GitLab

Artificial basic intelligence (AGI) is a kind of artificial intelligence (AI) that matches or goes beyond human cognitive capabilities across a wide range of cognitive tasks. This contrasts with narrow AI, which is restricted to particular tasks. [1] Artificial superintelligence (ASI), on the other hand, describes AGI that significantly goes beyond human cognitive abilities. AGI is considered among the meanings of strong AI.

Creating AGI is a primary goal of AI research study and of companies such as OpenAI [2] and Meta. [3] A 2020 survey recognized 72 active AGI research study and oke.zone advancement jobs across 37 countries. [4]
The timeline for attaining AGI stays a subject of ongoing dispute among researchers and professionals. As of 2023, some argue that it might be possible in years or years; others maintain it may take a century or longer; a minority believe it might never ever be accomplished; and another minority declares that it is currently here. [5] [6] Notable AI researcher Geoffrey Hinton has revealed issues about the rapid progress towards AGI, recommending it could be attained sooner than many expect. [7]
There is dispute on the precise meaning of AGI and concerning whether contemporary large language designs (LLMs) such as GPT-4 are early types of AGI. [8] AGI is a typical topic in sci-fi and futures research studies. [9] [10]
Contention exists over whether AGI represents an existential threat. [11] [12] [13] Many experts on AI have actually mentioned that alleviating the risk of human termination postured by AGI needs to be a global concern. [14] [15] Others find the development of AGI to be too remote to provide such a risk. [16] [17]
Terminology

AGI is likewise referred to as strong AI, [18] [19] full AI, [20] human-level AI, [5] human-level smart AI, or general smart action. [21]
Some academic sources schedule the term "strong AI" for computer programs that experience sentience or awareness. [a] In contrast, weak AI (or narrow AI) has the ability to fix one particular problem but does not have general cognitive abilities. [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 same sense as people. [a]
Related principles include artificial superintelligence and transformative AI. An artificial superintelligence (ASI) is a hypothetical type of AGI that is a lot more normally smart than human beings, [23] while the notion of transformative AI relates to AI having a large impact on society, for instance, comparable to the farming or industrial revolution. [24]
A structure for passfun.awardspace.us categorizing AGI in levels was proposed in 2023 by Google DeepMind researchers. They specify five levels of AGI: emerging, skilled, expert, virtuoso, and superhuman. For example, a qualified AGI is defined as an AI that outperforms 50% of knowledgeable grownups in a large variety of non-physical jobs, and a superhuman AGI (i.e. a synthetic superintelligence) is similarly defined however with a threshold of 100%. They consider large language models like ChatGPT or LLaMA 2 to be instances of emerging AGI. [25]
Characteristics

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

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

• communicate in natural language
• if needed, incorporate these skills in completion of any given goal

Many interdisciplinary techniques (e.g. cognitive science, computational intelligence, and choice making) think about extra characteristics such as imagination (the ability to form novel mental images and concepts) [28] and autonomy. [29]
Computer-based systems that exhibit numerous of these capabilities exist (e.g. see computational imagination, automated reasoning, choice support system, robot, evolutionary calculation, smart representative). There is argument about whether modern AI systems have them to an appropriate degree.

Physical qualities

Other capabilities are considered desirable in intelligent systems, as they might impact intelligence or help in its expression. These consist of: [30]
- the ability to sense (e.g. see, hear, etc), and - the ability to act (e.g. relocation and control objects, modification location to explore, etc).
This consists of the capability to spot and react to threat. [31]
Although the capability to sense (e.g. see, hear, and so on) and the capability to act (e.g. relocation and control items, change location to explore, etc) can be preferable for some smart systems, [30] these physical abilities are not strictly required for an entity to qualify as AGI-particularly under the thesis that large language models (LLMs) might currently be or end up being AGI. Even from a less optimistic point of view on LLMs, there is no firm requirement for an AGI to have a human-like kind; being a silicon-based computational system suffices, provided it can process input (language) from the external world in location of human senses. This analysis aligns with the understanding that AGI has never been proscribed a specific physical personification and therefore does not require a capacity for locomotion or traditional "eyes and ears". [32]
Tests for human-level AGI

Several tests indicated to validate human-level AGI have been considered, consisting of: [33] [34]
The concept of the test is that the machine has to try and pretend to be a guy, by answering questions put to it, and it will just pass if the pretence is reasonably persuading. A significant portion of a jury, who should not be professional about makers, must be taken in by the pretence. [37]
AI-complete issues

An issue is informally called "AI-complete" or "AI-hard" if it is thought that in order to solve it, one would require to implement AGI, since the service is beyond the capabilities of a purpose-specific algorithm. [47]
There are numerous issues that have actually been conjectured to require basic intelligence to resolve as well as human beings. Examples include computer system vision, natural language understanding, and handling unanticipated situations while solving any real-world problem. [48] Even a particular job like translation requires a device to check out and write in both languages, follow the author's argument (reason), understand the context (understanding), and consistently recreate the author's initial intent (social intelligence). All of these problems need to be solved concurrently in order to reach human-level machine performance.

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

Classical AI

Modern AI research began in the mid-1950s. [50] The very first generation of AI scientists were persuaded that synthetic general intelligence was possible which it would exist in just a couple of decades. [51] AI pioneer Herbert A. Simon composed in 1965: "makers will be capable, within twenty years, of doing any work a male can do." [52]
Their predictions were the inspiration for Stanley Kubrick and Arthur C. Clarke's character HAL 9000, who embodied what AI researchers believed they could develop by the year 2001. AI pioneer Marvin Minsky was a specialist [53] on the task of making HAL 9000 as sensible as possible according to the agreement forecasts of the time. He stated in 1967, "Within a generation ... the problem of producing 'expert system' will substantially be solved". [54]
Several classical AI projects, 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 became obvious that scientists had grossly undervalued the problem of the task. Funding firms became skeptical of AGI and put scientists under increasing pressure to produce useful "used AI". [c] In the early 1980s, Japan's Fifth Generation Computer Project revived interest in AGI, setting out a ten-year timeline that consisted of AGI goals like "carry on a table talk". [58] In response to this and the success of professional systems, both industry and federal government pumped money into the field. [56] [59] However, confidence in AI amazingly collapsed in the late 1980s, and the goals of the Fifth Generation Computer Project were never ever fulfilled. [60] For the 2nd time in twenty years, AI scientists who anticipated the imminent achievement of AGI had been misinterpreted. By the 1990s, AI scientists had a credibility for making vain promises. They became hesitant to make forecasts at all [d] and avoided reference of "human level" artificial intelligence for fear of being labeled "wild-eyed dreamer [s]. [62]
Narrow AI research

In the 1990s and early 21st century, mainstream AI accomplished commercial success and academic respectability by focusing on particular sub-problems where AI can produce verifiable results and business applications, such as speech acknowledgment and suggestion algorithms. [63] These "applied AI" systems are now utilized thoroughly throughout the technology market, and research study in this vein is heavily moneyed in both academia and industry. As of 2018 [update], development in this field was thought about an emerging pattern, and a fully grown stage was anticipated to be reached in more than 10 years. [64]
At the millenium, lots of mainstream AI researchers [65] hoped that strong AI might be developed by combining programs that solve different sub-problems. Hans Moravec composed in 1988:

