Artificial basic intelligence (AGI) is a kind of expert system (AI) that matches or surpasses human cognitive capabilities across a broad range of cognitive jobs. This contrasts with narrow AI, which is limited to specific tasks. [1] Artificial superintelligence (ASI), on the other hand, describes AGI that significantly exceeds human cognitive abilities. AGI is considered among the meanings of strong AI.

Creating AGI is a primary objective of AI research study and of companies such as OpenAI [2] and Meta. [3] A 2020 study determined 72 active AGI research study and development projects throughout 37 nations. [4]
The timeline for attaining AGI stays a topic of continuous argument amongst researchers and experts. Since 2023, some argue that it might be possible in years or decades; others preserve it may 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 researcher Geoffrey Hinton has actually expressed concerns about the rapid progress towards AGI, suggesting it could be attained quicker than many anticipate. [7]
There is argument on the precise meaning of AGI and regarding whether contemporary large language models (LLMs) such as GPT-4 are early forms of AGI. [8] AGI is a common subject in science fiction and futures research studies. [9] [10]
Contention exists over whether AGI represents an existential threat. [11] [12] [13] Many experts on AI have actually stated that reducing the danger of human extinction posed by AGI ought to be a global concern. [14] [15] Others discover the development of AGI to be too remote to present such a danger. [16] [17]
Terminology

AGI is also called strong AI, [18] [19] full AI, [20] human-level AI, [5] human-level intelligent AI, or basic intelligent action. [21]
Some scholastic sources book the term "strong AI" for computer programs that experience sentience or awareness. [a] In contrast, weak AI (or narrow AI) is able to solve one particular issue however lacks basic cognitive abilities. [22] [19] Some scholastic sources use "weak AI" to refer more broadly to any programs that neither experience consciousness nor have a mind in the very same sense as human beings. [a]
Related concepts include artificial superintelligence and transformative AI. A synthetic superintelligence (ASI) is a theoretical kind of AGI that is a lot more usually intelligent than human beings, [23] while the concept of transformative AI connects to AI having a big influence on society, for instance, similar to the agricultural or industrial transformation. [24]
A structure for categorizing AGI in levels was proposed in 2023 by Google DeepMind researchers. They define five levels of AGI: emerging, competent, professional, virtuoso, and superhuman. For example, a qualified AGI is defined as an AI that exceeds 50% of skilled adults in a large range of non-physical tasks, and a superhuman AGI (i.e. an artificial superintelligence) is similarly specified however with a limit of 100%. They think about large language models like ChatGPT or LLaMA 2 to be circumstances of emerging AGI. [25]
Characteristics

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

Researchers usually hold that intelligence is required to do all of the following: [27]
factor, use method, fix puzzles, and make judgments under unpredictability represent knowledge, consisting of common sense knowledge plan learn

• interact in natural language
• if essential, incorporate these abilities in completion of any given objective

Many interdisciplinary methods (e.g. cognitive science, computational intelligence, and decision making) think about additional traits such as imagination (the capability to form unique psychological images and concepts) [28] and autonomy. [29]
Computer-based systems that display much of these capabilities exist (e.g. see computational creativity, automated reasoning, decision support group, robot, evolutionary computation, smart agent). There is debate about whether contemporary AI systems have them to an appropriate degree.

Physical traits

Other abilities are thought about preferable in smart systems, as they may impact intelligence or help in its expression. These include: [30]
- the ability to sense (e.g. see, hear, and so on), and - the ability to act (e.g. move and manipulate things, change area to check out, and so on).
This consists of the ability to detect and react to danger. [31]
Although the ability to sense (e.g. see, hear, etc) and the capability to act (e.g. relocation and control objects, modification place to explore, etc) can be preferable for some smart systems, [30] these physical abilities are not strictly needed for an entity to qualify as AGI-particularly under the thesis that large language models (LLMs) might currently be or end up being AGI. Even from a less optimistic point of view on LLMs, there is no company requirement for an AGI to have a human-like type; being a silicon-based computational system is adequate, offered it can process input (language) from the external world in location of human senses. This analysis lines up with the understanding that AGI has actually never ever been proscribed a specific physical embodiment and therefore does not demand a capability for mobility or traditional "eyes and ears". [32]
Tests for human-level AGI

Several tests meant to validate human-level AGI have actually been thought about, including: [33] [34]
The idea of the test is that the device has to try and pretend to be a man, by addressing concerns put to it, and it will just pass if the pretence is fairly persuading. A significant part of a jury, who should not be professional about makers, must 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 resolve it, one would need to carry out AGI, due to the fact that the service is beyond the capabilities of a purpose-specific algorithm. [47]
There are lots of issues that have been conjectured to require general intelligence to fix in addition to people. Examples include computer system vision, natural language understanding, and handling unanticipated situations while resolving any real-world problem. [48] Even a particular job like translation requires a maker to check out and compose in both languages, follow the author's argument (reason), comprehend the context (knowledge), and consistently reproduce the author's original intent (social intelligence). All of these problems require to be resolved at the same time in order to reach human-level machine efficiency.

However, many of these jobs can now be carried out by modern large language models. According to Stanford University's 2024 AI index, AI has reached human-level efficiency on numerous standards for reading understanding and visual thinking. [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 general intelligence was possible and kenpoguy.com that it would exist in simply a few years. [51] AI pioneer Herbert A. Simon wrote 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 researchers believed they could develop by the year 2001. AI leader Marvin Minsky was a specialist [53] on the project of making HAL 9000 as reasonable as possible according to the consensus predictions of the time. He said in 1967, "Within a generation ... the issue of producing 'expert system' will substantially be solved". [54]
Several classical AI jobs, such as Doug Lenat's Cyc project (that began in 1984), and Allen Newell's Soar job, were directed at AGI.

However, in the early 1970s, it ended up being apparent that scientists had actually grossly ignored the trouble of the task. Funding firms became skeptical 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 goals like "bring on a casual discussion". [58] In reaction to this and the success of specialist systems, both market and federal government pumped money 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 satisfied. [60] For the second time in 20 years, AI scientists who predicted the impending achievement of AGI had actually been mistaken. By the 1990s, AI researchers had a track record for making vain guarantees. They became hesitant to make predictions at all [d] and avoided mention of "human level" synthetic intelligence for fear of being labeled "wild-eyed dreamer [s]. [62]
Narrow AI research

In the 1990s and early 21st century, mainstream AI attained industrial success and academic respectability by focusing on specific sub-problems where AI can produce verifiable results and industrial applications, such as speech recognition and suggestion algorithms. [63] These "applied AI" systems are now utilized thoroughly throughout the technology industry, and research in this vein is greatly funded in both academic community and industry. As of 2018 [upgrade], development in this field was considered an emerging pattern, and a mature 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 could be developed by integrating programs that resolve different sub-problems. Hans Moravec wrote in 1988:

I am positive that this bottom-up route to synthetic intelligence will one day meet the traditional top-down path over half method, ready to offer the real-world competence and the commonsense understanding that has been so frustratingly evasive in thinking programs. Fully intelligent devices will result when the metaphorical golden spike is driven joining the 2 efforts. [65]
However, even at the time, this was challenged. For example, 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 factors to consider in this paper are legitimate, then this expectation is hopelessly modular and there is really just one feasible 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 path (or vice versa) - nor is it clear why we ought to even attempt to reach such a level, because it looks as if getting there would simply amount to uprooting our signs from their intrinsic meanings (consequently merely minimizing ourselves to the functional equivalent of a programmable computer). [66]
Modern artificial basic intelligence research study

The term "artificial basic intelligence" was utilized as early as 1997, by Mark Gubrud [67] in a discussion of the ramifications of fully automated military production and operations. A mathematical formalism of AGI was proposed by Marcus Hutter in 2000. Named AIXI, the proposed AGI representative maximises "the ability to please objectives in a wide variety of environments". [68] This type of AGI, characterized 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 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 preliminary outcomes". The very first summer season school in AGI was organized in Xiamen, China in 2009 [73] by the Xiamen university's Artificial Brain Laboratory and OpenCog. The first university course was offered in 2010 [74] and 2011 [75] at Plovdiv University, Bulgaria by Todor Arnaudov. MIT provided a course on AGI in 2018, arranged by Lex Fridman and including a variety of visitor lecturers.

