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

Creating AGI is a primary goal of AI research study and of business such as OpenAI [2] and Meta. [3] A 2020 survey identified 72 active AGI research study and advancement tasks across 37 countries. [4]
The timeline for achieving AGI stays a topic of ongoing argument amongst researchers and professionals. Since 2023, some argue that it may be possible in years or years; others preserve it may take a century or longer; a minority believe it may never be attained; and another minority claims that it is currently here. [5] [6] Notable AI scientist Geoffrey Hinton has expressed concerns about the quick progress towards AGI, suggesting it could be accomplished quicker than numerous expect. [7]
There is debate on the exact meaning of AGI and regarding whether modern big language designs (LLMs) such as GPT-4 are early forms of AGI. [8] AGI is a common topic in science fiction and futures studies. [9] [10]
Contention exists over whether AGI represents an existential threat. [11] [12] [13] Many specialists on AI have actually specified that reducing the risk of human termination posed by AGI must be an international priority. [14] [15] Others discover the development of AGI to be too remote to provide such a danger. [16] [17]
Terminology

AGI is also known as strong AI, [18] [19] complete AI, [20] human-level AI, [5] human-level intelligent AI, or general intelligent action. [21]
Some scholastic sources book the term "strong AI" for computer programs that experience sentience or awareness. [a] In contrast, weak AI (or narrow AI) has the ability to resolve one particular problem however does not have general cognitive capabilities. [22] [19] Some academic sources utilize "weak AI" to refer more broadly to any programs that neither experience consciousness nor have a mind in the very same sense as humans. [a]
Related concepts consist of synthetic superintelligence and transformative AI. A synthetic superintelligence (ASI) is a hypothetical type of AGI that is a lot more usually smart than humans, [23] while the idea of transformative AI connects to AI having a large effect on society, for instance, comparable to the agricultural or industrial transformation. [24]
A structure for classifying AGI in levels was proposed in 2023 by Google DeepMind researchers. They define five levels of AGI: emerging, qualified, expert, virtuoso, and superhuman. For instance, a skilled AGI is specified as an AI that outperforms 50% of proficient adults in a wide variety of non-physical jobs, and a superhuman AGI (i.e. a synthetic superintelligence) is likewise defined however with a limit of 100%. They think about big language designs like ChatGPT or LLaMA 2 to be instances of emerging AGI. [25]
Characteristics

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

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

• interact in natural language
• if essential, integrate these abilities in conclusion of any provided objective

Many interdisciplinary methods (e.g. cognitive science, computational intelligence, and choice making) think about additional characteristics such as imagination (the ability to form novel psychological images and principles) [28] and autonomy. [29]
Computer-based systems that exhibit a lot of these abilities exist (e.g. see computational creativity, automated reasoning, decision support group, robot, evolutionary computation, smart representative). There is argument about whether modern AI systems possess them to a sufficient degree.

Physical qualities

Other abilities are considered desirable 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 capability to act (e.g. move and control objects, change location to check out, etc).
This includes the ability to detect and respond to risk. [31]
Although the ability to sense (e.g. see, hear, and so on) and the capability to act (e.g. move and manipulate things, change area to check out, etc) can be desirable for some smart systems, [30] these physical capabilities are not strictly required for an entity to qualify as AGI-particularly under the thesis that big language designs (LLMs) might already be or end up being AGI. Even from a less optimistic viewpoint on LLMs, there is no firm requirement for akropolistravel.com an AGI to have a human-like kind; being a silicon-based computational system is sufficient, supplied it can process input (language) from the external world in place of human senses. This interpretation lines up with the understanding that AGI has actually never ever been proscribed a particular physical personification and hence does not require a capacity for mobility or conventional "eyes and ears". [32]
Tests for human-level AGI

Several tests suggested to confirm human-level AGI have actually been thought about, 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 just pass if the pretence is fairly persuading. A significant 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 implement AGI, due to the fact that the service is beyond the capabilities of a purpose-specific algorithm. [47]
There are lots of problems that have actually been conjectured to need basic intelligence to resolve in addition to humans. Examples include computer vision, natural language understanding, and handling unexpected circumstances while solving any real-world problem. [48] Even a specific task like translation needs a machine to read and write in both languages, follow the author's argument (factor), comprehend the context (knowledge), and consistently reproduce the author's initial intent (social intelligence). All of these problems need to be solved simultaneously in order to reach human-level device efficiency.

However, a number of these jobs can now be carried out by contemporary big language designs. According to Stanford University's 2024 AI index, AI has actually reached human-level performance on many standards for checking out comprehension and visual thinking. [49]
History

Classical AI

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

However, in the early 1970s, it became obvious that researchers had grossly ignored the trouble of the project. Funding agencies ended up being skeptical of AGI and put scientists under increasing pressure to produce useful "applied AI". [c] In the early 1980s, Japan's Fifth Generation Computer Project revived interest in AGI, setting out a ten-year timeline that consisted of AGI objectives like "bring on a casual conversation". [58] In response to this and asystechnik.com the success of expert systems, both industry and government pumped money into the field. [56] [59] However, self-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 scientists who forecasted the imminent achievement of AGI had actually been misinterpreted. By the 1990s, AI researchers had a reputation for making vain promises. They ended up being reluctant to make forecasts at all [d] and avoided reference of "human level" synthetic intelligence for fear of being labeled "wild-eyed dreamer [s]. [62]
Narrow AI research study

In the 1990s and early 21st century, mainstream AI accomplished industrial success and academic respectability by focusing on particular sub-problems where AI can produce proven outcomes and commercial applications, such as speech recognition and suggestion algorithms. [63] These "applied AI" systems are now used extensively throughout the technology market, and research in this vein is greatly funded in both academia and market. Since 2018 [upgrade], advancement in this field was thought about an emerging trend, and a fully grown phase was anticipated to be reached in more than 10 years. [64]
At the millenium, numerous traditional AI scientists [65] hoped that strong AI could be established by combining programs that resolve different sub-problems. Hans Moravec composed in 1988:

I am confident that this bottom-up route to expert system will one day fulfill the standard top-down path majority method, all set to provide the real-world competence and the commonsense understanding that has actually been so frustratingly evasive in thinking programs. Fully smart devices will result when the metaphorical golden spike is driven joining the two efforts. [65]
However, even at the time, this was challenged. For example, Stevan Harnad of Princeton University concluded his 1990 paper on the sign grounding hypothesis by mentioning:

The expectation has typically been voiced that "top-down" (symbolic) approaches to modeling cognition will in some way satisfy "bottom-up" (sensory) approaches somewhere in between. If the grounding considerations in this paper stand, then this expectation is hopelessly modular and there is really just one feasible path from sense to symbols: from the ground up. A free-floating symbolic level like the software application level of a computer will never be reached by this path (or vice versa) - nor is it clear why we need to even try to reach such a level, given that it appears arriving would simply total up to uprooting our signs from their intrinsic meanings (thereby simply reducing ourselves to the functional equivalent of a programmable computer system). [66]
Modern artificial basic intelligence research study

The term "synthetic basic intelligence" was utilized as early as 1997, by Mark Gubrud [67] in a discussion of the implications of completely automated military production and operations. A mathematical formalism of AGI was proposed by Marcus Hutter in 2000. Named AIXI, the proposed AGI representative increases "the capability to satisfy goals in a vast array of environments". [68] This type of AGI, characterized by the capability to increase a mathematical definition of intelligence instead of exhibit human-like behaviour, [69] was likewise called universal expert system. [70]
The term AGI was re-introduced and popularized by Shane Legg and Ben Goertzel around 2002. [71] AGI research study activity in 2006 was described by Pei Wang and Ben Goertzel [72] as "producing publications and initial outcomes". The very first summer season school in AGI was organized in Xiamen, China in 2009 [73] by the Xiamen university's Artificial Brain Laboratory and OpenCog. The first university course was offered in 2010 [74] and 2011 [75] at Plovdiv University, Bulgaria by Todor Arnaudov. MIT presented a course on AGI in 2018, arranged by Lex Fridman and including a number of guest lecturers.