I am positive that this bottom-up route to expert system will one day fulfill the standard top-down path more than half method, prepared to provide the real-world competence and the commonsense knowledge that has actually been so frustratingly evasive in reasoning 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 contested. For instance, Stevan Harnad of Princeton University concluded his 1990 paper on the symbol grounding hypothesis by stating:

The expectation has frequently been voiced that "top-down" (symbolic) approaches to modeling cognition will somehow 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 feasible route from sense to signs: from the ground up. A free-floating symbolic level like the software application level of a computer will never be reached by this route (or vice versa) - nor is it clear why we need to even try to reach such a level, because it appears arriving would simply amount to uprooting our signs from their intrinsic significances (thus simply minimizing ourselves to the practical equivalent of a programmable computer system). [66]
Modern synthetic basic intelligence research

The term "artificial general intelligence" was used as early as 1997, by Mark Gubrud [67] in a discussion of the implications of totally automated military production and operations. A mathematical formalism of AGI was proposed by Marcus Hutter in 2000. Named AIXI, the proposed AGI representative increases "the capability to satisfy objectives in a large variety of environments". [68] This kind of AGI, characterized by the ability to increase a mathematical definition of intelligence rather than display 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 study activity in 2006 was explained by Pei Wang and Ben Goertzel [72] as "producing publications and initial outcomes". The first summer 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 given up 2010 [74] and 2011 [75] at Plovdiv University, Bulgaria by Todor Arnaudov. MIT presented a course on AGI in 2018, arranged by Lex Fridman and including a number of visitor lecturers.

Since 2023 [update], a little number of computer researchers are active in AGI research study, and many add to a series of AGI conferences. However, significantly more scientists are interested in open-ended knowing, [76] [77] which is the idea of allowing AI to continuously discover and innovate like people do.

Feasibility

As of 2023, the advancement and potential achievement of AGI stays a subject of extreme debate within the AI neighborhood. While standard consensus held that AGI was a remote goal, current advancements have actually led some scientists and industry figures to declare that early kinds of AGI may already exist. [78] AI leader Herbert A. Simon speculated in 1965 that "machines will be capable, within twenty years, of doing any work a man can do". This forecast stopped working to come true. Microsoft co-founder Paul Allen believed that such intelligence is unlikely in the 21st century due to the fact that it would require "unforeseeable and fundamentally unforeseeable breakthroughs" and a "clinically deep understanding of cognition". [79] Writing in The Guardian, roboticist Alan Winfield declared the gulf between modern computing and human-level expert system is as wide as the gulf in between existing area flight and useful faster-than-light spaceflight. [80]
A further difficulty is the absence of clarity in specifying what intelligence involves. Does it need consciousness? Must it display the capability to set objectives in addition to pursue them? Is it simply a matter of scale such that if design sizes increase adequately, intelligence will emerge? Are facilities such as preparation, thinking, and causal understanding needed? Does intelligence need explicitly duplicating the brain and its specific faculties? Does it require feelings? [81]
Most AI scientists think strong AI can be accomplished in the future, however some thinkers, like Hubert Dreyfus and Roger Penrose, deny the possibility of achieving strong AI. [82] [83] John McCarthy is amongst those who think human-level AI will be accomplished, but that today level of development is such that a date can not accurately be forecasted. [84] AI professionals' views on the feasibility of AGI wax and subside. Four surveys conducted in 2012 and 2013 suggested that the median quote among specialists for when they would be 50% confident AGI would show up was 2040 to 2050, depending on the poll, with the mean being 2081. Of the experts, 16.5% addressed with "never ever" when asked the very same concern however with a 90% confidence rather. [85] [86] Further existing AGI development considerations 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 amount of time there is a strong predisposition towards anticipating the arrival of human-level AI as in between 15 and 25 years from the time the prediction was made". They analyzed 95 forecasts made between 1950 and 2012 on when human-level AI will come about. [87]
In 2023, Microsoft researchers published a detailed examination of GPT-4. They concluded: "Given the breadth and depth of GPT-4's abilities, we think that it could fairly be seen as an early (yet still insufficient) version of an artificial basic intelligence (AGI) system." [88] Another study in 2023 reported that GPT-4 outshines 99% of humans on the Torrance tests of creativity. [89] [90]
Blaise Agüera y Arcas and Peter Norvig wrote in 2023 that a considerable level of basic intelligence has actually currently been achieved with frontier models. They wrote that reluctance to this view originates from four main factors: a "healthy suspicion about metrics for AGI", an "ideological commitment to alternative AI theories or techniques", a "commitment to human (or biological) exceptionalism", or a "issue about the economic implications of AGI". [91]
2023 also marked the emergence of large multimodal models (large language designs efficient in processing or generating numerous 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 reacting represents a new, additional paradigm. It improves model outputs by investing more computing power when generating the answer, whereas the design 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, mentioning, "In my opinion, we have actually currently accomplished AGI and it's a lot more clear with O1." Kazemi clarified that while the AI is not yet "much better than any human at any job", it is "better than a lot of people at the majority of tasks." He also attended to criticisms that big language models (LLMs) simply follow predefined patterns, comparing their knowing procedure to the scientific technique of observing, hypothesizing, and validating. These statements have stimulated debate, as they count on a broad and non-traditional meaning of AGI-traditionally understood as AI that matches human intelligence across all domains. Critics argue that, while OpenAI's designs show impressive flexibility, they might not totally fulfill this requirement. Notably, Kazemi's comments came quickly after OpenAI got rid of "AGI" from the terms of its collaboration with Microsoft, triggering speculation about the business's strategic intentions. [95]
Timescales

Progress in expert system has historically gone through durations of fast progress separated by durations when development appeared to stop. [82] Ending each hiatus were basic advances in hardware, software application or both to create area for more progress. [82] [98] [99] For example, the hardware offered in the twentieth century was not adequate to execute deep learning, which needs great deals of GPU-enabled CPUs. [100]
In the intro to his 2006 book, [101] Goertzel says that estimates of the time required before a really flexible AGI is constructed vary from ten years to over a century. Since 2007 [upgrade], the agreement in the AGI research study community appeared to be that the timeline talked about by Ray Kurzweil in 2005 in The Singularity is Near [102] (i.e. in between 2015 and 2045) was possible. [103] Mainstream AI scientists have actually provided a wide variety of viewpoints on whether development will be this quick. A 2012 meta-analysis of 95 such viewpoints discovered a bias towards predicting that the start of AGI would occur within 16-26 years for modern and historical forecasts alike. That paper has actually been criticized for how it classified viewpoints as professional or non-expert. [104]
In 2012, Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton developed a neural network called AlexNet, which won the ImageNet competition with a top-5 test error rate of 15.3%, significantly better than the second-best entry's rate of 26.3% (the conventional method used a weighted amount of scores from different pre-defined classifiers). [105] AlexNet was considered the preliminary ground-breaker of the current deep learning wave. [105]
In 2017, scientists Feng Liu, Yong Shi, and Ying Liu performed intelligence tests on openly offered and freely available weak AI such as Google AI, Apple's Siri, and others. At the optimum, these AIs reached an IQ value of about 47, which corresponds around to a six-year-old kid in very first grade. A grownup concerns about 100 typically. Similar tests were carried out in 2014, with the IQ score reaching a maximum worth of 27. [106] [107]
In 2020, OpenAI established GPT-3, a language model efficient in carrying out many varied jobs without specific training. According to Gary Grossman in a VentureBeat article, while there is consensus that GPT-3 is not an example of AGI, it is thought about by some to be too advanced to be categorized as a narrow AI system. [108]
In the exact same year, Jason Rohrer used his GPT-3 account to develop a chatbot, and provided a chatbot-developing platform called "Project December". OpenAI requested modifications to the chatbot to comply with their safety standards; Rohrer disconnected Project December from the GPT-3 API. [109]
In 2022, DeepMind established Gato, a "general-purpose" system capable of performing more than 600 different jobs. [110]
In 2023, Microsoft Research released a research study on an early variation of OpenAI's GPT-4, competing that it showed more basic intelligence than previous AI designs and showed human-level performance in jobs spanning numerous domains, such as mathematics, coding, and law. This research study sparked a dispute on whether GPT-4 might be considered an early, insufficient variation of artificial basic intelligence, highlighting the need for more expedition and evaluation of such systems. [111]
In 2023, the AI scientist Geoffrey Hinton mentioned that: [112]
The idea that this things might really get smarter than people - a few people believed that, [...] But the majority of people believed it was way off. And I believed it was method off. I believed it was 30 to 50 years or perhaps longer away. Obviously, I no longer think that.

