Artificial general intelligence

Short description: Hypothetical human-level or stronger AI

Artificial intelligence

Major goals

* Knowledge reasoning
* Planning
* Machine learning
* Natural language processing
* Computer vision
* Robotics
* Artificial general intelligence

Approaches

* Symbolic
* Deep learning
* Bayesian networks
* Evolutionary algorithms

Philosophy

* Ethics
* Existential risk
* Turing test
* Chinese room
* Control problem
* Friendly AI

History

* Timeline
* Progress
* AI winter

Technology

* Applications
* Projects
* Programming languages

Glossary

* Glossary

* v
* t
* e

An artificial general intelligence (AGI) is a hypothetical type of intelligent agent[1] which, if realized, could learn to accomplish any intellectual task that human beings or animals can perform.[2][3] Alternatively, AGI has been defined as an autonomous system that surpasses human capabilities in the majority of economically valuable tasks.[4][dubious - discuss]Creating AGI is a primary goal of some artificial intelligence research and of companies such as OpenAI,[5] DeepMind, and Anthropic. AGI is a common topic in science fiction and futures studies.

The timeline for AGI development remains a subject of ongoing debate among researchers and experts. As of 2023[update], some[who?] argue that it may be possible in years or decades; others maintain it might take a century or longer; and a minority believe it may never be achieved.[6] There is debate on the exact definition of AGI, and regarding whether modern large language models (LLMs) such as GPT-4 are early, incomplete forms of AGI.[7]

Contention exists over the potential for AGI to pose a threat to humanity;[1] for example, OpenAI treats it as an existential risk, while others find the development of AGI to be too remote to present a risk.[8][6][9]

A 2020 survey identified 72 active AGI R&D projects spread across 37 countries.[10]

## Contents

* 1 Terminology
* 2 Characteristics
* 2.1 Intelligence traits
* 2.2 Physical traits
* 2.3 Tests for human-level AGI
* 2.4 AI-complete problems
* 3 History
* 3.1 Classical AI
* 3.2 Narrow AI research
* 3.3 Modern artificial general intelligence research
* 3.4 Feasibility
* 3.5 Timescales
* 4 Brain simulation
* 4.1 Whole brain emulation
* 4.2 Early estimates
* 4.3 Current research
* 4.4 Criticisms of simulation-based approaches
* 5 Philosophical perspective
* 5.1 "Strong AI" as defined in philosophy
* 5.2 Consciousness, self-awareness, sentience
* 6 Research challenges
* 7 Benefits
* 8 Risks
* 8.1 Existential risks
* 8.1.1 Risk of loss of control and human extinction
* 8.2 Mass unemployment
* 9 See also
* 10 Notes
* 11 References
* 12 Sources
* 13 Further reading
* 14 External links

## Terminology

AGI is also known as strong AI,[11][12] full AI,[13] human-level AI[6] or general intelligent action.[14] However, some academic sources reserve the term "strong AI" for computer programs that experience sentience or consciousness.[lower-alpha 1] In contrast, weak AI (or narrow AI) is able to solve one specific problem, but lacks general cognitive abilities.[15][12] Some academic sources use "weak AI" to refer more broadly to any programs that neither experience consciousness nor have a mind in the same sense as humans.[lower-alpha 1]

Related concepts include artificial superintelligence and transformative AI. An artificial superintelligence (ASI) is a hypothetical type of AGI that is much more generally intelligent than humans.[16] And the notion of transformative AI relates to AI having a large impact on society, for example similar to the agricultural revolution.[17]

## Characteristics

Main article: Artificial intelligence

Various criteria for intelligence have been proposed (most famously the Turing test) but no definition is broadly accepted.[lower-alpha 2]

### Intelligence traits

However, researchers generally hold that intelligence is required to do all of the following:[19]

* reason, use strategy, solve puzzles, and make judgments under uncertainty
* represent knowledge, including common sense knowledge
* plan
* learn
* communicate in natural language
* if necessary, integrate these skills in completion of any given goal

Many interdisciplinary approaches (e.g. cognitive science, computational intelligence, and decision making) consider additional traits such as imagination (the ability to form novel mental images and concepts)[20] and autonomy.[21]

Computer-based systems that exhibit many of these capabilities exist (e.g. see computational creativity, automated reasoning, decision support system, robot, evolutionary computation, intelligent agent). However, no consensus holds that modern AI systems possess them to an adequate degree.

### Physical traits

Other capabilities are considered desirable in intelligent systems, as they may affect intelligence or aid in its expression. These include:[22]

* the ability to sense (e.g. see, hear, etc.), and
* the ability to act (e.g. move and manipulate objects, change location to explore, etc.)

This includes the ability to detect and respond to hazard.[23]

### Tests for human-level AGI

Several tests meant to confirm human-level AGI have been considered, including:[24][25]

The Turing Test (Turing)
A machine and a human both converse unseen with a second human, who must evaluate which of the two is the machine, which passes the test if it can fool the evaluator a significant fraction of the time. Note: Turing does not prescribe what should qualify as intelligence, only that knowing that it is a machine should disqualify it. The AI Eugene Goostman achieved Turing's estimate of convincing 30% of judges that it was human in 2014.
The Robot College Student Test (Goertzel)
A machine enrolls in a university, taking and passing the same classes that humans would, and obtaining a degree. LLMs can now pass university degree-level exams without even attending the classes.[26]
The Employment Test (Nilsson)
A machine performs an economically important job at least as well as humans in the same job. AIs are now replacing humans in many roles as varied as fast food and marketing.[27]
The Ikea test (Marcus)
Also known as the Flat Pack Furniture Test. An AI views the parts and instructions of an Ikea flat-pack product, then controls a robot to assemble the furniture correctly.
The Coffee Test (Wozniak)
A machine is required to enter an average American home and figure out how to make coffee: find the coffee machine, find the coffee, add water, find a mug, and brew the coffee by pushing the proper buttons. This has not yet been completed.

### AI-complete problems

Main article: AI-complete

There are many problems that may require general intelligence to solve the problems as well as humans do. For example, even specific straightforward tasks, like machine translation, require that a machine read and write in both languages (NLP), follow the author's argument (reason), know what is being talked about (knowledge), and faithfully reproduce the author's original intent (social intelligence). All of these problems need to be solved simultaneously in order to reach human-level machine performance.

A problem is informally called "AI-complete" or "AI-hard" if it is believed that to solve it one would need to implement strong AI, because the solution is beyond the capabilities of a purpose-specific algorithm.[28]

AI-complete problems are hypothesised to include general computer vision, natural language understanding, and dealing with unexpected circumstances while solving any real-world problem.[29]

AI-complete problems cannot be solved with current computer technology alone, and require human computation. This limitation could be useful to test for the presence of humans, as CAPTCHAs aim to do; and for computer security to repel brute-force attacks.[30][31]

## History

### Classical AI

Main articles: History of artificial intelligence and Symbolic artificial intelligence

Modern AI research began in the mid-1950s.[32] The first generation of AI researchers were convinced that artificial general intelligence was possible and that it would exist in just a few decades.[33] AI pioneer Herbert A. Simon wrote in 1965: "machines will be capable, within twenty years, of doing any work a man can do."[34]

Their predictions were the inspiration for Stanley Kubrick and Arthur C. Clarke's character HAL 9000, who embodied what AI researchers believed they could create by the year 2001. AI pioneer Marvin Minsky was a consultant[35] on the project of making HAL 9000 as realistic as possible according to the consensus predictions of the time. He said in 1967, "Within a generation... the problem of creating 'artificial intelligence' will substantially be solved".[36]

Several classical AI projects, such as Doug Lenat's Cyc project (that began in 1984), and Allen Newell's Soar project, were directed at AGI.

However, in the early 1970s, it became obvious that researchers had grossly underestimated the difficulty of the project. Funding agencies became skeptical of AGI and put researchers under increasing pressure to produce useful "applied AI".[lower-alpha 3] In the early 1980s, Japan's Fifth Generation Computer Project revived interest in AGI, setting out a ten-year timeline that included AGI goals like "carry on a casual conversation".[40] In response to this and the success of expert systems, both industry and government pumped money into the field.[38][41] However, confidence in AI spectacularly collapsed in the late 1980s, and the goals of the Fifth Generation Computer Project were never fulfilled.[42] For the second time in 20 years, AI researchers who predicted the imminent achievement of AGI had been mistaken. By the 1990s, AI researchers had a reputation for making vain promises. They became reluctant to make predictions at all[lower-alpha 4] and avoided mention of "human level" artificial intelligence for fear of being labeled "wild-eyed dreamer[s]".[44]

### Narrow AI research

Main article: Artificial intelligence

In the 1990s and early 21st century, mainstream AI achieved commercial success and academic respectability by focusing on specific sub-problems where AI can produce verifiable results and commercial applications, such as speech recognition and recommendation algorithms.[45] These "applied AI" systems are now used extensively throughout the technology industry, and research in this vein is heavily funded in both academia and industry. As of 2018[update], development in this field was considered an emerging trend, and a mature stage was expected to be reached in more than 10 years.[46]

