Artificial general intelligence (AGI) is a kind of synthetic intelligence (AI) that matches or exceeds human cognitive capabilities across a wide variety of cognitive jobs. This contrasts with narrow AI, which is limited to particular tasks. [1] Artificial superintelligence (ASI), on the other hand, suvenir51.ru refers to AGI that considerably exceeds human cognitive abilities. AGI is considered one of the meanings of strong AI.

Creating AGI is a main goal of AI research and of companies such as OpenAI [2] and Meta. [3] A 2020 study recognized 72 active AGI research and development projects across 37 nations. [4]
The timeline for accomplishing AGI stays a subject of ongoing debate among researchers and specialists. Since 2023, some argue that it may be possible in years or years; others maintain it might take a century or longer; a minority think it may never be accomplished; and another minority claims that it is currently here. [5] [6] Notable AI scientist Geoffrey Hinton has actually revealed issues about the fast development towards AGI, recommending it could be attained quicker than numerous anticipate. [7]
There is argument on the exact meaning of AGI and relating to whether contemporary large language designs (LLMs) such as GPT-4 are early forms of AGI. [8] AGI is a common topic in sci-fi and futures research studies. [9] [10]
Contention exists over whether AGI represents an existential threat. [11] [12] [13] Many specialists on AI have actually stated that mitigating the danger of human termination positioned by AGI needs to be an international top priority. [14] [15] Others discover the development of AGI to be too remote to present such a threat. [16] [17]
Terminology

AGI is likewise called strong AI, [18] [19] complete AI, [20] human-level AI, [5] human-level intelligent AI, or basic intelligent action. [21]
Some scholastic sources schedule the term "strong AI" for computer programs that experience life or awareness. [a] On the other hand, weak AI (or narrow AI) is able to fix one specific problem however lacks general cognitive capabilities. [22] [19] Some academic sources use "weak AI" to refer more broadly to any programs that neither experience awareness nor have a mind in the very same sense as humans. [a]
Related principles include artificial superintelligence and transformative AI. A synthetic superintelligence (ASI) is a theoretical type of AGI that is much more generally smart than people, [23] while the concept of transformative AI relates to AI having a big effect on society, for instance, similar to the farming or industrial revolution. [24]
A structure for classifying AGI in levels was proposed in 2023 by Google DeepMind scientists. They specify five levels of AGI: emerging, proficient, freechat.mytakeonit.org specialist, virtuoso, and superhuman. For instance, a qualified AGI is defined as an AI that outshines 50% of knowledgeable adults in a large range of non-physical tasks, and wiki.dulovic.tech a superhuman AGI (i.e. a synthetic superintelligence) is similarly defined however with a limit of 100%. They consider big language designs 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 well-known meanings, and some scientists disagree with the more popular techniques. [b]
Intelligence traits

Researchers normally hold that intelligence is needed to do all of the following: [27]
factor, use technique, fix puzzles, and make judgments under unpredictability represent knowledge, including good sense knowledge plan discover

• interact in natural language
• if necessary, integrate these abilities in completion of any provided goal

Many interdisciplinary approaches (e.g. cognitive science, computational intelligence, and decision making) think about extra traits such as creativity (the ability to form novel psychological images and principles) [28] and autonomy. [29]
Computer-based systems that show a number of these capabilities exist (e.g. see computational imagination, automated thinking, choice support system, robotic, evolutionary calculation, intelligent agent). There is dispute about whether contemporary AI systems have them to an adequate degree.

Physical traits

Other capabilities are thought about desirable in smart systems, as they might affect intelligence or help in its expression. These include: [30]
- the ability to sense (e.g. see, hear, and so on), and - the capability to act (e.g. move and control things, modification place to check out, and so on).
This includes the capability to spot and react to danger. [31]
Although the ability to sense (e.g. see, hear, etc) and the ability to act (e.g. move and manipulate objects, modification area to check out, 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 big language designs (LLMs) might already be or end up being AGI. Even from a less optimistic viewpoint on LLMs, there is no firm requirement for an AGI to have a human-like kind; being a silicon-based computational system suffices, offered it can process input (language) from the external world in location of human senses. This interpretation lines up with the understanding that AGI has never ever been proscribed a specific physical embodiment and therefore does not demand a capability for mobility or conventional "eyes and ears". [32]
Tests for human-level AGI

Several tests implied to confirm 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 answering questions put to it, and it will only pass if the pretence is reasonably convincing. A significant part of a jury, who need to not be skilled about machines, must be taken in by the pretence. [37]
AI-complete issues

An issue is informally called "AI-complete" or "AI-hard" if it is thought that in order to solve it, one would need to carry out AGI, because the solution is beyond the abilities of a purpose-specific algorithm. [47]
There are lots of issues that have been conjectured to require basic intelligence to fix as well as people. Examples include computer vision, natural language understanding, and handling unexpected scenarios while fixing any real-world problem. [48] Even a specific task like translation needs a machine to check out and compose in both languages, follow the author's argument (reason), understand the context (understanding), and consistently recreate 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, numerous of these jobs can now be performed by modern-day large language models. According to Stanford University's 2024 AI index, AI has reached human-level performance on lots of criteria for reading understanding and visual reasoning. [49]
History

Classical AI

Modern AI research started in the mid-1950s. [50] The first generation of AI scientists were encouraged that artificial general intelligence was possible and that it would exist in just a couple of years. [51] AI leader Herbert A. Simon composed in 1965: "makers will be capable, within twenty years, of doing any work a male can do." [52]
Their forecasts were the inspiration for Stanley Kubrick and Arthur C. Clarke's character HAL 9000, who embodied what AI scientists thought they could produce by the year 2001. AI pioneer Marvin Minsky was an expert [53] on the task of making HAL 9000 as sensible as possible according to the consensus forecasts of the time. He said in 1967, "Within a generation ... the issue of creating 'synthetic intelligence' will significantly be fixed". [54]
Several classical AI projects, such as Doug Lenat's Cyc project (that started in 1984), and Allen Newell's Soar project, were directed at AGI.

However, in the early 1970s, it became obvious that researchers had grossly undervalued the problem of the task. 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 restored interest in AGI, setting out a ten-year timeline that objectives like "bring on a casual discussion". [58] In response to this and the success of specialist systems, both industry and federal government pumped money into the field. [56] [59] However, confidence in AI spectacularly collapsed in the late 1980s, and the objectives of the Fifth Generation Computer Project were never satisfied. [60] For the second time in twenty years, AI scientists who predicted the imminent achievement of AGI had been mistaken. By the 1990s, AI researchers had a credibility for making vain pledges. They became reluctant to make predictions at all [d] and avoided reference of "human level" synthetic intelligence for worry of being labeled "wild-eyed dreamer [s]. [62]
Narrow AI research study

In the 1990s and early 21st century, mainstream AI achieved commercial success and academic respectability by focusing on specific sub-problems where AI can produce verifiable outcomes and industrial applications, such as speech recognition and suggestion algorithms. [63] These "applied AI" systems are now utilized extensively throughout the technology industry, and research study in this vein is greatly funded in both academic community and market. Since 2018 [upgrade], development in this field was thought about an emerging pattern, and a fully grown phase was anticipated to be reached in more than 10 years. [64]
At the millenium, many traditional AI scientists [65] hoped that strong AI could be established by integrating programs that fix various sub-problems. Hans Moravec composed in 1988:

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

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

The term "synthetic general intelligence" was utilized as early as 1997, by Mark Gubrud [67] in a conversation of the ramifications of completely automated military production and operations. A mathematical formalism of AGI was proposed by Marcus Hutter in 2000. Named AIXI, the proposed AGI representative increases "the ability to satisfy objectives in a wide variety of environments". [68] This type of AGI, identified by the ability to increase a mathematical meaning of intelligence instead of display human-like behaviour, [69] was also called universal expert system. [70]
The term AGI was re-introduced and popularized by Shane Legg and Ben Goertzel around 2002. [71] AGI research study activity in 2006 was described by Pei Wang and Ben Goertzel [72] as "producing publications and initial results". The 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 featuring a number of guest speakers.

