Artificial general intelligence
Artificial general intelligence (AGI) is a hypothetical artificial intelligence that demonstrates human-like intelligence – the intelligence of a machine that could successfully perform any intellectual task that a human being can. It is a primary goal of artificial intelligence research and an important topic for science fiction writers and futurists. Artificial general intelligence is also referred to as "strong AI", "full AI" or as the ability to perform "general intelligent action". AGI is associated with traits such as consciousness, sentience, sapience, and self-awareness observed in living beings.
Some references emphasize a distinction between strong AI and "applied AI" (also called "narrow AI" or "weak AI"): the use of software to study or accomplish specific problem solving or reasoning tasks. Weak AI, in contrast to strong AI, does not attempt to simulate the full range of human cognitive abilities.
Strong AI (as defined above) should not be confused with John Searle's strong AI hypothesis. Strong AI refers the amount of intelligence a computer can display, whereas 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.
- 1 Requirements
- 2 Mainstream AI research
- 3 Artificial general intelligence research
- 4 Origin of the term: John Searle's strong AI
- 5 Possible explanations for the slow progress of AI research
- 6 Critique of Strong AI research
- 7 See also
- 8 Notes
- 9 References
- 10 External links
Many different definitions of intelligence have been proposed (such as being able to pass the Turing test) but there is to date no definition that satisfies everyone. However, there is wide agreement among artificial intelligence researchers that intelligence is required to do the following:
- reason, use strategy, solve puzzles, and make judgments under uncertainty;
- represent knowledge, including commonsense knowledge;
- 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. This would include an ability to detect and respond to hazard. 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) and autonomy. 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.
- 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 legal rights, analogous to the rights of animals. Also, 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 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.
Mainstream AI research
History of mainstream research into strong AI
Modern AI research began in the mid 1950s. The first generation of AI researchers were convinced that strong AI was possible and that it would exist in just a few decades. As 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 accurately embodied what AI researchers believed they could create by the year 2001. Of note is the fact that 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; Crevier quotes him as having said on the subject in 1967, "Within a generation...the problem of creating 'artificial intelligence' will substantially be solved,", although Minsky states that he was misquoted.
However, in the early 1970s, it became obvious that researchers had grossly underestimated the difficulty of the project. The agencies that funded AI became skeptical of strong AI and put researchers under increasing pressure to produce useful technology, or "applied AI". As the 1980s began, Japan's fifth generation computer project revived interest in strong AI, setting out a ten year timeline that included strong AI 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, the market for 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 had predicted the imminent arrival of strong AI had been shown to be fundamentally mistaken about what they could accomplish. By the 1990s, AI researchers had gained a reputation for making promises they could not keep. AI researchers became reluctant to make any kind of prediction at all and avoid any mention of "human level" artificial intelligence, for fear of being labeled a "wild-eyed dreamer."
Current mainstream AI research
In the 1990s and early 21st century, mainstream AI has achieved a far higher degree of commercial success and academic respectability by focusing on specific sub-problems where they can produce verifiable results and commercial applications, such as neural nets, computer vision or data mining. These "applied AI" applications are now used extensively throughout the technology industry and research in this vein is very heavily funded in both academia and industry.
Most mainstream AI researchers hope that strong AI can be developed by combining the programs that solve various subproblems using an integrated agent architecture, cognitive architecture or subsumption architecture. 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, it should be noted that much contention has existed in AI research, even with regards to the fundamental philosophies informing this field; for example, Harnad, S. from Princeton stated in the conclusion of his 1990 paper on the Symbol Grounding Hypothesis that "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)."
Artificial general intelligence research
Artificial general intelligence (AGI) describes research that aims to create machines capable of general intelligent action. The term was introduced by Mark Gubrud in 1997 in a discussion of the implications of fully automated military production and operations. The research objective is much older, for example Doug Lenat's Cyc project (that began in 1984), and Allen Newell's Soar project are regarded as within the scope of AGI. AGI research activity in 2006 was described by Pei Wang and Ben Goertzel as "producing publications and preliminary results". As yet, most AI researchers have devoted little attention to AGI, with some claiming that intelligence is too complex to be completely replicated in the near term. However, a small number of computer scientists are active in AGI research, and many of this group are contributing to a series of AGI conferences. The research is extremely diverse and often pioneering in nature. In the introduction to his book, Goertzel says that estimates of the time needed before a truly flexible AGI is built vary from 10 years to over a century, but the consensus in the AGI research community seems to be that the timeline discussed by Ray Kurzweil in "The Singularity is Near" (i.e. between 2015 and 2045) is plausible. Most mainstream AI researchers doubt that progress will be this rapid. Organizations actively pursuing AGI include Adaptive AI, the Machine Intelligence Research Institute, the OpenCog Foundation, Bitphase AI, TexAI., Numenta and the associated Redwood Neuroscience Institute, and AND Corporation.