Since 2023 [update], a little number of computer system researchers are active in AGI research, and lots of add to a series of AGI conferences. However, significantly more scientists have an interest in open-ended learning, [76] [77] which is the concept of permitting AI to continually find out and innovate like people do.

Feasibility

As of 2023, the development and potential achievement of AGI remains a topic of intense debate within the AI neighborhood. While traditional agreement held that AGI was a remote objective, current improvements have led some scientists and market figures to declare that early types of AGI may currently exist. [78] AI pioneer Herbert A. Simon speculated in 1965 that "devices will be capable, within twenty years, of doing any work a man can do". This forecast failed to come real. Microsoft co-founder Paul Allen thought that such intelligence is unlikely in the 21st century due to the fact that it would need "unforeseeable and basically unpredictable developments" and a "scientifically deep understanding of cognition". [79] Writing in The Guardian, roboticist Alan Winfield claimed the gulf in between modern-day computing and human-level expert system is as wide as the gulf in between existing area flight and practical faster-than-light spaceflight. [80]
A further challenge is the lack of clearness in specifying what intelligence involves. Does it require awareness? Must it display the ability to set goals as well as pursue them? Is it simply a matter of scale such that if design sizes increase adequately, intelligence will emerge? Are facilities such as preparation, reasoning, and causal understanding needed? Does intelligence need explicitly reproducing the brain and its particular faculties? Does it need feelings? [81]
Most AI researchers believe strong AI can be achieved in the future, but 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, but that today level of progress is such that a date can not precisely be anticipated. [84] AI experts' views on the feasibility of AGI wax and wane. Four surveys carried out in 2012 and 2013 suggested that the median quote amongst experts for when they would be 50% confident AGI would show up was 2040 to 2050, depending upon the survey, with the mean being 2081. Of the professionals, 16.5% addressed with "never" when asked the same question however with a 90% confidence rather. [85] [86] Further existing AGI development considerations can be found above Tests for validating human-level AGI.

A report by Stuart Armstrong and Kaj Sotala of the Machine Intelligence Research Institute found that "over [a] 60-year amount of time there is a strong bias towards forecasting the arrival of human-level AI as in between 15 and 25 years from the time the forecast was made". They evaluated 95 forecasts made between 1950 and 2012 on when human-level AI will happen. [87]
In 2023, Microsoft researchers published a comprehensive examination of GPT-4. They concluded: "Given the breadth and depth of GPT-4's abilities, we think that it might reasonably be deemed an early (yet still incomplete) version of an artificial general intelligence (AGI) system." [88] Another study in 2023 reported that GPT-4 outperforms 99% of humans on the Torrance tests of creativity. [89] [90]
Blaise Agüera y Arcas and Peter Norvig wrote in 2023 that a substantial level of basic intelligence has currently been attained with frontier models. They wrote that hesitation to this view originates from four primary reasons: a "healthy skepticism about metrics for AGI", an "ideological dedication to alternative AI theories or methods", a "devotion to human (or biological) exceptionalism", or a "concern about the financial ramifications of AGI". [91]
2023 also marked the development of big multimodal designs (big language designs efficient in processing or creating numerous modalities such as text, audio, and images). [92]
In 2024, OpenAI launched o1-preview, the first of a series of designs that "invest more time thinking before they respond". According to Mira Murati, this ability to think before reacting represents a brand-new, extra paradigm. It improves design outputs by spending more computing power when generating the answer, whereas the model scaling paradigm improves outputs by increasing the model size, training data and training calculate power. [93] [94]
An OpenAI worker, Vahid Kazemi, claimed in 2024 that the business had attained AGI, specifying, "In my viewpoint, we have already accomplished AGI and it's a lot more clear with O1." Kazemi clarified that while the AI is not yet "better than any human at any task", it is "much better than many human beings at the majority of tasks." He likewise resolved criticisms that large language models (LLMs) merely follow predefined patterns, comparing their knowing process to the scientific approach of observing, hypothesizing, and validating. These declarations have stimulated dispute, as they depend on a broad and non-traditional meaning of AGI-traditionally comprehended as AI that matches human intelligence across all domains. Critics argue that, while OpenAI's designs demonstrate amazing flexibility, they might not completely fulfill this requirement. Notably, Kazemi's comments came soon after OpenAI got rid of "AGI" from the terms of its partnership with Microsoft, prompting speculation about the business's strategic intentions. [95]
Timescales

Progress in expert system has actually historically gone through periods of fast development separated by durations when development appeared to stop. [82] Ending each hiatus were basic advances in hardware, software application or both to develop space for further development. [82] [98] [99] For example, the hardware readily available in the twentieth century was not enough to implement deep learning, which needs big numbers of GPU-enabled CPUs. [100]
In the introduction to his 2006 book, [101] Goertzel says that quotes of the time required before a truly versatile AGI is constructed vary from ten years to over a century. As of 2007 [upgrade], the consensus in the AGI research community appeared to be that the timeline talked about by Ray Kurzweil in 2005 in The Singularity is Near [102] (i.e. between 2015 and 2045) was possible. [103] Mainstream AI researchers have offered a vast array of viewpoints on whether development will be this rapid. A 2012 meta-analysis of 95 such opinions discovered a predisposition towards anticipating that the beginning of AGI would happen within 16-26 years for modern and historical predictions alike. That paper has been criticized for how it categorized 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 competition with a top-5 test mistake rate of 15.3%, significantly better than the second-best entry's rate of 26.3% (the conventional approach used a weighted sum of ratings from different pre-defined classifiers). [105] AlexNet was considered the preliminary ground-breaker of the current deep knowing wave. [105]
In 2017, scientists Feng Liu, Yong Shi, and Ying Liu carried out intelligence tests on openly offered and freely available weak AI such as Google AI, Apple's Siri, and others. At the optimum, these AIs reached an IQ worth of about 47, which corresponds approximately to a six-year-old kid in first grade. A grownup comes to about 100 usually. Similar tests were brought out in 2014, with the IQ score reaching a maximum worth of 27. [106] [107]
In 2020, OpenAI developed GPT-3, a language design efficient in performing many diverse jobs 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 thought about by some to be too advanced to be categorized as a narrow AI system. [108]
In the very same year, Jason Rohrer used his GPT-3 account to establish a chatbot, and provided a chatbot-developing platform called "Project December". OpenAI requested modifications 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 efficient in performing more than 600 various jobs. [110]
In 2023, Microsoft Research published a study on an early variation of OpenAI's GPT-4, contending that it displayed more basic intelligence than previous AI models and showed human-level efficiency in tasks covering several domains, such as mathematics, coding, and law. This research study triggered a dispute on whether GPT-4 could be considered an early, insufficient version of synthetic general intelligence, highlighting the requirement for more exploration and evaluation of such systems. [111]
In 2023, the AI researcher Geoffrey Hinton specified that: [112]
The idea that this things could actually get smarter than people - a couple of individuals thought that, [...] But many individuals believed it was way off. And I thought it was method off. I believed it was 30 to 50 years and even longer away. Obviously, I no longer think that.