As of 2023 [update], a small number of computer system scientists are active in AGI research study, and lots of contribute to a series of AGI conferences. However, significantly more researchers are interested in open-ended learning, [76] [77] which is the concept of allowing AI to continually learn and innovate like humans do.

Feasibility

As of 2023, the advancement and possible accomplishment of AGI stays a subject of extreme debate within the AI community. While conventional agreement held that AGI was a distant objective, recent improvements have led some researchers and market figures to declare that early types of AGI may currently exist. [78] AI pioneer Herbert A. Simon hypothesized in 1965 that "devices will be capable, within twenty years, of doing any work a male 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 fundamentally unforeseeable advancements" and a "clinically 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 large as the gulf between existing space flight and practical faster-than-light spaceflight. [80]
A more challenge is the lack of clearness in defining what intelligence involves. Does it require awareness? Must it display the capability to set goals in addition to pursue them? Is it simply a matter of scale such that if design sizes increase adequately, intelligence will emerge? Are centers such as preparation, reasoning, and causal understanding needed? Does intelligence require explicitly reproducing the brain and its specific professors? Does it require emotions? [81]
Most AI scientists think strong AI can be accomplished in the future, but some thinkers, like Hubert Dreyfus and Roger Penrose, deny the possibility of accomplishing strong AI. [82] [83] John McCarthy is among those who believe human-level AI will be achieved, however that today level of development is such that a date can not properly be predicted. [84] AI experts' views on the feasibility of AGI wax and subside. Four polls carried out in 2012 and 2013 suggested that the mean estimate amongst professionals for when they would be 50% confident AGI would get here was 2040 to 2050, depending upon the survey, with the mean being 2081. Of the professionals, 16.5% addressed with "never" when asked the exact same concern but with a 90% self-confidence instead. [85] [86] Further present AGI progress factors to consider can be found above Tests for verifying human-level AGI.

A report by Stuart Armstrong and Kaj Sotala of the Machine Intelligence Research Institute found that "over [a] 60-year timespan there is a strong bias towards anticipating the arrival of human-level AI as between 15 and 25 years from the time the prediction was made". They analyzed 95 predictions made in between 1950 and 2012 on when human-level AI will come about. [87]
In 2023, Microsoft scientists published a detailed assessment of GPT-4. They concluded: "Given the breadth and depth of GPT-4's capabilities, we think that it might fairly be considered as an early (yet still insufficient) version of an artificial basic intelligence (AGI) system." [88] Another research study in 2023 reported that GPT-4 outshines 99% of human beings 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 actually already been achieved with frontier models. They wrote that reluctance to this view comes from four main factors: a "healthy uncertainty about metrics for AGI", an "ideological commitment to alternative AI theories or methods", a "devotion to human (or biological) exceptionalism", or a "concern about the financial implications of AGI". [91]
2023 likewise marked the emergence of large multimodal models (large language designs capable of processing or generating 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 capability to think before responding represents a brand-new, additional paradigm. It improves design outputs by spending more computing power when creating the response, whereas the model scaling paradigm enhances outputs by increasing the design size, training information and training calculate power. [93] [94]
An OpenAI employee, Vahid Kazemi, claimed in 2024 that the business had actually accomplished AGI, stating, "In my opinion, we have currently achieved 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 job", it is "better than the majority of human beings at many tasks." He likewise attended to criticisms that big language models (LLMs) merely follow predefined patterns, comparing their knowing procedure to the clinical method of observing, hypothesizing, and validating. These statements have actually stimulated debate, as they rely on a broad and unconventional definition of AGI-traditionally comprehended as AI that matches human intelligence throughout all domains. Critics argue that, while OpenAI's designs demonstrate impressive versatility, they might not totally fulfill this standard. Notably, Kazemi's remarks came shortly after OpenAI got rid of "AGI" from the regards to its partnership with Microsoft, triggering speculation about the business's tactical intentions. [95]
Timescales

Progress in expert system has traditionally gone through durations of fast progress separated by periods when progress appeared to stop. [82] Ending each hiatus were essential advances in hardware, software or both to create area for more progress. [82] [98] [99] For example, the computer hardware available in the twentieth century was not enough to implement deep learning, which requires big numbers of GPU-enabled CPUs. [100]
In the intro to his 2006 book, [101] Goertzel states that quotes of the time required before a really flexible AGI is constructed vary from ten years to over a century. As of 2007 [upgrade], the agreement in the AGI research neighborhood seemed to be that the timeline discussed by Ray Kurzweil in 2005 in The Singularity is Near [102] (i.e. between 2015 and 2045) was plausible. [103] Mainstream AI scientists have provided a large range of viewpoints on whether progress will be this rapid. A 2012 meta-analysis of 95 such opinions found a predisposition towards anticipating that the beginning of AGI would happen within 16-26 years for modern and historic predictions alike. That paper has actually been criticized for how it categorized viewpoints as expert or non-expert. [104]
In 2012, Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton established a neural network called AlexNet, which won the ImageNet competition with a top-5 test error rate of 15.3%, substantially much better than the second-best entry's rate of 26.3% (the traditional method used a weighted sum of ratings from various pre-defined classifiers). [105] AlexNet was considered the preliminary ground-breaker of the existing deep knowing wave. [105]
In 2017, scientists Feng Liu, Yong Shi, and Ying Liu conducted intelligence tests on publicly available and easily available weak AI such as Google AI, Apple's Siri, and others. At the maximum, these AIs reached an IQ value of about 47, which corresponds around to a six-year-old kid in very first grade. An adult pertains to about 100 typically. Similar tests were performed in 2014, with the IQ score reaching a maximum value of 27. [106] [107]
In 2020, OpenAI established GPT-3, a language model capable of performing lots of diverse tasks without specific training. According to Gary Grossman in a VentureBeat article, while there is consensus that GPT-3 is not an example of AGI, it is considered by some to be too advanced to be classified as a narrow AI system. [108]
In the very same year, Jason Rohrer used his GPT-3 account to establish a chatbot, and provided a chatbot-developing platform called "Project December". OpenAI asked for changes to the chatbot to abide by their safety guidelines; Rohrer detached Project December from the GPT-3 API. [109]
In 2022, DeepMind developed Gato, a "general-purpose" system efficient in carrying out more than 600 different tasks. [110]
In 2023, Microsoft Research released a research study on an early variation of OpenAI's GPT-4, competing that it showed more general intelligence than previous AI designs and demonstrated human-level performance in jobs covering multiple domains, such as mathematics, coding, and law. This research triggered a dispute on whether GPT-4 might be considered an early, insufficient version of synthetic general intelligence, stressing the requirement for more expedition and assessment of such systems. [111]
In 2023, the AI scientist Geoffrey Hinton stated that: [112]
The concept that this things might really get smarter than people - a couple of individuals thought that, [...] But a lot of individuals thought it was way off. And I thought it was method off. I believed it was 30 to 50 years or even longer away. Obviously, I no longer believe that.