Whole brain emulation
A popular approach discussed 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. 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 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 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 x 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) neuron updates per second. Kurzweil looks at various estimates for the hardware required to equal the human brain and adopts a figure of 1016 computations per second (cps). He uses this figure to predict the necessary hardware will be available sometime between 2015 and 2025, if the current exponential growth in computer power continues.
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. 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 proposes virtual embodiment (like Second Life), but it is not yet known whether this would be sufficient.
Desktop computers using 2 GHz Intel Pentium microprocessors and capable of more than 109 cps 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 no such simulation exists. There are at least three reasons for this:
- Firstly, the neuron model seems to be oversimplified (see next section).
- Secondly, 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.
- Thirdly, even if our understanding of cognition advances sufficiently, early simulation programs are likely to be very inefficient and will, therefore, need considerably more hardware.
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.
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 only understood 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 a computer 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, 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. 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. 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. 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.
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.
Artificial consciousness research
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.
Origin of the term: John Searle's strong AI
The term "strong AI" was adopted from the name of a position in the philosophy of artificial intelligence first identified by John Searle as part of his Chinese room argument in 1980. He wanted to distinguish between two different hypotheses about artificial intelligence:
- 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, which is fundamentally different than the subject of this article, is common in academic AI research and textbooks.
The term "strong AI" is now used to describe any artificial intelligence system that acts like it has a mind, regardless of whether a philosopher would be able to determine if it actually has a mind or not. As Russell and Norvig write: "Most AI researchers take the weak AI hypothesis for granted, and don't care about the strong AI hypothesis." AI researchers are interested in a related statement:
- An artificial intelligence system can think (or act like it thinks) as well as or better than people do.
This assertion, which hinges on the breadth and power of machine intelligence, is the subject of this article.
Possible explanations for the slow progress of AI research
Since the launch of AI research in 1956, the growth of this field has slowed down over time and has stalled the aims of creating machines skilled with intelligent action at the human level. A possible explanation for this delay is that computers lack a sufficient scope of memory or processing power. In addition, the level of complexity that connects to the process of AI research may also limit the progress of AI research.
While most AI researchers believe that strong AI can be achieved in the future, there are some individuals like Hubert Dreyfus and Roger Penrose that deny the possibility of achieving AI. John McCarthy was one of various computer scientists who believe human-level AI will be accomplished, but a date cannot accurately be predicted.
Conceptual limitations are another possible reason for the slowness in AI research. AI researchers may need to modify the conceptual framework of their discipline in order to provide a stronger base and contribution to the quest of achieving strong AI. As William Clocksin wrote in 2003: "the framework starts from Weizenbaum’s observation that intelligence manifests itself only relative to specific social and cultural contexts".
Furthermore, AI researchers have been able to create computers that can perform jobs that are complicated for people to do, but conversely they have struggled to develop a computer that is capable of carrying out tasks that are simple for humans to do. A problem that is described by David Gelernter is that some people assume that thinking and reasoning are equivalent. However, the idea of whether thoughts and the creator of those thoughts are isolated individually has intrigued AI researchers.
The problems that have been encountered in AI research over the past decades have further impeded the progress of AI. The failed predictions that have been promised by AI researchers and the lack of a complete understanding of human behaviors have helped diminish the primary idea of human-level AI. Although the progress of AI research has brought both improvement and disappointment, most investigators have established optimism about potentially achieving the goal of AI in the 21st century.
Other possible reasons have been proposed for the lengthy research in the progress of strong AI. The intricacy of scientific problems and the need to fully understand the human brain through psychology and neurophysiology have limited many researchers from emulating the function of the human brain into a computer hardware. Many researchers tend to underestimate any doubt that is involved with future predictions of AI, but without taking those issues seriously can people then overlook solutions to problematic questions.
Clocksin says that a conceptual limitation that may impede the progress of AI research is that people may be using the wrong techniques for computer programs and implementation of equipment. When AI researchers first began to aim for the goal of artificial intelligence, a main interest was human reasoning. Researchers hoped to establish computational models of human knowledge through reasoning and to find out how to design a computer with a specific cognitive task.
