Strong AI is artificial intelligence that matches or exceeds human intelligence — the intelligence of a machine that can 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. Strong AI is also referred to as "artificial general intelligence" or as the ability to perform "general intelligent action". Science fiction associates strong AI with such human traits as consciousness, sentience, sapience and self-awareness.
Some references emphasize a distinction between strong AI and "applied AI" : the use of software to study or accomplish specific problem solving or reasoning tasks that do not encompass the full range of human cognitive abilities.
Many different definitions of intelligence have been proposed 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 and the ability to act in the world where intelligent behaviour is to be observed. This would include an ability to detect and respond to hazard. Some sources consider "salience" as an important trait. Salience is thought to be part of how humans evaluate novelty so are likely to be important in some degree, but not necessarily at a human level. Many interdisciplinary approaches to intelligence tend to emphasise the need to consider additional traits such as imagination and autonomy.
Computer based systems that exhibit many of these capabilities do exist, but not yet at human levels.
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.
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."
Artificial General Intelligence research
Artificial General Intelligence 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, 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" One recent addition is Numenta, a project based on the theories of Jeff Hawkins, the creator of the Palm Pilot. While Numenta takes a computational approach to general intelligence, Hawkins is also the founder of the Redwood Neuroscience Institute, which explores conscious thought from a biological perspective. AND Corporation has been active in this field since 1990, and has developed machine intelligence processes based on phase coherence principles, having strong similarities to digital holography and QM with respect to quantum collapse of the wave function. Ben Goertzel is pursuing an embodied AGI through the open-source OpenCog project. Current code includes embodied virtual pets capable of learning simple English-language commands, as well as integration with real-world robotics, being done at the robotics lab of Hugo de Garis at Xiamen University.
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 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 . Goertzel proposes virtual embodiment, 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, 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 .
Secondly, there is insufficient understanding of higher cognitive processes to establish accurately what the 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.