In May 2023, Demis Hassabis likewise said that "The development in the last few years has actually been pretty amazing", which he sees no reason that it would decrease, expecting AGI within a years and even a couple of years. [113] In March 2024, Nvidia's CEO, Jensen Huang, specified his expectation that within 5 years, AI would can passing any test a minimum of as well as humans. [114] In June 2024, the AI researcher Leopold Aschenbrenner, a former OpenAI worker, approximated AGI by 2027 to be "noticeably plausible". [115]
Whole brain emulation

While the development of transformer designs like in ChatGPT is thought about the most appealing path to AGI, [116] [117] entire brain emulation can serve as an alternative approach. With whole brain simulation, a brain model is developed by scanning and mapping a biological brain in detail, and after that copying and simulating it on a computer system or another computational device. The simulation design need to be sufficiently loyal to the initial, so that it acts in virtually the exact same method as the original brain. [118] Whole brain emulation is a kind of brain simulation that is gone over in computational neuroscience and neuroinformatics, and for medical research study functions. It has actually been talked about in expert system research study [103] as a method to strong AI. Neuroimaging technologies that might provide the necessary in-depth understanding are enhancing rapidly, and futurist Ray Kurzweil in the book The Singularity Is Near [102] anticipates that a map of adequate quality will become readily available on a comparable timescale to the computing power needed to emulate it.

Early estimates

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

Current research

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

Criticisms of simulation-based approaches

The artificial neuron model assumed by Kurzweil and used in numerous present artificial neural network implementations is basic compared with biological neurons. A brain simulation would likely need to catch the comprehensive cellular behaviour of biological neurons, presently comprehended only in broad overview. The overhead introduced by complete modeling of the biological, chemical, and physical details of neural behaviour (specifically on a molecular scale) would need computational powers a number of orders of magnitude bigger than Kurzweil's price quote. In addition, the quotes do not account for glial cells, which are known to play a function in cognitive processes. [125]
A basic criticism of the simulated brain approach stems from embodied cognition theory which asserts that human embodiment is an important aspect of human intelligence and is needed to ground significance. [126] [127] If this theory is appropriate, any fully practical brain design will need to incorporate more than simply the nerve cells (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 suffice.

Philosophical viewpoint

"Strong AI" as specified in approach

In 1980, thinker John Searle coined the term "strong AI" as part of his Chinese space argument. [128] He proposed a distinction between 2 hypotheses about synthetic intelligence: [f]
Strong AI hypothesis: A synthetic intelligence system can have "a mind" and "consciousness". Weak AI hypothesis: An artificial intelligence system can (only) act like it thinks and has a mind and awareness.
The first one he called "strong" since it makes a stronger declaration: it assumes something special has occurred to the maker that goes beyond those capabilities that we can evaluate. The behaviour of a "weak AI" maker would be exactly similar to a "strong AI" device, however the latter would also have subjective conscious experience. This usage is likewise common in scholastic AI research and textbooks. [129]
In contrast to Searle and traditional AI, some futurists such as Ray Kurzweil use the term "strong AI" to indicate "human level artificial general intelligence". [102] This is not the very same as Searle's strong AI, unless it is presumed that awareness is required for human-level AGI. Academic thinkers such as Searle do not think that holds true, and to most artificial intelligence scientists the question is out-of-scope. [130]
Mainstream AI is most thinking about how a program acts. [131] According to Russell and Norvig, "as long as the program works, they do not care if you call it real or a simulation." [130] If the program can act as if it has a mind, then there is no requirement to know if it really has mind - undoubtedly, there would be no other way to tell. For AI research, Searle's "weak AI hypothesis" is comparable to the statement "artificial basic intelligence is possible". Thus, according to Russell and Norvig, "most AI scientists take the weak AI hypothesis for approved, and don't 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 meanings, and some elements play substantial functions in science fiction and the principles of artificial intelligence:

Sentience (or "sensational awareness"): The ability to "feel" perceptions or emotions subjectively, rather than the ability to factor about understandings. Some philosophers, such as David Chalmers, use the term "awareness" to refer solely to phenomenal awareness, which is roughly equivalent to life. [132] Determining why and how subjective experience arises is called the tough issue of awareness. [133] Thomas Nagel described in 1974 that it "feels like" something to be mindful. If we are not mindful, then it does not feel like anything. Nagel utilizes the example of a bat: we can smartly ask "what does it seem like to be a bat?" However, we are unlikely to ask "what does it feel like to be a toaster?" Nagel concludes that a bat seems conscious (i.e., has consciousness) but a toaster does not. [134] In 2022, a Google engineer declared that the company's AI chatbot, LaMDA, had accomplished sentience, though this claim was widely challenged by other experts. [135]
Self-awareness: To have mindful awareness of oneself as a different individual, specifically to be purposely knowledgeable about one's own ideas. This is opposed to just being the "subject of one's thought"-an operating system or debugger is able to be "mindful of itself" (that is, to represent itself in the very same method it represents whatever else)-however this is not what people typically suggest when they utilize the term "self-awareness". [g]
These qualities have a moral measurement. AI life would generate concerns of well-being and legal security, likewise to animals. [136] Other aspects of awareness associated to cognitive capabilities are likewise appropriate to the idea of AI rights. [137] Finding out how to incorporate innovative AI with existing legal and social structures is an emergent issue. [138]
Benefits

AGI might have a wide array of applications. If oriented towards such objectives, AGI might assist alleviate different issues worldwide such as cravings, hardship and health issue. [139]
AGI might enhance productivity and effectiveness in many tasks. For instance, in public health, AGI could accelerate medical research study, especially against cancer. [140] It might look after the elderly, [141] and equalize access to fast, premium medical diagnostics. It might offer enjoyable, cheap and tailored education. [141] The need to work to subsist might become outdated if the wealth produced is appropriately rearranged. [141] [142] This also raises the question of the location of human beings in a radically automated society.

AGI could also help to make reasonable choices, and to anticipate and prevent catastrophes. It could also assist to profit of potentially disastrous innovations such as nanotechnology or climate engineering, while avoiding the associated dangers. [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 might take steps to significantly lower the dangers [143] while reducing the impact of these steps on our lifestyle.