At the turn of the century, many mainstream AI researchers[47] hoped that strong AI could be developed by combining programs that solve various sub-problems. Hans Moravec wrote in 1988:

> I am confident that this bottom-up route to artificial intelligence will one day meet the traditional top-down route more than half way, ready to provide the real-world competence and the commonsense knowledge that has been so frustratingly elusive in reasoning programs. Fully intelligent machines will result when the metaphorical golden spike is driven uniting the two efforts.[47]

However, even at the time, this was disputed. For example, Stevan Harnad of Princeton University concluded his 1990 paper on the Symbol Grounding Hypothesis by stating:

> The expectation has often been voiced that "top-down" (symbolic) approaches to modeling cognition will somehow meet "bottom-up" (sensory) approaches somewhere in between. If the grounding considerations in this paper are valid, then this expectation is hopelessly modular and there is really only one viable route from sense to symbols: from the ground up. A free-floating symbolic level like the software level of a computer will never be reached by this route (or vice versa) – nor is it clear why we should even try to reach such a level, since it looks as if getting there would just amount to uprooting our symbols from their intrinsic meanings (thereby merely reducing ourselves to the functional equivalent of a programmable computer).[48]

### Modern artificial general intelligence research

The term "artificial general intelligence" was used as early as 1997, by Mark Gubrud[49] in a discussion of the implications of fully automated military production and operations. A mathematical formalism of AGI was proposed by Marcus Hutter in 2000. Named AIXI, the proposed AGI agent maximises “the ability to satisfy goals in a wide range of environments”.[50] This type of AGI, characterized by the ability to maximise a mathematical definition of intelligence rather than exhibit human-like behaviour,[51] was also called universal artificial intelligence.[52]

The term AGI was re-introduced and popularized by Shane Legg and Ben Goertzel around 2002.[53] AGI research activity in 2006 was described by Pei Wang and Ben Goertzel[54] as "producing publications and preliminary results". The first summer school in AGI was organized in Xiamen, China in 2009[55] by the Xiamen university's Artificial Brain Laboratory and OpenCog. The first university course was given in 2010[56] and 2011[57] at Plovdiv University, Bulgaria by Todor Arnaudov. MIT presented a course on AGI in 2018, organized by Lex Fridman and featuring a number of guest lecturers.

As of 2023[update], a small number of computer scientists are active in AGI research, and many contribute to a series of AGI conferences. However, increasingly more researchers are interested in open-ended learning,[3][58] which is the idea of allowing AI to continuously learn and innovate like humans do. Although most open-ended learning works are still done on Minecraft,[19][22][50] its application can be extended to robotics and the sciences.

### Feasibility

As of 2023[update], complete forms of AGI remain speculative.[59][60] No system that meets the generally agreed upon criteria for AGI has yet been demonstrated. Opinions vary both on whether and when artificial general intelligence will arrive. 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 because it would require "unforeseeable and fundamentally unpredictable breakthroughs" and a "scientifically deep understanding of cognition".[61] Writing in The Guardian , roboticist Alan Winfield claimed the gulf between modern computing and human-level artificial intelligence is as wide as the gulf between current space flight and practical faster-than-light spaceflight.[62]

Most AI researchers believe strong AI can be achieved in the future, but some thinkers, like Hubert Dreyfus and Roger Penrose, deny the possibility of achieving strong AI.[63][64] John McCarthy is among those who believe human-level AI will be accomplished, but that the present level of progress is such that a date cannot accurately be predicted.[65] AI experts' views on the feasibility of AGI wax and wane. Four polls conducted in 2012 and 2013 suggested that the median estimate among experts for when they would be 50% confident AGI would arrive was 2040 to 2050, depending on the poll, with the mean being 2081. Of the experts, 16.5% answered with "never" when asked the same question but with a 90% confidence instead.[66][67] Further current AGI progress considerations 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 time frame there is a strong bias towards predicting the arrival of human-level AI as between 15 and 25 years from the time the prediction was made". They analyzed 95 predictions made between 1950 and 2012 on when human-level AI will come about.[68]

In 2023, Microsoft researchers published a detailed evaluation of GPT-4. They concluded: "Given the breadth and depth of GPT-4’s capabilities, we believe that it could reasonably be viewed as an early (yet still incomplete) version of an artificial general intelligence (AGI) system."[69] Another study in 2023 reported that GPT-4 outperforms 99% of humans on the Torrance tests of creative thinking.[70][71]

### Timescales

In the introduction to his 2006 book,[72] Goertzel says that estimates of the time needed before a truly flexible AGI is built vary from 10 years to over a century. As of 2007[update], the consensus in the AGI research community seemed to be that the timeline discussed by Ray Kurzweil in 2005 in The Singularity is Near[73] (i.e. between 2015 and 2045) was plausible.[74] Mainstream AI researchers have given a wide range of opinions on whether progress will be this rapid. A 2012 meta-analysis of 95 such opinions found a bias towards predicting that the onset of AGI would occur within 16–26 years for modern and historical predictions alike. That paper has been criticized for how it categorized opinions as expert or non-expert.[75]

In 2012, Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton developed a neural network called AlexNet, which won the ImageNet competition with a top-5 test error rate of 15.3%, significantly better than the second-best entry's rate of 26.3% (the traditional approach used a weighted sum of scores from different pre-defined classifiers).[76] AlexNet was regarded as the initial ground-breaker of the current deep learning wave.[76]

In 2017, researchers Feng Liu, Yong Shi, and Ying Liu conducted intelligence tests on publicly available and freely accessible weak AI such as Google AI, Apple's Siri, and others. At the maximum, these AIs reached an IQ value of about 47, which corresponds approximately to a six-year-old child in first grade. An adult comes to about 100 on average. Similar tests were carried out in 2014, with the IQ score reaching a maximum value of 27.[77][78]

In 2020, OpenAI developed GPT-3, a language model capable of performing many diverse tasks without specific training. According to Gary Grossman in a VentureBeat article, while there is consensus that GPT-3 is not an example of AGI, it is considered by some to be too advanced to be classified as a narrow AI system.[79]

In the same year, Jason Rohrer used his GPT-3 account to develop a chatbot, and provided a chatbot-developing platform called "Project December". OpenAI asked for changes to the chatbot to comply with their safety guidelines; Rohrer disconnected Project December from the GPT-3 API.[80]

In 2022, DeepMind developed Gato, a "general-purpose" system capable of performing more than 600 different tasks.[81]

In 2023, Microsoft Research published a study on an early version of OpenAI's GPT-4, contending that it exhibited more general intelligence than previous AI models and demonstrated human-level performance in tasks spanning multiple domains, such as mathematics, coding, and law. This research sparked a debate on whether GPT-4 could be considered an early, incomplete version of artificial general intelligence, emphasizing the need for further exploration and evaluation of such systems.[82]

In 2023, the AI researcher Geoffrey Hinton stated that:[83]

> The idea that this stuff could actually get smarter than people – a few people believed that, [...]. But most people thought it was way off. And I thought it was way off. I thought it was 30 to 50 years or even longer away. Obviously, I no longer think that.

## Brain simulation

### Whole brain emulation

Main article: Philosophy:Mind uploading

One possible approach to achieving AGI is whole brain emulation: A brain model is built by scanning and mapping a biological brain in detail, and then copying and simulating it on a computer system or another computational device. The simulation model must be sufficiently faithful to the original, so that it behaves in practically the same way as the original brain.[84] Whole brain emulation is discussed in computational neuroscience and neuroinformatics, in the context of brain simulation for medical research purposes. It has been discussed in artificial intelligence research[74] as an approach to strong AI. Neuroimaging technologies that could deliver the necessary detailed understanding are improving rapidly, and futurist Ray Kurzweil in the book The Singularity Is Near[73] predicts that a map of sufficient quality will become available on a similar timescale to the computing power required to emulate it.

### Early estimates

Estimates of how much processing power is needed to emulate a human brain at various levels (from Ray Kurzweil, Anders Sandberg and Nick Bostrom), along with the fastest supercomputer from TOP500 mapped by year. Note the logarithmic scale and exponential trendline, which assumes the computational capacity doubles every 1.1 years. Kurzweil believes that mind uploading will be possible at neural simulation, while the Sandberg, Bostrom report is less certain about where consciousness arises.[85]

For low-level brain simulation, a very powerful cluster of computers or GPUs would be required, given the enormous quantity of synapses within the human brain. Each of the 1011 (one hundred billion) neurons has on average 7,000 synaptic connections (synapses) to other neurons. The brain of a three-year-old child has about 1015 synapses (1 quadrillion). This number declines with age, stabilizing by adulthood. Estimates vary for an adult, ranging from 1014 to 5×1014 synapses (100 to 500 trillion).[86] An estimate of the brain's processing power, based on a simple switch model for neuron activity, is around 1014 (100 trillion) synaptic updates per second (SUPS).[87]

In 1997, Kurzweil looked at various estimates for the hardware required to equal the human brain and adopted a figure of 1016 computations per second (cps).[lower-alpha 5] (For comparison, if a "computation" was equivalent to one "floating-point operation" – a measure used to rate current supercomputers – then 1016 "computations" would be equivalent to 10 petaFLOPS, achieved in 2011, while 1018 was achieved in 2022.) He used this figure to predict the necessary hardware would be available sometime between 2015 and 2025, if the exponential growth in computer power at the time of writing continued.