In May 2023, Demis Hassabis similarly stated that "The development in the last few years has been pretty incredible", and that he sees no reason that it would slow down, expecting AGI within a years or even a couple of years. [113] In March 2024, Nvidia's CEO, Jensen Huang, mentioned his expectation that within 5 years, AI would be capable of passing any test at least as well as human beings. [114] In June 2024, the AI scientist Leopold Aschenbrenner, a previous OpenAI worker, approximated AGI by 2027 to be "strikingly possible". [115]
Whole brain emulation

While the development of transformer models like in ChatGPT is thought about the most appealing course to AGI, [116] [117] whole brain emulation can serve as an alternative approach. With entire brain simulation, a brain model is built by scanning and mapping a biological brain in information, and then copying and mimicing it on a computer system or another computational device. The simulation model should be adequately faithful to the initial, so that it behaves in practically 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 study purposes. It has actually been talked about in synthetic intelligence research study [103] as a technique to strong AI. Neuroimaging innovations that might provide the required comprehensive understanding are improving rapidly, and futurist Ray Kurzweil in the book The Singularity Is Near [102] anticipates that a map of enough quality will become readily available on a comparable timescale to the computing power needed to imitate it.

Early estimates

For low-level brain simulation, a very powerful cluster of computers or GPUs would be required, provided the huge amount of synapses within the human brain. Each of the 1011 (one hundred billion) neurons has on typical 7,000 synaptic connections (synapses) to other neurons. The brain of a three-year-old kid has about 1015 synapses (1 quadrillion). This number declines with age, supporting by the 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 a basic switch design for neuron activity, is around 1014 (100 trillion) synaptic updates per second (SUPS). [121]
In 1997, Kurzweil took a look at numerous quotes for the hardware required to equal the human brain and embraced a figure of 1016 computations per second (cps). [e] (For comparison, if a "computation" was comparable to one "floating-point operation" - a procedure used to rate present supercomputers - then 1016 "computations" would be comparable to 10 petaFLOPS, attained in 2011, while 1018 was achieved in 2022.) He used this figure to predict the required hardware would be available at some point between 2015 and 2025, if the exponential development in computer power at the time of writing continued.

Current research study

The Human Brain Project, an EU-funded initiative active from 2013 to 2023, has developed a particularly comprehensive and openly 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 used in many existing artificial neural network executions is easy compared with biological neurons. A brain simulation would likely have to capture the in-depth cellular behaviour of biological nerve cells, currently comprehended just in broad summary. The overhead introduced by full modeling of the biological, chemical, and physical details of neural behaviour (particularly on a molecular scale) would require computational powers numerous orders of magnitude larger than Kurzweil's price quote. In addition, the quotes do not represent glial cells, which are understood to play a function in cognitive processes. [125]
A fundamental criticism of the simulated brain method stems from embodied cognition theory which asserts that human embodiment is an important aspect of human intelligence and is essential to ground significance. [126] [127] If this theory is correct, any completely functional brain model will require to encompass more than just the nerve cells (e.g., a robotic body). Goertzel [103] proposes virtual embodiment (like in metaverses like Second Life) as an alternative, but it is unidentified whether this would be enough.

Philosophical point of view

"Strong AI" as specified in viewpoint

In 1980, thinker John Searle created the term "strong AI" as part of his Chinese room argument. [128] He proposed a difference in between 2 hypotheses about artificial intelligence: [f]
Strong AI hypothesis: An expert system system can have "a mind" and "consciousness". Weak AI hypothesis: An expert system system can (just) act like it believes and has a mind and awareness.
The first one he called "strong" since it makes a more powerful declaration: it presumes something special has actually happened to the machine that exceeds those abilities that we can test. The behaviour of a "weak AI" maker would be precisely identical to a "strong AI" machine, but the latter would also have subjective conscious experience. This use is also typical in scholastic AI research study and books. [129]
In contrast to Searle and mainstream AI, some futurists such as Ray Kurzweil utilize the term "strong AI" to suggest "human level synthetic basic intelligence". [102] This is not the very same as Searle's strong AI, unless it is assumed that consciousness is needed for human-level AGI. Academic thinkers such as Searle do not believe that holds true, 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 don't care if you call it real or a simulation." [130] If the program can act as if it has a mind, then there is no need to understand if it really has mind - indeed, there would be no chance to tell. For AI research study, Searle's "weak AI hypothesis" is comparable to the declaration "synthetic general intelligence is possible". Thus, according to Russell and Norvig, "most AI scientists take the weak AI hypothesis for given, and do not care about the strong AI hypothesis." [130] Thus, for scholastic AI research, "Strong AI" and "AGI" are two different things.

Consciousness

Consciousness can have numerous significances, and some elements play considerable roles in sci-fi and the ethics of expert system:

Sentience (or "sensational awareness"): The capability to "feel" understandings or emotions subjectively, rather than the ability to reason about perceptions. Some theorists, such as David Chalmers, utilize the term "consciousness" to refer specifically to remarkable awareness, which is roughly comparable to life. [132] Determining why and how subjective experience occurs is called the difficult issue of awareness. [133] Thomas Nagel explained in 1974 that it "seems like" something to be mindful. If we are not mindful, then it does not feel like anything. Nagel uses the example of a bat: we can sensibly ask "what does it feel like to be a bat?" However, we are unlikely to ask "what does it seem like to be a toaster?" Nagel concludes that a bat seems mindful (i.e., has consciousness) but a toaster does not. [134] In 2022, a Google engineer claimed that the company's AI chatbot, LaMDA, had achieved life, though this claim was widely contested by other professionals. [135]
Self-awareness: To have mindful awareness of oneself as a separate individual, especially to be purposely mindful of one's own thoughts. This is opposed to merely being the "subject of one's believed"-an operating system or debugger has the ability to be "familiar with itself" (that is, to represent itself in the exact same method it represents whatever else)-however this is not what people generally indicate when they utilize the term "self-awareness". [g]
These qualities have an ethical dimension. AI life would generate concerns of well-being and legal security, likewise to animals. [136] Other aspects of consciousness associated to cognitive abilities are also relevant to the principle of AI rights. [137] Determining how to integrate innovative AI with existing legal and social structures is an emergent concern. [138]
Benefits

AGI might have a variety of applications. If oriented towards such objectives, AGI might assist alleviate different problems on the planet such as hunger, poverty and illness. [139]
AGI might improve efficiency and efficiency in a lot of tasks. For example, in public health, AGI might accelerate medical research, especially against cancer. [140] It might look after the elderly, [141] and democratize access to fast, premium medical diagnostics. It might offer fun, low-cost and customized education. [141] The need to work to subsist might end up being obsolete if the wealth produced is correctly redistributed. [141] [142] This also raises the question of the place of people in a radically automated society.

AGI could likewise assist to make rational decisions, and to anticipate and avoid disasters. It could also help to profit of potentially catastrophic technologies such as nanotechnology or climate engineering, while avoiding the associated threats. [143] If an AGI's main goal is to avoid existential catastrophes such as human extinction (which could be hard if the Vulnerable World Hypothesis turns out to be real), [144] it could take procedures to dramatically decrease the risks [143] while minimizing the impact of these steps on our quality of life.

