Artificial basic intelligence (AGI) is a kind of synthetic intelligence (AI) that matches or exceeds human cognitive capabilities throughout a wide variety of cognitive jobs. This contrasts with narrow AI, which is restricted to specific jobs. [1] Artificial superintelligence (ASI), on the other hand, describes AGI that significantly goes beyond human cognitive abilities. AGI is considered among the definitions of strong AI.

is a primary objective of AI research and of business such as OpenAI [2] and Meta. [3] A 2020 survey identified 72 active AGI research and development jobs across 37 countries. [4]
The timeline for accomplishing AGI remains a topic of continuous dispute amongst scientists and specialists. As of 2023, some argue that it may be possible in years or years; others maintain it might take a century or longer; a minority believe it may never be achieved; and another minority claims that it is already here. [5] [6] Notable AI scientist Geoffrey Hinton has actually revealed issues about the quick development towards AGI, recommending it might be accomplished quicker than many expect. [7]
There is dispute on the specific definition of AGI and regarding whether modern big language models (LLMs) such as GPT-4 are early kinds of AGI. [8] AGI is a typical subject in sci-fi and futures studies. [9] [10]
Contention exists over whether AGI represents an existential danger. [11] [12] [13] Many experts on AI have actually stated that reducing the danger of human extinction positioned by AGI ought to be a worldwide top priority. [14] [15] Others discover the advancement of AGI to be too remote to provide such a risk. [16] [17]
Terminology

AGI is likewise understood 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 system programs that experience sentience or awareness. [a] On the other hand, weak AI (or narrow AI) has the ability to resolve one particular issue however does not have general cognitive capabilities. [22] [19] Some scholastic sources utilize "weak AI" to refer more broadly to any programs that neither experience consciousness nor have a mind in the very same sense as people. [a]
Related principles consist of artificial superintelligence and transformative AI. An artificial superintelligence (ASI) is a hypothetical type of AGI that is a lot more usually intelligent than people, [23] while the idea of transformative AI connects to AI having a large effect on society, for instance, comparable to the farming or commercial revolution. [24]
A framework for categorizing AGI in levels was proposed in 2023 by Google DeepMind scientists. They define five levels of AGI: emerging, competent, specialist, virtuoso, and superhuman. For instance, a competent AGI is defined as an AI that surpasses 50% of competent grownups in a large range of non-physical tasks, and a superhuman AGI (i.e. a synthetic superintelligence) is likewise specified however with a threshold of 100%. They consider big language models like ChatGPT or LLaMA 2 to be instances of emerging AGI. [25]
Characteristics

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

Researchers typically hold that intelligence is needed to do all of the following: [27]
reason, use technique, resolve puzzles, and make judgments under uncertainty represent knowledge, including sound judgment knowledge plan learn

• communicate in natural language
• if needed, incorporate these abilities in conclusion of any offered goal

Many interdisciplinary techniques (e.g. cognitive science, computational intelligence, and choice making) think about extra characteristics such as creativity (the ability to form novel psychological images and concepts) [28] and autonomy. [29]
Computer-based systems that show many of these capabilities exist (e.g. see computational imagination, automated thinking, decision assistance system, robotic, evolutionary calculation, intelligent agent). There is dispute about whether modern-day AI systems have them to a sufficient degree.

Physical characteristics

Other abilities are thought about desirable in smart systems, as they might impact intelligence or aid in its expression. These include: [30]
- the capability to sense (e.g. see, hear, and so on), and - the ability to act (e.g. relocation and manipulate objects, modification location to check out, etc).
This consists of the ability to identify and respond to risk. [31]
Although the capability to sense (e.g. see, hear, etc) and the ability to act (e.g. relocation and manipulate objects, modification location to explore, and so on) can be preferable for some smart systems, [30] these physical abilities are not strictly required for an entity to certify as AGI-particularly under the thesis that large language models (LLMs) may currently be or end up being AGI. Even from a less positive 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, supplied it can process input (language) from the external world in place of human senses. This analysis aligns with the understanding that AGI has never been proscribed a particular physical personification and thus does not demand a capability for mobility or traditional "eyes and ears". [32]
Tests for human-level AGI

Several tests indicated to validate human-level AGI have been considered, consisting of: [33] [34]
The concept of the test is that the maker needs to try and pretend to be a guy, by addressing questions put to it, and it will only pass if the pretence is fairly convincing. A substantial part of a jury, who need to not be skilled about makers, 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 fix it, one would need to carry out AGI, since the option is beyond the abilities of a purpose-specific algorithm. [47]
There are many problems that have been conjectured to require basic intelligence to resolve as well as people. Examples include computer system vision, natural language understanding, and handling unforeseen scenarios while solving any real-world problem. [48] Even a particular task like translation requires a machine to check out and compose in both languages, follow the author's argument (reason), comprehend the context (understanding), and faithfully reproduce the author's initial intent (social intelligence). All of these issues need to be fixed simultaneously in order to reach human-level device performance.

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

Classical AI

Modern AI research study started in the mid-1950s. [50] The very first generation of AI researchers were convinced that synthetic basic intelligence was possible which it would exist in simply a couple of years. [51] AI pioneer Herbert A. Simon composed in 1965: "devices 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 believed they might develop by the year 2001. AI leader Marvin Minsky was a consultant [53] on the task of making HAL 9000 as reasonable as possible according to the consensus forecasts of the time. He said in 1967, "Within a generation ... the problem of creating 'artificial intelligence' will significantly be solved". [54]
Several classical AI tasks, 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 became apparent that scientists had actually grossly ignored the trouble of the job. Funding companies became hesitant of AGI and put researchers under increasing pressure to produce helpful "used AI". [c] In the early 1980s, Japan's Fifth Generation Computer Project revived interest in AGI, setting out a ten-year timeline that consisted of AGI goals like "bring on a table talk". [58] In action to this and the success of professional systems, both industry and government pumped cash into the field. [56] [59] However, confidence in AI stunningly collapsed in the late 1980s, and the goals of the Fifth Generation Computer Project were never fulfilled. [60] For the second time in twenty years, AI scientists who anticipated the imminent accomplishment of AGI had actually been mistaken. By the 1990s, AI scientists had a track record for making vain pledges. They became unwilling to make forecasts at all [d] and prevented reference of "human level" synthetic intelligence for fear of being identified "wild-eyed dreamer [s]. [62]
Narrow AI research

In the 1990s and early 21st century, mainstream AI accomplished commercial success and scholastic respectability by focusing on particular sub-problems where AI can produce proven outcomes and business applications, such as speech acknowledgment and recommendation algorithms. [63] These "applied AI" systems are now used thoroughly throughout the innovation industry, and research study in this vein is heavily funded in both academic community and industry. Since 2018 [upgrade], advancement in this field was thought about an emerging pattern, and a fully grown stage was expected to be reached in more than ten years. [64]
At the turn of the century, numerous traditional AI researchers [65] hoped that strong AI might be developed by integrating programs that fix numerous sub-problems. Hans Moravec wrote in 1988:

I am confident that this bottom-up path to expert system will one day meet the traditional top-down path majority method, all set to provide the real-world proficiency and the commonsense knowledge that has been so frustratingly elusive in reasoning programs. Fully intelligent devices will result when the metaphorical golden spike is driven unifying the 2 efforts. [65]
However, even at the time, this was contested. For instance, Stevan Harnad of Princeton University concluded his 1990 paper on the symbol grounding hypothesis by mentioning:

The expectation has typically been voiced that "top-down" (symbolic) approaches to modeling cognition will in some way fulfill "bottom-up" (sensory) approaches someplace in between. If the grounding considerations in this paper stand, then this expectation is hopelessly modular and there is really just one viable path from sense to signs: from the ground up. A free-floating symbolic level like the software application level of a computer will never be reached by this route (or vice versa) - nor is it clear why we need to even attempt to reach such a level, given that it looks as if arriving would just amount to uprooting our signs from their intrinsic meanings (thus simply lowering ourselves to the practical equivalent of a programmable computer). [66]
Modern artificial basic intelligence research

The term "synthetic general intelligence" was used as early as 1997, by Mark Gubrud [67] in a discussion of the ramifications of totally automated military production and operations. A mathematical formalism of AGI was proposed by Marcus Hutter in 2000. Named AIXI, the proposed AGI agent increases "the capability to satisfy goals in a large range of environments". [68] This type of AGI, defined by the capability to maximise a mathematical definition of intelligence rather than show human-like behaviour, [69] was also called universal artificial intelligence. [70]
The term AGI was re-introduced and promoted 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 initial 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 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, organized by Lex Fridman and featuring a variety of guest lecturers.

