Artificial general intelligence (AGI) is a type of expert system (AI) that matches or goes beyond human cognitive abilities across a large range of cognitive tasks. This contrasts with narrow AI, which is restricted to particular jobs. [1] Artificial superintelligence (ASI), on the other hand, refers to AGI that considerably goes beyond human cognitive abilities. AGI is thought about one of the meanings of strong AI.

Creating AGI is a main goal of AI research and sciencewiki.science of business such as OpenAI [2] and Meta. [3] A 2020 survey recognized 72 active AGI research and development jobs throughout 37 nations. [4]
The timeline for attaining AGI stays a topic of ongoing dispute amongst researchers and specialists. As of 2023, some argue that it might be possible in years or decades; others maintain it might take a century or longer; a minority think it may never be achieved; and another minority declares that it is already here. [5] [6] Notable AI scientist Geoffrey Hinton has revealed concerns about the fast progress towards AGI, suggesting it could be attained earlier than many expect. [7]
There is dispute on the specific definition of AGI and concerning whether contemporary large language models (LLMs) such as GPT-4 are early forms of AGI. [8] AGI is a common topic in science fiction and futures studies. [9] [10]
Contention exists over whether AGI represents an existential danger. [11] [12] [13] Many specialists on AI have actually specified that alleviating the risk of human extinction posed by AGI ought to be a global concern. [14] [15] Others discover the advancement of AGI to be too remote to present such a danger. [16] [17]
Terminology

AGI is also understood as strong AI, [18] [19] complete AI, [20] human-level AI, [5] human-level intelligent AI, or general intelligent action. [21]
Some scholastic sources reserve the term "strong AI" for computer programs that experience life or awareness. [a] On the other hand, weak AI (or narrow AI) has the ability to fix one particular problem but lacks basic cognitive abilities. [22] [19] Some academic sources utilize "weak AI" to refer more broadly to any programs that neither experience awareness nor have a mind in the same sense as human beings. [a]
Related principles consist of synthetic superintelligence and transformative AI. A synthetic superintelligence (ASI) is a theoretical type of AGI that is much more typically intelligent than humans, [23] while the notion of transformative AI connects to AI having a large effect on society, for instance, similar to the farming or pipewiki.org industrial transformation. [24]
A framework for classifying AGI in levels was proposed in 2023 by Google DeepMind scientists. They specify 5 levels of AGI: emerging, qualified, expert, virtuoso, and superhuman. For example, a skilled AGI is specified as an AI that surpasses 50% of knowledgeable adults in a wide variety of non-physical tasks, and a superhuman AGI (i.e. an artificial superintelligence) is likewise defined however with a limit of 100%. They consider large language designs like ChatGPT or LLaMA 2 to be instances of emerging AGI. [25]
Characteristics

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

Researchers typically hold that intelligence is needed to do all of the following: [27]
reason, usage technique, fix puzzles, and make judgments under uncertainty represent understanding, including common sense knowledge strategy find out

• interact in natural language
• if essential, incorporate these abilities in conclusion of any given goal

Many interdisciplinary approaches (e.g. cognitive science, computational intelligence, and decision making) think about additional qualities such as imagination (the capability to form novel mental images and principles) [28] and autonomy. [29]
Computer-based systems that exhibit a number of these abilities exist (e.g. see computational imagination, automated thinking, decision support group, robot, evolutionary computation, smart agent). There is dispute about whether modern AI systems possess them to a sufficient degree.

Physical characteristics

Other abilities are thought about preferable in smart systems, as they might impact intelligence or aid in its expression. These include: [30]
- the ability to sense (e.g. see, hear, and so on), and - the ability to act (e.g. relocation and manipulate things, change area to explore, and so on).
This includes the capability to find and react to risk. [31]
Although the capability to sense (e.g. see, hear, and so on) and the ability to act (e.g. relocation and control things, modification area to explore, etc) can be desirable for some intelligent systems, [30] these physical capabilities are not strictly required for an entity to qualify as AGI-particularly under the thesis that large language designs (LLMs) might already be or end up being AGI. Even from a less optimistic viewpoint on LLMs, there is no company requirement for an AGI to have a human-like kind; being a silicon-based computational system suffices, offered it can process input (language) from the external world in place of human senses. This analysis lines up with the understanding that AGI has actually never ever been proscribed a particular physical embodiment and therefore does not require a capacity for photorum.eclat-mauve.fr locomotion or traditional "eyes and ears". [32]
Tests for human-level AGI

Several tests suggested to validate human-level AGI have been considered, consisting of: [33] [34]
The idea of the test is that the maker has to attempt and pretend to be a guy, by answering questions put to it, and it will only pass if the pretence is reasonably convincing. A substantial portion of a jury, who must not be skilled about machines, should be taken in by the pretence. [37]
AI-complete issues

An issue is informally called "AI-complete" or "AI-hard" if it is believed that in order to resolve it, one would require to implement AGI, due to the fact that the option is beyond the capabilities of a purpose-specific algorithm. [47]
There are lots of issues that have actually been conjectured to require basic intelligence to fix along with humans. Examples consist of computer system vision, natural language understanding, and dealing with unexpected situations while fixing any real-world issue. [48] Even a particular job like translation requires a maker to check out and compose in both languages, follow the author's argument (factor), understand the context (understanding), and consistently reproduce the author's initial intent (social intelligence). All of these problems need to be fixed concurrently in order to reach human-level machine performance.

However, much of these tasks can now be performed by modern large language models. According to Stanford University's 2024 AI index, AI has actually reached human-level performance on lots of 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 researchers were encouraged that artificial basic intelligence was possible and that it would exist in just a couple of decades. [51] AI pioneer Herbert A. Simon wrote in 1965: "makers will be capable, within twenty years, of doing any work a guy can do." [52]
Their forecasts were the motivation for Stanley Kubrick and Arthur C. Clarke's character HAL 9000, who embodied what AI scientists thought they might produce by the year 2001. AI pioneer Marvin Minsky was an expert [53] on the task of making HAL 9000 as reasonable as possible according to the agreement forecasts of the time. He stated in 1967, "Within a generation ... the problem of developing 'synthetic intelligence' will considerably be solved". [54]
Several classical AI projects, such as Doug Lenat's Cyc job (that began in 1984), and Allen Newell's Soar job, were directed at AGI.