In May 2023, Demis Hassabis similarly said that "The progress in the last couple of years has actually been quite incredible", which he sees no reason that it would decrease, anticipating AGI within a decade or perhaps a few years. [113] In March 2024, Nvidia's CEO, Jensen Huang, mentioned his expectation that within five years, AI would can passing any test a minimum of in addition to people. [114] In June 2024, the AI scientist Leopold Aschenbrenner, a former OpenAI employee, estimated AGI by 2027 to be "strikingly plausible". [115]
Whole brain emulation

While the advancement of transformer designs like in ChatGPT is thought about the most promising course to AGI, [116] [117] whole brain emulation can act as an alternative method. With whole brain simulation, a brain model is built by scanning and mapping a biological brain in detail, and after that copying and mimicing it on a computer system or another computational device. The simulation design must be sufficiently faithful to the original, so that it acts in practically the same method 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 actually been talked about in expert system research study [103] as a technique to strong AI. Neuroimaging technologies that might provide the required in-depth understanding are improving rapidly, and futurist Ray Kurzweil in the book The Singularity Is Near [102] predicts that a map of sufficient quality will become available on a similar timescale to the computing power needed to replicate it.

Early estimates

For low-level brain simulation, a really powerful cluster of computer systems or GPUs would be required, provided the enormous 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 neurons. The brain of a three-year-old child has about 1015 synapses (1 quadrillion). This number decreases with age, supporting by adulthood. 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 upon a basic switch design for nerve cell activity, is around 1014 (100 trillion) synaptic updates per second (SUPS). [121]
In 1997, Kurzweil looked at numerous price quotes for the hardware needed to equal the human brain and adopted a figure of 1016 computations per second (cps). [e] (For comparison, if a "computation" was equivalent to one "floating-point operation" - a procedure utilized to rate existing supercomputers - then 1016 "computations" would be comparable to 10 petaFLOPS, accomplished in 2011, while 1018 was accomplished in 2022.) He utilized this figure to predict the required hardware would be offered at some point between 2015 and 2025, if the rapid development in computer power at the time of composing continued.

Current research

The Human Brain Project, an EU-funded initiative active from 2013 to 2023, has established a particularly detailed and openly accessible 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 methods

The synthetic nerve cell model presumed by Kurzweil and utilized in lots of current artificial neural network executions is basic compared to biological neurons. A brain simulation would likely have to record the comprehensive cellular behaviour of biological nerve cells, currently understood only in broad outline. The overhead presented by complete modeling of the biological, chemical, and physical information of neural behaviour (particularly on a molecular scale) would require computational powers numerous orders of magnitude larger than Kurzweil's estimate. In addition, the quotes do not represent glial cells, which are understood to contribute in cognitive processes. [125]
A fundamental criticism of the simulated brain approach originates from embodied cognition theory which asserts that human personification is a vital aspect of human intelligence and is necessary to ground significance. [126] [127] If this theory is proper, any completely practical brain model will need to incorporate more than just the nerve cells (e.g., a robotic body). Goertzel [103] proposes virtual embodiment (like in metaverses like Second Life) as an option, but it is unidentified whether this would suffice.

Philosophical point of view

"Strong AI" as specified in viewpoint

In 1980, thinker John Searle created the term "strong AI" as part of his Chinese space argument. [128] He proposed a difference between two hypotheses about expert system: [f]
Strong AI hypothesis: A synthetic intelligence system can have "a mind" and "awareness". Weak AI hypothesis: An artificial intelligence system can (just) act like it thinks and has a mind and consciousness.
The first one he called "strong" due to the fact that it makes a more powerful statement: it assumes something unique has actually happened to the machine that surpasses those capabilities that we can test. The behaviour of a "weak AI" maker would be specifically identical to a "strong AI" machine, but the latter would likewise have subjective conscious experience. This use is likewise common in scholastic AI research and books. [129]
In contrast to Searle and mainstream AI, some futurists such as Ray Kurzweil use the term "strong AI" to mean "human level artificial general intelligence". [102] This is not the like Searle's strong AI, unless it is presumed that consciousness is essential for human-level AGI. Academic thinkers such as Searle do not think that is the case, and to most expert system scientists the concern is out-of-scope. [130]
Mainstream AI is most interested in how a program acts. [131] According to Russell and Norvig, "as long as the program works, they don't care if you call it real or a simulation." [130] If the program can act as if it has a mind, then there is no need to understand if it in fact has mind - undoubtedly, there would be no other way to inform. For AI research study, Searle's "weak AI hypothesis" is comparable to the statement "synthetic general intelligence is possible". Thus, according to Russell and Norvig, "most AI researchers take the weak AI hypothesis for granted, and do not care about the strong AI hypothesis." [130] Thus, for academic AI research study, "Strong AI" and "AGI" are 2 various things.

Consciousness

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

Sentience (or "sensational awareness"): The ability to "feel" perceptions or feelings subjectively, rather than the capability to reason about perceptions. Some theorists, such as David Chalmers, use the term "consciousness" to refer solely to extraordinary consciousness, which is roughly equivalent to life. [132] Determining why and how subjective experience arises is called the difficult problem of consciousness. [133] Thomas Nagel explained in 1974 that it "seems like" something to be mindful. If we are not mindful, then it does not feel like anything. Nagel 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 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 company's AI chatbot, LaMDA, had accomplished life, though this claim was extensively disputed by other experts. [135]
Self-awareness: To have conscious awareness of oneself as a separate person, particularly to be knowingly conscious of one's own thoughts. This is opposed to simply being the "topic of one's believed"-an operating system or debugger has the ability to be "familiar with itself" (that is, to represent itself in the exact same way it represents everything else)-but this is not what individuals normally suggest when they utilize the term "self-awareness". [g]
These characteristics have a moral dimension. AI sentience would provide rise to concerns of welfare and legal defense, similarly to animals. [136] Other aspects of awareness related to cognitive abilities are likewise relevant to the principle of AI rights. [137] Finding out how to integrate innovative AI with existing legal and social structures is an emergent concern. [138]
Benefits

AGI could have a variety of applications. If oriented towards such objectives, AGI could assist alleviate numerous problems on the planet such as hunger, poverty and health issues. [139]
AGI might enhance productivity and efficiency in a lot of tasks. For example, in public health, AGI could accelerate medical research, significantly against cancer. [140] It could take care of the senior, [141] and democratize access to fast, high-quality medical diagnostics. It could offer fun, inexpensive and personalized education. [141] The requirement to work to subsist might end up being obsolete if the wealth produced is correctly rearranged. [141] [142] This likewise raises the question of the place of humans in a significantly automated society.

