The philosophy of artificial intelligence is a branch of the philosophy of mind and the philosophy of computer science that explores artificial intelligence and its implications for knowledge and understanding of intelligence, ethics, consciousness, epistemology, and free will. Furthermore, the technology is concerned with the creation of artificial animals or artificial people (or, at least, artificial creatures; see artificial life), so the discipline is of considerable interest to philosophers. These factors contributed to the emergence of the philosophy of artificial intelligence.
The philosophy of artificial intelligence attempts to answer such questions as: define the philosophy of AI as consisting of the first two questions, and the additional question of the ethics of artificial intelligence. writes "In the current literature, philosophy has two chief roles: to determine whether or not such machines would be conscious, and, second, to predict whether or not such machines are possible." The last question bears on the first two.
Can a machine act intelligently? Can it solve any problem that a person would solve by thinking? Are human intelligence and machine intelligence the same? Is the human brain essentially a computer? Can a machine have a mind, mental states, and consciousness in the same sense that a human being can? Can it feel how things are? (i.e. does it have qualia?) Questions like these reflect the divergent interests of AI researchers, cognitive scientists and philosophers respectively. The scientific answers to these questions depend on the definition of "intelligence" and "consciousness" and exactly which "machines" are under discussion.
Important propositions in the philosophy of AI include some of the following:
Turing's "polite convention": If a machine behaves as intelligently as a human being, then it is as intelligent as a human being.This is a paraphrase of the essential point of the Turing test. , , , . The Dartmouth proposal: "Every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.". This assertion was printed in the program for the Dartmouth workshop of 1956, widely considered the "birth of AI." also . Allen Newell and Herbert A. Simon's physical symbol system hypothesis: "A physical symbol system has the necessary and sufficient means of general intelligent action." and . John Searle's strong AI hypothesis: "The appropriately programmed computer with the right inputs and outputs would thereby have a mind in exactly the same sense human beings have minds."This version is from , and is also quoted in . Searle's original formulation was "The appropriately programmed computer really is a mind, in the sense that computers given the right programs can be literally said to understand and have other cognitive states." . Strong AI is defined similarly by : "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." Hobbes' mechanism: "For 'reason'... is nothing but 'reckoning,' that is adding and subtracting, of the consequences of general names agreed upon for the 'marking' and 'signifying' of our thoughts..."
Machine intelligence In AI research, questions about "intelligence" are concerned with the behavior of machines, and is not generally concerned with the concept of thought.See , where they make the distinction between acting rationally and being rational, and define AI as the study of the former.
Definitions of intelligence Turing test
In 1949, computer scientist Alan Turing reduced the problem of defining intelligence to a simple question about conversation. He suggests that if a machine can answer any question posed to it, using the same words that an ordinary person would, then we may call that machine intelligent. and see , where they call his paper "famous" and write "Turing examined a wide variety of possible objections to the possibility of intelligent machines, including virtually all of those that have been raised in the half century since his paper appeared." A modern version of his experimental design would use an online chat room, where one of the participants is a real person and one of the participants is a computer program. The program passes the test if no one can tell which of the two participants is human. Turing notes that no one (except philosophers) ever asks the question "can people think?" He writes "instead of arguing continually over this point, it is usual to have a polite convention that everyone thinks"., under "The Argument from Consciousness". Turing's test extends this polite convention to machines, proposing that if a machine acts as intelligently as a human being, then it is as intelligent as a human being.
One criticism of the Turing test is that it only measures the "humanness" of the machine's behavior, rather than the "intelligence" of the behavior. Since human behavior and intelligent behavior are not exactly the same thing, the test fails to measure intelligence. Stuart J. Russell and Peter Norvig write that "aeronautical engineering texts do not define the goal of their field as 'making machines that fly so exactly like pigeons that they can fool other pigeons'"..
Intelligence as achieving goals
Twenty-first century AI research defines intelligence in terms of goal-directed behavior. It views intelligence as a set of problems that the machine is expected to solve – the more problems it can solve, and the better its solutions are, the more intelligent the program is. AI founder John McCarthy defined intelligence as "the computational part of the ability to achieve goals in the world."
Stuart Russell and Peter Norvig formalized this definition using abstract intelligent agents. An "agent" is something which perceives and acts in an environment. A "performance measure" defines what counts as success for the agent.. "If an agent acts so as to maximize the expected value of a performance measure based on past experience and knowledge then it is intelligent."Russell and Norvig would prefer the word "rational" to "intelligent".
Definitions like this one try to capture the essence of intelligence. They have the advantage that, unlike the Turing test, they do not also test for unintelligent human traits such as making typing mistakes. They have the disadvantage that they can fail to differentiate between "things that think" and "things that do not". By this definition, even a thermostat has a rudimentary intelligence. consider a thermostat a simple form of intelligent agent, known as a reflex agent. For an in-depth treatment of the role of the thermostat in philosophy see "4. Is Experience Ubiquitous?" subsections What is it like to be a thermostat?, Whither panpsychism?, and Constraining the double-aspect principle.
Arguments that a machine can display general intelligence
Argument #1: The brain can be simulated
Hubert Dreyfus describes this argument as claiming that "if the nervous system obeys the laws of physics and chemistry, which we have every reason to suppose it does, then ... we ... ought to be able to reproduce the behavior of the nervous system with some physical device". This argument, first introduced as early as 1943 and vividly described by Hans Moravec in 1988, is now associated with futurist Ray Kurzweil, who estimates that computer power will be sufficient for a complete brain simulation by the year 2029.. Also see and . The most extreme form of this argument (the brain replacement scenario) was put forward by Clark Glymour in the mid-1970s and was touched on by Zenon Pylyshyn and John Searle in 1980. A non-real-time simulation of a thalamocortical model that has the size of the human brain (1011 neurons) was performed in 2005, and it took 50 days to simulate 1 second of brain dynamics on a cluster of 27 processors.
Even AI's harshest critics (such as Hubert Dreyfus and John Searle) agree that a brain simulation is possible in theory. However, Searle points out that, in principle, anything can be simulated by a computer; thus, bringing the definition to its breaking point leads to the conclusion that any process at all can technically be considered "computation". "What we wanted to know is what distinguishes the mind from thermostats and livers," he writes. Thus, merely simulating the functioning of a living brain would in itself be an admission of ignorance regarding intelligence and the nature of the mind, like trying to build a jet airliner by copying a living bird precisely, feather by feather, with no theoretical understanding of aeronautical engineering.
Argument #2: Human thinking is symbol processing
In 1963, Allen Newell and Herbert A. Simon proposed that "symbol manipulation" was the essence of both human and machine intelligence. They wrote: "A physical symbol system has the necessary and sufficient means of general intelligent action." This claim is very strong: it implies both that human thinking is a kind of symbol manipulation (because a symbol system is necessary for intelligence) and that machines can be intelligent (because a symbol system is sufficient for intelligence).Searle writes "I like the straight forwardness of the claim." . Another version of this position was described by philosopher Hubert Dreyfus, who called it "the psychological assumption": "The mind can be viewed as a device operating on bits of information according to formal rules.". The "symbols" that Newell, Simon and Dreyfus discussed were word-like and high levelsymbols that directly correspond with objects in the world, such as <dog> and <tail>. Most AI programs written between 1956 and 1990 used this kind of symbol. Modern AI, based on statistics and mathematical optimization, does not use the high-level "symbol processing" that Newell and Simon discussed.
Arguments against symbol processing
These arguments show that human thinking does not consist (solely) of high level symbol manipulation. They do not show that artificial intelligence is impossible, only that more than symbol processing is required.
Gödelian anti-mechanist arguments