Artificial general intelligence (AGI) is a kind of artificial intelligence (AI) that matches or goes beyond human cognitive capabilities across a large range of cognitive jobs. This contrasts with narrow AI, which is limited to specific tasks. [1] Artificial superintelligence (ASI), on the other hand, refers to AGI that significantly exceeds human cognitive abilities. AGI is thought about among the definitions 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 identified 72 active AGI research study and advancement tasks across 37 countries. [4]
The timeline for accomplishing AGI stays a subject of continuous argument amongst researchers and experts. Since 2023, some argue that it may be possible in years or years; others maintain it may take a century or longer; a minority think it may never ever be achieved; and another minority claims that it is already here. [5] [6] Notable AI researcher Geoffrey Hinton has revealed concerns about the rapid progress towards AGI, suggesting it could be achieved faster than many anticipate. [7]
There is argument on the specific definition of AGI and regarding whether modern-day large language designs (LLMs) such as GPT-4 are early forms of AGI. [8] AGI is a typical subject in sci-fi and futures studies. [9] [10]
Contention exists over whether AGI represents an existential threat. [11] [12] [13] Many experts on AI have actually specified that alleviating the danger of human termination postured by AGI must be a global priority. [14] [15] Others find the advancement of AGI to be too remote to provide such a risk. [16] [17]
Terminology

AGI is also called strong AI, [18] [19] full AI, [20] human-level AI, [5] human-level smart AI, or general smart action. [21]
Some academic sources book the term "strong AI" for computer programs that experience life or trademarketclassifieds.com awareness. [a] On the other hand, weak AI (or narrow AI) has the ability to resolve one particular issue but does not have basic cognitive capabilities. [22] [19] Some academic sources use "weak AI" to refer more broadly to any programs that neither experience consciousness nor have a mind in the exact same sense as people. [a]
Related principles include synthetic superintelligence and transformative AI. A synthetic superintelligence (ASI) is a hypothetical kind of AGI that is a lot more usually smart than people, [23] while the concept of transformative AI connects to AI having a big influence on society, for example, comparable to the agricultural or industrial transformation. [24]
A framework for categorizing AGI in levels was proposed in 2023 by Google DeepMind researchers. They define 5 levels of AGI: emerging, qualified, professional, virtuoso, and superhuman. For example, a competent AGI is specified as an AI that outshines 50% of competent adults in a vast array of non-physical tasks, and a superhuman AGI (i.e. a synthetic superintelligence) is likewise specified however with a threshold of 100%. They think about big language models like ChatGPT or LLaMA 2 to be circumstances of emerging AGI. [25]
Characteristics

Various popular meanings of intelligence have actually been proposed. Among the leading proposals is the Turing test. However, there are other well-known definitions, and some scientists disagree with the more popular techniques. [b]
Intelligence qualities

Researchers generally hold that intelligence is needed to do all of the following: [27]
reason, usage strategy, solve puzzles, and make judgments under uncertainty represent understanding, including good sense knowledge plan discover

• interact in natural language
• if required, integrate these abilities in conclusion of any offered goal

Many interdisciplinary methods (e.g. cognitive science, computational intelligence, and decision making) think about extra traits such as imagination (the ability to form unique psychological images and ideas) [28] and autonomy. [29]
Computer-based systems that show much of these capabilities exist (e.g. see computational creativity, automated thinking, decision support group, robot, evolutionary computation, smart representative). There is debate about whether modern AI systems possess them to an appropriate degree.

Physical characteristics

Other capabilities are thought about preferable in smart systems, as they may impact intelligence or aid in its expression. These consist of: [30]
- the capability to sense (e.g. see, hear, etc), and - the capability to act (e.g. move and manipulate items, change location to explore, etc).
This includes the ability to spot and respond to risk. [31]
Although the ability to sense (e.g. see, hear, and so on) and the capability to act (e.g. relocation and control objects, change area to explore, and so on) can be desirable for some smart systems, [30] these physical abilities are not strictly needed for an entity to certify as AGI-particularly under the thesis that big language designs (LLMs) may already be or become AGI. Even from a less optimistic perspective on LLMs, there is no firm requirement for an AGI to have a human-like form; 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 aligns with the understanding that AGI has never been proscribed a particular physical personification and thus does not demand a capacity for mobility or conventional "eyes and ears". [32]
Tests for human-level AGI

Several tests suggested to validate human-level AGI have actually been considered, consisting of: [33] [34]
The idea of the test is that the machine needs to attempt and pretend to be a man, by answering questions put to it, and it will only pass if the pretence is fairly persuading. A considerable part of a jury, who should not be professional about machines, should be taken in by the pretence. [37]
AI-complete problems

An issue is informally called "AI-complete" or "AI-hard" if it is thought that in order to resolve it, one would require to execute AGI, since the solution is beyond the capabilities of a purpose-specific algorithm. [47]
There are numerous issues that have been conjectured to require basic intelligence to resolve in addition to humans. Examples include computer system vision, natural language understanding, and handling unanticipated scenarios while resolving any real-world issue. [48] Even a particular job like translation requires a machine to check out and compose in both languages, follow the author's argument (reason), comprehend the context (understanding), and faithfully reproduce the author's original intent (social intelligence). All of these issues need to be solved concurrently in order to reach human-level machine performance.

However, much of these tasks can now be carried out by contemporary big language designs. According to Stanford University's 2024 AI index, AI has actually reached human-level performance on many benchmarks for checking out understanding and visual reasoning. [49]
History

Classical AI

Modern AI research started in the mid-1950s. [50] The first generation of AI scientists were persuaded that synthetic basic intelligence was possible and that it would exist in simply a couple of years. [51] AI pioneer Herbert A. Simon composed in 1965: "machines 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 believed they could produce 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 agreement predictions of the time. He said in 1967, "Within a generation ... the problem of developing 'expert system' will significantly be fixed". [54]
Several classical AI tasks, such as Doug Lenat's Cyc job (that started in 1984), and Allen Newell's Soar task, were directed at AGI.

However, in the early 1970s, it ended up being apparent that scientists had actually grossly underestimated the problem of the project. Funding agencies became doubtful of AGI and put researchers under increasing pressure to produce beneficial "used AI". [c] In the early 1980s, Japan's Fifth Generation Computer Project restored interest in AGI, setting out a ten-year timeline that included AGI objectives like "continue a table talk". [58] In action to this and the success of professional systems, both industry and federal government pumped cash into the field. [56] [59] However, self-confidence in AI amazingly collapsed in the late 1980s, and the goals of the Fifth Generation Computer Project were never ever satisfied. [60] For the second time in twenty years, AI researchers who forecasted the impending accomplishment of AGI had been mistaken. By the 1990s, AI scientists had a reputation for making vain guarantees. They ended up being hesitant to make forecasts at all [d] and avoided mention of "human level" artificial intelligence for worry of being identified "wild-eyed dreamer [s]. [62]
Narrow AI research

In the 1990s and early 21st century, mainstream AI achieved business success and academic respectability by concentrating on specific sub-problems where AI can produce proven results and industrial applications, such as speech recognition and suggestion algorithms. [63] These "applied AI" systems are now utilized extensively throughout the technology market, and research study in this vein is heavily funded in both academic community and market. As of 2018 [update], development in this field was thought about an emerging trend, and a fully grown phase was expected to be reached in more than ten years. [64]
At the millenium, numerous mainstream AI scientists [65] hoped that strong AI could be established by integrating programs that solve numerous sub-problems. Hans Moravec wrote in 1988:

I am positive that this bottom-up route to artificial intelligence will one day meet the conventional top-down path over half way, all set to provide the real-world competence and the commonsense understanding that has actually been so frustratingly elusive in thinking programs. Fully intelligent machines will result when the metaphorical golden spike is driven joining the two efforts. [65]
However, even at the time, this was challenged. For instance, Stevan Harnad of Princeton University concluded his 1990 paper on the sign grounding hypothesis by specifying:

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 stand, then this expectation is hopelessly modular and there is really only one viable route from sense to symbols: from the ground up. A free-floating symbolic level like the software level of a computer will never be reached by this path (or vice versa) - nor is it clear why we should even try to reach such a level, considering that it looks as if getting there would simply total up to uprooting our symbols from their intrinsic meanings (therefore merely reducing ourselves to the functional equivalent of a programmable computer). [66]
Modern synthetic basic intelligence research study

The term "synthetic general intelligence" was utilized as early as 1997, by Mark Gubrud [67] in a conversation of the implications 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, defined by the ability to increase a mathematical definition of intelligence rather than exhibit human-like behaviour, [69] was likewise called universal artificial intelligence. [70]
The term AGI was re-introduced and popularized by Shane Legg and Ben Goertzel around 2002. [71] AGI research activity in 2006 was described by Pei Wang and Ben Goertzel [72] as "producing publications and initial outcomes". The first summer school in AGI was arranged in Xiamen, China in 2009 [73] by the Xiamen university's Artificial Brain Laboratory and OpenCog. The very first university course was offered in 2010 [74] and 2011 [75] at Plovdiv University, Bulgaria by Todor Arnaudov. MIT presented a course on AGI in 2018, arranged by Lex Fridman and including a number of guest speakers.

Since 2023 [upgrade], a small number of computer system scientists are active in AGI research, and many add to a series of AGI conferences. However, progressively more researchers are interested in open-ended knowing, [76] [77] which is the idea of enabling AI to constantly discover and innovate like human beings do.

Feasibility

Since 2023, the advancement and possible accomplishment of AGI remains a subject of intense argument within the AI neighborhood. While conventional consensus held that AGI was a distant objective, recent improvements have actually led some researchers and industry figures to claim that early forms of AGI may currently exist. [78] AI pioneer Herbert A. Simon speculated in 1965 that "makers will be capable, within twenty years, of doing any work a guy can do". This forecast failed to come real. Microsoft co-founder Paul Allen believed that such intelligence is not likely in the 21st century because it would need "unforeseeable and essentially unforeseeable developments" and a "clinically deep understanding of cognition". [79] Writing in The Guardian, roboticist Alan Winfield declared the gulf in between modern-day computing and human-level synthetic intelligence is as large as the gulf in between present space flight and useful faster-than-light spaceflight. [80]
A further obstacle is the absence of clearness in defining what intelligence requires. Does it need awareness? Must it show the capability to set goals as well as pursue them? Is it purely a matter of scale such that if design sizes increase adequately, intelligence will emerge? Are centers such as planning, thinking, and causal understanding needed? Does intelligence need clearly duplicating the brain and its particular faculties? Does it require emotions? [81]
Most AI researchers believe strong AI can be achieved in the future, however some thinkers, like Hubert Dreyfus and Roger Penrose, reject the possibility of accomplishing strong AI. [82] [83] John McCarthy is among those who believe human-level AI will be achieved, but that the present level of progress is such that a date can not accurately be predicted. [84] AI specialists' views on the expediency of AGI wax and wane. Four polls carried out in 2012 and 2013 recommended that the median price quote among professionals for when they would be 50% positive AGI would show up was 2040 to 2050, depending on the poll, with the mean being 2081. Of the professionals, 16.5% addressed with "never ever" when asked the exact same question however with a 90% self-confidence instead. [85] [86] Further existing AGI progress factors to consider can be discovered 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 amount of time there is a strong bias towards predicting the arrival of human-level AI as between 15 and 25 years from the time the forecast was made". They examined 95 predictions made between 1950 and 2012 on when human-level AI will happen. [87]
In 2023, Microsoft researchers released an in-depth assessment of GPT-4. They concluded: "Given the breadth and depth of GPT-4's abilities, we believe that it could fairly be considered as an early (yet still incomplete) variation of an artificial basic intelligence (AGI) system." [88] Another research 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 significant level of basic intelligence has actually currently been attained with frontier models. They wrote that reluctance to this view originates from four primary factors: a "healthy suspicion about metrics for AGI", an "ideological commitment to alternative AI theories or strategies", a "devotion to human (or biological) exceptionalism", or a "concern about the economic implications of AGI". [91]
2023 likewise marked the development of large multimodal models (big language models capable of processing or generating multiple modalities such as text, audio, and images). [92]
In 2024, OpenAI released o1-preview, the very first of a series of models that "invest more time thinking before they respond". According to Mira Murati, this ability to think before responding represents a brand-new, extra paradigm. It improves model outputs by investing more computing power when generating the response, whereas the model scaling paradigm enhances outputs by increasing the design size, training information and training compute power. [93] [94]
An OpenAI worker, Vahid Kazemi, claimed in 2024 that the business had accomplished AGI, specifying, "In my opinion, we have actually currently achieved AGI and it's even more clear with O1." Kazemi clarified that while the AI is not yet "much better than any human at any job", it is "better than most humans at many tasks." He likewise addressed criticisms that large language models (LLMs) merely follow predefined patterns, comparing their knowing process to the clinical method of observing, hypothesizing, and verifying. These declarations have actually stimulated debate, as they depend on a broad and unconventional meaning of AGI-traditionally understood as AI that matches human intelligence throughout all domains. Critics argue that, while OpenAI's models demonstrate exceptional flexibility, they may not completely meet this standard. Notably, Kazemi's comments came soon after OpenAI removed "AGI" from the regards to its partnership with Microsoft, triggering speculation about the business's tactical intentions. [95]
Timescales