As of 2023 [update], a little number of computer scientists are active in AGI research study, and many add to a series of AGI conferences. However, progressively more scientists have an interest in open-ended learning, [76] [77] which is the idea of enabling AI to continually learn and innovate like humans do.

Feasibility

As of 2023, the development and possible accomplishment of AGI remains a subject of intense dispute within the AI community. While conventional agreement held that AGI was a distant objective, current developments have actually led some researchers and market figures to declare that early types of AGI may already exist. [78] AI leader Herbert A. Simon speculated in 1965 that "devices will be capable, within twenty years, of doing any work a man can do". This forecast stopped working to come true. Microsoft co-founder Paul Allen thought that such intelligence is unlikely in the 21st century because it would require "unforeseeable and basically unforeseeable breakthroughs" and a "clinically deep understanding of cognition". [79] Writing in The Guardian, roboticist Alan Winfield declared the gulf between contemporary computing and human-level artificial intelligence is as large as the gulf in between present space flight and useful faster-than-light spaceflight. [80]
An additional obstacle is the absence of clearness in defining what intelligence entails. Does it need consciousness? Must it display the capability to set objectives along with pursue them? Is it simply a matter of scale such that if design sizes increase sufficiently, intelligence will emerge? Are centers such as planning, reasoning, and causal understanding required? Does intelligence require explicitly replicating the brain and its specific faculties? Does it need feelings? [81]
Most AI scientists believe strong AI can be attained in the future, however some thinkers, like Hubert Dreyfus and Roger Penrose, deny the possibility of attaining strong AI. [82] [83] John McCarthy is among those who think human-level AI will be achieved, however that today level of progress is such that a date can not precisely be predicted. [84] AI professionals' views on the feasibility of AGI wax and subside. Four polls carried out in 2012 and 2013 recommended that the average quote among experts for when they would be 50% confident AGI would arrive was 2040 to 2050, depending on the survey, with the mean being 2081. Of the professionals, 16.5% responded to with "never ever" when asked the same question but with a 90% confidence rather. [85] [86] Further existing AGI progress factors to consider can be discovered above Tests for validating human-level AGI.

A report by Stuart Armstrong and Kaj Sotala of the Machine Intelligence Research Institute discovered that "over [a] 60-year amount of time there is a strong predisposition towards anticipating the arrival of human-level AI as in between 15 and 25 years from the time the prediction was made". They examined 95 predictions made between 1950 and 2012 on when human-level AI will happen. [87]
In 2023, Microsoft scientists released a comprehensive evaluation of GPT-4. They concluded: "Given the breadth and depth of GPT-4's abilities, we think that it could reasonably be viewed as an early (yet still insufficient) version of a synthetic general intelligence (AGI) system." [88] Another study in 2023 reported that GPT-4 surpasses 99% of humans on the Torrance tests of creative thinking. [89] [90]
Blaise Agüera y Arcas and Peter Norvig wrote in 2023 that a considerable level of general intelligence has actually already been attained with frontier designs. They composed that reluctance to this view comes from four primary factors: a "healthy skepticism about metrics for AGI", an "ideological dedication to alternative AI theories or strategies", a "commitment to human (or biological) exceptionalism", or a "issue about the economic ramifications of AGI". [91]
2023 also marked the introduction of big multimodal designs (big language models efficient in processing or producing multiple modalities such as text, audio, and images). [92]
In 2024, OpenAI launched o1-preview, the very first of a series of models that "spend more time thinking before they respond". According to Mira Murati, this capability to believe before responding represents a new, extra paradigm. It improves model outputs by investing more computing power when creating 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 company had achieved AGI, stating, "In my opinion, we have currently accomplished AGI and it's a lot more clear with O1." Kazemi clarified that while the AI is not yet "much better than any human at any task", it is "better than a lot of people at the majority of jobs." He likewise resolved criticisms that big language designs (LLMs) simply follow predefined patterns, comparing their knowing process to the clinical method of observing, hypothesizing, and validating. These statements have actually sparked argument, as they count on a broad and non-traditional definition of AGI-traditionally understood as AI that matches human intelligence throughout all domains. Critics argue that, while OpenAI's designs demonstrate remarkable adaptability, they might not completely fulfill this standard. Notably, Kazemi's comments came quickly after OpenAI eliminated "AGI" from the terms of its collaboration with Microsoft, prompting speculation about the company's strategic objectives. [95]
Timescales

Progress in synthetic intelligence has actually historically gone through periods of rapid development separated by periods when development appeared to stop. [82] Ending each hiatus were essential advances in hardware, software application or both to create area for more development. [82] [98] [99] For instance, the computer hardware offered in the twentieth century was not adequate to carry out deep learning, which needs big numbers of GPU-enabled CPUs. [100]
In the intro to his 2006 book, [101] Goertzel states that estimates of the time needed before a truly versatile AGI is constructed vary from ten years to over a century. Since 2007 [upgrade], the agreement in the AGI research study community seemed 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 plausible. [103] Mainstream AI researchers have actually offered a large range of viewpoints on whether progress will be this rapid. A 2012 meta-analysis of 95 such viewpoints discovered a predisposition towards predicting that the start of AGI would happen within 16-26 years for modern and historical forecasts alike. That paper has actually been slammed for how it classified opinions as specialist or non-expert. [104]
In 2012, Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton developed a neural network called AlexNet, which won the ImageNet competitors with a top-5 test mistake rate of 15.3%, considerably better than the second-best entry's rate of 26.3% (the conventional approach used a weighted sum of ratings from various pre-defined classifiers). [105] AlexNet was considered the preliminary ground-breaker of the current deep knowing wave. [105]
In 2017, researchers Feng Liu, Yong Shi, and Ying Liu performed intelligence tests on openly available and freely accessible weak AI such as Google AI, Apple's Siri, and others. At the optimum, these AIs reached an IQ value of about 47, which corresponds roughly to a six-year-old kid in very first grade. A grownup comes to 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 capable of performing lots of varied tasks without particular training. According to Gary Grossman in a VentureBeat short article, while there is agreement that GPT-3 is not an example of AGI, it is considered by some to be too advanced to be classified as a narrow AI system. [108]
In the exact same year, Jason Rohrer utilized his GPT-3 account to establish a chatbot, and offered a chatbot-developing platform called "Project December". OpenAI asked for changes to the chatbot to abide by their safety standards; Rohrer detached Project December from the GPT-3 API. [109]
In 2022, DeepMind developed Gato, a "general-purpose" system capable of carrying out more than 600 different jobs. [110]
In 2023, Microsoft Research published a study on an early version of OpenAI's GPT-4, competing that it displayed more general intelligence than previous AI models and demonstrated human-level performance in tasks covering several domains, such as mathematics, coding, and law. This research stimulated a debate on whether GPT-4 might be thought about an early, incomplete variation of synthetic general intelligence, highlighting the need for further expedition and evaluation of such systems. [111]
In 2023, the AI researcher Geoffrey Hinton mentioned that: [112]
The idea that this stuff might actually get smarter than people - a few people believed that, [...] But many people believed it was way off. And I believed it was method off. I believed it was 30 to 50 years and even longer away. Obviously, I no longer believe that.

