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Artificial basic intelligence (AGI) is a type of synthetic intelligence (AI) that matches or surpasses human cognitive capabilities throughout a wide variety of cognitive tasks. This contrasts with narrow AI, which is limited to particular tasks. [1] Artificial superintelligence (ASI), on the other hand, refers to AGI that significantly surpasses human cognitive capabilities. AGI is considered one of the definitions of strong AI.

Creating AGI is a main objective of AI research and of business such as OpenAI [2] and Meta. [3] A 2020 study determined 72 active AGI research and advancement tasks across 37 countries. [4]
The timeline for achieving AGI remains a subject of continuous debate amongst researchers and experts. Since 2023, some argue that it may be possible in years or decades; others maintain it might take a century or longer; a minority think it might never be accomplished; and another minority declares that it is currently here. [5] [6] Notable AI scientist Geoffrey Hinton has actually expressed concerns about the rapid development towards AGI, recommending it could be accomplished sooner than numerous expect. [7]
There is argument on the specific definition of AGI and relating to whether modern large language designs (LLMs) such as GPT-4 are early types of AGI. [8] AGI is a common subject in science fiction and futures research studies. [9] [10]
Contention exists over whether AGI represents an existential threat. [11] [12] [13] Many experts on AI have specified that reducing the risk of human termination postured by AGI ought to be a global priority. [14] [15] Others discover the advancement of AGI to be too remote to present such a danger. [16] [17]
Terminology

AGI is also known 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 life or awareness. [a] On the other hand, weak AI (or narrow AI) is able to fix one specific issue but does not have basic cognitive capabilities. [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 exact same sense as humans. [a]
Related ideas include synthetic superintelligence and transformative AI. A synthetic superintelligence (ASI) is a hypothetical type of AGI that is far more usually intelligent than people, [23] while the idea of transformative AI associates with AI having a large influence on society, for example, similar to the farming or commercial transformation. [24]
A framework for classifying AGI in levels was proposed in 2023 by Google DeepMind researchers. They specify five levels of AGI: emerging, qualified, specialist, virtuoso, and superhuman. For example, a competent AGI is specified as an AI that exceeds 50% of knowledgeable grownups in a large range of non-physical jobs, and a superhuman AGI (i.e. an artificial superintelligence) is likewise defined but with a limit 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 actually been proposed. One of the leading proposals is the Turing test. However, there are other popular meanings, and some scientists disagree with the more popular techniques. [b]
Intelligence traits

Researchers normally hold that intelligence is needed to do all of the following: [27]
factor, use strategy, resolve puzzles, and make judgments under uncertainty represent knowledge, including good sense understanding strategy learn

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

Many interdisciplinary techniques (e.g. cognitive science, computational intelligence, and decision making) think about additional characteristics such as imagination (the ability to form unique psychological images and ideas) [28] and autonomy. [29]
Computer-based systems that exhibit a number of these abilities exist (e.g. see computational imagination, automated thinking, choice assistance system, robotic, evolutionary calculation, intelligent agent). There is debate about whether modern AI systems have them to an adequate degree.

Physical characteristics

Other capabilities are considered desirable in intelligent systems, as they might impact intelligence or aid in its expression. These include: [30]
- the ability to sense (e.g. see, hear, etc), and - the ability to act (e.g. move and manipulate items, change place to explore, etc).
This consists of the capability to find and respond to danger. [31]
Although the capability to sense (e.g. see, hear, etc) and the capability to act (e.g. relocation and manipulate things, change place 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 models (LLMs) may already be or become AGI. Even from a less positive point of view on LLMs, there is no firm requirement for archmageriseswiki.com an AGI to have a human-like form; being a silicon-based computational system suffices, supplied it can process input (language) from the external world in place of human senses. This interpretation aligns with the understanding that AGI has actually never ever been proscribed a specific physical personification and therefore does not demand a capability for mobility or conventional "eyes and ears". [32]
Tests for human-level AGI

Several tests suggested to verify human-level AGI have actually been considered, consisting of: [33] [34]
The concept of the test is that the device needs to try and pretend to be a male, by answering concerns put to it, and it will only pass if the pretence is reasonably convincing. A significant portion of a jury, who ought to not be professional about devices, should be taken in by the pretence. [37]
AI-complete issues

A problem is informally called "AI-complete" or "AI-hard" if it is believed that in order to resolve it, one would require to execute AGI, since the solution is beyond the capabilities of a purpose-specific algorithm. [47]
There are lots of issues that have actually been conjectured to require basic intelligence to solve along with human beings. Examples include computer vision, natural language understanding, and dealing with unexpected circumstances while solving any real-world problem. [48] Even a particular job like translation requires a machine to read and compose in both languages, follow the author's argument (factor), comprehend the context (understanding), and faithfully recreate the author's initial intent (social intelligence). All of these issues require to be resolved concurrently in order to reach human-level maker efficiency.

However, a number of these tasks can now be performed by modern-day big language models. According to Stanford University's 2024 AI index, AI has actually reached human-level performance on many benchmarks for reading comprehension and visual reasoning. [49]
History

Classical AI

Modern AI research started in the mid-1950s. [50] The very first generation of AI researchers were persuaded that artificial basic intelligence was possible and that it would exist in just a couple of decades. [51] AI leader Herbert A. Simon composed in 1965: "machines will be capable, within twenty years, of doing any work a guy can do." [52]
Their forecasts were the inspiration for Stanley Kubrick and Arthur C. Clarke's character HAL 9000, who embodied what AI scientists thought they might develop by the year 2001. AI leader Marvin Minsky was an expert [53] on the job of making HAL 9000 as realistic as possible according to the consensus predictions of the time. He said in 1967, "Within a generation ... the issue of developing 'expert system' will considerably be solved". [54]
Several classical AI tasks, such as Doug Lenat's Cyc project (that started in 1984), and Allen Newell's Soar task, were directed at AGI.

However, in the early 1970s, it ended up being obvious that scientists had actually grossly undervalued the difficulty of the project. Funding companies became hesitant of AGI and put scientists under increasing pressure to produce beneficial "used 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 goals like "bring on a table talk". [58] In action to this and the success of specialist systems, both industry and government pumped money into the field. [56] [59] However, confidence in AI stunningly collapsed in the late 1980s, and the goals of the Fifth Generation Computer Project were never satisfied. [60] For the second time in 20 years, AI researchers who anticipated the imminent accomplishment of AGI had actually been misinterpreted. By the 1990s, AI researchers had a track record for making vain pledges. They ended up being hesitant to make forecasts at all [d] and avoided reference of "human level" expert system for fear of being identified "wild-eyed dreamer [s]. [62]
Narrow AI research

In the 1990s and early 21st century, mainstream AI achieved industrial success and scholastic respectability by focusing on particular sub-problems where AI can produce proven outcomes and business applications, such as speech acknowledgment and recommendation algorithms. [63] These "applied AI" systems are now utilized thoroughly throughout the innovation industry, and research in this vein is greatly funded in both academic community and industry. Since 2018 [upgrade], advancement in this field was considered an emerging pattern, and a fully grown stage was expected to be reached in more than 10 years. [64]
At the millenium, many mainstream AI researchers [65] hoped that strong AI might be developed by combining programs that solve numerous sub-problems. Hans Moravec wrote in 1988:

I am confident that this bottom-up route to expert system will one day satisfy the traditional top-down route majority method, all set to provide the real-world skills and the commonsense knowledge that has been so frustratingly elusive in reasoning programs. Fully intelligent makers will result when the metaphorical golden spike is driven unifying 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 typically been voiced that "top-down" (symbolic) approaches to modeling cognition will in some way satisfy "bottom-up" (sensory) approaches someplace in between. If the grounding considerations in this paper stand, then this expectation is hopelessly modular and there is actually just one viable route from sense to signs: from the ground up. A free-floating symbolic level like the software application level of a computer will never ever be reached by this route (or vice versa) - nor is it clear why we must even attempt to reach such a level, considering that it looks as if getting there would simply total up to uprooting our symbols from their intrinsic meanings (consequently merely decreasing ourselves to the practical equivalent of a programmable computer). [66]
Modern artificial general intelligence research

The term "artificial general intelligence" was utilized as early as 1997, by Mark Gubrud [67] in a conversation of the implications of fully automated military production and operations. A mathematical formalism of AGI was proposed by Marcus Hutter in 2000. Named AIXI, the proposed AGI agent increases "the capability to please objectives in a wide variety of environments". [68] This kind of AGI, defined by the capability to increase a mathematical definition of intelligence instead of show human-like behaviour, [69] was also called universal expert system. [70]
The term AGI was re-introduced and popularized by Shane Legg and Ben Goertzel around 2002. [71] AGI research activity in 2006 was described by Pei Wang and Ben Goertzel [72] as "producing publications and initial outcomes". The first summertime 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 offered in 2010 [74] and 2011 [75] at Plovdiv University, Bulgaria by Todor Arnaudov. MIT provided a course on AGI in 2018, arranged by Lex Fridman and featuring a variety of guest lecturers.

As of 2023 [upgrade], a small number of computer researchers are active in AGI research study, and numerous add to a series of AGI conferences. However, progressively more scientists have an interest in open-ended knowing, [76] [77] which is the concept of permitting AI to constantly learn and innovate like humans do.

Feasibility

As of 2023, the development and potential achievement of AGI remains a topic of intense dispute within the AI community. While traditional agreement held that AGI was a far-off goal, current advancements have actually led some researchers and industry figures to claim that early kinds 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 male can do". This prediction stopped working to come real. Microsoft co-founder Paul Allen thought that such intelligence is unlikely in the 21st century since it would need "unforeseeable and fundamentally unforeseeable breakthroughs" and a "scientifically deep understanding of cognition". [79] Writing in The Guardian, roboticist Alan Winfield declared the gulf in between modern-day computing and human-level synthetic intelligence is as wide as the gulf in between existing space flight and practical faster-than-light spaceflight. [80]
An additional obstacle is the lack of clearness in specifying what intelligence requires. Does it require consciousness? Must it display the capability to set objectives as well as pursue them? Is it purely a matter of scale such that if model sizes increase adequately, intelligence will emerge? Are centers such as preparation, reasoning, and causal understanding required? Does intelligence require clearly replicating the brain and its specific professors? Does it require feelings? [81]
Most AI researchers believe strong AI can be accomplished in the future, but some thinkers, like Hubert Dreyfus and Roger Penrose, reject the possibility of achieving strong AI. [82] [83] John McCarthy is amongst those who believe human-level AI will be achieved, but that today level of development is such that a date can not precisely be forecasted. [84] AI specialists' views on the expediency of AGI wax and wane. Four surveys conducted in 2012 and 2013 suggested that the typical estimate among specialists 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 specialists, 16.5% responded to with "never" when asked the very same question but with a 90% self-confidence rather. [85] [86] Further current AGI progress factors to consider can be discovered above Tests for confirming 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 examined 95 forecasts made in between 1950 and 2012 on when human-level AI will happen. [87]
In 2023, Microsoft researchers published an in-depth examination of GPT-4. They concluded: "Given the breadth and depth of GPT-4's abilities, our company believe that it could fairly be seen as an early (yet still incomplete) version of an artificial basic intelligence (AGI) system." [88] Another study in 2023 reported that GPT-4 outperforms 99% of human beings on the Torrance tests of creativity. [89] [90]
Blaise Agüera y Arcas and Peter Norvig wrote in 2023 that a substantial level of basic intelligence has actually already been achieved with frontier models. They wrote that hesitation to this view originates from four main factors: a "healthy uncertainty about metrics for AGI", an "ideological dedication 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 big multimodal models (big language designs efficient in processing or creating several techniques such as text, audio, and images). [92]
In 2024, OpenAI launched o1-preview, the very first of a series of designs that "spend more time believing before they respond". According to Mira Murati, this ability to believe before responding represents a new, extra paradigm. It improves design outputs by spending more computing power when producing the response, whereas the model scaling paradigm enhances outputs by increasing the model size, training information and training calculate power. [93] [94]
An OpenAI worker, Vahid Kazemi, declared in 2024 that the business had attained AGI, stating, "In my opinion, we have actually currently accomplished AGI and it's even more clear with O1." Kazemi clarified that while the AI is not yet "better than any human at any task", it is "much better than many humans at many tasks." He also resolved criticisms that large language models (LLMs) merely follow predefined patterns, comparing their knowing procedure to the scientific approach of observing, hypothesizing, and confirming. These declarations have stimulated debate, as they rely on a broad and non-traditional definition of AGI-traditionally comprehended as AI that matches human intelligence throughout all domains. Critics argue that, while OpenAI's designs show exceptional flexibility, they may not completely fulfill this requirement. Notably, Kazemi's comments came soon after OpenAI eliminated "AGI" from the regards to its partnership with Microsoft, prompting speculation about the business's tactical intents. [95]
Timescales