Progress in expert system has actually traditionally gone through periods of quick development separated by periods when progress appeared to stop. [82] Ending each hiatus were basic advances in hardware, software application or both to create space for additional progress. [82] [98] [99] For instance, the hardware offered in the twentieth century was not enough to implement deep learning, which requires great deals of GPU-enabled CPUs. [100]
In the introduction to his 2006 book, [101] Goertzel says that estimates of the time needed before a truly flexible AGI is constructed vary from 10 years to over a century. Since 2007 [upgrade], the agreement in the AGI research community appeared to be that the timeline discussed by Ray Kurzweil in 2005 in The Singularity is Near [102] (i.e. in between 2015 and 2045) was plausible. [103] Mainstream AI scientists have actually offered a large range of viewpoints on whether development will be this fast. A 2012 meta-analysis of 95 such viewpoints found a bias towards predicting that the beginning of AGI would happen within 16-26 years for modern-day and historic predictions alike. That paper has been criticized for how it classified opinions as expert or non-expert. [104]
In 2012, Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton developed a neural network called AlexNet, which won the ImageNet competitors with a top-5 test error rate of 15.3%, substantially better than the second-best entry's rate of 26.3% (the standard approach used a weighted sum of scores from different pre-defined classifiers). [105] AlexNet was considered the preliminary ground-breaker of the existing deep knowing wave. [105]
In 2017, researchers Feng Liu, Yong Shi, and Ying Liu carried out intelligence tests on publicly offered and freely accessible 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 around to a six-year-old kid in very first grade. An adult concerns about 100 on average. Similar tests were performed in 2014, with the IQ score reaching an optimum worth of 27. [106] [107]
In 2020, OpenAI developed GPT-3, a language design capable of performing many varied jobs without particular training. According to Gary Grossman in a VentureBeat post, while there is consensus that GPT-3 is not an example of AGI, it is thought about by some to be too advanced to be classified as a narrow AI system. [108]
In the same year, Jason Rohrer utilized his GPT-3 account to develop a chatbot, and provided a chatbot-developing platform called "Project December". OpenAI requested modifications 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 capable of performing more than 600 different tasks. [110]
In 2023, Microsoft Research released a research study on an early variation of OpenAI's GPT-4, contending that it displayed more basic intelligence than previous AI designs and showed human-level efficiency in jobs covering multiple domains, such as mathematics, coding, and law. This research study stimulated a debate on whether GPT-4 could be thought about an early, incomplete version of artificial general intelligence, highlighting the requirement for further exploration and assessment of such systems. [111]
In 2023, the AI scientist Geoffrey Hinton mentioned that: [112]
The idea that this things could really get smarter than individuals - a couple of individuals believed that, [...] But many people believed it was method off. And I believed it was method off. I believed it was 30 to 50 years or perhaps longer away. Obviously, I no longer believe that.

In May 2023, Demis Hassabis similarly stated that "The development in the last couple of years has actually been pretty amazing", which he sees no factor why it would slow down, expecting AGI within a years or perhaps a couple of years. [113] In March 2024, Nvidia's CEO, Jensen Huang, mentioned his expectation that within 5 years, AI would can passing any test at least as well as people. [114] In June 2024, the AI scientist Leopold Aschenbrenner, a former OpenAI staff member, approximated AGI by 2027 to be "noticeably plausible". [115]
Whole brain emulation

While the development of transformer designs like in ChatGPT is considered the most appealing path to AGI, [116] [117] entire brain emulation can work as an alternative method. With whole brain simulation, a brain model is built by scanning and mapping a biological brain in detail, and then copying and replicating it on a computer system or another computational gadget. The simulation design need to be sufficiently devoted to the original, so that it behaves in practically the same way as the original brain. [118] Whole brain emulation is a kind of brain simulation that is discussed in computational neuroscience and neuroinformatics, and for medical research study functions. It has been gone over in expert system research [103] as a method to strong AI. Neuroimaging innovations that could provide the essential comprehensive understanding are improving quickly, and futurist Ray Kurzweil in the book The Singularity Is Near [102] anticipates that a map of enough quality will end up being offered on a similar timescale to the computing power required to imitate it.

Early approximates

For low-level brain simulation, a very effective cluster of computer systems or GPUs would be needed, provided the massive amount of synapses within the human brain. Each of the 1011 (one hundred billion) neurons has on average 7,000 synaptic connections (synapses) to other nerve cells. The brain of a three-year-old child has about 1015 synapses (1 quadrillion). This number decreases with age, stabilizing by their adult years. Estimates differ for an adult, varying from 1014 to 5 × 1014 synapses (100 to 500 trillion). [120] A price quote of the brain's processing power, based on an easy switch design for nerve cell activity, is around 1014 (100 trillion) synaptic updates per second (SUPS). [121]
In 1997, Kurzweil took a look at different price quotes for the hardware needed to equal the human brain and adopted a figure of 1016 computations per 2nd (cps). [e] (For comparison, if a "computation" was equivalent to one "floating-point operation" - a step used to rate present 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 required hardware would be offered sometime in between 2015 and 2025, if the exponential development in computer system power at the time of composing continued.

Current research

The Human Brain Project, an EU-funded initiative active from 2013 to 2023, has actually developed a particularly in-depth and openly available atlas of the human brain. [124] In 2023, scientists from Duke University carried out a high-resolution scan of a mouse brain.

Criticisms of simulation-based techniques

The artificial nerve cell design presumed by Kurzweil and utilized in many current synthetic neural network applications is easy compared to biological nerve cells. A brain simulation would likely have to catch the detailed cellular behaviour of biological nerve cells, currently understood just in broad outline. The overhead presented by full modeling of the biological, chemical, and physical information of neural behaviour (specifically on a molecular scale) would require computational powers numerous orders of magnitude larger than Kurzweil's quote. In addition, the price quotes do not account for glial cells, which are understood to play a role in cognitive procedures. [125]
An essential criticism of the simulated brain approach stems from embodied cognition theory which asserts that human personification is an essential aspect of human intelligence and is required to ground significance. [126] [127] If this theory is appropriate, any completely practical brain design will need to include more than simply the neurons (e.g., a robotic body). Goertzel [103] proposes virtual personification (like in metaverses like Second Life) as an option, but it is unknown whether this would suffice.

Philosophical perspective

"Strong AI" as specified in approach

In 1980, theorist John Searle created the term "strong AI" as part of his Chinese space argument. [128] He proposed a difference in between 2 hypotheses about expert system: [f]
Strong AI hypothesis: An artificial intelligence system can have "a mind" and "awareness". Weak AI hypothesis: An expert system system can (only) imitate it believes and has a mind and consciousness.
The very first one he called "strong" since it makes a stronger statement: it assumes something unique has actually happened to the device that surpasses those capabilities that we can check. The behaviour of a "weak AI" maker would be exactly identical to a "strong AI" maker, but the latter would also have subjective mindful experience. This usage is also typical in academic AI research study and books. [129]
In contrast to Searle and traditional AI, some futurists such as Ray Kurzweil use the term "strong AI" to indicate "human level synthetic basic intelligence". [102] This is not the same as Searle's strong AI, unless it is presumed that awareness is required for human-level AGI. Academic theorists such as Searle do not believe that holds true, and to most expert system researchers the question is out-of-scope. [130]
Mainstream AI is most thinking about how a program behaves. [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 behave as if it has a mind, then there is no need to know if it really has mind - indeed, there would be no way to tell. For AI research study, Searle's "weak AI hypothesis" is comparable to the statement "artificial general intelligence is possible". Thus, according to Russell and Norvig, "most AI researchers take the weak AI hypothesis for approved, and don't care about the strong AI hypothesis." [130] Thus, for academic AI research, "Strong AI" and "AGI" are 2 different things.

Consciousness

Consciousness can have different meanings, and some elements play considerable roles in sci-fi and the ethics of synthetic intelligence:

Sentience (or "remarkable awareness"): The capability to "feel" understandings or emotions subjectively, as opposed to the capability to reason about understandings. Some theorists, such as David Chalmers, use the term "consciousness" to refer specifically to phenomenal awareness, which is approximately comparable to sentience. [132] Determining why and how subjective experience occurs is called the difficult issue of consciousness. [133] Thomas Nagel explained 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 sensibly ask "what does it seem 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 appears to be mindful (i.e., has consciousness) however a toaster does not. [134] In 2022, a Google engineer claimed that the business's AI chatbot, LaMDA, had accomplished sentience, though this claim was extensively challenged by other professionals. [135]
Self-awareness: To have conscious awareness of oneself as a different individual, especially to be consciously knowledgeable about one's own thoughts. This is opposed to merely being the "subject of one's thought"-an os or debugger is able to be "knowledgeable about itself" (that is, to represent itself in the very same way it represents whatever else)-but this is not what people normally mean when they utilize the term "self-awareness". [g]
These qualities have an ethical dimension. AI sentience would trigger issues of well-being and legal security, similarly to animals. [136] Other aspects of awareness related to cognitive abilities are likewise pertinent to the principle of AI rights. [137] Finding out how to incorporate innovative AI with existing legal and social structures is an emerging problem. [138]
Benefits

AGI could have a wide array of applications. If oriented towards such objectives, AGI might help alleviate various issues in the world such as cravings, hardship and health problems. [139]
AGI might enhance performance and effectiveness in the majority of tasks. For example, in public health, AGI could speed up medical research, significantly versus cancer. [140] It might take care of the senior, [141] and democratize access to rapid, premium medical diagnostics. It might provide enjoyable, low-cost and customized education. [141] The requirement to work to subsist could become outdated if the wealth produced is properly rearranged. [141] [142] This likewise raises the question of the place of humans in a radically automated society.

