Artificial basic intelligence (AGI) is a type of synthetic intelligence (AI) that matches or surpasses human cognitive abilities across a wide variety of cognitive jobs. This contrasts with narrow AI, which is limited to specific jobs. [1] Artificial superintelligence (ASI), on the other hand, refers to AGI that greatly surpasses human cognitive abilities. AGI is considered one of the meanings of strong AI.

Creating AGI is a main goal of AI research and of companies such as OpenAI [2] and Meta. [3] A 2020 study determined 72 active AGI research and development tasks throughout 37 countries. [4]
The timeline for attaining AGI stays a topic of continuous argument amongst scientists and experts. Since 2023, some argue that it may be possible in years or years; others maintain it may take a century or longer; a minority believe it might never ever be achieved; and another minority declares that it is currently here. [5] [6] Notable AI researcher Geoffrey Hinton has revealed issues about the rapid progress towards AGI, suggesting it might be accomplished sooner than many expect. [7]
There is dispute on the precise definition of AGI and relating to whether modern-day large language models (LLMs) such as GPT-4 are early types of AGI. [8] AGI is a common topic in science fiction and futures studies. [9] [10]
Contention exists over whether AGI represents an existential danger. [11] [12] [13] Many professionals on AI have mentioned that mitigating the risk of human extinction posed by AGI ought to be a global concern. [14] [15] Others find the development of AGI to be too remote to present such a danger. [16] [17]
Terminology

AGI is also referred to as strong AI, [18] [19] full AI, [20] human-level AI, [5] human-level intelligent AI, or general intelligent action. [21]
Some academic sources book the term "strong AI" for computer programs that experience life or consciousness. [a] In contrast, weak AI (or narrow AI) is able to solve one specific problem however does not have basic cognitive abilities. [22] [19] Some scholastic sources utilize "weak AI" to refer more broadly to any programs that neither experience awareness nor have a mind in the same sense as people. [a]
Related concepts consist of synthetic superintelligence and transformative AI. A synthetic superintelligence (ASI) is a theoretical type of AGI that is much more generally intelligent than people, [23] while the notion of transformative AI associates with AI having a large effect on society, for instance, comparable to the agricultural or industrial transformation. [24]
A structure for categorizing AGI in levels was proposed in 2023 by Google DeepMind scientists. They define 5 levels of AGI: emerging, qualified, professional, virtuoso, and superhuman. For example, a proficient AGI is specified as an AI that outshines 50% of knowledgeable grownups in a large range of non-physical jobs, and a superhuman AGI (i.e. an artificial superintelligence) is similarly defined but 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. One of the leading proposals is the Turing test. However, there are other popular meanings, and some researchers disagree with the more popular approaches. [b]
Intelligence traits

Researchers typically hold that intelligence is required to do all of the following: [27]
factor, use strategy, solve puzzles, and make judgments under unpredictability represent understanding, consisting of typical sense knowledge plan discover

• interact in natural language
• if necessary, incorporate these skills in conclusion of any given goal

Many interdisciplinary techniques (e.g. cognitive science, computational intelligence, and choice making) think about extra qualities such as imagination (the capability to form unique mental images and principles) [28] and autonomy. [29]
Computer-based systems that exhibit a number of these abilities exist (e.g. see computational creativity, automated reasoning, choice assistance system, robotic, evolutionary computation, smart agent). There is argument about whether modern AI systems have them to an adequate degree.

Physical traits

Other abilities are considered desirable in intelligent systems, as they might affect 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 control things, change place to check out, etc).
This consists of the ability to discover and react to threat. [31]
Although the capability to sense (e.g. see, hear, and so on) and the capability to act (e.g. move and control things, place to check out, and so on) 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 big language designs (LLMs) might already be or become AGI. Even from a less optimistic point of view on LLMs, there is no firm requirement for an AGI to have a human-like form; being a silicon-based computational system suffices, provided it can process input (language) from the external world in location of human senses. This analysis aligns with the understanding that AGI has never been proscribed a particular physical embodiment and hence does not require a capability for locomotion or conventional "eyes and ears". [32]
Tests for human-level AGI

Several tests indicated to confirm human-level AGI have been considered, including: [33] [34]
The idea of the test is that the machine has to try and pretend to be a guy, by answering questions put to it, and it will only pass if the pretence is fairly persuading. A considerable part of a jury, who should not be skilled about makers, 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 fix 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 problems that have actually been conjectured to require basic intelligence to solve as well as humans. Examples consist of computer system vision, natural language understanding, and dealing with unanticipated situations while solving any real-world issue. [48] Even a particular job like translation needs a maker to read and write in both languages, follow the author's argument (factor), understand the context (knowledge), and consistently replicate the author's initial intent (social intelligence). All of these problems require to be fixed concurrently in order to reach human-level maker performance.

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

Classical AI

Modern AI research began in the mid-1950s. [50] The very first generation of AI researchers were encouraged that artificial general intelligence was possible and that it would exist in simply a couple of decades. [51] AI leader Herbert A. Simon composed in 1965: "makers will be capable, within twenty years, of doing any work a man can do." [52]
Their predictions were the motivation for Stanley Kubrick and Arthur C. Clarke's character HAL 9000, who embodied what AI scientists thought they could create by the year 2001. AI pioneer Marvin Minsky was a consultant [53] on the task of making HAL 9000 as realistic 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 fixed". [54]
Several classical AI projects, such as Doug Lenat's Cyc task (that began in 1984), and Allen Newell's Soar job, were directed at AGI.

However, in the early 1970s, it ended up being obvious that researchers had actually grossly ignored the problem of the task. Funding agencies ended up being hesitant of AGI and put scientists under increasing pressure to produce useful "applied AI". [c] In the early 1980s, Japan's Fifth Generation Computer Project restored interest in AGI, setting out a ten-year timeline that included AGI objectives like "continue a table talk". [58] In response to this and the success of professional systems, both market and government pumped money into the field. [56] [59] However, confidence in AI amazingly collapsed in the late 1980s, and the objectives of the Fifth Generation Computer Project were never satisfied. [60] For the second time in twenty years, AI researchers who anticipated the impending achievement of AGI had been misinterpreted. By the 1990s, AI researchers had a reputation for making vain pledges. They ended up being unwilling to make forecasts at all [d] and avoided reference of "human level" expert system for fear of being labeled "wild-eyed dreamer [s]. [62]
Narrow AI research study

In the 1990s and early 21st century, mainstream AI attained business success and scholastic respectability by concentrating on specific sub-problems where AI can produce proven outcomes and industrial applications, such as speech recognition and recommendation algorithms. [63] These "applied AI" systems are now utilized extensively throughout the technology market, and research study in this vein is heavily funded in both academic community and market. Since 2018 [update], advancement in this field was considered an emerging pattern, and a fully grown stage was anticipated to be reached in more than 10 years. [64]
At the millenium, lots of traditional AI researchers [65] hoped that strong AI could be developed by combining programs that resolve different sub-problems. Hans Moravec wrote in 1988:

I am positive that this bottom-up route to expert system will one day meet the standard top-down route more than half method, ready to supply the real-world skills and the commonsense knowledge that has been so frustratingly elusive in reasoning programs. Fully intelligent machines will result when the metaphorical golden spike is driven unifying the 2 efforts. [65]
However, even at the time, this was disputed. For instance, Stevan Harnad of Princeton University concluded his 1990 paper on the sign grounding hypothesis by stating:

The expectation has often been voiced that "top-down" (symbolic) approaches to modeling cognition will somehow meet "bottom-up" (sensory) approaches somewhere in between. If the grounding considerations in this paper stand, then this expectation is hopelessly modular and there is truly just one viable route from sense to signs: from the ground up. A free-floating symbolic level like the software application level of a computer system will never ever be reached by this route (or vice versa) - nor is it clear why we should even attempt to reach such a level, because it looks as if getting there would just amount to uprooting our signs from their intrinsic meanings (thereby merely reducing ourselves to the functional equivalent of a programmable computer). [66]
Modern synthetic basic intelligence research

The term "artificial general 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 agent maximises "the capability to satisfy goals in a vast array of environments". [68] This type of AGI, defined by the ability 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 popularized by Shane Legg and Ben Goertzel around 2002. [71] AGI research activity in 2006 was described by Pei Wang and Ben Goertzel [72] as "producing publications and preliminary results". The very first summer school in AGI was arranged 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 variety of visitor lecturers.