As of 2023 [update], a little number of computer researchers are active in AGI research study, and many add to a series of AGI conferences. However, significantly more researchers are interested in open-ended learning, [76] [77] which is the concept of permitting AI to continuously learn and innovate like human beings do.

Feasibility

Since 2023, the advancement and prospective accomplishment of AGI remains a topic of intense argument within the AI community. While standard consensus held that AGI was a far-off goal, recent improvements have led some researchers and industry figures to declare that early forms of AGI might already exist. [78] AI pioneer Herbert A. Simon hypothesized in 1965 that "makers will be capable, within twenty years, of doing any work a male can do". This prediction stopped working to come true. Microsoft co-founder Paul Allen believed that such intelligence is unlikely in the 21st century due to the fact that it would require "unforeseeable and fundamentally unforeseeable advancements" and a "clinically deep understanding of cognition". [79] Writing in The Guardian, roboticist Alan Winfield claimed the gulf between modern-day computing and human-level artificial intelligence is as wide as the gulf in between existing area flight and useful faster-than-light spaceflight. [80]
A more difficulty is the absence of clarity in specifying what intelligence requires. Does it require consciousness? Must it display the capability to set goals as well as pursue them? Is it purely a matter of scale such that if design sizes increase sufficiently, intelligence will emerge? Are centers such as preparation, reasoning, and causal understanding needed? Does intelligence require explicitly reproducing the brain and its specific faculties? Does it need emotions? [81]
Most AI researchers believe strong AI can be accomplished in the future, but some thinkers, like Hubert Dreyfus and Roger Penrose, deny the possibility of accomplishing strong AI. [82] [83] John McCarthy is amongst those who believe human-level AI will be achieved, however that today level of development is such that a date can not precisely be predicted. [84] AI experts' views on the expediency of AGI wax and subside. Four polls conducted in 2012 and 2013 suggested that the mean price quote among professionals for when they would be 50% confident AGI would show up was 2040 to 2050, depending on the poll, with the mean being 2081. Of the experts, 16.5% addressed with "never ever" when asked the exact same concern but with a 90% confidence rather. [85] [86] Further current AGI progress considerations can be discovered above Tests for confirming human-level AGI.

A report by Stuart Armstrong and Kaj Sotala of the Machine Intelligence Research Institute discovered that "over [a] 60-year amount of time there is a strong bias towards forecasting the arrival of human-level AI as in between 15 and 25 years from the time the forecast was made". They examined 95 predictions made in between 1950 and 2012 on when human-level AI will come about. [87]
In 2023, Microsoft scientists published a comprehensive assessment of GPT-4. They concluded: "Given the breadth and depth of GPT-4's capabilities, our company believe that it could fairly be viewed as an early (yet still incomplete) version of an artificial basic intelligence (AGI) system." [88] Another research study in 2023 reported that GPT-4 surpasses 99% of human beings on the Torrance tests of imaginative thinking. [89] [90]
Blaise Agüera y Arcas and Peter Norvig composed in 2023 that a considerable level of general intelligence has actually already been accomplished with frontier designs. They composed that hesitation to this view comes from four main factors: a "healthy hesitation about metrics for AGI", an "ideological dedication to alternative AI theories or techniques", a "devotion to human (or biological) exceptionalism", or a "issue about the economic ramifications of AGI". [91]
2023 likewise marked the development of large multimodal designs (big language models efficient in processing or generating multiple techniques such as text, audio, and images). [92]
In 2024, OpenAI released o1-preview, the very first of a series of models that "invest more time believing before they react". According to Mira Murati, this ability to believe before responding represents a brand-new, additional paradigm. It enhances model outputs by spending more computing power when producing the response, whereas the model scaling paradigm improves outputs by increasing the model size, training information and training calculate power. [93] [94]
An OpenAI employee, Vahid Kazemi, declared in 2024 that the business had attained AGI, stating, "In my viewpoint, we have currently achieved AGI and it's even more clear with O1." Kazemi clarified that while the AI is not yet "much better than any human at any task", it is "better than most humans at most tasks." He likewise resolved criticisms that large language designs (LLMs) simply follow predefined patterns, comparing their learning process to the scientific approach of observing, assuming, and validating. These declarations have actually triggered dispute, as they depend 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 amazing flexibility, they might not completely satisfy this requirement. Notably, Kazemi's remarks came soon after OpenAI got rid of "AGI" from the terms of its partnership with Microsoft, triggering speculation about the company's tactical intentions. [95]
Timescales

Progress in synthetic intelligence has traditionally gone through durations of fast development separated by periods when development appeared to stop. [82] Ending each hiatus were basic advances in hardware, software or both to create space for additional development. [82] [98] [99] For example, the computer hardware offered in the twentieth century was not sufficient to implement deep knowing, which requires great deals of GPU-enabled CPUs. [100]
In the intro to his 2006 book, [101] Goertzel states that price quotes of the time needed before a genuinely versatile AGI is built differ from ten years to over a century. As of 2007 [update], the agreement in the AGI research study community 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 plausible. [103] Mainstream AI scientists have actually given a vast array of opinions on whether progress will be this quick. A 2012 meta-analysis of 95 such viewpoints discovered a bias towards predicting that the start of AGI would happen within 16-26 years for modern-day and historical forecasts alike. That paper has actually been slammed 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%, significantly much better than the second-best entry's rate of 26.3% (the standard technique utilized a weighted sum of scores from various pre-defined classifiers). [105] AlexNet was considered as the preliminary ground-breaker of the present deep learning wave. [105]
In 2017, scientists Feng Liu, Yong Shi, and Ying Liu conducted intelligence tests on publicly offered and easily 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 approximately to a six-year-old kid in very first grade. An adult comes to about 100 on average. Similar tests were performed in 2014, with the IQ score reaching a maximum value of 27. [106] [107]
In 2020, OpenAI established GPT-3, a language model efficient in carrying out lots of varied jobs without particular training. According to Gary Grossman in a VentureBeat short 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 classified as a narrow AI system. [108]
In the very same year, Jason Rohrer used his GPT-3 account to establish a chatbot, and provided a chatbot-developing platform called "Project December". OpenAI asked for changes to the chatbot to adhere to their security standards; Rohrer detached Project December from the GPT-3 API. [109]
In 2022, DeepMind developed Gato, a "general-purpose" system efficient in carrying out more than 600 different tasks. [110]
In 2023, Microsoft Research published a research study on an early variation of OpenAI's GPT-4, competing that it showed more basic intelligence than previous AI designs and demonstrated human-level performance in tasks covering numerous domains, such as mathematics, coding, and law. This research study stimulated an argument on whether GPT-4 could be thought about an early, incomplete variation of synthetic general intelligence, highlighting the requirement for further expedition and evaluation of such systems. [111]
In 2023, the AI scientist Geoffrey Hinton mentioned that: [112]
The concept that this things might actually get smarter than people - a couple of individuals believed that, [...] But the majority of people believed it was method off. And I thought it was way off. I believed it was 30 to 50 years or perhaps longer away. Obviously, I no longer think that.

