Everything about Artificial General Intelligence totally explained
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 anyone interested in the future, such as
science fiction writers and
futurists. Strong AI is also referred to as "
artificial general intelligence" or as the ability to perform "general intelligent action". Strong AI is also closely related to such traits as
sentience,
sapience,
self-awareness and
consciousness.
Some references emphasize a distinction between strong AI and "applied AI" (also called "narrow AI"): the use of software to study or accomplish specific
problem solving or
reasoning tasks that don't encompass (or in some cases are completely outside of) the full range of human cognitive abilities.
Requirements of strong AI
Many different definitions of intelligence have been proposed (such as the
Turing test) but there's to date no definition that satisfies everyone.
However, there
is wide agreement among
artificial intelligence researchers that an intelligent machine must at least have the ability to:
» *
reason, use strategy, solve puzzles, and make judgements under
uncertainty;
*
represent knowledge, including
commonsense knowledge;
» *
plan;
*
learn;
» * communicate in
natural language;
*
perceive, and especially
see;
» * have
social intelligence;
* be able to move and manipulate objects (
robotics);
» * and the ability to integrate all these skills towards common goals.
Together these skills provide a working definition of what intelligence is, and work is underway to design machines that have these abilities.
Strong AI is, at the very least, a system that can perform all these tasks as well as humans do or better.
There are other aspects of the human mind besides intelligence that also bear on the concept of strong AI:
» *
consciousness: To say there's something it would "be like" to have strong AI.
*
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." Sentience has a moral connotation: if a machine is sentient, then it has the same rights and responsibilities as any human being.
*
sapience: the capacity for wisdom.
It isn't clear whether any of these are
necessary for strong AI—for example, it isn't clear if consciousness is
necessary for a machine to reason as well as human beings can. It is also not clear whether any of these traits are
sufficient for intelligence: if a machine is built with a device that simulates the
neural correlates of consciousness, would it automatically have the ability to
represent knowledge or use
natural language?
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's clearly intelligent. For example, intelligent action may be
sufficient for sentience, rather than the other way around.
Research approaches
History of mainstream AI research
Modern AI research began in the middle 50s. 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 of making HAL 9000 as realistic as possible according to the consensus predictions of the time, having himself said on the subject in
1967, "Within a generation...the problem of creating 'artificial intelligence' will substantially be solved."
However, in the early 70s, 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 eighties 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 80s 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 couldn't 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." This is an unfortunate consequence of developing nascent technologies.
Mainstream AI research
For the most part, researchers today choose to focus on specific sub-problems where they can produce verifiable results and commercial applications, such as
neural nets,
computer vision or
data mining.
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
Artificial General Intelligence research aims to create AI that can replicate human-level intelligence completely, often called an Artificial General Intelligence (AGI) to distinguish from less ambitious AI projects. (The concept is derived from the
psychometric notion of
natural general intelligence (often denoted "
g")
(External Link
), though no adherence to any particular theory of
g is implied.) As yet, researchers have devoted little attention to AGI, with some claiming that intelligence is too complex to be completely replicated in the near term. Some small groups of computer scientists are doing AGI research, however. Organizations pursuing AGI include the
Adaptive AI,
Artificial General Intelligence Research Institute (AGIRI),
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.
Simulated human brain model
Simulated human brain model is one of the quickest means of achieving strong AI, as it doesn't require complete understanding of how intelligence works. Basically, a very powerful computer would simulate a human brain, often in the form of a network of neurons. For example, given a map of all (or most) of the neurons in a functional human brain, and a good understanding of how a single neuron works, it's theoretically possible for a computer program to simulate the working brain over time. Given some method of communication, this simulated brain might then be shown to be fully intelligent. The exact form of the simulation varies: instead of neurons, a simulation might use groups of neurons, or alternatively, individual molecules might be simulated. It's also unclear which portions of the human brain would need to be modeled: humans can still function while missing portions of their brains, and areas of the brain are associated with activities (such as breathing) that might not be necessary to think.