Since 2023 [update], a small number of computer researchers are active in AGI research, and many contribute to a series of AGI conferences. However, progressively more researchers are interested in open-ended knowing, [76] [77] which is the idea of permitting AI to continually learn and innovate like humans do.

Feasibility

As of 2023, the advancement and possible accomplishment of AGI stays a topic of extreme debate within the AI neighborhood. While conventional agreement held that AGI was a distant goal, current developments have led some scientists and industry figures to declare that early kinds of AGI might currently exist. [78] AI pioneer Herbert A. Simon hypothesized in 1965 that "machines will be capable, within twenty years, of doing any work a male can do". This prediction failed to come true. Microsoft co-founder Paul Allen thought that such intelligence is not likely in the 21st century since it would need "unforeseeable and essentially unpredictable breakthroughs" and a "clinically deep understanding of cognition". [79] Writing in The Guardian, roboticist Alan Winfield declared the gulf in between modern computing and human-level artificial intelligence is as large as the gulf between present space flight and useful faster-than-light spaceflight. [80]
A further obstacle is the lack of clearness in specifying what intelligence requires. Does it require awareness? Must it display the capability to set objectives as well as pursue them? Is it purely a matter of scale such that if model sizes increase adequately, intelligence will emerge? Are facilities such as planning, reasoning, and causal understanding needed? Does intelligence need explicitly replicating the brain and its particular faculties? Does it require emotions? [81]
Most AI researchers think strong AI can be achieved in the future, however some thinkers, like Hubert Dreyfus and Roger Penrose, reject the possibility of accomplishing strong AI. [82] [83] John McCarthy is among those who believe human-level AI will be accomplished, however that today level of development is such that a date can not accurately be forecasted. [84] AI specialists' views on the feasibility of AGI wax and wane. Four polls performed in 2012 and 2013 recommended that the mean quote among specialists for when they would be 50% positive AGI would arrive was 2040 to 2050, depending on the survey, with the mean being 2081. Of the specialists, 16.5% answered with "never ever" when asked the exact same question but with a 90% self-confidence rather. [85] [86] Further present AGI development factors to consider can be discovered above Tests for confirming human-level AGI.

A report by Stuart Armstrong and Kaj Sotala of the Machine Intelligence Research Institute discovered that "over [a] 60-year time frame there is a strong bias towards anticipating the arrival of human-level AI as in between 15 and 25 years from the time the forecast was made". They analyzed 95 predictions made between 1950 and 2012 on when human-level AI will come about. [87]
In 2023, Microsoft scientists published a detailed examination of GPT-4. They concluded: "Given the breadth and depth of GPT-4's abilities, our company believe that it could fairly be deemed an early (yet still incomplete) variation of a synthetic basic intelligence (AGI) system." [88] Another research study in 2023 reported that GPT-4 exceeds 99% of human beings on the Torrance tests of imaginative thinking. [89] [90]
Blaise Agüera y Arcas and Peter Norvig wrote in 2023 that a substantial level of basic intelligence has actually currently been accomplished with frontier models. They composed that unwillingness to this view comes from 4 main factors: a "healthy apprehension about metrics for AGI", an "ideological commitment to alternative AI theories or techniques", a "commitment to human (or biological) exceptionalism", or a "concern about the financial ramifications of AGI". [91]
2023 likewise marked the development of large multimodal designs (large language models efficient in processing or producing numerous methods such as text, audio, and images). [92]
In 2024, OpenAI released o1-preview, the first of a series of models that "invest more time thinking before they react". According to Mira Murati, this capability to think before responding represents a new, extra paradigm. It enhances model outputs by spending more computing power when creating the answer, whereas the model scaling paradigm improves outputs by increasing the design size, training data and training calculate power. [93] [94]
An OpenAI employee, Vahid Kazemi, claimed in 2024 that the company had achieved AGI, stating, "In my opinion, we have actually already achieved AGI and it's even more clear with O1." Kazemi clarified that while the AI is not yet "better than any human at any job", it is "much better than many people at a lot of tasks." He likewise addressed criticisms that large language models (LLMs) merely follow predefined patterns, comparing their knowing process to the scientific method of observing, hypothesizing, and validating. These statements have actually stimulated argument, as they depend on a broad and unconventional meaning of AGI-traditionally understood as AI that matches human intelligence across all domains. Critics argue that, while OpenAI's models show remarkable versatility, they might not fully satisfy this requirement. Notably, Kazemi's comments came soon after OpenAI eliminated "AGI" from the regards to its partnership with Microsoft, triggering speculation about the company's strategic intentions. [95]
Timescales

Progress in expert system has traditionally gone through periods of fast development separated by durations when progress appeared to stop. [82] Ending each hiatus were basic advances in hardware, software or both to produce area for further progress. [82] [98] [99] For instance, the hardware available in the twentieth century was not sufficient to execute deep learning, which requires large numbers of GPU-enabled CPUs. [100]
In the introduction to his 2006 book, [101] Goertzel says that estimates of the time needed before a genuinely versatile AGI is built vary from ten years to over a century. Since 2007 [upgrade], the consensus in the AGI research community seemed to be that the timeline talked about by Ray Kurzweil in 2005 in The Singularity is Near [102] (i.e. in between 2015 and 2045) was possible. [103] Mainstream AI researchers have actually offered a vast array of opinions on whether development will be this fast. A 2012 meta-analysis of 95 such viewpoints discovered a predisposition towards forecasting that the beginning of AGI would happen within 16-26 years for modern-day and historical predictions alike. That paper has been criticized for how it classified viewpoints as professional or non-expert. [104]
In 2012, Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton established a neural network called AlexNet, which won the ImageNet competitors with a top-5 test error rate of 15.3%, considerably better than the second-best entry's rate of 26.3% (the traditional method used a weighted amount of scores from different pre-defined classifiers). [105] AlexNet was concerned as the preliminary ground-breaker of the existing deep knowing wave. [105]
In 2017, scientists Feng Liu, Yong Shi, and Ying Liu performed intelligence tests on publicly readily available and freely available weak AI such as Google AI, Apple's Siri, and others. At the maximum, these AIs reached an IQ worth of about 47, which corresponds around to a six-year-old kid in very first grade. An adult concerns about 100 typically. Similar tests were performed in 2014, with the IQ score reaching a maximum worth of 27. [106] [107]
In 2020, OpenAI established GPT-3, a language design efficient in carrying out lots of diverse tasks without specific 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 changes to the chatbot to comply with their safety standards; Rohrer disconnected Project December from the GPT-3 API. [109]
In 2022, DeepMind established Gato, a "general-purpose" system efficient in carrying out more than 600 various tasks. [110]
In 2023, Microsoft Research published a study on an early version of OpenAI's GPT-4, competing that it showed more basic intelligence than previous AI designs and showed human-level efficiency in jobs covering several domains, such as mathematics, coding, and law. This research sparked an argument on whether GPT-4 could be thought about an early, incomplete version of artificial basic intelligence, stressing the requirement for more exploration and evaluation of such systems. [111]
In 2023, the AI scientist Geoffrey Hinton stated that: [112]
The concept that this things could in fact get smarter than people - a few individuals thought that, [...] But many individuals believed it was way off. And I thought it was method off. I thought it was 30 to 50 years or 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 actually been pretty incredible", which he sees no reason that it would decrease, anticipating AGI within a decade and even a few years. [113] In March 2024, Nvidia's CEO, Jensen Huang, mentioned his expectation that within 5 years, AI would can passing any test a minimum of in addition to people. [114] In June 2024, the AI scientist Leopold Aschenbrenner, a former OpenAI worker, estimated AGI by 2027 to be "strikingly plausible". [115]
Whole brain emulation