In May 2023, Demis Hassabis likewise stated that "The development in the last few years has been quite extraordinary", which he sees no reason that it would decrease, anticipating AGI within a decade or perhaps a couple of years. [113] In March 2024, Nvidia's CEO, Jensen Huang, mentioned his expectation that within 5 years, AI would be capable of passing any test at least in addition to people. [114] In June 2024, the AI scientist Leopold Aschenbrenner, a former OpenAI employee, estimated AGI by 2027 to be "noticeably plausible". [115]
Whole brain emulation

While the development of transformer designs like in ChatGPT is thought about the most promising course to AGI, [116] [117] entire brain emulation can act as an alternative approach. With entire brain simulation, a brain design is built by scanning and mapping a biological brain in detail, and after that copying and replicating it on a computer system or another computational gadget. The simulation design should be adequately devoted to the original, so that it behaves in virtually the very same way as the original brain. [118] Whole brain emulation is a type of brain simulation that is discussed in computational neuroscience and neuroinformatics, and for medical research study functions. It has actually been discussed in artificial intelligence research [103] as a method to strong AI. Neuroimaging technologies that might deliver the needed in-depth understanding are enhancing quickly, and futurist Ray Kurzweil in the book The Singularity Is Near [102] anticipates that a map of adequate quality will become readily available on a comparable timescale to the computing power required to emulate it.

Early estimates

For low-level brain simulation, a really effective cluster of computers or GPUs would be required, offered the enormous amount 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 neurons. The brain of a three-year-old kid has about 1015 synapses (1 quadrillion). This number decreases with age, supporting by adulthood. 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 a basic switch model for neuron activity, is around 1014 (100 trillion) synaptic updates per second (SUPS). [121]
In 1997, Kurzweil took a look at different estimates for the hardware needed to equal the human brain and embraced a figure of 1016 computations per second (cps). [e] (For contrast, if a "computation" was comparable to one "floating-point operation" - a step utilized to rate current supercomputers - then 1016 "computations" would be comparable to 10 petaFLOPS, achieved in 2011, while 1018 was achieved in 2022.) He used this figure to anticipate the essential hardware would be available at some point between 2015 and 2025, if the rapid growth in computer power at the time of writing continued.

Current research

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

Criticisms of simulation-based techniques

The synthetic neuron design presumed by Kurzweil and used in lots of present synthetic neural network applications is easy compared to biological neurons. A brain simulation would likely have to catch the detailed cellular behaviour of biological nerve cells, presently comprehended only in broad overview. The overhead introduced by full modeling of the biological, chemical, and physical information of neural behaviour (especially on a molecular scale) would need computational powers several orders of magnitude bigger than Kurzweil's quote. In addition, the estimates do not represent glial cells, which are understood to contribute in cognitive processes. [125]
A basic criticism of the simulated brain method obtains from embodied cognition theory which asserts that human personification is a vital aspect of human intelligence and is required to ground significance. [126] [127] If this theory is correct, any completely functional brain design will need to incorporate more than simply the neurons (e.g., a robotic body). Goertzel [103] proposes virtual personification (like in metaverses like Second Life) as an option, but it is unidentified whether this would suffice.

Philosophical point of view

"Strong AI" as defined in philosophy

In 1980, philosopher John Searle created the term "strong AI" as part of his Chinese space argument. [128] He proposed a distinction between two hypotheses about expert system: [f]
Strong AI hypothesis: A synthetic intelligence system can have "a mind" and "consciousness". Weak AI hypothesis: An artificial intelligence system can (only) imitate it thinks and has a mind and awareness.
The first one he called "strong" since it makes a stronger declaration: it presumes something unique has actually occurred to the device that exceeds those capabilities that we can test. The behaviour of a "weak AI" machine would be exactly similar to a "strong AI" machine, however the latter would also have subjective conscious experience. This use is likewise common in scholastic AI research and textbooks. [129]
In contrast to Searle and mainstream AI, some futurists such as Ray Kurzweil use the term "strong AI" to imply "human level artificial basic intelligence". [102] This is not the same as Searle's strong AI, unless it is presumed that awareness is required for human-level AGI. Academic philosophers such as Searle do not believe that is the case, and to most synthetic intelligence scientists the question is out-of-scope. [130]
Mainstream AI is most interested in how a program behaves. [131] According to Russell and Norvig, "as long as the program works, they don't care if you call it genuine or a simulation." [130] If the program can act as if it has a mind, then there is no need to know if it actually has mind - undoubtedly, there would be no other way to tell. For AI research, Searle's "weak AI hypothesis" is comparable to the declaration "artificial general intelligence is possible". Thus, according to Russell and Norvig, "most AI researchers take the weak AI hypothesis for granted, and don't care about the strong AI hypothesis." [130] Thus, for scholastic AI research study, "Strong AI" and "AGI" are 2 different things.

Consciousness

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

Sentience (or "extraordinary awareness"): The ability to "feel" understandings or emotions subjectively, as opposed to the ability to factor about perceptions. Some theorists, such as David Chalmers, use the term "consciousness" to refer solely to sensational awareness, which is approximately comparable to sentience. [132] Determining why and how subjective experience arises is called the tough issue of consciousness. [133] Thomas Nagel discussed in 1974 that it "feels like" something to be mindful. If we are not mindful, then it does not feel like anything. Nagel uses the example of a bat: we can sensibly ask "what does it seem like to be a bat?" However, we are unlikely to ask "what does it seem like to be a toaster?" Nagel concludes that a bat seems mindful (i.e., has awareness) however a toaster does not. [134] In 2022, a Google engineer claimed that the company's AI chatbot, LaMDA, had actually accomplished sentience, though this claim was extensively disputed by other specialists. [135]
Self-awareness: To have conscious awareness of oneself as a separate person, specifically to be consciously aware of one's own thoughts. This is opposed to simply being the "topic of one's believed"-an operating system or debugger has the ability to be "familiar with itself" (that is, to represent itself in the same way it represents whatever else)-but this is not what people generally mean when they utilize the term "self-awareness". [g]
These qualities have a moral measurement. AI sentience would offer rise to concerns of well-being and legal security, likewise to animals. [136] Other aspects of consciousness related to cognitive abilities are likewise pertinent to the idea of AI rights. [137] Determining how to incorporate advanced AI with existing legal and social frameworks is an emergent concern. [138]
Benefits

AGI might have a variety of applications. If oriented towards such goals, AGI might help mitigate various problems worldwide such as hunger, hardship and health issue. [139]
AGI might enhance efficiency and efficiency in a lot of tasks. For instance, in public health, AGI could accelerate medical research, notably versus cancer. [140] It could take care of the elderly, [141] and democratize access to rapid, top quality medical diagnostics. It might provide fun, cheap and tailored education. [141] The need to work to subsist could become outdated if the wealth produced is properly redistributed. [141] [142] This also raises the question of the place of human beings in a significantly automated society.