However, in the early 1970s, it ended up being obvious that scientists had grossly ignored the difficulty of the project. Funding companies became doubtful of AGI and put scientists under increasing pressure to produce useful "applied AI". [c] In the early 1980s, Japan's Fifth Generation Computer Project revived interest in AGI, setting out a ten-year timeline that included AGI objectives like "bring on a casual discussion". [58] In action to this and the success of specialist systems, both industry and federal government pumped money into the field. [56] [59] However, confidence in AI spectacularly collapsed in the late 1980s, and the objectives of the Fifth Generation Computer Project were never fulfilled. [60] For the 2nd time in twenty years, AI scientists who anticipated the impending achievement of AGI had actually been misinterpreted. By the 1990s, AI scientists had a track record for making vain guarantees. They ended up being unwilling to make forecasts at all [d] and prevented 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 business success and scholastic 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 innovation market, and research study in this vein is greatly funded in both academic community and market. Since 2018 [upgrade], development in this field was thought about an emerging pattern, and a fully grown phase was expected to be reached in more than 10 years. [64]
At the turn of the century, many mainstream AI scientists [65] hoped that strong AI might be established by integrating programs that fix various sub-problems. Hans Moravec wrote in 1988:

I am confident that this bottom-up path to artificial intelligence will one day meet the standard top-down path over half way, prepared to provide the real-world competence and the commonsense knowledge that has actually been so frustratingly elusive in thinking programs. Fully smart makers will result when the metaphorical golden spike is driven joining the 2 efforts. [65]
However, even at the time, this was disputed. For example, Stevan Harnad of Princeton University concluded his 1990 paper on the symbol grounding hypothesis by specifying:

The expectation has often been voiced that "top-down" (symbolic) approaches to modeling cognition will in some way meet "bottom-up" (sensory) approaches somewhere in between. If the grounding considerations in this paper are valid, then this expectation is hopelessly modular and there is really just one feasible path from sense to symbols: from the ground up. A free-floating symbolic level like the software level of a computer system will never ever be reached by this route (or vice versa) - nor is it clear why we need to even attempt to reach such a level, considering that it looks as if getting there would just amount to uprooting our symbols from their intrinsic significances (thereby simply lowering ourselves to the functional equivalent of a programmable computer). [66]
Modern artificial basic intelligence research study

The term "synthetic basic intelligence" was used as early as 1997, by Mark Gubrud [67] in a discussion of the ramifications of totally automated military production and operations. A mathematical formalism of AGI was proposed by Marcus Hutter in 2000. Named AIXI, the proposed AGI representative maximises "the ability to please goals in a wide variety of environments". [68] This kind of AGI, identified by the capability to increase a mathematical definition of intelligence instead of exhibit human-like behaviour, [69] was likewise called universal expert system. [70]
The term AGI was re-introduced and promoted by Shane Legg and Ben Goertzel around 2002. [71] AGI research activity in 2006 was explained by Pei Wang and Ben Goertzel [72] as "producing publications and preliminary results". The very first summer school in AGI was arranged in Xiamen, China in 2009 [73] by the Xiamen university's Artificial Brain Laboratory and OpenCog. The first university course was given up 2010 [74] and 2011 [75] at Plovdiv University, Bulgaria by Todor Arnaudov. MIT presented a course on AGI in 2018, organized by Lex Fridman and featuring a variety of visitor speakers.

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

Feasibility

Since 2023, the development and possible achievement of AGI remains a topic of intense debate within the AI neighborhood. While traditional agreement held that AGI was a far-off goal, current advancements have led some researchers and industry figures to declare that early forms of AGI may already exist. [78] AI pioneer Herbert A. Simon speculated in 1965 that "machines will be capable, within twenty years, of doing any work a man can do". This prediction failed to come real. 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 unpredictable developments" and a "clinically deep understanding of cognition". [79] Writing in The Guardian, roboticist Alan Winfield declared the gulf between modern-day computing and human-level synthetic intelligence is as broad as the gulf between present area flight and practical faster-than-light spaceflight. [80]
An additional obstacle is the absence of clarity in specifying what intelligence requires. Does it require consciousness? Must it display the capability to set goals as well as pursue them? Is it simply a matter of scale such that if model sizes increase sufficiently, intelligence will emerge? Are centers such as planning, thinking, and causal understanding needed? Does intelligence require explicitly duplicating the brain and its specific professors? Does it require feelings? [81]
Most AI scientists believe strong AI can be attained in the future, however some thinkers, like Hubert Dreyfus and Roger Penrose, reject the possibility of accomplishing strong AI. [82] [83] John McCarthy is amongst those who believe human-level AI will be achieved, but that the present level of development is such that a date can not accurately be forecasted. [84] AI professionals' views on the expediency of AGI wax and subside. Four surveys conducted in 2012 and 2013 suggested that the average estimate amongst professionals for when they would be 50% positive AGI would get here was 2040 to 2050, depending on the poll, with the mean being 2081. Of the professionals, 16.5% answered with "never ever" when asked the exact same concern but with a 90% self-confidence rather. [85] [86] Further existing AGI development factors to consider can be found above Tests for verifying human-level AGI.

A report by Stuart Armstrong and Kaj Sotala of the Machine Intelligence Research Institute found that "over [a] 60-year time frame there is a strong predisposition towards predicting the arrival of human-level AI as in between 15 and 25 years from the time the prediction was made". They analyzed 95 predictions made between 1950 and 2012 on when human-level AI will come about. [87]
In 2023, Microsoft scientists released a comprehensive examination of GPT-4. They concluded: "Given the breadth and depth of GPT-4's abilities, our company believe that it might fairly be seen as an early (yet still insufficient) variation of an artificial general 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 general intelligence has actually currently been accomplished with frontier models. They wrote that hesitation to this view originates from four primary reasons: a "healthy apprehension about metrics for AGI", an "ideological commitment to alternative AI theories or methods", a "dedication to human (or biological) exceptionalism", or a "issue about the economic implications of AGI". [91]
2023 also marked the introduction of large multimodal models (large language models efficient in processing or generating numerous modalities such as text, audio, and images). [92]
In 2024, OpenAI launched o1-preview, the very first of a series of models that "invest more time believing before they react". According to Mira Murati, this capability to believe before responding represents a brand-new, additional paradigm. It enhances model outputs by spending more computing power when producing the answer, whereas the design scaling paradigm improves outputs by increasing the model size, training data and training compute power. [93] [94]
An OpenAI worker, Vahid Kazemi, declared in 2024 that the company had attained AGI, stating, "In my viewpoint, we have currently achieved 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 "much better than the majority of human beings at most jobs." He likewise attended to criticisms that large language models (LLMs) simply follow predefined patterns, comparing their knowing procedure to the clinical method of observing, assuming, and validating. These statements have actually triggered argument, as they depend on a broad and unconventional meaning of AGI-traditionally comprehended as AI that matches human intelligence across all domains. Critics argue that, while OpenAI's designs demonstrate impressive flexibility, they may not fully satisfy this requirement. Notably, Kazemi's comments came shortly after OpenAI eliminated "AGI" from the regards to its collaboration with Microsoft, prompting speculation about the business's strategic intentions. [95]
Timescales