While the development of transformer designs like in ChatGPT is considered the most promising path to AGI, [116] [117] entire brain emulation can function as an alternative method. With entire brain simulation, a brain design is developed by scanning and mapping a biological brain in detail, and after that copying and imitating it on a computer system or another computational gadget. The simulation design need to be sufficiently devoted to the original, so that it behaves in almost the exact same way as the original brain. [118] Whole brain emulation is a type of brain simulation that is gone over in computational neuroscience and neuroinformatics, and for medical research study purposes. It has actually been gone over in expert system research [103] as an approach to strong AI. Neuroimaging innovations that might deliver the required comprehensive understanding are enhancing rapidly, and futurist Ray Kurzweil in the book The Singularity Is Near [102] predicts that a map of adequate quality will become available on a comparable timescale to the computing power required to imitate it.

Early estimates

For low-level brain simulation, an extremely effective cluster of computer systems or GPUs would be needed, provided the huge quantity of synapses within the human brain. Each of the 1011 (one hundred billion) nerve cells 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 declines with age, stabilizing by their adult years. Estimates differ for an adult, varying from 1014 to 5 × 1014 synapses (100 to 500 trillion). [120] An estimate of the brain's processing power, based upon an easy switch model for nerve cell activity, is around 1014 (100 trillion) synaptic updates per second (SUPS). [121]
In 1997, Kurzweil took a look at various estimates for the hardware needed to equal the human brain and adopted a figure of 1016 computations per 2nd (cps). [e] (For contrast, if a "calculation" was comparable to one "floating-point operation" - a step utilized to rate present supercomputers - then 1016 "calculations" would be equivalent to 10 petaFLOPS, attained in 2011, while 1018 was attained in 2022.) He utilized this figure to predict the needed hardware would be available sometime between 2015 and 2025, if the rapid growth in computer system power at the time of composing continued.

Current research study

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

Criticisms of simulation-based approaches

The artificial nerve cell design assumed by Kurzweil and utilized in lots of existing synthetic neural network applications is easy compared to biological neurons. A brain simulation would likely need to capture the in-depth cellular behaviour of biological nerve cells, currently comprehended just in broad outline. The overhead presented by full modeling of the biological, chemical, and physical details of neural behaviour (specifically on a molecular scale) would need computational powers numerous orders of magnitude bigger than Kurzweil's estimate. In addition, the quotes do not represent glial cells, which are understood to contribute in cognitive procedures. [125]
A basic criticism of the simulated brain technique derives from embodied cognition theory which asserts that human personification is an essential element of human intelligence and is essential to ground meaning. [126] [127] If this theory is right, any totally functional brain model will require to incorporate more than simply the neurons (e.g., a robotic body). Goertzel [103] proposes virtual embodiment (like in metaverses like Second Life) as an option, but it is unknown whether this would be enough.

Philosophical viewpoint

"Strong AI" as specified in philosophy

In 1980, philosopher John Searle coined the term "strong AI" as part of his Chinese room argument. [128] He proposed a distinction between 2 hypotheses about artificial intelligence: [f]
Strong AI hypothesis: An expert system system can have "a mind" and "consciousness". Weak AI hypothesis: A synthetic intelligence system can (only) act like it believes and has a mind and consciousness.
The very first one he called "strong" because it makes a stronger statement: it assumes something special has happened to the maker that exceeds those abilities that we can test. The behaviour of a "weak AI" machine would be specifically identical to a "strong AI" machine, however the latter would likewise have subjective mindful experience. This use is also common in scholastic AI research and textbooks. [129]
In contrast to Searle and traditional AI, some futurists such as Ray Kurzweil utilize the term "strong AI" to imply "human level synthetic basic intelligence". [102] This is not the exact same as Searle's strong AI, unless it is presumed that consciousness is essential for human-level AGI. Academic theorists such as Searle do not believe that holds true, and to most artificial intelligence scientists the concern is out-of-scope. [130]
Mainstream AI is most interested in how a program acts. [131] According to Russell and Norvig, "as long as the program works, they do not care if you call it genuine or a simulation." [130] If the program can behave as if it has a mind, then there is no need to know if it actually has mind - indeed, there would be no chance to inform. For AI research study, Searle's "weak AI hypothesis" is equivalent to the declaration "synthetic general intelligence is possible". Thus, according to Russell and Norvig, "most AI scientists take the weak AI hypothesis for approved, and do not care about the strong AI hypothesis." [130] Thus, for scholastic AI research, "Strong AI" and "AGI" are 2 different things.

Consciousness

Consciousness can have various significances, and some aspects play significant functions in science fiction and the principles of artificial intelligence:

Sentience (or "phenomenal awareness"): The capability to "feel" understandings or feelings subjectively, as opposed to the capability to factor about understandings. Some thinkers, such as David Chalmers, use the term "consciousness" to refer exclusively to phenomenal awareness, which is approximately comparable to sentience. [132] Determining why and how subjective experience develops is understood as the difficult issue of awareness. [133] Thomas Nagel discussed in 1974 that it "feels like" something to be conscious. If we are not mindful, then it doesn't seem like anything. Nagel utilizes the example of a bat: we can smartly ask "what does it feel like to be a bat?" However, we are not likely to ask "what does it seem like to be a toaster?" Nagel concludes that a bat appears to be conscious (i.e., has awareness) however a toaster does not. [134] In 2022, a Google engineer declared that the company's AI chatbot, LaMDA, had accomplished life, though this claim was commonly disputed by other experts. [135]
Self-awareness: To have conscious awareness of oneself as a separate person, particularly to be consciously aware of one's own thoughts. This is opposed to simply being the "subject of one's believed"-an os or debugger is able to be "familiar with itself" (that is, to represent itself in the same method it represents everything else)-however this is not what individuals usually suggest when they use the term "self-awareness". [g]
These traits have a moral dimension. AI life would generate concerns of welfare and legal protection, similarly to animals. [136] Other elements of consciousness associated to cognitive abilities are also appropriate to the idea of AI rights. [137] Finding out how to integrate advanced AI with existing legal and social structures is an emerging problem. [138]
Benefits

AGI could have a wide array of applications. If oriented towards such objectives, AGI might help alleviate numerous problems in the world such as hunger, poverty and illness. [139]
AGI might improve efficiency and performance in many jobs. For instance, in public health, AGI might speed up medical research study, especially versus cancer. [140] It might take care of the senior, [141] and equalize access to fast, premium medical diagnostics. It might provide enjoyable, inexpensive and personalized education. [141] The requirement to work to subsist could end up being obsolete if the wealth produced is appropriately rearranged. [141] [142] This likewise raises the question of the location of people in a significantly automated society.