### Current research

This book may need to be updated to reflect current knowledge.

|

Some research projects are investigating brain simulation using more sophisticated neural models, implemented on conventional computing architectures. The Artificial Intelligence System project implemented non-real time simulations of a "brain" (with 1011 neurons) in 2005. It took 50 days on a cluster of 27 processors to simulate 1 second of a model.[90] The Blue Brain project used one of the fastest supercomputer architectures, IBM's Blue Gene platform, to create a real time simulation of a single rat neocortical column consisting of approximately 10,000 neurons and 108 synapses in 2006.[91] A longer-term goal is to build a detailed, functional simulation of the physiological processes in the human brain: "It is not impossible to build a human brain and we can do it in 10 years," Henry Markram, director of the Blue Brain Project, said in 2009 at the TED conference in Oxford.[92] Neuro-silicon interfaces have been proposed as an alternative implementation strategy that may scale better.[93]

Hans Moravec addressed the above arguments ("brains are more complicated", "neurons have to be modeled in more detail") in his 1997 paper "When will computer hardware match the human brain?".[89] He measured the ability of existing software to simulate the functionality of neural tissue, specifically the retina. His results[specify] do not depend on the number of glial cells, nor on what kinds of processing neurons perform where.

The actual complexity of modeling biological neurons has been explored in OpenWorm project that aimed at complete simulation of a worm that has only 302 neurons in its neural network (among about 1000 cells in total). The animal's neural network was well documented before the start of the project. However, although the task seemed simple at the beginning, the models based on a generic neural network did not work. Currently,Template:Current event inline efforts focus on precise emulation of biological neurons (partly on the molecular level), but the result cannot yet be called a total success.

In 2023, researchers performed a high-resolution scan of a mouse brain.[94]

### Criticisms of simulation-based approaches

The artificial neuron model assumed by Kurzweil and used in many current artificial neural network implementations is simple compared with biological neurons. A brain simulation would likely have to capture the detailed cellular behaviour of biological neurons, presently understood only in broad outline. The overhead introduced by full modeling of the biological, chemical, and physical details of neural behaviour (especially on a molecular scale) would require computational powers several orders of magnitude larger than Kurzweil's estimate. In addition, the estimates do not account for glial cells, which are known to play a role in cognitive processes.[95]

A fundamental criticism of the simulated brain approach derives from embodied cognition theory which asserts that human embodiment is an essential aspect of human intelligence and is necessary to ground meaning.[96][94] If this theory is correct, any fully functional brain model will need to encompass more than just the neurons (e.g., a robotic body). Goertzel[74] proposes virtual embodiment (like in metaverses like Second Life) as an option, but it is unknown whether this would be sufficient.

## Philosophical perspective

### "Strong AI" as defined in philosophy

In 1980, philosopher John Searle coined the term "strong AI" as part of his Chinese room argument.[97] He wanted to distinguish between two different hypotheses about artificial intelligence:[lower-alpha 6]

* Strong AI hypothesis: An artificial intelligence system can have "a mind" and "consciousness".
* Weak AI hypothesis: An artificial intelligence system can (only) act like it thinks and has a mind and consciousness.

The first one he called "strong" because it makes a stronger statement: it assumes something special has happened to the machine that goes beyond those abilities that we can test. The behaviour of a "weak AI" machine would be precisely identical to a "strong AI" machine, but the latter would also have subjective conscious experience. This usage is also common in academic AI research and textbooks.[98]

In contrast to Searle and mainstream AI, some futurists such as Ray Kurzweil use the term "strong AI" to mean "human level artificial general intelligence".[73] This is not the same as Searle's strong AI, unless it is assumed that consciousness is necessary for human-level AGI. Academic philosophers such as Searle do not believe that is the case, and to most artificial intelligence researchers the question is out-of-scope.[99]

Mainstream AI is most interested in how a program behaves.[100] According to Russell and Norvig, "as long as the program works, they don't care if you call it real or a simulation."[99] If the program can behave as if it has a mind, then there is no need to know if it actually has mind – indeed, there would be no way to tell. For AI research, Searle's "weak AI hypothesis" is equivalent to the statement "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."[99] Thus, for academic AI research, "Strong AI" and "AGI" are two different things.

### Consciousness, self-awareness, sentience

Other aspects of the human mind besides intelligence are relevant to the concept of AGI or "strong AI", and these play a major role in science fiction and the ethics of artificial intelligence:

* consciousness: To have subjective experience. Thomas Nagel explains that it "feels like" something to be conscious. If we are not conscious, then it doesn't feel like anything. Nagel uses the example of a bat: we can sensibly ask "what does it feel like to be a bat?" However, we are unlikely to ask "what does it feel like to be a toaster?" Nagel concludes that a bat appears to be conscious (i.e. has consciousness) but a toaster does not.[101]
* self-awareness: To have conscious awareness of oneself as a separate individual, especially to be consciously aware of one's own thoughts. This is opposed to simply being the "subject of one's thought" – an operating system or debugger is able to be "aware of itself" (that is, to represent itself in the same way it represents everything else) but this is not what people typically mean when they use the term "self-awareness".[lower-alpha 7]
* sentience: The ability to "feel" perceptions or emotions subjectively, as opposed to the ability to reason about perceptions or, in regard to emotions, to be aware that the situation requires urgency, kindness or aggression. For example, we can build a machine that knows which objects in its field of view are red, but this machine will not necessarily know what red looks like.

These traits have a moral dimension, because a machine with this form of "strong AI" may have rights, analogous to the rights of non-human animals. Preliminary work has been conducted on integrating strong AI with existing legal and social frameworks, focusing on the legal position and rights of 'strong' AI.[103]

It remains to be shown whether "artificial consciousness" is necessary for AGI. However, many AGI researchers regard research that investigates possibilities for implementing consciousness as vital.[104]

Bill Joy, among others, argues a machine with these traits may be a threat to human life or dignity.[105]

## Research challenges

See also: History of artificial intelligence § The problems, and History of artificial intelligence § Predictions (or "Where is HAL 9000?")

Progress in artificial intelligence has historically gone through periods of rapid progress separated by periods when progress appeared to stop.[63] Ending each hiatus were fundamental advances in hardware, software or both to create space for further progress.[63][106][107] For example, the computer hardware available in the twentieth century was not sufficient to implement deep learning, which requires large numbers of GPU-enabled CPUs.[108]

A further challenge is the lack of clarity in defining what intelligence entails. Does it require consciousness? Must it display the ability to set goals as well as pursue them? Is it purely a matter of scale such that if model sizes increase sufficiently, intelligence will emerge? Are facilities such as planning, reasoning, and causal understanding required? Does intelligence require explicitly replicating the brain and its specific faculties? Does it require emotions?[109]

## Benefits

AGI could have a wide variety of applications. If oriented towards such goals, AGI could help mitigate various problems in the world such as hunger, poverty and health problems.[110]

AGI could improve the productivity and efficiency in most jobs. For example, in public health, AGI could accelerate medical research, notably against cancer.[111] It could take care of the elderly,[112] and democratize access to rapid, high-quality medical diagnostics. It could offer fun, cheap and personalized education.[112] For virtually any job that benefits society if done well, it would probably sooner or later be preferable to leave it to an AGI. The need to work to subsist could become obsolete if the wealth produced is properly redistributed.[112][113] This also raises the question of the place of humans in a radically automated society.

AGI could also help to make rational decisions, and to anticipate and prevent disasters. It could also help to reap the benefits of potentially catastrophic technologies such as nanotechnology or climate engineering, while avoiding the associated risks.[114] If an AGI's primary goal is to prevent existential catastrophes such as human extinction (which could be difficult if the Vulnerable World Hypothesis turns out to be true),[115] it could take measures to drastically reduce the risks[114] while minimizing the impact of these measures on our quality of life.

