GitLab

Artificial general intelligence (AGI) is a kind of artificial intelligence (AI) that matches or goes beyond human cognitive capabilities across a vast array of cognitive tasks. This contrasts with narrow AI, which is limited to specific tasks. [1] Artificial superintelligence (ASI), on the other hand, refers to AGI that significantly surpasses human cognitive abilities. AGI is thought about one of the meanings of strong AI.

Creating AGI is a main goal of AI research and of companies such as OpenAI [2] and Meta. [3] A 2020 survey identified 72 active AGI research study and advancement projects across 37 countries. [4]
The timeline for accomplishing AGI stays a subject of continuous dispute amongst scientists and professionals. As of 2023, some argue that it may be possible in years or decades; others keep it may take a century or longer; a minority believe it may never ever be attained; and another minority declares that it is already here. [5] [6] Notable AI scientist Geoffrey Hinton has revealed issues about the rapid progress towards AGI, suggesting it could be achieved sooner than many expect. [7]
There is argument on the specific meaning of AGI and regarding whether contemporary big language models (LLMs) such as GPT-4 are early forms of AGI. [8] AGI is a common topic in science fiction and futures research studies. [9] [10]
Contention exists over whether AGI represents an existential risk. [11] [12] [13] Many experts on AI have actually mentioned that reducing the risk of human extinction posed by AGI needs to be a worldwide concern. [14] [15] Others find the advancement of AGI to be too remote to provide such a danger. [16] [17]
Terminology

AGI is likewise understood as strong AI, [18] [19] complete AI, [20] human-level AI, [5] human-level intelligent AI, or general smart action. [21]
Some academic sources reserve the term "strong AI" for computer system programs that experience life or awareness. [a] In contrast, weak AI (or narrow AI) is able to solve one specific issue but does not have basic cognitive abilities. [22] [19] Some scholastic sources use "weak AI" to refer more broadly to any programs that neither experience consciousness nor have a mind in the same sense as human beings. [a]
Related ideas include artificial superintelligence and transformative AI. An artificial superintelligence (ASI) is a theoretical type of AGI that is far more normally intelligent than human beings, [23] while the idea of transformative AI connects to AI having a big effect on society, for instance, similar to the farming or commercial revolution. [24]
A framework for classifying AGI in levels was proposed in 2023 by Google DeepMind researchers. They define five levels of AGI: emerging, competent, expert, virtuoso, and superhuman. For example, a qualified AGI is specified as an AI that outshines 50% of knowledgeable grownups in a wide variety of non-physical jobs, and a superhuman AGI (i.e. a synthetic superintelligence) is similarly defined but with a threshold of 100%. They think about big language designs like ChatGPT or LLaMA 2 to be circumstances of emerging AGI. [25]
Characteristics

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

Researchers usually hold that intelligence is needed to do all of the following: [27]
factor, usage technique, resolve puzzles, and make judgments under unpredictability represent knowledge, consisting of common sense knowledge strategy discover

• communicate in natural language
• if required, incorporate these skills in conclusion of any offered objective

Many interdisciplinary approaches (e.g. cognitive science, computational intelligence, and choice making) consider additional traits such as imagination (the ability to form unique psychological images and ideas) [28] and autonomy. [29]
Computer-based systems that show a number of these abilities exist (e.g. see computational creativity, automated thinking, choice support system, robotic, evolutionary calculation, intelligent agent). There is dispute about whether modern-day AI systems have them to a sufficient degree.

Physical characteristics

Other capabilities are thought about preferable in intelligent systems, as they may impact intelligence or aid in its expression. These consist of: [30]
- the ability to sense (e.g. see, hear, and so on), and - the ability to act (e.g. relocation and manipulate items, modification area to explore, etc).
This includes the ability to spot and respond to risk. [31]
Although the ability to sense (e.g. see, hear, and so on) and the ability to act (e.g. relocation and control things, change place to check out, and so on) can be preferable for some intelligent systems, [30] these physical capabilities are not strictly needed for an entity to qualify as AGI-particularly under the thesis that large language designs (LLMs) might currently be or become AGI. Even from a less optimistic point of view on LLMs, there is no firm requirement for an AGI to have a human-like type; being a silicon-based computational system is enough, offered it can process input (language) from the external world in location of human senses. This interpretation lines up with the understanding that AGI has never ever been proscribed a specific physical personification and hence does not require a capability for locomotion or traditional "eyes and ears". [32]
Tests for human-level AGI

Several tests implied to confirm human-level AGI have been considered, including: [33] [34]
The idea of the test is that the maker needs to try and pretend to be a man, by responding to concerns put to it, and it will only pass if the pretence is reasonably persuading. A considerable part of a jury, who should not be professional about makers, 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 fix it, one would need to carry out AGI, because the service is beyond the abilities of a purpose-specific algorithm. [47]
There are many problems that have actually been conjectured to need general intelligence to solve along with human beings. Examples include computer system vision, natural language understanding, and dealing with unforeseen circumstances while any real-world problem. [48] Even a specific job like translation needs a device to read and write in both languages, follow the author's argument (reason), understand the context (knowledge), and faithfully recreate the author's original intent (social intelligence). All of these problems need to be resolved all at once 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 efficiency on lots of standards for reading comprehension and visual thinking. [49]
History

Classical AI

Modern AI research study started in the mid-1950s. [50] The first generation of AI scientists were encouraged that artificial general intelligence was possible and that it would exist in just a couple of years. [51] AI leader Herbert A. Simon wrote in 1965: "machines will be capable, within twenty years, of doing any work a man 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 pioneer Marvin Minsky was an expert [53] on the project of making HAL 9000 as reasonable as possible according to the agreement predictions of the time. He stated in 1967, "Within a generation ... the issue of producing 'expert system' will significantly be resolved". [54]
Several classical AI tasks, such as Doug Lenat's Cyc project (that started in 1984), and Allen Newell's Soar job, were directed at AGI.

However, in the early 1970s, it ended up being obvious that researchers had grossly underestimated the difficulty of the project. Funding firms ended up being doubtful of AGI and put researchers under increasing pressure to produce beneficial "used AI". [c] In the early 1980s, Japan's Fifth Generation Computer Project revived interest in AGI, setting out a ten-year timeline that consisted of AGI goals like "carry on a table talk". [58] In reaction to this and the success of specialist systems, both market and federal government pumped cash into the field. [56] [59] However, self-confidence in AI marvelously collapsed in the late 1980s, and the goals of the Fifth Generation Computer Project were never ever fulfilled. [60] For the second time in twenty years, AI researchers who predicted the impending achievement of AGI had actually been misinterpreted. By the 1990s, AI scientists had a track record for making vain promises. They ended up being unwilling to make predictions at all [d] and avoided reference of "human level" expert system for worry of being labeled "wild-eyed dreamer [s]. [62]
Narrow AI research study

In the 1990s and early 21st century, mainstream AI accomplished business success and academic respectability by focusing on particular sub-problems where AI can produce verifiable results and business applications, such as speech recognition and suggestion algorithms. [63] These "applied AI" systems are now used extensively throughout the innovation market, and research study in this vein is greatly moneyed in both academia and market. As of 2018 [upgrade], development in this field was considered an emerging pattern, and a fully grown phase was expected to be reached in more than ten years. [64]
At the turn of the century, numerous mainstream AI researchers [65] hoped that strong AI could be established by combining programs that resolve various sub-problems. Hans Moravec composed in 1988:

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

The expectation has actually typically been voiced that "top-down" (symbolic) approaches to modeling cognition will in some way fulfill "bottom-up" (sensory) approaches someplace in between. If the grounding factors to consider in this paper stand, then this expectation is hopelessly modular and there is actually only one viable path 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 path (or vice versa) - nor is it clear why we should even try to reach such a level, since it appears getting there would just amount to uprooting our signs from their intrinsic meanings (consequently simply reducing ourselves to the functional equivalent of a programmable computer). [66]
Modern artificial general intelligence research study

The term "artificial basic intelligence" was used as early as 1997, by Mark Gubrud [67] in a discussion of the ramifications of completely automated military production and operations. A mathematical formalism of AGI was proposed by Marcus Hutter in 2000. Named AIXI, the proposed AGI representative maximises "the ability to satisfy goals in a large range of environments". [68] This kind of AGI, defined by the ability to increase a mathematical meaning of intelligence instead of display human-like behaviour, [69] was also called universal artificial intelligence. [70]
The term AGI was re-introduced and popularized by Shane Legg and Ben Goertzel around 2002. [71] AGI research activity in 2006 was described by Pei Wang and Ben Goertzel [72] as "producing publications and initial outcomes". The first 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 presented a course on AGI in 2018, arranged by Lex Fridman and featuring a variety of guest lecturers.

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

Feasibility

Since 2023, the development and possible accomplishment of AGI stays a topic of extreme dispute within the AI community. While conventional consensus held that AGI was a far-off goal, current advancements have actually led some researchers and industry figures to declare that early forms of AGI may currently 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 true. Microsoft co-founder Paul Allen believed that such intelligence is unlikely in the 21st century since it would need "unforeseeable and basically unpredictable advancements" 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 expert system is as wide as the gulf in between current area flight and useful faster-than-light spaceflight. [80]
A more difficulty is the absence of clarity in specifying what intelligence involves. Does it need consciousness? Must it display the capability to set objectives in addition to pursue them? Is it purely a matter of scale such that if model sizes increase sufficiently, intelligence will emerge? Are centers such as planning, reasoning, and causal understanding needed? Does intelligence require explicitly duplicating the brain and its specific professors? Does it need feelings? [81]
Most AI scientists believe strong AI can be attained in the future, but 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, however that the present level of progress is such that a date can not precisely be predicted. [84] AI professionals' views on the feasibility of AGI wax and subside. Four polls carried out in 2012 and 2013 suggested that the typical price quote amongst specialists for when they would be 50% positive AGI would show up was 2040 to 2050, depending on the poll, with the mean being 2081. Of the experts, 16.5% responded to with "never" when asked the very same question but with a 90% confidence instead. [85] [86] Further existing AGI development factors to consider can be found 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 timespan there is a strong predisposition towards anticipating the arrival of human-level AI as in between 15 and 25 years from the time the prediction was made". They evaluated 95 forecasts made between 1950 and 2012 on when human-level AI will come about. [87]
In 2023, Microsoft scientists published an in-depth examination of GPT-4. They concluded: "Given the breadth and depth of GPT-4's capabilities, our company believe that it might reasonably be viewed as an early (yet still incomplete) version of a synthetic general intelligence (AGI) system." [88] Another study in 2023 reported that GPT-4 exceeds 99% of human beings on the Torrance tests of imaginative thinking. [89] [90]
Blaise Agüera y Arcas and Peter Norvig wrote in 2023 that a substantial level of general intelligence has actually currently been attained with frontier models. They wrote that reluctance to this view originates from 4 primary reasons: a "healthy apprehension about metrics for AGI", an "ideological dedication to alternative AI theories or strategies", a "commitment to human (or biological) exceptionalism", or a "concern about the financial ramifications of AGI". [91]
2023 likewise marked the emergence of big multimodal designs (large language designs capable of processing or generating several methods such as text, audio, and images). [92]
In 2024, OpenAI released o1-preview, the first of a series of models that "spend more time thinking before they react". According to Mira Murati, this ability to think before responding represents a brand-new, extra paradigm. It improves design outputs by spending more computing power when generating the answer, whereas the model scaling paradigm enhances outputs by increasing the model size, training information and training compute power. [93] [94]
An OpenAI employee, Vahid Kazemi, claimed in 2024 that the business had accomplished AGI, specifying, "In my opinion, we have actually already achieved AGI and it's even more clear with O1." Kazemi clarified that while the AI is not yet "much better than any human at any job", it is "much better than the majority of human beings at many tasks." He likewise addressed criticisms that big language models (LLMs) merely follow predefined patterns, comparing their knowing process to the clinical approach of observing, hypothesizing, and verifying. These statements have stimulated debate, as they rely on a broad and unconventional meaning of AGI-traditionally understood as AI that matches human intelligence throughout all domains. Critics argue that, while OpenAI's designs demonstrate remarkable versatility, they may not fully meet this standard. Notably, Kazemi's comments came quickly after OpenAI got rid of "AGI" from the terms of its partnership with Microsoft, triggering speculation about the company's tactical intents. [95]
Timescales

Progress in synthetic intelligence has actually traditionally gone through durations of quick development separated by durations when progress appeared to stop. [82] Ending each hiatus were basic advances in hardware, software or both to create area for more progress. [82] [98] [99] For instance, the hardware offered 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 says that quotes of the time needed before a truly versatile AGI is developed vary from 10 years to over a century. Since 2007 [upgrade], the agreement in the AGI research community seemed to be that the timeline talked about by Ray Kurzweil in 2005 in The Singularity is Near [102] (i.e. between 2015 and 2045) was possible. [103] Mainstream AI researchers have actually given a wide variety of opinions on whether development will be this rapid. A 2012 meta-analysis of 95 such viewpoints discovered a predisposition towards anticipating that the onset of AGI would occur within 16-26 years for contemporary and historic predictions alike. That paper has actually been criticized for how it categorized opinions as professional 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 traditional technique utilized a weighted sum of scores from different pre-defined classifiers). [105] AlexNet was considered the preliminary ground-breaker of the current deep learning wave. [105]
In 2017, scientists Feng Liu, Yong Shi, and Ying Liu conducted intelligence tests on openly readily available and freely accessible weak AI such as Google AI, Apple's Siri, and others. At the optimum, these AIs reached an IQ worth of about 47, which corresponds around to a six-year-old child in very first grade. An adult concerns about 100 typically. 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 capable of carrying out lots of varied jobs without specific training. According to Gary Grossman in a VentureBeat post, while there is consensus that GPT-3 is not an example of AGI, it is thought about by some to be too advanced to be categorized as a narrow AI system. [108]
In the exact same year, Jason Rohrer used his GPT-3 account to establish a chatbot, and supplied a chatbot-developing platform called "Project December". OpenAI requested changes to the chatbot to abide by their security guidelines; Rohrer disconnected Project December from the GPT-3 API. [109]
In 2022, DeepMind established Gato, a "general-purpose" system capable of carrying out more than 600 different jobs. [110]
In 2023, Microsoft Research released a study on an early version of OpenAI's GPT-4, contending that it displayed more general intelligence than previous AI models and demonstrated human-level efficiency in jobs covering several domains, such as mathematics, coding, and law. This research stimulated an argument on whether GPT-4 might be considered an early, incomplete version of synthetic basic intelligence, highlighting the requirement for more exploration and evaluation of such systems. [111]
In 2023, the AI scientist Geoffrey Hinton mentioned that: [112]
The idea that this stuff might really get smarter than individuals - a few people thought that, [...] But many people believed it was method off. And I thought it was way off. I believed it was 30 to 50 years or even 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 actually been quite unbelievable", which he sees no reason that it would decrease, anticipating AGI within a years or perhaps a couple of years. [113] In March 2024, Nvidia's CEO, Jensen Huang, mentioned his expectation that within 5 years, AI would can passing any test a minimum of in addition to people. [114] In June 2024, the AI scientist Leopold Aschenbrenner, a previous OpenAI staff member, estimated AGI by 2027 to be "noticeably plausible". [115]
Whole brain emulation