AGI might likewise assist to make logical choices, and to prepare for and avoid disasters. It could also assist to gain the benefits of possibly devastating innovations such as nanotechnology or environment engineering, while preventing the associated risks. [143] If an AGI's main goal is to avoid existential catastrophes such as human extinction (which could be challenging if the Vulnerable World Hypothesis ends up being true), [144] it might take measures to considerably reduce the dangers [143] while reducing the effect of these steps on our quality of life.

Risks

Existential threats

AGI may represent several types of existential threat, which are risks that threaten "the early termination of Earth-originating intelligent life or the irreversible and extreme damage of its capacity for desirable future advancement". [145] The threat of human termination from AGI has actually been the subject of many debates, but there is also the possibility that the advancement of AGI would lead to a permanently problematic future. Notably, it might be used to spread out and protect the set of worths of whoever develops it. If mankind still has moral blind areas comparable to slavery in the past, AGI might irreversibly entrench it, preventing moral development. [146] Furthermore, AGI could help with mass surveillance and indoctrination, which might be used to develop a stable repressive around the world totalitarian regime. [147] [148] There is likewise a threat for the devices themselves. If makers that are sentient or otherwise worthy of moral consideration are mass created in the future, taking part in a civilizational course that indefinitely neglects their well-being and interests could be an existential catastrophe. [149] [150] Considering just how much AGI might enhance mankind's future and aid decrease other existential dangers, Toby Ord calls these existential threats "an argument for proceeding 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 danger requires more attention, is questionable however has been endorsed in 2023 by lots of public figures, AI scientists 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 extensive indifference:

So, dealing with possible futures of enormous benefits and dangers, the specialists are surely doing everything possible to guarantee the very best result, right? Wrong. If an exceptional alien civilisation sent us a message saying, 'We'll arrive in a few years,' would we simply respond, 'OK, call us when you get here-we'll leave the lights on?' Probably not-but this is more or less what is taking place with AI. [153]
The potential fate of humanity has in some cases been compared to the fate of gorillas threatened by human activities. The comparison states that greater intelligence enabled humanity to dominate gorillas, which are now vulnerable in manner ins which they could not have actually anticipated. As a result, the gorilla has become an endangered species, not out of malice, however merely as a collateral damage from human activities. [154]
The skeptic Yann LeCun considers that AGIs will have no desire to control mankind and that we must beware not to anthropomorphize them and analyze their intents as we would for human beings. He said that people won't be "smart sufficient to create super-intelligent machines, yet unbelievably dumb to the point of giving it moronic objectives without any safeguards". [155] On the other side, the concept of critical merging suggests that almost whatever their goals, smart agents will have reasons to try to survive and get more power as intermediary actions to achieving these goals. Which this does not require having emotions. [156]
Many scholars who are worried about existential risk supporter for more research study into resolving the "control problem" to address the concern: what kinds of safeguards, algorithms, or architectures can developers implement to increase the likelihood 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 safety preventative measures in order to launch items before rivals), [159] and the usage of AI in weapon systems. [160]
The thesis that AI can pose existential threat likewise has detractors. Skeptics generally state that AGI is unlikely in the short-term, or that concerns about AGI distract from other problems connected to present AI. [161] Former Google fraud czar Shuman Ghosemajumder considers that for lots of people beyond the technology market, existing chatbots and LLMs are already perceived as though they were AGI, resulting in further misunderstanding and worry. [162]
Skeptics in some cases charge that the thesis is crypto-religious, with an irrational belief in the possibility of superintelligence changing an irrational belief in an omnipotent God. [163] Some researchers think that the interaction projects on AI existential threat by specific AI groups (such as OpenAI, Anthropic, DeepMind, and Conjecture) may be an at effort at regulatory capture and to pump up interest in their items. [164] [165]
In 2023, the CEOs of Google DeepMind, OpenAI and Anthropic, along with other industry leaders and scientists, issued a joint declaration asserting that "Mitigating the danger of termination from AI should be a worldwide concern along with other societal-scale dangers such as pandemics and nuclear war." [152]
Mass joblessness

Researchers from OpenAI estimated that "80% of the U.S. labor force might have at least 10% of their work jobs affected by the introduction of LLMs, while around 19% of employees may see at least 50% of their jobs impacted". [166] [167] They think about office workers to be the most exposed, for instance mathematicians, accountants or web designers. [167] AGI might have a much better autonomy, capability to make decisions, to user interface with other computer tools, but also to control robotized bodies.

According to Stephen Hawking, the result of automation on the lifestyle will depend upon how the wealth will be redistributed: [142]
Everyone can enjoy a life of luxurious leisure if the machine-produced wealth is shared, or many people can wind up badly poor if the machine-owners effectively lobby versus wealth redistribution. Up until now, the pattern appears to be towards the 2nd option, with innovation driving ever-increasing inequality

Elon Musk considers that the automation of society will need governments to embrace a universal basic earnings. [168]
See also

Artificial brain - Software and hardware with cognitive capabilities comparable to those of the animal or human brain AI effect AI security - Research location on making AI safe and helpful AI alignment - AI conformance to the intended goal A.I. Rising - 2018 movie directed by Lazar Bodroža Artificial intelligence Automated artificial intelligence - 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 study centre General video game playing - Ability of synthetic intelligence to play different games Generative synthetic intelligence - AI system efficient in producing content in reaction to prompts Human Brain Project - Scientific research project Intelligence amplification - Use of infotech to enhance human intelligence (IA). Machine principles - Moral behaviours of manufactured makers. Moravec's paradox. Multi-task knowing - Solving multiple machine discovering tasks at the very same time. Neural scaling law - Statistical law in artificial intelligence. Outline of artificial intelligence - Overview of and topical guide to synthetic intelligence. Transhumanism - Philosophical motion. Synthetic intelligence - Alternate term for or kind of artificial intelligence. Transfer knowing - Artificial intelligence strategy. Loebner Prize - Annual AI competitors. Hardware for synthetic intelligence - Hardware specifically designed and optimized for expert system. Weak synthetic intelligence - Form of expert system.
Notes

^ a b See below for the origin of the term "strong AI", and see the academic meaning of "strong AI" and weak AI in the short article Chinese space. ^ AI founder John McCarthy composes: "we can not yet identify in general what sort of computational treatments we wish to call intelligent. " [26] (For a discussion of some meanings of intelligence utilized by expert system scientists, see viewpoint of artificial intelligence.). ^ The Lighthill report specifically slammed AI's "grand objectives" and led the taking apart of AI research in England. [55] In the U.S., DARPA became figured out to fund only "mission-oriented direct research study, instead of fundamental undirected research study". [56] [57] ^ As AI creator John McCarthy composes "it would be a fantastic relief to the rest of the workers in AI if the developers of brand-new general formalisms would express their hopes in a more protected 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 approximately represent 1014 cps. Moravec talks in terms of MIPS, not "cps", which is a non-standard term Kurzweil introduced. ^ As specified in a standard AI book: "The assertion that devices might possibly act wisely (or, perhaps better, act as if they were smart) is called the 'weak AI' hypothesis by thinkers, and the assertion that machines that do so are in fact thinking (as opposed to simulating thinking) is called the 'strong AI' hypothesis." [121] ^ Alan Turing made this point in 1950. [36] References