Risks

Existential dangers

AGI might represent several kinds of existential threat, which are risks that threaten "the premature extinction of Earth-originating smart life or the permanent and extreme damage of its potential for desirable future advancement". [145] The risk of human extinction from AGI has been the topic of many disputes, however there is likewise the possibility that the development of AGI would lead to a completely flawed future. Notably, it could be utilized to spread out and protect the set of values of whoever develops it. If mankind still has ethical blind spots similar to slavery in the past, AGI might irreversibly entrench it, preventing ethical progress. [146] Furthermore, AGI might assist in mass security and indoctrination, which might be used to produce a steady repressive worldwide totalitarian regime. [147] [148] There is also a threat for the machines themselves. If devices that are sentient or otherwise worthwhile of ethical factor to consider are mass created in the future, taking part in a civilizational path that forever ignores their well-being and interests might be an existential catastrophe. [149] [150] Considering how much AGI might enhance humankind's future and help in reducing other existential dangers, Toby Ord calls these existential risks "an argument for proceeding with due care", not for "abandoning AI". [147]
Risk of loss of control and human extinction

The thesis that AI postures an existential threat for human beings, which this danger needs 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 widespread indifference:

So, dealing with possible futures of enormous advantages and dangers, the specialists are certainly doing whatever possible to make sure the very best result, right? Wrong. If a superior alien civilisation sent us a message stating, 'We'll get here 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 potential fate of humankind has sometimes been compared to the fate of gorillas threatened by human activities. The comparison specifies that higher intelligence permitted humanity to control gorillas, which are now susceptible in manner ins which they could not have actually prepared for. As a result, the gorilla has actually become an endangered types, not out of malice, however just as a collateral damage from human activities. [154]
The skeptic Yann LeCun considers that AGIs will have no desire to dominate mankind and that we need to beware not to anthropomorphize them and translate their intents as we would for human beings. He stated that people will not be "wise adequate to develop super-intelligent makers, yet extremely silly to the point of providing it moronic objectives with no safeguards". [155] On the other side, the principle of instrumental convergence suggests that nearly whatever their goals, intelligent agents will have reasons to attempt to make it through and get more power as intermediary steps to attaining these objectives. Which this does not need having feelings. [156]
Many scholars who are worried about existential threat advocate for more research into fixing the "control issue" to answer the concern: what kinds of safeguards, algorithms, or architectures can developers carry out to maximise the possibility that their recursively-improving AI would continue to behave in a friendly, instead of destructive, way after it reaches superintelligence? [157] [158] Solving the control problem is complicated by the AI arms race (which could cause a race to the bottom of security preventative measures in order to release items before competitors), [159] and the usage of AI in weapon systems. [160]
The thesis that AI can present existential threat also has detractors. Skeptics typically say that AGI is not likely in the short-term, or that concerns about AGI distract from other issues associated with existing AI. [161] Former Google scams czar Shuman Ghosemajumder considers that for lots of people beyond the innovation industry, existing chatbots and LLMs are already perceived as though they were AGI, leading to additional misconception and fear. [162]
Skeptics sometimes charge that the thesis is crypto-religious, with an illogical belief in the possibility of superintelligence changing an irrational belief in a supreme God. [163] Some scientists believe that the communication projects on AI existential threat by particular AI groups (such as OpenAI, Anthropic, DeepMind, and Conjecture) may be an at attempt 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 market leaders and researchers, released a joint statement asserting that "Mitigating the danger of extinction from AI must be a global priority alongside other societal-scale risks such as pandemics and nuclear war." [152]
Mass unemployment

Researchers from OpenAI estimated that "80% of the U.S. workforce might have at least 10% of their work jobs affected by the intro of LLMs, while around 19% of workers might see at least 50% of their jobs affected". [166] [167] They consider workplace 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 interface with other computer tools, but likewise to manage robotized bodies.

According to Stephen Hawking, the result of automation on the quality of life will depend on how the wealth will be rearranged: [142]
Everyone can delight in a life of luxurious leisure if the machine-produced wealth is shared, or the majority of people can end up miserably bad if the machine-owners effectively lobby versus wealth redistribution. Up until now, the pattern appears to be towards the 2nd option, with innovation driving ever-increasing inequality

Elon Musk thinks about that the automation of society will require governments to adopt a universal standard income. [168]
See likewise

Artificial brain - Software and hardware with cognitive abilities comparable to those of the animal or human brain AI impact AI security - Research location on making AI safe and helpful AI positioning - AI conformance to the intended objective A.I. Rising - 2018 movie directed by Lazar Bodroža Expert system Automated artificial intelligence - Process of automating the application of machine learning BRAIN Initiative - Collaborative public-private research study effort announced by the Obama administration China Brain Project Future of Humanity Institute - Defunct Oxford interdisciplinary research centre General video game playing - Ability of artificial intelligence to play various video games Generative expert system - AI system efficient in generating material in reaction to triggers Human Brain Project - Scientific research project Intelligence amplification - Use of infotech to enhance human intelligence (IA). Machine ethics - Moral behaviours of man-made devices. Moravec's paradox. Multi-task knowing - Solving numerous device finding out tasks at the exact same time. Neural scaling law - Statistical law in device learning. Outline of artificial intelligence - Overview of and topical guide to synthetic intelligence. Transhumanism - Philosophical motion. Synthetic intelligence - Alternate term for or type of expert system. Transfer learning - Machine knowing technique. Loebner Prize - Annual AI competitors. Hardware for artificial intelligence - Hardware specifically designed and enhanced for artificial intelligence. Weak expert system - Form of synthetic intelligence.
Notes

^ a b See below for the origin of the term "strong AI", and see the scholastic definition of "strong AI" and weak AI in the short article Chinese room. ^ AI creator John McCarthy writes: "we can not yet identify in basic what type of computational procedures we desire to call intelligent. " [26] (For a conversation of some meanings of intelligence utilized by expert system researchers, see viewpoint of artificial intelligence.). ^ The Lighthill report particularly slammed AI's "grand goals" and led the taking apart of AI research study in England. [55] In the U.S., DARPA became identified to fund just "mission-oriented direct research, rather than basic undirected research study". [56] [57] ^ As AI creator John McCarthy composes "it would be an excellent relief to the rest of the employees in AI if the developers of new basic formalisms would reveal their hopes in a more safeguarded form than has actually sometimes held true." [61] ^ In "Mind Children" [122] 1015 cps is utilized. More recently, in 1997, [123] Moravec argued for 108 MIPS which would roughly correspond to 1014 cps. Moravec talks in regards to MIPS, not "cps", which is a non-standard term Kurzweil presented. ^ As specified in a standard AI textbook: "The assertion that makers might perhaps act intelligently (or, possibly 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 simulating thinking) is called the 'strong AI' hypothesis." [121] ^ Alan Turing made this point in 1950. [36] References