## Risks

### Existential risks

Main articles: Unsolved:Existential risk from artificial general intelligence and AI safety

AGI may represent multiple types of existential risk, which are risks that threaten "the premature extinction of Earth-originating intelligent life or the permanent and drastic destruction of its potential for desirable future development".[116] The risk of human extinction from AGI has been the topic of many debates, but there is also the possibility that the development of AGI would lead to a permanently flawed future. Notably, it could be used to spread and preserve the set of values of whoever develops it. If humanity still has moral blind spots similar to slavery in the past, AGI might irreversibly entrench it, preventing moral progress.[117] Furthermore, AGI could facilitate mass surveillance and indoctrination, which could be used to create a stable repressive worldwide totalitarian regime.[118][119] There is also a risk for the machines themselves. If machines that are sentient or otherwise worthy of moral consideration are mass created in the future, engaging in a civilizational path that indefinitely neglects their welfare and interests could be an existential catastrophe.[120][121] Considering how much AGI could improve humanity's future and help reduce other existential risks, Toby Ord calls these existential risks "an argument for proceeding with due caution", not for "abandoning AI".[118]

#### Risk of loss of control and human extinction

The thesis that AI poses an existential risk for humans, and that this risk needs more attention, is controversial 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.[122][123]

In 2014, Stephen Hawking criticized widespread indifference:

“  | So, facing possible futures of incalculable benefits and risks, the experts are surely doing everything possible to ensure the best outcome, right? Wrong. If a superior alien civilisation sent us a message saying, 'We'll arrive in a few decades,' would we just reply, 'OK, call us when you get here—we'll leave the lights on?' Probably not—but this is more or less what is happening with AI.[124] | ”

|
|

The potential fate of humanity has sometimes been compared to the fate of gorillas threatened by human activities. The comparison states that greater intelligence allowed humanity to dominate gorillas, which are now vulnerable in ways that they could not have anticipated. As a result, the gorilla has become an endangered species, not out of malice, but simply as a collateral damage from human activities.[125]

The skeptic Yann LeCun considers that AGIs will have no desire to dominate humanity and that we should be careful not to anthropomorphize them and interpret their intents as we would for humans. He said that people won't be "smart enough to design super-intelligent machines, yet ridiculously stupid to the point of giving it moronic objectives with no safeguards".[126] On the other side, the concept of instrumental convergence suggests that almost whatever their goals, intelligent agents will have reasons to try to survive and acquire more power as intermediary steps to achieving these goals. And that this does not require having emotions.[127]

Many scholars who are concerned about existential risk advocate for more research into solving the "control problem" to answer the question: what types of safeguards, algorithms, or architectures can programmers implement to maximise the probability that their recursively-improving AI would continue to behave in a friendly, rather than destructive, manner after it reaches superintelligence?[128][129] Solving the control problem is complicated by the AI arms race (which could lead to a race to the bottom of safety precautions in order to release products before competitors),[130] and the use of AI in weapon systems.[131]

The thesis that AI can pose existential risk also has detractors. Skeptics usually say that AGI is unlikely in the short-term, or that concerns about AGI distract from other issues related to current AI.[132] Former Google fraud czar Shuman Ghosemajumder considers that for many people outside of the technology industry, existing chatbots and LLMs are already perceived as though they were AGI, leading to further misunderstanding and fear.[133]

Skeptics sometimes charge that the thesis is crypto-religious, with an irrational belief in the possibility of superintelligence replacing an irrational belief in an omnipotent God.[134] Some researchers believe that the communication campaigns on AI existential risk by certain AI groups (such as OpenAI, Anthropic, DeepMind, and Conjecture) may be an at attempt at regulatory capture and to inflate interest in their products.[135][136]

In 2023, the CEOs of Google DeepMind, OpenAI and Anthropic, along with other industry leaders and researchers, issued a joint statement asserting that "Mitigating the risk of extinction from AI should be a global priority alongside other societal-scale risks such as pandemics and nuclear war."[123]

### Mass unemployment

Further information: Engineering:Technological unemployment

Researchers from OpenAI estimated that "80% of the U.S. workforce could have at least 10% of their work tasks affected by the introduction of LLMs, while around 19% of workers may see at least 50% of their tasks impacted".[137][138] They consider office workers to be the most exposed, for example mathematicians, accountants or web designers.[138] AGI could have a better autonomy, ability to make decisions, to interface with other computer tools, but also to control robotized bodies.

According to Stephen Hawking, the outcome of automation on the quality of life will depend on how the wealth will be redistributed:[113]

“  | Everyone can enjoy a life of luxurious leisure if the machine-produced wealth is shared, or most people can end up miserably poor if the machine-owners successfully lobby against wealth redistribution. So far, the trend seems to be toward the second option, with technology driving ever-increasing inequality  | ”

|
|

Elon Musk considers that the automation of society will require governments to adopt a universal basic income.[139]

## See also

* Neural scaling law - Statistical law in machine learning
* Artificial brain
* AI effect
* AI safety - Artificial intelligence field of study
* AI alignment - AI conformance to the intended objective
* A.I. Rising - 2018 film directed by Lazar Bodroža
* Automated machine learning - Process of automating the application of machine learning
* BRAIN Initiative
* China Brain Project
* Future of Humanity Institute
* General game playing - Learning to play multiple games successfully
* Generative artificial intelligence - Subset of AI using generative models
* Human Brain Project - Scientific research project
* Intelligence amplification - Use of information technology to augment human intelligence (IA)
* Machine ethics - Moral behaviours of man-made machines
* Multi-task learning - Solving multiple machine learning tasks at the same time
* Outline of artificial intelligence - Overview of and topical guide to artificial intelligence
* Transhumanism - Philosophical movement
* Synthetic intelligence - Alternate term for or form of artificial intelligence
* Transfer learning - Machine learning technique
* Loebner Prize
* Hardware for artificial intelligence - Hardware specially designed and optimized for artificial intelligence
* Weak artificial intelligence - Form of artificial intelligence

## Notes

1. ↑ 1.0 1.1 See below for the origin of the term "strong AI", and see the academic definition of "strong AI" and weak AI in the article Chinese room.
2. ↑ AI founder John McCarthy writes: "we cannot yet characterize in general what kinds of computational procedures we want to call intelligent."[18] (For a discussion of some definitions of intelligence used by artificial intelligence researchers, see philosophy of artificial intelligence.)
3. ↑ The Lighthill report specifically criticized AI's "grandiose objectives" and led the dismantling of AI research in England.[37] In the U.S., DARPA became determined to fund only "mission-oriented direct research, rather than basic undirected research".[38][39]
4. ↑ As AI founder John McCarthy writes "it would be a great relief to the rest of the workers in AI if the inventors of new general formalisms would express their hopes in a more guarded form than has sometimes been the case."[43]
5. ↑ In "Mind Children"[88] 1015 cps is used. More recently, in 1997,[89] Moravec argued for 108 MIPS which would roughly correspond to 1014 cps. Moravec talks in terms of MIPS, not "cps", which is a non-standard term Kurzweil introduced.
6. ↑ As defined in a standard AI textbook: "The assertion that machines could possibly act intelligently (or, perhaps better, act as if they were intelligent) is called the 'weak AI' hypothesis by philosophers, and the assertion that machines that do so are actually thinking (as opposed to simulating thinking) is called the 'strong AI' hypothesis."[87]
7. ↑ Alan Turing made this point in 1950.[102]

