Artificial general intelligence

Not to be confused with Generative artificial intelligence.

Unsolved problem in computer science:

Could an artificial general intelligence be created? If so, how?

(more unsolved problems in computer science)

An artificial general intelligence (AGI) is a type of hypothetical intelligent agent. The AGI concept is that it can learn to accomplish any intellectual task that human beings or animals can perform. Alternatively, AGI has been defined as an autonomous system that surpasses human capabilities in the majority of economically valuable tasks. Creating AGI is a primary goal of some artificial intelligence research and companies such as OpenAI, 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. Some 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. Additionally, there is debate regarding whether modern deep learning systems, such as GPT-4, are an early yet incomplete form of AGI or if new approaches are required.

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

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

Terminology

AGI is also known as strong AI, full AI, or general intelligent action. However, some academic sources reserve the term "strong AI" for computer programs that experience sentience or consciousness. In contrast, weak AI (or narrow AI) is able to solve one specific problem, but lacks general cognitive abilities. 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.

Related concepts include human-level AI, transformative AI, and superintelligence.

Characteristics

Main article: Artificial intelligence

Various criteria for intelligence have been proposed (most famously the Turing test) but no definition is broadly accepted.

Intelligence traits

However, researchers generally hold that intelligence is required to do the following:

• reason, use strategy, solve puzzles, and make judgments under uncertainty;
• represent knowledge, including common sense knowledge;
• plan;
• learn;
• communicate in natural language;

and, 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) and autonomy.

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:

• 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.

Mathematical formalisms

A mathematically precise specification 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”. This type of AGI, characterized by the ability to maximise a mathematical definition of intelligence rather than exhibit human-like behaviour, is also called universal artificial intelligence.

In 2015 Jan Leike and Marcus Hutter showed that Legg-Hutter intelligence - "an agent’s ability to achieve goals in a wide range of environments" - is measured with respect to "a fixed Universal Turing Machine(UTM). AIXI is the most intelligent policy if it uses the same UTM", a result which "undermines all existing optimality properties for AIXI".

Tests for testing human-level AGI

Several tests meant to confirm human-level AGI have been considered, including:

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.

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.

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, if machines are to solve the problems as well as people 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.

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

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.

History

Classical AI

Main articles: History of artificial intelligence and Symbolic artificial intelligence

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

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 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".

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". 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". In response to this and the success of expert systems, both industry and government pumped money back into the field. However, confidence in AI spectacularly collapsed in the late 1980s, and the goals of the Fifth Generation Computer Project were never fulfilled. 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 and avoided mention of "human level" artificial intelligence for fear of being labeled "wild-eyed dreamer[s]".

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 artificial neural networks and statistical machine learning. 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 development on this field was considered an emerging trend, and a mature stage was expected to happen in more than 10 years.

Most mainstream AI researchers hope that strong AI can 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.

However, this is 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).

Modern artificial general intelligence research

The term "artificial general intelligence" was used as early as 1997, by Mark Gubrud in a discussion of the implications of fully automated military production and operations. The term was re-introduced and popularized by Shane Legg and Ben Goertzel around 2002. AGI research activity in 2006 was described by Pei Wang and Ben Goertzel as "producing publications and preliminary results". The first summer school in AGI was organized in Xiamen, China in 2009 by the Xiamen university's Artificial Brain Laboratory and OpenCog. The first university course was given in 2010 and 2011 at Plovdiv University, Bulgaria by Todor Arnaudov. MIT presented a course in AGI in 2018, organized by Lex Fridman and featuring a number of guest lecturers.

As of 2023, 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, 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, its application can be extended to robotics and the sciences.

Feasibility

As of 2022, AGI remains speculative. No such system 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". 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.

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. 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. AI experts' views on the feasibility of AGI wax and wane. Four polls conducted in 2012 and 2013 suggested that the median guess 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. 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.

Timescales

In the introduction to his 2006 book, 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 the consensus in the AGI research community seemed to be that the timeline discussed by Ray Kurzweil in The Singularity is Near (i.e. between 2015 and 2045) was plausible. 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.

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). AlexNet was regarded as the initial ground-breaker of the current deep learning wave.

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.

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 classify as a narrow AI system.

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.

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

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.