AGI could likewise assist to make reasonable choices, and to anticipate and prevent catastrophes. It might also help to gain the advantages of potentially catastrophic technologies such as nanotechnology or environment engineering, while preventing the associated dangers. [143] If an AGI's primary objective is to avoid existential catastrophes such as human extinction (which might be tough if the Vulnerable World Hypothesis ends up being true), [144] it might take measures to dramatically reduce the risks [143] while reducing the impact of these procedures on our quality of life.

Risks

Existential threats

AGI might represent multiple types of existential danger, which are dangers that threaten "the early extinction of Earth-originating intelligent life or the irreversible and extreme destruction of its potential for preferable future development". [145] The risk of human extinction from AGI has been the subject of numerous disputes, but there is also the possibility that the advancement of AGI would cause a completely flawed future. Notably, it might be used to spread and protect the set of values of whoever develops it. If humankind still has ethical blind areas similar to slavery in the past, AGI may irreversibly entrench it, avoiding moral progress. [146] Furthermore, AGI might help with mass security and brainwashing, which could be used to create a steady repressive worldwide totalitarian program. [147] [148] There is likewise a threat for the devices themselves. If makers that are sentient or otherwise worthy of moral factor to consider are mass developed in the future, engaging in a civilizational course that forever ignores their well-being and interests might be an existential catastrophe. [149] [150] Considering just how much AGI could improve mankind's future and help in reducing other existential risks, Toby Ord calls these existential threats "an argument for proceeding with due care", not for "abandoning AI". [147]
Risk of loss of control and human extinction

The thesis that AI presents an existential danger for human beings, and that this danger requires more attention, is questionable but has been endorsed in 2023 by many 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 criticized extensive indifference:

So, facing possible futures of incalculable advantages and risks, the specialists are surely doing everything possible to guarantee the very best outcome, right? Wrong. If a remarkable alien civilisation sent us a message stating, 'We'll arrive in a few decades,' would we simply reply, '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 possible fate of mankind has actually sometimes been compared to the fate of gorillas threatened by human activities. The contrast mentions that greater intelligence allowed mankind to control gorillas, which are now vulnerable in manner ins which they could not have actually anticipated. As a result, the gorilla has actually become an endangered species, not out of malice, however simply as a collateral damage from human activities. [154]
The skeptic Yann LeCun thinks about that AGIs will have no desire to control humankind which we need to take care not to anthropomorphize them and interpret their intents as we would for human beings. He said that people won't be "wise enough to develop super-intelligent devices, yet ridiculously foolish to the point of providing it moronic objectives without any safeguards". [155] On the other side, the principle of critical merging suggests that nearly whatever their objectives, intelligent agents will have factors to try to make it through and get more power as intermediary steps to attaining these objectives. And that this does not require having feelings. [156]
Many scholars who are worried about existential risk advocate for more research into fixing the "control issue" to answer the concern: what kinds of safeguards, algorithms, or architectures can developers execute to maximise the probability 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 issue is complicated by the AI arms race (which could cause a race to the bottom of safety precautions in order to launch items before competitors), [159] and making use of AI in weapon systems. [160]
The thesis that AI can pose existential danger also has detractors. Skeptics typically say that AGI is unlikely in the short-term, or that concerns about AGI sidetrack from other problems associated with present AI. [161] Former Google scams czar Shuman Ghosemajumder considers that for many individuals beyond the innovation industry, existing chatbots and LLMs are currently perceived as though they were AGI, resulting in additional misunderstanding and fear. [162]
Skeptics in some cases charge that the thesis is crypto-religious, with an illogical belief in the possibility of superintelligence replacing an irrational belief in an omnipotent God. [163] Some scientists think that the communication campaigns on AI existential danger by particular AI groups (such as OpenAI, Anthropic, DeepMind, and Conjecture) might be an at attempt at regulative capture and to inflate interest in their products. [164] [165]
In 2023, the CEOs of Google DeepMind, OpenAI and Anthropic, along with other market leaders and researchers, provided a joint declaration asserting that "Mitigating the danger of extinction from AI ought to be a worldwide concern together with other societal-scale threats such as pandemics and nuclear war." [152]
Mass joblessness

Researchers from OpenAI estimated that "80% of the U.S. workforce could have at least 10% of their work tasks impacted by the intro of LLMs, while around 19% of workers may see at least 50% of their jobs impacted". [166] [167] They consider office workers to be the most exposed, for instance mathematicians, accounting professionals or web designers. [167] AGI might have a better autonomy, capability to make decisions, to interface with other computer tools, however likewise to control robotized bodies.

According to Stephen Hawking, the outcome of automation on the lifestyle will depend on how the wealth will be redistributed: [142]
Everyone can delight in a life of luxurious leisure if the machine-produced wealth is shared, or most people can end up badly poor if the machine-owners successfully lobby against wealth redistribution. Up until now, the trend appears to be toward the 2nd option, with technology driving ever-increasing inequality

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

Artificial brain - Software and hardware with cognitive capabilities similar to those of the animal or human brain AI result AI safety - Research location on making AI safe and helpful AI positioning - AI conformance to the designated objective A.I. Rising - 2018 movie directed by Lazar Bodroža Expert system Automated machine knowing - Process of automating the application of maker knowing BRAIN Initiative - Collaborative public-private research 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 games Generative expert system - AI system efficient in producing content in reaction to triggers Human Brain Project - Scientific research study project Intelligence amplification - Use of details innovation to augment human intelligence (IA). Machine ethics - Moral behaviours of man-made makers. Moravec's paradox. Multi-task learning - Solving multiple machine finding out jobs at the very same time. Neural scaling law - Statistical law in artificial intelligence. Outline of expert system - Overview of and topical guide to expert system. Transhumanism - Philosophical motion. Synthetic intelligence - Alternate term for or form of artificial intelligence. Transfer knowing - Machine learning method. Loebner Prize - Annual AI competitors. Hardware for artificial intelligence - Hardware specially designed and enhanced for expert system. Weak expert system - Form of artificial intelligence.
Notes