In May 2023, Demis Hassabis similarly said that "The progress in the last few years has actually been quite extraordinary", and that 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, specified his expectation that within five years, AI would can passing any test at least along with human beings. [114] In June 2024, the AI scientist Leopold Aschenbrenner, a former OpenAI worker, approximated AGI by 2027 to be "noticeably possible". [115]
Whole brain emulation

While the advancement of transformer designs like in ChatGPT is thought about the most promising course to AGI, [116] [117] entire brain emulation can act as an alternative approach. With entire brain simulation, a brain model is constructed by scanning and mapping a biological brain in detail, and after that copying and mimicing it on a computer system or another computational gadget. The simulation design need to be adequately devoted to the original, so that it behaves in almost the very same method as the initial brain. [118] Whole brain emulation is a kind of brain simulation that is talked about in computational neuroscience and neuroinformatics, and for medical research functions. It has been gone over in artificial intelligence research [103] as an approach to strong AI. Neuroimaging innovations that could deliver the essential 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 appear on a comparable timescale to the computing power required to replicate it.

Early approximates

For low-level brain simulation, a really powerful cluster of computer systems or GPUs would be required, given the massive quantity of synapses within the human brain. Each of the 1011 (one hundred billion) nerve cells has on typical 7,000 synaptic connections (synapses) to other nerve cells. The brain of a three-year-old kid has about 1015 synapses (1 quadrillion). This number declines with age, supporting by their adult years. Estimates vary for an adult, ranging from 1014 to 5 × 1014 synapses (100 to 500 trillion). [120] A price quote of the brain's processing power, based on a simple switch design for neuron activity, is around 1014 (100 trillion) synaptic updates per second (SUPS). [121]
In 1997, Kurzweil looked at numerous price quotes for the hardware needed to equate to the human brain and embraced a figure of 1016 calculations per second (cps). [e] (For contrast, if a "computation" was comparable to one "floating-point operation" - a measure used to rate current supercomputers - then 1016 "computations" would be comparable to 10 petaFLOPS, accomplished in 2011, while 1018 was attained in 2022.) He utilized this figure to forecast the essential hardware would be readily available sometime between 2015 and 2025, if the rapid growth in computer power at the time of composing continued.

Current research

The Human Brain Project, an EU-funded initiative active from 2013 to 2023, has actually developed an especially in-depth and openly available 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 methods

The synthetic neuron design assumed by Kurzweil and utilized in many present artificial neural network implementations is easy compared with biological neurons. A brain simulation would likely have to record the in-depth cellular behaviour of biological nerve cells, presently understood only in broad outline. The overhead presented by complete modeling of the biological, chemical, and physical details of neural behaviour (especially on a molecular scale) would need computational powers numerous orders of magnitude bigger than Kurzweil's quote. In addition, the price quotes do not account for glial cells, which are understood to play a function in cognitive processes. [125]
A fundamental criticism of the simulated brain method originates from embodied cognition theory which asserts that human embodiment is an essential element of human intelligence and is required to ground meaning. [126] [127] If this theory is correct, any totally practical brain design will require to encompass more than simply the neurons (e.g., a robotic body). Goertzel [103] proposes virtual personification (like in metaverses like Second Life) as an alternative, but it is unknown whether this would be sufficient.

Philosophical perspective

"Strong AI" as specified in viewpoint

In 1980, theorist John Searle coined the term "strong AI" as part of his Chinese room argument. [128] He proposed a distinction between two hypotheses about synthetic intelligence: [f]
Strong AI hypothesis: An artificial intelligence system can have "a mind" and "awareness". Weak AI hypothesis: An expert system system can (only) imitate it believes and has a mind and awareness.
The very first one he called "strong" since it makes a more powerful statement: it presumes something special has actually taken place to the maker that exceeds those abilities that we can test. The behaviour of a "weak AI" maker would be precisely similar to a "strong AI" maker, but the latter would also have subjective conscious experience. This use is also typical in academic AI research study and textbooks. [129]
In contrast to Searle and traditional AI, some futurists such as Ray Kurzweil use the term "strong AI" to mean "human level artificial basic intelligence". [102] This is not the exact same as Searle's strong AI, unless it is assumed that consciousness is required for human-level AGI. Academic philosophers such as Searle do not believe that is the case, and to most expert system scientists the concern is out-of-scope. [130]
Mainstream AI is most thinking about how a program acts. [131] According to Russell and Norvig, "as long as the program works, they do not care if you call it real or a simulation." [130] If the program can behave as if it has a mind, then there is no need to understand if it in fact has mind - indeed, there would be no other way to tell. For AI research, Searle's "weak AI hypothesis" is equivalent to the declaration "artificial general intelligence is possible". Thus, according to Russell and Norvig, "most AI scientists take the weak AI hypothesis for given, and utahsyardsale.com don't care about the strong AI hypothesis." [130] Thus, for academic AI research, "Strong AI" and "AGI" are 2 different things.

Consciousness

Consciousness can have different meanings, and some aspects play significant functions in sci-fi and the ethics of artificial intelligence:

Sentience (or "remarkable consciousness"): The capability to "feel" perceptions or feelings subjectively, instead of the capability to factor about understandings. Some philosophers, such as David Chalmers, use the term "awareness" to refer exclusively to phenomenal awareness, which is approximately equivalent to life. [132] Determining why and how subjective experience arises is called the hard problem of awareness. [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 feel 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 awareness) however a toaster does not. [134] In 2022, a Google engineer claimed that the company's AI chatbot, LaMDA, had actually accomplished sentience, though this claim was widely disputed by other professionals. [135]
Self-awareness: To have mindful awareness of oneself as a different person, specifically to be purposely knowledgeable about one's own thoughts. This is opposed to simply being the "topic of one's thought"-an operating system or debugger is able to be "familiar with itself" (that is, to represent itself in the exact same way it represents everything else)-but this is not what people generally imply when they use the term "self-awareness". [g]
These traits have a moral dimension. AI life would trigger issues of well-being and legal protection, similarly to animals. [136] Other aspects of awareness related to cognitive abilities are also appropriate to the principle of AI rights. [137] Determining how to integrate innovative AI with existing legal and social structures is an emergent issue. [138]
Benefits

AGI might have a variety of applications. If oriented towards such goals, AGI might help mitigate numerous issues worldwide such as appetite, poverty and health problems. [139]
AGI could improve productivity and performance in most tasks. For example, in public health, AGI might speed up medical research study, significantly versus cancer. [140] It might take care of the elderly, [141] and democratize access to rapid, premium medical diagnostics. It could offer fun, low-cost and individualized education. [141] The requirement to work to subsist could end up being outdated if the wealth produced is appropriately rearranged. [141] [142] This likewise raises the concern of the place of humans in a radically automated society.

AGI might also assist to make reasonable choices, and to expect and prevent catastrophes. It could likewise help to profit of possibly catastrophic technologies such as nanotechnology or climate engineering, while preventing the associated risks. [143] If an AGI's primary objective is to prevent existential disasters such as human termination (which might be tough if the Vulnerable World Hypothesis turns out to be true), [144] it could take steps to considerably reduce the risks [143] while decreasing the effect of these measures on our lifestyle.