^ Krishna, Sri (9 February 2023). "What is artificial narrow intelligence (ANI)?". VentureBeat. Retrieved 1 March 2024. ANI is created to carry out a single job. ^ "OpenAI Charter". OpenAI. Retrieved 6 April 2023. Our mission is to make sure that synthetic basic intelligence benefits all of mankind. ^ Heath, Alex (18 January 2024). "Mark Zuckerberg's brand-new objective is producing synthetic general 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 Study of Artificial General Intelligence Projects for Ethics, Risk, and Policy (PDF) (Report). Global Catastrophic Risk Institute. Retrieved 28 November 2024. 72 AGI R&D jobs were identified as being active in 2020. ^ a b c "AI timelines: What do specialists in synthetic 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 Times. Retrieved 18 May 2023. ^ "AI pioneer Geoffrey Hinton stops Google and alerts of risk ahead". The New York Times. 1 May 2023. Retrieved 2 May 2023. It is hard to see how you can avoid 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 explores 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 alter. All that you change 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 Artificial Intelligence". The New York City Times. The genuine hazard is not AI itself but the way we deploy it. ^ "Impressed by artificial intelligence? Experts say AGI is following, and it has 'existential' risks". ABC News. 23 March 2023. Retrieved 6 April 2023. AGI might present existential dangers 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 development that humanity needs to make. ^ Roose, Kevin (30 May 2023). "A.I. Poses 'Risk of Extinction,' Industry Leaders Warn". The New York Times. Mitigating the danger of termination from AI should be a worldwide top priority. ^ "Statement on AI Risk". Center for AI Safety. Retrieved 1 March 2024. AI specialists caution of threat of extinction from AI. ^ Mitchell, Melanie (30 May 2023). "Are AI's Doomsday Scenarios Worth Taking Seriously?". The New York Times. We are far from producing makers that can outthink us in basic ways. ^ LeCun, Yann (June 2023). "AGI does not provide an existential risk". 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 original on 14 August 2005: Kurzweil describes strong AI as "machine intelligence with the full series of human intelligence.". ^ "The Age of Expert System: 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 use for "human-level" intelligence in the physical symbol system hypothesis. ^ "The Open University on Strong and Weak AI". Archived from the initial on 25 September 2009. Retrieved 8 October 2007. ^ "What is artificial superintelligence (ASI)?|Definition from TechTarget". Enterprise AI. Retrieved 8 October 2023. ^ "Artificial intelligence is changing our world - it is on everyone to make certain that it goes well". Our World in Data. Retrieved 8 October 2023. ^ Dickson, Ben (16 November 2023). "Here is how far we are to accomplishing AGI, according to DeepMind". VentureBeat. ^ McCarthy, John (2007a). "Basic Questions". Stanford University. Archived from the initial on 26 October 2007. Retrieved 6 December 2007. ^ This list of smart traits is based upon the topics covered by major AI textbooks, including: 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 shapes the method we think: a new view of intelligence (The MIT Press, 2007). ISBN 0-2621-6239-3. ^ White, R. W. (1959 ). "Motivation reconsidered: The principle of skills". Psychological Review. 66 (5 ): 297-333. doi:10.1037/ h0040934. PMID 13844397. S2CID 37385966. ^ White, R. W. (1959 ). "Motivation reassessed: The idea of proficiency". Psychological Review. 66 (5 ): 297-333. doi:10.1037/ h0040934. PMID 13844397. S2CID 37385966. ^ Muehlhauser, Luke (11 August 2013). "What is AGI?". Machine Intelligence Research Institute. Archived from the initial on 25 April 2014. Retrieved 1 May 2014. ^ "What is Artificial General Intelligence (AGI)?|4 Tests For Ensuring Artificial General Intelligence". Talky Blog. 13 July 2019. Archived from the original on 17 July 2019. Retrieved 17 July 2019. ^ Kirk-Giannini, Cameron Domenico; Goldstein, Simon (16 October 2023). "AI is closer than ever to passing the Turing test for 'intelligence'. What occurs when it does?". The Conversation. Retrieved 22 September 2024. ^ a b Turing 1950. ^ Turing, Alan (1952 ). B. Jack Copeland (ed.). Can Automatic Calculating Machines Be Said To Think?. Oxford: Oxford University Press. pp. 487-506. ISBN 978-0-1982-5079-1. ^ "Eugene Goostman is a real kid - the Turing Test states so". The Guardian. 9 June 2014. ISSN 0261-3077. Retrieved 3 March 2024. ^ "Scientists challenge whether computer system 'Eugene Goostman' passed Turing test". BBC News. 9 June 2014. Retrieved 3 March 2024. ^ Jones, Cameron R.; Bergen, Benjamin K. (9 May 2024). "People can not distinguish GPT-4 from a human in a Turing test". arXiv:2405.08007 [cs.HC] ^ Varanasi, Lakshmi (21 March 2023). "AI designs like ChatGPT and forum.batman.gainedge.org GPT-4 are acing whatever from the bar examination to AP Biology. Here's a list of tough examinations both AI versions have passed". Business Insider. Retrieved 30 May 2023. ^ Naysmith, Caleb (7 February 2023). "6 Jobs Expert System Is Already Replacing and How Investors Can 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 unreliable. 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 suggested testing an AI chatbot's ability to turn $100,000 into $1 million to determine human-like intelligence". Business Insider. Retrieved 3 March 2024. ^ Suleyman, Mustafa (14 July 2023). "Mustafa Suleyman: My brand-new Turing test would see if AI can make $1 million". MIT Technology Review. Retrieved 3 March 2024. ^ Shapiro, Stuart C. (1992 ). "Expert System" (PDF). In Stuart C. Shapiro (ed.). Encyclopedia of Artificial Intelligence (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). 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 original on 6 May 2022. Retrieved 12 March 2022. ^ Simon 1965, p. 96 priced estimate in Crevier 1993, p. 109. ^ "Scientist on the Set: An Interview with Marvin Minsky". Archived from the original on 16 July 2012. Retrieved 5 April 2008. ^ Marvin Minsky to Darrach (1970 ), priced estimate in Crevier (1993, p. 109). ^ Lighthill 1973; Howe 1994. ^ a b NRC 1999, "Shift to Applied Research Increases Investment". ^ Crevier 1993, pp. 115-117; Russell & Norvig 2003, pp. 21-22. ^ Crevier 1993, p. 211, Russell & Norvig 2003, p. 24 and see also Feigenbaum & McCorduck 1983. ^ Crevier 1993, pp. 161-162, 197-203, 240; Russell & Norvig 2003, p. 25. ^ Crevier 1993, pp. 209-212. ^ McCarthy, John (2000 ). "Reply to Lighthill". Stanford University. Archived from the initial on 30 September 2008. Retrieved 29 September 2007. ^ Markoff, John (14 October 2005). "Behind Expert system, a Squadron of Bright Real People". The New York City Times. Archived from the original on 2 February 2023. Retrieved 18 February 2017. At its low point, some computer scientists and software engineers avoided the term expert system 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 initial on 22 May 2019. Retrieved 7 May 2019. ^ a b Moravec 1988, p. 20 ^ Harnad, S. (1990 ). "The Symbol Grounding Problem". Physica D. 42 (1-3): 335-346. arXiv: cs/9906002. Bibcode:1990 PhyD ... 