Strong AI

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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 (subjective experience and thought), sentience (subjective feelings and emotion), sapience (wisdom) and self-awareness (identification of oneself as a separate individual, especially to be aware of one’s own thoughts). 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 that do not encompass (or in some cases are completely outside of) the full range of human cognitive abilities.

Many different definitions of intelligence have been proposed (such as being able to pass the Turing test — a test to see if a computer can trick a person into believing that the computer is a person too) 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; 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 behavior is to be observed. This would include an ability to detect and respond to hazard. Some sources consider ‘salience’ (the capacity for recognizing importance) 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 (e.g. cognitive science) tend to emphasize 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, but not at human levels. For example, intelligent agents,  autonomous entities which observe and act upon an environment and direct activity towards achieving goals (i.e. it is rational).

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, sentience, sapience, and self-awareness. 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. 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 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.

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 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; he endeavoured to make HAL 9000 as realistic as possible according to the consensus predictions of the time.

By 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 (a government initiated research project to develop a computer that transcends microprocessors)  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.’

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 (an artificial system made of artificial neuron cells modeled after the way the human brain works), computer vision (the ability of a computer to make sense of visual input, e.g. facial recognition), or data mining (also called knowledge discovery in databases). 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 ‘agent architecture,’ a blueprint for rational, autonomous software agents and ‘subsumption architecture’ (a reactive robot architecture heavily associated with behavior-based robotics). 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 (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. But, 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, Artificial General Intelligence Research Institute (AGIRI), the Singularity Institute for Artificial Intelligence, and TexAI. 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 quantum mechanics, having strong similarities to digital holography. 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.

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. Neuroimaging technologies, that could deliver the necessary detailed understanding are improving rapidly; Kurzweil 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 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 1 quadrillion synapses. This number declines with age, stabilizing by adulthood. Estimates vary for an adult, ranging from 100 to 500 trillion.

An estimate of the brain’s processing power, based on a simple switch model for neuron activity, is around 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 more than a quadrillion computations per second. 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 key fundamental criticism of the simulated brain approach derives from embodied cognition, which argues that the nature of the human mind is largely determined by the form of the human body. 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 the online virtual world 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 a billion computations per second 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. 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.

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 (non-neuronal cells that provide support and protection for neurons) synapses are currently unquantified but are known to be extremely numerous.

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 behavior 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 behavior(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 to one, 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 several billion 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 millions of synapses in 2006. There have also been controversial claims to have simulated a cat brain. Neuro-silicon interfaces (brain implants) have been proposed as an alternative implementation strategy that may scale better.

Moravec addressed the above arguments (‘brains are more complicated,’ ‘neurons have to be modeled in more detail’) in his 1997 paper. 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 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: Someone who knows only English sits alone in a room following English instructions for manipulating strings of Chinese characters, such that to those outside the room it appears as if someone in the room understands Chinese. The argument is intended to show that while suitably programmed computers may appear to converse in natural language, they are not capable of understanding language, even in principle.

He wanted to distinguish between two different hypotheses about artificial intelligence. An artificial intelligence system can think and have a mind. Or, 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.

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.

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 Galernter is that some people assume that thinking and reasoning mean the same definition. However, the idea of whether thoughts and the creator of those thoughts are isolated individually has intrigued AI 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.’

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