Progress in expert system has actually traditionally gone through durations of quick development separated by periods when progress appeared to stop. [82] Ending each hiatus were fundamental advances in hardware, software or both to create area for additional development. [82] [98] [99] For example, the hardware offered in the twentieth century was not adequate to carry out deep learning, which requires big numbers of GPU-enabled CPUs. [100]
In the introduction to his 2006 book, [101] Goertzel states that estimates of the time required before a truly versatile AGI is developed differ from 10 years to over a century. Since 2007 [upgrade], the consensus in the AGI research study neighborhood seemed to be that the timeline gone over 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 given a large range of opinions on whether progress will be this fast. A 2012 meta-analysis of 95 such opinions found a predisposition towards predicting that the onset of AGI would occur within 16-26 years for modern-day and historical predictions alike. That paper has been slammed for how it classified viewpoints as specialist or non-expert. [104]
In 2012, Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton established a neural network called AlexNet, which won the ImageNet competitors with a top-5 test mistake rate of 15.3%, substantially better than the second-best entry's rate of 26.3% (the conventional approach utilized a weighted sum of ratings from different pre-defined classifiers). [105] AlexNet was considered the initial ground-breaker of the current deep knowing wave. [105]
In 2017, researchers Feng Liu, Yong Shi, and Ying Liu conducted intelligence tests on publicly readily available and easily accessible 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 child in first grade. A grownup pertains to about 100 usually. Similar tests were performed in 2014, with the IQ score reaching an optimum worth of 27. [106] [107]
In 2020, OpenAI established GPT-3, a language model capable of performing numerous diverse jobs 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 thought about 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 develop a chatbot, and supplied a chatbot-developing platform called "Project December". OpenAI requested changes to the chatbot to adhere to their safety standards; Rohrer disconnected Project December from the GPT-3 API. [109]
In 2022, DeepMind developed Gato, a "general-purpose" system capable of performing more than 600 various tasks. [110]
In 2023, Microsoft Research released a study on an early variation of OpenAI's GPT-4, competing that it showed more general intelligence than previous AI designs and demonstrated human-level performance in tasks covering several domains, such as mathematics, coding, and law. This research study sparked a debate on whether GPT-4 might be thought about an early, insufficient version of synthetic basic intelligence, emphasizing the requirement for additional exploration and examination of such systems. [111]
In 2023, the AI researcher Geoffrey Hinton stated that: [112]
The concept that this things might in fact get smarter than people - a couple of people believed that, [...] But a lot of individuals believed it was way off. And I thought it was way off. I believed it was 30 to 50 years and even longer away. Obviously, I no longer think that.

In May 2023, Demis Hassabis similarly stated that "The progress in the last few years has actually been quite amazing", which he sees no reason it would decrease, expecting AGI within a years and even a couple of years. [113] In March 2024, Nvidia's CEO, Jensen Huang, mentioned his expectation that within 5 years, AI would can passing any test at least as well as humans. [114] In June 2024, the AI researcher Leopold Aschenbrenner, a previous OpenAI employee, estimated AGI by 2027 to be "strikingly possible". [115]
Whole brain emulation

While the development of transformer models like in ChatGPT is thought about the most appealing path to AGI, [116] [117] whole brain emulation can act as an alternative technique. With entire brain simulation, a brain design is developed by scanning and mapping a biological brain in detail, and after that copying and mimicing it on a computer system or another computational gadget. The simulation design must be adequately devoted to the initial, so that it acts in virtually the exact same method as the initial brain. [118] Whole brain emulation is a type of brain simulation that is gone over in computational neuroscience and neuroinformatics, and for medical research purposes. It has actually been discussed in expert system research [103] as a technique to strong AI. Neuroimaging innovations that might deliver the required detailed understanding are enhancing rapidly, and futurist Ray Kurzweil in the book The Singularity Is Near [102] forecasts that a map of sufficient quality will appear on a comparable timescale to the computing power required to emulate it.

Early estimates

For low-level brain simulation, a very effective cluster of computers or GPUs would be needed, provided the huge amount 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 kid has about 1015 synapses (1 quadrillion). This number decreases with age, supporting by the adult years. Estimates differ for an adult, ranging from 1014 to 5 × 1014 synapses (100 to 500 trillion). [120] An estimate of the brain's processing power, based upon an easy switch model for neuron activity, is around 1014 (100 trillion) synaptic updates per second (SUPS). [121]
In 1997, Kurzweil looked at numerous price quotes for the hardware required to equal the human brain and adopted a figure of 1016 calculations per second (cps). [e] (For comparison, if a "calculation" was comparable to one "floating-point operation" - a measure used to rate existing supercomputers - then 1016 "computations" would be comparable to 10 petaFLOPS, attained in 2011, while 1018 was achieved in 2022.) He used this figure to forecast the necessary hardware would be offered sometime in between 2015 and 2025, if the rapid development in computer system power at the time of composing continued.

Current research study

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

Criticisms of simulation-based approaches

The synthetic neuron design assumed by Kurzweil and utilized in many existing artificial neural network applications is easy compared with biological neurons. A brain simulation would likely need to record the detailed cellular behaviour of biological neurons, currently understood only in broad outline. The overhead introduced by full modeling of the biological, chemical, and physical details of neural behaviour (especially on a molecular scale) would need computational powers several orders of magnitude bigger than Kurzweil's estimate. In addition, the estimates do not account for glial cells, which are understood to contribute in cognitive processes. [125]
A fundamental criticism of the simulated brain approach obtains from embodied cognition theory which asserts that human personification is an essential element of human intelligence and is essential to ground significance. [126] [127] If this theory is right, any completely functional brain design 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 a choice, but it is unidentified whether this would be adequate.

Philosophical point of view

"Strong AI" as defined in viewpoint

In 1980, thinker John Searle coined the term "strong AI" as part of his Chinese room argument. [128] He proposed a distinction between 2 hypotheses about expert system: [f]
Strong AI hypothesis: An artificial intelligence system can have "a mind" and "consciousness". Weak AI hypothesis: An expert system system can (just) act like it believes and has a mind and consciousness.
The very first one he called "strong" since it makes a more powerful statement: it assumes something special has actually taken place to the maker that exceeds those abilities that we can test. The behaviour of a "weak AI" machine would be precisely identical to a "strong AI" machine, but the latter would likewise have subjective conscious experience. This use is likewise common in scholastic AI research study and books. [129]
In contrast to Searle and mainstream AI, some futurists such as Ray Kurzweil utilize the term "strong AI" to mean "human level artificial basic intelligence". [102] This is not the like Searle's strong AI, unless it is assumed that consciousness is essential for human-level AGI. Academic thinkers such as Searle do not think that is the case, and to most synthetic intelligence scientists the concern is out-of-scope. [130]
Mainstream AI is most thinking about how a program behaves. [131] According to Russell and Norvig, "as long as the program works, they do not care if you call it genuine or a simulation." [130] If the program can behave as if it has a mind, then there is no need to understand if it really has mind - undoubtedly, there would be no chance to inform. For AI research, 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 given, and do not care about the strong AI hypothesis." [130] Thus, for academic AI research, "Strong AI" and "AGI" are 2 various things.

Consciousness

Consciousness can have various meanings, and some aspects play substantial functions in sci-fi and the ethics of synthetic intelligence:

Sentience (or "extraordinary consciousness"): The capability to "feel" perceptions or feelings subjectively, instead of the capability to factor about perceptions. Some thinkers, such as David Chalmers, use the term "consciousness" to refer solely to remarkable consciousness, which is roughly comparable to sentience. [132] Determining why and how subjective experience emerges is called the tough issue of consciousness. [133] Thomas Nagel explained in 1974 that it "seems like" something to be mindful. If we are not mindful, then it doesn't feel like anything. Nagel utilizes the example of a bat: we can sensibly ask "what does it feel like to be a bat?" However, we are not likely to ask "what does it feel like to be a toaster?" Nagel concludes that a bat appears to be mindful (i.e., has awareness) however a toaster does not. [134] In 2022, a Google engineer declared that the company's AI chatbot, LaMDA, had attained life, though this claim was extensively contested by other professionals. [135]
Self-awareness: To have mindful awareness of oneself as a separate person, specifically to be consciously familiar with one's own ideas. This is opposed to merely being the "subject of one's believed"-an operating system or debugger is able to be "knowledgeable about itself" (that is, to represent itself in the same way it represents whatever else)-but this is not what individuals generally mean when they utilize the term "self-awareness". [g]
These traits have an ethical dimension. AI life would offer increase to concerns of well-being and legal defense, similarly to animals. [136] Other aspects of awareness associated to cognitive capabilities are also pertinent to the concept of AI rights. [137] Figuring out how to incorporate sophisticated AI with existing legal and social structures is an emergent issue. [138]
Benefits

AGI could have a wide range of applications. If oriented towards such objectives, AGI might help reduce various problems in the world such as appetite, poverty and health problems. [139]
AGI could enhance performance and performance in the majority of jobs. For instance, in public health, AGI might accelerate medical research study, notably against cancer. [140] It could take care of the senior, [141] and equalize access to quick, top quality medical diagnostics. It could provide enjoyable, cheap and tailored education. [141] The need to work to subsist might become outdated if the wealth produced is effectively rearranged. [141] [142] This likewise raises the question of the place of people in a drastically automated society.