^ a b See listed below for the origin of the term "strong AI", and see the academic meaning of "strong AI" and weak AI in the post Chinese room. ^ AI founder John McCarthy composes: "we can not yet characterize in general what kinds of computational procedures we want to call smart. " [26] (For a conversation of some meanings of intelligence utilized by artificial intelligence researchers, see viewpoint of expert system.). ^ The Lighthill report particularly criticized AI's "grandiose goals" and led the taking apart of AI research study in England. [55] In the U.S., DARPA ended up being figured out to fund just "mission-oriented direct research study, instead of fundamental undirected research study". [56] [57] ^ As AI creator John McCarthy composes "it would be a terrific relief to the remainder of the workers in AI if the inventors of new general formalisms would reveal their hopes in a more secured type than has in some cases been the case." [61] ^ In "Mind Children" [122] 1015 cps is utilized. More just recently, in 1997, [123] Moravec argued for 108 MIPS which would roughly correspond to 1014 cps. Moravec talks in terms of MIPS, not "cps", which is a non-standard term Kurzweil presented. ^ As defined in a standard AI textbook: "The assertion that machines might perhaps act intelligently (or, possibly better, act as if they were smart) is called the 'weak AI' hypothesis by thinkers, and the assertion that machines that do so are actually 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 synthetic 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 mission is to guarantee that artificial basic intelligence benefits all of humankind. ^ Heath, Alex (18 January 2024). "Mark Zuckerberg's brand-new objective is developing artificial general intelligence". The Verge. Retrieved 13 June 2024. Our vision is to develop AI that is much better than human-level at all of the human senses. ^ Baum, Seth D. (2020 ). A Study of Artificial General Intelligence Projects for Ethics, Risk, and Policy (PDF) (Report). Global Catastrophic Risk Institute. Retrieved 28 November 2024. 72 AGI R&D jobs were recognized as being active in 2020. ^ a b c "AI timelines: What do professionals in expert system anticipate for the future?". Our World in Data. Retrieved 6 April 2023. ^ Metz, Cade (15 May 2023). "Some Researchers Say A.I. Is Already Here, Stirring Debate in Tech Circles". The New York Times. Retrieved 18 May 2023. ^ "AI pioneer Geoffrey Hinton quits Google and cautions 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 stars from utilizing it for bad things. ^ Bubeck, Sébastien; Chandrasekaran, Varun; Eldan, Ronen; Gehrke, Johannes; Horvitz, Eric (2023 ). "Sparks of Artificial General Intelligence: Early explores GPT-4". arXiv preprint. arXiv:2303.12712. GPT-4 shows triggers of AGI. ^ Butler, Octavia E. (1993 ). Parable of the Sower. Grand Central Publishing. ISBN 978-0-4466-7550-5. All that you touch you alter. All that you change modifications 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 Times. The genuine threat is not AI itself however the method we deploy it. ^ "Impressed by artificial intelligence? Experts say AGI is coming next, and it has 'existential' dangers". ABC News. 23 March 2023. Retrieved 6 April 2023. AGI might pose existential dangers to humanity. ^ Bostrom, Nick (2014 ). Superintelligence: Paths, Dangers, Strategies. Oxford University Press. ISBN 978-0-1996-7811-2. The first superintelligence will be the last invention that humankind requires to make. ^ Roose, Kevin (30 May 2023). "A.I. Poses 'Risk of Extinction,' Industry Leaders Warn". The New York Times. Mitigating the danger of extinction from AI must be a worldwide concern. ^ "Statement on AI Risk". Center for AI Safety. Retrieved 1 March 2024. AI specialists 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 devices that can outthink us in general ways. ^ LeCun, Yann (June 2023). "AGI does not present an existential threat". Medium. There is no reason to fear AI as an existential danger. ^ Kurzweil 2005, p. 260. ^ a b Kurzweil, Ray (5 August 2005), "Long Live AI", Forbes, archived from the original on 14 August 2005: Kurzweil explains strong AI as "device intelligence with the full range of human intelligence.". ^ "The Age of Expert System: 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 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. ^ "Expert system is transforming our world - it is on everyone to make certain that it works out". Our World in Data. Retrieved 8 October 2023. ^ Dickson, Ben (16 November 2023). "Here is how far we are to achieving AGI, according to DeepMind". VentureBeat. ^ McCarthy, John (2007a). "Basic Questions". Stanford University. Archived from the original on 26 October 2007. Retrieved 6 December 2007. ^ This list of smart qualities is based upon the subjects covered by major AI textbooks, including: Russell & Norvig 2003, Luger & Stubblefield 2004, Poole, Mackworth & Goebel 1998 and Nilsson 1998. ^ Johnson 1987. ^ de Charms, R. (1968 ). Personal causation. New York City: Academic Press. ^ a b Pfeifer, R. and Bongard J. C., How the body forms the way we believe: a brand-new view of intelligence (The MIT Press, 2007). ISBN 0-2621-6239-3. ^ White, R. W. (1959 ). "Motivation reconsidered: The principle of proficiency". Psychological Review. 66 (5 ): 297-333. doi:10.1037/ h0040934. PMID 13844397. S2CID 37385966. ^ White, R. W. (1959 ). "Motivation reconsidered: The idea of skills". Psychological Review. 66 (5 ): 297-333. doi:10.1037/ h0040934. PMID 13844397. S2CID 37385966. ^ Muehlhauser, Luke (11 August 2013). "What is AGI?". Machine Intelligence Research Institute. Archived from the original on 25 April 2014. Retrieved 1 May 2014. ^ "What is Artificial General Intelligence (AGI)?|4 Tests For Ensuring Artificial General Intelligence". Talky Blog. 13 July 2019. Archived from the original on 17 July 2019. Retrieved 17 July 2019. ^ Kirk-Giannini, Cameron Domenico; Goldstein, Simon (16 October 2023). "AI is closer than ever to passing the Turing test for 'intelligence'. What occurs when it does?". The Conversation. Retrieved 22 September 2024. ^ a b Turing 1950. ^ Turing, Alan (1952 ). B. Jack Copeland (ed.). Can Automatic Calculating Machines Be Said To Think?. Oxford: Oxford University Press. pp. 487-506. ISBN 978-0-1982-5079-1. ^ "Eugene Goostman is a real boy - the Turing Test says 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 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 whatever from the bar exam to AP Biology. Here's a list of difficult examinations both AI variations have passed". Business Insider. Retrieved 30 May 2023. ^ Naysmith, Caleb (7 February 2023). "6 Jobs Expert System Is Already Replacing and How Investors Can Capitalize on It". Retrieved 30 May 2023. ^ Turk, Victoria (28 January 2015). "The Plan to Replace the Turing Test with a 'Turing Olympics'". Vice. Retrieved 3 March 2024. ^ Gopani, Avi (25 May 2022). "Turing Test is unreliable. The Winograd Schema is obsolete. Coffee is the response". Analytics India Magazine. Retrieved 3 March 2024. ^ Bhaimiya, Sawdah (20 June 2023). "DeepMind's co-founder suggested checking 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 Expert System (Second ed.). New York City: John Wiley. pp. 54-57. Archived (PDF) from the original on 1 February 2016. (Section 4 is on "AI-Complete Tasks".). ^ Yampolskiy, Roman V. (2012 ). Xin-She Yang (ed.). "Turing Test as a Defining Feature of AI-Completeness" (PDF). 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 Expert System. 