 8 November 2023. ^ Sandberg & Boström 2008. ^ Drachman 2005. ^ a b Russell & Norvig 2003. ^ Moravec 1988, p. 61. ^ Moravec 1998. ^ Holmgaard Mersh, Amalie (15 September 2023). "Decade-long European research study project 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 large range of views in current research study, all of which need grounding to some degree ^ Thornton, Angela (26 June 2023). "How submitting our minds to a computer system might end up being possible". The Conversation. Retrieved 8 November 2023. ^ Searle 1980 ^ For example: Russell & Norvig 2003, Oxford University Press Dictionary of Psychology Archived 3 December 2007 at the Wayback Machine (priced estimate in" Encyclopedia.com"),. MIT Encyclopedia of Cognitive Science Archived 19 July 2008 at the Wayback Machine (quoted in "AITopics"),. Will Biological Computers Enable Artificially Intelligent Machines to Become Persons? Archived 13 May 2008 at the Wayback Machine Anthony Tongen.


^ a b c Russell & Norvig 2003, p. 947. ^ though see Explainable synthetic intelligence for curiosity by the field about why a program behaves the method it does. ^ Chalmers, David J. (9 August 2023). "Could a Large Language Model Be Conscious?". Boston Review. ^ Seth, Anil. "Consciousness". New Scientist. Retrieved 5 September 2024. ^ Nagel 1974. ^ "The Google engineer who thinks the business'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 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 expense outweigh 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 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, threats, methods (Reprinted with corrections 2017 ed.). Oxford, United Kingdom; New York City, 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 human beings will ruin ourselves". Vox. Retrieved 8 June 2023. ^ Doherty, Ben (17 May 2018). "Climate alter an 'existential security threat' to Australia, Senate questions states". The Guardian. ISSN 0261-3077. Retrieved 16 July 2023. ^ MacAskill, William (2022 ). What we owe the future. New York, NY: Basic Books. ISBN 978-1-5416-1862-6. ^ a b Ord, Toby (2020 ). "Chapter 5: Future Risks, Unaligned 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 implications of artificial intelligence - however are we taking AI seriously enough?'". The Independent (UK). Archived from the original on 25 September 2015. Retrieved 3 December 2014. ^ Herger, Mario. "The Gorilla Problem - Enterprise Garage". Retrieved 7 June 2023. ^ "The fascinating Facebook argument in between Yann LeCun, Stuart Russel and Yoshua Bengio about the dangers of strong AI". The interesting Facebook dispute in between Yann LeCun, Stuart Russel and Yoshua Bengio about the dangers of strong AI (in French). Retrieved 8 June 2023. ^ "Will Expert System Doom The Mankind Within The Next 100 Years?". HuffPost. 22 August 2014. Retrieved 8 June 2023. ^ Sotala, Kaj; Yampolskiy, Roman V. (19 December 2014). "Responses to disastrous AGI 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 threats spiralling out of control, report alerts". 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 postured but artificial intelligence 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, caution researchers". The Telegraph. Retrieved 7 December 2023. ^ Davidson, John (30 October 2023). "Google Brain founder says huge tech is lying about AI extinction danger". Australian Financial Review. Archived from the initial on 7 December 2023. Retrieved 7 December 2023. ^ Eloundou, Tyna; Manning, Sam; Mishkin, Pamela; Rock, Daniel (17 March 2023). "GPTs are GPTs: An early take a look at the labor market impact capacity of big language models". OpenAI. Retrieved 7 June 2023. ^ a b Hurst, Luke (23 March 2023). "OpenAI states 80% of workers might see their jobs impacted by AI. These are the jobs most impacted". euronews. Retrieved 8 June 2023. ^ Sheffey, Ayelet (20 August 2021). "Elon Musk says we need universal basic income because 'in the future, physical work 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 Search for Expert System. New York City, NY: BasicBooks. ISBN 0-465-02997-3. Darrach, Brad (20 November 1970), "Meet Shakey, the First Electronic Person", Life Magazine, pp. 58-68. Drachman, D. (2005 ), "Do we have brain to spare?", Neurology, 64 (12 ): 2004-2005, doi:10.1212/ 01. WNL.0000166914.38327. BB, PMID 15985565, S2CID 38482114. Feigenbaum, Edward A.; McCorduck, Pamela (1983 ), The Fifth Generation: Expert System and Japan's Computer Challenge to the World, Michael Joseph, ISBN 978-0-7181-2401-4. Goertzel, Ben; Pennachin, Cassio, eds. (2006 ), Artificial General Intelligence (PDF), Springer, ISBN 978-3-5402-3733-4, archived from the initial (PDF) on 20 March 2013. Goertzel, Ben (December 2007), "Human-level artificial 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 