42..335 H. doi:10.1016/ 0167-2789( 90 )90087-6. S2CID 3204300. ^ Gubrud 1997 ^ Hutter, Marcus (2005 ). Universal Expert System: Sequential Decisions Based on Algorithmic Probability. Texts in Theoretical Computer Technology an EATCS Series. Springer. doi:10.1007/ b138233. ISBN 978-3-5402-6877-2. S2CID 33352850. Archived from the initial on 19 July 2022. Retrieved 19 July 2022. ^ Legg, Shane (2008 ). Machine Super Intelligence (PDF) (Thesis). University of Lugano. Archived (PDF) from the initial on 15 June 2022. Retrieved 19 July 2022. ^ Goertzel, Ben (2014 ). Artificial General Intelligence. Lecture Notes in Computer Science. Vol. 8598. Journal of Artificial General Intelligence. doi:10.1007/ 978-3-319-09274-4. ISBN 978-3-3190-9273-7. S2CID 8387410. ^ "Who coined the term "AGI"?". goertzel.org. Archived from the initial on 28 December 2018. Retrieved 28 December 2018., via 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 summer season school: June 22 - July 3, 2009, OpenCog Lab: July 6-9, 2009". Archived from the original on 28 September 2020. Retrieved 11 May 2020. ^ "Избираеми дисциплини 2009/2010 - пролетен триместър" [Elective courses 2009/2010 - spring trimester] Факултет по математика и информатика [Faculty of Mathematics and Informatics] (in Bulgarian). Archived from the initial 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 limitations of maker intelligence: Despite progress in device intelligence, artificial general intelligence is still a significant challenge". EMBO Reports. 20 (10 ): e49177. doi:10.15252/ embr.201949177. ISSN 1469-221X. PMC 6776890. PMID 31531926. ^ Bubeck, Sébastien; Chandrasekaran, Varun; Eldan, Ronen; Gehrke, Johannes; Horvitz, Eric; Kamar, Ece; Lee, Peter; Lee, Yin Tat; Li, Yuanzhi; Lundberg, Scott; Nori, Harsha; Palangi, Hamid; Ribeiro, Marco Tulio; Zhang, Yi (27 March 2023). "Sparks of Artificial General Intelligence: Early explores GPT-4". arXiv:2303.12712 [cs.CL] ^ "Microsoft Researchers Claim GPT-4 Is Showing "Sparks" of AGI". Futurism. 23 March 2023. Retrieved 13 December 2023. ^ Allen, Paul; Greaves, Mark (12 October 2011). "The Singularity Isn't Near". MIT Technology Review. Retrieved 17 September 2014. ^ Winfield, Alan. "Expert system will not develop into a Frankenstein's monster". The Guardian. Archived from the initial on 17 September 2014. Retrieved 17 September 2014. ^ Deane, George (2022 ). "Machines That Feel and Think: The Role of Affective Feelings and Mental Action in (Artificial) General Intelligence". Artificial Life. 28 (3 ): 289-309. doi:10.1162/ artl_a_00368. ISSN 1064-5462. PMID 35881678. S2CID 251069071. ^ a b c Clocksin 2003. ^ Fjelland, Ragnar (17 June 2020). "Why 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 expert system bring us paradise or damage?". The New Yorker. Archived from the original on 28 January 2016. Retrieved 7 February 2016. ^ Müller, V. C., & Bostrom, N. (2016 ). Future progress in expert system: A study of skilled viewpoint. In Fundamental issues 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, 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 general intelligence with hybrid Tianjic chip architecture". Nature. 572 (7767 ): 106-111. Bibcode:2019 Natur.572..106 P. doi:10.1038/ s41586-019-1424-8. ISSN 1476-4687. PMID 31367028. S2CID 199056116. Archived from the initial on 29 August 2022. Retrieved 29 August 2022. ^ Pandey, Mohit; Fernandez, Michael; Gentile, Francesco; Isayev, Olexandr; Tropsha, Alexander; Stern, Abraham C.; Cherkasov, Artem (March 2022). "The transformational function of GPU computing and deep 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 Artificial Intelligence". Annals of Data Science. 4 (2 ): 179-191. arXiv:1709.10242. doi:10.1007/ s40745-017-0109-0. S2CID 37900130. ^ Brien, Jörn (5 October 2017). "Google-KI doppelt so schlau wie Siri" [Google AI is two times as smart as Siri - however a six-year-old beats both] (in German). Archived from the original on 3 January 2019. Retrieved 2 January 2019. ^ Grossman, Gary (3 September 2020). "We're entering the AI twilight zone in between narrow and general AI". VentureBeat. Archived from the initial on 4 September 2020. Retrieved 5 September 2020. Certainly, too, there are those who declare we are currently seeing an early example of an AGI system in the just 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 consensus is that it is not AGI. ... If absolutely nothing else, GPT-3 tells us there is a happy medium in between narrow and general AI. ^ Quach, Katyanna. "A developer constructed an AI chatbot using GPT-3 that assisted a guy speak once again to his late fiancée. OpenAI shut it down". The Register. Archived from the initial on 16 October 2021. Retrieved 16 October 2021. ^ Wiggers, Kyle (13 May 2022), "DeepMind's new AI can carry out over 600 tasks, from playing games to controlling robotics", TechCrunch, archived from the original on 16 June 2022, obtained 12 June 2022. ^ Bubeck, Sébastien; Chandrasekaran, Varun; Eldan, Ronen; Gehrke, Johannes; Horvitz, Eric; Kamar, Ece; Lee, Peter; Lee, Yin Tat; Li, Yuanzhi; Lundberg, Scott; Nori, Harsha; Palangi, Hamid; Ribeiro, Marco Tulio; Zhang, Yi (22 March 2023). "Sparks of Artificial General Intelligence: Early 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 Times. ISSN 0362-4331. Retrieved 7 June 2023. ^ Bove, Tristan. "A.I. could measure up to human intelligence in 'simply a couple of years,' says CEO of Google's main A.I. research laboratory". 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 define Artificial General Intelligence". Fast Company. ^ Nosta, John (5 January 2024). "The Accelerating Path to Artificial General Intelligence". Psychology Today. Retrieved 30 March 2024. ^ Hickey, Alex. "Whole Brain Emulation: A Giant Step for Neuroscience". Tech Brew. Retrieved 8 November 2023. ^ Sandberg & Boström 2008. ^ Drachman 2005. ^ a b Russell & Norvig 2003. ^ Moravec 1988, p. 61. ^ Moravec 1998. ^ Holmgaard Mersh, Amalie (15 September 2023). "Decade-long European research study job maps the human brain". euractiv. ^ Swaminathan, Nikhil (January-February 2011). "Glia-the other brain cells". Discover. Archived from the initial on 8 February 2014. Retrieved 24 January 2014. ^ de Vega, Glenberg & Graesser 2008. A broad variety of views in existing research, 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 instance: Russell & Norvig 2003, Oxford University Press Dictionary of Psychology Archived 3 December 2007 at the Wayback Machine (priced quote in" Encyclopedia.com"),. MIT Encyclopedia of Cognitive Science Archived 19 July 2008 at the Wayback Machine (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 way it does. ^ Chalmers, David J. (9 August 2023). "Could a Big Language Model Be Conscious?". Boston Review. ^ Seth, Anil. "Consciousness". New Scientist. Retrieved 5 September 2024. ^ Nagel 1974. ^ "The Google engineer who thinks the business's AI has actually come to life". The Washington Post. 11 June 2022. Retrieved 12 June 2023. ^ Kateman, Brian (24 July 2023). "AI Should Be Terrified of Humans". TIME. Retrieved 5 September 2024. ^ Nosta, John (18 December 2023). "Should Expert System Have Rights?". Psychology Today. Retrieved 5 September 2024. ^ Akst, Daniel (10 April 2023). "Should Robots With Artificial Intelligence Have Moral or Legal Rights?". The Wall Street Journal. ^ "Artificial General Intelligence - Do [es] the expense outweigh advantages?". 