Risks

Existential threats

AGI may represent multiple types of existential risk, which are risks that threaten "the early extinction of Earth-originating smart life or the permanent and drastic destruction of its potential for preferable future advancement". [145] The risk of human termination from AGI has been the subject of many disputes, however there is likewise the possibility that the development of AGI would cause a permanently problematic future. Notably, it might be utilized to spread and protect the set of worths of whoever develops it. If humanity still has ethical blind areas comparable to slavery in the past, AGI may irreversibly entrench it, avoiding moral progress. [146] Furthermore, AGI could facilitate mass surveillance and brainwashing, which might be used to create a stable repressive around the world totalitarian routine. [147] [148] There is likewise a danger for the machines themselves. If makers that are sentient or otherwise worthy of ethical factor to consider are mass produced in the future, taking part in a civilizational path that indefinitely neglects their well-being and interests might be an existential disaster. [149] [150] Considering how much AGI could improve humanity's future and help in reducing other existential risks, Toby Ord calls these existential threats "an argument for proceeding with due care", not for "abandoning AI". [147]
Risk of loss of control and human termination

The thesis that AI poses an existential threat for humans, and that this danger needs more attention, is controversial however has been backed in 2023 by lots of public figures, AI researchers and CEOs of AI business such as Elon Musk, Bill Gates, Geoffrey Hinton, Yoshua Bengio, Demis Hassabis and Sam Altman. [151] [152]
In 2014, Stephen Hawking criticized prevalent indifference:

So, dealing with possible futures of enormous advantages and risks, the specialists are definitely doing everything possible to guarantee the very best outcome, right? Wrong. If an exceptional alien civilisation sent us a message stating, 'We'll arrive in a couple of decades,' would we just respond, 'OK, call us when you get here-we'll leave the lights on?' Probably not-but this is more or less what is happening with AI. [153]
The prospective fate of mankind has actually sometimes been compared to the fate of gorillas threatened by human activities. The contrast specifies that greater intelligence permitted humanity to control gorillas, which are now susceptible in manner ins which they could not have actually prepared for. As an outcome, the gorilla has actually become a threatened types, not out of malice, but merely as a collateral damage from human activities. [154]
The skeptic Yann LeCun considers that AGIs will have no desire to dominate mankind which we must take care not to anthropomorphize them and interpret their intents as we would for human beings. He said that people will not be "smart adequate to develop super-intelligent makers, yet ridiculously silly to the point of giving it moronic objectives with no safeguards". [155] On the other side, the principle of instrumental convergence recommends that almost whatever their goals, smart representatives will have reasons to attempt to survive and acquire more power as intermediary steps to achieving these objectives. Which this does not require having emotions. [156]
Many scholars who are concerned about existential risk supporter for more research into resolving the "control problem" to respond to the question: what kinds of safeguards, algorithms, or architectures can developers carry out to increase the probability that their recursively-improving AI would continue to act in a friendly, rather than devastating, way after it reaches superintelligence? [157] [158] Solving the control issue is complicated by the AI arms race (which could lead to a race to the bottom of safety precautions in order to launch products before rivals), [159] and making use of AI in weapon systems. [160]
The thesis that AI can pose existential risk also has detractors. Skeptics normally state that AGI is unlikely in the short-term, or that issues about AGI sidetrack from other issues associated with present AI. [161] Former Google fraud czar Shuman Ghosemajumder considers that for many individuals outside of the innovation industry, existing chatbots and LLMs are currently perceived as though they were AGI, resulting in additional misconception and fear. [162]
Skeptics sometimes charge that the thesis is crypto-religious, with an illogical belief in the possibility of superintelligence replacing an irrational belief in a supreme God. [163] Some researchers believe that the interaction projects on AI existential danger by specific AI groups (such as OpenAI, Anthropic, DeepMind, and Conjecture) might be an at effort at regulative capture and to inflate interest in their items. [164] [165]
In 2023, the CEOs of Google DeepMind, OpenAI and Anthropic, along with other industry leaders and scientists, released a joint declaration asserting that "Mitigating the danger of termination from AI should be a worldwide concern together with other societal-scale dangers such as pandemics and nuclear war." [152]
Mass joblessness

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

According to Stephen Hawking, the result of automation on the quality of life will depend upon how the wealth will be redistributed: [142]
Everyone can take pleasure in a life of glamorous leisure if the machine-produced wealth is shared, or most individuals can end up miserably bad if the machine-owners effectively lobby against wealth redistribution. Up until now, 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 embrace a universal basic income. [168]
See also

Artificial brain - Software and hardware with cognitive capabilities comparable to those of the animal or human brain AI effect AI safety - Research location on making AI safe and useful AI positioning - AI conformance to the intended goal A.I. Rising - 2018 movie directed by Lazar Bodroža Artificial intelligence Automated maker knowing - Process of automating the application of maker knowing BRAIN Initiative - Collaborative public-private research effort revealed by the Obama administration China Brain Project Future of Humanity Institute - Defunct Oxford interdisciplinary research centre General video game playing - Ability of synthetic intelligence to play various video games Generative expert system - AI system efficient in generating content in action to triggers Human Brain Project - Scientific research study project Intelligence amplification - Use of infotech to augment human intelligence (IA). Machine principles - Moral behaviours of manufactured makers. Moravec's paradox. Multi-task knowing - Solving multiple device discovering tasks at the exact same time. Neural scaling law - Statistical law in device learning. Outline of synthetic intelligence - Overview of and topical guide to expert system. Transhumanism - Philosophical movement. Synthetic intelligence - Alternate term for or form of expert system. Transfer learning - Artificial intelligence technique. Loebner Prize - Annual AI competition. Hardware for expert system - Hardware specifically designed and enhanced for expert system. Weak expert system - Form of artificial intelligence.
Notes

^ a b See listed below for the origin of the term "strong AI", and see the academic definition of "strong AI" and weak AI in the article Chinese space. ^ AI creator John McCarthy composes: "we can not yet define in basic what type of computational procedures we wish to call smart. " [26] (For a conversation of some meanings of intelligence used by artificial intelligence researchers, see viewpoint of expert system.). ^ The Lighthill report particularly criticized AI's "grandiose objectives" and led the taking apart of AI research in England. [55] In the U.S., DARPA became identified to money only "mission-oriented direct research, instead of basic undirected research". [56] [57] ^ As AI creator John McCarthy writes "it would be a fantastic relief to the remainder of the employees in AI if the inventors of new basic formalisms would reveal their hopes in a more secured type than has sometimes held true." [61] ^ In "Mind Children" [122] 1015 cps is used. More just recently, in 1997, [123] Moravec argued for 108 MIPS which would roughly correspond to 1014 cps. Moravec talks in regards to MIPS, not "cps", which is a non-standard term Kurzweil presented. ^ As defined in a basic AI book: "The assertion that devices could perhaps act smartly (or, maybe much better, act as if they were intelligent) is called the 'weak AI' hypothesis by theorists, and the assertion that devices that do so are in fact believing (instead of mimicing thinking) is called the 'strong AI' hypothesis." [121] ^ Alan Turing made this point in 1950. [36] References