While the development of transformer designs like in ChatGPT is considered the most appealing path to AGI, [116] [117] whole brain emulation can function as an alternative method. With whole brain simulation, a brain model is built by scanning and mapping a biological brain in information, and after that copying and mimicing it on a computer system or another computational gadget. The simulation design should be sufficiently loyal to the initial, so that it acts in almost the exact same method as the initial brain. [118] Whole brain emulation is a type of brain simulation that is talked about in computational neuroscience and neuroinformatics, and for medical research purposes. It has been discussed in artificial intelligence research study [103] as an approach to strong AI. Neuroimaging technologies that might deliver the necessary in-depth understanding are enhancing rapidly, and futurist Ray Kurzweil in the book The Singularity Is Near [102] forecasts that a map of adequate quality will become offered on a similar timescale to the computing power needed to imitate it.

Early approximates

For low-level brain simulation, an extremely powerful cluster of computer systems or GPUs would be required, given the huge quantity of synapses within the human brain. Each of the 1011 (one hundred billion) nerve cells has on average 7,000 synaptic connections (synapses) to other nerve cells. The brain of a three-year-old child has about 1015 synapses (1 quadrillion). This number declines with age, stabilizing by their adult years. Estimates vary for an adult, varying from 1014 to 5 × 1014 synapses (100 to 500 trillion). [120] A quote of the brain's processing power, based upon a basic switch model for neuron activity, is around 1014 (100 trillion) synaptic updates per second (SUPS). [121]
In 1997, Kurzweil looked at different price quotes for the hardware needed to equal the human brain and embraced a figure of 1016 calculations per second (cps). [e] (For comparison, if a "computation" was equivalent to one "floating-point operation" - a measure utilized to rate present supercomputers - then 1016 "calculations" would be equivalent to 10 petaFLOPS, achieved in 2011, while 1018 was achieved in 2022.) He used this figure to predict the required hardware would be readily available sometime between 2015 and 2025, if the exponential 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 established a particularly comprehensive and publicly accessible atlas of the human brain. [124] In 2023, researchers from Duke University performed a high-resolution scan of a mouse brain.

Criticisms of simulation-based approaches

The artificial nerve cell design presumed by Kurzweil and used in numerous existing artificial neural network implementations is easy compared with biological nerve cells. A brain simulation would likely need to record the in-depth cellular behaviour of biological nerve cells, currently comprehended only in broad overview. The overhead introduced by full modeling of the biological, chemical, and physical information of neural behaviour (specifically on a molecular scale) would require computational powers several orders of magnitude larger than Kurzweil's price quote. In addition, the quotes do not represent glial cells, which are known to play a function in cognitive procedures. [125]
A basic criticism of the simulated brain method derives from embodied cognition theory which asserts that human embodiment is an essential aspect of human intelligence and is needed to ground meaning. [126] [127] If this theory is appropriate, any totally functional brain model will need to include more than simply the nerve cells (e.g., a robotic body). Goertzel [103] proposes virtual personification (like in metaverses like Second Life) as an option, however it is unknown whether this would suffice.

Philosophical perspective

"Strong AI" as defined in philosophy

In 1980, thinker John Searle coined the term "strong AI" as part of his Chinese space argument. [128] He proposed a distinction in between 2 hypotheses about expert system: [f]
Strong AI hypothesis: An artificial intelligence system can have "a mind" and "awareness". Weak AI hypothesis: An expert system system can (just) imitate it thinks and has a mind and awareness.
The very first one he called "strong" because it makes a more powerful statement: it assumes something special has actually taken place to the device that goes beyond those abilities that we can evaluate. The behaviour of a "weak AI" machine would be specifically identical to a "strong AI" maker, however the latter would likewise have subjective mindful experience. This usage is likewise common in scholastic AI research study and textbooks. [129]
In contrast to Searle and mainstream AI, some futurists such as Ray Kurzweil use the term "strong AI" to indicate "human level synthetic general intelligence". [102] This is not the like Searle's strong AI, unless it is assumed that awareness is needed for human-level AGI. Academic thinkers such as Searle do not think that is the case, and to most synthetic intelligence researchers the concern is out-of-scope. [130]
Mainstream AI is most interested in how a program acts. [131] According to Russell and Norvig, "as long as the program works, they don't care if you call it real or a simulation." [130] If the program can act as if it has a mind, then there is no need to know 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 granted, and do not care about the strong AI hypothesis." [130] Thus, for academic AI research study, "Strong AI" and "AGI" are 2 various things.

Consciousness

Consciousness can have various significances, and some elements play significant functions in science fiction and the principles of expert system:

Sentience (or "phenomenal awareness"): The capability to "feel" understandings or feelings subjectively, as opposed to the capability to reason about understandings. Some philosophers, such as David Chalmers, utilize the term "consciousness" to refer specifically to extraordinary awareness, which is approximately equivalent to life. [132] Determining why and how subjective experience develops is called the tough problem of consciousness. [133] Thomas Nagel described in 1974 that it "seems like" something to be conscious. If we are not mindful, then it doesn't seem like anything. Nagel uses the example of a bat: we can smartly ask "what does it seem 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 seems mindful (i.e., has consciousness) however a toaster does not. [134] In 2022, a Google engineer declared that the company's AI chatbot, LaMDA, had achieved sentience, though this claim was extensively challenged by other experts. [135]
Self-awareness: To have mindful awareness of oneself as a different individual, specifically to be purposely familiar with one's own thoughts. This is opposed to just being the "subject of one's thought"-an os or debugger is able to be "aware of itself" (that is, to represent itself in the very same method it represents whatever else)-however this is not what individuals generally indicate when they use the term "self-awareness". [g]
These traits have a moral measurement. AI sentience would generate issues of well-being and legal defense, likewise to animals. [136] Other elements of consciousness associated to cognitive capabilities are also relevant to the concept of AI rights. [137] Finding out how to incorporate sophisticated AI with existing legal and social frameworks is an emerging problem. [138]
Benefits

AGI could have a wide variety of applications. If oriented towards such goals, AGI might help reduce numerous issues worldwide such as hunger, poverty and health issues. [139]
AGI might improve performance and effectiveness in many tasks. For example, in public health, AGI could speed up medical research study, notably versus cancer. [140] It could look after the senior, [141] and democratize access to quick, premium medical diagnostics. It could provide fun, cheap and individualized education. [141] The requirement to work to subsist might end up being outdated if the wealth produced is effectively rearranged. [141] [142] This also raises the question of the location of human beings in a radically automated society.