original 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 initial on 29 May 2011, retrieved 7 May 2011. Howe, J. (November 1994), Artificial Intelligence at Edinburgh University: a Point of view, archived from the initial on 17 August 2007, recovered 30 August 2007. Johnson, Mark (1987 ), The body in the mind, Chicago, ISBN 978-0-2264-0317-5. Kurzweil, Ray (2005 ), The Singularity is Near, Viking Press. Lighthill, Professor Sir James (1973 ), "Artificial Intelligence: A General Survey", Expert System: a paper seminar, Science Research Council. Luger, George; Stubblefield, William (2004 ), Artificial Intelligence: Structures and Strategies for Complex Problem Solving (5th 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 solve issues and attain objectives worldwide as well as people. Moravec, Hans (1988 ), Mind Children, Harvard University Press Moravec, Hans (1998 ), "When will computer system hardware match the human brain?", Journal of Evolution and Technology, vol. 1, archived from the initial on 15 June 2006, retrieved 23 June 2006 Nagel (1974 ), "What Is it Like to Be a Bat" (PDF), Philosophical Review, 83 (4 ): 435-50, doi:10.2307/ 2183914, JSTOR 2183914, archived (PDF) from the initial on 16 October 2011, retrieved 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, recovered 29 September 2007 Poole, David; Mackworth, Alan; Goebel, Randy (1998 ), Computational Intelligence: A Sensible Approach, New York: Oxford University Press, archived from the original on 25 July 2009, obtained 6 December 2007 Russell, Stuart J.; Norvig, Peter (2003 ), Expert System: A Modern Approach (second ed.), Upper Saddle River, New Jersey: Prentice Hall, ISBN 0-13-790395-2 Sandberg, Anders; Boström, Nick (2008 ), Whole Brain Emulation: A Roadmap (PDF), Technical Report # 2008-3, Future of Humanity Institute, Oxford University, archived (PDF) from the initial on 25 March 2020, 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, obtained 3 September 2020 Simon, H. A. (1965 ), The Shape of Automation for Men and Management, New York City: Harper & Row Turing, Alan (October 1950). "Computing Machinery and Intelligence". Mind. 59 (236 ): 433-460. doi:10.1093/ mind/LIX.236.433. ISSN 1460-2113. JSTOR 2251299. S2CID 14636783.
de Vega, Manuel; Glenberg, Arthur; Graesser, Arthur, eds. (2008 ), Symbols and Embodiment: Debates on significance and cognition, Oxford University Press, ISBN 978-0-1992-1727-4 Wang, Pei; Goertzel, Ben (2007 ). "Introduction: Aspects of Artificial General Intelligence". Advances in Artificial General Intelligence: Concepts, Architectures and Algorithms: Proceedings of the AGI Workshop 2006. IOS Press. pp. 1-16. ISBN 978-1-5860-3758-1. Archived from the original on 18 February 2021. Retrieved 13 December 2020 - through ResearchGate.
Further reading

Aleksander, Igor (1996 ), Impossible Minds, World Scientific Publishing Company, ISBN 978-1-8609-4036-1 Azevedo FA, Carvalho LR, Grinberg LT, Farfel J, et al. (April 2009), "Equal 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 original on 18 February 2021, obtained 4 September 2013 - by means of ResearchGate Berglas, Anthony (January 2012) [2008], Expert System Will Kill Our Grandchildren (Singularity), archived from the original on 23 July 2014, obtained 31 August 2012 Cukier, Kenneth, "Ready for Robots? How to Think about the Future of AI", Foreign Affairs, vol. 98, no. 4 (July/August 2019), pp. 192-98. George Dyson, historian of computing, composes (in what may be called "Dyson's Law") that "Any system easy sufficient to be understandable will not be complicated enough to act smartly, while any system made complex enough to behave smartly will be too made complex to understand." (p. 197.) Computer scientist Alex Pentland writes: "Current AI machine-learning algorithms are, at their core, dead easy silly. 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, recovered 25 July 2010. Gleick, James, "The Fate of Free Will" (evaluation of Kevin J. Mitchell, Free Agents: How Evolution Gave Us Free Choice, Princeton University Press, 2023, 333 pp.), The New York City Review of Books, vol. LXXI, no. 1 (18 January 2024), pp. 27-28, 30. "Agency is what identifies us from makers. For biological animals, reason and purpose come from acting on the planet and experiencing the repercussions. Artificial intelligences - disembodied, complete strangers to blood, sweat, and tears - have no occasion 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: Residing 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 anticipate that those who want to get rich from AI are going to have the interests of the rest of us close at heart,' ... writes [Gary Marcus] 'We can't rely on federal governments driven by campaign financing contributions [from