^ Krishna, Sri (9 February 2023). "What is synthetic narrow intelligence (ANI)?". VentureBeat. Retrieved 1 March 2024. ANI is designed to carry out a single task. ^ "OpenAI Charter". OpenAI. Retrieved 6 April 2023. Our objective is to ensure that artificial general intelligence advantages all of mankind. ^ Heath, Alex (18 January 2024). "Mark Zuckerberg's 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 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 identified as being active in 2020. ^ a b c "AI timelines: What do specialists in expert system expect for the future?". Our World in Data. Retrieved 6 April 2023. ^ Metz, Cade (15 May 2023). "Some Researchers Say A.I. Is Already Here, Stirring Debate in Tech Circles". The New York Times. Retrieved 18 May 2023. ^ "AI pioneer Geoffrey Hinton stops Google and alerts of danger ahead". The New York Times. 1 May 2023. Retrieved 2 May 2023. It is tough to see how you can prevent the bad actors from utilizing it for bad things. ^ Bubeck, Sébastien; Chandrasekaran, Varun; Eldan, Ronen; Gehrke, Johannes; Horvitz, Eric (2023 ). "Sparks of Artificial General Intelligence: Early explores GPT-4". arXiv preprint. arXiv:2303.12712. GPT-4 shows stimulates of AGI. ^ Butler, Octavia E. (1993 ). Parable of the Sower. Grand Central Publishing. ISBN 978-0-4466-7550-5. All that you touch you 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 Expert System". The New York City Times. The real hazard is not AI itself but the method we deploy it. ^ "Impressed by artificial intelligence? Experts state AGI is following, and it has 'existential' dangers". ABC News. 23 March 2023. Retrieved 6 April 2023. AGI could position existential threats to humankind. ^ Bostrom, Nick (2014 ). Superintelligence: Paths, Dangers, Strategies. Oxford University Press. ISBN 978-0-1996-7811-2. The first superintelligence will be the last creation that mankind requires to make. ^ Roose, Kevin (30 May 2023). "A.I. Poses 'Risk of Extinction,' Industry Leaders Warn". The New York City Times. Mitigating the threat of termination from AI need to be an international priority. ^ "Statement on AI Risk". Center for AI Safety. Retrieved 1 March 2024. AI professionals alert of danger of termination from AI. ^ Mitchell, Melanie (30 May 2023). "Are AI's Doomsday Scenarios Worth Taking Seriously?". The New York Times. We are far from producing machines that can outthink us in general methods. ^ LeCun, Yann (June 2023). "AGI does not provide an existential risk". Medium. There is no factor to fear AI as an existential hazard. ^ Kurzweil 2005, p. 260. ^ a b Kurzweil, Ray (5 August 2005), "Long Live AI", Forbes, archived from the initial on 14 August 2005: Kurzweil describes strong AI as "device intelligence with the complete variety of human intelligence.". ^ "The Age of Artificial Intelligence: George John at TEDxLondonBusinessSchool 2013". Archived from the initial on 26 February 2014. Retrieved 22 February 2014. ^ Newell & Simon 1976, This is the term they utilize for "human-level" intelligence in the physical symbol system hypothesis. ^ "The Open University on Strong and Weak AI". Archived from the original on 25 September 2009. Retrieved 8 October 2007. ^ "What is artificial superintelligence (ASI)?|Definition from TechTarget". Enterprise AI. Retrieved 8 October 2023. ^ "Expert system is transforming our world - it is on everybody to ensure 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 original on 26 October 2007. Retrieved 6 December 2007. ^ This list of intelligent traits is based on the subjects covered by major AI books, consisting of: Russell & Norvig 2003, Luger & Stubblefield 2004, Poole, Mackworth & Goebel 1998 and Nilsson 1998. ^ Johnson 1987. ^ de Charms, R. (1968 ). Personal causation. New York: Academic Press. ^ a b Pfeifer, R. and Bongard J. C., How the body forms the way we believe: a brand-new view of intelligence (The MIT Press, 2007). ISBN 0-2621-6239-3. ^ White, R. W. (1959 ). "Motivation reevaluated: The principle of proficiency". Psychological Review. 66 (5 ): 297-333. doi:10.1037/ h0040934. PMID 13844397. S2CID 37385966. ^ White, R. W. (1959 ). "Motivation reassessed: The principle of proficiency". Psychological Review. 66 (5 ): 297-333. doi:10.1037/ h0040934. PMID 13844397. S2CID 37385966. ^ Muehlhauser, Luke (11 August 2013). "What is AGI?". Machine Intelligence Research Institute. Archived from the initial on 25 April 2014. Retrieved 1 May 2014. ^ "What is Artificial General Intelligence (AGI)?|4 Tests For Ensuring Artificial General Intelligence". Talky Blog. 13 July 2019. Archived from the original on 17 July 2019. Retrieved 17 July 2019. ^ Kirk-Giannini, Cameron Domenico; Goldstein, Simon (16 October 2023). "AI is closer than ever to passing the Turing test for 'intelligence'. What takes place when it does?". The Conversation. Retrieved 22 September 2024. ^ a b Turing 1950. ^ Turing, Alan (1952 ). B. Jack Copeland (ed.). Can Automatic Calculating Machines Be Said To Think?. Oxford: Oxford University Press. pp. 487-506. ISBN 978-0-1982-5079-1. ^ "Eugene Goostman is a genuine kid - the Turing Test says so". The Guardian. 9 June 2014. ISSN 0261-3077. Retrieved 3 March 2024. ^ "Scientists contest whether computer system 'Eugene Goostman' passed Turing test". BBC News. 9 June 2014. Retrieved 3 March 2024. ^ Jones, Cameron R.; Bergen, Benjamin K. (9 May 2024). "People can not identify GPT-4 from a human in a Turing test". arXiv:2405.08007 [cs.HC] ^ Varanasi, Lakshmi (21 March 2023). "AI designs like ChatGPT and GPT-4 are acing everything from the bar exam to AP Biology. Here's a list of difficult 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 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 answer". Analytics India Magazine. Retrieved 3 March 2024. ^ Bhaimiya, Sawdah (20 June 2023). "DeepMind's co-founder suggested evaluating an AI chatbot's capability to turn $100,000 into $1 million to measure human-like intelligence". Business Insider. Retrieved 3 March 2024. ^ Suleyman, Mustafa (14 July 2023). "Mustafa Suleyman: My new Turing test would see if AI can make $1 million". MIT Technology Review. Retrieved 3 March 2024. ^ Shapiro, Stuart C. (1992 ). "Expert System" (PDF). In Stuart C. Shapiro (ed.). Encyclopedia of Expert System (Second ed.). New York City: John Wiley. pp. 54-57. Archived (PDF) from the original on 1 February 2016. (Section 4 is on "AI-Complete Tasks".). ^ Yampolskiy, Roman V. (2012 ). Xin-She Yang (ed.). "Turing Test as a Defining Feature of AI-Completeness" (PDF). Expert System, Evolutionary Computation and Metaheuristics (AIECM): 3-17. Archived (PDF) from the initial on 22 May 2013. ^ "AI Index: State of AI in 13 Charts". Stanford University Human-Centered Artificial Intelligence. 15 April 2024. Retrieved 27 May 2024. ^ Crevier 1993, pp. 48-50. ^ Kaplan, Andreas (2022 ). "Artificial Intelligence, Business and Civilization - Our Fate Made in Machines". Archived from the initial on 6 May 2022. Retrieved 12 March 2022. ^ Simon 1965, p. 96 priced quote in Crevier 1993, p. 109. ^ "Scientist on the Set: An Interview with Marvin Minsky". Archived from the initial on 16 July 2012. Retrieved 5 April 2008. ^ Marvin Minsky to Darrach (1970 ), 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 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 researchers and software engineers avoided the term artificial intelligence 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 original on 22 May 2019. Retrieved 7 May 2019. ^ a b Moravec 1988, p. 20 ^ Harnad, S. (1990 ). "The Symbol Grounding Problem". Physica D. 42 (1-3): 335-346. arXiv: cs/9906002. Bibcode:1990 PhyD ... 42..335 H. doi:10.1016/ 0167-2789( 90 )90087-6. S2CID 3204300. ^ Gubrud 1997 ^ Hutter, Marcus (2005 ). Universal Expert System: Sequential Decisions Based 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 original on 15 June 2022. Retrieved 19 July 2022. ^ Goertzel, Ben (2014 ). Artificial General Intelligence. Lecture Notes in Computer Technology. 