AGI might likewise assist to make rational decisions, and to anticipate and avoid disasters. It might likewise help to profit of possibly catastrophic technologies such as nanotechnology or environment engineering, while preventing the associated dangers. [143] If an AGI's primary objective is to prevent existential disasters such as human termination (which might be hard if the Vulnerable World Hypothesis turns out to be real), [144] it could take steps to dramatically reduce the dangers [143] while reducing the effect of these steps on our quality of life.

Risks

Existential dangers

AGI might represent several types of existential risk, which are threats that threaten "the early extinction of Earth-originating smart life or the irreversible and drastic destruction of its potential for desirable future advancement". [145] The risk of human termination from AGI has actually been the topic of lots of arguments, but there is likewise the possibility that the advancement of AGI would lead to a completely problematic future. Notably, it could be used to spread and preserve the set of worths of whoever develops it. If humanity still has ethical blind areas similar to slavery in the past, AGI might irreversibly entrench it, preventing moral progress. [146] Furthermore, AGI could facilitate mass security and brainwashing, which could be used to produce a steady repressive worldwide totalitarian program. [147] [148] There is also a risk for the machines themselves. If devices that are sentient or otherwise deserving of moral factor to consider are mass developed in the future, participating in a civilizational course that forever disregards their welfare and interests might be an existential catastrophe. [149] [150] Considering just how much AGI might enhance humanity's future and help in reducing other existential dangers, Toby Ord calls these existential risks "an argument for continuing with due care", not for "abandoning AI". [147]
Risk of loss of control and human termination

The thesis that AI poses an existential risk for people, which this danger needs more attention, is questionable however has actually been backed in 2023 by lots of public figures, AI scientists and CEOs of AI companies such as Elon Musk, Bill Gates, Geoffrey Hinton, Yoshua Bengio, Demis Hassabis and Sam Altman. [151] [152]
In 2014, Stephen Hawking slammed extensive indifference:

So, facing possible futures of incalculable advantages and risks, the specialists are undoubtedly doing everything possible to ensure the finest result, right? Wrong. If a superior alien civilisation sent us a message stating, 'We'll get here 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 happening with AI. [153]
The possible fate of humankind has sometimes been compared to the fate of gorillas threatened by human activities. The contrast specifies that higher intelligence permitted humanity to control gorillas, which are now vulnerable in methods that they could not have actually prepared for. As a result, the gorilla has become a threatened species, not out of malice, however just as a civilian casualties from human activities. [154]
The skeptic Yann LeCun thinks about that AGIs will have no desire to dominate humankind which we should beware not to anthropomorphize them and analyze their intents as we would for people. He stated that individuals won't be "smart adequate to develop super-intelligent makers, yet unbelievably foolish to the point of providing it moronic goals without any safeguards". [155] On the other side, the concept of important convergence recommends that almost whatever their goals, smart representatives will have factors to try to survive and get more power as intermediary actions to attaining these goals. And that this does not need having emotions. [156]
Many scholars who are concerned about existential threat advocate for more research into resolving the "control problem" to address the concern: what types of safeguards, algorithms, or architectures can programmers implement to increase the likelihood that their recursively-improving AI would continue to act in a friendly, rather than harmful, manner 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 security preventative measures in order to release products before competitors), [159] and using AI in weapon systems. [160]
The thesis that AI can present existential threat likewise has detractors. Skeptics typically state that AGI is not likely in the short-term, or that concerns about AGI sidetrack from other concerns connected to current AI. [161] Former Google scams czar Shuman Ghosemajumder considers that for many people outside of the innovation market, existing chatbots and LLMs are already perceived as though they were AGI, causing more misconception and fear. [162]
Skeptics often charge that the thesis is crypto-religious, with an irrational 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 threat by specific AI groups (such as OpenAI, Anthropic, DeepMind, and Conjecture) might be an at effort at regulative capture and to inflate interest in their products. [164] [165]
In 2023, the CEOs of Google DeepMind, OpenAI and Anthropic, along with other industry leaders and scientists, issued a joint declaration asserting that "Mitigating the risk of termination from AI ought to be a global priority together with other societal-scale threats such as pandemics and nuclear war." [152]
Mass joblessness

Researchers from OpenAI approximated 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 employees may see a minimum of 50% of their tasks affected". [166] [167] They think about office employees to be the most exposed, for example mathematicians, accountants or web designers. [167] AGI could have a much better autonomy, capability to make decisions, to interface with other computer system tools, however likewise to control robotized bodies.

According to Stephen Hawking, the outcome of automation on the lifestyle will depend upon how the wealth will be redistributed: [142]
Everyone can take pleasure in a life of luxurious leisure if the machine-produced wealth is shared, or many people can end up badly poor if the machine-owners effectively lobby versus wealth redistribution. Up until now, the trend appears to be towards the second alternative, with technology driving ever-increasing inequality

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

Artificial brain - Software and hardware with cognitive capabilities comparable to those of the animal or human brain AI impact AI safety - Research location on making AI safe and advantageous AI positioning - AI conformance to the desired objective A.I. Rising - 2018 movie directed by Lazar Bodroža Expert system Automated machine learning - Process of automating the application of device learning BRAIN Initiative - Collaborative public-private research initiative announced by the Obama administration China Brain Project Future of Humanity Institute - Defunct Oxford interdisciplinary research study centre General game playing - Ability of expert system to play different games Generative synthetic intelligence - AI system capable of producing material in response to prompts Human Brain Project - Scientific research study task Intelligence amplification - Use of details technology to augment human intelligence (IA). Machine ethics - Moral behaviours of manufactured machines. Moravec's paradox. Multi-task learning - Solving several maker finding out jobs at the same time. Neural scaling law - Statistical law in artificial intelligence. Outline of artificial intelligence - Overview of and topical guide to artificial intelligence. Transhumanism - Philosophical motion. Synthetic intelligence - Alternate term for or form of artificial intelligence. Transfer knowing - Machine knowing technique. Loebner Prize - Annual AI competitors. Hardware for artificial intelligence - Hardware specifically created and optimized 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 short article Chinese room. ^ AI founder John McCarthy composes: "we can not yet characterize in general what sort of computational treatments we wish to call smart. " [26] (For a conversation of some meanings of intelligence used by synthetic intelligence researchers, see philosophy of expert system.). ^ The Lighthill report particularly criticized AI's "grand objectives" and led the taking apart of AI research study in England. [55] In the U.S., DARPA ended up being figured out to money just "mission-oriented direct research, instead of standard undirected research study". [56] [57] ^ As AI creator John McCarthy composes "it would be an excellent relief to the rest of the employees in AI if the developers of new general formalisms would express their hopes in a more protected type than has actually sometimes been the case." [61] ^ In "Mind Children" [122] 1015 cps is used. More just recently, in 1997, [123] Moravec argued for 108 MIPS which would approximately correspond to 1014 cps. Moravec talks in terms of MIPS, not "cps", which is a non-standard term Kurzweil introduced. ^ As specified in a basic AI textbook: "The assertion that machines might perhaps act intelligently (or, maybe better, act as if they were smart) is called the 'weak AI' hypothesis by thinkers, and the assertion that machines that do so are in fact believing (rather than replicating thinking) is called the 'strong AI' hypothesis." [121] ^ Alan Turing made this point in 1950. [36] References