15 April 2024. Retrieved 27 May 2024. ^ Crevier 1993, pp. 48-50. ^ Kaplan, Andreas (2022 ). "Expert System, Business and Civilization - Our Fate Made in Machines". Archived from the original on 6 May 2022. Retrieved 12 March 2022. ^ Simon 1965, p. 96 estimated in Crevier 1993, p. 109. ^ "Scientist on the Set: An Interview with Marvin Minsky". Archived from the 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 also Feigenbaum & McCorduck 1983. ^ Crevier 1993, pp. 161-162, 197-203, 240; Russell & Norvig 2003, p. 25. ^ Crevier 1993, pp. 209-212. ^ McCarthy, John (2000 ). "Reply to Lighthill". Stanford University. Archived from the original on 30 September 2008. Retrieved 29 September 2007. ^ Markoff, John (14 October 2005). "Behind 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 system scientists and software engineers avoided the term expert system for fear of being considered as wild-eyed dreamers. ^ Russell & Norvig 2003, pp. 25-26 ^ "Trends in the Emerging Tech Hype Cycle". Gartner Reports. Archived from the original on 22 May 2019. Retrieved 7 May 2019. ^ a b Moravec 1988, p. 20 ^ Harnad, S. (1990 ). "The Symbol Grounding Problem". Physica D. 42 (1-3): 335-346. arXiv: cs/9906002. Bibcode:1990 PhyD ... 42..335 H. doi:10.1016/ 0167-2789( 90 )90087-6. S2CID 3204300. ^ Gubrud 1997 ^ Hutter, Marcus (2005 ). Universal Expert System: Sequential Decisions Based on Algorithmic Probability. Texts in Theoretical Computer Technology an EATCS Series. Springer. doi:10.1007/ b138233. ISBN 978-3-5402-6877-2. S2CID 33352850. Archived from the initial on 19 July 2022. Retrieved 19 July 2022. ^ Legg, Shane (2008 ). Machine Super Intelligence (PDF) (Thesis). University of Lugano. Archived (PDF) from the initial on 15 June 2022. Retrieved 19 July 2022. ^ Goertzel, Ben (2014 ). Artificial General Intelligence. Lecture Notes in Computer Science. Vol. 8598. Journal of Artificial General Intelligence. doi:10.1007/ 978-3-319-09274-4. ISBN 978-3-3190-9273-7. S2CID 8387410. ^ "Who coined the term "AGI"?". goertzel.org. Archived from the initial on 28 December 2018. Retrieved 28 December 2018., 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 original on 26 July 2020. Retrieved 11 May 2020. ^ "Избираеми дисциплини 2010/2011 - зимен триместър" [Elective courses 2010/2011 - winter trimester] Факултет по математика и информатика [Faculty of Mathematics and Informatics] (in Bulgarian). Archived from the initial 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 maker intelligence, synthetic basic intelligence is still a significant obstacle". EMBO Reports. 20 (10 ): e49177. doi:10.15252/ embr.201949177. ISSN 1469-221X. PMC 6776890. PMID 31531926. ^ Bubeck, Sébastien; Chandrasekaran, Varun; Eldan, Ronen; Gehrke, Johannes; Horvitz, Eric; Kamar, Ece; Lee, Peter; Lee, Yin Tat; Li, Yuanzhi; Lundberg, Scott; Nori, Harsha; Palangi, Hamid; Ribeiro, Marco Tulio; Zhang, Yi (27 March 2023). "Sparks of Artificial General Intelligence: Early explores GPT-4". arXiv:2303.12712 [cs.CL] ^ "Microsoft Researchers Claim GPT-4 Is Showing "Sparks" of AGI". Futurism. 23 March 2023. Retrieved 13 December 2023. ^ Allen, Paul; Greaves, Mark (12 October 2011). "The Singularity Isn't Near". MIT Technology Review. Retrieved 17 September 2014. ^ Winfield, Alan. "Expert system will not develop into a Frankenstein's monster". The Guardian. Archived from the initial on 17 September 2014. Retrieved 17 September 2014. ^ Deane, George (2022 ). "Machines That Feel and Think: The Role of Affective Feelings and Mental Action in (Artificial) General Intelligence". Artificial Life. 28 (3 ): 289-309. doi:10.1162/ artl_a_00368. ISSN 1064-5462. PMID 35881678. S2CID 251069071. ^ a b c Clocksin 2003. ^ Fjelland, Ragnar (17 June 2020). "Why general synthetic intelligence will not be realized". Humanities and Social Sciences Communications. 7 (1 ): 1-9. doi:10.1057/ s41599-020-0494-4. hdl:11250/ 2726984. ISSN 2662-9992. S2CID 219710554. ^ McCarthy 2007b. ^ Khatchadourian, Raffi (23 November 2015). "The Doomsday Invention: Will expert system bring us utopia or damage?". The New Yorker. Archived from the original on 28 January 2016. Retrieved 7 February 2016. ^ Müller, V. C., & Bostrom, N. (2016 ). Future development in expert system: A survey of professional viewpoint. In Fundamental problems of expert system (pp. 555-572). Springer, Cham. ^ Armstrong, Stuart, and Kaj Sotala. 2012. "How We're Predicting AI-or Failing To." In Beyond AI: Artificial Dreams, edited by Jan Romportl, Pavel Ircing, Eva Žáčková, Michal Polák and Radek Schuster, 52-75. Plzeň: University of West Bohemia ^ "Microsoft Now Claims GPT-4 Shows 'Sparks' of General Intelligence". 24 March 2023. ^ Shimek, Cary (6 July 2023). "AI Outperforms Humans in Creativity Test". Neuroscience News. Retrieved 20 October 2023. ^ Guzik, Erik E.; Byrge, Christian; Gilde, Christian (1 December 2023). "The creativity of devices: AI takes the Torrance Test". Journal of Creativity. 33 (3 ): 100065. doi:10.1016/ j.yjoc.2023.100065. ISSN 2713-3745. S2CID 261087185. ^ Arcas, Blaise Agüera y (10 October 2023). "Artificial General Intelligence Is Already Here". Noema. ^ Zia, Tehseen (8 January 2024). "Unveiling of Large Multimodal Models: Shaping the Landscape of Language Models in 2024". Unite.ai. Retrieved 26 May 2024. ^ "Introducing OpenAI o1-preview". OpenAI. 12 September 2024. ^ Knight, Will. "OpenAI Announces a Brand-new AI Model, Code-Named Strawberry, That Solves Difficult Problems Step by Step". Wired. ISSN 1059-1028. Retrieved 17 September 2024. ^ "OpenAI Employee Claims AGI Has Been Achieved". Orbital Today. 13 December 2024. Retrieved 27 December 2024. ^ "AI Index: State of AI in 13 Charts". hai.stanford.edu. 15 April 2024. Retrieved 7 June 2024. ^ "Next-Gen AI: OpenAI and Meta's Leap Towards Reasoning Machines". Unite.ai. 19 April 2024. Retrieved 7 June 2024. ^ James, Alex P. (2022 ). "The Why, What, and How of Artificial General Intelligence Chip Development". IEEE Transactions on Cognitive and Developmental Systems. 14 (2 ): 333-347. arXiv:2012.06338. doi:10.1109/ TCDS.2021.3069871. ISSN 2379-8920. S2CID 228376556. Archived from the initial on 28 August 2022. Retrieved 28 August 2022. ^ Pei, Jing; Deng, Lei; Song, Sen; Zhao, Mingguo; Zhang, Youhui; Wu, Shuang; Wang, Guanrui; Zou, Zhe; Wu, Zhenzhi; He, Wei; Chen, Feng; Deng, Ning; Wu, Si; Wang, Yu; Wu, Yujie (2019 ). "Towards synthetic general intelligence with hybrid Tianjic chip architecture". Nature. 572 (7767 ): 106-111. Bibcode:2019 Natur.572..106 P. doi:10.1038/ s41586-019-1424-8. ISSN 1476-4687. PMID 31367028. S2CID 199056116. Archived from the original on 29 August 2022. Retrieved 29 August 2022. ^ Pandey, Mohit; Fernandez, Michael; Gentile, Francesco; Isayev, Olexandr; Tropsha, Alexander; Stern, Abraham C.; Cherkasov, Artem (March 2022). "The transformational function of GPU computing and deep knowing in drug discovery". Nature Machine Intelligence. 4 (3 ): 211-221. doi:10.1038/ s42256-022-00463-x. ISSN 2522-5839. S2CID 252081559. ^ Goertzel & Pennachin 2006. ^ a b c (Kurzweil 2005, p. 260). ^ a b c Goertzel 2007. ^ Grace, Katja (2016 ). "Error in Armstrong and Sotala 2012". AI Impacts (blog site). Archived from the original on 4 December 2020. Retrieved 24 August 2020. ^ a b Butz, Martin V. (1 March 2021). "Towards Strong AI". KI - Künstliche Intelligenz. 