AGI could also help to make logical choices, and to expect and avoid catastrophes. It might likewise help to profit of possibly devastating innovations such as nanotechnology or climate engineering, while avoiding the associated dangers. [143] If an AGI's main goal is to prevent existential disasters such as human extinction (which could be challenging if the Vulnerable World Hypothesis ends up being true), [144] it could take procedures to significantly lower the risks [143] while minimizing the impact of these procedures on our quality of life.

Risks

Existential risks

AGI may represent numerous types of existential threat, which are threats that threaten "the early extinction of Earth-originating smart life or the permanent and extreme damage of its capacity for preferable future development". [145] The risk of human extinction from AGI has been the topic of numerous arguments, but there is also the possibility that the advancement of AGI would result in a permanently problematic future. Notably, it could be utilized to spread out and protect the set of worths of whoever establishes it. If humanity still has moral blind spots comparable to slavery in the past, AGI might irreversibly entrench it, avoiding ethical development. [146] Furthermore, AGI could help with mass monitoring and brainwashing, which could be utilized to develop a stable repressive worldwide totalitarian regime. [147] [148] There is likewise a risk for the devices themselves. If machines that are sentient or otherwise worthy of ethical factor to consider are mass created in the future, engaging in a civilizational path that indefinitely neglects their well-being and interests could be an existential disaster. [149] [150] Considering how much AGI might enhance humanity's future and help minimize other existential threats, Toby Ord calls these existential risks "an argument for continuing with due care", not for "deserting AI". [147]
Risk of loss of control and human extinction

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

So, dealing with possible futures of enormous benefits and dangers, the professionals are certainly doing whatever possible to make sure the very best result, right? Wrong. If a superior alien civilisation sent us a message saying, 'We'll get here in a few years,' would we just reply, 'OK, call us when you get here-we'll leave the lights on?' Probably not-but this is basically what is occurring with AI. [153]
The possible fate of mankind has actually sometimes been compared to the fate of gorillas threatened by human activities. The contrast mentions that higher intelligence allowed mankind to control gorillas, which are now vulnerable in manner ins which they could not have anticipated. As a result, the gorilla has ended up being a threatened species, not out of malice, however simply as a collateral damage from human activities. [154]
The skeptic Yann LeCun thinks about that AGIs will have no desire to dominate humankind which we need to beware not to anthropomorphize them and analyze their intents as we would for human beings. He said that individuals won't be "smart adequate to create super-intelligent machines, yet extremely silly to the point of offering it moronic objectives with no safeguards". [155] On the other side, the concept of important merging suggests that nearly whatever their goals, smart agents will have reasons to attempt to survive and obtain more power as intermediary actions to accomplishing these goals. And that this does not require having emotions. [156]
Many scholars who are concerned about existential danger advocate for more research study into resolving the "control issue" to address the question: what types of safeguards, algorithms, or architectures can developers implement to maximise the probability that their recursively-improving AI would continue to behave in a friendly, rather than devastating, manner after it reaches superintelligence? [157] [158] Solving the control problem is made complex by the AI arms race (which could lead to a race to the bottom of safety preventative measures in order to launch products before competitors), [159] and making use of AI in weapon systems. [160]
The thesis that AI can posture existential threat also has critics. Skeptics usually state that AGI is not likely in the short-term, or that concerns about AGI sidetrack from other problems related to current AI. [161] Former Google scams czar Shuman Ghosemajumder considers that for many individuals beyond the innovation industry, existing chatbots and LLMs are already viewed as though they were AGI, causing more misunderstanding and fear. [162]
Skeptics often charge that the thesis is crypto-religious, with an irrational belief in the possibility of superintelligence replacing an illogical belief in a supreme God. [163] Some researchers believe that the interaction projects on AI existential threat by specific AI groups (such as OpenAI, Anthropic, DeepMind, and Conjecture) may be an at attempt at regulative capture and to inflate interest in their items. [164] [165]
In 2023, the CEOs of Google DeepMind, OpenAI and Anthropic, in addition to other industry leaders and researchers, provided a joint statement asserting that "Mitigating the danger of termination from AI need to be a global top priority along with other societal-scale threats such as pandemics and war." [152]
Mass unemployment

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

According to Stephen Hawking, the outcome of automation on the quality of life will depend upon how the wealth will be redistributed: [142]
Everyone can take pleasure in a life of glamorous 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 seems to be toward the 2nd option, with innovation driving ever-increasing inequality