^ Krishna, Sri (9 February 2023). "What is artificial narrow intelligence (ANI)?". VentureBeat. Retrieved 1 March 2024. ANI is designed to carry out a single task. ^ "OpenAI Charter". OpenAI. Retrieved 6 April 2023. Our mission is to ensure that synthetic general intelligence benefits all of mankind. ^ Heath, Alex (18 January 2024). "Mark Zuckerberg's brand-new objective is developing artificial basic intelligence". The Verge. Retrieved 13 June 2024. Our vision is to build AI that is much better than human-level at all of the human senses. ^ Baum, Seth D. (2020 ). A 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 identified as being active in 2020. ^ a b c "AI timelines: What do experts 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 City Times. Retrieved 18 May 2023. ^ "AI pioneer Geoffrey Hinton stops Google and alerts of threat ahead". The New York Times. 1 May 2023. Retrieved 2 May 2023. It is tough to see how you can prevent the bad stars from using it for bad things. ^ Bubeck, Sébastien; Chandrasekaran, Varun; Eldan, Ronen; Gehrke, Johannes; Horvitz, Eric (2023 ). "Sparks of Artificial General Intelligence: Early experiments with 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 Expert System". The New York Times. The real hazard is not AI itself but the way we release it. ^ "Impressed by artificial intelligence? Experts state AGI is following, and it has 'existential' risks". ABC News. 23 March 2023. Retrieved 6 April 2023. AGI might present existential dangers to mankind. ^ Bostrom, Nick (2014 ). Superintelligence: Paths, Dangers, Strategies. Oxford University Press. ISBN 978-0-1996-7811-2. The first superintelligence will be the last creation that mankind needs to make. ^ Roose, Kevin (30 May 2023). "A.I. Poses 'Risk of Extinction,' Industry Leaders Warn". The New York Times. Mitigating the risk of extinction from AI ought to 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 City Times. We are far from creating machines that can outthink us in general methods. ^ LeCun, Yann (June 2023). "AGI does not provide an existential danger". 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 "device intelligence with the full series 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 utilize for "human-level" intelligence in the physical symbol system hypothesis. ^ "The Open University on Strong and Weak AI". Archived from the original on 25 September 2009. Retrieved 8 October 2007. ^ "What is synthetic superintelligence (ASI)?|Definition from TechTarget". Enterprise AI. Retrieved 8 October 2023. ^ "Expert system is changing our world - it is on everyone to make certain that it goes well". Our World in Data. Retrieved 8 October 2023. ^ Dickson, Ben (16 November 2023). "Here is how far we are to attaining AGI, according to DeepMind". VentureBeat. ^ McCarthy, John (2007a). "Basic Questions". Stanford University. Archived from the initial on 26 October 2007. Retrieved 6 December 2007. ^ This list of smart characteristics is based upon the subjects covered by significant AI books, consisting of: Russell & Norvig 2003, Luger & Stubblefield 2004, Poole, Mackworth & Goebel 1998 and Nilsson 1998. ^ Johnson 1987. ^ de Charms, R. (1968 ). Personal causation. New York City: 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 principle of skills". Psychological Review. 66 (5 ): 297-333. doi:10.1037/ h0040934. PMID 13844397. S2CID 37385966. ^ White, R. W. (1959 ). "Motivation reconsidered: The concept 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 initial on 25 April 2014. Retrieved 1 May 2014. ^ "What is Artificial General Intelligence (AGI)?|4 Tests For Ensuring Artificial General Intelligence". Talky Blog. 13 July 2019. Archived from the 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 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 kid - the Turing Test states so". The Guardian. 9 June 2014. ISSN 0261-3077. Retrieved 3 March 2024. ^ "Scientists dispute whether computer system 'Eugene Goostman' passed Turing test". BBC News. 9 June 2014. Retrieved 3 March 2024. ^ Jones, Cameron R.; Bergen, Benjamin K. (9 May 2024). "People can not distinguish GPT-4 from a human in a Turing test". arXiv:2405.08007 [cs.HC] ^ Varanasi, Lakshmi (21 March 2023). "AI designs like ChatGPT and GPT-4 are acing everything from the bar examination to AP Biology. Here's a list of tough exams both AI versions 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 suggested testing an AI chatbot's ability to turn $100,000 into $1 million to measure human-like intelligence". Business Insider. Retrieved 3 March 2024. ^ Suleyman, Mustafa (14 July 2023). "Mustafa Suleyman: My new Turing test would see if AI can make $1 million". MIT Technology Review. Retrieved 3 March 2024. ^ Shapiro, Stuart C. (1992 ). "Artificial Intelligence" (PDF). In Stuart C. Shapiro (ed.). Encyclopedia of Artificial Intelligence (Second ed.). New York: John Wiley. pp. 54-57. Archived (PDF) from the initial on 1 February 2016. (Section 4 is on "AI-Complete Tasks".). ^ Yampolskiy, Roman V. (2012 ). Xin-She Yang (ed.). "Turing Test as a Defining Feature of AI-Completeness" (PDF). Artificial Intelligence, Evolutionary Computation and Metaheuristics (AIECM): 3-17. Archived (PDF) from the initial on 22 May 2013. ^ "AI Index: State of AI in 13 Charts". Stanford University Human-Centered Expert System. 15 April 2024. Retrieved 27 May 2024. ^ Crevier 1993, pp. 48-50. ^ Kaplan, Andreas (2022 ). "Artificial Intelligence, Business and Civilization - Our Fate Made in Machines". Archived from the initial on 6 May 2022. Retrieved 12 March 2022. ^ Simon 1965, p. 96 estimated in Crevier 1993, p. 109. ^ "Scientist on the Set: An Interview with Marvin Minsky". Archived from the original on 16 July 2012. Retrieved 5 April 2008. ^ Marvin Minsky to Darrach (1970 ), quoted in Crevier (1993, p. 109). ^ Lighthill 1973; Howe 1994. ^ a b NRC 1999, "Shift to Applied Research Increases Investment". ^ Crevier 1993, pp. 115-117; Russell & Norvig 2003, pp. 21-22. ^ Crevier 1993, p. 211, Russell & Norvig 2003, p. 24 and see likewise Feigenbaum & McCorduck 1983. ^ Crevier 1993, pp. 161-162, 197-203, 240; Russell & Norvig 2003, p. 25. ^ Crevier 1993, pp. 209-212. ^ McCarthy, John (2000 ). "Respond to Lighthill". Stanford University. Archived from the initial on 30 September 2008. Retrieved 29 September 2007. ^ Markoff, John (14 October 2005). "Behind Expert system, wifidb.science 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 application engineers avoided the term expert system for worry of being viewed as wild-eyed dreamers. ^ Russell & Norvig 2003, pp. 25-26 ^ "Trends in the Emerging Tech Hype Cycle". Gartner Reports. Archived from the 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 Upon Algorithmic