tech business] to press back.' ... Marcus details the needs that people ought to make of their governments and the tech business. They consist of openness on how AI systems work; compensation for individuals if their data [are] used to train LLMs (big language model) s and the right to consent to this usage; and the ability to hold tech business liable for the damages they bring on by getting rid of Section 230, enforcing money penalites, and passing more stringent item liability laws ... Marcus also recommends ... that a new, AI-specific federal company, similar to the FDA, the FCC, or the FTC, might supply the most robust oversight ... [T] he Fordham law teacher Chinmayi Sharma ... recommends ... develop [ing] a professional licensing program for engineers that would function in a comparable way to medical licenses, malpractice matches, and the Hippocratic oath in medicine. 'What if, like doctors,' she asks ..., 'AI engineers likewise vowed to do no harm?'" (p. 46.). Holte, R. C.; Choueiry, B. Y. (2003 ), "Abstraction and reformulation in 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 stymied humans for decades, reveals the constraints of natural-language-processing algorithms", Scientific American, vol. 329, no. 4 (November 2023), pp. 81-82. "This murder secret competitors has exposed that although NLP (natural-language processing) designs are capable of amazing accomplishments, their capabilities are quite restricted by the quantity of context they receive. This [...] might trigger [difficulties] for scientists who wish to utilize them to do things such as analyze ancient languages. In many cases, there are few historical records on long-gone civilizations to act as training information for such a function." (p. 82.). Immerwahr, Daniel, "Your Lying Eyes: People now use A.I. to generate phony videos identical from genuine ones. How much does it matter?", The New Yorker, 20 November 2023, pp. 54-59. "If by 'deepfakes' we suggest reasonable videos produced utilizing expert system that actually deceive people, then they hardly exist. The fakes aren't deep, and the deeps aren't phony. [...] A.I.-generated videos are not, in general, operating in our media as counterfeited evidence. Their role better looks like that of animations, particularly smutty ones." (p. 59.). - Leffer, Lauren, "The Risks of Trusting AI: We should avoid humanizing machine-learning models 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 discussion?", The New Yorker, 7 October 2024, pp. 12-16. Marcus, Gary, "Artificial Confidence: Even the newest, buzziest systems of artificial 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 (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, retrieved 29 September 2007. Newell, Allen; Simon, H. A. (1963 ), "GPS: A Program that Simulates Human Thought", in Feigenbaum, E. A.; Feldman, J. (eds.), Computers and Thought, New York City: McGraw-Hill. Omohundro, Steve (2008 ), The Nature of Self-Improving Artificial Intelligence, presented and distributed at the 2007 Singularity Summit, San Francisco, California. Press, Eyal, "In Front of Their Faces: Does facial-recognition innovation lead cops to neglect contradictory proof?", The New Yorker, 20 November 2023, pp. 20-26. Roivainen, Eka, "AI's IQ: ChatGPT aced a [basic intelligence] test but showed that intelligence can not be determined by IQ alone", Scientific American, vol. 329, no. 1 (July/August 2023), p. 7. "Despite its high IQ, ChatGPT fails at tasks that need genuine humanlike thinking or an understanding of the physical and social world ... ChatGPT appeared unable to factor logically and attempted to count on its vast database of ... realities 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 effective however undependable. Rules-based systems can not deal with scenarios their developers did not anticipate. Learning systems are limited by the data on which they were trained. AI failures have already caused catastrophe. Advanced auto-pilot functions in cars, although they perform well in some situations, have driven automobiles without cautioning into trucks, concrete barriers, and parked vehicles. In the incorrect scenario, AI systems go from supersmart to superdumb in an instant. When an opponent is attempting to manipulate and hack an AI system, the risks are even greater." (p. 140.). Sutherland, J. G. (1990 ), "Holographic Model of Memory, Learning, and Expression", International Journal of Neural Systems, vol. 1-3, pp. 256-267. - Vincent, James, "Horny Robot Baby Voice: James Vincent on AI chatbots", London Review of Books, vol. 46, no. 19 (10 October 2024), pp. 29-32." [AI chatbot] programs are enabled by brand-new technologies but rely on the timelelss human propensity to anthropomorphise." (p. 29.). Williams, R. W.; Herrup, K.