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., through Life 3.0: 'The term "AGI" was promoted by ... Shane Legg, Mark Gubrud and Ben Goertzel' ^ Wang & Goertzel 2007 ^ "First International Summer School in Artificial General Intelligence, Main summertime school: June 22 - July 3, 2009, OpenCog Lab: July 6-9, 2009". Archived from the original on 28 September 2020. Retrieved 11 May 2020. ^ "Избираеми дисциплини 2009/2010 - пролетен триместър" [Elective courses 2009/2010 - spring trimester] Факултет по математика и информатика [Faculty of Mathematics and Informatics] (in Bulgarian). Archived from the original on 26 July 2020. Retrieved 11 May 2020. ^ "Избираеми дисциплини 2010/2011 - зимен триместър" [Elective courses 2010/2011 - winter 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. ^ Shevlin, Henry; Vold, Karina; Crosby, Matthew; Halina, Marta (4 October 2019). "The limitations of maker intelligence: Despite development in machine intelligence, artificial general intelligence is still a significant difficulty". EMBO Reports. 20 (10 ): e49177. doi:10.15252/ embr.201949177. ISSN 1469-221X. PMC 6776890. PMID 31531926. ^ Bubeck, Sébastien; Chandrasekaran, Varun; Eldan, Ronen; Gehrke, Johannes; Horvitz, Eric; Kamar, Ece; Lee, Peter; Lee, Yin Tat; Li, Yuanzhi; Lundberg, Scott; Nori, Harsha; Palangi, Hamid; Ribeiro, Marco Tulio; Zhang, Yi (27 March 2023). "Sparks of Artificial General Intelligence: Early try outs GPT-4". arXiv:2303.12712 [cs.CL] ^ "Microsoft Researchers Claim GPT-4 Is Showing "Sparks" of AGI". Futurism. 23 March 2023. Retrieved 13 December 2023. ^ Allen, Paul; Greaves, Mark (12 October 2011). "The Singularity Isn't Near". MIT Technology Review. Retrieved 17 September 2014. ^ Winfield, Alan. "Artificial intelligence will not turn 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 artificial intelligence will not be recognized". Humanities and Social Sciences Communications. 7 (1 ): 1-9. doi:10.1057/ s41599-020-0494-4. hdl:11250/ 2726984. ISSN 2662-9992. S2CID 219710554. ^ McCarthy 2007b. ^ Khatchadourian, Raffi (23 November 2015). "The Doomsday Invention: Will expert system 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 progress in expert system: A survey of skilled opinion. In Fundamental concerns of expert system (pp. 555-572). Springer, Cham. ^ Armstrong, Stuart, and Kaj Sotala. 2012. "How We're Predicting AI-or Failing To." In Beyond AI: Artificial Dreams, 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 creativity of makers: AI takes the Torrance Test". Journal of Creativity. 33 (3 ): 100065. doi:10.1016/ j.yjoc.2023.100065. ISSN 2713-3745. S2CID 261087185. ^ Arcas, Blaise Agüera y (10 October 2023). "Artificial General Intelligence Is Already Here". Noema. ^ Zia, Tehseen (8 January 2024). "Unveiling of Large Multimodal Models: Shaping the Landscape of Language Models in 2024". Unite.ai. Retrieved 26 May 2024. ^ "Introducing OpenAI o1-preview". OpenAI. 12 September 2024. ^ Knight, Will. "OpenAI Announces a New AI Model, Code-Named Strawberry, That Solves Difficult Problems Step by Step". Wired. ISSN 1059-1028. Retrieved 17 September 2024. ^ "OpenAI Employee Claims AGI Has Been Achieved". Orbital Today. 13 December 2024. Retrieved 27 December 2024. ^ "AI Index: State of AI in 13 Charts". hai.stanford.edu. 15 April 2024. Retrieved 7 June 2024. ^ "Next-Gen AI: OpenAI and Meta's Leap Towards Reasoning Machines". Unite.ai. 19 April 2024. Retrieved 7 June 2024. ^ James, Alex P. (2022 ). "The Why, What, and How of Artificial General Intelligence Chip Development". IEEE Transactions on Cognitive and Developmental Systems. 14 (2 ): 333-347. arXiv:2012.06338. doi:10.1109/ TCDS.2021.3069871. ISSN 2379-8920. S2CID 228376556. Archived from the 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 artificial basic intelligence with hybrid Tianjic chip architecture". Nature. 572 (7767 ): 106-111. Bibcode:2019 Natur.572..106 P. doi:10.1038/ s41586-019-1424-8. ISSN 1476-4687. PMID 31367028. S2CID 199056116. Archived from the original on 29 August 2022. Retrieved 29 August 2022. ^ Pandey, Mohit; Fernandez, Michael; Gentile, Francesco; Isayev, Olexandr; Tropsha, Alexander; Stern, Abraham C.; Cherkasov, Artem (March 2022). "The transformational role of GPU computing and deep learning in drug discovery". Nature Machine Intelligence. 4 (3 ): 211-221. doi:10.1038/ s42256-022-00463-x. ISSN 2522-5839. S2CID 252081559. ^ Goertzel & Pennachin 2006. ^ a b c (Kurzweil 2005, p. 260). ^ a b c Goertzel 2007. ^ Grace, Katja (2016 ). "Error in Armstrong and Sotala 2012". AI Impacts (blog). Archived from the initial on 4 December 2020. Retrieved 24 August 2020. ^ a b Butz, Martin V. (1 March 2021). "Towards Strong AI". KI - Künstliche Intelligenz. 35 (1 ): 91-101. doi:10.1007/ s13218-021-00705-x. ISSN 1610-1987. S2CID 256065190. ^ Liu, Feng; Shi, Yong; Liu, Ying (2017 ). "Intelligence Quotient and Intelligence Grade of 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 wise as Siri - but a six-year-old beats both] (in German). Archived from the initial on 3 January 2019. Retrieved 2 January 2019. ^ Grossman, Gary (3 September 2020). "We're entering the AI twilight zone in between narrow and basic AI". VentureBeat. Archived from the original on 4 September 2020. Retrieved 5 September 2020. Certainly, too, there are those who claim we are already seeing an early example of an AGI system in the recently announced GPT-3 natural language processing (NLP) neural network. ... So is GPT-3 the very first example of an AGI system? This is debatable, however the agreement is that it is not AGI. ... If absolutely nothing else, GPT-3 tells us there is a middle ground between narrow and basic AI. ^ Quach, Katyanna. "A designer constructed an AI chatbot using GPT-3 that helped a man speak again to his late fiancée. OpenAI shut it down". The Register. Archived from the initial on 16 October 2021. Retrieved 16 October 2021. ^ Wiggers, Kyle (13 May 2022), "DeepMind's brand-new AI can perform over 600 tasks, from playing games to controlling robots", TechCrunch, archived from the initial on 16 June 2022, obtained 12 June 2022. ^ Bubeck, Sébastien; Chandrasekaran, Varun; Eldan, Ronen; Gehrke, Johannes; Horvitz, Eric; Kamar, Ece; Lee, Peter; Lee, Yin Tat; Li, Yuanzhi; Lundberg, Scott; Nori, Harsha; Palangi, Hamid; Ribeiro, Marco Tulio; Zhang, Yi (22 March 2023). "Sparks of Artificial General Intelligence: Early explores GPT-4". arXiv:2303.12712 [cs.CL] ^ Metz, Cade (1 May 2023). "' The Godfather of A.I.' Leaves Google and Warns of Danger Ahead". The New York City Times. ISSN 0362-4331. Retrieved 7 June 2023. ^ Bove, Tristan. "A.I. could equal human intelligence in 'just a couple of years,' says CEO of Google's main A.I. research study lab". Fortune. Retrieved 4 September 2024. ^ Nellis, Stephen (2 March 2024). "Nvidia CEO states AI could pass human tests in five years". Reuters. ^ Aschenbrenner, Leopold. "SITUATIONAL AWARENESS, utahsyardsale.com The Decade Ahead". ^ Sullivan, Mark (18 October 2023). "Why everybody seems to disagree on how to specify Artificial General Intelligence". Fast Company. ^ Nosta, John (5 January 2024). "The Accelerating Path to Artificial General Intelligence". Psychology Today. Retrieved 30 March 2024. ^ Hickey, Alex. "Whole Brain Emulation: A Giant Step for Neuroscience". Tech Brew. Retrieved 8 November 2023. ^ Sandberg & Boström 2008. ^ Drachman 2005. ^ a b Russell & Norvig 2003. ^ Moravec 1988, p. 61. ^ Moravec 1998. ^ Holmgaard Mersh, Amalie (15 September 2023). "Decade-long European research 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 large range of views in present research, all of which require grounding to some degree ^ Thornton, Angela (26 June 2023). "How submitting our minds to a computer system may become possible". The Conversation. Retrieved 8 November 2023. ^ Searle 1980 ^ For example: Russell & Norvig 2003, Oxford University Press Dictionary of Psychology Archived 3 December 2007 at the Wayback Machine (quoted in" Encyclopedia.com"),. MIT Encyclopedia of Cognitive Science Archived 19 July 2008 at the Wayback Machine (priced quote in "AITopics"),. Will Biological Computers Enable Artificially Intelligent Machines to Become Persons? Archived 13 May 2008 at the Wayback Machine Anthony Tongen.