AGI could also assist to make reasonable decisions, and to expect and prevent disasters. It might likewise assist to profit of potentially disastrous innovations such as nanotechnology or environment engineering, while avoiding the associated risks. [143] If an AGI's main objective is to prevent existential catastrophes such as human termination (which could be hard if the Vulnerable World Hypothesis ends up being real), [144] it might take measures to considerably minimize the dangers [143] while decreasing the effect of these procedures on our quality of life.

Risks

Existential dangers

AGI may represent multiple types of existential danger, which are dangers that threaten "the early extinction of Earth-originating intelligent life or the permanent and extreme destruction of its potential for desirable future advancement". [145] The danger of human termination from AGI has been the topic of lots of disputes, but there is also the possibility that the advancement of AGI would result in a completely problematic future. Notably, it might be used to spread out and protect the set of values of whoever establishes it. If humanity still has moral blind areas similar to slavery in the past, AGI might irreversibly entrench it, preventing moral development. [146] Furthermore, AGI might facilitate mass surveillance and brainwashing, which could be used to produce a stable repressive around the world totalitarian regime. [147] [148] There is also a threat for the machines themselves. If makers that are sentient or otherwise deserving of ethical consideration are mass developed in the future, taking part in a civilizational course that indefinitely overlooks their well-being and interests could be an existential catastrophe. [149] [150] Considering just how much AGI could improve mankind's future and assistance minimize other existential dangers, Toby Ord calls these existential risks "an argument for continuing with due care", not for "abandoning AI". [147]
Risk of loss of control and human extinction

The thesis that AI postures an existential danger for people, which this danger needs more attention, is questionable but has actually been endorsed in 2023 by many public figures, AI researchers and CEOs of AI business such as Elon Musk, Bill Gates, Geoffrey Hinton, Yoshua Bengio, Demis Hassabis and Sam Altman. [151] [152]
In 2014, Stephen Hawking slammed widespread indifference:

So, facing possible futures of enormous advantages and threats, the specialists are definitely doing whatever possible to ensure the best outcome, right? Wrong. If a superior alien civilisation sent us a message stating, 'We'll arrive in a couple of decades,' would we simply 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 potential fate of mankind has actually in some cases been compared to the fate of gorillas threatened by human activities. The contrast specifies that greater intelligence allowed humankind to dominate gorillas, which are now vulnerable in methods that they could not have prepared for. As an outcome, the gorilla has actually ended up being a threatened species, not out of malice, however simply as a civilian casualties from human activities. [154]
The skeptic Yann LeCun considers that AGIs will have no desire to control humanity which we need to take care not to anthropomorphize them and interpret their intents as we would for humans. He said that people won't be "clever sufficient to create super-intelligent machines, yet unbelievably foolish to the point of offering it moronic objectives with no safeguards". [155] On the other side, the principle of crucial merging suggests that practically whatever their objectives, intelligent agents will have factors to attempt to make it through and get more power as intermediary actions to accomplishing these goals. Which this does not require having emotions. [156]
Many scholars who are concerned about existential danger advocate for more research into solving the "control issue" to address the concern: what types of safeguards, algorithms, or architectures can programmers execute to maximise the possibility that their recursively-improving AI would continue to act in a friendly, instead of harmful, 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 rivals), [159] and making use of AI in weapon systems. [160]
The thesis that AI can position existential danger likewise has critics. Skeptics normally state that AGI is unlikely in the short-term, or that concerns about AGI sidetrack from other issues associated with existing AI. [161] Former Google fraud czar Shuman Ghosemajumder considers that for numerous people beyond the innovation industry, existing chatbots and LLMs are currently viewed as though they were AGI, causing additional misunderstanding 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 scientists think that the communication projects on AI existential threat by specific AI groups (such as OpenAI, Anthropic, DeepMind, and Conjecture) might be an at effort at regulative capture and to inflate interest in their products. [164] [165]
In 2023, the CEOs of Google DeepMind, OpenAI and Anthropic, in addition to other market leaders and researchers, released a joint declaration asserting that "Mitigating the danger of extinction from AI must be a global priority together with other societal-scale dangers such as pandemics and nuclear war." [152]
Mass joblessness

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

According to Stephen Hawking, the result of automation on the lifestyle will depend upon how the wealth will be rearranged: [142]
Everyone can take pleasure in a life of glamorous leisure if the machine-produced wealth is shared, or most individuals can end up miserably poor if the machine-owners successfully lobby versus wealth redistribution. Up until now, the pattern seems to be toward the second alternative, with innovation driving ever-increasing inequality

Elon Musk thinks about that the automation of society will need governments to embrace a universal fundamental earnings. [168]
See likewise

Artificial brain - Software and hardware with cognitive capabilities comparable to those of the animal or human brain AI effect AI safety - Research area on making AI safe and advantageous AI alignment - AI conformance to the designated objective A.I. Rising - 2018 film 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 expert system to play different games Generative expert system - AI system efficient in creating material in response to prompts Human Brain Project - Scientific research task Intelligence amplification - Use of info technology to enhance human intelligence (IA). Machine ethics - Moral behaviours of manufactured machines. Moravec's paradox. Multi-task knowing - Solving numerous maker discovering tasks at the very same time. Neural scaling law - Statistical law in device knowing. Outline of expert system - Overview of and topical guide to expert system. Transhumanism - Philosophical motion. Synthetic intelligence - Alternate term for or type of expert system. Transfer knowing - Artificial intelligence method. Loebner Prize - Annual AI competitors. Hardware for synthetic intelligence - Hardware specifically designed and optimized for synthetic intelligence. Weak artificial intelligence - Form of expert system.
Notes

^ a b See below for the origin of the term "strong AI", and see the academic meaning of "strong AI" and weak AI in the short article Chinese space. ^ AI creator John McCarthy writes: "we can not yet identify in general what kinds of computational procedures we want to call intelligent. " [26] (For a conversation of some definitions of intelligence utilized by artificial intelligence scientists, see philosophy of synthetic intelligence.). ^ The Lighthill report particularly criticized AI's "grand objectives" and led the dismantling of AI research in England. [55] In the U.S., DARPA became figured out to money only "mission-oriented direct research, instead of basic undirected research study". [56] [57] ^ As AI founder John McCarthy composes "it would be a great relief to the remainder of the workers in AI if the inventors of new basic formalisms would express their hopes in a more guarded type than has actually sometimes 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 approximately correspond to 1014 cps. Moravec talks in terms of MIPS, not "cps", which is a non-standard term Kurzweil presented. ^ As specified in a standard AI book: "The assertion that makers could potentially act intelligently (or, perhaps better, act as if they were intelligent) is called the 'weak AI' hypothesis by thinkers, and the assertion that machines that do so are really believing (as opposed to imitating thinking) is called the 'strong AI' hypothesis." [121] ^ Alan Turing made this point in 1950. [36] References

