Philosophy of artificial intelligence

The philosophy of artificial intelligence considers the relationship between machines and thought and attempts to answer such question as:

• Can a machine act intelligently? Can it solve any problem that a person would solve by thinking?
• Can a machine have a mind, mental states and consciousness in the same sense humans do? Can it feel?
• Are human intelligence and machine intelligence the same? Is the human brain essentially a computer?

These three questions reflect the divergent interests of AI researchers, philosophers and cognitive scientists respectively. The answers to these questions depend on how one defines "intelligence" or "consciousness" and exactly which "machines" are under discussion.

Important propositions in the philosophy of AI include:

• Turing's "polite convention": If a machine acts as intelligently as a human being, then it is as intelligent as a human being.
• The Dartmouth proposal: "Every aspect of learning or any other feature of intelligence can be so precisely described that a machine can be made to simulate it."
• Newell and Simon's physical symbol system hypothesis: "A physical symbol system has the necessary and sufficient means of general intelligent action."
• 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."
• Hobbes' mechanism: "Reason is nothing but reckoning."

Can a machine display general intelligence?

Is it possible to create a machine that can solve all the problems humans solve using their intelligence? This is the question that AI researchers are most interested in answering. It defines the scope of what machines will be able to do in the future and guides the direction of AI research. It only concerns the behavior of machines and ignores the issues of interest to psychologists, cognitive scientists and philosophers; to answer this question, it doesn't matter whether a machine is really thinking (as a person thinks) or is just acting like it is thinking.

The basic position of most AI researchers is summed up in this statement, which appeared in the proposal for the Dartmouth Conferences of 1956:

• Every aspect of learning or any other feature of intelligence can be so precisely described that a machine can be made to simulate it.

Arguments against the basic premise must show that building a working AI system is impossible, because there is some practical limit to the abilities of computers or that there is some special quality of the human mind that is necessary for thinking and yet can't be duplicated by a machine (or by the methods of current AI research). Arguments in favor of the basic premise must show that such a system is possible.

The first step to answering the question is to clearly define "intelligence."

Intelligence

Turing test

Alan Turing, in a famous and seminal 1950 paper, reduced the problem of defining intelligence to a simple question about conversation. He suggests that: if a machine can answer any question put to it, using the same words that an ordinary person would, then we may call that machine intelligent. 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." Turing's test extends this polite convention to machines:

• If a machine acts as intelligently as human being, then it is as intelligent as a human being.

Human intelligence vs. intelligence in general

One criticism of the Turing test is that it is explicitly anthropomorphic. If our ultimate goal is to create machines that are more intelligent than people, why should we insist that our machines must closely resemble people? Russell and 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.'" Recent AI research defines intelligence in terms of 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 maximize the expected value of a performance measure based on past experience and knowledge then it is intelligent.

Definitions like this one try to capture the essence of intelligence. They have the advantage that, unlike the Turing test, they don't also test for human traits that we may not want to consider intelligent, like the ability to be insulted or the temptation to lie. They have the disadvantage that they fail to make the commonsense differentiation between "things that think" and "things that don't". By this definition, even a thermostat has a rudimentary intelligence.

Arguments that a machine can display general intelligence

The brain can be simulated

An MRI scan of a normal adult human brain

Marvin Minsky writes 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.. 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.

Few disagree that a brain simulation is possible in theory, even critics of AI such as Hubert Dreyfus and John Searle. However, Searle points out that, in principle, anything can be simulated by a computer, and so any process at all can be considered "computation", if you're willing to stretch the definition to the breaking point. "What we wanted to know is what distinguishes the mind from thermostats and livers," he writes. Any argument that involves simply copying a brain is an argument that admits that we know nothing about how intelligence works. "If we had to know how the brain worked to do AI, we wouldn't bother with AI."

Human thinking is symbol processing

In 1963, Alan Newell and Herbert 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). 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.

A distinction is usually made between the kind of high level symbols that directly correspond with objects in the world, such as and and the more complex "symbols" that are present in a machine like a neural network. Early research into AI, called "good old fashioned artificial intelligence" (GOFAI) by John Haugeland, focused on these kind of high level symbols.

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.