^ a b c Russell & Norvig 2003, p. 947. ^ though see Explainable expert system for interest 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 company's AI has actually come to life". The Washington Post. 11 June 2022. Retrieved 12 June 2023. ^ Kateman, Brian (24 July 2023). "AI Should Be Terrified of Humans". TIME. Retrieved 5 September 2024. ^ Nosta, John (18 December 2023). "Should 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 cost surpass advantages?". 23 August 2021. Retrieved 7 June 2023. ^ "How we can Take advantage of Advancing Artificial General Intelligence (AGI) - Unite.AI". www.unite.ai. 7 April 2020. Retrieved 7 June 2023. ^ a b c Talty, Jules; Julien, Stephan. "What Will Our Society Look Like When Expert System Is Everywhere?". Smithsonian Magazine. Retrieved 7 June 2023. ^ a b Stevenson, Matt (8 October 2015). "Answers to Stephen Hawking's AMA are Here!". Wired. ISSN 1059-1028. Retrieved 8 June 2023. ^ a b Bostrom, Nick (2017 ). " § Preferred order of arrival". Superintelligence: courses, dangers, strategies (Reprinted with corrections 2017 ed.). Oxford, UK; New York City, New York City, USA: Oxford University Press. ISBN 978-0-1996-7811-2. ^ Piper, Kelsey (19 November 2018). "How technological development is making it likelier than ever that human beings will ruin ourselves". Vox. Retrieved 8 June 2023. ^ Doherty, Ben (17 May 2018). "Climate alter an 'existential security danger' to Australia, Senate inquiry 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 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 City Times. ISSN 0362-4331. Retrieved 24 December 2023. ^ a b "Statement on AI Risk". Center for AI Safety. 30 May 2023. Retrieved 8 June 2023. ^ "Stephen Hawking: photorum.eclat-mauve.fr 'Transcendence looks at the ramifications of artificial intelligence - but 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 remarkable Facebook dispute in between Yann LeCun, Stuart Russel and Yoshua Bengio about the threats of strong AI". The remarkable Facebook dispute between Yann LeCun, Stuart Russel and Yoshua Bengio about the risks of strong AI (in French). Retrieved 8 June 2023. ^ "Will Artificial Intelligence Doom The Human Race Within The Next 100 Years?". HuffPost. 22 August 2014. Retrieved 8 June 2023. ^ Sotala, Kaj; Yampolskiy, Roman V. (19 December 2014). "Responses to devastating AGI risk: a survey". Physica Scripta. 90 (1 ): 018001. doi:10.1088/ 0031-8949/90/ 1/018001. ISSN 0031-8949. ^ Bostrom, Nick (2014 ). Superintelligence: Paths, Dangers, Strategies (First ed.). Oxford University Press. ISBN 978-0-1996-7811-2. ^ Chow, Andrew R.; Perrigo, Billy (16 February 2023). "The AI Arms Race Is On. Start Worrying". TIME. Retrieved 24 December 2023. ^ Tetlow, Gemma (12 January 2017). "AI arms race threats spiralling out of control, report warns". Financial Times. Archived from the original on 11 April 2022. Retrieved 24 December 2023. ^ Milmo, Dan; Stacey, Kiran (25 September 2023). "Experts disagree over threat presented but expert system can not be disregarded". The Guardian. ISSN 0261-3077. Retrieved 24 December 2023. ^ "Humanity, Security & AI, thatswhathappened.wiki Oh My! (with Ian Bremmer & Shuman Ghosemajumder)". CAFE. 20 July 2023. Retrieved 15 September 2023. ^ Hamblin, James (9 May 2014). "But What Would the End of Humanity Mean for Me?". The Atlantic. Archived from the original on 4 June 2014. Retrieved 12 December 2015. ^ Titcomb, James (30 October 2023). "Big Tech is stoking worries over AI, alert scientists". The Telegraph. Retrieved 7 December 2023. ^ Davidson, John (30 October 2023). "Google Brain creator says huge tech is lying about AI extinction risk". Australian Financial Review. Archived from the original on 7 December 2023. Retrieved 7 December 2023. ^ Eloundou, Tyna; Manning, Sam; Mishkin, Pamela; Rock, Daniel (17 March 2023). "GPTs are GPTs: An early appearance at the labor market effect potential of large language designs". OpenAI. Retrieved 7 June 2023. ^ a b Hurst, Luke (23 March 2023). "OpenAI states 80% of employees might see their tasks impacted by AI. These are the tasks most affected". euronews. Retrieved 8 June 2023. ^ Sheffey, Ayelet (20 August 2021). "Elon Musk states we need universal fundamental income since '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 initial on 18 June 2022. Retrieved 22 September 2021. Chalmers, David (1996 ), The Conscious Mind, Oxford University Press. Clocksin, William (August 2003), "Expert system and the future", Philosophical Transactions of the Royal Society A, vol. 361, no. 1809, pp. 1721-1748, Bibcode:2003 RSPTA.361.1721 C, doi:10.1098/ rsta.2003.1232, PMID 12952683, S2CID 31032007. Crevier, Daniel (1993 ). AI: The Tumultuous Search for Artificial Intelligence. New York City, NY: BasicBooks. ISBN 0-465-02997-3. Darrach, Brad (20 November 1970), "Meet Shakey, the First Electronic Person", Life Magazine, pp. 58-68. Drachman, D. (2005 ), "Do we have brain to spare?", Neurology, 64 (12 ): 2004-2005, doi:10.1212/ 01. WNL.0000166914.38327. BB, PMID 15985565, S2CID 38482114. Feigenbaum, Edward A.; McCorduck, Pamela (1983 ), The Fifth Generation: Expert System and Japan's Computer Challenge to the World, Michael Joseph, ISBN 978-0-7181-2401-4. Goertzel, Ben; Pennachin, Cassio, eds. (2006 ), Artificial General Intelligence (PDF), Springer, ISBN 978-3-5402-3733-4, archived from the original (PDF) on 20 March 2013. Goertzel, Ben (December 2007), "Human-level artificial general intelligence and the possibility of a technological singularity: a response 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 original on 29 May 2011, retrieved 7 May 2011. Howe, J. (November 1994), Expert System at Edinburgh University: a Viewpoint, archived from the original on 17 August 2007, obtained 30 August 2007. Johnson, Mark (1987 ), The body in the mind, Chicago, ISBN 978-0-2264-0317-5. Kurzweil, Ray (2005 ), The Singularity is Near, Viking Press. Lighthill, Professor Sir James (1973 ), "Artificial Intelligence: A General Survey", 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 supreme effort is to make computer programs that can solve problems and accomplish goals on the planet in addition to humans. 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 original 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, obtained 7 November 2009 Newell, Allen; Simon, H. A. (1976 ). "Computer Technology as Empirical Inquiry: Symbols and Search". Communications of the ACM. 19 (3 ): 113-126. doi:10.1145/ 360018.360022. Nilsson, Nils (1998 ), Expert System: A New Synthesis, Morgan Kaufmann Publishers, ISBN 978-1-5586-0467-4 NRC (1999 ), "Developments in Artificial Intelligence", Funding a Revolution: Government Support for Computing Research, National Academy Press, archived from the original on 12 January 2008, retrieved 29 September 2007 Poole, David; Mackworth, Alan; Goebel, Randy (1998 ), Computational Intelligence: A Rational 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 original on 25 March 2020, recovered 5 April 2009 Searle, John (1980 ), "Minds, Brains and Programs" (PDF), Behavioral and Brain Sciences, 3 (3 ): 417-457, doi:10.1017/ S0140525X00005756, S2CID 55303721, archived (PDF) from the original on 17 March 2019, retrieved 3 September 2020 Simon, H. A. (1965 ), The Shape of Automation for Men and Management, New York: Harper & Row Turing, Alan (October 1950). "Computing Machinery and Intelligence". Mind. 59 (236 ): 433-460. doi:10.1093/ mind/LIX.236.433. ISSN 1460-2113. JSTOR 2251299. S2CID 14636783.