The practice of abstraction, which people tend to redefine when working with a particular context in research, provides researchers with a concentration on just a few concepts. The most productive use of abstraction in AI research comes from planning and problem solving. Although the aim is to increase the speed of a computation, the role of abstraction has posed questions about the involvement of abstraction operators.
A possible reason for the slowness in AI relates to the acknowledgement by many AI researchers that heuristics is a section that contains a significant breach between computer performance and human performance. The specific functions that are programmed to a computer may be able to account for many of the requirements that allow it to match human intelligence. These explanations are not necessarily guaranteed to be the fundamental causes for the delay in achieving strong AI, but they are widely agreed by numerous researchers.
There have been many AI researchers that debate over the idea whether machines should be created with emotions. There are no emotions in typical models of AI and some researchers say programming emotions into machines allows them to have a mind of their own. Emotion sums up the experiences of humans because it allows them to remember those experiences.
As David Gelernter writes, “No computer will be creative unless it can simulate all the nuances of human emotion.” This concern about emotion has posed problems for AI researchers and it connects to the concept of strong AI as its research progresses into the future.
Critique of Strong AI research
If research into Strong AI produced sufficiently intelligent software, it might be able to reprogram and improve itself. The improved software would be even better at improving itself, leading to recursive self-improvement. The new intelligence could thus increase exponentially and dramatically surpass humans.
Hyper-intelligent software may not necessarily decide to support the continued existence of mankind, and would be extremely difficult to stop. This topic has also recently begun to be discussed in academic publications as a real source of risks to civilization, humans, and planet Earth.
One proposal to deal with this is to ensure that the first generally intelligent AI is 'Friendly AI', and will then be able to control subsequently developed AIs. Some question whether this kind of check could really remain in place.
- Outline of artificial intelligence
- History of artificial intelligence
- Ethics of artificial intelligence
- Future of artificial intelligence
- Synthetic intelligence
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- The Age of Artificial Intelligence: George John at TEDxLondonBusinessSchool 2013
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- Encyclopædia Britannica Strong AI, applied AI, and cognitive simulation or Jack Copeland What is artificial intelligence? on AlanTuring.net
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- 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.
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- Crevier 1993, pp. 211, Russell & Norvig 2003, p. 24 and see also Feigenbaum & McCorduck 1983
- Crevier 1993, pp. 161–162,197–203,240; Russell & Norvig 2003, p. 25; NRC 1999, under "Shift to Applied Research Increases Investment"
- Crevier 1993, pp. 209–212
- 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." McCarthy, John (2000). "Reply to Lighthill". Stanford University.
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- In "Mind Children" Moravec 1988, p. 61 1015 cps is used. More recently, in 1997, <http://www.transhumanist.com/volume1/moravec.htm> 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.
- de Vega, Glenberg & Graesser 2008. A wide range of views in current research, all of which require grounding to some degree
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- 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." (Russell & Norvig 2003)
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- Wang, Pei (2006), website Artificial General Intelligence : A Gentle Introduction
- Yudkowsky, Eliezer (2006), Goertzel, Ben; Pennachin, Cassio, eds., Artificial General Intelligence, Springer, doi:10.1146/annurev.psych.49.1.585;jsessionid=o4K4TVe3OdBd, ISBN 3-540-23733-X
- 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.
- Yudkowsky, Eliezer (2008), "Artificial Intelligence as a Positive and Negative Factor in Global Risk", Global Catastrophic Risks (Oxford University Press).
- The AGI portal maintained by Pei Wang
- GPAI Project A Mass Collaboration for Strong AI
- AND Corporation - a neuromorphic model based on holographic neural processing
- Expanding Frontiers of Humanoid Robots
- GAIuS - Intelligent Artifact's framework for developing general intelligence agents
- AI lectures from Tokyo hosted by Rolf Pfeifer
- Artificial General Intelligence Research Institute
- The Genesis Group at MIT's CSAIL — Modern research on the computations that underlay human intelligence
- Essentials of general intelligence, article at Adaptive AI.
- OpenCog - open source project to develop a human-level AI
- Wiki of the Artificial General Intelligence Research Institute
- Problems with Thinking Robots
- Simulating logical human thought
- Texai, An open source project to create artificial intelligence
- The Game of Intelligent Design, An online game that promotes the development of artificial general intelligence
- The Practical Strong AI Project, A Practical approach and strategy for strong AI.
- Artificial Intelligence as a Positive and Negative Factor in Global Risk