Probability. Texts in Theoretical Computer Science an EATCS Series. Springer. doi:10.1007/ b138233. ISBN 978-3-5402-6877-2. S2CID 33352850. Archived from the original on 19 July 2022. Retrieved 19 July 2022. ^ Legg, Shane (2008 ). Machine Super Intelligence (PDF) (Thesis). University of Lugano. Archived (PDF) from the initial on 15 June 2022. Retrieved 19 July 2022. ^ Goertzel, Ben (2014 ). Artificial General Intelligence. Lecture Notes in Computer Technology. Vol. 8598. Journal of Artificial General Intelligence. doi:10.1007/ 978-3-319-09274-4. ISBN 978-3-3190-9273-7. S2CID 8387410. ^ "Who coined the term "AGI"?". goertzel.org. Archived from the original on 28 December 2018. Retrieved 28 December 2018., through Life 3.0: 'The term "AGI" was popularized 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 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 season 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 device intelligence: Despite development in device intelligence, artificial general intelligence is still a major challenge". EMBO Reports. 20 (10 ): e49177. doi:10.15252/ embr.201949177. ISSN 1469-221X. PMC 6776890. PMID 31531926. ^ Bubeck, Sébastien; Chandrasekaran, Varun; Eldan, Ronen; Gehrke, Johannes; Horvitz, Eric; Kamar, Ece; Lee, Peter; Lee, Yin Tat; Li, Yuanzhi; Lundberg, Scott; Nori, Harsha; Palangi, Hamid; Ribeiro, Marco Tulio; Zhang, Yi (27 March 2023). "Sparks of Artificial General Intelligence: Early explores GPT-4". arXiv:2303.12712 [cs.CL] ^ "Microsoft Researchers Claim GPT-4 Is Showing "Sparks" of AGI". Futurism. 23 March 2023. Retrieved 13 December 2023. ^ Allen, Paul; Greaves, Mark (12 October 2011). "The Singularity Isn't Near". MIT Technology Review. Retrieved 17 September 2014. ^ Winfield, Alan. "Expert system will not turn 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 basic artificial 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 expert system bring us paradise or damage?". The New Yorker. Archived from the original on 28 January 2016. Retrieved 7 February 2016. ^ Müller, V. C., & Bostrom, N. (2016 ). Future progress in expert system: A survey of skilled viewpoint. In Fundamental concerns of artificial intelligence (pp. 555-572). Springer, Cham. ^ Armstrong, Stuart, and Kaj Sotala. 2012. "How We're Predicting AI-or Failing To." In Beyond AI: Artificial Dreams, modified by Jan Romportl, Pavel Ircing, Eva Žáčková, Michal Polák and Radek Schuster, 52-75. Plzeň: University of West Bohemia ^ "Microsoft Now Claims GPT-4 Shows 'Sparks' of General Intelligence". 24 March 2023. ^ Shimek, Cary (6 July 2023). "AI Outperforms Humans in Creativity Test". Neuroscience News. Retrieved 20 October 2023. ^ Guzik, Erik E.; Byrge, Christian; Gilde, Christian (1 December 2023). "The creativity of machines: AI takes the Torrance Test". Journal of Creativity. 33 (3 ): 100065. doi:10.1016/ j.yjoc.2023.100065. ISSN 2713-3745. S2CID 261087185. ^ Arcas, Blaise Agüera y (10 October 2023). "Artificial General Intelligence Is Already Here". Noema. ^ Zia, Tehseen (8 January 2024). "Unveiling of Large Multimodal Models: Shaping the Landscape of Language Models in 2024". Unite.ai. Retrieved 26 May 2024. ^ "Introducing OpenAI o1-preview". OpenAI. 12 September 2024. ^ Knight, Will. "OpenAI Announces a 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 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 role of GPU computing and deep learning in drug discovery". Nature Machine Intelligence. 4 (3 ): 211-221. doi:10.1038/ s42256-022-00463-x. ISSN 2522-5839. S2CID 252081559. ^ Goertzel & Pennachin 2006. ^ a b c (Kurzweil 2005, p. 260). ^ a b c Goertzel 2007. ^ Grace, Katja (2016 ). "Error in Armstrong and Sotala 2012". AI Impacts (blog site). Archived from the original on 4 December 2020. Retrieved 24 August 2020. ^ a b Butz, Martin V. (1 March 2021). "Towards Strong AI". KI - Künstliche Intelligenz. 35 (1 ): 91-101. doi:10.1007/ s13218-021-00705-x. ISSN 1610-1987. S2CID 256065190. ^ Liu, Feng; Shi, Yong; Liu, Ying (2017 ). "Intelligence Quotient and Intelligence Grade of Artificial Intelligence". Annals of Data Science. 4 (2 ): 179-191. arXiv:1709.10242. doi:10.1007/ s40745-017-0109-0. S2CID 37900130. ^ Brien, Jörn (5 October 2017). "Google-KI doppelt so schlau wie Siri" [Google AI is two times as wise as Siri - but a six-year-old beats both] (in German). Archived from the initial 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 declare we are already seeing an early example of an AGI system in the 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 nothing else, GPT-3 tells us there is a middle ground in between narrow and basic AI. ^ Quach, Katyanna. "A developer built an AI chatbot utilizing GPT-3 that helped a male speak once again to his late fiancée. OpenAI shut it down". The Register. Archived from the initial on 16 October 2021. Retrieved 16 October 2021. ^ Wiggers, Kyle (13 May 2022), "DeepMind's brand-new AI can perform over 600 tasks, from playing games to controlling robots", TechCrunch, archived from the original on 16 June 2022, retrieved 12 June 2022. ^ Bubeck, Sébastien; Chandrasekaran, Varun; Eldan, Ronen; Gehrke, Johannes; Horvitz, Eric; Kamar, Ece; Lee, Peter; Lee, Yin Tat; Li, Yuanzhi; Lundberg, Scott; Nori, Harsha; Palangi, Hamid; Ribeiro, Marco Tulio; Zhang, Yi (22 March 2023). "Sparks of Artificial General Intelligence: Early try outs GPT-4". arXiv:2303.12712 [cs.CL] ^ Metz, Cade (1 May 2023). "' The Godfather of A.I.' Leaves Google and Warns of Danger Ahead". The New York City Times. ISSN 0362-4331. Retrieved 7 June 2023. ^ Bove, Tristan. "A.I. could match human intelligence in 'just a few years,' says CEO of Google's main A.I. research study laboratory". Fortune. Retrieved 4 September 2024. ^ Nellis, Stephen (2 March 2024). "Nvidia CEO states AI might pass human tests in 5 years". Reuters. ^ Aschenbrenner, Leopold. "SITUATIONAL AWARENESS, The Decade Ahead". ^ Sullivan, Mark (18 October 2023). "Why everyone appears to disagree on how to define Artificial General Intelligence". Fast Company. ^ Nosta, John (5 January 2024). "The Accelerating Path to Artificial General Intelligence". Psychology Today. Retrieved 30 March 2024. ^ Hickey, Alex. "Whole Brain Emulation: A Huge Step for Neuroscience". Tech Brew. Retrieved 8 November 2023. ^ Sandberg & Boström 2008. ^ Drachman 2005. ^ a b Russell & Norvig 2003. ^ Moravec 1988, p. 61. ^ Moravec 1998. ^ Holmgaard Mersh, Amalie (15 September 2023). "Decade-long European research study job maps the human brain". euractiv. ^ Swaminathan, Nikhil (January-February 2011). "Glia-the other brain cells". Discover. Archived from the original on 8 February 2014. Retrieved 24 January 2014. ^ de Vega, Glenberg & Graesser 2008. A large range of views in current research study, all of which need 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 instance: 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 interest by the field about why a program acts the method 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 Artificial Intelligence Have Moral or Legal Rights?". The Wall Street Journal. ^ "Artificial General Intelligence - Do [es] the cost exceed advantages?". 