Risks

Existential dangers

AGI may represent multiple types of existential risk, which are risks that threaten "the premature extinction of Earth-originating smart life or the long-term and extreme destruction of its potential for preferable future advancement". [145] The risk of human extinction from AGI has been the topic of lots of disputes, however there is likewise the possibility that the development of AGI would result in a completely problematic future. Notably, it might be used to spread out and preserve the set of worths of whoever establishes it. If humankind still has moral blind spots similar to slavery in the past, AGI might irreversibly entrench it, avoiding ethical development. [146] Furthermore, AGI might assist in mass surveillance and brainwashing, which might be used to develop a stable repressive worldwide totalitarian routine. [147] [148] There is likewise a danger for the devices themselves. If devices that are sentient or otherwise worthy of moral consideration are mass developed in the future, participating in a civilizational path that forever disregards their welfare and interests could be an existential catastrophe. [149] [150] Considering just how much AGI might improve mankind's future and help in reducing other existential threats, Toby Ord calls these existential risks "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, which this danger requires more attention, is controversial however has been backed in 2023 by numerous 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 prevalent indifference:

So, dealing with possible futures of incalculable advantages and dangers, the experts are surely doing everything possible to ensure the very best result, right? Wrong. If a remarkable alien civilisation sent us a message stating, 'We'll show up in a few decades,' would we just reply, 'OK, call us when you get here-we'll leave the lights on?' Probably not-but this is basically what is occurring with AI. [153]
The possible fate of mankind has actually sometimes been compared to the fate of gorillas threatened by human activities. The contrast specifies that higher intelligence permitted humankind to dominate gorillas, which are now vulnerable in methods that they could not have actually expected. As a result, the gorilla has actually ended up being an endangered species, not out of malice, however simply as a security damage from human activities. [154]
The skeptic Yann LeCun considers that AGIs will have no desire to control mankind which we should take care not to anthropomorphize them and analyze their intents as we would for human beings. He stated that individuals will not be "smart adequate to develop super-intelligent machines, yet ridiculously dumb to the point of giving it moronic objectives without any safeguards". [155] On the other side, the idea of critical convergence recommends that almost whatever their objectives, intelligent agents will have factors to try to survive and acquire more power as intermediary actions to achieving these goals. Which this does not need having emotions. [156]
Many scholars who are concerned about existential risk supporter for more research into resolving the "control problem" to address the question: what kinds of safeguards, algorithms, or architectures can programmers implement to increase the likelihood 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 problem is made complex by the AI arms race (which could cause a race to the bottom of safety precautions in order to release items before competitors), [159] and making use of AI in weapon systems. [160]
The thesis that AI can posture existential threat also has detractors. Skeptics normally say that AGI is unlikely in the short-term, or that concerns about AGI sidetrack from other concerns connected to current AI. [161] Former Google fraud czar Shuman Ghosemajumder thinks about that for lots of people outside of the technology industry, existing chatbots and LLMs are already viewed as though they were AGI, leading to additional misconception and worry. [162]
Skeptics often charge that the thesis is crypto-religious, with an irrational belief in the possibility of superintelligence replacing an unreasonable belief in a supreme God. [163] Some researchers believe that the communication campaigns on AI existential danger by particular AI groups (such as OpenAI, Anthropic, DeepMind, and Conjecture) might be an at effort at regulatory capture and to inflate interest in their items. [164] [165]
In 2023, the CEOs of Google DeepMind, OpenAI and Anthropic, in addition to other industry leaders and scientists, released a joint declaration asserting that "Mitigating the threat of termination from AI must be a global top priority alongside other societal-scale risks such as pandemics and nuclear war." [152]
Mass unemployment

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

According to Stephen Hawking, the outcome of automation on the quality of life will depend on how the wealth will be rearranged: [142]
Everyone can delight in a life of elegant leisure if the machine-produced wealth is shared, or many people can end up miserably poor if the machine-owners successfully lobby versus wealth redistribution. Up until now, the trend seems to be toward the second choice, with technology driving ever-increasing inequality

Elon Musk thinks about that the automation of society will need federal governments to adopt a universal basic income. [168]
See also

Artificial brain - Software and hardware with cognitive abilities comparable to those of the animal or human brain AI result AI security - Research area on making AI safe and advantageous AI positioning - AI conformance to the desired goal A.I. Rising - 2018 movie directed by Lazar Bodroža Expert system Automated device knowing - Process of automating the application of maker learning BRAIN Initiative - Collaborative public-private research initiative revealed by the Obama administration China Brain Project Future of Humanity Institute - Defunct Oxford interdisciplinary research centre General game playing - Ability of expert system to play various video games Generative expert system - AI system capable of producing content in reaction to prompts Human Brain Project - Scientific research project Intelligence amplification - Use of infotech to enhance human intelligence (IA). Machine ethics - Moral behaviours of manufactured makers. Moravec's paradox. Multi-task learning - Solving several machine discovering tasks at the same time. Neural scaling law - Statistical law in maker knowing. Outline of artificial intelligence - Overview of and topical guide to artificial intelligence. Transhumanism - Philosophical movement. Synthetic intelligence - Alternate term for or type of synthetic intelligence. Transfer learning - Machine learning technique. Loebner Prize - Annual AI competitors. Hardware for expert system - Hardware specifically designed and enhanced for expert system. Weak expert system - Form of synthetic intelligence.
Notes