Lucas, Penrose and Gödel

In 1931 Kurt Gödel proved that it is always possible to create statements that a formal system (such as an AI program) could not prove. A human being, however, can (with some thought) see the truth of these "Gödel statements". This proved to philosopher John Lucas that human reason would always be superior to machines. He wrote "Gödel's theorem seems to me to prove that mechanism is false, that is, that minds cannot be explained as machines." Roger Penrose expanded on this argument in his 1989 book The Emperor's New Mind, where he speculated that quantum mechanical processes inside individual neurons gave humans this special advantage over machines.

Douglas Hofstadter, in his Pulitzer prize winning book Gödel, Escher, Bach: An Eternal Golden Braid, explains that these "Gödel-statements" always refer to the system itself, similar to the way the Epimenides paradox uses statements that refer to themselves, such as "this statement is false" or "I am lying". But, of course, the Epimenides paradox applies to anything that makes statements, whether they are machines or humans, even Lucas himself. Consider:

• Lucas can't assert the truth of this statement.

This statement is true but can't be asserted by Lucas. This shows that Lucas himself is subject to the same limits that he describes for machines, as are all people, and so Lucas's argument is pointless.

Further, Russell and Norvig note that Gödel's argument only applies to what can theoretically be proved, given an infinite amount of memory and time. In practice, real machines (including humans) have finite resources and will have difficulty proving many theorems. It is not necessary to prove everything in order to be intelligent.

Dreyfus: the primacy of unconscious skills

Hubert Dreyfus argued that human intelligence and expertise depended primarily on unconscious instincts rather than conscious symbolic manipulation, and argued that these unconscious skills would never be captured in formal rules.

Dreyfus's argument had been anticipated by Turing in his 1950 paper Computing machinery and intelligence, where he had classified this as the "argument from the informality of behavior." Turing argued in response that, just because we don't know the rules that govern a complex behavior, this does not mean that no such rules exist. He wrote: "we cannot so easily convince ourselves of the absence of complete laws of behaviour ... The only way we know of for finding such laws is scientific observation, and we certainly know of no circumstances under which we could say, 'We have searched enough. There are no such laws.'"

Russell and Norvig point out that, in the years since Dreyfus published his critique, progress has been made towards discovering the "rules" that govern unconscious reasoning. The situated movement in robotics research attempts to capture our unconscious skills at perception and attention. Computational intelligence paradigms, such as neural nets, evolutionary algorithms and so on are mostly directed at simulated unconscious reasoning and learning. Research into commonsense knowledge has focused on reproducing the "background" or context of knowledge. In fact, AI research in general has moved away from high level symbol manipulation or "GOFAI", towards new models that are intended to capture more of our unconscious reasoning. Historian and AI researcher Daniel Crevier wrote that "time has proven the accuracy and perceptiveness of some of Dreyfus's comments. Had he formulated them less aggressively, constructive actions they suggested might have been taken much earlier."

Can a machine have a mind, consciousness and mental states?

This is a philosophical question, related to the problem of other minds and the hard problem of consciousness. The question revolves around a position defined by John Searle as "strong AI":

• A physical symbol system can have a mind and mental states.

Searle distinguished this position from what he called "weak AI":

• A physical symbol system can act intelligently.

Searle introduced the terms to isolate strong AI from weak AI so he could focus on what he thought was the more interesting and debatable issue. He argued that even if we assume that we had a computer program that acted exactly like a human mind, there would still be a difficult philosophical question that needed to be answered.

Neither of Searle's two positions are of great concern to AI research, since they do not directly answer the question "can a machine display general intelligence?" (unless it can also be shown that consciousness is necessary for intelligence). Turing wrote "I do not wish to give the impression that I think there is no mystery about consciousness ... [b]ut I do not think these mysteries necessarily need to be solved before we can answer the question [of whether machines can think]." Russell and Norvig agree: "Most AI researchers take the weak AI hypothesis for granted, and don't care about the strong AI hypothesis."

Before we can answer this question, we must be clear what we mean by "minds", "mental states" and "consciousness".

Consciousness, minds, mental states, meaning

Representation of consciousness from the 17th century.

The words "mind" and "consciousness" are used by different communities in different ways. Some new age thinkers, for example, use the word "consciousness" to describe something similar to Bergson's "élan vital": an invisible, energetic fluid that permeates life and especially the mind. Science fiction writers use the word to describe some essential property that makes us human: a machine or alien that is "conscious" will be presented as a fully human character, with intelligence, desires, will, insight, pride and so on. (Science fiction writers also use the words "sentience", "sapience," "self-awareness" or "ghost" (as in the Ghost in the Shell manga and anime series) to describe this essential human property.) For others, the words "mind" or "consciousness" are used as a kind of secular synonym for the soul.