Progress in expert system has actually traditionally gone through periods of rapid development separated by periods when development appeared to stop. [82] Ending each hiatus were fundamental advances in hardware, software or both to develop area for further progress. [82] [98] [99] For example, the computer hardware readily available in the twentieth century was not sufficient to carry out deep learning, which requires large numbers of GPU-enabled CPUs. [100]
In the introduction to his 2006 book, [101] Goertzel states that quotes of the time required before a really versatile AGI is built vary from 10 years to over a century. As of 2007 [update], the consensus in the AGI research study neighborhood 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 plausible. [103] Mainstream AI researchers have provided a vast array of opinions on whether development will be this rapid. A 2012 meta-analysis of 95 such opinions discovered a predisposition towards anticipating that the start of AGI would occur within 16-26 years for modern and historical predictions alike. That paper has been criticized for how it categorized viewpoints as specialist or non-expert. [104]
In 2012, Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton developed a neural network called AlexNet, which won the ImageNet competition with a top-5 test error rate of 15.3%, substantially better than the second-best entry's rate of 26.3% (the standard approach utilized a weighted sum of ratings from various pre-defined classifiers). [105] AlexNet was concerned as the initial ground-breaker of the existing deep learning wave. [105]
In 2017, scientists Feng Liu, Yong Shi, and Ying Liu conducted intelligence tests on openly offered and freely available weak AI such as Google AI, Apple's Siri, and others. At the maximum, these AIs reached an IQ worth of about 47, which corresponds roughly to a six-year-old child in very first grade. A grownup comes to about 100 usually. Similar tests were performed in 2014, with the IQ rating reaching a maximum value of 27. [106] [107]
In 2020, OpenAI developed GPT-3, a language design efficient in performing lots of varied tasks without particular training. According to Gary Grossman in a VentureBeat article, while there is agreement that GPT-3 is not an example of AGI, it is considered by some to be too advanced to be categorized as a narrow AI system. [108]
In the exact same year, Jason Rohrer utilized his GPT-3 account to establish a chatbot, and provided a chatbot-developing platform called "Project December". OpenAI asked for changes to the chatbot to abide by their security guidelines; Rohrer detached Project December from the GPT-3 API. [109]
In 2022, DeepMind developed Gato, a "general-purpose" system efficient in carrying out more than 600 different jobs. [110]
In 2023, Microsoft Research published a study on an early version of OpenAI's GPT-4, competing that it displayed more general intelligence than previous AI designs and showed human-level performance in tasks covering several domains, such as mathematics, coding, and law. This research study stimulated a debate on whether GPT-4 might be considered an early, incomplete version of artificial basic intelligence, stressing the need for more exploration and examination of such systems. [111]
In 2023, the AI researcher Geoffrey Hinton mentioned that: [112]
The idea that this things could in fact get smarter than individuals - a few individuals believed that, [...] But the majority of people thought it was method off. And I believed it was way 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 stated that "The progress in the last couple of years has been pretty amazing", which he sees no factor why it would decrease, 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 five years, AI would be capable of passing any test a minimum of in addition to humans. [114] In June 2024, the AI researcher Leopold Aschenbrenner, a previous OpenAI staff member, estimated AGI by 2027 to be "noticeably plausible". [115]
Whole brain emulation

While the advancement of transformer models like in ChatGPT is thought about the most promising course to AGI, [116] [117] entire brain emulation can act as an alternative approach. With entire brain simulation, a brain design is built by scanning and mapping a biological brain in information, and then copying and simulating it on a computer system or another computational gadget. The simulation model need to be sufficiently faithful to the initial, so that it behaves in virtually the very 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 purposes. It has actually been discussed in artificial intelligence research study [103] as an approach to strong AI. Neuroimaging innovations that could 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 enough quality will become offered on a similar timescale to the computing power required to replicate it.

Early estimates

For low-level brain simulation, a very powerful cluster of computers or GPUs would be needed, given the enormous quantity of synapses within the human brain. Each of the 1011 (one hundred billion) nerve cells has on typical 7,000 synaptic connections (synapses) to other nerve cells. The brain of a three-year-old child has about 1015 synapses (1 quadrillion). This number declines with age, supporting by adulthood. Estimates vary for an adult, varying from 1014 to 5 × 1014 synapses (100 to 500 trillion). [120] An estimate of the brain's processing power, based on a simple switch design for nerve cell activity, is around 1014 (100 trillion) synaptic updates per second (SUPS). [121]
In 1997, Kurzweil looked at different estimates for the hardware required to equal the human brain and embraced a figure of 1016 computations per 2nd (cps). [e] (For contrast, if a "computation" was equivalent to one "floating-point operation" - a step utilized to rate existing supercomputers - then 1016 "computations" would be comparable to 10 petaFLOPS, achieved in 2011, while 1018 was accomplished in 2022.) He utilized this figure to forecast the needed hardware would be available at some point in between 2015 and 2025, if the rapid growth in computer system power at the time of composing continued.

Current research study

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

Criticisms of simulation-based approaches

The artificial nerve cell design assumed by Kurzweil and utilized in many existing artificial neural network implementations is simple compared with biological nerve cells. A brain simulation would likely have to capture the comprehensive cellular behaviour of biological neurons, currently comprehended just in broad summary. The overhead presented by complete modeling of the biological, chemical, and physical details of neural behaviour (specifically on a molecular scale) would need computational powers several orders of magnitude bigger than Kurzweil's quote. In addition, the estimates do not account for glial cells, which are understood to contribute in cognitive procedures. [125]
A basic criticism of the simulated brain approach obtains from embodied cognition theory which asserts that human embodiment is a necessary aspect of human intelligence and is essential to ground meaning. [126] [127] If this theory is correct, any fully practical brain model will need to encompass more than just the nerve cells (e.g., a robotic body). Goertzel [103] proposes virtual personification (like in metaverses like Second Life) as an alternative, however it is unknown whether this would be sufficient.

Philosophical perspective

"Strong AI" as defined 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 artificial intelligence: [f]
Strong AI hypothesis: An artificial intelligence system can have "a mind" and "consciousness". Weak AI hypothesis: An expert system system can (just) imitate it thinks and has a mind and awareness.
The very first one he called "strong" because it makes a stronger statement: it presumes something special has happened to the device that surpasses those abilities that we can check. The behaviour of a "weak AI" device would be specifically identical to a "strong AI" device, however the latter would also have subjective mindful experience. This usage is also common in scholastic AI research study and textbooks. [129]
In contrast to Searle and traditional AI, some futurists such as Ray Kurzweil utilize the term "strong AI" to mean "human level artificial basic intelligence". [102] This is not the same as Searle's strong AI, unless it is assumed that awareness is required for human-level AGI. Academic theorists such as Searle do not believe that is the case, and to most artificial intelligence scientists the question is out-of-scope. [130]
Mainstream AI is most interested in how a program behaves. [131] According to Russell and Norvig, "as long as the works, they do not care if you call it real or a simulation." [130] If the program can behave as if it has a mind, then there is no need to know if it actually has mind - certainly, there would be no method to tell. For AI research study, Searle's "weak AI hypothesis" is comparable to the declaration "synthetic general intelligence is possible". Thus, according to Russell and Norvig, "most AI researchers take the weak AI hypothesis for approved, and don't 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 different meanings, and some elements play substantial roles in sci-fi and the ethics of expert system:

Sentience (or "incredible consciousness"): The ability to "feel" understandings or emotions subjectively, as opposed to the ability to factor about perceptions. Some theorists, such as David Chalmers, utilize the term "awareness" to refer solely to extraordinary consciousness, which is approximately equivalent to sentience. [132] Determining why and how subjective experience occurs is called the hard problem of awareness. [133] Thomas Nagel discussed in 1974 that it "seems like" something to be conscious. If we are not conscious, then it does not feel like anything. Nagel utilizes the example of a bat: we can smartly ask "what does it feel like to be a bat?" However, we are not likely to ask "what does it seem like to be a toaster?" Nagel concludes that a bat appears to be mindful (i.e., has awareness) but a toaster does not. [134] In 2022, a Google engineer claimed that the company's AI chatbot, LaMDA, had achieved sentience, though this claim was widely challenged by other professionals. [135]
Self-awareness: To have conscious awareness of oneself as a separate individual, especially to be purposely familiar with one's own thoughts. This is opposed to merely being the "subject of one's believed"-an operating system or debugger has the ability to be "familiar with itself" (that is, to represent itself in the exact same method it represents whatever else)-however this is not what individuals generally mean when they use the term "self-awareness". [g]
These qualities have a moral measurement. AI life would give increase to issues of well-being and legal security, similarly to animals. [136] Other elements of awareness associated to cognitive capabilities are also pertinent to the idea of AI rights. [137] Determining how to incorporate advanced AI with existing legal and social structures is an emerging problem. [138]
Benefits

AGI might have a wide range of applications. If oriented towards such objectives, AGI could assist alleviate various problems on the planet such as appetite, hardship and health issue. [139]
AGI might enhance performance and effectiveness in the majority of tasks. For example, in public health, AGI could accelerate medical research, especially versus cancer. [140] It might look after the senior, [141] and democratize access to rapid, top quality medical diagnostics. It might use fun, cheap and tailored education. [141] The need to work to subsist might become obsolete if the wealth produced is correctly redistributed. [141] [142] This also raises the concern of the location of human beings in a drastically automated society.

AGI could also help to make logical decisions, and to expect and avoid disasters. It could likewise assist to enjoy the advantages of possibly disastrous innovations such as nanotechnology or environment engineering, while preventing the associated threats. [143] If an AGI's primary objective is to prevent existential disasters such as human termination (which could be challenging if the Vulnerable World Hypothesis ends up being true), [144] it might take procedures to considerably lower the dangers [143] while minimizing the effect of these steps on our lifestyle.

Risks

Existential threats

AGI might represent multiple kinds of existential threat, which are dangers that threaten "the early termination of Earth-originating smart life or the long-term and extreme damage of its potential for preferable future development". [145] The danger of human termination from AGI has actually been the subject of many arguments, but there is likewise the possibility that the development of AGI would lead to a permanently problematic future. Notably, it could be used to spread out and protect the set of values of whoever develops it. If humanity still has moral blind areas comparable to slavery in the past, AGI might irreversibly entrench it, avoiding moral progress. [146] Furthermore, AGI could facilitate mass monitoring and brainwashing, which could be used to produce a steady repressive around the world totalitarian regime. [147] [148] There is likewise a threat for the devices themselves. If makers that are sentient or otherwise deserving of ethical factor to consider are mass produced in the future, participating in a civilizational path that forever disregards their well-being and interests could be an existential catastrophe. [149] [150] Considering how much AGI might improve humanity's future and help in reducing other existential threats, Toby Ord calls these existential dangers "an argument for continuing with due care", not for "deserting AI". [147]
Risk of loss of control and human extinction

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

So, facing possible futures of incalculable advantages and threats, the professionals are surely doing whatever possible to guarantee the very best result, right? Wrong. If a superior alien civilisation sent us a message stating, 'We'll arrive in a few years,' would we just respond, 'OK, call us when you get here-we'll leave the lights on?' Probably not-but this is more or less what is taking place with AI. [153]
The possible fate of humankind has actually sometimes been compared to the fate of gorillas threatened by human activities. The contrast specifies that greater intelligence enabled humankind to dominate gorillas, which are now vulnerable in methods that they might not have prepared for. As an outcome, the gorilla has actually ended up being an endangered types, not out of malice, but just as a security damage from human activities. [154]
The skeptic Yann LeCun considers that AGIs will have no desire to control mankind and that we must beware not to anthropomorphize them and translate their intents as we would for people. He stated that people won't be "clever sufficient to develop super-intelligent makers, yet ridiculously foolish to the point of offering it moronic goals without any safeguards". [155] On the other side, the idea of critical merging recommends that practically whatever their goals, smart agents will have reasons to attempt to endure and acquire more power as intermediary steps to attaining these objectives. And that this does not require having emotions. [156]
Many scholars who are worried about existential danger supporter for more research into solving the "control problem" to address the question: what kinds of safeguards, algorithms, or architectures can programmers carry out to increase the likelihood that their recursively-improving AI would continue to behave in a friendly, rather than devastating, way after it reaches superintelligence? [157] [158] Solving the control issue is complicated by the AI arms race (which might cause a race to the bottom of safety precautions in order to launch products before competitors), [159] and the usage of AI in weapon systems. [160]
The thesis that AI can position existential risk also has critics. Skeptics typically say that AGI is not likely in the short-term, or that issues about AGI sidetrack from other concerns related to present AI. [161] Former Google fraud czar Shuman Ghosemajumder considers that for lots of people outside of the technology market, existing chatbots and LLMs are currently viewed as though they were AGI, causing additional misconception and fear. [162]
Skeptics in some cases charge that the thesis is crypto-religious, with an illogical belief in the possibility of superintelligence changing an unreasonable belief in a supreme God. [163] Some researchers think that the interaction campaigns on AI existential danger by certain AI groups (such as OpenAI, Anthropic, DeepMind, and Conjecture) might be an at attempt at regulative capture and to inflate interest in their items. [164] [165]
In 2023, the CEOs of Google DeepMind, OpenAI and Anthropic, in addition to other market leaders and researchers, released a joint statement asserting that "Mitigating the danger of extinction from AI ought to be a global concern together with other societal-scale dangers such as pandemics and nuclear war." [152]
Mass joblessness

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

According to Stephen Hawking, the outcome of automation on the lifestyle will depend on how the wealth will be rearranged: [142]
Everyone can enjoy a life of elegant leisure if the machine-produced wealth is shared, or many people can wind up badly poor if the machine-owners effectively lobby against wealth redistribution. So far, the trend appears to be toward the 2nd choice, with innovation driving ever-increasing inequality

Elon Musk considers that the automation of society will need governments to adopt a universal basic earnings. [168]
See also