de Vega, Manuel; Glenberg, Arthur; Graesser, Arthur, eds. (2008 ), Symbols and Embodiment: Debates on significance and cognition, Oxford University Press, ISBN 978-0-1992-1727-4 Wang, Pei; Goertzel, Ben (2007 ). "Introduction: Aspects of Artificial General Intelligence". Advances in Artificial General Intelligence: Concepts, Architectures and Algorithms: Proceedings of the AGI Workshop 2006. IOS Press. pp. 1-16. ISBN 978-1-5860-3758-1. Archived from the initial on 18 February 2021. Retrieved 13 December 2020 - 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, recovered 4 September 2013 - by means of ResearchGate Berglas, Anthony (January 2012) [2008], Artificial Intelligence Will Kill Our Grandchildren (Singularity), archived from the initial on 23 July 2014, obtained 31 August 2012 Cukier, Kenneth, "Ready for Robots? How to Think about the Future of AI", Foreign Affairs, vol. 98, no. 4 (July/August 2019), pp. 192-98. George Dyson, historian of computing, writes (in what might be called "Dyson's Law") that "Any system simple adequate to be understandable will not be made complex enough to behave intelligently, while any system made complex enough to act intelligently will be too made complex to comprehend." (p. 197.) Computer scientist Alex Pentland writes: "Current AI machine-learning algorithms are, at their core, dead easy dumb. 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 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 differentiates us from machines. For biological animals, reason and purpose come from acting in the world and experiencing the consequences. Expert systems - disembodied, complete strangers to blood, sweat, and tears - have no event for that." (p. 30.). Halal, William E. "TechCast Article Series: The Automation of Thought" (PDF). Archived from the initial (PDF) on 6 June 2013. - Halpern, Sue, "The Coming Tech Autocracy" (evaluation of Verity Harding, AI Needs You: How We Can Change AI's Future and Save Our Own, Princeton University Press, 274 pp.; Gary Marcus, Taming Silicon Valley: How We Can Ensure That AI Works for Us, MIT Press, 235 pp.; Daniela Rus and Gregory Mone, The Mind's Mirror: Risk and Reward in the Age of AI, Norton, 280 pp.; Madhumita Murgia, Code Dependent: Living in the Shadow of AI, Henry Holt, 311 pp.), The New York City Review of Books, vol. LXXI, no. 17 (7 November 2024), pp. 44-46. "' We can't realistically anticipate that those who want to get abundant 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 project financing contributions [from tech business] to push back.' ... Marcus information the needs that residents must make from their federal governments and the tech business. They include transparency on how AI systems work; settlement for people if their data [are] used to train LLMs (large language design) s and the right to grant this use; and the ability to hold tech business responsible for the damages they trigger by getting rid of Section 230, enforcing money penalites, and passing stricter item liability laws ... Marcus also suggests ... that a brand-new, AI-specific federal agency, similar to the FDA, the FCC, or the FTC, might supply the most robust oversight ... [T] he Fordham law professor Chinmayi Sharma ... suggests ... develop [ing] a professional licensing routine for engineers that would work in a similar method to medical licenses, malpractice matches, and the Hippocratic oath in medication. 'What if, like physicians,' she asks ..., 'AI engineers also vowed to do no harm?'" (p. 46.). Holte, R. C.; Choueiry, B. Y. (2003 ), "Abstraction and reformulation in synthetic 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 stymied people for decades, exposes the limitations of natural-language-processing algorithms", Scientific American, vol. 329, no. 4 (November 2023), pp. 81-82. "This murder mystery competitors has revealed that although NLP (natural-language processing) designs can unbelievable accomplishments, their capabilities are really much restricted by the quantity of context they receive. This [...] might trigger [troubles] for researchers who wish to utilize them to do things such as evaluate ancient languages. Sometimes, there are couple of historical records on long-gone civilizations to serve as training information for such a purpose." (p. 82.). Immerwahr, Daniel, "Your Lying Eyes: People now utilize A.I. to produce fake videos equivalent from genuine ones. Just how much does it matter?", The New Yorker, 20 November 2023, pp. 54-59. "If by 'deepfakes' we mean practical videos produced using artificial intelligence that really deceive individuals, then they barely exist. The phonies 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 function much better resembles that of animations, particularly smutty ones." (p. 59.). - Leffer, Lauren, "The Risks of Trusting AI: We should prevent humanizing machine-learning models utilized in clinical research", Scientific American, vol. 330, no. 6 (June 2024), pp. 80-81. Lepore, Jill, "The Chit-Chatbot: Is talking with a device a conversation?", The New Yorker, 7 October 2024, pp. 12-16. Marcus, Gary, "Artificial Confidence: Even the most recent, buzziest systems of synthetic general intelligence are stymmied by the usual issues", Scientific American, vol. 327, no. 4 (October 2022), pp. 42-45. McCarthy, John (October 2007), "From here to human-level AI", Expert System, 171 (18 ): 1174-1182, doi:10.1016/ j.artint.2007.10.009. McCorduck, Pamela (2004 ), Machines Who Think (2nd ed.), Natick, Massachusetts: A. K. Peters, ISBN 1-5688-1205-1. Moravec, Hans (1976 ), The Role of Raw Power in Intelligence, archived from the original on 3 March 2016, obtained 29 September 2007. Newell, Allen; Simon, H. A. (1963 ), "GPS: A Program that Simulates Human Thought", in Feigenbaum, E. A.; Feldman, J. (eds.), Computers and Thought, New York City: McGraw-Hill. Omohundro, Steve (2008 ), The Nature of Self-Improving Expert system, provided and distributed at the 2007 Singularity Summit, San Francisco, California. Press, Eyal, "In Front of Their Faces: Does facial-recognition innovation lead police to neglect contradictory proof?", The New Yorker, 20 November 2023, pp. 20-26. Roivainen, Eka, "AI's IQ: ChatGPT aced a [basic intelligence] test however revealed 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 genuine humanlike thinking or an understanding of the physical and social world ... ChatGPT appeared unable to reason logically and tried to depend on its large database of ... facts derived from online texts. " - Scharre, Paul, "Killer Apps: The Real Dangers of an AI Arms Race", Foreign Affairs, vol. 98, no. 3 (May/June 2019), pp. 135-44. "Today's AI innovations are effective but undependable. Rules-based systems can not handle situations their developers did not anticipate. Learning systems are restricted by the information on which they were trained. AI failures have actually already resulted in tragedy. Advanced auto-pilot functions in vehicles, although they carry out well in some circumstances, have driven vehicles without cautioning into trucks, concrete barriers, and parked automobiles. In the wrong scenario, AI systems go from supersmart to superdumb in an instant. When an opponent is trying to control 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 enabled by new technologies but rely on the timelelss human propensity to anthropomorphise." (p. 29.). Williams, R. W.; Herrup, K.