^ a b See below for the origin of the term "strong AI", and see the scholastic meaning of "strong AI" and weak AI in the short article Chinese room. ^ AI founder John McCarthy composes: "we can not yet characterize in general what sort of computational treatments we want to call intelligent. " [26] (For a discussion of some meanings of intelligence used by expert system researchers, see philosophy of expert system.). ^ The Lighthill report particularly slammed AI's "grandiose objectives" and led the dismantling of AI research in England. [55] In the U.S., DARPA ended up being identified to fund only "mission-oriented direct research study, instead of basic undirected research". [56] [57] ^ As AI creator John McCarthy composes "it would be a terrific relief to the rest of the employees in AI if the developers of brand-new basic formalisms would reveal their hopes in a more safeguarded type than has actually in some cases been the case." [61] ^ In "Mind Children" [122] 1015 cps is used. More just recently, in 1997, [123] Moravec argued for 108 MIPS which would roughly represent 1014 cps. Moravec talks in terms of MIPS, not "cps", which is a non-standard term Kurzweil introduced. ^ As defined in a basic AI textbook: "The assertion that devices might potentially act smartly (or, possibly much better, act as if they were smart) is called the 'weak AI' hypothesis by theorists, and the assertion that devices that do so are in fact believing (rather than 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 synthetic narrow intelligence (ANI)?". VentureBeat. Retrieved 1 March 2024. ANI is developed to perform a single job. ^ "OpenAI Charter". OpenAI. Retrieved 6 April 2023. Our objective is to make sure that artificial general intelligence benefits all of mankind. ^ Heath, Alex (18 January 2024). "Mark Zuckerberg's brand-new goal is developing artificial 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 projects were recognized as being active in 2020. ^ a b c "AI timelines: What do experts in artificial intelligence 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 leader Geoffrey Hinton gives up Google and warns of threat ahead". The New York Times. 1 May 2023. Retrieved 2 May 2023. It is difficult to see how you can avoid the bad actors from utilizing it for bad things. ^ Bubeck, Sébastien; Chandrasekaran, Varun; Eldan, Ronen; Gehrke, Johannes; Horvitz, Eric (2023 ). "Sparks of Artificial General Intelligence: Early 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 Times. The real risk is not AI itself however the way we deploy it. ^ "Impressed by expert system? Experts say AGI is coming next, and it has 'existential' dangers". ABC News. 23 March 2023. Retrieved 6 April 2023. AGI could position existential dangers to humanity. ^ Bostrom, Nick (2014 ). Superintelligence: Paths, Dangers, Strategies. Oxford University Press. ISBN 978-0-1996-7811-2. The 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 ought to be a worldwide concern. ^ "Statement on AI Risk". Center for AI Safety. Retrieved 1 March 2024. AI experts warn of threat of termination from AI. ^ Mitchell, Melanie (30 May 2023). "Are AI's Doomsday Scenarios Worth Taking Seriously?". The New York City Times. We are far from developing machines that can outthink us in general ways. ^ LeCun, Yann (June 2023). "AGI does not present an existential danger". 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 original on 14 August 2005: Kurzweil describes strong AI as "maker intelligence with the complete range of human intelligence.". ^ "The Age of Artificial Intelligence: George John at TEDxLondonBusinessSchool 2013". Archived from the original on 26 February 2014. Retrieved 22 February 2014. ^ Newell & Simon 1976, This is the term they use for "human-level" intelligence in the physical symbol system hypothesis. ^ "The Open University on Strong and Weak AI". Archived from the original on 25 September 2009. Retrieved 8 October 2007. ^ "What is synthetic superintelligence (ASI)?|Definition from TechTarget". Enterprise AI. Retrieved 8 October 2023. ^ "Artificial intelligence is transforming our world - it is on everyone 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 achieving 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 characteristics is based upon the topics covered by significant AI books, consisting of: Russell & Norvig 2003, Luger & Stubblefield 2004, Poole, Mackworth & Goebel 1998 and Nilsson 1998. ^ Johnson 1987. ^ de Charms, R. (1968 ). Personal causation. New York: Academic Press. ^ a b Pfeifer, R. and Bongard J. C., How the body shapes the way we think: a brand-new view of intelligence (The MIT Press, 2007). ISBN 0-2621-6239-3. ^ White, R. W. (1959 ). "Motivation reconsidered: The concept of skills". Psychological Review. 66 (5 ): 297-333. doi:10.1037/ h0040934. PMID 13844397. S2CID 37385966. ^ White, R. W. (1959 ). "Motivation reassessed: The principle of skills". Psychological Review. 66 (5 ): 297-333. doi:10.1037/ h0040934. PMID 13844397. S2CID 37385966. ^ Muehlhauser, Luke (11 August 2013). "What is AGI?". Machine Intelligence Research Institute. Archived from the original on 25 April 2014. Retrieved 1 May 2014. ^ "What is Artificial General Intelligence (AGI)?|4 Tests For Ensuring Artificial General Intelligence". Talky Blog. 13 July 2019. Archived from the 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 real kid - the Turing Test says so". The Guardian. 9 June 2014. ISSN 0261-3077. Retrieved 3 March 2024. ^ "Scientists contest whether computer 'Eugene Goostman' passed Turing test". BBC News. 9 June 2014. Retrieved 3 March 2024. ^ Jones, Cameron R.; Bergen, Benjamin K. (9 May 2024). "People can not differentiate GPT-4 from a human in a Turing test". arXiv:2405.08007 [cs.HC] ^ Varanasi, Lakshmi (21 March 2023). "AI models like ChatGPT and GPT-4 are acing everything from the bar test to AP Biology. Here's a list of difficult examinations both AI variations have actually passed". Business Insider. Retrieved 30 May 2023. ^ Naysmith, Caleb (7 February 2023). "6 Jobs Expert System Is Already Replacing and How Investors Can Capitalize on It". Retrieved 30 May 2023. ^ Turk, Victoria (28 January 2015). "The Plan to Replace the Turing Test with a 'Turing Olympics'". Vice. Retrieved 3 March 2024. ^ Gopani, Avi (25 May 2022). "Turing Test is unreliable. The Winograd Schema is obsolete. Coffee is the response". Analytics India Magazine. Retrieved 3 March 2024. ^ Bhaimiya, Sawdah (20 June 2023). "DeepMind's co-founder suggested testing an AI chatbot's capability to turn $100,000 into $1 million to determine human-like intelligence". Business Insider. Retrieved 3 March 2024. ^ Suleyman, Mustafa (14 July 2023). "Mustafa Suleyman: My new Turing test would see if AI can make $1 million". MIT Technology Review. Retrieved 3 March 2024. ^ Shapiro, Stuart C. (1992 ). "Artificial Intelligence" (PDF). In Stuart C. Shapiro (ed.). Encyclopedia of Expert System (Second ed.). New York: John Wiley. pp. 54-57. Archived (PDF) from the initial on 1 February 2016. (Section 4 is on "AI-Complete Tasks".). ^ Yampolskiy, Roman V. (2012 ). Xin-She Yang (ed.). "Turing Test as a Defining Feature of AI-Completeness" (PDF). Artificial Intelligence, Evolutionary Computation and Metaheuristics (AIECM): 3-17. Archived (PDF) from the initial on 22 May 2013. ^ "AI Index: photorum.eclat-mauve.fr 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 estimated in Crevier 1993, p. 109. ^ "Scientist on the Set: An Interview with Marvin Minsky". Archived from the original on 16 July 2012. Retrieved 5 April 2008. ^ Marvin Minsky to Darrach (1970 ), priced 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 ). "Respond to Lighthill". Stanford University. Archived from the initial on 30 September 2008. Retrieved 29 September 2007. ^ Markoff, John (14 October 2005). "Behind Expert system, a Squadron of Bright Real People". The New York Times. Archived from the original on 2 February 2023. Retrieved 18 February 2017. At its low point, some computer system scientists and software application engineers avoided the term expert system for fear of being viewed as wild-eyed dreamers. ^ Russell & Norvig 2003, pp. 25-26 ^ "Trends in the Emerging Tech Hype Cycle". Gartner Reports. Archived from the initial on 22 May 2019. Retrieved 7 May 2019. ^ a b Moravec 1988, p. 20 ^ Harnad, S. (1990 ). "The Symbol Grounding Problem". Physica D. 42 (1-3): 335-346. arXiv: cs/9906002. Bibcode:1990 PhyD ... 42..335 H. doi:10.1016/ 0167-2789( 90 )90087-6. S2CID 3204300. ^ Gubrud 1997 ^ Hutter, Marcus (2005 ). Universal Expert System: Sequential Decisions Based Upon Algorithmic Probability. Texts in Theoretical Computer Science an EATCS Series. Springer. doi:10.1007/ b138233. ISBN 978-3-5402-6877-2. S2CID 33352850. Archived from the initial on 19 July 2022. Retrieved 19 July 2022. ^ Legg, Shane (2008 ). Machine Super Intelligence (PDF) (Thesis). University of Lugano. Archived (PDF) from the 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 created 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 promoted by ... Shane Legg, Mark Gubrud and Ben Goertzel' ^ Wang & Goertzel 2007 ^ "First International Summer School in Artificial General Intelligence, Main summer 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 original on 26 July 2020. Retrieved 11 May 2020. ^ Shevlin, Henry; Vold, Karina; Crosby, Matthew; Halina, Marta (4 October 2019). "The limitations of machine intelligence: Despite development in maker intelligence, synthetic general intelligence is still a major challenge". EMBO Reports. 20 (10 ): e49177. doi:10.15252/ embr.201949177. ISSN 1469-221X. PMC 6776890. PMID 31531926. ^ Bubeck, Sébastien; Chandrasekaran, Varun; Eldan, Ronen; Gehrke, Johannes; Horvitz, Eric; Kamar, Ece; Lee, Peter; Lee, Yin Tat; Li, Yuanzhi; Lundberg, Scott; Nori, Harsha; Palangi, Hamid; Ribeiro, Marco Tulio; Zhang, Yi (27 March 2023). "Sparks of Artificial General Intelligence: Early 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 become a Frankenstein's beast". The Guardian. Archived from the initial on 17 September 2014. Retrieved 17 September 2014. ^ Deane, George (2022 ). "Machines That Feel and Think: The Role of Affective Feelings and Mental Action in (Artificial) General Intelligence". Artificial Life. 28 (3 ): 289-309. doi:10.1162/ artl_a_00368. ISSN 1064-5462. PMID 35881678. S2CID 251069071. ^ a b c Clocksin 2003. ^ Fjelland, Ragnar (17 June 2020). "Why 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 synthetic intelligence 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 expert viewpoint. In Fundamental issues of synthetic intelligence (pp. 555-572). Springer, Cham. ^ Armstrong, Stuart, and Kaj Sotala. 