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

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

^ a b See below for the origin of the term "strong AI", and see the scholastic meaning of "strong AI" and weak AI in the article Chinese room. ^ AI founder John McCarthy composes: "we can not yet identify in basic what kinds of computational procedures we desire to call smart. " [26] (For a discussion of some definitions of intelligence utilized by synthetic intelligence scientists, see philosophy of expert system.). ^ The Lighthill report particularly slammed AI's "grandiose objectives" and led the dismantling of AI research in England. [55] In the U.S., DARPA became figured out to fund just "mission-oriented direct research study, rather than fundamental undirected research". [56] [57] ^ As AI founder John McCarthy writes "it would be a fantastic relief to the remainder of the employees in AI if the creators of brand-new general formalisms would reveal their hopes in a more safeguarded kind than has actually in some cases been the case." [61] ^ In "Mind Children" [122] 1015 cps is utilized. More recently, in 1997, [123] Moravec argued for 108 MIPS which would approximately correspond to 1014 cps. Moravec talks in regards to MIPS, not "cps", which is a non-standard term Kurzweil introduced. ^ As defined in a basic AI textbook: "The assertion that machines might perhaps act wisely (or, maybe better, act as if they were intelligent) is called the 'weak AI' hypothesis by philosophers, and the assertion that devices that do so are in fact believing (as opposed to 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 synthetic narrow intelligence (ANI)?". VentureBeat. Retrieved 1 March 2024. ANI is created to carry out a single job. ^ "OpenAI Charter". OpenAI. Retrieved 6 April 2023. Our mission is to guarantee that artificial basic intelligence benefits all of humanity. ^ Heath, Alex (18 January 2024). "Mark Zuckerberg's new goal is producing 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 Survey of Artificial General Intelligence Projects for Ethics, Risk, and Policy (PDF) (Report). Global Catastrophic Risk Institute. Retrieved 28 November 2024. 72 AGI R&D projects were determined as being active in 2020. ^ a b c "AI timelines: What do professionals in synthetic intelligence anticipate for the future?". Our World in Data. Retrieved 6 April 2023. ^ Metz, Cade (15 May 2023). "Some Researchers Say A.I. Is Already Here, Stirring Debate in Tech Circles". The New York Times. Retrieved 18 May 2023. ^ "AI leader Geoffrey Hinton quits Google and cautions of risk ahead". The New York City Times. 1 May 2023. Retrieved 2 May 2023. It is tough to see how you can prevent the bad actors from using it for bad things. ^ Bubeck, Sébastien; Chandrasekaran, Varun; Eldan, Ronen; Gehrke, Johannes; Horvitz, Eric (2023 ). "Sparks of Artificial General Intelligence: Early explores GPT-4". arXiv preprint. arXiv:2303.12712. GPT-4 reveals 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 change. All that you change changes you. ^ Vinge, Vernor (1992 ). A Fire Upon the Deep. Tor Books. ISBN 978-0-8125-1528-2. The Singularity is coming. ^ Morozov, Evgeny (30 June 2023). "The True Threat of Expert System". The New York Times. The real risk is not AI itself but the method we release it. ^ "Impressed by expert system? Experts say AGI is coming next, and it has 'existential' threats". ABC News. 23 March 2023. Retrieved 6 April 2023. AGI could 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 innovation that humanity needs to make. ^ Roose, Kevin (30 May 2023). "A.I. Poses 'Risk of Extinction,' Industry Leaders Warn". The New York City Times. Mitigating the danger of termination from AI need to be a global priority. ^ "Statement on AI Risk". Center for AI Safety. Retrieved 1 March 2024. AI experts warn of risk of termination from AI. ^ Mitchell, Melanie (30 May 2023). "Are AI's Doomsday Scenarios Worth Taking Seriously?". The New York Times. We are far from producing makers that can outthink us in general ways. ^ LeCun, Yann (June 2023). "AGI does not provide an existential risk". Medium. There is no reason to fear AI as an existential danger. ^ Kurzweil 2005, p. 260. ^ a b Kurzweil, Ray (5 August 2005), "Long Live AI", Forbes, archived from the original on 14 August 2005: Kurzweil describes strong AI as "maker intelligence with the full series of human intelligence.". ^ "The Age of Expert System: mariskamast.net George John at TEDxLondonBusinessSchool 2013". Archived from the initial on 26 February 2014. Retrieved 22 February 2014. ^ Newell & Simon 1976, This is the term they utilize for "human-level" intelligence in the physical sign system hypothesis. ^ "The Open University on Strong and Weak AI". Archived from the original on 25 September 2009. Retrieved 8 October 2007. ^ "What is artificial superintelligence (ASI)?|Definition from TechTarget". Enterprise AI. Retrieved 8 October 2023. ^ "Expert system is transforming our world - it is on everyone to make sure that it goes well". Our World in Data. Retrieved 8 October 2023. ^ Dickson, Ben (16 November 2023). "Here is how far we are to achieving AGI, according to DeepMind". VentureBeat. ^ McCarthy, John (2007a). "Basic Questions". Stanford University. Archived from the initial on 26 October 2007. Retrieved 6 December 2007. ^ This list of smart characteristics is based on the subjects covered by major AI books, including: Russell & Norvig 2003, Luger & Stubblefield 2004, Poole, Mackworth & Goebel 1998 and Nilsson 1998. ^ Johnson 1987. ^ de Charms, R. (1968 ). Personal causation. New York City: Academic Press. ^ a b Pfeifer, R. and Bongard J. C., How the body forms the method we think: a new view of intelligence (The MIT Press, 2007). ISBN 0-2621-6239-3. ^ White, R. W. (1959 ). "Motivation reassessed: The principle of proficiency". Psychological Review. 66 (5 ): 297-333. doi:10.1037/ h0040934. PMID 13844397. S2CID 37385966. ^ White, R. W. (1959 ). "Motivation reevaluated: The idea of proficiency". Psychological Review. 66 (5 ): 297-333. doi:10.1037/ h0040934. PMID 13844397. S2CID 37385966. ^ Muehlhauser, Luke (11 August 2013). "What is AGI?". Machine Intelligence Research Institute. Archived from the initial on 25 April 2014. Retrieved 1 May 2014. ^ "What is Artificial General Intelligence (AGI)?|4 Tests For Ensuring Artificial General Intelligence". Talky Blog. 13 July 2019. Archived from the initial on 17 July 2019. Retrieved 17 July 2019. ^ Kirk-Giannini, Cameron Domenico; Goldstein, Simon (16 October 2023). "AI is closer than ever to passing the Turing test for 'intelligence'. What happens when it does?". The Conversation. Retrieved 22 September 2024. ^ a b Turing 1950. ^ Turing, Alan (1952 ). B. Jack Copeland (ed.). Can Automatic Calculating Machines Be Said To Think?. Oxford: Oxford University Press. pp. 487-506. ISBN 978-0-1982-5079-1. ^ "Eugene Goostman is a genuine young boy - the Turing Test says 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 challenging examinations both AI variations have actually passed". Business Insider. Retrieved 30 May 2023. ^ Naysmith, Caleb (7 February 2023). "6 Jobs Artificial Intelligence Is Already Replacing and How Investors Can Capitalize on It". Retrieved 30 May 2023. ^ Turk, Victoria (28 January 2015). "The Plan to Replace the Turing Test with a 'Turing Olympics'". Vice. Retrieved 3 March 2024. ^ Gopani, Avi (25 May 2022). "Turing Test is 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 capability to turn $100,000 into $1 million to measure human-like intelligence". Business Insider. Retrieved 3 March 2024. ^ Suleyman, Mustafa (14 July 2023). "Mustafa Suleyman: My brand-new Turing test would see if AI can make $1 million". MIT Technology Review. Retrieved 3 March 2024. ^ Shapiro, Stuart C. (1992 ). "Expert System" (PDF). In Stuart C. Shapiro (ed.). Encyclopedia of Expert System (Second ed.). New York: John Wiley. pp. 54-57. Archived (PDF) from the initial on 1 February 2016. (Section 4 is on "AI-Complete Tasks".). ^ Yampolskiy, Roman V. (2012 ). Xin-She Yang (ed.). "Turing Test as a Specifying Feature of AI-Completeness" (PDF). Artificial Intelligence, Evolutionary Computation and Metaheuristics (AIECM): 3-17. Archived (PDF) from the initial on 22 May 2013. ^ "AI Index: State of AI in 13 Charts". Stanford University Human-Centered Expert System. 