Artificial brain - Software and hardware with cognitive abilities similar to those of the animal or human brain AI effect AI security - Research area on making AI safe and useful AI positioning - AI conformance to the desired objective A.I. Rising - 2018 film directed by Lazar Bodroža Artificial intelligence Automated artificial intelligence - Process of automating the application of maker knowing BRAIN Initiative - Collaborative public-private research study effort announced by the Obama administration China Brain Project Future of Humanity Institute - Defunct Oxford interdisciplinary research centre General video game playing - Ability of artificial intelligence to play various games Generative artificial intelligence - AI system capable of creating material in response to prompts Human Brain Project - Scientific research study project Intelligence amplification - Use of infotech to augment human intelligence (IA). Machine principles - Moral behaviours of man-made makers. Moravec's paradox. Multi-task learning - Solving numerous device discovering jobs at the very same time. Neural scaling law - Statistical law in artificial intelligence. Outline of expert system - Overview of and topical guide to synthetic intelligence. Transhumanism - Philosophical motion. Synthetic intelligence - Alternate term for or form of expert system. Transfer knowing - Machine learning method. Loebner Prize - Annual AI competitors. Hardware for synthetic intelligence - Hardware specially created and enhanced for expert system. Weak synthetic intelligence - Form of expert system.
Notes

^ a b See below for the origin of the term "strong AI", and see the scholastic definition of "strong AI" and weak AI in the article Chinese space. ^ AI creator John McCarthy writes: "we can not yet characterize in general what kinds of computational procedures we desire to call intelligent. " [26] (For a discussion of some definitions of intelligence utilized by artificial intelligence scientists, see approach of expert system.). ^ The Lighthill report specifically criticized AI's "grandiose goals" and led the dismantling of AI research study in England. [55] In the U.S., DARPA ended up being figured out to fund only "mission-oriented direct research, rather than fundamental undirected research study". [56] [57] ^ As AI creator John McCarthy composes "it would be a great relief to the rest of the workers in AI if the creators of new basic formalisms would express their hopes in a more protected form than has actually often held true." [61] ^ In "Mind Children" [122] 1015 cps is utilized. More just recently, in 1997, [123] Moravec argued for 108 MIPS which would roughly represent 1014 cps. Moravec talks in regards to MIPS, not "cps", which is a non-standard term Kurzweil introduced. ^ As defined in a basic AI textbook: "The assertion that makers might perhaps act smartly (or, perhaps better, act as if they were intelligent) is called the 'weak AI' hypothesis by theorists, and the assertion that makers that do so are actually thinking (rather than mimicing thinking) is called the 'strong AI' hypothesis." [121] ^ Alan Turing made this point in 1950. [36] References