^ Krishna, Sri (9 February 2023). "What is synthetic narrow intelligence (ANI)?". VentureBeat. Retrieved 1 March 2024. ANI is developed to carry out a single task. ^ "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 new objective is producing synthetic general intelligence". The Verge. Retrieved 13 June 2024. Our vision is to build AI that is much better than human-level at all of the human senses. ^ Baum, Seth D. (2020 ). A Survey of Artificial General Intelligence Projects for Ethics, Risk, and Policy (PDF) (Report). Global Catastrophic Risk Institute. Retrieved 28 November 2024. 72 AGI R&D projects were determined as being active in 2020. ^ a b c "AI timelines: What do professionals 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 City Times. Retrieved 18 May 2023. ^ "AI leader Geoffrey Hinton gives up Google and warns of risk ahead". The New York City Times. 1 May 2023. Retrieved 2 May 2023. It is hard to see how you can prevent the bad actors from using it for bad things. ^ Bubeck, Sébastien; Chandrasekaran, Varun; Eldan, Ronen; Gehrke, Johannes; Horvitz, Eric (2023 ). "Sparks of Artificial General Intelligence: Early 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 change changes you. ^ Vinge, Vernor (1992 ). A Fire Upon the Deep. Tor Books. ISBN 978-0-8125-1528-2. The Singularity is coming. ^ Morozov, Evgeny (30 June 2023). "The True Threat of Expert System". The New York City Times. The real threat is not AI itself but the way we deploy it. ^ "Impressed by synthetic intelligence? Experts say AGI is following, and it has 'existential' threats". ABC News. 23 March 2023. Retrieved 6 April 2023. AGI might pose existential risks to humankind. ^ Bostrom, Nick (2014 ). Superintelligence: Paths, Dangers, Strategies. Oxford University Press. ISBN 978-0-1996-7811-2. The first superintelligence will be the last development 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 extinction from AI need to be an international top priority. ^ "Statement on AI Risk". Center for AI Safety. Retrieved 1 March 2024. AI specialists warn of risk of extinction from AI. ^ Mitchell, Melanie (30 May 2023). "Are AI's Doomsday Scenarios Worth Taking Seriously?". The New York City Times. We are far from creating devices that can outthink us in general ways. ^ LeCun, Yann (June 2023). "AGI does not present an existential danger". Medium. There is no reason to fear AI as an existential threat. ^ Kurzweil 2005, p. 260. ^ a b Kurzweil, Ray (5 August 2005), "Long Live AI", Forbes, archived from the initial on 14 August 2005: Kurzweil describes strong AI as "device intelligence with the full series of human intelligence.". ^ "The Age of Artificial Intelligence: George John at TEDxLondonBusinessSchool 2013". Archived from the initial on 26 February 2014. Retrieved 22 February 2014. ^ Newell & Simon 1976, This is the term they utilize for "human-level" intelligence in the physical sign system hypothesis. ^ "The Open University on Strong and Weak AI". Archived from the initial 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 changing our world - it is on everyone 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 qualities 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 method we believe: a brand-new view of intelligence (The MIT Press, 2007). ISBN 0-2621-6239-3. ^ White, R. W. (1959 ). "Motivation reassessed: 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 idea of proficiency". Psychological Review. 66 (5 ): 297-333. doi:10.1037/ h0040934. PMID 13844397. S2CID 37385966. ^ Muehlhauser, Luke (11 August 2013). "What is AGI?". Machine Intelligence Research Institute. Archived from the initial on 25 April 2014. Retrieved 1 May 2014. ^ "What is Artificial General Intelligence (AGI)?|4 Tests For Ensuring Artificial General Intelligence". Talky Blog. 13 July 2019. Archived from the initial on 17 July 2019. Retrieved 17 July 2019. ^ Kirk-Giannini, Cameron Domenico; Goldstein, Simon (16 October 2023). "AI is closer than ever to passing the Turing test for 'intelligence'. What happens when it does?". The Conversation. Retrieved 22 September 2024. ^ a b Turing 1950. ^ Turing, Alan (1952 ). B. Jack Copeland (ed.). Can Automatic Calculating Machines Be Said To Think?. Oxford: Oxford University Press. pp. 487-506. ISBN 978-0-1982-5079-1. ^ "Eugene Goostman is a real boy - the Turing Test states so". The Guardian. 9 June 2014. ISSN 0261-3077. Retrieved 3 March 2024. ^ "Scientists challenge 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 designs like ChatGPT and GPT-4 are acing everything from the bar test to AP Biology. Here's a list of tough examinations 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 Take Advantage Of 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 answer". Analytics India Magazine. Retrieved 3 March 2024. ^ Bhaimiya, Sawdah (20 June 2023). "DeepMind's co-founder recommended testing an AI chatbot's ability to turn $100,000 into $1 million to measure human-like intelligence". Business Insider. Retrieved 3 March 2024. ^ Suleyman, Mustafa (14 July 2023). "Mustafa Suleyman: My new Turing test would see if AI can make $1 million". MIT Technology Review. Retrieved 3 March 2024. ^ Shapiro, Stuart C. (1992 ). "Artificial Intelligence" (PDF). In Stuart C. Shapiro (ed.). Encyclopedia of Artificial Intelligence (Second ed.). New York City: John Wiley. pp. 54-57. Archived (PDF) from the original on 1 February 2016. (Section 4 is on "AI-Complete Tasks".). ^ Yampolskiy, Roman V. (2012 ). Xin-She Yang (ed.). "Turing Test as a Defining Feature of AI-Completeness" (PDF). Artificial Intelligence, Evolutionary Computation and Metaheuristics (AIECM): 3-17. Archived (PDF) from the original on 22 May 2013. ^ "AI Index: State of AI in 13 Charts". Stanford University Human-Centered Artificial Intelligence. 15 April 2024. Retrieved 27 May 2024. ^ Crevier 1993, pp. 48-50. ^ Kaplan, Andreas (2022 ). "Artificial Intelligence, Business and Civilization - Our Fate Made in Machines". Archived from the initial on 6 May 2022. Retrieved 12 March 2022. ^ Simon 1965, p. 96 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 ), priced quote in Crevier (1993, p. 109). ^ Lighthill 1973; Howe 1994. ^ a b NRC 1999, "Shift to Applied Research Increases Investment". ^ Crevier 1993, pp. 115-117; Russell & Norvig 2003, pp. 21-22. ^ Crevier 1993, p. 211, Russell & Norvig 2003, p. 24 and see likewise Feigenbaum & McCorduck 1983. ^ Crevier 1993, pp. 161-162, 197-203, 240; Russell & Norvig 2003, p. 25. ^ Crevier 1993, pp. 209-212. ^ McCarthy, John (2000 ). "Reply to Lighthill". Stanford University. Archived from the original on 30 September 2008. Retrieved 29 September 2007. ^ Markoff, John (14 October 2005). "Behind Expert system, a Squadron of Bright Real People". The New York Times. Archived from the initial on 2 February 2023. Retrieved 18 February 2017. At its low point, some computer scientists and software engineers prevented the term synthetic intelligence for fear of being deemed wild-eyed dreamers. ^ Russell & Norvig 2003, pp. 25-26 ^ "Trends in the Emerging Tech Hype Cycle". Gartner Reports. Archived from the initial on 22 May 2019. Retrieved 7 May 2019. ^ a b Moravec 1988, p. 20 ^ Harnad, S. (1990 ). "The Symbol Grounding Problem". Physica D. 42 (1-3): 335-346. arXiv: cs/9906002. Bibcode:1990 PhyD ... 