15 April 2024. Retrieved 27 May 2024. ^ Crevier 1993, pp. 48-50. ^ Kaplan, Andreas (2022 ). "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 priced estimate 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 ), priced quote in Crevier (1993, p. 109). ^ Lighthill 1973; Howe 1994. ^ a b NRC 1999, "Shift to Applied Research Increases Investment". ^ Crevier 1993, pp. 115-117; Russell & Norvig 2003, pp. 21-22. ^ Crevier 1993, p. 211, Russell & Norvig 2003, p. 24 and see also Feigenbaum & McCorduck 1983. ^ Crevier 1993, pp. 161-162, 197-203, 240; Russell & Norvig 2003, p. 25. ^ Crevier 1993, pp. 209-212. ^ McCarthy, John (2000 ). "Respond to Lighthill". Stanford University. Archived from the original on 30 September 2008. Retrieved 29 September 2007. ^ Markoff, John (14 October 2005). "Behind Artificial Intelligence, a Squadron of Bright Real People". The New York Times. Archived from the original on 2 February 2023. Retrieved 18 February 2017. At its low point, some computer researchers and software engineers avoided the term artificial intelligence for worry of being considered as wild-eyed dreamers. ^ Russell & Norvig 2003, pp. 25-26 ^ "Trends in the Emerging Tech Hype Cycle". Gartner Reports. Archived from the initial on 22 May 2019. Retrieved 7 May 2019. ^ a b Moravec 1988, p. 20 ^ Harnad, S. (1990 ). "The Symbol Grounding Problem". Physica D. 42 (1-3): 335-346. arXiv: cs/9906002. Bibcode:1990 PhyD ... 42..335 H. doi:10.1016/ 0167-2789( 90 )90087-6. S2CID 3204300. ^ Gubrud 1997 ^ Hutter, Marcus (2005 ). Universal Expert System: Sequential Decisions Based on Algorithmic Probability. Texts in Theoretical Computer Technology 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 Science. Vol. 8598. Journal of Artificial General Intelligence. doi:10.1007/ 978-3-319-09274-4. ISBN 978-3-3190-9273-7. S2CID 8387410. ^ "Who created 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 original 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 machine intelligence: Despite progress in device intelligence, synthetic 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 try outs GPT-4". arXiv:2303.12712 [cs.CL] ^ "Microsoft Researchers Claim GPT-4 Is Showing "Sparks" of AGI". Futurism. 23 March 2023. Retrieved 13 December 2023. ^ Allen, Paul; Greaves, Mark (12 October 2011). "The Singularity Isn't Near". MIT Technology Review. Retrieved 17 September 2014. ^ Winfield, Alan. "Artificial intelligence will not become a Frankenstein's beast". The Guardian. Archived from the original on 17 September 2014. Retrieved 17 September 2014. ^ Deane, George (2022 ). "Machines That Feel and Think: The Role of Affective Feelings and Mental Action in (Artificial) General Intelligence". Artificial Life. 28 (3 ): 289-309. doi:10.1162/ artl_a_00368. ISSN 1064-5462. PMID 35881678. S2CID 251069071. ^ a b c Clocksin 2003. ^ Fjelland, Ragnar (17 June 2020). "Why basic artificial intelligence will not be realized". Humanities and Social Sciences Communications. 7 (1 ): 1-9. doi:10.1057/ s41599-020-0494-4. hdl:11250/ 2726984. ISSN 2662-9992. S2CID 219710554. ^ McCarthy 2007b. ^ Khatchadourian, Raffi (23 November 2015). "The Doomsday Invention: photorum.eclat-mauve.fr Will synthetic intelligence bring us utopia or destruction?". The New Yorker. Archived from the initial on 28 January 2016. Retrieved 7 February 2016. ^ Müller, V. C., & Bostrom, N. (2016 ). Future development in synthetic intelligence: A study of skilled opinion. In Fundamental problems of synthetic intelligence (pp. 555-572). Springer, Cham. ^ Armstrong, Stuart, and Kaj Sotala. 2012. "How We're Predicting AI-or Failing To." In Beyond AI: Artificial Dreams, 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 Brand-new AI Model, Code-Named Strawberry, That Solves Difficult Problems Step by Step". Wired. ISSN 1059-1028. Retrieved 17 September 2024. ^ "OpenAI Employee Claims AGI Has Been Achieved". Orbital Today. 13 December 2024. Retrieved 27 December 2024. ^ "AI Index: State of AI in 13 Charts". hai.stanford.edu. 15 April 2024. Retrieved 7 June 2024. ^ "Next-Gen AI: OpenAI and Meta's Leap Towards Reasoning Machines". Unite.ai. 19 April 2024. Retrieved 7 June 2024. ^ James, Alex P. (2022 ). "The Why, What, and How of Artificial General Intelligence Chip Development". IEEE Transactions on Cognitive and Developmental Systems. 14 (2 ): 333-347. arXiv:2012.06338. doi:10.1109/ TCDS.2021.3069871. ISSN 2379-8920. S2CID 228376556. Archived from the initial on 28 August 2022. Retrieved 28 August 2022. ^ Pei, Jing; Deng, Lei; Song, Sen; Zhao, Mingguo; Zhang, Youhui; Wu, Shuang; Wang, Guanrui; Zou, Zhe; Wu, Zhenzhi; He, Wei; Chen, Feng; Deng, Ning; Wu, Si; Wang, Yu; Wu, Yujie (2019 ). "Towards synthetic basic intelligence with hybrid Tianjic chip architecture". Nature. 572 (7767 ): 106-111. Bibcode:2019 Natur.572..106 P. doi:10.1038/ s41586-019-1424-8. ISSN 1476-4687. PMID 31367028. S2CID 199056116. Archived from the 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 knowing in drug discovery". Nature Machine Intelligence. 4 (3 ): 211-221. doi:10.1038/ s42256-022-00463-x. ISSN 2522-5839. S2CID 252081559. ^ Goertzel & Pennachin 2006. ^ a b c (Kurzweil 2005, p. 260). ^ a b c Goertzel 2007. ^ Grace, Katja (2016 ). "Error in Armstrong and Sotala 2012". AI Impacts (blog site). Archived from the initial 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 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 entering the AI golden zone between narrow and basic AI". VentureBeat. Archived from the initial on 4 September 2020. Retrieved 5 September 2020. Certainly, too, there are those who declare we are already seeing an early example of an AGI system in the recently revealed GPT-3 natural language processing (NLP) neural network. ... So is GPT-3 the first example of an AGI system? This is arguable, but the agreement is that it is not AGI. ... If absolutely nothing else, GPT-3 tells us there is a happy medium between narrow and basic AI. ^ Quach, Katyanna. "A developer built an AI chatbot using GPT-3 that assisted a guy speak once again to his late fiancée. OpenAI shut it down". The Register. Archived from the original on 16 October 2021. Retrieved 16 October 2021. ^ Wiggers, Kyle (13 May 2022), "DeepMind's new AI can carry out over 600 tasks, from playing games to controlling robots", TechCrunch, archived from the original on 16 June 2022, recovered 12 June 2022. ^ Bubeck, Sébastien; Chandrasekaran, Varun; Eldan, Ronen; Gehrke, Johannes; Horvitz, Eric; Kamar, Ece; Lee, Peter; Lee, Yin Tat; Li, Yuanzhi; Lundberg, Scott; Nori, Harsha; Palangi, Hamid; Ribeiro, Marco Tulio; Zhang, Yi (22 March 2023). "Sparks of Artificial General Intelligence: Early explores GPT-4". arXiv:2303.12712 [cs.CL] ^ Metz, Cade (1 May 2023). "' The Godfather of A.I.' Leaves Google and Warns of Danger Ahead". The New York Times. ISSN 0362-4331. Retrieved 7 June 2023. ^ Bove, Tristan. "A.I. might rival human intelligence in 'just a couple of years,' states CEO of Google's main A.I. research laboratory". Fortune. Retrieved 4 September 2024. ^ Nellis, Stephen (2 March 2024). "Nvidia CEO states AI might pass human tests in five years". Reuters. ^ Aschenbrenner, Leopold. "SITUATIONAL AWARENESS, The Decade Ahead". ^ Sullivan, Mark (18 October 2023). "Why everybody appears to disagree on how to specify Artificial General Intelligence". Fast Company. ^ Nosta, John (5 January 2024). "The Accelerating Path to Artificial General Intelligence". Psychology Today. Retrieved 30 