^ Krishna, Sri (9 February 2023). "What is artificial narrow intelligence (ANI)?". VentureBeat. Retrieved 1 March 2024. ANI is designed to carry out a single job. ^ "OpenAI Charter". OpenAI. Retrieved 6 April 2023. Our objective is to make sure that artificial basic intelligence advantages all of mankind. ^ Heath, Alex (18 January 2024). "Mark Zuckerberg's brand-new goal is developing synthetic general intelligence". The Verge. Retrieved 13 June 2024. Our vision is to develop AI that is 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 synthetic 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 quits Google and warns of threat ahead". The New York City Times. 1 May 2023. Retrieved 2 May 2023. It is hard 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 reveals 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 change. All that you alter changes you. ^ Vinge, Vernor (1992 ). A Fire Upon the Deep. Tor Books. ISBN 978-0-8125-1528-2. The Singularity is coming. ^ Morozov, Evgeny (30 June 2023). "The True Threat of Artificial Intelligence". The New York City Times. The real hazard is not AI itself however the method we release it. ^ "Impressed by expert system? Experts say AGI is following, and it has 'existential' threats". ABC News. 23 March 2023. Retrieved 6 April 2023. AGI might posture existential threats to mankind. ^ Bostrom, Nick (2014 ). Superintelligence: Paths, Dangers, Strategies. Oxford University Press. ISBN 978-0-1996-7811-2. The very first superintelligence will be the last invention that mankind needs to make. ^ Roose, Kevin (30 May 2023). "A.I. Poses 'Risk of Extinction,' Industry Leaders Warn". The New York Times. Mitigating the threat of termination from AI must be an international top priority. ^ "Statement on AI Risk". Center for AI Safety. Retrieved 1 March 2024. AI professionals alert of risk of extinction from AI. ^ Mitchell, Melanie (30 May 2023). "Are AI's Doomsday Scenarios Worth Taking Seriously?". The New York Times. We are far from creating makers that can outthink us in general ways. ^ LeCun, Yann (June 2023). "AGI does not present an existential threat". 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 explains strong AI as "machine intelligence with the complete variety of human intelligence.". ^ "The Age of Expert System: George John at TEDxLondonBusinessSchool 2013". Archived from the original on 26 February 2014. Retrieved 22 February 2014. ^ Newell & Simon 1976, This is the term they use for "human-level" intelligence in the physical symbol system hypothesis. ^ "The Open University on Strong and Weak AI". Archived from the original on 25 September 2009. Retrieved 8 October 2007. ^ "What is artificial superintelligence (ASI)?|Definition from TechTarget". Enterprise AI. Retrieved 8 October 2023. ^ "Artificial intelligence is changing our world - it is on all of us to make sure that it works out". Our World in Data. Retrieved 8 October 2023. ^ Dickson, Ben (16 November 2023). "Here is how far we are to attaining AGI, according to DeepMind". VentureBeat. ^ McCarthy, John (2007a). "Basic Questions". Stanford University. Archived from the original on 26 October 2007. Retrieved 6 December 2007. ^ This list of intelligent traits is based on the topics covered by major AI textbooks, consisting of: Russell & Norvig 2003, Luger & Stubblefield 2004, Poole, Mackworth & Goebel 1998 and Nilsson 1998. ^ Johnson 1987. ^ de Charms, R. (1968 ). Personal causation. New York City: Academic Press. ^ a b Pfeifer, R. and Bongard J. C., How the body shapes the way we believe: a new view of intelligence (The MIT Press, 2007). ISBN 0-2621-6239-3. ^ White, R. W. (1959 ). "Motivation reevaluated: The concept of skills". Psychological Review. 66 (5 ): 297-333. doi:10.1037/ h0040934. PMID 13844397. S2CID 37385966. ^ White, R. W. (1959 ). "Motivation reevaluated: 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 original on 25 April 2014. Retrieved 1 May 2014. ^ "What is Artificial General Intelligence (AGI)?|4 Tests For Ensuring Artificial General Intelligence". Talky Blog. 13 July 2019. Archived from the initial on 17 July 2019. Retrieved 17 July 2019. ^ Kirk-Giannini, Cameron Domenico; Goldstein, Simon (16 October 2023). "AI is closer than ever to passing the Turing test for 'intelligence'. What occurs when it does?". The Conversation. Retrieved 22 September 2024. ^ a b Turing 1950. ^ Turing, Alan (1952 ). B. Jack Copeland (ed.). Can Automatic Calculating Machines Be Said To Think?. Oxford: Oxford University Press. pp. 487-506. ISBN 978-0-1982-5079-1. ^ "Eugene Goostman is a genuine boy - the Turing Test says so". The Guardian. 9 June 2014. ISSN 0261-3077. Retrieved 3 March 2024. ^ "Scientists challenge whether computer system 'Eugene Goostman' passed Turing test". BBC News. 9 June 2014. Retrieved 3 March 2024. ^ Jones, Cameron R.; Bergen, Benjamin K. (9 May 2024). "People can not distinguish GPT-4 from a human in a Turing test". arXiv:2405.08007 [cs.HC] ^ Varanasi, Lakshmi (21 March 2023). "AI designs like ChatGPT and GPT-4 are acing everything from the bar examination to AP Biology. Here's a list of hard exams both AI variations have actually passed". Business Insider. Retrieved 30 May 2023. ^ Naysmith, Caleb (7 February 2023). "6 Jobs Artificial Intelligence Is Already Replacing and How Investors Can Capitalize on It". Retrieved 30 May 2023. ^ Turk, Victoria (28 January 2015). "The Plan to Replace the Turing Test with a 'Turing Olympics'". Vice. Retrieved 3 March 2024. ^ Gopani, Avi (25 May 2022). "Turing Test is unreliable. The Winograd Schema is outdated. Coffee is the response". 