^ Krishna, Sri (9 February 2023). "What is synthetic narrow intelligence (ANI)?". VentureBeat. Retrieved 1 March 2024. ANI is created to carry out a single task. ^ "OpenAI Charter". OpenAI. Retrieved 6 April 2023. Our objective is to guarantee that artificial general intelligence advantages all of humankind. ^ Heath, Alex (18 January 2024). "Mark Zuckerberg's brand-new objective is producing artificial basic 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 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 pioneer Geoffrey Hinton gives up Google and cautions of danger ahead". The New York 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 try outs GPT-4". arXiv preprint. arXiv:2303.12712. GPT-4 shows stimulates of AGI. ^ Butler, Octavia E. (1993 ). Parable of the Sower. Grand Central Publishing. ISBN 978-0-4466-7550-5. All that you touch you alter. All that you change 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 City Times. The genuine threat is not AI itself but the method we release it. ^ "Impressed by artificial intelligence? Experts say AGI is coming next, and it has 'existential' threats". ABC News. 23 March 2023. Retrieved 6 April 2023. AGI might present existential risks to humankind. ^ Bostrom, Nick (2014 ). Superintelligence: Paths, Dangers, Strategies. Oxford University Press. ISBN 978-0-1996-7811-2. The very first superintelligence will be the last development 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 should be an international priority. ^ "Statement on AI Risk". Center for AI Safety. Retrieved 1 March 2024. AI professionals alert of threat of termination from AI. ^ Mitchell, Melanie (30 May 2023). "Are AI's Doomsday Scenarios Worth Taking Seriously?". The New York Times. We are far from developing devices that can outthink us in basic 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 initial on 14 August 2005: Kurzweil describes strong AI as "maker intelligence with the full range 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 use for "human-level" intelligence in the physical sign system hypothesis. ^ "The Open University on Strong and Weak AI". Archived from the original on 25 September 2009. Retrieved 8 October 2007. ^ "What is synthetic superintelligence (ASI)?|Definition from TechTarget". Enterprise AI. Retrieved 8 October 2023. ^ "Artificial intelligence is transforming our world - it is on all of us 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 characteristics is based upon the subjects covered by major AI books, consisting of: Russell & Norvig 2003, Luger & Stubblefield 2004, Poole, Mackworth & Goebel 1998 and Nilsson 1998. ^ Johnson 1987. ^ de Charms, R. (1968 ). Personal causation. New York: Academic Press. ^ a b Pfeifer, R. and Bongard J. C., How the body shapes the way we think: a brand-new view of intelligence (The MIT Press, 2007). ISBN 0-2621-6239-3. ^ White, R. W. (1959 ). "Motivation reassessed: The concept of competence". 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 original on 25 April 2014. Retrieved 1 May 2014. ^ "What is Artificial General Intelligence (AGI)?|4 Tests For Ensuring Artificial General Intelligence". Talky Blog. 13 July 2019. Archived from the initial on 17 July 2019. Retrieved 17 July 2019. ^ Kirk-Giannini, Cameron Domenico; Goldstein, Simon (16 October 2023). "AI is closer than ever to passing the Turing test for 'intelligence'. What 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 young boy - the Turing Test says so". The Guardian. 9 June 2014. ISSN 0261-3077. Retrieved 3 March 2024. ^ "Scientists contest whether computer 'Eugene Goostman' passed Turing test". BBC News. 9 June 2014. Retrieved 3 March 2024. ^ Jones, Cameron R.; Bergen, Benjamin K. (9 May 2024). "People can not distinguish GPT-4 from a human in a Turing test". arXiv:2405.08007 [cs.HC] ^ Varanasi, Lakshmi (21 March 2023). "AI models like ChatGPT and GPT-4 are acing everything from the bar exam to AP Biology. Here's a list of difficult tests both AI variations have actually passed". Business Insider. Retrieved 30 May 2023. ^ Naysmith, Caleb (7 February 2023). "6 Jobs Expert System Is Already Replacing and How Investors Can 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 undependable. The Winograd Schema is obsolete. Coffee is the answer". Analytics India Magazine. Retrieved 3 March 2024. ^ Bhaimiya, Sawdah (20 June 2023). "DeepMind's co-founder 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 brand-new Turing test would see if AI can make $1 million". MIT Technology Review. Retrieved 3 March 2024. ^ Shapiro, Stuart C. (1992 ). "Expert System" (PDF). In Stuart C. Shapiro (ed.). Encyclopedia of Artificial Intelligence (Second ed.). New York: John Wiley. pp. 54-57. Archived (PDF) from the initial on 1 February 2016. (Section 4 is on "AI-Complete Tasks".). ^ Yampolskiy, Roman V. (2012 ). Xin-She Yang (ed.). "Turing Test as a Specifying Feature of AI-Completeness" (PDF). Artificial Intelligence, Evolutionary Computation and Metaheuristics (AIECM): 3-17. Archived (PDF) from the initial on 22 May 2013. ^ "AI Index: State of AI in 13 Charts". Stanford University Human-Centered Expert System. 15 April 2024. Retrieved 27 May 2024. ^ Crevier 1993, pp. 48-50. ^ Kaplan, Andreas (2022 ). "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 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 ), estimated in Crevier (1993, p. 109). ^ Lighthill 1973; Howe 1994. ^ a b NRC 1999, "Shift to Applied Research Increases Investment". ^ Crevier 1993, pp. 115-117; Russell & Norvig 2003, pp. 21-22. ^ Crevier 1993, p. 211, Russell & Norvig 2003, p. 24 and see also Feigenbaum & McCorduck 1983. ^ Crevier 1993, pp. 161-162, 197-203, 240; Russell & Norvig 2003, p. 25. ^ Crevier 1993, pp. 209-212. ^ McCarthy, John (2000 ). "Respond to Lighthill". Stanford University. Archived from the original on 30 September 2008. Retrieved 29 September 2007. ^ Markoff, John (14 October 2005). "Behind Artificial Intelligence, a Squadron of Bright Real People". The New York Times. Archived from the original on 2 February 2023. Retrieved 18 February 2017. At its low point, some computer scientists and software engineers prevented 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 Science an EATCS Series. Springer. doi:10.1007/ b138233. ISBN 978-3-5402-6877-2. S2CID 33352850. Archived from the initial on 19 July 2022. Retrieved 19 July 2022. ^ Legg, Shane (2008 ). Machine Super Intelligence (PDF) (Thesis). University of Lugano. Archived (PDF) from the initial on 15 June 2022. Retrieved 19 July 2022. ^ Goertzel, Ben (2014 ). Artificial General Intelligence. Lecture Notes in Computer Technology. Vol. 8598. Journal of Artificial General Intelligence. doi:10.1007/ 978-3-319-09274-4. ISBN 978-3-3190-9273-7. S2CID 8387410. ^ "Who created the term "AGI"?". goertzel.org. Archived from the initial on 28 December 2018. Retrieved 28 December 2018., through Life 3.0: 'The term "AGI" was promoted by ... Shane Legg, Mark Gubrud and Ben Goertzel' ^ Wang & Goertzel 2007 ^ "First International Summer School in Artificial General Intelligence, Main 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 limitations of maker intelligence: Despite progress in maker intelligence, artificial 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. "Artificial intelligence will not become a Frankenstein's beast". The Guardian. Archived from the original on 17 September 2014. Retrieved 17 September 2014. ^ Deane, George (2022 ). "Machines That Feel and Think: The Role of Affective Feelings and Mental Action in (Artificial) General Intelligence". Artificial Life. 28 (3 ): 289-309. doi:10.1162/ artl_a_00368. ISSN 1064-5462. PMID 35881678. S2CID 251069071. ^ a b c Clocksin 2003. ^ Fjelland, Ragnar (17 June 2020). "Why general synthetic intelligence will not be understood". Humanities and Social Sciences Communications. 