## References

1. ↑ 1.0 1.1 Morozov, Evgeny (June 30, 2023). "The True Threat of Artificial Intelligence". The New York Times. Archived from the original on June 30, 2023. https://archive.today/20230630093626/https://www.nytimes.com/2023/06/30/opinion/artificial-intelligence-danger.html. Retrieved June 30, 2023.
2. ↑ Hodson, Hal (1 March 2019). "DeepMind and Google: the battle to control artificial intelligence". 1843. https://www.economist.com/1843/2019/03/01/deepmind-and-google-the-battle-to-control-artificial-intelligence. "AGI stands for Artificial General Intelligence, a hypothetical computer program..."
3. ↑ 3.0 3.1 Shevlin, Henry; Vold, Karina; Crosby, Matthew; Halina, Marta (2019-10-04). "The limits of machine intelligence: Despite progress in machine intelligence, artificial general intelligence is still a major challenge" (in en). EMBO Reports 20 (10): e49177. doi:10.15252/embr.201949177. ISSN 1469-221X. PMID 31531926.
4. ↑ Sullivan, Mark (18 October 2023). "Why everyone seems to disagree on how to define Artificial General Intelligence". Fast Company. https://www.fastcompany.com/90968623/why-everyone-seems-to-disagree-on-how-to-define-artificial-general-intelligence.
5. ↑ "OpenAI Charter" (in en-US). https://openai.com/charter.
6. ↑ 6.0 6.1 6.2 "AI timelines: What do experts in artificial intelligence expect for the future?". https://ourworldindata.org/ai-timelines.
7. ↑ "Microsoft Researchers Claim GPT-4 Is Showing "Sparks" of AGI". https://futurism.com/gpt-4-sparks-of-agi.
8. ↑ "Impressed by artificial intelligence? Experts say AGI is coming next, and it has 'existential' risks" (in en-AU). ABC News. 2023-03-23. https://www.abc.net.au/news/2023-03-24/what-is-agi-artificial-general-intelligence-ai-experts-risks/102035132.
9. ↑ "Artificial general intelligence: Are we close, and does it even make sense to try?" (in en). https://www.technologyreview.com/2020/10/15/1010461/artificial-general-intelligence-robots-ai-agi-deepmind-google-openai/.
10. ↑ Baum, Seth, A Survey of Artificial General Intelligence Projects for Ethics, Risk, and Policy, Global Catastrophic Risk Institute Working Paper 20, https://gcrinstitute.org/papers/055_agi-2020.pdf, retrieved 13 January 2022
11. ↑ Kurzweil 2005, p. 260.
12. ↑ 12.0 12.1 Kurzweil, Ray (5 August 2005a), "Long Live AI", Forbes, archived from the original on 2005-08-14, https://web.archive.org/web/20050814000557/https://www.forbes.com/home/free_forbes/2005/0815/030.html : Kurzweil describes strong AI as "machine intelligence with the full range of human intelligence."
13. ↑ "The Age of Artificial Intelligence: George John at TEDxLondonBusinessSchool 2013". http://tedxtalks.ted.com/video/The-Age-of-Artificial-Intellige.
14. ↑ Newell & Simon 1976, This is the term they use for "human-level" intelligence in the physical symbol system hypothesis.
15. ↑ "The Open University on Strong and Weak AI". http://www.open2.net/nextbigthing/ai/ai_in_depth/in_depth.htm.
16. ↑ "What is artificial superintelligence (ASI)? | Definition from TechTarget" (in en). https://www.techtarget.com/searchenterpriseai/definition/artificial-superintelligence-ASI.
17. ↑ "Artificial intelligence is transforming our world – it is on all of us to make sure that it goes well". https://ourworldindata.org/ai-impact.
18. ↑ McCarthy, John (2007a). "Basic Questions". Stanford University. http://www-formal.stanford.edu/jmc/whatisai/node1.html.
19. ↑ 19.0 19.1 This list of intelligent traits is based on the topics covered by major AI textbooks, including: Russell & Norvig 2003, Luger & Stubblefield 2004, Poole, Mackworth & Goebel 1998 and Nilsson 1998.
20. ↑ Johnson 1987
21. ↑ de Charms, R. (1968). Personal causation. New York: Academic Press.
22. ↑ 22.0 22.1 Pfeifer, R. and Bongard J. C., How the body shapes the way we think: a new view of intelligence (The MIT Press, 2007). ISBN 0-262-16239-3
23. ↑ White, R. W. (1959). "Motivation reconsidered: The concept of competence". Psychological Review 66 (5): 297–333. doi:10.1037/h0040934. PMID 13844397.
24. ↑ Muehlhauser, Luke (11 August 2013). "What is AGI?". Machine Intelligence Research Institute. http://intelligence.org/2013/08/11/what-is-agi/.
25. ↑ "What is Artificial General Intelligence (AGI)? | 4 Tests For Ensuring Artificial General Intelligence" (in en-US). 13 July 2019. https://www.talkyblog.com/artificial_general_intelligence_agi/.
26. ↑ Varanasi, Lakshmi (21 March 2023). "AI models like ChatGPT and GPT-4 are acing everything from the bar exam to AP Biology. Here's a list of difficult exams both AI versions have passed.". https://www.businessinsider.com/list-here-are-the-exams-chatgpt-has-passed-so-far-2023-1.
27. ↑ Naysmith, Caleb (7 February 2023). "6 Jobs Artificial Intelligence Is Already Replacing and How Investors Can Capitalize on It". https://www.yahoo.com/now/6-jobs-artificial-intelligence-already-150339825.html.
28. ↑ Shapiro, Stuart C. (1992). "Artificial Intelligence". in Stuart C. Shapiro. Encyclopedia of Artificial Intelligence (Second ed.). New York: John Wiley. pp. 54–57. http://www.cse.buffalo.edu/~shapiro/Papers/ai.pdf. (Section 4 is on "AI-Complete Tasks".)
29. ↑ Yampolskiy, Roman V. (2012). Xin-She Yang. ed. "Turing Test as a Defining Feature of AI-Completeness". Artificial Intelligence, Evolutionary Computation and Metaheuristics (AIECM): 3–17. http://cecs.louisville.edu/ry/TuringTestasaDefiningFeature04270003.pdf.
30. ↑ Luis von Ahn, Manuel Blum, Nicholas Hopper, and John Langford. CAPTCHA: Using Hard AI Problems for Security . In Proceedings of Eurocrypt, Vol. 2656 (2003), pp. 294–311.
31. ↑ Bergmair, Richard (7 January 2006). "Natural Language Steganography and an "AI-complete" Security Primitive". CiteSeer.
32. ↑ Crevier 1993, pp. 48–50
33. ↑ Kaplan, Andreas (2022). "Artificial Intelligence, Business and Civilization – Our Fate Made in Machines". https://www.routledge.com/Artificial-Intelligence-Business-and-Civilization-Our-Fate-Made-in-Machines/Kaplan/p/book/9781032155319.
34. ↑ Simon 1965, p. 96 quoted in Crevier 1993, p. 109
35. ↑ "Scientist on the Set: An Interview with Marvin Minsky". http://mitpress.mit.edu/e-books/Hal/chap2/two1.html.
36. ↑ Marvin Minsky to (Darrach 1970), quoted in (Crevier 1993).
37. ↑ Lighthill 1973; Howe 1994
38. ↑ 38.0 38.1 NRC 1999, "Shift to Applied Research Increases Investment".
39. ↑ Crevier 1993, pp. 115–117; Russell & Norvig 2003, pp. 21–22.
40. ↑ Crevier 1993, p. 211, Russell & Norvig 2003, p. 24 and see also Feigenbaum & McCorduck 1983
41. ↑ Crevier 1993, pp. 161–162, 197–203, 240; Russell & Norvig 2003, p. 25.
42. ↑ Crevier 1993, pp. 209–212
43. ↑ McCarthy, John (2000). "Reply to Lighthill". Stanford University. http://www-formal.stanford.edu/jmc/reviews/lighthill/lighthill.html.
44. ↑ Markoff, John (14 October 2005). "Behind Artificial Intelligence, a Squadron of Bright Real People". The New York Times. https://www.nytimes.com/2005/10/14/technology/14artificial.html?ei=5070&en=11ab55edb7cead5e&ex=1185940800&adxnnl=1&adxnnlx=1185805173-o7WsfW7qaP0x5/NUs1cQCQ. "At its low point, some computer scientists and software engineers avoided the term artificial intelligence for fear of being viewed as wild-eyed dreamers."
45. ↑ Russell & Norvig 2003, pp. 25–26
46. ↑ "Trends in the Emerging Tech Hype Cycle". Gartner Reports. https://blogs.gartner.com/smarterwithgartner/files/2018/08/PR_490866_5_Trends_in_the_Emerging_Tech_Hype_Cycle_2018_Hype_Cycle.png.
47. ↑ 47.0 47.1 Moravec 1988, p. 20
48. ↑ Harnad, S. (1990). "The Symbol Grounding Problem". Physica D 42 (1–3): 335–346. doi:10.1016/0167-2789(90)90087-6. Bibcode: 1990PhyD...42..335H.
49. ↑ Gubrud 1997