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 brand-new Turing test would see if AI can make $1 million". MIT Technology Review. Retrieved 3 March 2024. ^ Shapiro, Stuart C. (1992 ). "Expert System" (PDF). In Stuart C. Shapiro (ed.). Encyclopedia of Artificial Intelligence (Second ed.). New York: John Wiley. pp. 54-57. Archived (PDF) from the original on 1 February 2016. (Section 4 is on "AI-Complete Tasks".). ^ Yampolskiy, Roman V. (2012 ). Xin-She Yang (ed.). "Turing Test as a Defining Feature of AI-Completeness" (PDF). Artificial Intelligence, Evolutionary Computation and Metaheuristics (AIECM): 3-17. Archived (PDF) from the initial on 22 May 2013. ^ "AI Index: State of AI in 13 Charts". Stanford University Human-Centered Expert System. 15 April 2024. Retrieved 27 May 2024. ^ Crevier 1993, pp. 48-50. ^ Kaplan, Andreas (2022 ). "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 quoted 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 ), quoted in Crevier (1993, p. 109). ^ Lighthill 1973; Howe 1994. ^ a b NRC 1999, "Shift to Applied Research Increases Investment". ^ Crevier 1993, pp. 115-117; Russell & Norvig 2003, pp. 21-22. ^ Crevier 1993, p. 211, Russell & Norvig 2003, p. 24 and see likewise Feigenbaum & McCorduck 1983. ^ Crevier 1993, pp. 161-162, 197-203, 240; Russell & Norvig 2003, p. 25. ^ Crevier 1993, pp. 209-212. ^ McCarthy, John (2000 ). "Reply to Lighthill". Stanford University. Archived from the original on 30 September 2008. Retrieved 29 September 2007. ^ Markoff, John (14 October 2005). "Behind Artificial Intelligence, a Squadron of Bright Real People". The New York Times. Archived from the original on 2 February 2023. Retrieved 18 February 2017. At its low point, some computer system researchers and software application engineers prevented 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 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 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 created 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 summer season school: June 22 - July 3, 2009, OpenCog Lab: July 6-9, 2009". Archived from the original on 28 September 2020. Retrieved 11 May 2020. ^ "Избираеми дисциплини 2009/2010 - пролетен триместър" [Elective courses 2009/2010 - spring trimester] Факултет по математика и информатика [Faculty of Mathematics and Informatics] (in Bulgarian). Archived from the initial on 26 July 2020. Retrieved 11 May 2020. ^ "Избираеми дисциплини 2010/2011 - зимен триместър" [Elective courses 2010/2011 - winter trimester] Факултет по математика и информатика [Faculty of Mathematics and Informatics] (in Bulgarian). Archived from the original on 26 July 2020. Retrieved 11 May 2020. ^ Shevlin, Henry; Vold, Karina; Crosby, Matthew; Halina, Marta (4 October 2019). "The limits of device intelligence: Despite progress in maker intelligence, synthetic general intelligence is still a significant challenge". EMBO Reports. 20 (10 ): e49177. doi:10.15252/ embr.201949177. ISSN 1469-221X. PMC 6776890. PMID 31531926. ^ Bubeck, Sébastien; Chandrasekaran, Varun; Eldan, Ronen; Gehrke, Johannes; Horvitz, Eric; Kamar, Ece; Lee, Peter; Lee, Yin Tat; Li, Yuanzhi; Lundberg, Scott; Nori, Harsha; Palangi, Hamid; Ribeiro, Marco Tulio; Zhang, Yi (27 March 2023). "Sparks of Artificial General Intelligence: Early explores GPT-4". arXiv:2303.12712 [cs.CL] ^ "Microsoft Researchers Claim GPT-4 Is Showing "Sparks" of AGI". Futurism. 23 March 2023. Retrieved 13 December 2023. ^ Allen, Paul; Greaves, Mark (12 October 2011). "The Singularity Isn't Near". MIT Technology Review. Retrieved 17 September 2014. ^ Winfield, Alan. "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 basic artificial intelligence will not be realized". Humanities and Social Sciences Communications. 7 (1 ): 1-9. doi:10.1057/ s41599-020-0494-4. hdl:11250/ 2726984. ISSN 2662-9992. S2CID 219710554. ^ McCarthy 2007b. ^ Khatchadourian, Raffi (23 November 2015). "The Doomsday Invention: Will synthetic intelligence bring us utopia or damage?". The New Yorker. Archived from the initial on 28 January 2016. Retrieved 7 February 2016. ^ Müller, V. C., & Bostrom, N. (2016 ). Future progress in expert system: A study of expert viewpoint. In Fundamental issues of expert system (pp. 555-572). Springer, Cham. ^ Armstrong, Stuart, and Kaj Sotala. 2012. "How We're Predicting AI-or Failing To." In Beyond AI: Artificial Dreams, modified by Jan Romportl, Pavel Ircing, Eva Žáčková, Michal Polák and Radek Schuster, 52-75. Plzeň: University of West Bohemia ^ "Microsoft Now Claims GPT-4 Shows 'Sparks' of General Intelligence". 24 March 2023. ^ Shimek, Cary (6 July 2023). "AI Outperforms Humans in Creativity Test". Neuroscience News. Retrieved 20 October 2023. ^ Guzik, Erik E.; Byrge, Christian; Gilde, Christian (1 December 2023). "The originality of 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 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 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 original on 4 December 2020. Retrieved 24 August 2020. ^ a b Butz, Martin V. (1 March 2021). "Towards Strong AI". KI - Künstliche Intelligenz. 35 (1 ): 91-101. doi:10.1007/ s13218-021-00705-x. ISSN 1610-1987. S2CID 256065190. ^ Liu, Feng; Shi, Yong; Liu, Ying (2017 ). "Intelligence Quotient and Intelligence Grade of Artificial Intelligence". Annals of Data Science. 