In 2023, the AI researcher Geoffrey Hinton stated that:

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: 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 copying its state into a computer system or another computational device. The computer runs a simulation model sufficiently faithful to the original that it behaves in practically the same way as the original brain. Whole brain emulation is discussed in computational neuroscience and neuroinformatics, in the context of brain simulation for medical research purposes. It is discussed in artificial intelligence research 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 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.

For low-level brain simulation, an extremely powerful computer would be required. The human brain has a huge number of synapses. 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). 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).

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). (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.

Modelling the neurons in more detail

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.

Current research

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. 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. 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. Neuro-silicon interfaces have been proposed as an alternative implementation strategy that may scale better.

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?". He measured the ability of existing software to simulate the functionality of neural tissue, specifically the retina. His results 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, efforts focus on precise emulation of biological neurons (partly on the molecular level), but the result cannot yet be called a total success.

Criticisms of simulation-based approaches

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. 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 proposes virtual embodiment (like in Second Life) as an option, but it is unknown whether this would be sufficient.

Desktop computers using microprocessors capable of more than 10⁹ cps (Kurzweil's non-standard unit "computations per second", see above) have been available since 2005. According to the brain power estimates used by Kurzweil (and Moravec), such a computer should be capable of supporting a simulation of a bee brain, but despite some interest no such simulation exists. There are several reasons for this:

1. The neuron model seems to be oversimplified (see next section).
2. There is insufficient understanding of higher cognitive processes to establish accurately what the brain's neural activity (observed using techniques such as functional magnetic resonance imaging) correlates with.
3. Even if our understanding of cognition advances sufficiently, early simulation programs are likely to be very inefficient and will, therefore, need considerably more hardware.
4. The brain of an organism, while critical, may not be an appropriate boundary for a cognitive model. To simulate a bee brain, it may be necessary to simulate the body, and the environment. The Extended Mind thesis formalises this philosophical concept, and research into cephalopods demonstrated clear examples of a decentralized system.

In addition, the scale of the human brain is not currently well-constrained. One estimate puts the human brain at about 100 billion neurons and 100 trillion synapses. Another estimate is 86 billion neurons of which 16.3 billion are in the cerebral cortex and 69 billion in the cerebellum. Glial cell synapses are currently unquantified but are known to be extremely numerous.

Philosophical perspective

See also: Philosophy of artificial intelligence and Turing test

"Strong AI" as defined in philosophy

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

• Strong AI hypothesis: An artificial intelligence system can "think"—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.

Mainstream AI is most interested in how a program behaves. According to Russell and Norvig, "as long as the program works, they don't care if you call it real or a simulation." 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." Thus, for academic AI research, "Strong AI" and "AGI" are two very different things.

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". This is not the same as Searle's strong AI, unless you assume 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.

Consciousness

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

• consciousness: To have subjective experience and thought.
• self-awareness: To be aware of oneself as a separate individual, especially to be aware of one's own thoughts.
• sentience: The ability to "feel" perceptions or emotions subjectively.
• sapience: The capacity for wisdom.

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 full ethical agents with existing legal and social frameworks, focusing on the legal position and rights of 'strong' AI. Bill Joy, among others, argues a machine with these traits may be a threat to human life or dignity.

It remains to be shown whether any of these traits are necessary for strong AI. The role of consciousness is not clear, and there is no agreed test for its presence. If a machine is built with a device that simulates the neural correlates of consciousness, would it automatically have self-awareness? It is possible that some of these traits naturally emerge from a fully intelligent machine. It is also possible that people will ascribe these properties to machines once they begin to act in a way that is clearly intelligent.

Artificial consciousness research

Main article: Artificial consciousness

Although the role of consciousness in strong AI/AGI is debatable, many AGI researchers regard research that investigates possibilities for implementing consciousness as vital. In an early effort Igor Aleksander argued that the principles for creating a conscious machine already existed but that it would take forty years to train such a machine to understand language.

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 gone through periods of rapid progress separated by periods when progress appeared to stop. Ending each hiatus fundamental advances in hardware, software or both to create space for further progress. 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.

The field has also oscillated between approaches to the problem. At times, effort has focused on explicit accumulation of facts and logic, as in expert systems. At other times, systems were expected to build their own g via machine learning, as in artificial neural networks.

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? Gelernter writes, "No computer will be creative unless it can simulate all the nuances of human emotion."