42..335 H. doi:10.1016/ 0167-2789( 90 )90087-6. S2CID 3204300. ^ Gubrud 1997 ^ Hutter, Marcus (2005 ). Universal Expert System: Sequential Decisions Based on Algorithmic Probability. Texts in Theoretical Computer Science an EATCS Series. Springer. doi:10.1007/ b138233. ISBN 978-3-5402-6877-2. S2CID 33352850. Archived from the original 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 original on 28 December 2018. Retrieved 28 December 2018., by means of Life 3.0: 'The term "AGI" was promoted by ... Shane Legg, Mark Gubrud and Ben Goertzel' ^ Wang & Goertzel 2007 ^ "First International Summer School in Artificial General Intelligence, Main summertime school: June 22 - July 3, 2009, OpenCog Lab: July 6-9, 2009". Archived from the initial on 28 September 2020. Retrieved 11 May 2020. ^ "Избираеми дисциплини 2009/2010 - пролетен триместър" [Elective courses 2009/2010 - spring trimester] Факултет по математика и информатика [Faculty of Mathematics and Informatics] (in Bulgarian). Archived from the 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 machine intelligence: Despite development in machine intelligence, synthetic basic 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 GPT-4". arXiv:2303.12712 [cs.CL] ^ "Microsoft Researchers Claim GPT-4 Is Showing "Sparks" of AGI". Futurism. 23 March 2023. Retrieved 13 December 2023. ^ Allen, Paul; Greaves, Mark (12 October 2011). "The Singularity Isn't Near". MIT Technology Review. Retrieved 17 September 2014. ^ Winfield, Alan. "Expert system will not develop into a Frankenstein's 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 realized". Humanities and Social Sciences Communications. 7 (1 ): 1-9. doi:10.1057/ s41599-020-0494-4. hdl:11250/ 2726984. ISSN 2662-9992. S2CID 219710554. ^ McCarthy 2007b. ^ Khatchadourian, Raffi (23 November 2015). "The Doomsday Invention: Will expert system bring us utopia or destruction?". The New Yorker. Archived from the initial on 28 January 2016. Retrieved 7 February 2016. ^ Müller, V. C., & Bostrom, N. (2016 ). Future development in expert system: A study of expert viewpoint. In Fundamental concerns 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, 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 makers: AI takes the Torrance Test". Journal of Creativity. 33 (3 ): 100065. doi:10.1016/ j.yjoc.2023.100065. ISSN 2713-3745. S2CID 261087185. ^ Arcas, Blaise Agüera y (10 October 2023). "Artificial General Intelligence Is Already Here". Noema. ^ Zia, Tehseen (8 January 2024). "Unveiling of Large Multimodal Models: Shaping the Landscape of Language Models in 2024". Unite.ai. Retrieved 26 May 2024. ^ "Introducing OpenAI o1-preview". OpenAI. 12 September 2024. ^ Knight, Will. "OpenAI Announces a Brand-new AI Model, Code-Named Strawberry, That Solves Difficult Problems Step by Step". Wired. ISSN 1059-1028. Retrieved 17 September 2024. ^ "OpenAI Employee Claims AGI Has Been Achieved". Orbital Today. 13 December 2024. Retrieved 27 December 2024. ^ "AI Index: State of AI in 13 Charts". hai.stanford.edu. 15 April 2024. Retrieved 7 June 2024. ^ "Next-Gen AI: OpenAI and Meta's Leap Towards Reasoning Machines". Unite.ai. 19 April 2024. Retrieved 7 June 2024. ^ James, Alex P. (2022 ). "The Why, What, and How of Artificial General Intelligence Chip Development". IEEE Transactions on Cognitive and Developmental Systems. 14 (2 ): 333-347. arXiv:2012.06338. doi:10.1109/ TCDS.2021.3069871. ISSN 2379-8920. S2CID 228376556. Archived from the initial on 28 August 2022. Retrieved 28 August 2022. ^ Pei, Jing; Deng, Lei; Song, Sen; Zhao, Mingguo; Zhang, Youhui; Wu, Shuang; Wang, Guanrui; Zou, Zhe; Wu, Zhenzhi; He, Wei; Chen, Feng; Deng, Ning; Wu, Si; Wang, Yu; Wu, Yujie (2019 ). "Towards synthetic basic intelligence with hybrid Tianjic chip architecture". Nature. 572 (7767 ): 106-111. Bibcode:2019 Natur.572..106 P. doi:10.1038/ s41586-019-1424-8. ISSN 1476-4687. PMID 31367028. S2CID 199056116. Archived from the original on 29 August 2022. Retrieved 29 August 2022. ^ Pandey, Mohit; Fernandez, Michael; Gentile, Francesco; Isayev, Olexandr; Tropsha, Alexander; Stern, Abraham C.; Cherkasov, Artem (March 2022). "The transformational role of GPU computing and deep learning in drug discovery". Nature Machine Intelligence. 4 (3 ): 211-221. doi:10.1038/ s42256-022-00463-x. ISSN 2522-5839. S2CID 252081559. ^ Goertzel & Pennachin 2006. ^ a b c (Kurzweil 2005, p. 260). ^ a b c Goertzel 2007. ^ Grace, Katja (2016 ). "Error in Armstrong and Sotala 2012". AI Impacts (blog site). Archived from the 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 Expert System". 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 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 in between narrow and general AI". VentureBeat. Archived from the initial on 4 September 2020. Retrieved 5 September 2020. Certainly, too, there are those who declare we are already seeing an early example of an AGI system in the just recently announced GPT-3 natural language processing (NLP) neural network. ... So is GPT-3 the 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 between narrow and basic AI. ^ Quach, Katyanna. "A designer built an AI chatbot using GPT-3 that assisted a guy speak once again to his late fiancée. OpenAI shut it down". The Register. Archived from the original on 16 October 2021. Retrieved 16 October 2021. ^ Wiggers, Kyle (13 May 2022), "DeepMind's new AI can carry out over 600 tasks, from playing games to managing robotics", TechCrunch, archived from the original on 16 June 2022, obtained 12 June 2022. ^ Bubeck, Sébastien; Chandrasekaran, Varun; Eldan, Ronen; Gehrke, Johannes; Horvitz, Eric; Kamar, Ece; Lee, Peter; Lee, Yin Tat; Li, Yuanzhi; Lundberg, Scott; Nori, Harsha; Palangi, Hamid; Ribeiro, Marco Tulio; Zhang, Yi (22 March 2023). "Sparks of Artificial General Intelligence: Early try outs GPT-4". arXiv:2303.12712 [cs.CL] ^ Metz, Cade (1 May 2023). "' The Godfather of A.I.' Leaves Google and Warns of Danger Ahead". The New York Times. ISSN 0362-4331. Retrieved 7 June 2023. ^ Bove, Tristan. "A.I. might rival human intelligence in 'simply 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 states AI could pass human tests in 5 years". Reuters. ^ Aschenbrenner, Leopold. "SITUATIONAL AWARENESS, The Decade Ahead". ^ Sullivan, Mark (18 October 2023). "Why everybody appears to disagree on how to specify Artificial General Intelligence". Fast Company. ^ Nosta, John (5 January 2024). "The Accelerating Path to Artificial General Intelligence". Psychology Today. Retrieved 30 March 2024. ^ Hickey, Alex. "Whole Brain Emulation: A Giant Step for Neuroscience". Tech Brew. Retrieved 8 November 2023. ^ Sandberg & Boström 2008. ^ Drachman 2005. ^ a b Russell & Norvig 2003. ^ Moravec 1988, p. 61. ^ Moravec 1998. ^ Holmgaard Mersh, Amalie (15 September 2023). "Decade-long European research job maps the human brain". euractiv. ^ Swaminathan, Nikhil (January-February 2011). "Glia-the other brain cells". Discover. Archived from the original on 8 February 2014. Retrieved 24 January 2014. ^ de Vega, Glenberg & Graesser 2008. A vast array of views in existing research, all of which need grounding to some degree ^ Thornton, Angela (26 June 2023). "How publishing our minds to a computer system 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 (priced quote in "AITopics"),. Will Biological Computers Enable Artificially Intelligent Machines to Become Persons? Archived 13 May 2008 at the Wayback Machine Anthony Tongen.