4 (2 ): 179-191. arXiv:1709.10242. doi:10.1007/ s40745-017-0109-0. S2CID 37900130. ^ Brien, Jörn (5 October 2017). "Google-KI doppelt so schlau wie Siri" [Google AI is twice as clever as Siri - however a six-year-old beats both] (in German). Archived from the original on 3 January 2019. Retrieved 2 January 2019. ^ Grossman, Gary (3 September 2020). "We're going into the AI golden zone between narrow and general AI". VentureBeat. Archived from the original on 4 September 2020. Retrieved 5 September 2020. Certainly, too, there are those who declare we are currently seeing an early example of an AGI system in the just recently revealed GPT-3 natural language processing (NLP) neural network. ... So is GPT-3 the first example of an AGI system? This is debatable, but the consensus is that it is not AGI. ... If absolutely nothing else, GPT-3 informs us there is a happy medium between narrow and general AI. ^ Quach, Katyanna. "A designer built an AI chatbot utilizing GPT-3 that assisted a male speak again to his late fiancée. OpenAI shut it down". The Register. Archived from the initial on 16 October 2021. Retrieved 16 October 2021. ^ Wiggers, Kyle (13 May 2022), "DeepMind's new AI can carry out over 600 jobs, from playing games to controlling robotics", TechCrunch, archived from the original on 16 June 2022, recovered 12 June 2022. ^ Bubeck, Sébastien; Chandrasekaran, Varun; Eldan, Ronen; Gehrke, Johannes; Horvitz, Eric; Kamar, Ece; Lee, Peter; Lee, Yin Tat; Li, Yuanzhi; Lundberg, Scott; Nori, Harsha; Palangi, Hamid; Ribeiro, Marco Tulio; Zhang, Yi (22 March 2023). "Sparks of Artificial General Intelligence: Early experiments with GPT-4". arXiv:2303.12712 [cs.CL] ^ Metz, Cade (1 May 2023). "' The Godfather of A.I.' Leaves Google and Warns of Danger Ahead". The New York City Times. ISSN 0362-4331. Retrieved 7 June 2023. ^ Bove, Tristan. "A.I. could rival human intelligence in 'simply a few years,' says CEO of Google's main A.I. research study lab". Fortune. Retrieved 4 September 2024. ^ Nellis, Stephen (2 March 2024). "Nvidia CEO says AI could pass human tests in five years". Reuters. ^ Aschenbrenner, Leopold. "SITUATIONAL AWARENESS, The Decade Ahead". ^ Sullivan, Mark (18 October 2023). "Why everyone seems 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 broad variety of views in current research study, 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 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 synthetic intelligence for curiosity by the field about why a program behaves the method it does. ^ Chalmers, David J. (9 August 2023). "Could a Big Language Model Be Conscious?". Boston Review. ^ Seth, Anil. "Consciousness". New Scientist. Retrieved 5 September 2024. ^ Nagel 1974. ^ "The Google engineer who thinks the business's AI has actually come to life". The Washington Post. 11 June 2022. Retrieved 12 June 2023. ^ Kateman, Brian (24 July 2023). "AI Should Be Terrified of Humans". TIME. Retrieved 5 September 2024. ^ Nosta, John (18 December 2023). "Should Artificial Intelligence Have Rights?". Psychology Today. Retrieved 5 September 2024. ^ Akst, Daniel (10 April 2023). "Should Robots With Expert System Have Moral or Legal Rights?". The Wall Street Journal. ^ "Artificial General Intelligence - Do [es] the cost surpass benefits?". 23 August 2021. Retrieved 7 June 2023. ^ "How we can Gain from Advancing Artificial General Intelligence (AGI) - Unite.AI". www.unite.ai. 7 April 2020. Retrieved 7 June 2023. ^ a b c Talty, Jules; Julien, Stephan. "What Will Our Society Appear Like When Artificial Intelligence Is Everywhere?". Smithsonian Magazine. Retrieved 7 June 2023. ^ a b Stevenson, Matt (8 October 2015). "Answers to Stephen Hawking's AMA are Here!". Wired. ISSN 1059-1028. Retrieved 8 June 2023. ^ a b Bostrom, Nick (2017 ). " § Preferred order of arrival". Superintelligence: courses, dangers, techniques (Reprinted with corrections 2017 ed.). Oxford, United Kingdom; New York, New York City, USA: Oxford University Press. ISBN 978-0-1996-7811-2. ^ Piper, Kelsey (19 November 2018). "How technological progress is making it likelier than ever that humans will destroy ourselves". Vox. Retrieved 8 June 2023. ^ Doherty, Ben (17 May 2018). "Climate change an 'existential security risk' 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: 'Transcendence takes a look at the ramifications of synthetic 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 remarkable Facebook argument between Yann LeCun, Stuart Russel and Yoshua Bengio about the dangers of strong AI". The remarkable Facebook argument in between Yann LeCun, Stuart Russel and Yoshua Bengio about the dangers of strong AI (in French). Retrieved 8 June 2023. ^ "Will Expert System Doom The 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 catastrophic AGI danger: a survey". Physica Scripta. 90 (1 ): 018001. doi:10.1088/ 0031-8949/90/ 1/018001. ISSN 0031-8949. ^ Bostrom, Nick (2014 ). Superintelligence: Paths, Dangers, Strategies (First ed.). Oxford University Press. ISBN 978-0-1996-7811-2. ^ Chow, Andrew R.; Perrigo, Billy (16 February 2023). "The AI Arms Race Is On. Start Worrying". TIME. Retrieved 24 December 2023. ^ Tetlow, Gemma (12 January 2017). "AI arms race dangers 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 posed however synthetic intelligence can not be disregarded". The Guardian. ISSN 0261-3077. Retrieved 24 December 2023. ^ "Humanity, Security & AI, Oh My! (with Ian Bremmer & Shuman Ghosemajumder)". CAFE. 20 July 2023. Retrieved 15 September 2023. ^ Hamblin, James (9 May 2014). "But What Would completion of Humanity Mean for Me?". The Atlantic. Archived from the original on 4 June 2014. Retrieved 12 December 2015. ^ Titcomb, James (30 October 2023). "Big Tech is stiring fears over AI, alert researchers". The Telegraph. Retrieved 7 December 2023. ^ Davidson, John (30 October 2023). "Google Brain creator states huge tech is lying about AI extinction danger". 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 big language designs". OpenAI. Retrieved 7 June 2023. ^ a b Hurst, Luke (23 March 2023). "OpenAI states 80% of workers could see their jobs impacted by AI. These are the jobs most impacted". euronews. Retrieved 8 June 2023. ^ Sheffey, Ayelet (20 August 2021). "Elon Musk says we require universal fundamental earnings due to the fact that 'in the future, physical work will be a choice'". Business Insider. Archived from the initial on 9 July 2023. Retrieved 8 June 2023. Sources