AGI might also help to make logical decisions, and to expect and avoid disasters. It could likewise help to reap the benefits of possibly catastrophic innovations such as nanotechnology or climate engineering, while preventing the associated risks. [143] If an AGI's main objective is to avoid existential disasters such as human termination (which could be difficult if the Vulnerable World Hypothesis ends up being true), [144] it could take steps to dramatically minimize the dangers [143] while minimizing the impact of these measures on our lifestyle.

Risks

Existential dangers

AGI may represent several types of existential danger, which are risks that threaten "the premature extinction of Earth-originating intelligent life or the irreversible and drastic damage of its potential for preferable future development". [145] The danger of human termination from AGI has been the topic of numerous disputes, but there is likewise the possibility that the development of AGI would lead to a completely problematic future. Notably, it could be utilized to spread and maintain the set of worths of whoever establishes it. If mankind still has moral blind areas similar to slavery in the past, AGI might irreversibly entrench it, avoiding moral development. [146] Furthermore, AGI could assist in mass surveillance and indoctrination, which could be utilized to create a steady repressive worldwide totalitarian routine. [147] [148] There is likewise a threat for the machines themselves. If devices that are sentient or otherwise worthy of moral factor to consider are mass developed in the future, engaging in a civilizational course that forever neglects their well-being and interests might be an existential disaster. [149] [150] Considering just how much AGI could improve humanity's future and aid minimize other existential threats, Toby Ord calls these existential dangers "an argument for proceeding with due care", not for "abandoning AI". [147]
Risk of loss of control and human extinction

The thesis that AI postures an existential danger for humans, which this risk needs more attention, is questionable but has been endorsed in 2023 by many public figures, AI researchers 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 benefits and threats, the professionals are undoubtedly doing whatever possible to make sure the very best outcome, right? Wrong. If a remarkable alien civilisation sent us a message saying, 'We'll show up in a few decades,' would we just respond, 'OK, call us when you get here-we'll leave the lights on?' Probably not-but this is basically what is happening with AI. [153]
The prospective fate of mankind has actually sometimes been compared to the fate of gorillas threatened by human activities. The comparison states that higher intelligence allowed humankind to control gorillas, which are now susceptible in ways that they could not have anticipated. As an outcome, the gorilla has actually ended up being a threatened species, not out of malice, but simply as a collateral damage from human activities. [154]
The skeptic Yann LeCun considers that AGIs will have no desire to dominate mankind which we ought to beware not to anthropomorphize them and translate their intents as we would for people. He said that individuals will not be "clever adequate to develop super-intelligent devices, yet unbelievably silly to the point of providing it moronic objectives with no safeguards". [155] On the other side, the idea of critical merging recommends that practically whatever their objectives, intelligent representatives will have reasons to attempt to make it through and obtain more power as intermediary actions to accomplishing these goals. Which this does not need having emotions. [156]
Many scholars who are worried about existential danger supporter for more research study into fixing the "control issue" to respond to the concern: what kinds of safeguards, algorithms, or architectures can programmers carry out to increase the probability that their recursively-improving AI would continue to act in a friendly, rather than destructive, way after it reaches superintelligence? [157] [158] Solving the control problem is made complex by the AI arms race (which might result in a race to the bottom of security preventative measures in order to release products before rivals), [159] and using AI in weapon systems. [160]
The thesis that AI can pose existential danger also has detractors. Skeptics typically state that AGI is not likely in the short-term, or that issues about AGI distract from other issues associated with current AI. [161] Former Google scams czar Shuman Ghosemajumder thinks about that for lots of people beyond the innovation industry, existing chatbots and LLMs are currently perceived as though they were AGI, leading to additional misunderstanding and worry. [162]
Skeptics sometimes charge that the thesis is crypto-religious, with an irrational belief in the possibility of superintelligence changing an irrational belief in a supreme God. [163] Some scientists believe that the interaction campaigns on AI existential danger by particular AI groups (such as OpenAI, Anthropic, DeepMind, and Conjecture) might be an at effort at regulatory capture and to inflate interest in their items. [164] [165]
In 2023, the CEOs of Google DeepMind, OpenAI and Anthropic, along with other industry leaders and scientists, provided a joint statement asserting that "Mitigating the risk of extinction from AI must be an international priority alongside other societal-scale threats such as pandemics and nuclear war." [152]
Mass joblessness

Researchers from OpenAI estimated that "80% of the U.S. labor force could have at least 10% of their work tasks affected by the intro of LLMs, while around 19% of workers might see at least 50% of their jobs affected". [166] [167] They consider workplace workers to be the most exposed, for instance mathematicians, accounting professionals or web designers. [167] AGI might have a much better autonomy, capability to make choices, 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 enjoy a life of elegant leisure if the machine-produced wealth is shared, or many individuals can end up badly poor if the machine-owners effectively lobby against wealth redistribution. So far, the trend seems to be towards the second alternative, with technology driving ever-increasing inequality

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

Artificial brain - Software and hardware with cognitive abilities similar to those of the animal or human brain AI effect AI safety - Research location on making AI safe and beneficial AI positioning - AI conformance to the designated objective A.I. Rising - 2018 film directed by Lazar Bodroža Expert system Automated artificial intelligence - Process of automating the application of artificial intelligence 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 game playing - Ability of artificial intelligence to play different games Generative expert system - AI system capable of generating material in action to triggers Human Brain Project - Scientific research task Intelligence amplification - Use of infotech to augment human intelligence (IA). Machine ethics - Moral behaviours of manufactured makers. Moravec's paradox. Multi-task knowing - Solving several device finding out jobs at the very same time. Neural scaling law - Statistical law in artificial intelligence. Outline of expert system - Overview of and topical guide to expert system. Transhumanism - Philosophical motion. Synthetic intelligence - Alternate term for or kind of artificial intelligence. Transfer learning - Machine learning strategy. Loebner Prize - Annual AI competitors. Hardware for artificial intelligence - Hardware specially created and enhanced for synthetic intelligence. Weak synthetic intelligence - Form of synthetic intelligence.
Notes