March 2024. ^ Hickey, Alex. "Whole Brain Emulation: A Giant Step for Neuroscience". Tech Brew. Retrieved 8 November 2023. ^ Sandberg & Boström 2008. ^ Drachman 2005. ^ a b Russell & Norvig 2003. ^ Moravec 1988, p. 61. ^ Moravec 1998. ^ Holmgaard Mersh, Amalie (15 September 2023). "Decade-long European research project maps the human brain". euractiv. ^ Swaminathan, Nikhil (January-February 2011). "Glia-the other brain cells". Discover. Archived from the original on 8 February 2014. Retrieved 24 January 2014. ^ de Vega, Glenberg & Graesser 2008. A wide variety of views in present research study, all of which require grounding to some degree ^ Thornton, Angela (26 June 2023). "How uploading our minds to a computer might end up being possible". The Conversation. Retrieved 8 November 2023. ^ Searle 1980 ^ For example: Russell & Norvig 2003, Oxford University Press Dictionary of Psychology Archived 3 December 2007 at the Wayback Machine (priced quote in" Encyclopedia.com"),. MIT Encyclopedia of Cognitive Science Archived 19 July 2008 at the Wayback Machine (priced 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 synthetic intelligence for interest by the field about why a program behaves the way it does. ^ Chalmers, David J. (9 August 2023). "Could a Big Language Model Be Conscious?". Boston Review. ^ Seth, Anil. "Consciousness". New Scientist. Retrieved 5 September 2024. ^ Nagel 1974. ^ "The Google engineer who thinks the business's AI has come to life". The Washington Post. 11 June 2022. Retrieved 12 June 2023. ^ Kateman, Brian (24 July 2023). "AI Should Be Terrified of Humans". TIME. Retrieved 5 September 2024. ^ Nosta, John (18 December 2023). "Should Expert System Have Rights?". Psychology Today. Retrieved 5 September 2024. ^ Akst, Daniel (10 April 2023). "Should Robots With Artificial Intelligence Have Moral or Legal Rights?". The Wall Street Journal. ^ "Artificial General Intelligence - Do [es] the expense outweigh 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 Appear 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, methods (Reprinted with corrections 2017 ed.). Oxford, United Kingdom; New York City, New York, USA: Oxford University Press. ISBN 978-0-1996-7811-2. ^ Piper, Kelsey (19 November 2018). "How technological development is making it likelier than ever that human beings will ruin ourselves". Vox. Retrieved 8 June 2023. ^ Doherty, Ben (17 May 2018). "Climate alter an 'existential security danger' to Australia, Senate questions states". The Guardian. ISSN 0261-3077. Retrieved 16 July 2023. ^ MacAskill, William (2022 ). What we owe the future. New York City, NY: Basic Books. ISBN 978-1-5416-1862-6. ^ a b Ord, Toby (2020 ). "Chapter 5: Future Risks, Unaligned Expert System". The Precipice: Existential Risk and the Future of Humanity. Bloomsbury Publishing. ISBN 978-1-5266-0021-9. ^ Al-Sibai, Noor (13 February 2022). "OpenAI Chief Scientist Says Advanced AI May Already Be Conscious". Futurism. Retrieved 24 December 2023. ^ Samuelsson, Paul Conrad (2019 ). "Artificial Consciousness: Our Greatest Ethical Challenge". Philosophy Now. Retrieved 23 December 2023. ^ Kateman, Brian (24 July 2023). "AI Should Be Terrified of Humans". TIME. Retrieved 23 December 2023. ^ Roose, Kevin (30 May 2023). "A.I. Poses 'Risk of Extinction,' Industry Leaders Warn". The New York City Times. ISSN 0362-4331. Retrieved 24 December 2023. ^ a b "Statement on AI Risk". Center for AI Safety. 30 May 2023. Retrieved 8 June 2023. ^ "Stephen Hawking: 'Transcendence takes a look at the implications of expert system - however are we taking AI seriously enough?'". The Independent (UK). Archived from the original on 25 September 2015. Retrieved 3 December 2014. ^ Herger, Mario. "The Gorilla Problem - Enterprise Garage". Retrieved 7 June 2023. ^ "The remarkable Facebook dispute between Yann LeCun, Stuart Russel and Yoshua Bengio about the risks of strong AI". The fascinating Facebook dispute in between Yann LeCun, Stuart Russel and Yoshua Bengio about the dangers of strong AI (in French). Retrieved 8 June 2023. ^ "Will Artificial Intelligence Doom The Mankind Within The Next 100 Years?". HuffPost. 22 August 2014. Retrieved 8 June 2023. ^ Sotala, Kaj; Yampolskiy, Roman V. (19 December 2014). "Responses to disastrous AGI threat: a survey". Physica Scripta. 90 (1 ): 018001. doi:10.1088/ 0031-8949/90/ 1/018001. ISSN 0031-8949. ^ Bostrom, Nick (2014 ). Superintelligence: Paths, Dangers, Strategies (First ed.). Oxford University Press. ISBN 978-0-1996-7811-2. ^ Chow, Andrew R.; Perrigo, Billy (16 February 2023). "The AI Arms Race Is On. Start Worrying". TIME. Retrieved 24 December 2023. ^ Tetlow, Gemma (12 January 2017). "AI arms race threats spiralling out of control, report warns". 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 positioned however synthetic intelligence can not be overlooked". The Guardian. ISSN 0261-3077. Retrieved 24 December 2023. ^ "Humanity, Security & AI, Oh My! (with Ian Bremmer & Shuman Ghosemajumder)". CAFE. 20 July 2023. Retrieved 15 September 2023. ^ Hamblin, James (9 May 2014). "But What Would completion of Humanity Mean for Me?". The Atlantic. Archived from the initial on 4 June 2014. Retrieved 12 December 2015. ^ Titcomb, James (30 October 2023). "Big Tech is stiring worries over AI, alert researchers". The Telegraph. Retrieved 7 December 2023. ^ Davidson, John (30 October 2023). "Google Brain creator says big tech is lying about AI extinction 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 take a look at the labor vetlek.ru market impact capacity of large language models". OpenAI. Retrieved 7 June 2023. ^ a b Hurst, Luke (23 March 2023). "OpenAI says 80% of employees could see their jobs affected by AI. These are the jobs most impacted". euronews. Retrieved 8 June 2023. ^ Sheffey, Ayelet (20 August 2021). "Elon Musk states we require universal fundamental income due to the fact that 'in the future, manual labor 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), "Artificial intelligence and the future", Philosophical Transactions of the Royal Society A, vol. 361, no. 1809, pp. 1721-1748, Bibcode:2003 RSPTA.361.1721 C, doi:10.1098/ rsta.2003.1232, PMID 12952683, S2CID 31032007. Crevier, Daniel (1993 ). AI: The Tumultuous Search for Artificial Intelligence. New York City, NY: BasicBooks. ISBN 0-465-02997-3. Darrach, Brad (20 November 1970), "Meet Shakey, the First Electronic Person", Life Magazine, pp. 58-68. Drachman, D. (2005 ), "Do we have brain to spare?", Neurology, 64 (12 ): 2004-2005, doi:10.1212/ 01. WNL.0000166914.38327. BB, PMID 15985565, S2CID 38482114. Feigenbaum, Edward A.; McCorduck, Pamela (1983 ), The Fifth Generation: Artificial Intelligence and Japan's Computer Challenge to the World, Michael Joseph, ISBN 978-0-7181-2401-4. Goertzel, Ben; Pennachin, Cassio, eds. (2006 ), Artificial General Intelligence (PDF), Springer, ISBN 978-3-5402-3733-4, archived from the initial (PDF) on 20 March 2013. Goertzel, Ben (December 2007), "Human-level artificial general intelligence and the possibility of a technological singularity: a reaction to Ray Kurzweil's The Singularity Is Near, and McDermott's review of Kurzweil", Artificial Intelligence, vol. 171, no. 18, Special Review Issue, pp. 1161-1173, doi:10.1016/ j.artint.2007.10.011, archived from the initial on 7 January 2016, obtained 1 April 2009. Gubrud, Mark (November 1997), "Nanotechnology and International Security", Fifth Foresight Conference on Molecular Nanotechnology, archived from the original on 29 May 2011, retrieved 7 May 2011. Howe, J. (November 1994), Expert System at Edinburgh University: a Point of view, archived from the original on 17 August 2007, obtained 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 (fifth ed.), The Benjamin/Cummings Publishing Company, Inc., p. 720, ISBN 978-0-8053-4780-7. McCarthy, John (2007b). What is Expert system?. Stanford University. The ultimate effort is to make