35 (1 ): 91-101. doi:10.1007/ s13218-021-00705-x. ISSN 1610-1987. S2CID 256065190. ^ Liu, Feng; Shi, Yong; Liu, Ying (2017 ). "Intelligence Quotient and Intelligence Grade of 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 - 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 getting in the AI golden zone between narrow and basic AI". VentureBeat. Archived from the original on 4 September 2020. Retrieved 5 September 2020. Certainly, too, there are those who claim we are already seeing an early example of an AGI system in the just recently announced GPT-3 natural language processing (NLP) neural network. ... So is GPT-3 the very first example of an AGI system? This is debatable, but the consensus is that it is not AGI. ... If nothing else, GPT-3 tells us there is a happy medium between narrow and general AI. ^ Quach, Katyanna. "A developer 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 brand-new AI can carry out over 600 jobs, from playing games to controlling robots", TechCrunch, archived from the initial 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 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 'just a few years,' says CEO of Google's primary A.I. research study lab". Fortune. Retrieved 4 September 2024. ^ Nellis, Stephen (2 March 2024). "Nvidia CEO states AI might pass human tests in five years". Reuters. ^ Aschenbrenner, Leopold. "SITUATIONAL AWARENESS, The Decade Ahead". ^ Sullivan, Mark (18 October 2023). "Why everyone 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 study project 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 large range 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 might 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 (priced quote 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 artificial intelligence for curiosity by the field about why a program behaves the way it does. ^ Chalmers, David J. (9 August 2023). "Could a Large Language Model Be Conscious?". Boston Review. ^ Seth, Anil. "Consciousness". New Scientist. Retrieved 5 September 2024. ^ Nagel 1974. ^ "The Google engineer who thinks the 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 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 exceed 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 Look 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: paths, dangers, techniques (Reprinted with corrections 2017 ed.). Oxford, UK; 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 human beings will damage ourselves". Vox. Retrieved 8 June 2023. ^ Doherty, Ben (17 May 2018). "Climate alter an 'existential security danger' to Australia, Senate questions says". 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 Artificial Intelligence". The Precipice: Existential Risk and the Future of Humanity. Bloomsbury Publishing. ISBN 978-1-5266-0021-9. ^ Al-Sibai, Noor (13 February 2022). "OpenAI Chief Scientist Says Advanced AI May Already Be Conscious". Futurism. Retrieved 24 December 2023. ^ Samuelsson, Paul Conrad (2019 ). "Artificial Consciousness: Our Greatest Ethical Challenge". Philosophy Now. Retrieved 23 December 2023. ^ Kateman, Brian (24 July 2023). "AI Should Be Terrified of Humans". TIME. Retrieved 23 December 2023. ^ Roose, Kevin (30 May 2023). "A.I. Poses 'Risk of Extinction,' Industry Leaders Warn". The New York Times. ISSN 0362-4331. Retrieved 24 December 2023. ^ a b "Statement on AI Risk". Center for AI Safety. 30 May 2023. Retrieved 8 June 2023. ^ "Stephen Hawking: 'Transcendence looks 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 fascinating Facebook argument in between Yann LeCun, Stuart Russel and Yoshua Bengio about the risks of strong AI". The interesting Facebook debate between Yann LeCun, Stuart Russel and Yoshua Bengio about the threats of strong AI (in French). Retrieved 8 June 2023. ^ "Will Artificial Intelligence Doom The 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 disastrous AGI threat: a survey". Physica Scripta. 90 (1 ): 018001. doi:10.1088/ 0031-8949/90/ 1/018001. ISSN 0031-8949. ^ Bostrom, Nick (2014 ). Superintelligence: Paths, Dangers, Strategies (First ed.). Oxford University Press. ISBN 978-0-1996-7811-2. ^ Chow, Andrew R.; Perrigo, Billy (16 February 2023). "The AI Arms Race Is On. Start Worrying". TIME. Retrieved 24 December 2023. ^ Tetlow, Gemma (12 January 2017). "AI arms race threats spiralling out of control, report cautions". Financial Times. Archived from the initial on 11 April 2022. Retrieved 24 December 2023. ^ Milmo, Dan; Stacey, Kiran (25 September 2023). "Experts disagree over threat presented however artificial intelligence can not be overlooked". The Guardian. ISSN 0261-3077. Retrieved 24 December 2023. ^ "Humanity, Security & AI, Oh My! (with Ian Bremmer & Shuman Ghosemajumder)". CAFE. 20 July 2023. Retrieved 15 September 2023. ^ Hamblin, James (9 May 2014). "But What Would completion of Humanity Mean for Me?". The Atlantic. Archived from the initial on 4 June 2014. Retrieved 12 December 2015. ^ Titcomb, James (30 October 2023). "Big Tech is stiring fears over AI, caution researchers". The Telegraph. Retrieved 7 December 2023. ^ Davidson, John (30 October 2023). "Google Brain founder states huge tech is lying about AI threat". Australian Financial Review. Archived from the initial on 7 December 2023. Retrieved 7 December 2023. ^ Eloundou, Tyna; Manning, Sam; Mishkin, Pamela; Rock, Daniel (17 March 2023). "GPTs are GPTs: An early take a look at the labor market impact potential of big language models". OpenAI. Retrieved 7 June 2023. ^ a b Hurst, Luke (23 March 2023). "OpenAI states 80% of employees could see their tasks impacted by AI. These are the tasks most impacted". euronews. Retrieved 8 June 2023. ^ Sheffey, Ayelet (20 August 2021). "Elon Musk states we need universal fundamental earnings because 'in the future, physical work will be a choice'". 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 Look For Artificial Intelligence. New York City, NY: BasicBooks. ISBN 0-465-02997-3. Darrach, Brad (20 November 1970), "Meet Shakey, the First Electronic Person", Life Magazine, pp. 58-68. Drachman, D. (2005 ), "Do we have brain to spare?", Neurology, 64 (12 ): 2004-2005, doi:10.1212/ 01. WNL.0000166914.38327. BB, PMID 15985565, S2CID 38482114. Feigenbaum, Edward A.; McCorduck, Pamela (1983 ), The Fifth Generation: 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 synthetic general intelligence and the possibility of a technological singularity: a response to Ray Kurzweil's The Singularity Is Near, and McDermott's critique of Kurzweil", Expert system, vol. 171, no. 18, Special Review Issue, pp. 1161-1173, doi:10.1016/ j.artint.2007.10.011, archived from the original on 7 January 2016, retrieved 1 April 2009. Gubrud, Mark (November 1997), "Nanotechnology and International Security", Fifth Foresight Conference on Molecular Nanotechnology, archived from the initial on 29 May 2011, obtained 7 May 2011. Howe, J. (November 1994), Expert System at Edinburgh University: a Point of view, 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 ), "Expert System: A General Survey", Artificial Intelligence: a paper symposium, Science Research Council. Luger, George; Stubblefield, William (2004 ), Artificial Intelligence: Structures and Strategies for Complex Problem Solving (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 ultimate effort is to make computer system programs that can resolve problems and achieve objectives in the world along with humans. Moravec, Hans (1988 ), Mind Children, Harvard University Press Moravec, Hans (1998 ), "When will computer hardware match the human brain?", Journal of Evolution and Technology, vol. 1, archived from the original on 15 June 2006, retrieved 23 June 2006 Nagel (1974 ), "What Is it Like to Be a Bat" (PDF), Philosophical Review, 83 (4 ): 435-50, doi:10.2307/ 2183914, JSTOR 2183914, archived (PDF) from the original 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, obtained 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, recovered 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, obtained 5 April 2009 Searle, John (1980 ), "Minds, Brains and Programs" (PDF), Behavioral and Brain Sciences, 3 (3 ): 417-457, doi:10.1017/ S0140525X00005756, S2CID 55303721, archived (PDF) from the original on 17 March 2019, retrieved 3 September 2020 Simon, H. A. (1965 ), The Shape of Automation for Men and Management, New York City: Harper & Row Turing, Alan (October 1950). "Computing Machinery and Intelligence". Mind. 59 (236 ): 433-460. doi:10.1093/ mind/LIX.236.433. ISSN 1460-2113. JSTOR 2251299. S2CID 14636783.
