Artificial general intelligence (AGI) is the hypothetical[1] intelligence of a machine that has the capacity to understand or learn any intellectual task that a human being can. It is a primary goal of some artificial intelligence research and a common topic in science fiction and futures studies. AGI can also be referred to as strong AI,[2][3][4] full AI,[5] or general intelligent action.[6] Some academic sources reserve the term "strong AI" for machines that can experience consciousness.[7] Today's AI is speculated to be many years, if not decades, away from AGI.[8][9]
Some authorities emphasize a distinction between strong AI and applied AI,[10] also called narrow AI[3] or weak AI.[11] In contrast to strong AI, weak AI is not intended to perform human cognitive abilities. Rather, weak AI is limited to the use of software to study or accomplish specific problem solving or reasoning tasks.
As of 2017, over forty organizations are researching AGI.[12]
Various criteria for intelligence have been proposed (most famously the Turing test) but to date, there is no definition that satisfies everyone.[13] However, there is wide agreement among artificial intelligence researchers that intelligence is required to do the following:[14]
reason, use strategy, solve puzzles, and make judgments under uncertainty;
represent knowledge, including commonsense knowledge;
plan;
learn;
communicate in natural language;
and integrate all these skills towards common goals.
Other important capabilities include the ability to sense (e.g. see) and the ability to act (e.g. move and manipulate objects) in the world where intelligent behaviour is to be observed.[15] This would include an ability to detect and respond to hazard.[16] Many interdisciplinary approaches to intelligence (e.g. cognitive science, computational intelligence and decision making) tend to emphasise the need to consider additional traits such as imagination (taken as the ability to form mental images and concepts that were not programmed in)[17] and autonomy.[18] Computer based systems that exhibit many of these capabilities do exist (e.g. see computational creativity, automated reasoning, decision support system, robot, evolutionary computation, intelligent agent), but not yet at human levels.
The following tests to confirm human-level AGI have been considered:[19][20]
The Turing Test (Turing)A machine and a human both converse sight 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 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.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.The Employment Test (Nilsson)A machine works an economically important job, performing at least as well as humans in the same job.
Chinese researchers Feng Liu, Yong Shi and Ying Liu conducted intelligence tests in the summer of 2017 with publicly available and freely accessible weak AI such as Google AI or Apple's Siri and others. At the maximum, these AI reached a value of about 47, which corresponds approximately to a six-year-old child in first grade. An adult comes to about 100 on average. In 2014, similar tests were carried out in which the AI reached a maximum value of 27.[21][22]
The most difficult problems for computers are informally known as "AI-complete" or "AI-hard", implying that solving them is equivalent to the general aptitude of human intelligence, or strong AI, beyond the capabilities of a purpose-specific algorithm.[23]
AI-complete problems are hypothesised to include general computer vision, natural language understanding, and dealing with unexpected circumstances while solving any real world problem.[24]
AI-complete problems cannot be solved with current computer technology alone, and also require human computation. This property could be useful, for example, to test for the presence of humans, as CAPTCHAs aim to do; and for computer security to repel brute-force attacks.[25][26]
A popular discussed approach to achieving general intelligent action is whole brain emulation. A low-level 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 so faithful to the original that it will behave in essentially the same way as the original brain, or for all practical purposes, indistinguishably.[53] 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[46] 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[45] predicts that a map of sufficient quality will become available on a similar timescale to the required computing power.
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. It has been estimated that 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).[55] 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).[56] 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).[57] (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). 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.
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 the broadest of outlines. 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 at least as numerous as neurons, and which may outnumber neurons by as much as 10:1, and are now known to play a role in cognitive processes.
There are some research projects that 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.[59] The Blue Brain project used one of the fastest supercomputer architectures in the world, 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.[60] 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.[61] There have also been controversial claims to have simulated a cat brain. Neuro-silicon interfaces have been proposed as an alternative implementation strategy that may scale better.[62]
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?".[63] 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 was aimed on 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 has been 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, the efforts are focused on precise emulation of biological neurons (partly on the molecular level), but the result cannot be called a total success yet. Even if the number of issues to be solved in a human-brain-scale model is not proportional to the number of neurons, the amount of work along this path is obvious.