50. ↑ 50.0 50.1 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 978-3-540-26877-2. https://link.springer.com/book/10.1007/b138233. Retrieved 19 July 2022.
51. ↑ Legg, Shane (2008). Machine Super Intelligence (PDF) (Thesis). University of Lugano. Archived (PDF) from the original on 15 June 2022. Retrieved 19 July 2022.
52. ↑ Goertzel, Ben (2014). Artificial General Intelligence. Lecture Notes in Computer Science. 8598. Journal of Artificial General Intelligence. doi:10.1007/978-3-319-09274-4. ISBN 978-3-319-09273-7. https://www.researchgate.net/publication/271390398.
53. ↑ "Who coined the term "AGI"?" (in en-US). http://goertzel.org/who-coined-the-term-agi/. , via Life 3.0: 'The term "AGI" was popularized by... Shane Legg, Mark Gubrud and Ben Goertzel'
54. ↑ Wang & Goertzel 2007
55. ↑ "First International Summer School in Artificial General Intelligence, Main summer school: June 22 – July 3, 2009, OpenCog Lab: July 6-9, 2009". https://goertzel.org/AGI_Summer_School_2009.htm.
56. ↑ "Избираеми дисциплини 2009/2010 – пролетен триместър" (in bg). http://fmi-plovdiv.org/index.jsp?id=1054&ln=1.
57. ↑ "Избираеми дисциплини 2010/2011 – зимен триместър" (in bg). http://fmi.uni-plovdiv.bg/index.jsp?id=1139&ln=1.
58. ↑ 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 (2023-03-27). "Sparks of Artificial General Intelligence: Early experiments with GPT-4". arXiv:2303.12712 [cs.CL].
59. ↑ Boucher, Philip (March 2019). ﻿How artificial intelligence works﻿ (Report). European Parliamentary Research Service. https://www.europarl.europa.eu/at-your-service/files/be-heard/religious-and-non-confessional-dialogue/events/en-20190319-how-artificial-intelligence-works.pdf. Retrieved March 3, 2020. "Today's AI is powerful and useful, but remains far from speculated AGI or ASI."
60. ↑ itu.int: Beyond Mad?: The Race For Artificial General Intelligence , "AGI represents a level of power that remains firmly in the realm of speculative fiction as on date." February 2, 2018, retrieved March 3, 2020
61. ↑ Allen, Paul; Greaves, Mark (October 12, 2011). "The Singularity Isn't Near". MIT Technology Review. http://www.technologyreview.com/view/425733/paul-allen-the-singularity-isnt-near/.
62. ↑ Winfield, Alan. "Artificial intelligence will not turn into a Frankenstein's monster". The Guardian. https://www.theguardian.com/technology/2014/aug/10/artificial-intelligence-will-not-become-a-frankensteins-monster-ian-winfield.
63. ↑ 63.0 63.1 63.2 Clocksin 2003.
64. ↑ Fjelland, Ragnar (2020-06-17). "Why general artificial intelligence will not be realized" (in en). Humanities and Social Sciences Communications 7 (1): 1–9. doi:10.1057/s41599-020-0494-4. ISSN 2662-9992.
65. ↑ McCarthy 2007.
66. ↑ Khatchadourian, Raffi (23 November 2015). "The Doomsday Invention: Will artificial intelligence bring us utopia or destruction?". The New Yorker. http://www.newyorker.com/magazine/2015/11/23/doomsday-invention-artificial-intelligence-nick-bostrom.
67. ↑ Müller, V. C., & Bostrom, N. (2016). Future progress in artificial intelligence: A survey of expert opinion. In Fundamental issues of artificial intelligence (pp. 555–572). Springer, Cham.
68. ↑ Armstrong, Stuart, and Kaj Sotala. 2012. “How We’re Predicting AI—or Failing To.” In Beyond AI: Artificial Dreams, edited by Jan Romportl, Pavel Ircing, Eva Zackova, Michal Polak, and Radek Schuster, 52–75. Pilsen: University of West Bohemia
69. ↑ "Microsoft Now Claims GPT-4 Shows 'Sparks' of General Intelligence". 24 March 2023. https://www.vice.com/en/article/g5ypex/microsoft-now-claims-gpt-4-shows-sparks-of-general-intelligence.
70. ↑ Shimek, Cary (2023-07-06). "AI Outperforms Humans in Creativity Test". https://neurosciencenews.com/ai-creativity-23585/.
71. ↑ Guzik, Erik E.; Byrge, Christian; Gilde, Christian (2023-12-01). "The originality of machines: AI takes the Torrance Test". Journal of Creativity 33 (3): 100065. doi:10.1016/j.yjoc.2023.100065. ISSN 2713-3745. https://www.sciencedirect.com/science/article/pii/S2713374523000249.
72. ↑ Goertzel & Pennachin 2006.
73. ↑ 73.0 73.1 73.2 (Kurzweil 2005)
74. ↑ 74.0 74.1 74.2 Goertzel 2007.
75. ↑ Grace, Katja (2016). "Error in Armstrong and Sotala 2012". https://aiimpacts.org/error-in-armstrong-and-sotala-2012/.
76. ↑ 76.0 76.1 Butz, Martin V. (2021-03-01). "Towards Strong AI" (in en). KI – Künstliche Intelligenz 35 (1): 91–101. doi:10.1007/s13218-021-00705-x. ISSN 1610-1987.
77. ↑ Liu, Feng; Shi, Yong; Liu, Ying (2017). "Intelligence Quotient and Intelligence Grade of Artificial Intelligence". Annals of Data Science 4 (2): 179–191. doi:10.1007/s40745-017-0109-0.
78. ↑ Brien, Jörn (2017-10-05). "Google-KI doppelt so schlau wie Siri" (in de). https://t3n.de/news/iq-kind-schlauer-google-ki-siri-864003.
79. ↑ Grossman, Gary (September 3, 2020). "We're entering the AI twilight zone between narrow and general AI". VentureBeat. https://venturebeat.com/2020/09/03/were-entering-the-ai-twilight-zone-between-narrow-and-general-ai/. ""Certainly, too, there are those who claim we are already seeing an early example of an AGI system in the recently announced GPT-3 natural language processing (NLP) neural network. ... So is GPT-3 the first example of an AGI system? This is debatable, but the consensus is that it is not AGI. ... If nothing else, GPT-3 tells us there is a middle ground between narrow and general AI.""
80. ↑ Quach, Katyanna. "A developer built an AI chatbot using GPT-3 that helped a man speak again to his late fiancée. OpenAI shut it down". The Register. https://www.theregister.com/2021/09/08/project_december_openai_gpt_3/.
81. ↑ Wiggers, Kyle (May 13, 2022), "DeepMind's new AI can perform over 600 tasks, from playing games to controlling robots", TechCrunch, https://techcrunch.com/2022/05/13/deepminds-new-ai-can-perform-over-600-tasks-from-playing-games-to-controlling-robots/, retrieved 12 June 2022
82. ↑ 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 experiments with GPT-4". arXiv:2303.12712 [cs.CL].
83. ↑ Metz, Cade (2023-05-01). "'The Godfather of A.I.' Leaves Google and Warns of Danger Ahead" (in en-US). The New York Times. ISSN 0362-4331. https://www.nytimes.com/2023/05/01/technology/ai-google-chatbot-engineer-quits-hinton.html.
84. ↑ Hickey, Alex. "Whole Brain Emulation: A Giant Step for Neuroscience" (in en-us). https://www.emergingtechbrew.com/stories/2019/08/15/whole-brain-emulation-giant-step-neuroscience.
85. ↑ Sandberg & Boström 2008.
86. ↑ Drachman 2005.
87. ↑ 87.0 87.1 Russell & Norvig 2003.
88. ↑ Moravec 1988, p. 61.
89. ↑ 89.0 89.1 Moravec 1998.
90. ↑ Izhikevich, Eugene M.; Edelman, Gerald M. (4 March 2008). "Large-scale model of mammalian thalamocortical systems". PNAS 105 (9): 3593–3598. doi:10.1073/pnas.0712231105. PMID 18292226. PMC 2265160. Bibcode: 2008PNAS..105.3593I. http://vesicle.nsi.edu/users/izhikevich/publications/large-scale_model_of_human_brain.pdf. Retrieved 23 June 2015.
91. ↑ "Project Milestones". Blue Brain. http://bluebrain.epfl.ch/Jahia/site/bluebrain/op/edit/pid/19085.
92. ↑ "Artificial brain '10 years away'". BBC News. 22 July 2009. http://news.bbc.co.uk/1/hi/technology/8164060.stm.
93. ↑ University of Calgary news , NBC News news
94. ↑ 94.0 94.1 Thornton, Angela (2023-06-26). "How uploading our minds to a computer might become possible" (in en-US). http://theconversation.com/how-uploading-our-minds-to-a-computer-might-become-possible-206804.
95. ↑ Swaminathan, Nikhil (Jan–Feb 2011). "Glia—the other brain cells". Discover. http://discovermagazine.com/2011/jan-feb/62. Retrieved 24 January 2014.
96. ↑ de Vega, Glenberg & Graesser 2008. A wide range of views in current research, all of which require grounding to some degree