de Vega, Manuel; Glenberg, Arthur; Graesser, Arthur, eds. (2008 ), Symbols and Embodiment: Debates on meaning and cognition, Oxford University Press, ISBN 978-0-1992-1727-4 Wang, Pei; Goertzel, Ben (2007 ). "Introduction: Aspects of Artificial General Intelligence". Advances in Artificial General Intelligence: Concepts, Architectures and Algorithms: Proceedings of the AGI Workshop 2006. IOS Press. pp. 1-16. ISBN 978-1-5860-3758-1. Archived from the original on 18 February 2021. Retrieved 13 December 2020 - by means of 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], Artificial Intelligence Will Kill Our Grandchildren (Singularity), archived from the original on 23 July 2014, recovered 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 basic adequate to be easy to understand will not be made complex enough to behave intelligently, while any system complicated enough to act intelligently will be too complicated to understand." (p. 197.) Computer scientist Alex Pentland writes: "Current AI machine-learning algorithms are, at their core, dead easy foolish. They work, but they work by brute force." (p. 198.). Gelernter, David, Dream-logic, the Internet and Artificial Thought, Edge, archived from the original on 26 July 2010, obtained 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 makers. For biological animals, reason and purpose originate from acting on the planet and experiencing the effects. Expert systems - disembodied, 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 initial (PDF) on 6 June 2013. - Halpern, Sue, "The Coming Tech Autocracy" (review of Verity Harding, AI Needs You: How We Can Change AI's Future and Save Our Own, Princeton University Press, 274 pp.; Gary Marcus, Taming Silicon Valley: How We Can Ensure That AI Works for Us, MIT Press, 235 pp.; Daniela Rus and Gregory Mone, The Mind's Mirror: Risk and Reward in the Age of AI, Norton, 280 pp.; Madhumita Murgia, Code Dependent: Residing In the Shadow of AI, Henry Holt, 311 pp.), The New York Review of Books, vol. LXXI, no. 17 (7 November 2024), pp. 44-46. "' We can't realistically expect that those who want to get abundant from AI are going to have the interests of the rest people close at heart,' ... composes [Gary Marcus] 'We can't count on federal governments driven by project financing contributions [from tech business] to push back.' ... Marcus information the needs that residents should make of their governments and the tech business. They include transparency on how AI systems work; payment for individuals if their data [are] utilized to train LLMs (large language design) s and the right to grant this use; and the ability to hold tech business liable for the harms they bring on by getting rid of Section 230, enforcing money penalites, and passing stricter item liability laws ... Marcus also suggests ... that a new, AI-specific federal firm, similar to the FDA, the FCC, or the FTC, might offer the most robust oversight ... [T] he Fordham law teacher Chinmayi Sharma ... suggests ... develop [ing] a professional licensing program for engineers that would function in a comparable method to medical licenses, malpractice matches, and the Hippocratic oath in medicine. 'What if, like medical professionals,' she asks ..., 'AI engineers also pledged 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 baffled people for decades, exposes the constraints of natural-language-processing algorithms", Scientific American, vol. 329, no. 4 (November 2023), pp. 81-82. "This murder secret competitors has revealed that although NLP (natural-language processing) models are capable of amazing tasks, their capabilities are really much restricted by the quantity of context they get. This [...] could cause [difficulties] for researchers who wish to utilize them to do things such as evaluate ancient languages. In some cases, there are couple of historical records on long-gone civilizations to work as training data for such a purpose." (p. 82.). Immerwahr, Daniel, "Your Lying Eyes: People now use A.I. to produce phony videos indistinguishable from genuine ones. Just how much does it matter?", The New Yorker, 20 November 2023, pp. 54-59. "If by 'deepfakes' we suggest sensible videos produced using synthetic intelligence that actually trick people, then they barely exist. The fakes 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 role much 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 utilized in clinical research study", Scientific American, vol. 330, no. 6 (June 2024), pp. 80-81. Lepore, Jill, "The Chit-Chatbot: Is talking with a machine a conversation?", The New Yorker, 7 October 2024, pp. 12-16. Marcus, Gary, "Artificial Confidence: Even the most recent, buzziest systems of artificial general intelligence are stymmied by the usual problems", Scientific American, vol. 327, no. 4 (October 2022), pp. 42-45. McCarthy, John (October 2007), "From here to human-level AI", Expert System, 171 (18 ): 1174-1182, doi:10.1016/ j.artint.2007.10.009. McCorduck, Pamela (2004 ), Machines Who Think (2nd ed.), Natick, Massachusetts: A. K. Peters, ISBN 1-5688-1205-1. Moravec, Hans (1976 ), The Role of Raw Power in Intelligence, archived from the original on 3 March 2016, 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: McGraw-Hill. Omohundro, Steve (2008 ), The Nature of Self-Improving Expert system, presented and distributed at the 2007 Singularity Summit, San Francisco, California. Press, Eyal, "In Front of Their Faces: Does facial-recognition technology lead cops to disregard inconsistent evidence?", The New Yorker, 20 November 2023, pp. 20-26. Roivainen, Eka, "AI's IQ: ChatGPT aced a [basic intelligence] test but revealed that intelligence can not be determined by IQ alone", Scientific American, vol. 329, no. 1 (July/August 2023), p. 7. "Despite its high IQ, ChatGPT stops working at jobs that need real humanlike reasoning or an understanding of the physical and social world ... ChatGPT appeared not able to reason logically and attempted to count on its large 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 effective however unreliable. Rules-based systems can not deal with circumstances their programmers did not prepare for. Learning systems are limited by the data on which they were trained. AI failures have already led to disaster. Advanced autopilot functions in automobiles, although they perform well in some situations, have driven cars without cautioning into trucks, concrete barriers, and parked cars and trucks. In the incorrect situation, AI systems go from supersmart to superdumb in an instant. When an enemy is trying to control and hack an AI system, the threats are even greater." (p. 140.). Sutherland, J. G. (1990 ), "Holographic Model of Memory, Learning, and Expression", International Journal of Neural Systems, vol. 1-3, pp. 256-267. - Vincent, James, "Horny Robot Baby Voice: James Vincent on AI chatbots", London Review of Books, vol. 46, no. 19 (10 October 2024), pp. 29-32." [AI chatbot] programs are made possible by new innovations but count on the timelelss human tendency to anthropomorphise." (p. 29.). Williams, R. W.; Herrup, K.