2012. "How We're Predicting AI-or Failing To." In Beyond AI: Artificial Dreams, edited by Jan Romportl, Pavel Ircing, Eva Žáčková, Michal Polák and Radek Schuster, 52-75. Plzeň: University of West Bohemia ^ "Microsoft Now Claims GPT-4 Shows 'Sparks' of General Intelligence". 24 March 2023. ^ Shimek, Cary (6 July 2023). "AI Outperforms Humans in Creativity Test". Neuroscience News. Retrieved 20 October 2023. ^ Guzik, Erik E.; Byrge, Christian; Gilde, Christian (1 December 2023). "The originality of machines: AI takes the Torrance Test". Journal of Creativity. 33 (3 ): 100065. doi:10.1016/ j.yjoc.2023.100065. ISSN 2713-3745. S2CID 261087185. ^ Arcas, Blaise Agüera y (10 October 2023). "Artificial General Intelligence Is Already Here". Noema. ^ Zia, Tehseen (8 January 2024). "Unveiling of Large Multimodal Models: Shaping the Landscape of Language Models in 2024". Unite.ai. Retrieved 26 May 2024. ^ "Introducing OpenAI o1-preview". OpenAI. 12 September 2024. ^ Knight, Will. "OpenAI Announces a Brand-new AI Model, Code-Named Strawberry, That Solves Difficult Problems Step by Step". Wired. ISSN 1059-1028. Retrieved 17 September 2024. ^ "OpenAI Employee Claims AGI Has Been Achieved". Orbital Today. 13 December 2024. Retrieved 27 December 2024. ^ "AI Index: State of AI in 13 Charts". hai.stanford.edu. 15 April 2024. Retrieved 7 June 2024. ^ "Next-Gen AI: OpenAI and Meta's Leap Towards Reasoning Machines". Unite.ai. 19 April 2024. Retrieved 7 June 2024. ^ James, Alex P. (2022 ). "The Why, What, and How of Artificial General Intelligence Chip Development". IEEE Transactions on Cognitive and Developmental Systems. 14 (2 ): 333-347. arXiv:2012.06338. doi:10.1109/ TCDS.2021.3069871. ISSN 2379-8920. S2CID 228376556. Archived from the initial on 28 August 2022. Retrieved 28 August 2022. ^ Pei, Jing; Deng, Lei; Song, Sen; Zhao, Mingguo; Zhang, Youhui; Wu, Shuang; Wang, Guanrui; Zou, Zhe; Wu, Zhenzhi; He, Wei; Chen, Feng; Deng, Ning; Wu, Si; Wang, Yu; Wu, Yujie (2019 ). "Towards synthetic basic intelligence with hybrid Tianjic chip architecture". Nature. 572 (7767 ): 106-111. Bibcode:2019 Natur.572..106 P. doi:10.1038/ s41586-019-1424-8. ISSN 1476-4687. PMID 31367028. S2CID 199056116. Archived from the original on 29 August 2022. Retrieved 29 August 2022. ^ Pandey, Mohit; Fernandez, Michael; Gentile, Francesco; Isayev, Olexandr; Tropsha, Alexander; Stern, Abraham C.; Cherkasov, Artem (March 2022). "The transformational function of GPU computing and users.atw.hu 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). 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 original on 3 January 2019. Retrieved 2 January 2019. ^ Grossman, Gary (3 September 2020). "We're going into the AI twilight zone between narrow and basic AI". VentureBeat. Archived from the initial on 4 September 2020. Retrieved 5 September 2020. Certainly, too, there are those who declare we are already seeing an early example of an AGI system in the just recently revealed GPT-3 natural language processing (NLP) neural network. ... So is GPT-3 the very first example of an AGI system? This is arguable, however the consensus is that it is not AGI. ... If absolutely nothing else, GPT-3 tells us there is a middle ground in between narrow and basic AI. ^ Quach, Katyanna. "A developer constructed an AI chatbot utilizing GPT-3 that assisted a man speak once again to his late fiancée. OpenAI shut it down". The Register. Archived from the initial on 16 October 2021. Retrieved 16 October 2021. ^ Wiggers, Kyle (13 May 2022), "DeepMind's new AI can perform over 600 tasks, from playing video games to managing robotics", TechCrunch, archived from the initial on 16 June 2022, retrieved 12 June 2022. ^ Bubeck, Sébastien; Chandrasekaran, Varun; Eldan, Ronen; Gehrke, Johannes; Horvitz, Eric; Kamar, Ece; Lee, Peter; Lee, Yin Tat; Li, Yuanzhi; Lundberg, Scott; Nori, Harsha; Palangi, Hamid; Ribeiro, Marco Tulio; Zhang, Yi (22 March 2023). "Sparks of Artificial General Intelligence: Early try outs GPT-4". arXiv:2303.12712 [cs.CL] ^ Metz, Cade (1 May 2023). "' The Godfather of A.I.' Leaves Google and Warns of Danger Ahead". The New York City Times. ISSN 0362-4331. Retrieved 7 June 2023. ^ Bove, Tristan. "A.I. could match human intelligence in 'just a few years,' states 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 five years". Reuters. ^ Aschenbrenner, Leopold. "SITUATIONAL AWARENESS, The Decade Ahead". ^ Sullivan, Mark (18 October 2023). "Why everybody 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 Huge Step for Neuroscience". Tech Brew. Retrieved 8 November 2023. ^ Sandberg & Boström 2008. ^ Drachman 2005. ^ a b Russell & Norvig 2003. ^ Moravec 1988, p. 61. ^ Moravec 1998. ^ Holmgaard Mersh, Amalie (15 September 2023). "Decade-long European research job maps the human brain". euractiv. ^ Swaminathan, Nikhil (January-February 2011). "Glia-the other brain cells". Discover. Archived from the original on 8 February 2014. Retrieved 24 January 2014. ^ de Vega, Glenberg & Graesser 2008. A vast array of views in current research, all of which require grounding to some degree ^ Thornton, Angela (26 June 2023). "How uploading our minds to a computer may 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 (estimated 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 expert system for interest by the field about why a program acts the way it does. ^ Chalmers, David J. (9 August 2023). "Could a Large Language Model Be Conscious?". Boston Review. ^ Seth, Anil. "Consciousness". New Scientist. Retrieved 5 September 2024. ^ Nagel 1974. ^ "The Google engineer who thinks the business's AI has come to life". The Washington Post. 11 June 2022. Retrieved 12 June 2023. ^ Kateman, Brian (24 July 2023). "AI Should Be Terrified of Humans". TIME. Retrieved 5 September 2024. ^ Nosta, John (18 December 2023). "Should Expert System Have Rights?". Psychology Today. Retrieved 5 September 2024. ^ Akst, Daniel (10 April 2023). "Should Robots With Expert System Have Moral or Legal Rights?". The Wall Street Journal. ^ "Artificial General Intelligence - Do [es] the cost outweigh advantages?". 23 August 2021. Retrieved 7 June 2023. ^ "How we can Benefit 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, risks, methods (Reprinted with corrections 2017 ed.). Oxford, UK; New York City, New York, USA: Oxford University Press. ISBN 978-0-1996-7811-2. ^ Piper, Kelsey (19 November 2018). "How technological development is making it likelier than ever that people will damage ourselves". Vox. Retrieved 8 June 2023. ^ Doherty, Ben (17 May 2018). "Climate change an 'existential security danger' to Australia, Senate inquiry states". The Guardian. ISSN 0261-3077. Retrieved 16 July 2023. ^ MacAskill, William (2022 ). What we owe the future. New York, NY: Basic Books. ISBN 978-1-5416-1862-6. ^ a b Ord, Toby (2020 ). "Chapter 5: Future Risks, Unaligned Expert System". The Precipice: Existential Risk and the Future of Humanity. Bloomsbury Publishing. ISBN 978-1-5266-0021-9. ^ Al-Sibai, Noor (13 February 2022). "OpenAI Chief Scientist Says Advanced AI May Already Be Conscious". Futurism. Retrieved 24 December 2023. ^ Samuelsson, Paul Conrad (2019 ). "Artificial Consciousness: Our Greatest Ethical Challenge". Philosophy Now. Retrieved 23 December 2023. ^ Kateman, Brian (24 July 2023). "AI Should Be Terrified of Humans". TIME. Retrieved 23 December 2023. ^ Roose, Kevin (30 May 2023). "A.I. Poses 'Risk of Extinction,' Industry Leaders Warn". The New York 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: 'Transcendence looks at the ramifications of artificial intelligence - but 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 interesting Facebook debate in between Yann LeCun, Stuart Russel and Yoshua Bengio about the threats of strong AI". The remarkable Facebook argument 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 Mankind Within The Next 100 Years?". HuffPost. 22 August 2014. Retrieved 8 June 2023. ^ Sotala, Kaj; Yampolskiy, Roman V. (19 December 2014). "Responses to devastating AGI threat: a survey". Physica Scripta. 90 (1 ): 018001. doi:10.1088/ 0031-8949/90/ 1/018001. ISSN 0031-8949. ^ Bostrom, Nick (2014 ). Superintelligence: Paths, Dangers, Strategies (First ed.). Oxford University Press. ISBN 978-0-1996-7811-2. ^ Chow, Andrew R.; Perrigo, Billy (16 February 2023). "The AI Arms Race Is On. Start Worrying". TIME. Retrieved 24 December 2023. ^ Tetlow, Gemma (12 January 2017). "AI arms race risks spiralling out of control, report 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 danger posed but synthetic intelligence can not be neglected". The Guardian. ISSN 0261-3077. Retrieved 24 December 2023. ^ "Humanity, Security & AI, Oh My! (with Ian Bremmer & Shuman Ghosemajumder)". CAFE. 20 July 2023. Retrieved 15 September 2023. ^ Hamblin, James (9 May 2014). "But What Would completion of Humanity Mean for Me?". The Atlantic. Archived from the initial on 4 June 2014. Retrieved 12 December 2015. ^ Titcomb, James (30 October 2023). "Big Tech is stiring worries over AI, caution scientists". The Telegraph. Retrieved 7 December 2023. ^ Davidson, John (30 October 2023). "Google Brain creator says huge tech is lying about AI extinction threat". Australian Financial Review. Archived from the original on 7 December 2023. Retrieved 7 December 2023. ^ Eloundou, Tyna; Manning, Sam; Mishkin, Pamela; Rock, Daniel (17 March 2023). "GPTs are GPTs: An early take a look at the labor market effect potential of big language designs". OpenAI. Retrieved 7 June 2023. ^ a b Hurst, Luke (23 March 2023). "OpenAI states 80% of employees could see their jobs affected by AI. These are the jobs most impacted". euronews. Retrieved 8 June 2023. ^ Sheffey, Ayelet (20 August 2021). "Elon Musk says we need universal basic earnings due to the fact that 'in the future, manual labor will be an option'". Business Insider. Archived from the original on 9 July 2023. Retrieved 8 June 2023. Sources