For philosophers, neuroscientists and cognitive scientists, the words are used in a way that is both more precise and more mundane: they refer to the familiar, everyday experience of having a "thought in your head", like a perception, a dream, an intention or a plan, and to the way we know something, or mean something or understand something. "It's not hard to give a commonsense definition of consciousness" observes philosopher John Searle. What is mysterious and fascinating is not so much what it is but how it is: how does a lump of fatty tissue and electricity give rise to this (familiar) experience of perceiving, meaning or thinking?

Philosophers call this the hard problem of consciousness. It is the latest version of a classic problem in the philosophy of mind called the "mind-body problem." A related problem is the problem of meaning or understanding (which philosophers call "intentionality"): what is the connection between our thoughts (i.e. patterns of neurons) and what we are thinking about (i.e. objects and situations out in the world)? A third issue is the problem of experience (or "phenomenology"): If two people see the same thing, do they have the same experience? Or are there things "inside their head" (called "qualia") that can be different from person to person?

Neurobiologists believe all these problems will be solved as we begin to identify the neural correlates of consciousness: the actual machinery in our heads that creates the mind, experience and understanding. Even the harshest critics of artificial intelligence agree that the brain is just a machine, and that consciousness and intelligence are the result of physical processes in the brain. The difficult philosophical question is this: can a computer program, running on a digital machine that shuffles the binary digits of zero and one, duplicate the ability of the neurons to create minds, with mental states (like understanding or perceiving), and ultimately, the experience of consciousness?

Arguments that a computer can't have a mind and mental states

Searle's Chinese room

John Searle asks us to consider a thought experiment: suppose we have written a computer program that passes the Turing Test and demonstrates "general intelligent action." Suppose, specifically that the program can converse in fluent Chinese. Write the program on 3x5 cards and give them to an ordinary person. Lock the person into a room and have him follow the instructions on the cards. He will copy out Chinese characters and pass them in and out of the room through a slot. From the outside, it will appear that the Chinese room contains a fully intelligent person who speaks Chinese. The question is this: is there anyone (or anything) in the room that understands Chinese? That is, is there anything that has the mental state of understanding, or which has conscious awareness of what is being discussed in Chinese? The man is clearly not aware. The room can't be aware. The cards certainly aren't aware. Searle concludes that the Chinese room, or any other physical symbol system, cannot have a mind.

Searle goes on to argue that actual mental states and consciousness require (yet to be described) "actual physical-chemical properties of actual human brains." He argues there are special "causal properties" of brains and neurons that gives rise to minds: in his words "brains cause minds."

Related arguments: Leibniz' mill, Block's telephone exchange and blockhead

Gottfried Leibniz made essentially the same argument as Searle in 1714, using the thought experiment of expanding the brain until it was the size of a mill. In 1974, Lawrence Davis imagined duplicating the brain using telephone lines and offices staffed by people, and in 1978 Ned Block envisioned the entire population of China involved in such a brain simulation. This thought experiment is called "the Chinese Nation" or "the Chinese Gym". Ned Block also proposed his "blockhead" argument, which is a version of the Chinese room in which the program has been re-factored into a simple set of rules of the form "see this, do that", removing all mystery from the program.

Responses to the Chinese Room

Responses to the Chinese room emphasize several different points.

1. The systems reply and the virtual mind reply: This reply argues that the system, including the man, the program, the room, and the cards, is what understands Chinese. Searle claims that the man in the room is the only thing which could possibly "have a mind" or "understand", but others disagree, arguing that it is possible for there to be two minds in the same physical place, similar to the way a computer can simultaneously "be" two machines at once: one physical (like a Macintosh) and one "virtual" (like a word processor).
2. Speed, power and complexity replies: Several critics point out that the man in the room would probably take millions of years to respond to a simple question, and would require "filing cabinets" of astronomical proportions. This brings the clarity of Searle's intuition into doubt.
3. Robot reply: To truly understand, some believe the Chinese Room needs eyes and hands. Hans Moravec writes: 'If we could graft a robot to a reasoning program, we wouldn't need a person to provide the meaning anymore: it would come from the physical world."
4. Brain simulator reply: What if the program simulates the sequence of nerve firings at the synapses of an actual brain of an actual Chinese speaker? The man in the room would be simulating an actual brain. This is a variation on the "systems reply" that appears more plausible because "the system" now clearly operates like a human brain, which strengthens the intuition that there is something besides the man in the room that could understand Chinese.
5. Other minds reply and the epiphenomena reply: Several people have noted that Searle's argument is just a version of the problem of other minds, applied to machines. Since it's difficult to decide if people are "actually" thinking, we shouldn't be surprised that it's difficult to answer the same question about machines. A related idea is that Searle's "causal properties" of neurons are epiphenomenal: they have no effect on the real world. Why would natural selection create them in the first place, if they make no difference to behavior?