Benefits

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

AGI could improve the productivity and efficiency in most jobs. For example, in public health, AGI could accelerate medical research, notably against cancer. It could take care of the elderly, and democratize access to rapid, high-quality medical diagnostics. It could offer fun, cheap and personalized education. 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. 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. 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), it could take measures to drastically reduce the risks while minimizing the impact of these measures on our quality of life.

Risks

Potential threat to human existence

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

The thesis that AI poses an existential risk for humans, and that this risk needs much more attention than it currently gets, is controversial but has been endorsed by many public figures including Elon Musk, Bill Gates, and Stephen Hawking. AI researchers like Stuart J. Russell, Roman Yampolskiy, and Alexey Turchin, also support the basic thesis of a potential threat to humanity. Gates states he does not "understand why some people are not concerned", and Hawking criticized widespread indifference in his 2014 editorial:

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.

The fate of humanity has sometimes been compared to the fate of gorillas threatened by human activities. Additional intelligence caused humanity to dominate gorillas, which are now vulnerable in ways that they could not have anticipated. The gorilla has become an endangered species, not out of malice, but simply as a collateral damage from human activities.

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". 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. Nick Bostrom gives the thought experiment of the paper clips optimizer:

Suppose we have an AI whose only goal is to make as many paper clips as possible. The AI will realize quickly that it would be much better if there were no humans because humans might decide to switch it off. Because if humans do so, there would be fewer paper clips. Also, human bodies contain a lot of atoms that could be made into paper clips. The future that the AI would be trying to gear towards would be one in which there were a lot of paper clips but no humans.

A 2021 systematic review of the risks associated with AGI, while noting the paucity of data, found the following potential threats: "AGI removing itself from the control of human owners/managers, being given or developing unsafe goals, development of unsafe AGI, AGIs with poor ethics, morals and values; inadequate management of AGI, and existential risks".

Many scholars who are concerned about existential risk advocate (possibly massive) research into solving the difficult "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? Solving the control problem is complicated by the AI arms race, which will almost certainly see the militarization and weaponization of AGI by more than one nation-state, resulting in AGI-enabled warfare, and in the case of AI misalignment, AGI-directed warfare, potentially against all humanity.

The thesis that AI can pose existential risk also has detractors. 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. Jaron Lanier argued in 2014 that the idea that then-current machines were in any way intelligent is "an illusion" and a "stupendous con" by the wealthy.

Much criticism argues that AGI is unlikely in the short term. Computer scientist Gordon Bell argues that the human race will destroy itself before it reaches the technological singularity. Gordon Moore, the original proponent of Moore's Law, declares: "I am a skeptic. I don't believe [a technological singularity] is likely to happen, at least for a long time. And I don't know why I feel that way." Former Baidu Vice President and Chief Scientist Andrew Ng said in 2015 that worrying about AI existential risk is "like worrying about overpopulation on Mars when we have not even set foot on the planet yet."

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."

Mass unemployment

Further information: 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". They consider office workers to be the most exposed, for example mathematicians, accountants or web designers. 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:

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.

See also

• Neural scaling law – Law in machine learning
• Artificial brain – Software and hardware with cognitive abilities similar to those of the animal or human brain
• AI safety – Research area on making AI safe and beneficial
• 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 – Collaborative public-private research initiative announced by the Obama administration
• China Brain Project
• Future of Humanity Institute – Defunct Oxford interdisciplinary research centre
• General game playing – Learning to play multiple games successfully
• Generative artificial intelligence – AI system capable of generating content in response to prompts
• 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 – Annual AI competition
• Hardware for artificial intelligence – Hardware specially designed and optimized for artificial intelligence
• Weak artificial intelligence – Form of artificial intelligence