UNESCO Science Report: the Race Against Time for Smarter Development. Paris: UNESCO. 11 June 2021. ISBN 978-9-2310-0450-6. Archived from the original on 18 June 2022. Retrieved 22 September 2021. Chalmers, David (1996 ), The Conscious Mind, Oxford University Press. Clocksin, William (August 2003), "Artificial intelligence and the future", Philosophical Transactions of the Royal Society A, vol. 361, no. 1809, pp. 1721-1748, Bibcode:2003 RSPTA.361.1721 C, doi:10.1098/ rsta.2003.1232, PMID 12952683, S2CID 31032007. Crevier, Daniel (1993 ). AI: The Tumultuous Look For Artificial Intelligence. New York, 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 initial (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", Artificial Intelligence, vol. 171, no. 18, Special Review Issue, pp. 1161-1173, doi:10.1016/ j.artint.2007.10.011, archived from the original on 7 January 2016, retrieved 1 April 2009. Gubrud, Mark (November 1997), "Nanotechnology and International Security", Fifth Foresight Conference on Molecular Nanotechnology, archived from the initial on 29 May 2011, retrieved 7 May 2011. Howe, J. (November 1994), Artificial Intelligence at Edinburgh University: a Point of view, archived from the initial on 17 August 2007, 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 symposium, Science Research Council. Luger, George; Stubblefield, William (2004 ), Artificial Intelligence: Structures and Strategies for Complex Problem Solving (fifth ed.), The Benjamin/Cummings Publishing Company, Inc., p. 720, ISBN 978-0-8053-4780-7. McCarthy, John (2007b). What is Artificial Intelligence?. Stanford University. The supreme effort is to make computer system programs that can fix issues and achieve objectives worldwide in addition to human beings. Moravec, Hans (1988 ), Mind Children, Harvard University Press Moravec, Hans (1998 ), "When will hardware match the human brain?", Journal of Evolution and Technology, vol. 1, archived from the initial on 15 June 2006, retrieved 23 June 2006 Nagel (1974 ), "What Is it Like to Be a Bat" (PDF), Philosophical Review, 83 (4 ): 435-50, doi:10.2307/ 2183914, JSTOR 2183914, archived (PDF) from the original on 16 October 2011, retrieved 7 November 2009 Newell, Allen; Simon, H. A. (1976 ). "Computer Science as Empirical Inquiry: Symbols and Search". Communications of the ACM. 19 (3 ): 113-126. doi:10.1145/ 360018.360022. Nilsson, Nils (1998 ), Artificial Intelligence: A New Synthesis, Morgan Kaufmann Publishers, ISBN 978-1-5586-0467-4 NRC (1999 ), "Developments in Expert System", Funding a Transformation: Government Support for Computing Research, National Academy Press, archived from the original on 12 January 2008, recovered 29 September 2007 Poole, David; Mackworth, Alan; Goebel, Randy (1998 ), Computational Intelligence: A Logical Approach, New York City: Oxford University Press, archived from the initial on 25 July 2009, obtained 6 December 2007 Russell, Stuart J.; Norvig, Peter (2003 ), Expert System: A Modern Approach (2nd ed.), Upper Saddle River, New Jersey: Prentice Hall, ISBN 0-13-790395-2 Sandberg, Anders; Boström, Nick (2008 ), Whole Brain Emulation: A Roadmap (PDF), Technical Report # 2008-3, Future of Humanity Institute, Oxford University, archived (PDF) from the initial on 25 March 2020, 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, obtained 3 September 2020 Simon, H. A. (1965 ), The Shape of Automation for Men and Management, New York City: Harper & Row Turing, Alan (October 1950). "Computing Machinery and Intelligence". Mind. 59 (236 ): 433-460. doi:10.1093/ mind/LIX.236.433. ISSN 1460-2113. JSTOR 2251299. S2CID 14636783.
de Vega, Manuel; Glenberg, Arthur; Graesser, Arthur, eds. (2008 ), Symbols and Embodiment: Debates on meaning and cognition, Oxford University Press, ISBN 978-0-1992-1727-4 Wang, Pei; Goertzel, Ben (2007 ). "Introduction: Aspects of Artificial General Intelligence". Advances in Artificial General Intelligence: Concepts, Architectures and Algorithms: Proceedings of the AGI Workshop 2006. IOS Press. pp. 1-16. ISBN 978-1-5860-3758-1. Archived from the original on 18 February 2021. Retrieved 13 December 2020 - via ResearchGate.
Further reading