7 (1 ): 1-9. doi:10.1057/ s41599-020-0494-4. hdl:11250/ 2726984. ISSN 2662-9992. S2CID 219710554. ^ McCarthy 2007b. ^ Khatchadourian, Raffi (23 November 2015). "The Doomsday Invention: Will synthetic intelligence bring us paradise or damage?". The New Yorker. Archived from the initial on 28 January 2016. Retrieved 7 February 2016. ^ Müller, V. C., & Bostrom, N. (2016 ). Future progress in expert system: A survey of expert opinion. In Fundamental problems of synthetic intelligence (pp. 555-572). Springer, Cham. ^ Armstrong, Stuart, and Kaj Sotala. 2012. "How We're Predicting AI-or Failing To." In Beyond AI: Artificial Dreams, edited by Jan Romportl, Pavel Ircing, Eva Žáčková, Michal Polák and Radek Schuster, 52-75. Plzeň: University of West Bohemia ^ "Microsoft Now Claims GPT-4 Shows 'Sparks' of General Intelligence". 24 March 2023. ^ Shimek, Cary (6 July 2023). "AI Outperforms Humans in Creativity Test". Neuroscience News. Retrieved 20 October 2023. ^ Guzik, Erik E.; Byrge, Christian; Gilde, Christian (1 December 2023). "The 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 original on 28 August 2022. Retrieved 28 August 2022. ^ Pei, Jing; Deng, Lei; Song, Sen; Zhao, Mingguo; Zhang, Youhui; Wu, Shuang; Wang, Guanrui; Zou, Zhe; Wu, Zhenzhi; He, Wei; Chen, Feng; Deng, Ning; Wu, Si; Wang, Yu; Wu, Yujie (2019 ). "Towards artificial 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 initial 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). Archived from the original on 4 December 2020. Retrieved 24 August 2020. ^ a b Butz, Martin V. (1 March 2021). "Towards Strong AI". KI - Künstliche Intelligenz. 35 (1 ): 91-101. doi:10.1007/ s13218-021-00705-x. ISSN 1610-1987. S2CID 256065190. ^ Liu, Feng; Shi, Yong; Liu, Ying (2017 ). "Intelligence Quotient and Intelligence Grade of Artificial Intelligence". Annals of Data Science. 4 (2 ): 179-191. arXiv:1709.10242. doi:10.1007/ s40745-017-0109-0. S2CID 37900130. ^ Brien, Jörn (5 October 2017). "Google-KI doppelt so schlau wie Siri" [Google AI is twice as clever as Siri - but a six-year-old beats both] (in German). Archived from the initial on 3 January 2019. Retrieved 2 January 2019. ^ Grossman, Gary (3 September 2020). "We're going into the AI twilight 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 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 happy medium between narrow and general AI. ^ Quach, Katyanna. "A developer built an AI chatbot utilizing GPT-3 that assisted a guy speak 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 perform over 600 jobs, from playing games to managing robotics", TechCrunch, archived from the initial on 16 June 2022, retrieved 12 June 2022. ^ Bubeck, Sébastien; Chandrasekaran, Varun; Eldan, Ronen; Gehrke, Johannes; Horvitz, Eric; Kamar, Ece; Lee, Peter; Lee, Yin Tat; Li, Yuanzhi; Lundberg, Scott; Nori, Harsha; Palangi, Hamid; Ribeiro, Marco Tulio; Zhang, Yi (22 March 2023). "Sparks of Artificial General Intelligence: Early explores GPT-4". arXiv:2303.12712 [cs.CL] ^ Metz, Cade (1 May 2023). "' The Godfather of A.I.' Leaves Google and Warns of Danger Ahead". The New York Times. ISSN 0362-4331. Retrieved 7 June 2023. ^ Bove, Tristan. "A.I. could match human intelligence in 'simply a couple of years,' says CEO of Google's primary A.I. research study lab". Fortune. Retrieved 4 September 2024. ^ Nellis, Stephen (2 March 2024). "Nvidia CEO says AI could pass human tests in five years". Reuters. ^ Aschenbrenner, Leopold. "SITUATIONAL AWARENESS, The Decade Ahead". ^ Sullivan, Mark (18 October 2023). "Why everybody appears to disagree on how to define Artificial General Intelligence". Fast Company. ^ Nosta, John (5 January 2024). "The Accelerating Path to Artificial General Intelligence". Psychology Today. Retrieved 30 March 2024. ^ Hickey, Alex. "Whole Brain Emulation: A 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 task maps the human brain". euractiv. ^ Swaminathan, Nikhil (January-February 2011). "Glia-the other brain cells". Discover. Archived from the initial on 8 February 2014. Retrieved 24 January 2014. ^ de Vega, Glenberg & Graesser 2008. A wide variety of views in present research, all of which need grounding to some degree ^ Thornton, Angela (26 June 2023). "How publishing our minds to a computer system might become possible". The Conversation. Retrieved 8 November 2023. ^ Searle 1980 ^ For example: Russell & Norvig 2003, Oxford University Press Dictionary of Psychology Archived 3 December 2007 at the Wayback Machine (priced estimate in" Encyclopedia.com"),. MIT Encyclopedia of Cognitive Science Archived 19 July 2008 at the Wayback Machine (priced estimate 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 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 come to life". The Washington Post. 11 June 2022. Retrieved 12 June 2023. ^ Kateman, Brian (24 July 2023). "AI Should Be Terrified of Humans". TIME. Retrieved 5 September 2024. ^ Nosta, John (18 December 2023). "Should Expert System Have Rights?". Psychology Today. Retrieved 5 September 2024. ^ Akst, Daniel (10 April 2023). "Should Robots With Artificial Intelligence Have Moral or Legal Rights?". The Wall Street Journal. ^ "Artificial General Intelligence - Do [es] the cost surpass benefits?". 23 August 2021. Retrieved 7 June 2023. ^ "How we can Gain from Advancing Artificial General Intelligence (AGI) - Unite.AI". www.unite.ai. 7 April 2020. Retrieved 7 June 2023. ^ a b c Talty, Jules; Julien, Stephan. "What Will Our Society Appear Like When Artificial Intelligence Is Everywhere?". Smithsonian Magazine. Retrieved 7 June 2023. ^ a b Stevenson, Matt (8 October 2015). "Answers to Stephen Hawking's AMA are Here!". Wired. ISSN 1059-1028. Retrieved 8 June 2023. ^ a b Bostrom, Nick (2017 ). " § Preferred order of arrival". Superintelligence: paths, dangers, 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 progress is making it likelier than ever that people will damage ourselves". Vox. Retrieved 8 June 2023. ^ Doherty, Ben (17 May 2018). "Climate change an 'existential security threat' to Australia, Senate questions states". The Guardian. ISSN 0261-3077. Retrieved 16 July 2023. ^ MacAskill, William (2022 ). What we owe the future. New York City, NY: Basic Books. ISBN 978-1-5416-1862-6. ^ a b Ord, Toby (2020 ). "Chapter 5: Future Risks, Unaligned 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 City Times. ISSN 0362-4331. Retrieved 24 December 2023. ^ a b "Statement on AI Risk". Center for AI Safety. 30 May 2023. Retrieved 8 June 2023. ^ "Stephen Hawking: 'Transcendence takes a look at the implications of synthetic intelligence - however 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 dispute between Yann LeCun, Stuart Russel and Yoshua Bengio about the threats of strong AI". The interesting Facebook debate in between Yann LeCun, Stuart Russel and Yoshua Bengio about the dangers 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 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 threats spiralling out of control, report alerts". Financial Times. Archived from the original on 11 April 2022. Retrieved 24 December 2023. ^ Milmo, Dan; Stacey, Kiran (25 September 2023). "Experts disagree over danger positioned but expert system 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 the End 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 stoking worries over AI, alert scientists". The Telegraph. Retrieved 7 December 2023. ^ Davidson, John (30 October 2023). "Google Brain creator says big tech is lying about AI extinction 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 capacity of big language models". OpenAI. Retrieved 7 June 2023. ^ a b Hurst, Luke (23 March 2023). "OpenAI states 80% of employees could see their jobs impacted by AI. These are the jobs most affected". euronews. Retrieved 8 June 2023. ^ Sheffey, Ayelet (20 August 2021). "Elon Musk states we need universal basic income because 'in the future, physical work will be an option'". Business Insider. Archived from the original on 9 July 2023. Retrieved 8 June 2023. Sources