UNESCO Science Report: the Race Against Time for Smarter Development. Paris: UNESCO. 11 June 2021. ISBN 978-9-2310-0450-6. Archived from the initial on 18 June 2022. Retrieved 22 September 2021. Chalmers, David (1996 ), The Conscious Mind, Oxford University Press. Clocksin, William (August 2003), "Expert system and the future", Philosophical Transactions of the Royal Society A, vol. 361, no. 1809, pp. 1721-1748, Bibcode:2003 RSPTA.361.1721 C, doi:10.1098/ rsta.2003.1232, PMID 12952683, S2CID 31032007. Crevier, Daniel (1993 ). AI: The Tumultuous Search for Expert System. 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: 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 reaction to Ray Kurzweil's The Singularity Is Near, and McDermott's review of Kurzweil", Expert system, vol. 171, no. 18, Special Review Issue, pp. 1161-1173, doi:10.1016/ j.artint.2007.10.011, archived from the initial on 7 January 2016, recovered 1 April 2009. Gubrud, Mark (November 1997), "Nanotechnology and International Security", Fifth Foresight Conference on Molecular Nanotechnology, archived from the initial on 29 May 2011, recovered 7 May 2011. Howe, J. (November 1994), Expert System at Edinburgh University: a Perspective, 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", Expert System: a paper seminar, Science Research Council. Luger, George; Stubblefield, William (2004 ), Expert System: 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 Expert system?. Stanford University. The supreme effort is to make computer programs that can resolve issues and achieve goals worldwide as well as people. 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, obtained 23 June 2006 Nagel (1974 ), "What Is it Like to Be a Bat" (PDF), Philosophical Review, 83 (4 ): 435-50, doi:10.2307/ 2183914, JSTOR 2183914, archived (PDF) from the initial on 16 October 2011, retrieved 7 November 2009 Newell, Allen; Simon, H. A. (1976 ). "Computer Science as Empirical Inquiry: Symbols and Search". Communications of the ACM. 19 (3 ): 113-126. doi:10.1145/ 360018.360022. Nilsson, Nils (1998 ), Expert System: A New Synthesis, Morgan Kaufmann Publishers, ISBN 978-1-5586-0467-4 NRC (1999 ), "Developments in Expert System", Funding a Transformation: Government Support for Computing Research, National Academy Press, archived from the original on 12 January 2008, obtained 29 September 2007 Poole, David; Mackworth, Alan; Goebel, Randy (1998 ), Computational Intelligence: A Logical Approach, New York City: Oxford University Press, archived from the original on 25 July 2009, recovered 6 December 2007 Russell, Stuart J.; Norvig, Peter (2003 ), 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, recovered 3 September 2020 Simon, H. A. (1965 ), The Shape of Automation for Men and Management, New York City: Harper & Row Turing, Alan (October 1950). "Computing Machinery and Intelligence". Mind. 59 (236 ): 433-460. doi:10.1093/ mind/LIX.236.433. ISSN 1460-2113. JSTOR 2251299. S2CID 14636783.
de Vega, Manuel; Glenberg, Arthur; Graesser, Arthur, eds. (2008 ), Symbols and Embodiment: Debates on significance and cognition, Oxford University Press, ISBN 978-0-1992-1727-4 Wang, Pei; Goertzel, Ben (2007 ). "Introduction: Aspects of Artificial General Intelligence". Advances in Artificial General Intelligence: Concepts, Architectures and Algorithms: Proceedings of the AGI Workshop 2006. IOS Press. pp. 1-16. ISBN 978-1-5860-3758-1. Archived from the original on 18 February 2021. Retrieved 13 December 2020 - 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 initial on 18 February 2021, obtained 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, retrieved 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 sufficient to be understandable will not be complicated enough to behave intelligently, while any system made complex enough to behave intelligently will be too made complex to comprehend." (p. 197.) Computer researcher Alex Pentland writes: "Current AI machine-learning algorithms are, at their core, dead easy foolish. They work, however they work by strength." (p. 198.). Gelernter, David, Dream-logic, the Internet and Artificial Thought, Edge, archived from the initial on 26 July 2010, retrieved 25 July 2010. Gleick, James, "The Fate of Free Choice" (review of Kevin J. Mitchell, Free Agents: How Evolution Gave Us Free Choice, Princeton University Press, 2023, 333 pp.), The New York City Review of Books, vol. LXXI, no. 1 (18 January 2024), pp. 27-28, 30. "Agency is what identifies us from devices. For biological creatures, factor and function come from acting on the planet and garagesale.es experiencing the effects. Artificial intelligences - disembodied, complete strangers to blood, sweat, and tears - have no event for that." (p. 30.). Halal, William E. "TechCast Article Series: The Automation of Thought" (PDF). Archived from the initial (PDF) on 6 June 2013. - Halpern, Sue, "The Coming Tech Autocracy" (review of Verity Harding, AI Needs You: How We Can Change AI's Future and Save Our Own, Princeton University Press, 274 pp.; Gary Marcus, Taming Silicon Valley: How We Can Ensure That AI Works for Us, MIT Press, 235 pp.; Daniela Rus and Gregory Mone, The Mind's Mirror: Risk and Reward in the Age of AI, Norton, 280 pp.; Madhumita Murgia, Code Dependent: Living in the Shadow of AI, Henry Holt, 311 pp.), The New York Review of Books, vol. LXXI, no. 17 (7 November 2024), pp. 44-46. "' We can't realistically expect that those who intend 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 depend on governments driven by campaign financing contributions [from tech business] to press back.' ... Marcus information the needs that citizens need to make from their governments and the tech companies. They include transparency on how AI systems work; compensation for people if their information [are] utilized to train LLMs (big language design) s and the right to grant this usage; and the capability to hold tech companies accountable for the harms they bring on by eliminating Section 230, imposing cash penalites, and passing stricter item liability laws ... Marcus also suggests ... that a new, AI-specific federal company, similar to the FDA, the FCC, or the FTC, may provide the most robust oversight ... [T] he Fordham law professor Chinmayi Sharma ... recommends ... develop [ing] a professional licensing regime for engineers that would function in a similar method to medical licenses, malpractice fits, and the Hippocratic oath in medicine. 'What if, like physicians,' she asks ..., 'AI engineers also promised 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 actually baffled humans for decades, exposes the restrictions of natural-language-processing algorithms", Scientific American, vol. 329, no. 4 (November 2023), pp. 81-82. "This murder mystery competition has actually exposed that although NLP (natural-language processing) designs are capable of unbelievable feats, their abilities are very much limited by the quantity of context they receive. This [...] could cause [troubles] for scientists who hope to utilize them to do things such as evaluate ancient languages. Sometimes, there are few historic records on long-gone civilizations to serve as training information for such a purpose." (p. 82.). Immerwahr, Daniel, "Your Lying Eyes: People now use A.I. to create fake videos equivalent from genuine ones. How much does it matter?", The New Yorker, 20 November 2023, pp. 54-59. "If by 'deepfakes' we indicate practical videos produced utilizing synthetic intelligence that actually deceive people, then they hardly exist. The phonies aren't deep, and the deeps aren't fake. [...] A.I.-generated videos are not, in general, operating in our media as counterfeited evidence. Their function better looks like that of animations, particularly smutty ones." (p. 59.). - Leffer, Lauren, "The Risks of Trusting AI: We need to avoid 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 maker a discussion?", The New Yorker, 7 October 2024, pp. 12-16. Marcus, Gary, "Artificial Confidence: Even the most recent, buzziest systems of artificial basic intelligence are stymmied by the usual problems", Scientific American, vol. 327, no. 4 (October 2022), pp. 42-45. McCarthy, John (October 2007), "From here to human-level AI", Artificial Intelligence, 171 (18 ): 1174-1182, doi:10.1016/ j.artint.2007.10.009. McCorduck, Pamela (2004 ), Machines Who Think (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 initial on 3 March 2016, recovered 29 September 2007. Newell, Allen; Simon, H. A. (1963 ), "GPS: A Program that Simulates Human Thought", in Feigenbaum, E. A.; Feldman, J. (eds.), Computers and Thought, New York City: McGraw-Hill. Omohundro, Steve (2008 ), The Nature of Self-Improving Artificial Intelligence, provided and distributed at the 2007 Singularity Summit, San Francisco, California. Press, Eyal, "In Front of Their Faces: Does facial-recognition technology lead police to disregard contradictory proof?", The New Yorker, 20 November 2023, pp. 20-26. Roivainen, Eka, "AI's IQ: ChatGPT aced a [basic intelligence] test however showed that intelligence can not be determined by IQ alone", Scientific American, vol. 329, no. 1 (July/August 2023), p. 7. "Despite its high IQ, ChatGPT stops working at jobs that need real humanlike reasoning or an understanding of the physical and social world ... ChatGPT seemed unable to factor logically and tried to depend on its huge database of ... facts stemmed from online texts. " - Scharre, Paul, "Killer Apps: The Real Dangers of an AI Arms Race", Foreign Affairs, vol. 98, no. 3 (May/June 2019), pp. 135-44. "Today's AI innovations are effective but undependable. Rules-based systems can not deal with scenarios their programmers did not expect. Learning systems are limited by the information on which they were trained. AI failures have actually currently caused tragedy. Advanced autopilot features in cars and trucks, although they carry out well in some circumstances, have driven cars without warning into trucks, concrete barriers, and parked vehicles. In the wrong circumstance, AI systems go from supersmart to superdumb in an instant. When an enemy is attempting to control and hack an AI system, the threats are even higher." (p. 140.). Sutherland, J. G. (1990 ), "Holographic Model of Memory, Learning, and Expression", International Journal of Neural Systems, vol. 1-3, pp. 256-267. - Vincent, James, "Horny Robot Baby Voice: James Vincent on AI chatbots", London Review of Books, vol. 46, no. 19 (10 October 2024), pp. 29-32." [AI chatbot] programs are enabled by brand-new innovations however depend on the timelelss human propensity to anthropomorphise." (p. 29.). Williams, R. W.; Herrup, K.