^ a b See below for the origin of the term "strong AI", and see the academic definition of "strong AI" and weak AI in the short article Chinese space. ^ AI creator John McCarthy writes: "we can not yet identify in general what type of computational procedures we want to call smart. " [26] (For a discussion of some meanings of intelligence utilized by synthetic intelligence researchers, see approach of expert system.). ^ The Lighthill report particularly slammed AI's "grandiose objectives" and led the taking apart of AI research study in England. [55] In the U.S., DARPA became figured out to money only "mission-oriented direct research, rather than standard undirected research". [56] [57] ^ As AI creator John McCarthy writes "it would be a fantastic relief to the remainder of the workers in AI if the creators of brand-new general formalisms would express their hopes in a more guarded form than has in some cases held true." [61] ^ In "Mind Children" [122] 1015 cps is utilized. More recently, in 1997, [123] Moravec argued for 108 MIPS which would approximately represent 1014 cps. Moravec talks in regards to MIPS, not "cps", which is a non-standard term Kurzweil introduced. ^ As specified in a basic AI textbook: "The assertion that machines could potentially act wisely (or, possibly better, act as if they were intelligent) is called the 'weak AI' hypothesis by thinkers, and the assertion that makers that do so are actually thinking (instead of imitating thinking) is called the 'strong AI' hypothesis." [121] ^ Alan Turing made this point in 1950. [36] References