23 August 2021. Retrieved 7 June 2023. ^ "How we can Take advantage of 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 Expert System Is Everywhere?". Smithsonian Magazine. Retrieved 7 June 2023. ^ a b Stevenson, Matt (8 October 2015). "Answers to Stephen Hawking's AMA are Here!". Wired. ISSN 1059-1028. Retrieved 8 June 2023. ^ a b Bostrom, Nick (2017 ). " § Preferred order of arrival". Superintelligence: paths, dangers, strategies (Reprinted with corrections 2017 ed.). Oxford, UK; New York City, New York City, USA: Oxford University Press. ISBN 978-0-1996-7811-2. ^ Piper, Kelsey (19 November 2018). "How technological development is making it likelier than ever that humans will ruin ourselves". Vox. Retrieved 8 June 2023. ^ Doherty, Ben (17 May 2018). "Climate change an 'existential security risk' to Australia, Senate inquiry says". The Guardian. ISSN 0261-3077. Retrieved 16 July 2023. ^ MacAskill, William (2022 ). What we owe the future. New York City, NY: Basic Books. ISBN 978-1-5416-1862-6. ^ a b Ord, Toby (2020 ). "Chapter 5: Future Risks, Unaligned 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 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 argument in between Yann LeCun, iuridictum.pecina.cz Stuart Russel and Yoshua Bengio about the risks of strong AI". The fascinating Facebook argument between Yann LeCun, Stuart Russel and Yoshua Bengio about the threats of strong AI (in French). Retrieved 8 June 2023. ^ "Will Expert System Doom The Mankind Within The Next 100 Years?". HuffPost. 22 August 2014. Retrieved 8 June 2023. ^ Sotala, Kaj; Yampolskiy, Roman V. (19 December 2014). "Responses to catastrophic AGI threat: a survey". Physica Scripta. 90 (1 ): 018001. doi:10.1088/ 0031-8949/90/ 1/018001. ISSN 0031-8949. ^ Bostrom, Nick (2014 ). Superintelligence: Paths, Dangers, Strategies (First ed.). Oxford University Press. ISBN 978-0-1996-7811-2. ^ Chow, Andrew R.; Perrigo, Billy (16 February 2023). "The AI Arms Race Is On. Start Worrying". TIME. Retrieved 24 December 2023. ^ Tetlow, Gemma (12 January 2017). "AI arms race risks spiralling out of control, report 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 danger posed but 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 original on 4 June 2014. Retrieved 12 December 2015. ^ Titcomb, James (30 October 2023). "Big Tech is stoking fears over AI, warn scientists". The Telegraph. Retrieved 7 December 2023. ^ Davidson, John (30 October 2023). "Google Brain creator states huge tech is lying about AI termination danger". Australian Financial Review. Archived from the original on 7 December 2023. Retrieved 7 December 2023. ^ Eloundou, Tyna; Manning, Sam; Mishkin, Pamela; Rock, Daniel (17 March 2023). "GPTs are GPTs: An early look at the labor market effect capacity of big language models". OpenAI. Retrieved 7 June 2023. ^ a b Hurst, Luke (23 March 2023). "OpenAI says 80% of workers might see their tasks affected by AI. These are the tasks most affected". euronews. Retrieved 8 June 2023. ^ Sheffey, Ayelet (20 August 2021). "Elon Musk states we need universal standard income due to the fact that '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 original on 18 June 2022. Retrieved 22 September 2021. Chalmers, David (1996 ), The Conscious Mind, Oxford University Press. Clocksin, William (August 2003), "Expert system and the future", Philosophical Transactions of the Royal Society A, vol. 361, no. 1809, pp. 1721-1748, Bibcode:2003 RSPTA.361.1721 C, doi:10.1098/ rsta.2003.1232, PMID 12952683, S2CID 31032007. Crevier, Daniel (1993 ). AI: The Tumultuous Search for Expert System. New York 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 synthetic basic intelligence and the possibility of a technological singularity: a response to Ray Kurzweil's The Singularity Is Near, and McDermott's critique of Kurzweil", 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 original on 29 May 2011, recovered 7 May 2011. Howe, J. (November 1994), Expert System at Edinburgh University: a Perspective, archived from the initial on 17 August 2007, recovered 30 August 2007. Johnson, Mark (1987 ), The body in the mind, Chicago, ISBN 978-0-2264-0317-5. Kurzweil, Ray (2005 ), The Singularity is Near, Viking Press. Lighthill, Professor Sir James (1973 ), "Expert System: A General Survey", Artificial Intelligence: a paper symposium, 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 Artificial Intelligence?. Stanford University. The supreme effort is to make computer programs that can solve issues and accomplish objectives worldwide along with people. Moravec, Hans (1988 ), Mind Children, Harvard University Press Moravec, Hans (1998 ), "When will hardware match the human brain?", Journal of Evolution and Technology, vol. 1, archived from the initial on 15 June 2006, 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, 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 ), Expert System: A New Synthesis, Morgan Kaufmann Publishers, ISBN 978-1-5586-0467-4 NRC (1999 ), "Developments in Expert System", Funding a Transformation: Government Support for Computing Research, National Academy Press, archived from the initial on 12 January 2008, obtained 29 September 2007 Poole, David; Mackworth, Alan; Goebel, Randy (1998 ), Computational Intelligence: A Logical Approach, New York: Oxford University Press, archived from the original on 25 July 2009, retrieved 6 December 2007 Russell, Stuart J.; Norvig, Peter (2003 ), Artificial Intelligence: A Modern Approach (2nd ed.), Upper Saddle River, New Jersey: Prentice Hall, ISBN 0-13-790395-2 Sandberg, Anders; Boström, Nick (2008 ), Whole Brain Emulation: A Roadmap (PDF), Technical Report # 2008-3, Future of Humanity Institute, Oxford University, archived (PDF) from the initial 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 original on 17 March 2019, recovered 3 September 2020 Simon, H. A. (1965 ), The Shape of Automation for Men and Management, New York City: Harper & Row Turing, Alan (October 1950). "Computing Machinery and Intelligence". Mind. 59 (236 ): 433-460. doi:10.1093/ mind/LIX.236.433. ISSN 1460-2113. JSTOR 2251299. S2CID 14636783.