computer programs that can resolve problems and attain objectives on the planet in addition to people. Moravec, Hans (1988 ), Mind Children, Harvard University Press Moravec, Hans (1998 ), "When will hardware match the human brain?", Journal of Evolution and Technology, vol. 1, archived from the original on 15 June 2006, retrieved 23 June 2006 Nagel (1974 ), "What Is it Like to Be a Bat" (PDF), Philosophical Review, 83 (4 ): 435-50, doi:10.2307/ 2183914, JSTOR 2183914, archived (PDF) from the original 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 Transformation: Government Support for Computing Research, National Academy Press, archived from the original on 12 January 2008, obtained 29 September 2007 Poole, David; Mackworth, Alan; Goebel, Randy (1998 ), Computational Intelligence: A Sensible Approach, New York City: Oxford University Press, archived from the initial on 25 July 2009, obtained 6 December 2007 Russell, Stuart J.; Norvig, Peter (2003 ), Artificial Intelligence: A Modern Approach (second 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, recovered 5 April 2009 Searle, John (1980 ), "Minds, Brains and Programs" (PDF), Behavioral and Brain Sciences, 3 (3 ): 417-457, doi:10.1017/ S0140525X00005756, S2CID 55303721, archived (PDF) from the initial on 17 March 2019, retrieved 3 September 2020 Simon, H. A. (1965 ), The Shape of Automation for Men and Management, New York: Harper & Row Turing, Alan (October 1950). "Computing Machinery and Intelligence". Mind. 59 (236 ): 433-460. doi:10.1093/ mind/LIX.236.433. ISSN 1460-2113. JSTOR 2251299. S2CID 14636783.
de Vega, Manuel; Glenberg, Arthur; Graesser, Arthur, eds. (2008 ), Symbols and Embodiment: Debates on significance 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 original on 18 February 2021, retrieved 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, obtained 31 August 2012 Cukier, Kenneth, "Ready for Robots? How to Consider the Future of AI", Foreign Affairs, vol. 98, no. 4 (July/August 2019), pp. 192-98. George Dyson, historian of computing, writes (in what might be called "Dyson's Law") that "Any system simple enough to be reasonable will not be complicated enough to act wisely, while any system complicated enough to behave smartly will be too made complex to understand." (p. 197.) Computer researcher Alex Pentland composes: "Current AI machine-learning algorithms are, at their core, dead basic silly. 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, recovered 25 July 2010. Gleick, James, "The Fate of Free Choice" (evaluation of Kevin J. Mitchell, Free Agents: How Evolution Gave Us Free Choice, Princeton University Press, 2023, 333 pp.), The New York City Review of Books, vol. LXXI, no. 1 (18 January 2024), pp. 27-28, 30. "Agency is what distinguishes us from devices. For biological animals, factor and purpose come from acting on the planet and experiencing the effects. Expert systems - disembodied, complete strangers to blood, sweat, and tears - have no celebration for that." (p. 30.). Halal, William E. "TechCast Article Series: The Automation of Thought" (PDF). Archived from the original (PDF) on 6 June 2013. - Halpern, Sue, "The Coming Tech Autocracy" (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: 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 anticipate that those who wish to get abundant 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 finance contributions [from tech business] to push back.' ... Marcus information the demands that citizens should make from their governments and the tech business. They consist of openness on how AI systems work; payment for individuals if their information [are] used to train LLMs (large language model) s and the right to approval to this usage; and the capability to hold tech business accountable for the damages they cause by eliminating Section 230, enforcing cash penalites, and passing stricter item liability laws ... Marcus also suggests ... that a brand-new, AI-specific federal agency, akin to the FDA, the FCC, or the FTC, might supply the most robust oversight ... [T] he Fordham law professor Chinmayi Sharma ... suggests ... establish [ing] a professional licensing program for engineers that would operate in a comparable method to medical licenses, malpractice fits, and the Hippocratic oath in medication. 'What if, like medical professionals,' she asks ..., 'AI engineers likewise pledged 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, which has actually stymied people for decades, exposes the restrictions of natural-language-processing algorithms", Scientific American, vol. 329, no. 4 (November 2023), pp. 81-82. "This murder secret competitors has revealed that although NLP (natural-language processing) models are capable of extraordinary accomplishments, their capabilities are very much restricted by the amount of context they get. This [...] could cause [problems] for researchers who want to utilize them to do things such as analyze ancient languages. Sometimes, there are couple of historical records on long-gone civilizations to work as training data for such a function." (p. 82.). Immerwahr, Daniel, "Your Lying Eyes: People now use A.I. to create phony videos indistinguishable from real ones. Just how much does it matter?", The New Yorker, 20 November 2023, pp. 54-59. "If by 'deepfakes' we mean reasonable videos produced utilizing expert system that actually deceive people, then they barely exist. The fakes aren't deep, and the deeps aren't fake. [...] A.I.-generated videos are not, in general, running in our media as counterfeited proof. Their function much better resembles that of animations, specifically smutty ones." (p. 59.). - Leffer, Lauren, "The Risks of Trusting AI: We need to avoid humanizing machine-learning models utilized in clinical research study", Scientific American, vol. 330, no. 6 (June 2024), pp. 80-81. Lepore, Jill, "The Chit-Chatbot: Is talking with a machine a discussion?", The New Yorker, 7 October 2024, pp. 12-16. Marcus, Gary, "Artificial Confidence: Even the newest, buzziest systems of synthetic basic intelligence are stymmied by the usual issues", Scientific American, vol. 327, no. 4 (October 2022), pp. 42-45. McCarthy, John (October 2007), "From here to human-level AI", Expert System, 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 Expert system, provided and distributed at the 2007 Singularity Summit, San Francisco, California. Press, Eyal, "In Front of Their Faces: Does facial-recognition technology lead police to neglect inconsistent proof?", The New Yorker, 20 November 2023, pp. 20-26. Roivainen, Eka, "AI's IQ: ChatGPT aced a [basic intelligence] test however revealed that intelligence can not be measured by IQ alone", Scientific American, vol. 329, no. 1 (July/August 2023), p. 7. "Despite its high IQ, ChatGPT stops working at jobs that need real humanlike thinking or an understanding of the physical and social world ... ChatGPT seemed unable to reason realistically and tried to depend on its vast database of ... realities originated from online texts. " - Scharre, Paul, "Killer Apps: The Real Dangers of an AI Arms Race", Foreign Affairs, vol. 98, no. 3 (May/June 2019), pp. 135-44. "Today's AI technologies are effective however undependable. Rules-based systems can not handle circumstances their programmers did not expect. Learning systems are restricted by the data on which they were trained. AI failures have actually currently led to tragedy. Advanced auto-pilot functions in cars, although they perform well in some circumstances, have driven automobiles without warning into trucks, concrete barriers, and parked vehicles. In the incorrect circumstance, AI systems go from supersmart to superdumb in an immediate. When an opponent 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 innovations but depend on the timelelss human tendency to anthropomorphise." (p. 29.). Williams, R. W.; Herrup, K.