^ Krishna, Sri (9 February 2023). "What is artificial narrow intelligence (ANI)?". VentureBeat. Retrieved 1 March 2024. ANI is developed to carry out a single job. ^ "OpenAI Charter". OpenAI. Retrieved 6 April 2023. Our objective is to make sure that artificial general intelligence advantages all of mankind. ^ Heath, Alex (18 January 2024). "Mark Zuckerberg's brand-new objective is developing synthetic basic intelligence". The Verge. Retrieved 13 June 2024. Our vision is to construct AI that is much better than human-level at all of the human senses. ^ Baum, Seth D. (2020 ). A Survey of Artificial General Intelligence Projects for Ethics, Risk, and Policy (PDF) (Report). Global Catastrophic Risk Institute. Retrieved 28 November 2024. 72 AGI R&D tasks were identified 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 leader Geoffrey Hinton gives up Google and warns of danger ahead". The New York Times. 1 May 2023. Retrieved 2 May 2023. It is hard to see how you can avoid the bad actors from using it for bad things. ^ Bubeck, Sébastien; Chandrasekaran, Varun; Eldan, Ronen; Gehrke, Johannes; Horvitz, Eric (2023 ). "Sparks of Artificial General Intelligence: Early experiments with GPT-4". arXiv preprint. arXiv:2303.12712. GPT-4 reveals stimulates of AGI. ^ Butler, Octavia E. (1993 ). Parable of the Sower. Grand Central Publishing. ISBN 978-0-4466-7550-5. All that you touch you change. All that you change changes you. ^ Vinge, Vernor (1992 ). A Fire Upon the Deep. Tor Books. ISBN 978-0-8125-1528-2. The Singularity is coming. ^ Morozov, Evgeny (30 June 2023). "The True Threat of Artificial Intelligence". The New York City Times. The real danger is not AI itself but the way we deploy 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 risks to humanity. ^ Bostrom, Nick (2014 ). Superintelligence: Paths, Dangers, Strategies. Oxford University Press. ISBN 978-0-1996-7811-2. The first superintelligence will be the last creation 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 risk of extinction from AI should be a global concern. ^ "Statement on AI Risk". Center for AI Safety. Retrieved 1 March 2024. AI specialists warn of risk of extinction from AI. ^ Mitchell, Melanie (30 May 2023). "Are AI's Doomsday Scenarios Worth Taking Seriously?". The New York City Times. We are far from developing makers that can outthink us in general ways. ^ LeCun, Yann (June 2023). "AGI does not provide an existential danger". Medium. There is no factor to fear AI as an existential threat. ^ Kurzweil 2005, p. 260. ^ a b Kurzweil, Ray (5 August 2005), "Long Live AI", Forbes, archived from the original on 14 August 2005: Kurzweil explains strong AI as "device intelligence with the full variety of human intelligence.". ^ "The Age of Expert System: git.soy.dog George John at TEDxLondonBusinessSchool 2013". Archived from the original on 26 February 2014. Retrieved 22 February 2014. ^ Newell & Simon 1976, This is the term they utilize 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. ^ "Expert system is changing our world - it is on everybody to ensure that it works out". Our World in Data. Retrieved 8 October 2023. ^ Dickson, Ben (16 November 2023). "Here is how far we are to attaining AGI, according to DeepMind". VentureBeat. ^ McCarthy, John (2007a). "Basic Questions". Stanford University. Archived from the original on 26 October 2007. Retrieved 6 December 2007. ^ This list of smart traits is based on the topics 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 method we believe: a brand-new view of intelligence (The MIT Press, 2007). ISBN 0-2621-6239-3. ^ White, R. W. (1959 ). "Motivation reevaluated: The principle of proficiency". Psychological Review. 66 (5 ): 297-333. doi:10.1037/ h0040934. PMID 13844397. S2CID 37385966. ^ White, R. W. (1959 ). "Motivation reassessed: 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 original on 25 April 2014. Retrieved 1 May 2014. ^ "What is Artificial General Intelligence (AGI)?|4 Tests For Ensuring Artificial General Intelligence". Talky Blog. 13 July 2019. Archived from the original on 17 July 2019. Retrieved 17 July 2019. ^ Kirk-Giannini, Cameron Domenico; Goldstein, Simon (16 October 2023). "AI is closer than ever to passing the Turing test for 'intelligence'. What takes place when it does?". The Conversation. Retrieved 22 September 2024. ^ a b Turing 1950. ^ Turing, Alan (1952 ). B. Jack Copeland (ed.). Can Automatic Calculating Machines Be Said To Think?. Oxford: Oxford University Press. pp. 487-506. ISBN 978-0-1982-5079-1. ^ "Eugene Goostman is a genuine young boy - the Turing Test states so". The Guardian. 9 June 2014. ISSN 0261-3077. Retrieved 3 March 2024. ^ "Scientists challenge whether computer system 'Eugene Goostman' passed Turing test". BBC News. 9 June 2014. Retrieved 3 March 2024. ^ Jones, Cameron R.; Bergen, Benjamin K. (9 May 2024). "People can not differentiate GPT-4 from a human in a Turing test". arXiv:2405.08007 [cs.HC] ^ Varanasi, Lakshmi (21 March 2023). "AI designs like ChatGPT and GPT-4 are acing everything from the bar test to AP Biology. Here's a list of tough exams both AI variations have passed". Business Insider. Retrieved 30 May 2023. ^ Naysmith, Caleb (7 February 2023). "6 Jobs Expert System Is Already Replacing and How Investors Can 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 outdated. Coffee is the response". Analytics India Magazine. Retrieved 3 March 2024. ^ Bhaimiya, Sawdah (20 June 2023). "DeepMind's co-founder recommended checking 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 ). "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 original 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). Expert System, Evolutionary Computation and Metaheuristics (AIECM): 3-17. Archived (PDF) from the original on 22 May 2013. ^ "AI Index: State of AI in 13 Charts". Stanford University Human-Centered Expert System. 15 April 2024. Retrieved 27 May 2024. ^ Crevier 1993, pp. 48-50. ^ Kaplan, Andreas (2022 ). "Expert System, Business and Civilization - Our Fate Made in Machines". Archived from the 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 ), 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 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 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 City Times. Archived from the initial on 2 February 2023. Retrieved 18 February 2017. At its low point, some computer scientists and software engineers avoided the term artificial intelligence for fear of being seen as wild-eyed dreamers. ^ Russell & Norvig 2003, pp. 25-26 ^ "Trends in the Emerging Tech Hype Cycle". Gartner Reports. Archived from the 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 Artificial Intelligence: Sequential Decisions Based on Algorithmic Probability. Texts in Theoretical Computer Science an EATCS Series. Springer. doi:10.1007/ b138233. ISBN 978-3-5402-6877-2. S2CID 33352850. Archived from the original on 19 July 2022. Retrieved 19 July 2022. ^ Legg, Shane (2008 ). Machine Super Intelligence (PDF) (Thesis). University of Lugano. Archived (PDF) from the original on 15 June 2022. Retrieved 19 July 2022. ^ Goertzel, Ben (2014 ). Artificial General Intelligence. Lecture Notes in Computer Technology. Vol. 8598. Journal of Artificial General Intelligence. doi:10.1007/ 978-3-319-09274-4. ISBN 978-3-3190-9273-7. S2CID 8387410. ^ "Who created the term "AGI"?". goertzel.org. Archived from the original on 28 December 2018. Retrieved 28 December 2018., via 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 original on 28 September 2020. Retrieved 11 May 2020. ^ "Избираеми дисциплини 2009/2010 - пролетен триместър" [Elective courses 2009/2010 - spring trimester] Факултет по математика и информатика [Faculty of Mathematics and Informatics] (in Bulgarian). Archived from the initial on 26 July 2020. Retrieved 11 May 2020. ^ "Избираеми дисциплини 2010/2011 - зимен триместър" [Elective courses 2010/2011 - winter 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 limits of device intelligence: Despite progress in maker intelligence, artificial general intelligence is still a significant difficulty". 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 experiments with GPT-4". arXiv:2303.12712 [cs.CL] ^ "Microsoft Researchers Claim GPT-4 Is Showing "Sparks" of AGI". Futurism. 23 March 2023. Retrieved 13 December 2023. ^ Allen, Paul; Greaves, Mark (12 October 2011). "The Singularity Isn't Near". MIT Technology Review. Retrieved 17 September 2014. ^ Winfield, Alan. "Expert system will not develop into a Frankenstein's beast". The Guardian. Archived from the initial on 17 September 2014. Retrieved 17 September 2014. ^ Deane, George (2022 ). "Machines That Feel and Think: The Role of Affective Feelings and Mental Action in (Artificial) General Intelligence". Artificial Life. 28 (3 ): 289-309. doi:10.1162/ artl_a_00368. ISSN 1064-5462. PMID 35881678. S2CID 251069071. ^ a b c Clocksin 2003. ^ Fjelland, Ragnar (17 June 2020). "Why general synthetic intelligence will not be recognized". 