^ a b c Russell & Norvig 2003, p. 947. ^ though see Explainable expert system for curiosity by the field about why a program behaves the way it does. ^ Chalmers, David J. (9 August 2023). "Could a Big Language Model Be Conscious?". Boston Review. ^ Seth, Anil. "Consciousness". New Scientist. Retrieved 5 September 2024. ^ Nagel 1974. ^ "The Google engineer who thinks the company's AI has actually come to life". The Washington Post. 11 June 2022. Retrieved 12 June 2023. ^ Kateman, Brian (24 July 2023). "AI Should Be Terrified of Humans". TIME. Retrieved 5 September 2024. ^ Nosta, John (18 December 2023). "Should Expert System Have Rights?". Psychology Today. Retrieved 5 September 2024. ^ Akst, Daniel (10 April 2023). "Should Robots With Expert System Have Moral or Legal Rights?". The Wall Street Journal. ^ "Artificial General Intelligence - Do [es] the expense exceed advantages?". 23 August 2021. Retrieved 7 June 2023. ^ "How we can Gain from Advancing Artificial General Intelligence (AGI) - Unite.AI". www.unite.ai. 7 April 2020. Retrieved 7 June 2023. ^ a b c Talty, Jules; Julien, Stephan. "What Will Our Society Appear Like When 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, risks, methods (Reprinted with corrections 2017 ed.). Oxford, United Kingdom; New York, 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 humans will ruin ourselves". Vox. Retrieved 8 June 2023. ^ Doherty, Ben (17 May 2018). "Climate alter 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 Times. ISSN 0362-4331. Retrieved 24 December 2023. ^ a b "Statement on AI Risk". Center for AI Safety. 30 May 2023. Retrieved 8 June 2023. ^ "Stephen Hawking: 'Transcendence looks at the implications of synthetic intelligence - but are we taking AI seriously enough?'". The Independent (UK). Archived from the original on 25 September 2015. Retrieved 3 December 2014. ^ Herger, Mario. "The Gorilla Problem - Enterprise Garage". Retrieved 7 June 2023. ^ "The fascinating Facebook dispute in between Yann LeCun, wiki.vifm.info Stuart Russel and Yoshua Bengio about the threats of strong AI". The fascinating Facebook debate between Yann LeCun, Stuart Russel and Yoshua Bengio about the risks 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 devastating AGI danger: a study". Physica Scripta. 90 (1 ): 018001. doi:10.1088/ 0031-8949/90/ 1/018001. ISSN 0031-8949. ^ Bostrom, Nick (2014 ). Superintelligence: Paths, Dangers, Strategies (First ed.). Oxford University Press. ISBN 978-0-1996-7811-2. ^ Chow, Andrew R.; Perrigo, Billy (16 February 2023). "The AI Arms Race Is On. Start Worrying". TIME. Retrieved 24 December 2023. ^ Tetlow, Gemma (12 January 2017). "AI arms race dangers spiralling out of control, report alerts". Financial Times. Archived from the initial on 11 April 2022. Retrieved 24 December 2023. ^ Milmo, Dan; Stacey, Kiran (25 September 2023). "Experts disagree over danger posed however synthetic intelligence can not be neglected". The Guardian. ISSN 0261-3077. Retrieved 24 December 2023. ^ "Humanity, asteroidsathome.net 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 worries over AI, warn researchers". The Telegraph. Retrieved 7 December 2023. ^ Davidson, John (30 October 2023). "Google Brain creator says big tech is lying about AI extinction risk". Australian Financial Review. Archived from the original on 7 December 2023. Retrieved 7 December 2023. ^ Eloundou, Tyna; Manning, Sam; Mishkin, Pamela; Rock, Daniel (17 March 2023). "GPTs are GPTs: An early look at the labor market impact capacity of large language models". OpenAI. Retrieved 7 June 2023. ^ a b Hurst, Luke (23 March 2023). "OpenAI says 80% of workers 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 states we require universal standard earnings because '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 initial on 18 June 2022. Retrieved 22 September 2021. Chalmers, David (1996 ), The Conscious Mind, Oxford University Press. Clocksin, William (August 2003), "Artificial intelligence and the future", Philosophical Transactions of the Royal Society A, vol. 361, no. 1809, pp. 1721-1748, Bibcode:2003 RSPTA.361.1721 C, doi:10.1098/ rsta.2003.1232, PMID 12952683, S2CID 31032007. Crevier, Daniel (1993 ). AI: The Tumultuous Search for 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 original (PDF) on 20 March 2013. Goertzel, Ben (December 2007), "Human-level artificial basic 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, recovered 1 April 2009. Gubrud, Mark (November 1997), "Nanotechnology and International Security", Fifth Foresight Conference on Molecular Nanotechnology, archived from the original on 29 May 2011, retrieved 7 May 2011. Howe, J. (November 1994), Expert System at Edinburgh University: a Perspective, archived from the initial on 17 August 2007, recovered 30 August 2007. Johnson, Mark (1987 ), The body in the mind, Chicago, ISBN 978-0-2264-0317-5. Kurzweil, Ray (2005 ), The Singularity is Near, Viking Press. Lighthill, Professor Sir James (1973 ), "Artificial Intelligence: A General Survey", Expert System: a paper seminar, Science Research Council. Luger, George; Stubblefield, William (2004 ), Artificial Intelligence: Structures and Strategies for Complex Problem Solving (5th ed.), The Benjamin/Cummings Publishing Company, Inc., p. 720, ISBN 978-0-8053-4780-7. McCarthy, John (2007b). What is Expert system?. Stanford University. The supreme effort is to make computer programs that can fix issues and achieve objectives on the planet along with 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 original on 15 June 2006, recovered 23 June 2006 Nagel (1974 ), "What Is it Like to Be a Bat" (PDF), Philosophical Review, 83 (4 ): 435-50, doi:10.2307/ 2183914, JSTOR 2183914, archived (PDF) from the initial on 16 October 2011, retrieved 7 November 2009 Newell, Allen; Simon, H. A. (1976 ). "Computer Technology as Empirical Inquiry: Symbols and Search". Communications of the ACM. 19 (3 ): 113-126. doi:10.1145/ 360018.360022. Nilsson, Nils (1998 ), Artificial Intelligence: A New Synthesis, Morgan Kaufmann Publishers, ISBN 978-1-5586-0467-4 NRC (1999 ), "Developments in Expert System", Funding a Revolution: Government Support for Computing Research, National Academy Press, archived from the original on 12 January 2008, retrieved 29 September 2007 Poole, David; Mackworth, Alan; Goebel, Randy (1998 ), Computational Intelligence: A Sensible Approach, New York: Oxford University Press, archived from the initial on 25 July 2009, retrieved 6 December 2007 Russell, Stuart J.; Norvig, Peter (2003 ), Artificial Intelligence: 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 original on 25 March 2020, retrieved 5 April 2009 Searle, John (1980 ), "Minds, Brains and Programs" (PDF), Behavioral and Brain Sciences, 3 (3 ): 417-457, doi:10.1017/ S0140525X00005756, S2CID 55303721, archived (PDF) from the initial on 17 March 2019, obtained 3 September 2020 Simon, H. A. (1965 ), The Shape of Automation for Men and Management, New York: Harper & Row Turing, Alan (October 1950). "Computing Machinery and Intelligence". Mind. 59 (236 ): 433-460. doi:10.1093/ mind/LIX.236.433. ISSN 1460-2113. JSTOR 2251299. S2CID 14636783.
de Vega, Manuel; Glenberg, Arthur; Graesser, Arthur, eds. (2008 ), Symbols and Embodiment: Debates on meaning and cognition, Oxford University Press, ISBN 978-0-1992-1727-4 Wang, Pei; Goertzel, Ben (2007 ). "Introduction: Aspects of Artificial General Intelligence". Advances in Artificial General Intelligence: Concepts, Architectures and Algorithms: Proceedings of the AGI Workshop 2006. IOS Press. pp. 1-16. ISBN 978-1-5860-3758-1. Archived from the initial on 18 February 2021. Retrieved 13 December 2020 - through ResearchGate.
Further reading