97. ↑ Searle 1980
98. ↑ For example:
* Russell & Norvig 2003,
* Oxford University Press Dictionary of Psychology (quoted in " Encyclopedia.com"),
* MIT Encyclopedia of Cognitive Science (quoted in "AITopics"),
* Will Biological Computers Enable Artificially Intelligent Machines to Become Persons? Anthony Tongen
99. ↑ 99.0 99.1 99.2 Russell & Norvig 2003, p. 947.
100. ↑ though see Explainable artificial intelligence for curiosity by the field about why a program behaves the way it does
101. ↑ Nagel 1974.
102. ↑ Turing 1950.
103. ↑ Sotala, Kaj; Yampolskiy, Roman V. (2014-12-19). "Responses to catastrophic AGI risk: a survey". Physica Scripta 90 (1): 018001. doi:10.1088/0031-8949/90/1/018001. ISSN 0031-8949.
104. ↑ Yudkowsky 2006.
105. ↑ Joy, Bill (April 2000). "Why the future doesn't need us". Wired.
106. ↑ 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. doi:10.1109/TCDS.2021.3069871. ISSN 2379-8920. https://ieeexplore.ieee.org/document/9390376. Retrieved 28 August 2022.
107. ↑ Pei, Jing; Deng, Lei; Song, Sen; Zhao, Mingguo; Zhang, Youhui; Wu, Shuang; Wang, Guanrui; Zou, Zhe et al. (2019). "Towards artificial general intelligence with hybrid Tianjic chip architecture" (in en). Nature 572 (7767): 106–111. doi:10.1038/s41586-019-1424-8. ISSN 1476-4687. PMID 31367028. Bibcode: 2019Natur.572..106P. https://www.nature.com/articles/s41586-019-1424-8. Retrieved 29 August 2022.
108. ↑ 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" (in en). Nature Machine Intelligence 4 (3): 211–221. doi:10.1038/s42256-022-00463-x. ISSN 2522-5839.
109. ↑ 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. http://dx.doi.org/10.1162/artl_a_00368.
110. ↑ "Artificial General Intelligence – Do[es the cost outweigh benefits?"] (in en-US). 2021-08-23. https://coe-dsai.nasscom.in/artificial-general-intelligence-do-the-cost-outweigh-benefits/.
111. ↑ "How we can Benefit from Advancing Artificial General Intelligence (AGI) – Unite.AI". https://www.unite.ai/artificial-general-intelligence-agi/.
112. ↑ 112.0 112.1 112.2 Talty, Jules; Julien, Stephan. "What Will Our Society Look Like When Artificial Intelligence Is Everywhere?" (in en-us). https://www.smithsonianmag.com/innovation/artificial-intelligence-future-scenarios-180968403/.
113. ↑ 113.0 113.1 Stevenson, Matt (2015-10-08). "Answers to Stephen Hawking's AMA are Here!" (in en-US). Wired. ISSN 1059-1028. https://www.wired.com/brandlab/2015/10/stephen-hawkings-ama/. Retrieved 2023-06-08.
114. ↑ 114.0 114.1 Bostrom, Nick (2017). "§ Preferred order of arrival" (in en). Superintelligence: paths, dangers, strategies (Reprinted with corrections 2017 ed.). Oxford, United Kingdom; New York, New York, USA: Oxford University Press. ISBN 978-0-19-967811-2.
115. ↑ Piper, Kelsey (2018-11-19). "How technological progress is making it likelier than ever that humans will destroy ourselves" (in en). https://www.vox.com/future-perfect/2018/11/19/18097663/nick-bostrom-vulnerable-world-global-catastrophic-risks.
116. ↑ Doherty, Ben (2018-05-17). "Climate change an 'existential security risk' to Australia, Senate inquiry says" (in en-GB). The Guardian. ISSN 0261-3077. https://www.theguardian.com/environment/2018/may/18/climate-change-an-existential-security-risk-to-australia-senate-inquiry-says.
117. ↑ MacAskill, William (2022). What we owe the future. New York, NY: Basic Books. ISBN 978-1-5416-1862-6.
118. ↑ 118.0 118.1 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.
119. ↑ Al-Sibai, Noor (13 February 2022). "OpenAI Chief Scientist Says Advanced AI May Already Be Conscious". https://futurism.com/the-byte/openai-already-sentient.
120. ↑ Samuelsson, Paul Conrad (2019). "Artificial Consciousness: Our Greatest Ethical Challenge". https://philosophynow.org/issues/132/Artificial_Consciousness_Our_Greatest_Ethical_Challenge.
121. ↑ Kateman, Brian (2023-07-24). "AI Should Be Terrified of Humans" (in en). https://time.com/6296234/ai-should-be-terrified-of-humans/.
122. ↑ Roose, Kevin (2023-05-30). "A.I. Poses ‘Risk of Extinction,’ Industry Leaders Warn" (in en-US). The New York Times. ISSN 0362-4331. https://www.nytimes.com/2023/05/30/technology/ai-threat-warning.html.
123. ↑ 123.0 123.1 "Statement on AI Risk". May 30, 2023. https://www.safe.ai/statement-on-ai-risk.
124. ↑ "Stephen Hawking: 'Transcendence looks at the implications of artificial intelligence – but are we taking AI seriously enough?'". The Independent (UK). https://www.independent.co.uk/news/science/stephen-hawking-transcendence-looks-at-the-implications-of-artificial-intelligence--but-are-we-taking-ai-seriously-enough-9313474.html.
125. ↑ Herger, Mario. "The Gorilla Problem – Enterprise Garage" (in en-US). https://www.enterprisegarage.io/2019/10/the-gorilla-problem/.
126. ↑ "The fascinating Facebook debate between Yann LeCun, Stuart Russel and Yoshua Bengio about the risks of strong AI" (in fr). https://www.parlonsfutur.com/blog/the-fascinating-facebook-debate-between-yann-lecun-stuart-russel-and-yoshua.
127. ↑ "Will Artificial Intelligence Doom The Human Race Within The Next 100 Years?" (in en). 2014-08-22. https://www.huffpost.com/entry/artificial-intelligence-oxford_n_5689858.
128. ↑ Sotala, Kaj; Yampolskiy, Roman V. (2014-12-19). "Responses to catastrophic AGI risk: a survey". Physica Scripta 90 (1): 018001. doi:10.1088/0031-8949/90/1/018001. ISSN 0031-8949.
129. ↑ Bostrom, Nick (2014). Superintelligence: Paths, Dangers, Strategies (First ed.). Oxford University Press. ISBN 978-0199678112.
130. ↑ Chow, Andrew R.; Perrigo, Billy (2023-02-16). "The AI Arms Race Is On. Start Worrying" (in en). https://time.com/6255952/ai-impact-chatgpt-microsoft-google/.
131. ↑ Tetlow, Gemma (January 12, 2017). "AI arms race risks spiralling out of control, report warns". https://www.ft.com/content/b56d57e8-d822-11e6-944b-e7eb37a6aa8e.
132. ↑ Milmo, Dan; Stacey, Kiran (2023-09-25). "Experts disagree over threat posed but artificial intelligence cannot be ignored" (in en-GB). The Guardian. ISSN 0261-3077. https://www.theguardian.com/technology/2023/sep/25/experts-disagree-over-threat-posed-but-artificial-intelligence-cannot-be-ignored-ai.
133. ↑ "Humanity, Security & AI, Oh My! (with Ian Bremmer & Shuman Ghosemajumder)" (in en-US). 2023-07-20. https://cafe.com/stay-tuned/humanity-security-ai-oh-my-with-ian-bremmer-shuman-ghosemajumder/.
134. ↑ Hamblin, James (9 May 2014). "But What Would the End of Humanity Mean for Me?". The Atlantic. https://www.theatlantic.com/health/archive/2014/05/but-what-does-the-end-of-humanity-mean-for-me/361931/. Retrieved 12 December 2015.
135. ↑ "Big Tech is stoking fears over AI, warn scientists" (in en). https://www.telegraph.co.uk/business/2023/10/30/big-tech-stoking-fears-over-ai-warn-scientists/.
136. ↑ "Google Brain founder says big tech is lying about AI extinction danger" (in en). https://www.afr.com/technology/google-brain-founder-says-big-tech-is-lying-about-ai-human-extinction-danger-20231027-p5efnz.
137. ↑ "GPTs are GPTs: An early look at the labor market impact potential of large language models" (in en-US). https://openai.com/research/gpts-are-gpts.
138. ↑ 138.0 138.1 "80% of workers will be exposed to AI. These jobs will be most affected" (in en). 2023-03-23. https://www.euronews.com/next/2023/03/23/openai-says-80-of-workers-could-see-their-jobs-impacted-by-ai-these-are-the-jobs-most-affe.
139. ↑ Sheffey, Ayelet. "Elon Musk says we need universal basic income because 'in the future, physical work will be a choice'" (in en-US). https://www.businessinsider.com/elon-musk-universal-basic-income-physical-work-choice-2021-8.