Aleksander, Igor (1996 ), Impossible Minds, World Scientific Publishing Company, ISBN 978-1-8609-4036-1 Azevedo FA, Carvalho LR, Grinberg LT, Farfel J, et al. (April 2009), "Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain", The Journal of Comparative Neurology, 513 (5 ): 532-541, doi:10.1002/ cne.21974, PMID 19226510, S2CID 5200449, archived from the original on 18 February 2021, recovered 4 September 2013 - through ResearchGate Berglas, Anthony (January 2012) [2008], Expert System Will Kill Our Grandchildren (Singularity), archived from the original on 23 July 2014, obtained 31 August 2012 Cukier, Kenneth, "Ready for Robots? How to 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 easy adequate to be easy to understand will not be complicated enough to act wisely, while any system made complex enough to act smartly will be too made complex to understand." (p. 197.) Computer researcher Alex Pentland writes: "Current AI machine-learning algorithms are, at their core, dead simple foolish. They work, however they work by brute force." (p. 198.). Gelernter, David, Dream-logic, the Internet and Artificial Thought, Edge, archived from the initial on 26 July 2010, obtained 25 July 2010. Gleick, James, "The Fate of Free Choice" (evaluation of Kevin J. Mitchell, Free Agents: How Evolution Gave Us Free Will, Princeton University Press, 2023, 333 pp.), The New York City Review of Books, vol. LXXI, no. 1 (18 January 2024), pp. 27-28, 30. "Agency is what identifies us from makers. For biological creatures, reason and purpose come from acting in the world and experiencing the consequences. Artificial intelligences - disembodied, strangers to blood, sweat, and tears - have no occasion for that." (p. 30.). Halal, William E. "TechCast Article Series: The Automation of Thought" (PDF). Archived from the initial (PDF) on 6 June 2013. - Halpern, Sue, "The Coming Tech Autocracy" (evaluation of Verity Harding, AI Needs You: How We Can Change AI's Future and Save Our Own, Princeton University Press, 274 pp.; Gary Marcus, Taming Silicon Valley: How We Can Ensure That AI Works for Us, MIT Press, 235 pp.; Daniela Rus and Gregory Mone, The Mind's Mirror: Risk and Reward in the Age of AI, Norton, 280 pp.; Madhumita Murgia, Code Dependent: Residing In the Shadow of AI, Henry Holt, 311 pp.), The New York City Review of Books, vol. LXXI, no. 17 (7 November 2024), pp. 44-46. "' We can't reasonably anticipate that those who hope to get rich from AI are going to have the interests of the rest people close at heart,' ... [Gary Marcus] 'We can't depend on federal governments driven by project financing contributions [from tech companies] to push back.' ... Marcus details the demands that residents ought to make of their federal governments and the tech companies. They include openness on how AI systems work; settlement for individuals if their data [are] utilized to train LLMs (big language design) s and the right to permission to this usage; and the ability to hold tech business accountable for the harms they trigger by removing Section 230, enforcing cash penalites, and passing stricter product liability laws ... Marcus likewise suggests ... that a brand-new, AI-specific federal firm, comparable to the FDA, the FCC, or the FTC, might supply the most robust oversight ... [T] he Fordham law professor Chinmayi Sharma ... recommends ... establish [ing] a professional licensing program for engineers that would operate in a similar way to medical licenses, malpractice suits, and the Hippocratic oath in medication. 'What if, like physicians,' she asks ..., 'AI engineers likewise pledged to do no harm?'" (p. 46.). Holte, R. C.; Choueiry, B. Y. (2003 ), "Abstraction and reformulation in expert system", Philosophical Transactions of the Royal Society B, vol. 358, no. 1435, pp. 1197-1204, doi:10.1098/ rstb.2003.1317, PMC 1693218, PMID 12903653. Hughes-Castleberry, Kenna, "A Murder Mystery Puzzle: The literary puzzle Cain's Jawbone, which has stumped human beings for years, exposes the restrictions of natural-language-processing algorithms", Scientific American, vol. 329, no. 4 (November 2023), pp. 81-82. "This murder mystery competitors has exposed that although NLP (natural-language processing) models are capable of incredible feats, their capabilities are really much restricted by the amount of context they receive. This [...] might cause [difficulties] for researchers who want to utilize them to do things such as evaluate ancient languages. Sometimes, there are few historic records on long-gone civilizations to work as training data for such a function." (p. 82.). Immerwahr, Daniel, "Your Lying Eyes: People now utilize A.I. to create fake videos indistinguishable from genuine ones. Just how much does it matter?", The New Yorker, 20 November 2023, pp. 54-59. "If by 'deepfakes' we imply sensible videos produced utilizing expert system that really deceive individuals, then they hardly exist. The fakes aren't deep, and the deeps aren't fake. [...] A.I.-generated videos are not, in basic, running in our media as counterfeited proof. Their function much better looks like that of cartoons, specifically smutty ones." (p. 59.). - Leffer, Lauren, "The Risks of Trusting AI: We should prevent humanizing machine-learning designs used 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 discussion?", The New Yorker, 7 October 2024, pp. 12-16. Marcus, Gary, "Artificial Confidence: Even the newest, buzziest systems of artificial general intelligence are stymmied by the exact same old issues", Scientific American, vol. 327, no. 4 (October 2022), pp. 42-45. McCarthy, John (October 2007), "From here to human-level AI", Artificial Intelligence, 171 (18 ): 1174-1182, doi:10.1016/ j.artint.2007.10.009. McCorduck, Pamela (2004 ), Machines Who Think (second ed.), Natick, Massachusetts: A. K. Peters, ISBN 1-5688-1205-1. Moravec, Hans (1976 ), The Role of Raw Power in Intelligence, archived from the initial on 3 March 2016, obtained 29 September 2007. Newell, Allen; Simon, H. A. (1963 ), "GPS: A Program that Simulates Human Thought", in Feigenbaum, E. A.; Feldman, J. (eds.), Computers and Thought, New York: McGraw-Hill. Omohundro, Steve (2008 ), The Nature of Self-Improving Artificial Intelligence, presented and distributed at the 2007 Singularity Summit, San Francisco, California. Press, Eyal, "In Front of Their Faces: Does facial-recognition innovation lead authorities to ignore contradictory proof?", The New Yorker, 20 November 2023, pp. 20-26. Roivainen, Eka, "AI's IQ: ChatGPT aced a [basic intelligence] test but revealed that intelligence can not be determined by IQ alone", Scientific American, vol. 329, no. 1 (July/August 2023), p. 7. "Despite its high IQ, ChatGPT fails at tasks that require genuine humanlike reasoning or an understanding of the physical and social world ... ChatGPT appeared not able to factor realistically and attempted to depend on its large database of ... truths obtained from online texts. " - Scharre, Paul, "Killer Apps: The Real Dangers of an AI Arms Race", Foreign Affairs, vol. 98, no. 3 (May/June 2019), pp. 135-44. "Today's AI technologies are effective but unreliable. Rules-based systems can not handle scenarios their programmers did not anticipate. Learning systems are limited by the data on which they were trained. AI failures have already led to tragedy. Advanced autopilot features in vehicles, although they perform well in some scenarios, have driven vehicles without cautioning into trucks, concrete barriers, and parked vehicles. In the incorrect situation, AI systems go from supersmart to superdumb in an instant. When an opponent is trying to control and hack an AI system, the risks are even greater." (p. 140.). Sutherland, J. G. (1990 ), "Holographic Model of Memory, Learning, and Expression", International Journal of Neural Systems, vol. 1-3, pp. 256-267. - Vincent, James, "Horny Robot Baby Voice: James Vincent on AI chatbots", London Review of Books, vol. 46, no. 19 (10 October 2024), pp. 29-32." [AI chatbot] programs are enabled by new technologies however rely on the timelelss human propensity to anthropomorphise." (p. 29.). Williams, R. W.; Herrup, K.