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 artificial intelligence bring us paradise or destruction?". 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 professional viewpoint. 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, 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 basic intelligence with hybrid Tianjic chip architecture". Nature. 572 (7767 ): 106-111. Bibcode:2019 Natur.572..106 P. doi:10.1038/ s41586-019-1424-8. ISSN 1476-4687. PMID 31367028. S2CID 199056116. Archived from the 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 role of GPU computing and deep learning in drug discovery". Nature Machine Intelligence. 4 (3 ): 211-221. doi:10.1038/ s42256-022-00463-x. ISSN 2522-5839. S2CID 252081559. ^ Goertzel & Pennachin 2006. ^ a b c (Kurzweil 2005, p. 260). ^ a b c Goertzel 2007. ^ Grace, Katja (2016 ). "Error in Armstrong and Sotala 2012". AI Impacts (blog site). Archived from the original on 4 December 2020. Retrieved 24 August 2020. ^ a b Butz, Martin V. (1 March 2021). "Towards Strong AI". KI - Künstliche Intelligenz. 35 (1 ): 91-101. doi:10.1007/ s13218-021-00705-x. ISSN 1610-1987. S2CID 256065190. ^ Liu, Feng; Shi, Yong; Liu, Ying (2017 ). "Intelligence Quotient and Intelligence Grade of Expert System". Annals of Data Science. 4 (2 ): 179-191. arXiv:1709.10242. doi:10.1007/ s40745-017-0109-0. S2CID 37900130. ^ Brien, Jörn (5 October 2017). "Google-KI doppelt so schlau wie Siri" [Google AI is twice as wise as Siri - however 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 entering 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 revealed GPT-3 natural language processing (NLP) neural network. ... So is GPT-3 the first example of an AGI system? This is debatable, but the consensus is that it is not AGI. ... If nothing else, GPT-3 tells us there is a happy medium between narrow and general AI. ^ Quach, Katyanna. "A developer developed an AI chatbot utilizing GPT-3 that assisted a male speak again to his late fiancée. OpenAI shut it down". The Register. Archived from the 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 video games to controlling robots", TechCrunch, archived from the initial on 16 June 2022, obtained 12 June 2022. ^ Bubeck, Sébastien; Chandrasekaran, Varun; Eldan, Ronen; Gehrke, Johannes; Horvitz, Eric; Kamar, Ece; Lee, Peter; Lee, Yin Tat; Li, Yuanzhi; Lundberg, Scott; Nori, Harsha; Palangi, Hamid; Ribeiro, Marco Tulio; Zhang, Yi (22 March 2023). "Sparks of Artificial General Intelligence: Early 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 City Times. ISSN 0362-4331. Retrieved 7 June 2023. ^ Bove, Tristan. "A.I. might match human intelligence in 'just a couple of years,' says CEO of Google's primary A.I. research lab". Fortune. Retrieved 4 September 2024. ^ Nellis, Stephen (2 March 2024). "Nvidia CEO says AI could pass human tests in 5 years". Reuters. ^ Aschenbrenner, Leopold. "SITUATIONAL AWARENESS, The Decade Ahead". ^ Sullivan, Mark (18 October 2023). "Why everyone seems to disagree on how to specify Artificial General Intelligence". Fast Company. ^ Nosta, John (5 January 2024). "The Accelerating Path to Artificial General Intelligence". Psychology Today. Retrieved 30 March 2024. ^ Hickey, Alex. "Whole Brain Emulation: A Huge Step for Neuroscience". Tech Brew. Retrieved 8 November 2023. ^ Sandberg & Boström 2008. ^ Drachman 2005. ^ a b Russell & Norvig 2003. ^ Moravec 1988, p. 61. ^ Moravec 1998. ^ Holmgaard Mersh, Amalie (15 September 2023). "Decade-long European research job maps the human brain". euractiv. ^ Swaminathan, Nikhil (January-February 2011). "Glia-the other brain cells". Discover. Archived from the original on 8 February 2014. Retrieved 24 January 2014. ^ de Vega, Glenberg & Graesser 2008. A large range of views in present research, all of which need grounding to some degree ^ Thornton, Angela (26 June 2023). "How uploading our minds to a computer might become possible". The Conversation. Retrieved 8 November 2023. ^ Searle 1980 ^ For instance: Russell & Norvig 2003, Oxford University Press Dictionary of Psychology Archived 3 December 2007 at the Wayback Machine (estimated in" Encyclopedia.com"),. MIT Encyclopedia of Cognitive Science Archived 19 July 2008 at the Wayback Machine (priced 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 expert system for interest by the field about why a program acts the method it does. ^ Chalmers, David J. (9 August 2023). "Could a Big Language Model Be Conscious?". Boston Review. ^ Seth, Anil. "Consciousness". New Scientist. Retrieved 5 September 2024. ^ Nagel 1974. ^ "The Google engineer who believes the business's AI has actually come to life". The Washington Post. 11 June 2022. Retrieved 12 June 2023. ^ Kateman, Brian (24 July 2023). "AI Should Be Terrified of Humans". TIME. Retrieved 5 September 2024. ^ Nosta, John (18 December 2023). "Should 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 outweigh benefits?". 23 August 2021. Retrieved 7 June 2023. ^ "How we can Benefit from Advancing Artificial General Intelligence (AGI) - Unite.AI". www.unite.ai. 7 April 2020. Retrieved 7 June 2023. ^ a b c Talty, Jules; Julien, Stephan. "What Will Our Society Appear Like When Artificial Intelligence Is Everywhere?". Smithsonian Magazine. Retrieved 7 June 2023. ^ a b Stevenson, Matt (8 October 2015). "Answers to Stephen Hawking's AMA are Here!". Wired. ISSN 1059-1028. Retrieved 8 June 2023. ^ a b Bostrom, Nick (2017 ). " § Preferred order of arrival". Superintelligence: courses, dangers, strategies (Reprinted with corrections 2017 ed.). Oxford, UK; 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 human beings will destroy ourselves". Vox. Retrieved 8 June 2023. ^ Doherty, Ben (17 May 2018). "Climate alter an 'existential security threat' to Australia, Senate query says". The Guardian. ISSN 0261-3077. Retrieved 16 July 2023. ^ MacAskill, William (2022 ). What we owe the future. New York, NY: Basic Books. ISBN 978-1-5416-1862-6. ^ a b Ord, Toby (2020 ). "Chapter 5: Future Risks, Unaligned Artificial Intelligence". The Precipice: Existential Risk and the Future of Humanity. Bloomsbury Publishing. ISBN 978-1-5266-0021-9. ^ Al-Sibai, Noor (13 February 2022). "OpenAI Chief Scientist Says Advanced AI May Already Be Conscious". Futurism. Retrieved 24 December 2023. ^ Samuelsson, Paul Conrad (2019 ). "Artificial Consciousness: Our Greatest Ethical Challenge". Philosophy Now. Retrieved 23 December 2023. ^ Kateman, Brian (24 July 2023). "AI Should Be Terrified of Humans". TIME. Retrieved 23 December 2023. ^ Roose, Kevin (30 May 2023). "A.I. Poses 'Risk of Extinction,' Industry Leaders Warn". The New York 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 looks at the ramifications of expert system - but are we taking AI seriously enough?'". The Independent (UK). Archived from the initial on 25 September 2015. Retrieved 3 December 2014. ^ Herger, Mario. "The Gorilla Problem - Enterprise Garage". Retrieved 7 June 2023. ^ "The fascinating Facebook dispute in between Yann LeCun, Stuart Russel and Yoshua Bengio about the threats of strong AI". The remarkable Facebook dispute in between Yann LeCun, Stuart Russel and Yoshua Bengio about the risks 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 devastating AGI threat: a survey". Physica Scripta. 90 (1 ): 018001. doi:10.1088/ 0031-8949/90/ 1/018001. ISSN 0031-8949. ^ Bostrom, Nick (2014 ). Superintelligence: Paths, Dangers, Strategies (First ed.). Oxford University Press. ISBN 978-0-1996-7811-2. ^ Chow, Andrew R.; Perrigo, Billy (16 February 2023). "The AI Arms Race Is On. Start Worrying". TIME. Retrieved 24 December 2023. ^ Tetlow, Gemma (12 January 2017). "AI arms race threats spiralling out of control, report 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 hazard posed however artificial intelligence can not be disregarded". The Guardian. ISSN 0261-3077. Retrieved 24 December 2023. ^ "Humanity, Security & AI, Oh My! (with Ian Bremmer & Shuman Ghosemajumder)". CAFE. 20 July 2023. Retrieved 15 September 2023. ^ Hamblin, James (9 May 2014). "But What Would completion of Humanity Mean for Me?". The Atlantic. Archived from the original on 4 June 2014. Retrieved 12 December 2015. ^ Titcomb, James (30 October 2023). "Big Tech is stiring fears over AI, warn scientists". The Telegraph. Retrieved 7 December 2023. ^ Davidson, John (30 October 2023). "Google Brain founder says big tech is lying about AI extinction risk". 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 look at the labor humanlove.stream market effect potential of large language models". OpenAI. Retrieved 7 June 2023. ^ a b Hurst, Luke (23 March 2023). "OpenAI says 80% of employees could see their tasks affected by AI. These are the tasks most impacted". euronews. Retrieved 8 June 2023. ^ Sheffey, Ayelet (20 August 2021). "Elon Musk states we require universal standard earnings because 'in the future, manual labor will be an option'". Business Insider. Archived from the original on 9 July 2023. Retrieved 8 June 2023. Sources