This approach would require three things:
- Hardware. An extremely powerful computer would be required for such a model. Futurist Ray Kurzweil estimates 10 million MIPS, or ten petaflops. At least one special-purpose petaflops computer has already been built (the Riken MDGRAPE-3) and there are nine current computing projects (such as BlueGene/P) to build more general purpose petaflops computers all of which should be completed by 2008, if not sooner.(External Link) Most other attempted estimates of the brain's computational power equivalent have been rather higher, ranging from 100 million MIPS (100 petaflops) to 100 billion MIPS (100,000 petaflops). Using accurate Top500 projections, it might be estimated that such levels of computing power might be reached using the top-performing CPU-based supercomputers to be by ~2015 (for 100 petaflops), up to a more conservative estimate of ~2025 (for 100,000 petaflops). However, considering that GPU processing and Stream Processing power appears to double every year, these estimates will be reached much sooner using GPGPU processing as high-end GPU's set to arrive in early 2008 are already going to be able to process over 1 teraflop, which is 20x more powerful than a standard quad-core CPU. It should also be noted, however, that the overhead introduced by the modeling of the biological, chemical, and physical details of neural behaviour (especially on a molecular scale) might require a simulator to have access to computational power much greater than that of the brain itself and that current simulations and estimates don't account for the importance of Glial cells which outnumber neurons 10:1.
- Software. Software to simulate the function of a brain would be required. This assumes that the human mind is the central nervous system and is governed by physical laws. Constructing the simulation would require a great deal of knowledge about the physical and functional operation of the human brain, and might require detailed information about a particular human brain's structure. Information would be required both of the function of different types of neurons, and of how they're connected. Note that the particular form of the software dictates the hardware necessary to run it. For example, an extremely detailed simulation including molecules or small groups of molecules would require enormously more processing power than a simulation that models neurons using a simple equation, and a more accurate model of a neuron would be expected to be much more expensive computationally than a simple model. The more neurons in the simulation, the more processing power it would require.
- Understanding. Finally, it requires sufficient understanding thereof to be able to model it mathematically. This could be done either by understanding the central nervous system, or by mapping and copying it. Neuroimaging technologies are improving rapidly, and Kurzweil predicts that a map of sufficient quality will become available on a similar timescale to the required computing power. However, the simulation would also have to capture the detailed cellular behaviour of neurons and glial cells, presently only understood in the broadest of outlines.
Once such a model is built, it'll be easily altered and thus open to trial-and-error experimentation. This is likely to lead to huge advances in understanding, allowing the model's intelligence to be improved/motivations altered.
The
Blue Brain project aims to use one of the fastest supercomputer architectures in the world,
IBM's
Blue Gene platform, to simulate a single
neocortical column consisting of approximately 60,000 neurons and 5km of interconnecting synapses. The eventual goal of the project is to use supercomputers to replicate an entire brain.
The brain gets its power from performing many parallel operations, a standard computer from performing operations very quickly. It should be noted, however, that supercomputers also perform many operations in parallel. Good examples of this are the Cray and NEC vector computers which operate as a single machine but perform many calculations at once. Another example is any form of cluster computing, where multiple single computers operate as one.
The human brain has roughly 100 billion neurons operating simultaneously, connected by roughly 100 trillion synapses. By comparison, a modern computer
microprocessor uses only 1.7 billion transistors.
(External Link) Although estimates of the brain's processing power put it at around 10
14 neuron updates per second, it's expected that the first unoptimized simulations of a human brain will require a computer capable of 10
18 FLOPS. By comparison a general purpose CPU (circa 2006) operates at a few GFLOPS (10
9 FLOPS). (each FLOP may require as many as 20,000 logic operations).
However, a neuron is estimated to spike 200 times per second (this giving an upper limit on the number of operations). Signals between them are transmitted at a maximum speed of 150 meters per second. A modern 2GHz processor operates at 2 billion cycles per second, or 10,000,000 times faster than a human neuron, and signals in electronic computers travel at roughly half the speed of light; faster than signals in humans by a factor of 1,000,000. The brain consumes about 20W of power whereas supercomputers may use as much as 1MW or an order of 100,000 more (note:
Landauer limit is 3.5x10
20 op/sec/watt at room temperature).
Neuro-silicon interfaces have also been proposed.
(External Link) (External Link)
Critics of this approach believe that it's possible to achieve AI directly without imitating nature and often have used the analogy that early attempts to construct flying machines modeled them after birds, but that modern aircraft don't look like birds. The direct approach is used in
AI - What is this, where it's shown that if we've a formal definition of AI, it can be found by enumerating all possible programs and then testing each of them to see whether it has produced Artificial Intelligence, or has not.
Artificial consciousness research
Artificial consciousness research aims to create and study artificially conscious systems.
Professor Igor Aleksander argues 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.