Is thinking a kind of computation?

This issue is of primary importance to cognitive scientists, who study the nature of human thinking and problem solving.

The computational theory of mind or "computationalism" claims that the relationship between mind and body is similar (if not identical) to the relationship between a running program and a computer. The idea has philosophical roots in Hobbes (who claimed reasoning was "nothing more than reckoning"), Leibniz (who attempted to create a logical calculus of all human ideas), Hume (who thought perception could be reduced to "atomic impressions") and even Kant (who analyzed all experience as controlled by formal rules). The latest version is associated with philosophers Hilary Putnam and Jerry Fodor.

This question bears on our earlier questions: if the human brain is a kind of computer then computers can be both intelligent and conscious, answering both the practical and philosophical questions of AI. In terms of the practical question of AI ("Can a machine display general intelligence?"), some versions of computationalism make the claim that (as Hobbes wrote):

• Reasoning is nothing but reckoning

In other words, our intelligence derives from a form of calculation, similar to arithmetic. This is the physical symbol system hypothesis discussed above, and it implies that artificial intelligence is possible. In terms of the philosophical question of AI ("Can a machine have mind, mental states and consciousness?"), most versions of computationalism claim that (as Stevan Harnad characterizes it):

• Mental states are just implementations of (the right) computer programs

This is John Searle's "strong AI" discussed above, and it is the real target of the Chinese Room argument (according to Harnad).

Other related questions

Alan Turing noted that there are many arguments of the form "a machine will never do X", where X can be many things, such as:

Be kind, resourceful, beautiful, friendly, have initiative, have a sense of humor, tell right from wrong, make mistakes, fall in love, enjoy strawberries and cream, make someone fall in love with it, learn from experience, use words properly, be the subject of its own thought, have as much diversity of behaviour as a man, do something really new.

Turing argues that these objections are often based on naive assumptions about the versatility of machines or are "disguised forms of the argument from consciousness". Writing a program that exhibits one of these behaviors "will not make much of an impression." All of these arguments are tangential to the basic premise of AI, unless it can be shown that one of these traits is essential for general intelligence.

Can a machine have emotions?

Hans Moravec believes "I think robots in general will be quite emotional about being nice people" and describes emotions in terms of the behaviors they cause. Fear is a source of urgency. Empathy is a necessary component of good human computer interaction. He says robots "will try to please you in an apparently selfless manner because it will get a thrill out of this positive reinforcement. You can interpret this as a kind of love." Daniel Crevier writes "Moravec's point is that emotions are just devices for channeling behavior in a direction beneficial to the survival of one's species."

The question of whether the machine actually feels an emotion, or whether it merely acts as if feeling an emotion is the philosophical question, "can a machine be conscious?" in another form.

Can a machine be self aware?

"Self awareness", as noted above, is sometimes used by science fiction writers as a name for the essential human property that makes a character fully human. Turing strips away all other properties of human beings and reduces the question to "can a machine be the subject of its own thought?" Can it think about itself? Viewed in this way, it is obvious that a program can be written that can report on its own internal states, such as a debugger.

Can a machine be original or creative?

Turing reduces this to the question of whether a machine can “take us by surprise" and argues that this is obviously true, as any programmer can attest. He notes that, with enough storage capacity, a computer can behave in an astronomical number of different ways. It must be possible, even trivial, for a computer that can represent ideas to combine them in new ways. (Douglas Lenat's Automated Mathematician, as one example, combined ideas to discover new mathematical truths.)

Can a machine have a soul?

Finally, those who believe in the existence of a soul would argue that

• Thinking is a function of man’s immortal soul

Alan Turing called this “the theological objection” and writes

In attempting to construct such machines we should not be irreverently usurping His power of creating souls, any more than we are in the procreation of children: rather we are, in either case, instruments of His will providing mansions for the souls that He creates.