de Vega, Manuel; Glenberg, Arthur; Graesser, Arthur, eds. (2008 ), Symbols and Embodiment: Debates on 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 original on 18 February 2021, obtained 4 September 2013 - via ResearchGate Berglas, Anthony (January 2012) [2008], Expert System Will Kill Our Grandchildren (Singularity), archived from the initial on 23 July 2014, recovered 31 August 2012 Cukier, Kenneth, "Ready for Robots? How to Think of the Future of AI", Foreign Affairs, vol. 98, no. 4 (July/August 2019), pp. 192-98. George Dyson, historian of computing, writes (in what might be called "Dyson's Law") that "Any system easy adequate to be easy to understand will not be made complex enough to behave wisely, while any system complicated enough to behave smartly will be too complicated to understand." (p. 197.) Computer researcher Alex Pentland composes: "Current AI machine-learning algorithms are, at their core, dead easy dumb. 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" (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 machines. For biological animals, factor and function come from acting worldwide and the effects. Expert systems - disembodied, strangers to blood, sweat, and tears - have no occasion for that." (p. 30.). Halal, William E. "TechCast Article Series: The Automation of Thought" (PDF). Archived from the initial (PDF) on 6 June 2013. - Halpern, Sue, "The Coming Tech Autocracy" (evaluation of Verity Harding, AI Needs You: How We Can Change AI's Future and Save Our Own, Princeton University Press, 274 pp.; Gary Marcus, Taming Silicon Valley: How We Can Ensure That AI Works for Us, MIT Press, 235 pp.; Daniela Rus and Gregory Mone, The Mind's Mirror: Risk and Reward in the Age of AI, Norton, 280 pp.; Madhumita Murgia, Code Dependent: 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 reasonably expect that those who intend to get rich from AI are going to have the interests of the rest people close at heart,' ... writes [Gary Marcus] 'We can't rely on federal governments driven by campaign financing contributions [from tech companies] to push back.' ... Marcus details the demands that citizens need to make of their governments and the tech companies. They include openness on how AI systems work; compensation for people if their information [are] used to train LLMs (large language model) s and the right to consent to this use; and the capability to hold tech companies liable for the harms they bring on by removing Section 230, enforcing cash penalites, and passing more stringent item liability laws ... Marcus likewise suggests ... that a new, AI-specific federal agency, similar to the FDA, the FCC, or the FTC, might supply the most robust oversight ... [T] he Fordham law teacher Chinmayi Sharma ... suggests ... develop [ing] a professional licensing routine for engineers that would function in a comparable way to medical licenses, malpractice suits, and the Hippocratic oath in medication. 'What if, like medical professionals,' she asks ..., 'AI engineers likewise swore to do no damage?'" (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, disgaeawiki.info which has actually puzzled human beings for decades, reveals the constraints of natural-language-processing algorithms", Scientific American, vol. 329, no. 4 (November 2023), pp. 81-82. "This murder secret competition has revealed that although NLP (natural-language processing) designs can unbelievable accomplishments, their abilities are very much limited by the amount of context they receive. This [...] could cause [troubles] for scientists who want to use them to do things such as evaluate ancient languages. In some cases, there are couple of historic records on long-gone civilizations to act as training information for such a function." (p. 82.). Immerwahr, Daniel, "Your Lying Eyes: People now utilize A.I. to create 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 imply practical videos produced utilizing expert system that in fact trick people, then they barely exist. The phonies aren't deep, and the deeps aren't fake. [...] A.I.-generated videos are not, in basic, operating in our media as counterfeited evidence. Their function better resembles that of cartoons, specifically smutty ones." (p. 59.). - Leffer, Lauren, "The Risks of Trusting AI: We need to avoid humanizing machine-learning models utilized in clinical research", Scientific American, vol. 330, no. 6 (June 2024), pp. 80-81. Lepore, Jill, "The Chit-Chatbot: Is talking with a machine a discussion?", The New Yorker, 7 October 2024, pp. 12-16. Marcus, Gary, "Artificial Confidence: Even the most recent, buzziest systems of artificial basic intelligence are stymmied by the usual issues", Scientific American, vol. 327, no. 4 (October 2022), pp. 42-45. McCarthy, John (October 2007), "From here to human-level AI", Artificial Intelligence, 171 (18 ): 1174-1182, doi:10.1016/ j.artint.2007.10.009. McCorduck, Pamela (2004 ), Machines Who Think (2nd ed.), Natick, Massachusetts: A. K. Peters, ISBN 1-5688-1205-1. Moravec, Hans (1976 ), The Role of Raw Power in Intelligence, archived from the original on 3 March 2016, recovered 29 September 2007. Newell, Allen; Simon, H. A. (1963 ), "GPS: A Program that Simulates Human Thought", in Feigenbaum, E. A.; Feldman, J. (eds.), Computers and Thought, New York: McGraw-Hill. Omohundro, Steve (2008 ), The Nature of Self-Improving Expert system, provided and dispersed at the 2007 Singularity Summit, San Francisco, California. Press, Eyal, "In Front of Their Faces: Does facial-recognition innovation lead authorities to overlook contradictory evidence?", The New Yorker, 20 November 2023, pp. 20-26. Roivainen, Eka, "AI's IQ: ChatGPT aced a [basic intelligence] test but showed that intelligence can not be 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 require genuine humanlike reasoning or an understanding of the physical and social world ... ChatGPT seemed not able to reason realistically and tried to depend on its vast database of ... truths stemmed from online texts. " - Scharre, Paul, "Killer Apps: The Real Dangers of an AI Arms Race", Foreign Affairs, vol. 98, no. 3 (May/June 2019), pp. 135-44. "Today's AI innovations are effective however unreliable. Rules-based systems can not deal with circumstances their programmers did not anticipate. Learning systems are limited by the data on which they were trained. AI failures have actually currently caused catastrophe. Advanced autopilot functions in automobiles, although they carry out well in some scenarios, have driven cars and trucks without cautioning into trucks, concrete barriers, and parked automobiles. In the wrong situation, AI systems go from supersmart to superdumb in an immediate. When an enemy is trying to control and hack an AI system, the dangers 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 technologies however rely on the timelelss human tendency to anthropomorphise." (p. 29.). Williams, R. W.; Herrup, K.