A fundamental criticism of the simulated brain approach derives from embodied cognition where human embodiment is taken as an essential aspect of human intelligence. Many researchers believe that embodiment is necessary to ground meaning.[64] If this view is correct, any fully functional brain model will need to encompass more than just the neurons (i.e., a robotic body). Goertzel[46] proposes virtual embodiment (like in Second Life), but it is not yet known 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), this computer should be capable of supporting a simulation of a bee brain, but despite some interest[65] no such simulation exists[citation needed]. There are at least three reasons for this:
The neuron model seems to be oversimplified (see next section).
There is insufficient understanding of higher cognitive processes[66] to establish accurately what the brain's neural activity, observed using techniques such as functional magnetic resonance imaging, correlates with.
Even if our understanding of cognition advances sufficiently, early simulation programs are likely to be very inefficient and will, therefore, need considerably more hardware.
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 formalizes the philosophical concept, and research into cephalopods has demonstrated clear examples of a decentralized system.[67]
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.[68][69] Another estimate is 86 billion neurons of which 16.3 billion are in the cerebral cortex and 69 billion in the cerebellum.[70] Glial cell synapses are currently unquantified but are known to be extremely numerous.
In 1980, philosopher John Searle coined the term "strong AI" as part of his Chinese room argument.[71] He wanted to distinguish between two different hypotheses about artificial intelligence:[72]
An artificial intelligence system can think and have a mind. (The word "mind" has a specific meaning for philosophers, as used in "the mind body problem" or "the philosophy of mind".)
An artificial intelligence system can (only) act like it thinks and has a mind.
The first one is called "the strong AI hypothesis" and the second is "the weak AI hypothesis" because the first one makes the stronger statement: it assumes something special has happened to the machine that goes beyond all its abilities that we can test. Searle referred to the "strong AI hypothesis" as "strong AI". This usage is also common in academic AI research and textbooks.[73]
The weak AI hypothesis is equivalent to the hypothesis that artificial general intelligence is possible. According to Russell and Norvig, "Most AI researchers take the weak AI hypothesis for granted, and don't care about the strong AI hypothesis."[74]
In contrast to Searle, Ray Kurzweil uses the term "strong AI" to describe any artificial intelligence system that acts like it has a mind,[45] regardless of whether a philosopher would be able to determine if it actually has a mind or not. In science fiction, AGI is associated with traits such as consciousness, sentience, sapience, and self-awareness observed in living beings. However, according to Searle, it is an open question whether general intelligence is sufficient for consciousness. "Strong AI" (as defined above by Kurzweil) should not be confused with Searle's "strong AI hypothesis." The strong AI hypothesis is the claim that a computer which behaves as intelligently as a person must also necessarily have a mind and consciousness. AGI refers only to the amount of intelligence that the machine displays, with or without a mind.
There are other aspects of the human mind besides intelligence that are relevant to the concept of strong AI which play a major role in science fiction and the ethics of artificial intelligence:
consciousness: To have subjective experience and thought.[75]
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 legal rights, analogous to the rights of non-human animals. As such, preliminary work has been conducted on approaches to integrating full ethical agents with existing legal and social frameworks. These approaches have focused on the legal position and rights of 'strong' AI.[76]
However, Bill Joy, among others, argues a machine with these traits may be a threat to human life or dignity.[77] It remains to be shown whether any of these traits are necessary for strong AI. The role of consciousness is not clear, and currently 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 also possible that some of these properties, such as sentience, naturally emerge from a fully intelligent machine, or that it becomes natural to ascribe these properties to machines once they begin to act in a way that is clearly intelligent. For example, intelligent action may be sufficient for sentience, rather than the other way around.
Although the role of consciousness in strong AI/AGI is debatable, many AGI researchers[78] regard research that investigates possibilities for implementing consciousness as vital. In an early effort Igor Aleksander[79] 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.
https://en.m.wikipedia.org/wiki/Artificial_general_intelligence
(However, as I say, Technology is a Physical version of Spirituality/Magick.)
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