UNESCO Science Report: the Race Against Time for Smarter Development. Paris: UNESCO. 11 June 2021. ISBN 978-9-2310-0450-6. Archived from the 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 Look For Expert System. New York, NY: BasicBooks. ISBN 0-465-02997-3. Darrach, Brad (20 November 1970), "Meet Shakey, the First Electronic Person", Life Magazine, pp. 58-68. Drachman, D. (2005 ), "Do we have brain to spare?", Neurology, 64 (12 ): 2004-2005, doi:10.1212/ 01. WNL.0000166914.38327. BB, PMID 15985565, S2CID 38482114. Feigenbaum, Edward A.; McCorduck, Pamela (1983 ), The Fifth Generation: 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 basic intelligence and the possibility of a technological singularity: a reaction to Ray Kurzweil's The Singularity Is Near, and McDermott's critique of Kurzweil", Artificial Intelligence, vol. 171, no. 18, Special Review Issue, pp. 1161-1173, doi:10.1016/ j.artint.2007.10.011, archived from the initial on 7 January 2016, retrieved 1 April 2009. Gubrud, Mark (November 1997), "Nanotechnology and International Security", Fifth Foresight Conference on Molecular Nanotechnology, archived from the initial on 29 May 2011, recovered 7 May 2011. Howe, J. (November 1994), Artificial Intelligence at Edinburgh University: a Viewpoint, archived from the initial on 17 August 2007, recovered 30 August 2007. Johnson, Mark (1987 ), The body in the mind, Chicago, ISBN 978-0-2264-0317-5. Kurzweil, Ray (2005 ), The Singularity is Near, Viking Press. Lighthill, Professor Sir James (1973 ), "Artificial Intelligence: A General Survey", Expert System: a paper seminar, Science Research Council. Luger, George; Stubblefield, William (2004 ), 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 ultimate effort is to make computer programs that can solve problems and attain objectives in the world as well as humans. 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, obtained 7 November 2009 Newell, Allen; Simon, H. A. (1976 ). "Computer Technology as Empirical Inquiry: Symbols and Search". Communications of the ACM. 19 (3 ): 113-126. doi:10.1145/ 360018.360022. Nilsson, Nils (1998 ), Expert System: A New Synthesis, Morgan Kaufmann Publishers, ISBN 978-1-5586-0467-4 NRC (1999 ), "Developments in Artificial Intelligence", Funding a Revolution: Government Support for Computing Research, National Academy Press, archived from the original on 12 January 2008, obtained 29 September 2007 Poole, David; Mackworth, Alan; Goebel, Randy (1998 ), Computational Intelligence: A Rational Approach, New York City: Oxford University Press, archived from the original on 25 July 2009, obtained 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, obtained 5 April 2009 Searle, John (1980 ), "Minds, Brains and Programs" (PDF), Behavioral and Brain Sciences, 3 (3 ): 417-457, doi:10.1017/ S0140525X00005756, S2CID 55303721, archived (PDF) from the initial on 17 March 2019, obtained 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 - 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 numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain", The Journal of Comparative Neurology, 513 (5 ): 532-541, doi:10.1002/ cne.21974, PMID 19226510, S2CID 5200449, archived from the initial on 18 February 2021, obtained 4 September 2013 - via ResearchGate Berglas, Anthony (January 2012) [2008], Artificial Intelligence Will Kill Our Grandchildren (Singularity), archived from the initial on 23 July 2014, obtained 31 August 2012 Cukier, Kenneth, "Ready for Robots? How to Think about the Future of AI", Foreign Affairs, vol. 98, no. 4 (July/August 2019), pp. 192-98. George Dyson, historian of computing, writes (in what might be called "Dyson's Law") that "Any system easy adequate to be easy to understand will not be complicated enough to act wisely, while any system complicated enough to behave wisely will be too complicated to comprehend." (p. 197.) Computer researcher Alex Pentland composes: "Current AI machine-learning algorithms are, at their core, dead easy stupid. They work, but they work by strength." (p. 198.). Gelernter, David, Dream-logic, the Internet and Artificial Thought, Edge, archived from the original on 26 July 2010, obtained 25 July 2010. Gleick, James, "The Fate of Free Will" (review of Kevin J. Mitchell, Free Agents: How Evolution Gave Us Free Choice, Princeton University Press, 2023, 333 pp.), The New York Review of Books, vol. LXXI, no. 1 (18 January 2024), pp. 27-28, 30. "Agency is what distinguishes us from makers. For biological creatures, reason and purpose come from acting worldwide and experiencing the effects. Expert systems - disembodied, complete strangers to blood, sweat, and tears - have no event for that." (p. 30.). Halal, William E. "TechCast Article Series: The Automation of Thought" (PDF). Archived from the initial (PDF) on 6 June 2013. - Halpern, Sue, "The Coming Tech Autocracy" (review of Verity Harding, AI Needs You: How We Can Change AI's Future and Save Our Own, Princeton University Press, 274 pp.; Gary Marcus, Taming Silicon Valley: How We Can Ensure That AI Works for Us, MIT Press, 235 pp.; Daniela Rus and Gregory Mone, The Mind's Mirror: Risk and Reward in the Age of AI, Norton, 280 pp.; Madhumita Murgia, Code Dependent: Residing In the Shadow of AI, Henry Holt, 311 pp.), The New York Review of Books, vol. LXXI, no. 17 (7 November 2024), pp. 44-46. "' We can't reasonably expect that those who intend to get rich from AI are going to have the interests of the rest of us close at heart,' ... writes [Gary Marcus] 'We can't rely on governments driven by project financing contributions [from tech business] to push back.' ... Marcus information the needs that people need to make from their federal governments and the tech business. They consist of transparency on how AI systems work; payment for individuals if their information [are] used to train LLMs (big language model) s and the right to approval to this use; and the capability to hold tech companies accountable for the damages they bring on by removing Section 230, enforcing money penalites, and passing stricter item liability laws ... Marcus also recommends ... that a brand-new, AI-specific federal company, similar to the FDA, the FCC, or the FTC, may supply the most robust oversight ... [T] he Fordham law professor Chinmayi Sharma ... suggests ... establish [ing] a professional licensing routine for engineers that would function in a similar way to medical licenses, malpractice matches, and the Hippocratic oath in medication. 'What if, like doctors,' she asks ..., 'AI engineers also swore 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 stumped people for decades, reveals the constraints 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) designs can amazing accomplishments, their abilities are very much restricted by the quantity of context they get. This [...] could trigger [troubles] for researchers who want to utilize them to do things such as evaluate ancient languages. Sometimes, there are few historical 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 generate phony videos identical from real ones. How much does it matter?", The New Yorker, 20 November 2023, pp. 54-59. "If by 'deepfakes' we mean practical videos produced using artificial intelligence that actually deceive individuals, then they hardly exist. The fakes aren't deep, and the deeps aren't phony. [...] A.I.-generated videos are not, in basic, operating in our media as counterfeited proof. Their role much better resembles that of animations, specifically smutty ones." (p. 59.). - Leffer, Lauren, "The Risks of Trusting AI: We must avoid humanizing machine-learning designs used in scientific research", Scientific American, vol. 330, no. 6 (June 2024), pp. 80-81. Lepore, Jill, "The Chit-Chatbot: Is talking with a maker a conversation?", The New Yorker, 7 October 2024, pp. 12-16. Marcus, Gary, "Artificial Confidence: Even the latest, buzziest systems of artificial basic intelligence are stymmied by the same old problems", Scientific American, vol. 327, no. 4 (October 2022), pp. 42-45. McCarthy, John (October 2007), "From here to human-level AI", Artificial Intelligence, 171 (18 ): 1174-1182, doi:10.1016/ j.artint.2007.10.009. McCorduck, Pamela (2004 ), Machines Who Think (second ed.), Natick, Massachusetts: A. K. Peters, ISBN 1-5688-1205-1. Moravec, Hans (1976 ), The Role of Raw Power in Intelligence, archived from the initial on 3 March 2016, retrieved 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 Artificial Intelligence, presented and distributed at the 2007 Singularity Summit, San Francisco, California. Press, Eyal, "In Front of Their Faces: Does facial-recognition innovation lead cops to disregard contradictory proof?", 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 need genuine humanlike thinking or an understanding of the physical and social world ... ChatGPT appeared not able to reason logically and tried to rely on its large database of ... facts stemmed from online texts. " - Scharre, Paul, "Killer Apps: The Real Dangers of an AI Arms Race", Foreign Affairs, vol. 98, no. 3 (May/June 2019), pp. 135-44. "Today's AI innovations are effective but undependable. Rules-based systems can not handle circumstances their programmers did not prepare for. Learning systems are limited by the information on which they were trained. AI failures have actually already resulted in disaster. Advanced auto-pilot functions in cars, although they carry out well in some scenarios, have driven automobiles without cautioning into trucks, concrete barriers, and parked cars and trucks. In the incorrect situation, AI systems go from supersmart to superdumb in an instant. When an enemy is trying to control and hack an AI system, the 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 made possible by new innovations but count on the timelelss human propensity to anthropomorphise." (p. 29.). Williams, R. W.; Herrup, K.