UNESCO Science Report: the Race Against Time for Smarter Development. Paris: UNESCO. 11 June 2021. ISBN 978-9-2310-0450-6. Archived from the original on 18 June 2022. Retrieved 22 September 2021. Chalmers, David (1996 ), The Conscious Mind, Oxford University Press. Clocksin, William (August 2003), "Artificial intelligence and the future", Philosophical Transactions of the Royal Society A, vol. 361, no. 1809, pp. 1721-1748, Bibcode:2003 RSPTA.361.1721 C, doi:10.1098/ rsta.2003.1232, PMID 12952683, S2CID 31032007. Crevier, Daniel (1993 ). AI: The Tumultuous Search for Expert System. 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 initial (PDF) on 20 March 2013. Goertzel, Ben (December 2007), "Human-level artificial general intelligence and the possibility of a technological singularity: a reaction to Ray Kurzweil's The Singularity Is Near, and McDermott's review of Kurzweil", 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, obtained 7 May 2011. Howe, J. (November 1994), Expert System at Edinburgh University: a Point of view, archived from the original on 17 August 2007, retrieved 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 seminar, Science Research Council. Luger, George; Stubblefield, William (2004 ), Expert System: Structures and Strategies for Complex Problem Solving (5th ed.), The Benjamin/Cummings Publishing Company, Inc., p. 720, ISBN 978-0-8053-4780-7. McCarthy, John (2007b). What is Expert system?. Stanford University. The supreme effort is to make computer system programs that can resolve problems and accomplish goals in the world in addition to people. Moravec, Hans (1988 ), Mind Children, Harvard University Press Moravec, Hans (1998 ), "When will hardware match the human brain?", Journal of Evolution and Technology, vol. 1, archived from the original on 15 June 2006, retrieved 23 June 2006 Nagel (1974 ), "What Is it Like to Be a Bat" (PDF), Philosophical Review, 83 (4 ): 435-50, doi:10.2307/ 2183914, JSTOR 2183914, archived (PDF) from the initial on 16 October 2011, recovered 7 November 2009 Newell, Allen; Simon, H. A. (1976 ). "Computer Science as Empirical Inquiry: Symbols and Search". Communications of the ACM. 19 (3 ): 113-126. doi:10.1145/ 360018.360022. Nilsson, Nils (1998 ), Artificial Intelligence: A New Synthesis, Morgan Kaufmann Publishers, ISBN 978-1-5586-0467-4 NRC (1999 ), "Developments in Expert System", Funding a Revolution: Government Support for Computing Research, National Academy Press, archived from the initial on 12 January 2008, retrieved 29 September 2007 Poole, David; Mackworth, Alan; Goebel, Randy (1998 ), Computational Intelligence: A Logical Approach, New York City: Oxford University Press, archived from the original on 25 July 2009, retrieved 6 December 2007 Russell, Stuart J.; Norvig, Peter (2003 ), Expert System: A Modern Approach (2nd ed.), Upper Saddle River, New Jersey: Prentice Hall, ISBN 0-13-790395-2 Sandberg, Anders; Boström, Nick (2008 ), Whole Brain Emulation: A Roadmap (PDF), Technical Report # 2008-3, Future of Humanity Institute, Oxford University, archived (PDF) from the 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, retrieved 3 September 2020 Simon, H. A. (1965 ), The Shape of Automation for Men and Management, New York: Harper & Row Turing, Alan (October 1950). "Computing Machinery and Intelligence". Mind. 59 (236 ): 433-460. doi:10.1093/ mind/LIX.236.433. ISSN 1460-2113. JSTOR 2251299. S2CID 14636783.
de Vega, Manuel; Glenberg, Arthur; Graesser, Arthur, eds. (2008 ), Symbols and Embodiment: Debates on 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 - 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 initial on 18 February 2021, retrieved 4 September 2013 - by means of ResearchGate Berglas, Anthony (January 2012) [2008], Artificial Intelligence Will Kill Our Grandchildren (Singularity), archived from the initial on 23 July 2014, recovered 31 August 2012 Cukier, Kenneth, "Ready for Robots? How to Consider the Future of AI", Foreign Affairs, vol. 98, no. 4 (July/August 2019), pp. 192-98. George Dyson, historian of computing, writes (in what may be called "Dyson's Law") that "Any system easy sufficient to be reasonable will not be made complex enough to act intelligently, while any system complicated enough to behave wisely will be too made complex to understand." (p. 197.) Computer scientist Alex Pentland composes: "Current AI machine-learning algorithms are, at their core, dead simple stupid. 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, retrieved 25 July 2010. Gleick, James, "The Fate of Free Will" (evaluation of Kevin J. Mitchell, Free Agents: How Evolution Gave Us Free Choice, Princeton University Press, 2023, 333 pp.), The New York City Review of Books, vol. LXXI, no. 1 (18 January 2024), pp. 27-28, 30. "Agency is what distinguishes us from devices. For biological creatures, reason and purpose come from acting worldwide 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 expect that those who wish to get rich from AI are going to have the interests of the rest people close at heart,' ... composes [Gary Marcus] 'We can't rely on governments driven by campaign financing contributions [from tech business] to press back.' ... Marcus details the demands that people ought to make of their governments and the tech business. They consist of transparency on how AI systems work; settlement for individuals if their information [are] utilized to train LLMs (large language design) s and the right to approval to this usage; and the capability to hold tech companies responsible for the damages they trigger by eliminating Section 230, enforcing money penalites, and passing more stringent item liability laws ... Marcus likewise recommends ... that a brand-new, AI-specific federal agency, comparable to the FDA, the FCC, or the FTC, may supply the most robust oversight ... [T] he Fordham law teacher Chinmayi Sharma ... suggests ... establish [ing] an expert licensing regime for engineers that would operate in a similar method to medical licenses, malpractice suits, and the Hippocratic oath in medicine. 'What if, like doctors,' she asks ..., 'AI engineers likewise pledged to do no damage?'" (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 actually puzzled people for decades, reveals the limitations of natural-language-processing algorithms", Scientific American, vol. 329, no. 4 (November 2023), pp. 81-82. "This murder mystery competitors has actually exposed that although NLP (natural-language processing) models can unbelievable accomplishments, their abilities are extremely much restricted by the amount of context they receive. This [...] could trigger [difficulties] for scientists who intend to utilize them to do things such as examine ancient languages. Sometimes, there are few historic records on long-gone civilizations to function as training information for such a function." (p. 82.). Immerwahr, Daniel, "Your Lying Eyes: People now utilize A.I. to generate fake videos equivalent from genuine ones. Just how much does it matter?", The New Yorker, 20 November 2023, pp. 54-59. "If by 'deepfakes' we suggest practical videos produced using artificial intelligence that in fact trick people, then they hardly exist. The fakes aren't deep, and the deeps aren't fake. [...] A.I.-generated videos are not, in general, running in our media as counterfeited proof. Their function much better looks like that of cartoons, especially smutty ones." (p. 59.). - Leffer, Lauren, "The Risks of Trusting AI: We should avoid humanizing machine-learning designs utilized in scientific 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 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, obtained 29 September 2007. Newell, Allen; Simon, H. A. (1963 ), "GPS: A Program that Simulates Human Thought", in Feigenbaum, E. A.; Feldman, J. (eds.), Computers and Thought, New York City: McGraw-Hill. Omohundro, Steve (2008 ), The Nature of Self-Improving Expert system, provided and distributed at the 2007 Singularity Summit, San Francisco, California. Press, Eyal, "In Front of Their Faces: Does facial-recognition technology lead authorities to disregard contradictory proof?", The New Yorker, 20 November 2023, pp. 20-26. Roivainen, Eka, "AI's IQ: ChatGPT aced a [standard intelligence] test however 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 fails at tasks that need real humanlike reasoning or photorum.eclat-mauve.fr an understanding of the physical and social world ... ChatGPT appeared not able to reason realistically and tried to count on its huge database of ... facts originated 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 scenarios their programmers did not prepare for. Learning systems are restricted by the data on which they were trained. AI failures have currently resulted in disaster. Advanced autopilot features in automobiles, although they perform well in some circumstances, have actually driven cars without alerting into trucks, concrete barriers, and parked cars. In the wrong circumstance, AI systems go from supersmart to superdumb in an immediate. When an opponent is trying to control and hack an AI system, the risks are even higher." (p. 140.). Sutherland, J. G. (1990 ), "Holographic Model of Memory, Learning, and Expression", International Journal of Neural Systems, vol. 1-3, pp. 256-267. - Vincent, James, "Horny Robot Baby Voice: James Vincent on AI chatbots", London Review of Books, vol. 46, no. 19 (10 October 2024), pp. 29-32." [AI chatbot] programs are enabled by brand-new innovations but rely on the timelelss human propensity to anthropomorphise." (p. 29.). Williams, R. W.; Herrup, K.