Aleksander, Igor (1996 ), Impossible Minds, World Scientific Publishing Company, ISBN 978-1-8609-4036-1 Azevedo FA, Carvalho LR, Grinberg LT, Farfel J, et al. (April 2009), "Equal varieties of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain", The Journal of Comparative Neurology, 513 (5 ): 532-541, doi:10.1002/ cne.21974, PMID 19226510, S2CID 5200449, archived from the original on 18 February 2021, recovered 4 September 2013 - by means of ResearchGate Berglas, Anthony (January 2012) [2008], Expert System Will Kill Our Grandchildren (Singularity), archived from the initial 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, composes (in what may be called "Dyson's Law") that "Any system easy adequate to be reasonable will not be made complex enough to behave intelligently, while any system complicated enough to act wisely will be too made complex to understand." (p. 197.) Computer scientist Alex Pentland writes: "Current AI machine-learning algorithms are, at their core, dead easy 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 Choice, Princeton University Press, 2023, 333 pp.), The New York 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 function come from acting in the world and experiencing the repercussions. Artificial intelligences - disembodied, complete strangers to blood, sweat, and tears - have no celebration for that." (p. 30.). Halal, William E. "TechCast Article Series: The Automation of Thought" (PDF). Archived from the original (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 City Review of Books, vol. LXXI, no. 17 (7 November 2024), pp. 44-46. "' We can't realistically anticipate that those who want to get abundant from AI are going to have the interests of the rest people close at heart,' ... writes [Gary Marcus] 'We can't depend on federal governments driven by campaign financing contributions [from tech companies] to press back.' ... Marcus information the demands that people ought to make of their federal governments and the tech business. They consist of openness on how AI systems work; settlement for people if their information [are] used to train LLMs (large language design) s and the right to approval to this use; and the ability to hold tech companies accountable for the damages they bring on by removing Section 230, imposing money penalites, and passing more stringent product liability laws ... Marcus also suggests ... that a brand-new, AI-specific federal company, akin to the FDA, the FCC, or the FTC, might offer the most robust oversight ... [T] he Fordham law teacher Chinmayi Sharma ... recommends ... establish [ing] an expert licensing program for engineers that would operate in a similar method to medical licenses, malpractice matches, and the Hippocratic oath in medicine. 'What if, like medical professionals,' she asks ..., 'AI engineers likewise vowed to do no harm?'" (p. 46.). Holte, R. C.; Choueiry, B. Y. (2003 ), "Abstraction and reformulation in expert system", Philosophical Transactions of the Royal Society B, vol. 358, no. 1435, pp. 1197-1204, doi:10.1098/ rstb.2003.1317, PMC 1693218, PMID 12903653. Hughes-Castleberry, Kenna, "A Murder Mystery Puzzle: The literary puzzle Cain's Jawbone, which has stumped humans for years, reveals 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 can unbelievable accomplishments, their capabilities are extremely much restricted by the quantity of context they get. This [...] could trigger [difficulties] for researchers who wish to utilize them to do things such as evaluate ancient languages. In many cases, there are few historical records on long-gone civilizations to serve as training information for such a purpose." (p. 82.). Immerwahr, Daniel, "Your Lying Eyes: People now utilize A.I. to generate phony videos identical from genuine ones. Just how much does it matter?", The New Yorker, 20 November 2023, pp. 54-59. "If by 'deepfakes' we indicate reasonable videos produced using expert system that really deceive individuals, then they barely exist. The phonies aren't deep, and the deeps aren't fake. [...] A.I.-generated videos are not, in general, running in our media as counterfeited evidence. Their function much better looks like that of animations, particularly smutty ones." (p. 59.). - Leffer, Lauren, "The Risks of Trusting AI: We must avoid humanizing machine-learning designs used in clinical research study", 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 synthetic basic intelligence are stymmied by the usual issues", Scientific American, vol. 327, no. 4 (October 2022), pp. 42-45. McCarthy, John (October 2007), "From here to human-level AI", 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 original 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 Expert system, provided and dispersed at the 2007 Singularity Summit, San Francisco, California. Press, Eyal, "In Front of Their Faces: Does facial-recognition technology lead police to disregard contradictory evidence?", The New Yorker, 20 November 2023, pp. 20-26. Roivainen, Eka, "AI's IQ: ChatGPT aced a [basic intelligence] test but showed that intelligence can not be measured by IQ alone", Scientific American, vol. 329, no. 1 (July/August 2023), p. 7. "Despite its high IQ, ChatGPT stops working at tasks that need 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 huge database of ... truths derived from online texts. " - Scharre, Paul, "Killer Apps: The Real Dangers of an AI Arms Race", Foreign Affairs, vol. 98, no. 3 (May/June 2019), pp. 135-44. "Today's AI technologies are powerful but undependable. Rules-based systems can not handle scenarios their developers did not prepare for. Learning systems are restricted by the information on which they were trained. AI failures have actually currently caused tragedy. Advanced auto-pilot functions in cars and trucks, although they perform well in some scenarios, have actually driven cars and trucks without warning into trucks, concrete barriers, and parked cars. In the wrong scenario, AI systems go from supersmart to superdumb in an immediate. When an opponent is trying to manipulate 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 made possible by new technologies but depend on the timelelss human propensity to anthropomorphise." (p. 29.). Williams, R. W.; Herrup, K.