Franklin’s Intelligent Distribution Agent
Stan Franklin (1995, 2003) defines an
autonomous agent as possessing
functional consciousness when it's capable of several of the functions of consciousness as identified by
Bernard Baars’
Global Workspace Theory, . His brain child IDA (Intelligent Distribution Agent) is a software implementation of GWT, which makes it functionally conscious by definition. IDA’s task is to negotiate new assignments for sailors in the US Navy after they end a tour of duty, by matching each individual’s skills and preferences with the Navy’s needs. IDA interacts with Navy databases and communicates with the sailors via natural language email dialog while obeying a large set of Navy policies. The IDA computational model was developed during 1996-2001 at Stan Franklin’s "Conscious" Software Research Group at the
University of Memphis. It "consists of approximately a quarter-million lines of
Java code, and almost completely consumes the resources of a 2001 high-end workstation." It relies heavily on
codelets, which are "special purpose, relatively independent, mini-agent[s] typically implemented as a small piece of code running as a separate thread." In IDA’s top-down architecture, high-level cognitive functions are explicitly modeled; see and for details. While IDA is functionally conscious by definition, Franklin does “not attribute
phenomenal consciousness to [his] own 'conscious' software agent, IDA, in spite of her many human-like behaviours. This in spite of watching several US Navy detailers repeatedly nodding their heads saying 'Yes, that’s how I do it' while watching IDA’s internal and external actions as she performs her task."
Ron Sun's cognitive architecture CLARION
CLARION posits a two-level representation that explains the distinction between conscious and unconscious mental processes.
CLARION has been successful in accounting for a variety of psychological data. A number of well-known skill learning tasks have been simulated using CLARION that span the spectrum ranging from simple reactive skills to complex cognitive skills. The tasks include serial reaction time (SRT) tasks, artificial grammar learning (AGL) tasks, process control (PC) tasks, the categorical inference (CI) task, the alphabetical arithmetic (AA) task, and the Tower of Hanoi (TOH) task . Among them, SRT, AGL, and PC are typical implicit learning tasks, very much relevant to the issue of consciousness as they operationalized the notion of consciousness in the context of psychological experiments .
The simulations using CLARION provide detailed, process-based interpretations of experimental data related to consciousness, in the context of a broadly scoped cognitive architecture and a unified theory of cognition. Such interpretations are important for a precise, process-based understanding of consciousness and other aspects of cognition, leading up to better appreciations of the role of consciousness in human cognition . CLARION also makes quantitative and qualitative predictions regarding cognition in the areas of memory, learning, motivation, meta-cognition, and so on. These predictions either have been experimentally tested already or are in the process of being tested.
Haikonen’s cognitive architecture
Pentti considers classical rule-based computing inadequate for achieving AC: "the brain is definitely not a computer. Thinking isn't an execution of programmed strings of commands. The brain isn't a numerical calculator either. We don't think by numbers." Rather than trying to achieve
mind and
consciousness by identifying and implementing their underlying computational rules, Haikonen proposes "a special
cognitive architecture to reproduce the
processes of
perception,
inner imagery,
inner speech,
pain,
pleasure,
emotions and the
cognitive functions behind these. This bottom-up architecture would produce higher-level functions by the power of the elementary processing units, the
artificial neurons, without
algorithms or
programs". Haikonen believes that, when implemented with sufficient complexity, this architecture will develop consciousness, which he considers to be "a style and way of operation, characterized by distributed signal representation, perception process, cross-modality reporting and availability for retrospection." Haikonen isn't alone in this process view of consciousness, or the view that AC will spontaneously emerge in
autonomous agents that have a suitable neuro-inspired architecture of complexity; these are shared by many, for example and . A low-complexity implementation of the architecture proposed by was reportedly not capable of AC, but did exhibit emotions as expected .
Self-awareness research
Self-awareness in robots is being investigated by Junichi Takeno
(External Link) at
Meiji University in Japan. Takeno is asserting that he's developed a robot capable of discriminating between a self-image in a mirror and any other having an identical image to it
(External Link)(External Link), and this claim has been already reviewed.
Emergence
Some have suggested that intelligence can arise as an emergent quality from the convergence of random, man-made technologies. Human sentience — or any other biological and naturally occurring intelligence — arises out of the natural process of species evolution and an individual's experiences. Discussion of this eventuality is currently limited to
fiction and theory.
Origin of the term: John Searle's strong AI
The term "strong AI" was adopted from the name of an argument 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.
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,
AI researchers are interested in a related statement (that some sources confusingly call "the strong AI hypothesis"):
An artificial intelligence system can think (or act like it thinks) as well or better than people do.
This assertion, which hinges on the breadth and power of machine intelligence, is the subject of this article.Further Information
Get more info on 'Artificial General Intelligence'.
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