Sources

• UNESCO Science Report: the Race Against Time for Smarter Development. Paris: UNESCO. 11 June 2021. ISBN 978-92-3-100450-6. Archived from the original on 18 June 2022. Retrieved 22 September 2021.
• Aleksander, Igor (1996), Impossible Minds, World Scientific Publishing Company, ISBN 978-1-86094-036-1
• 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): 532–41, doi:10.1002/cne.21974, PMID 19226510, S2CID 5200449, archived from the original on 18 February 2021, retrieved 4 September 2013
• Berglas, Anthony (2008), Artificial Intelligence will Kill our Grandchildren, archived from the original on 23 July 2014, 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, Bibcode:2003RSPTA.361.1721C, doi:10.1098/rsta.2003.1232, PMID 12952683, S2CID 31032007.
• 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, pp. 58–68.
• Drachman, D (2005), "Do we have brain to spare?", Neurology, 64 (12): 2004–5, 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
• Gelernter, David (2010), Dream-logic, the Internet and Artificial Thought, archived from the original on 26 July 2010, retrieved 25 July 2010
• Goertzel, Ben; Pennachin, Cassio, eds. (2006), Artificial General Intelligence (PDF), Springer, ISBN 978-3-540-23733-4, archived from the original (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 critique of Kurzweil", Artificial Intelligence, 171 (18, Special Review Issue): 1161–1173, doi:10.1016/j.artint.2007.10.011, archived from the original on 7 January 2016, retrieved 1 April 2009.
• Gubrud, Mark (November 1997), "Nanotechnology and International Security", Fifth Foresight Conference on Molecular Nanotechnology, archived from the original on 29 May 2011, retrieved 7 May 2011
• Halal, William E. "TechCast Article Series: The Automation of Thought" (PDF). Archived from the original (PDF) on 6 June 2013.
• Holte, RC; Choueiry, BY (2003), "Abstraction and reformulation in artificial intelligence", Philosophical Transactions of the Royal Society B, 358 (1435): 1197–1204, doi:10.1098/rstb.2003.1317, PMC 1693218, PMID 12903653.
• Howe, J. (November 1994), Artificial Intelligence at Edinburgh University : a Perspective, archived from the original on 17 August 2007, 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
• McCarthy, John (October 2007), "From here to human-level AI", Artificial Intelligence, 171 (18): 1174–1182, doi:10.1016/j.artint.2007.10.009.
• McCorduck, Pamela (2004), Machines Who Think (2nd ed.), Natick, Massachusetts: A. K. Peters, ISBN 1-5688-1205-1
• Moravec, Hans (1976), The Role of Raw Power in Intelligence, archived from the original on 3 March 2016, 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, 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, retrieved 7 November 2009.
• 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
• 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, archived from the original on 12 January 2008, retrieved 29 September 2007
• Omohundro, Steve (2008), The Nature of Self-Improving Artificial Intelligence, presented and distributed at the 2007 Singularity Summit, San Francisco, CA.
• Poole, David; Mackworth, Alan; Goebel, Randy (1998), Computational Intelligence: A Logical Approach, New York: Oxford University Press, archived from the original on 25 July 2009, retrieved 6 December 2007
• Russell, Stuart J.; Norvig, Peter (2003), Artificial Intelligence: A Modern Approach (2nd ed.), Upper Saddle River, New Jersey: Prentice Hall, ISBN 0-13-790395-2
• Sandberg, Anders; Boström, Nick (2008), Whole Brain Emulation: A Roadmap (PDF), Technical Report #2008‐3, Future of Humanity Institute, Oxford University, archived (PDF) from the original on 25 March 2020, retrieved 5 April 2009
• Searle, John (1980), "Minds, Brains and Programs" (PDF), Behavioral and Brain Sciences, 3 (3): 417–457, doi:10.1017/S0140525X00005756, S2CID 55303721, archived (PDF) from the original on 17 March 2019, 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.
• 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. pp. 1–16. ISBN 978-1-58603-758-1. Archived from the original on 18 February 2021. Retrieved 13 December 2020.
• Williams RW, Herrup K (1988), "The control of neuron number", Annual Review of Neuroscience, 11: 423–53, doi:10.1146/annurev.ne.11.030188.002231, PMID 3284447.
• Yudkowsky, Eliezer (2006), "Artificial General Intelligence" (PDF), Annual Review of Psychology, 49, Springer: 585–612, doi:10.1146/annurev.psych.49.1.585, ISBN 978-3-540-23733-4, PMID 9496632, archived from the original (PDF) on 11 April 2009.
• Yudkowsky, Eliezer (2008), "Artificial Intelligence as a Positive and Negative Factor in Global Risk", Global Catastrophic Risks, Bibcode:2008gcr..book..303Y, doi:10.1093/oso/9780198570509.003.0021, ISBN 9780198570509.
• 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, PMC 1693211, PMID 12903672.

Further reading

• Eka Roivainen, "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."

External links

• The AGI portal maintained by Pei Wang
• The Genesis Group at MIT's CSAIL – Modern research on the computations that underlay human intelligence
• OpenCog – open source project to develop a human-level AI
• Simulating logical human thought
• What Do We Know about AI Timelines? – Literature review