23 August 2021. Retrieved 7 June 2023. ^ "How we can Gain from Advancing Artificial General Intelligence (AGI) - Unite.AI". www.unite.ai. 7 April 2020. Retrieved 7 June 2023. ^ a b c Talty, Jules; Julien, Stephan. "What Will Our Society Appear 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, UK; 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 human beings will ruin ourselves". Vox. Retrieved 8 June 2023. ^ Doherty, Ben (17 May 2018). "Climate alter an 'existential security danger' to Australia, Senate query says". 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 implications of expert system - however are we taking AI seriously enough?'". The Independent (UK). Archived from the initial on 25 September 2015. Retrieved 3 December 2014. ^ Herger, Mario. "The Gorilla Problem - Enterprise Garage". Retrieved 7 June 2023. ^ "The fascinating Facebook debate 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 threats 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 risk: a study". Physica Scripta. 90 (1 ): 018001. doi:10.1088/ 0031-8949/90/ 1/018001. ISSN 0031-8949. ^ Bostrom, Nick (2014 ). Superintelligence: Paths, Dangers, Strategies (First ed.). Oxford University Press. ISBN 978-0-1996-7811-2. ^ Chow, Andrew R.; Perrigo, Billy (16 February 2023). "The AI Arms Race Is On. Start Worrying". TIME. Retrieved 24 December 2023. ^ Tetlow, Gemma (12 January 2017). "AI arms race risks spiralling out of control, report warns". Financial Times. Archived from the initial on 11 April 2022. Retrieved 24 December 2023. ^ Milmo, Dan; Stacey, Kiran (25 September 2023). "Experts disagree over hazard posed but 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 completion 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 fears over AI, caution scientists". The Telegraph. Retrieved 7 December 2023. ^ Davidson, John (30 October 2023). "Google Brain creator states big tech is lying about AI termination 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 effect capacity of big language designs". OpenAI. Retrieved 7 June 2023. ^ a b Hurst, Luke (23 March 2023). "OpenAI states 80% of employees could 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 need universal basic earnings due to the fact that '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 original 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 Expert System. New York City, NY: BasicBooks. ISBN 0-465-02997-3. Darrach, Brad (20 November 1970), "Meet Shakey, the First Electronic Person", Life Magazine, pp. 58-68. Drachman, D. (2005 ), "Do we have brain to spare?", Neurology, 64 (12 ): 2004-2005, doi:10.1212/ 01. WNL.0000166914.38327. BB, PMID 15985565, S2CID 38482114. Feigenbaum, Edward A.; McCorduck, Pamela (1983 ), The Fifth Generation: Artificial Intelligence and Japan's Computer Challenge to the World, Michael Joseph, ISBN 978-0-7181-2401-4. Goertzel, Ben; Pennachin, Cassio, eds. (2006 ), Artificial General Intelligence (PDF), Springer, ISBN 978-3-5402-3733-4, archived from the 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 review of Kurzweil", Expert system, vol. 171, no. 18, Special Review Issue, pp. 1161-1173, doi:10.1016/ j.artint.2007.10.011, archived from the initial on 7 January 2016, obtained 1 April 2009. Gubrud, Mark (November 1997), "Nanotechnology and International Security", Fifth Foresight Conference on Molecular Nanotechnology, archived from the original on 29 May 2011, obtained 7 May 2011. Howe, J. (November 1994), Artificial Intelligence at Edinburgh University: a Perspective, archived from the initial on 17 August 2007, obtained 30 August 2007. Johnson, Mark (1987 ), The body in the mind, Chicago, ISBN 978-0-2264-0317-5. Kurzweil, Ray (2005 ), The Singularity is Near, Viking Press. Lighthill, Professor Sir James (1973 ), "Expert System: A General Survey", Artificial Intelligence: a paper seminar, Science Research Council. Luger, George; Stubblefield, William (2004 ), Expert System: Structures and Strategies for Complex Problem Solving (5th ed.), The Benjamin/Cummings Publishing Company, Inc., p. 720, ISBN 978-0-8053-4780-7. McCarthy, John (2007b). What is Artificial Intelligence?. Stanford University. The ultimate effort is to make computer system programs that can fix issues and attain goals worldwide in addition to humans. Moravec, Hans (1988 ), Mind Children, Harvard University Press Moravec, Hans (1998 ), "When will computer hardware match the human brain?", Journal of Evolution and Technology, vol. 1, archived from the initial on 15 June 2006, recovered 23 June 2006 Nagel (1974 ), "What Is it Like to Be a Bat" (PDF), Philosophical Review, 83 (4 ): 435-50, doi:10.2307/ 2183914, JSTOR 2183914, archived (PDF) from the initial on 16 October 2011, recovered 7 November 2009 Newell, Allen; Simon, H. A. (1976 ). "Computer Science as Empirical Inquiry: Symbols and Search". Communications of the ACM. 19 (3 ): 113-126. doi:10.1145/ 360018.360022. Nilsson, Nils (1998 ), Expert System: A New Synthesis, Morgan Kaufmann Publishers, ISBN 978-1-5586-0467-4 NRC (1999 ), "Developments in Artificial Intelligence", Funding a Revolution: Government Support for Computing Research, National Academy Press, archived from the initial on 12 January 2008, retrieved 29 September 2007 Poole, David; Mackworth, Alan; Goebel, Randy (1998 ), Computational Intelligence: A Logical Approach, New York City: Oxford University Press, archived from the original on 25 July 2009, recovered 6 December 2007 Russell, Stuart J.; Norvig, Peter (2003 ), Artificial Intelligence: A Modern Approach (second ed.), Upper Saddle River, New Jersey: Prentice Hall, ISBN 0-13-790395-2 Sandberg, Anders; Boström, Nick (2008 ), Whole Brain Emulation: A Roadmap (PDF), Technical Report # 2008-3, Future of Humanity Institute, Oxford University, archived (PDF) from the original on 25 March 2020, obtained 5 April 2009 Searle, John (1980 ), "Minds, Brains and Programs" (PDF), Behavioral and Brain Sciences, 3 (3 ): 417-457, doi:10.1017/ S0140525X00005756, S2CID 55303721, archived (PDF) from the initial on 17 March 2019, obtained 3 September 2020 Simon, H. A. (1965 ), The Shape of Automation for Men and Management, New York: Harper & Row Turing, Alan (October 1950). "Computing Machinery and Intelligence". Mind. 59 (236 ): 433-460. doi:10.1093/ mind/LIX.236.433. ISSN 1460-2113. JSTOR 2251299. S2CID 14636783.
de Vega, Manuel; Glenberg, Arthur; Graesser, Arthur, eds. (2008 ), Symbols and Embodiment: Debates on meaning and cognition, Oxford University Press, ISBN 978-0-1992-1727-4 Wang, Pei; Goertzel, Ben (2007 ). "Introduction: Aspects of Artificial General Intelligence". Advances in Artificial General Intelligence: Concepts, Architectures and Algorithms: Proceedings of the AGI Workshop 2006. IOS Press. pp. 1-16. ISBN 978-1-5860-3758-1. Archived from the initial on 18 February 2021. Retrieved 13 December 2020 - through ResearchGate.
Further reading