## Sources

* UNESCO Science Report: the Race Against Time for Smarter Development.. Paris: UNESCO. 11 June 2021. ISBN 978-92-3-100450-6. https://unesdoc.unesco.org/ark:/48223/pf0000377433/PDF/377433eng.pdf.multi. Retrieved 22 September 2021.
* Aleksander, Igor (1996), Impossible Minds, World Scientific Publishing Company, ISBN 978-1-86094-036-1, https://archive.org/details/impossiblemindsm0000alek
* "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, April 2009, doi:10.1002/cne.21974, PMID 19226510, https://www.researchgate.net/publication/24024444, retrieved 4 September 2013
* Berglas, Anthony (2008), Artificial Intelligence will Kill our Grandchildren, http://berglas.org/Articles/AIKillGrandchildren/AIKillGrandchildren.html, retrieved 31 August 2012
* 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 361 (1809): 1721–1748, doi:10.1098/rsta.2003.1232, PMID 12952683, Bibcode: 2003RSPTA.361.1721C .
* Crevier, Daniel (1993), AI: The Tumultuous Search for Artificial Intelligence, New York, NY: BasicBooks, ISBN 0-465-02997-3
* Darrach, Brad (20 November 1970), "Meet Shakey, the First Electronic Person", Life Magazine: 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 .
* 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
* Gelernter, David (2010), Dream-logic, the Internet and Artificial Thought, http://www.edge.org/3rd_culture/gelernter10.1/gelernter10.1_index.html, retrieved 25 July 2010
* Goertzel, Ben; Pennachin, Cassio, eds. (2006), Artificial General Intelligence, Springer, ISBN 978-3-540-23733-4, http://people.inf.elte.hu/csizsekp/ai/books/artificial-general-intelligence-cognitive-technologies.9783540237334.27156.pdf
* Goertzel, Ben (Dec 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 critique of Kurzweil", Artificial Intelligence 171 (18, Special Review Issue): 1161–1173, doi:10.1016/j.artint.2007.10.011, https://scholar.google.com/scholar?hl=sv&lr=&cluster=15189798216526465792, retrieved 1 April 2009 .
* Gubrud, Mark (November 1997), "Nanotechnology and International Security", Fifth Foresight Conference on Molecular Nanotechnology, http://www.foresight.org/Conferences/MNT05/Papers/Gubrud/, retrieved 7 May 2011
* Halal, William E.. "TechCast Article Series: The Automation of Thought". http://www.techcast.org/Upload/PDFs/633615794236495345_TCTheAutomationofThought.pdf.
* Holte, R. C.; Choueiry, B. Y. (2003), "Abstraction and reformulation in artificial intelligence", Philosophical Transactions of the Royal Society B 358 (1435): 1197–1204, doi:10.1098/rstb.2003.1317, PMID 12903653 .
* Howe, J. (November 1994), Artificial Intelligence at Edinburgh University: a Perspective, http://www.dai.ed.ac.uk/AI_at_Edinburgh_perspective.html, retrieved 30 August 2007
* Johnson, Mark (1987), The body in the mind, Chicago, ISBN 978-0-226-40317-5
* Kurzweil, Ray (2005), The Singularity is Near, Viking Press
* Lighthill, Professor Sir James (1973), "Artificial Intelligence: A General Survey", Artificial Intelligence: a paper symposium, Science Research Council
* Luger, George; Stubblefield, William (2004), Artificial Intelligence: Structures and Strategies for Complex Problem Solving (5th ed.), The Benjamin/Cummings Publishing Company, Inc., p. 720, ISBN 978-0-8053-4780-7, https://archive.org/details/artificialintell0000luge/page/720
* McCarthy, John (Oct 2007), "From here to human-level AI", Artificial Intelligence 171 (18): 1174–1182, doi:10.1016/j.artint.2007.10.009 .
* McCorduck, Pamela (2004), Machines Who Think (2nd ed.), Natick, MA: A. K. Peters, Ltd., ISBN 1-56881-205-1, http://www.pamelamc.com/html/machines_who_think.html
* Moravec, Hans (1976), The Role of Raw Power in Intelligence, http://www.frc.ri.cmu.edu/users/hpm/project.archive/general.articles/1975/Raw.Power.html, retrieved 29 September 2007
* Moravec, Hans (1988), Mind Children, Harvard University Press
* Moravec, Hans (1998), "When will computer hardware match the human brain?", Journal of Evolution and Technology 1, http://www.transhumanist.com/volume1/moravec.htm, retrieved 23 June 2006
* Nagel (1974), "What Is it Like to Be a Bat", Philosophical Review 83 (4): 435–50, doi:10.2307/2183914, http://organizations.utep.edu/Portals/1475/nagel_bat.pdf, retrieved 7 November 2009 .
* Newell, Allen; Simon, H. A. (1963), "GPS: A Program that Simulates Human Thought", in Feigenbaum, E. A.; Feldman, J., Computers and Thought, New York: McGraw-Hill
* Newell, Allen; Simon, H. A. (1976). "Computer Science as Empirical Inquiry: Symbols and Search". Communications of the ACM 19 (3): 113–126. doi:10.1145/360018.360022.
* Nilsson, Nils (1998), Artificial Intelligence: A New Synthesis, Morgan Kaufmann Publishers, ISBN 978-1-55860-467-4
* NRC (1999), "Developments in Artificial Intelligence", Funding a Revolution: Government Support for Computing Research, National Academy Press, http://www.nap.edu/readingroom/books/far/ch9.html, retrieved 29 September 2007
* Omohundro, Steve (2008), The Nature of Self-Improving Artificial Intelligence, presented and distributed at the 2007 Singularity Summit, San Francisco, California
* Poole, David; Mackworth, Alan; Goebel, Randy (1998), Computational Intelligence: A Logical Approach, New York: Oxford University Press, http://www.cs.ubc.ca/spider/poole/ci.html, retrieved 6 December 2007
* Russell, Stuart J.; Norvig, Peter (2003), Artificial Intelligence: A Modern Approach (2nd ed.), Upper Saddle River, New Jersey: Prentice Hall, ISBN 0-13-790395-2, http://aima.cs.berkeley.edu/
* Sandberg, Anders; Boström, Nick (2008), Whole Brain Emulation: A Roadmap, Technical Report #2008‐3, Future of Humanity Institute, Oxford University, http://www.fhi.ox.ac.uk/Reports/2008-3.pdf, retrieved 5 April 2009
* Searle, John (1980), "Minds, Brains and Programs", Behavioral and Brain Sciences 3 (3): 417–457, doi:10.1017/S0140525X00005756, http://cogprints.org/7150/1/10.1.1.83.5248.pdf, retrieved 3 September 2020
* Simon, H. A. (1965), The Shape of Automation for Men and Management, New York: Harper & Row
* Sutherland, J. G. (1990), "Holographic Model of Memory, Learning, and Expression", International Journal of Neural Systems 1–3: 256–267 .
* Turing, Alan (October 1950), "Computing Machinery and Intelligence", Mind LIX (236): 433–460, doi:10.1093/mind/LIX.236.433, ISSN 0026-4423
* de Vega, Manuel; Glenberg, Arthur; Graesser, Arthur, eds. (2008), Symbols and Embodiment: Debates on meaning and cognition, Oxford University Press, ISBN 978-0-19-921727-4 .
* 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 978-1-58603-758-1. https://www.researchgate.net/publication/234801154. Retrieved 13 December 2020.
* Williams, R. W.; Herrup, K. (1988), "The control of neuron number", Annual Review of Neuroscience 11: 423–453, doi:10.1146/annurev.ne.11.030188.002231, PMID 3284447 .
* Yudkowsky, Eliezer (2006), "Artificial General Intelligence", Annual Review of Psychology (Springer) 49: 585–612, doi:10.1146/annurev.psych.49.1.585, ISBN 978-3-540-23733-4, PMID 9496632, http://www.singinst.org/upload/LOGI//LOGI.pdf .
* Yudkowsky, Eliezer (2008), "Artificial Intelligence as a Positive and Negative Factor in Global Risk", Global Catastrophic Risks, doi:10.1093/oso/9780198570509.003.0021, ISBN 9780198570509, Bibcode: 2008gcr..book..303Y .
* Zucker, Jean-Daniel (July 2003), "A grounded theory of abstraction in artificial intelligence", Philosophical Transactions of the Royal Society B 358 (1435): 1293–1309, doi:10.1098/rstb.2003.1308, PMID 12903672 .

## Further reading

* 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 simple enough to be understandable will not be complicated enough to behave intelligently, while any system complicated enough to behave intelligently will be too complicated to understand." (p. 197.) Computer scientist Alex Pentland writes: "Current AI machine-learning algorithms are, at their core, dead simple stupid. They work, but they work by brute force." (p. 198.)
* Domingos, Pedro, "Our Digital Doubles: AI will serve our species, not control it", Scientific American, vol. 319, no. 3 (September 2018), pp. 88–93. "AIs are like autistic savants and will remain so for the foreseeable future.... AIs lack common sense and can easily make errors that a human never would... They are also liable to take our instructions too literally, giving us precisely what we asked for instead of what we actually wanted." (p. 93.)
* Gleick, James, "The Fate of Free Will" (review of Kevin J. Mitchell, Free Agents: How Evolution Gave Us Free Will, 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 machines. For biological creatures, reason and purpose come from acting in the world and experiencing the consequences. Artificial intelligences – disembodied, strangers to blood, sweat, and tears – have no occasion for that." (p. 30.)
* Hughes-Castleberry, Kenna, "A Murder Mystery Puzzle: The literary puzzle Cain's Jawbone, which has stumped humans for decades, reveals the limitations of natural-language-processing algorithms", Scientific American, vol. 329, no. 4 (November 2023), pp. 81–82. "This murder mystery competition has revealed that although NLP (natural-language processing) models are capable of incredible feats, their abilities are very much limited by the amount of context they receive. This [...] could cause [difficulties] for researchers who hope to use them to do things such as analyze ancient languages. In some cases, there are few historical records on long-gone civilizations to serve as training data for such a purpose." (p. 82.)
* Immerwahr, Daniel, "Your Lying Eyes: People now use A.I. to generate fake videos indistinguishable from real ones. How much does it matter?", The New Yorker, 20 November 2023, pp. 54–59. "If by 'deepfakes' we mean realistic videos produced using artificial intelligence 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, operating in our media as counterfeited evidence. Their role better resembles that of cartoons, especially smutty ones." (p. 59.)
* Marcus, Gary, "Am I Human?: Researchers need new ways to distinguish artificial intelligence from the natural kind", Scientific American, vol. 316, no. 3 (March 2017), pp. 61–63. Marcus points out a so far insuperable stumbling block to artificial intelligence: an incapacity for reliable disambiguation. "[V]irtually every sentence [that people generate] is ambiguous, often in multiple ways. Our brain is so good at comprehending language that we do not usually notice." A prominent example is the "pronoun disambiguation problem" ("PDP"): a machine has no way of determining to whom or what a pronoun in a sentence—such as "he", "she" or "it"—refers.
* Marcus, Gary, "Artificial Confidence: Even the newest, buzziest systems of artificial general intelligence are stymmied by the same old problems", Scientific American, vol. 327, no. 4 (October 2022), pp. 42–45.
* Press, Eyal, "In Front of Their Faces: Does facial-recognition technology lead police to ignore contradictory evidence?", The New Yorker, 20 November 2023, pp. 20–26.
* Roivainen, Eka, "AI's IQ: ChatGPT aced a [standard intelligence] test but showed that intelligence cannot be measured by IQ alone", Scientific American, vol. 329, no. 1 (July/August 2023), p. 7. "Despite its high IQ, ChatGPT fails at tasks that require real humanlike reasoning or an understanding of the physical and social world.... ChatGPT seemed unable to reason logically and tried to rely on its vast database of... facts derived from online texts."

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