Aleksander, Igor (1996 ), Impossible Minds, World Scientific Publishing Company, ISBN 978-1-8609-4036-1 Azevedo FA, Carvalho LR, Grinberg LT, Farfel J, et al. (April 2009), "Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain", The Journal of Comparative Neurology, 513 (5 ): 532-541, doi:10.1002/ cne.21974, PMID 19226510, S2CID 5200449, archived from the initial on 18 February 2021, retrieved 4 September 2013 - by means of ResearchGate Berglas, Anthony (January 2012) [2008], Artificial Intelligence Will Kill Our Grandchildren (Singularity), archived from the original on 23 July 2014, obtained 31 August 2012 Cukier, Kenneth, "Ready for Robots? How to Consider the Future of AI", Foreign Affairs, vol. 98, no. 4 (July/August 2019), pp. 192-98. George Dyson, historian of computing, writes (in what may be called "Dyson's Law") that "Any system basic enough to be understandable will not be made complex enough to act wisely, while any system made complex enough to act 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 easy foolish. They work, but they work by strength." (p. 198.). Gelernter, David, Dream-logic, the Internet and Artificial Thought, Edge, archived from the initial on 26 July 2010, retrieved 25 July 2010. Gleick, James, "The Fate of Free Choice" (review of Kevin J. Mitchell, Free Agents: How Evolution Gave Us Free Choice, Princeton University Press, 2023, 333 pp.), The New York City Review of Books, vol. LXXI, no. 1 (18 January 2024), pp. 27-28, 30. "Agency is what distinguishes us from devices. For biological creatures, reason and purpose come from acting worldwide and experiencing the effects. Artificial intelligences - disembodied, strangers to blood, sweat, and tears - have no event for that." (p. 30.). Halal, William E. "TechCast Article Series: The Automation of Thought" (PDF). Archived from the initial (PDF) on 6 June 2013. - Halpern, Sue, "The Coming Tech Autocracy" (review of Verity Harding, AI Needs You: How We Can Change AI's Future and Save Our Own, Princeton University Press, 274 pp.; Gary Marcus, Taming Silicon Valley: How We Can Ensure That AI Works for Us, MIT Press, 235 pp.; Daniela Rus and Gregory Mone, The Mind's Mirror: Risk and Reward in the Age of AI, Norton, 280 pp.; Madhumita Murgia, Code Dependent: Residing In the Shadow of AI, Henry Holt, 311 pp.), The New York Review of Books, vol. LXXI, no. 17 (7 November 2024), pp. 44-46. "' We can't realistically expect that those who want to get rich from AI are going to have the interests of the rest people close at heart,' ... composes [Gary Marcus] 'We can't depend on federal governments driven by campaign finance contributions [from tech business] to push back.' ... Marcus details the needs that citizens need to make of their governments and the tech business. They consist of transparency on how AI systems work; compensation for individuals if their data [are] utilized to train LLMs (big language design) s and the right to approval to this use; and the ability to hold tech business liable for the damages they bring on by eliminating Section 230, enforcing cash penalites, and passing stricter item liability laws ... Marcus likewise recommends ... that a brand-new, AI-specific federal company, comparable to the FDA, the FCC, or the FTC, might supply the most robust oversight ... [T] he Fordham law teacher Chinmayi Sharma ... recommends ... establish [ing] an expert licensing regime for engineers that would work in a comparable way to medical licenses, malpractice fits, and the Hippocratic oath in medicine. 'What if, like physicians,' she asks ..., 'AI engineers likewise swore to do no harm?'" (p. 46.). Holte, R. C.; Choueiry, B. Y. (2003 ), "Abstraction and complexityzoo.net reformulation in artificial intelligence", Philosophical Transactions of the Royal Society B, vol. 358, no. 1435, pp. 1197-1204, doi:10.1098/ rstb.2003.1317, PMC 1693218, PMID 12903653. Hughes-Castleberry, Kenna, "A Murder Mystery Puzzle: The literary puzzle Cain's Jawbone, which has actually puzzled people for decades, exposes the restrictions of natural-language-processing algorithms", Scientific American, vol. 329, no. 4 (November 2023), pp. 81-82. "This murder secret competitors has actually exposed that although NLP (natural-language processing) models can extraordinary accomplishments, their abilities are quite limited by the amount of context they get. This [...] might trigger [difficulties] for scientists who hope to use them to do things such as examine ancient languages. Sometimes, there are few historic records on long-gone civilizations to work 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. Just how much does it matter?", The New Yorker, 20 November 2023, pp. 54-59. "If by 'deepfakes' we mean realistic videos produced using expert system that in fact trick people, then they barely exist. The phonies aren't deep, and the deeps aren't phony. [...] A.I.-generated videos are not, in general, running in our media as counterfeited evidence. Their role much better looks like that of cartoons, especially smutty ones." (p. 59.). - Leffer, Lauren, "The Risks of Trusting AI: We should avoid humanizing machine-learning models used in scientific research study", Scientific American, vol. 330, no. 6 (June 2024), pp. 80-81. Lepore, Jill, "The Chit-Chatbot: Is talking with a machine a discussion?", The New Yorker, 7 October 2024, pp. 12-16. Marcus, Gary, "Artificial Confidence: Even the latest, buzziest systems of artificial general intelligence are stymmied by the usual problems", Scientific American, vol. 327, no. 4 (October 2022), pp. 42-45. McCarthy, John (October 2007), "From here to human-level AI", Artificial Intelligence, 171 (18 ): 1174-1182, doi:10.1016/ j.artint.2007.10.009. McCorduck, Pamela (2004 ), Machines Who Think (second ed.), Natick, Massachusetts: A. K. Peters, ISBN 1-5688-1205-1. Moravec, Hans (1976 ), The Role of Raw Power in Intelligence, archived from the initial on 3 March 2016, obtained 29 September 2007. Newell, Allen; Simon, H. A. (1963 ), "GPS: A Program that Simulates Human Thought", in Feigenbaum, E. A.; Feldman, J. (eds.), Computers and Thought, New York 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 technology lead cops to ignore inconsistent proof?", The New Yorker, 20 November 2023, pp. 20-26. Roivainen, Eka, "AI's IQ: ChatGPT aced a [basic intelligence] test but showed that intelligence can not be measured by IQ alone", Scientific American, vol. 329, no. 1 (July/August 2023), p. 7. "Despite its high IQ, ChatGPT fails at jobs that require real humanlike reasoning or an understanding of the physical and social world ... ChatGPT seemed not able to factor realistically and tried to count on its vast database of ... truths stemmed from online texts. " - Scharre, Paul, "Killer Apps: The Real Dangers of an AI Arms Race", Foreign Affairs, vol. 98, no. 3 (May/June 2019), pp. 135-44. "Today's AI innovations are powerful however unreliable. Rules-based systems can not handle scenarios their programmers did not prepare for. Learning systems are restricted by the information on which they were trained. AI failures have actually already resulted in catastrophe. Advanced autopilot features in vehicles, although they perform well in some circumstances, have actually driven vehicles without alerting into trucks, concrete barriers, and parked automobiles. In the incorrect scenario, AI systems go from supersmart to superdumb in an instant. When an opponent is trying to manipulate and hack an AI system, the threats 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 made possible by brand-new innovations however rely on the timelelss human propensity to anthropomorphise." (p. 29.). Williams, R. W.; Herrup, K.