Minds and Computers: An Introduction to the Philosophy of Artificial Intelligence


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1. The History of AI

There is no longer a clear distinction between humans and computers. Most conscious entities no longer have a permanent physical presence. The number of soft-ware based humans vastly exceeds those still using native neuron-cell based computation. Life expectancy is no longer a viable term in relation to intelligent beings. The philosophical implications are clear: if a properly programmed computer can reason, possess intelligence, then it may well be that our intelligence is a product of the same sort of processes that go on in computers - maybe our brains or minds are computers, running programs.

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Perhaps our reasoning is a form of computation. A host of issues: to understand the debate surrounding AI properly a variety of issues and arguments have to be considered, e. These arguments need to be considered. The question of whether intelligence requires consciousness is an interesting one. Before getting down to details, it is worth looking at one well-known issue connected with AI. Nearly everyone who has heard of AI has heard of the Turing Test. The general idea: if a machine passes the Turing Test, then we would be justified in taking the machine to be intelligent.

Simplifying somewhat, a machine would pass the Turing Test if it could pass itself off as a normal human being more than half the time. Running the Test involves having conversations with the machine, and with humans. You can ask what you want: you can tell jokes, inquire about political opinions, ask for verdicts of poets and novelists - nothing is ruled out.

The test can be criticized: e.

If so, it would fail the test, despite being intelligent. This seems a reasonable point. Arguably, it would. Among the books in the library will be all the books containing meaningful 2-party or 3-party dialogues between human beings. Every possible conversation is in there, in a book. Suppose we had a very powerful computer which could contain all these conversation- books in its memory, and quickly find any book it wanted.

You begin your conversation any way you like or you allow the machine to begin, with any of the possible conversational opening lines in its vast repertoire - including periods of silence. But suppose you start off. In response to your initial statement, e. The computer then quickly isolates the billions of books that contain these three opening lines of conversation, and from these many billions, randomly selects one. The fourth line in this conversation is:. And so it goes. You have a perfectly sensible conversation - no matter what gambit you adopt, no matter what you say, the computer comes up with a sensible, human-like, response.

But is it intelligent? Maybe: but this is wholly irrelevant. The question is: once built, if such a computer passes the Turing test, are we right to regard it as intelligent? In which case, behaviour isn't conceptually sufficient for intelligence. Merely producing intelligent-seeming output isn't enough - we doubt that the library-computer is intelligent because although it produces intelligent-seeming responses, what's going on inside it seems to be the wrong sort of process for intelligence.

Lesson: it does matter what goes on inside you. Black-box behaviourism only the input-outputs are relevant is false even for intelligence. Ordinary PCs are examples of these machines. What matters is software, not hardware. Well, how do computers work? One quick answer is that they process information. Another is that they store and shuffle symbols, according to fixed and precise rules. Perhaps the simplest form of pattern consists of a sequence of two elements.

The pattern consists of the way these elements are organized. A particular pattern would be:. Another reason: base 2 arithmetic consists of 1s and 0s, and any base 10 number can be represented in base 2, e.


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However, this connection with numbers is, in a way, accidental. What matters is the simple pattern, which can be represented in any number of ways. So: a computer is a device which can store remember and manipulate simple binary 2-element patterns, or bit-strings. Bit-strings are stored in what are called registers. Each register, a location in the computers memory, has an address, which is itself a bit-string. There are only a few of these primitive processes.

Other Bodies, Other Minds: A Machine Incarnation of an Old Philosophical Problem

They include operations like the following:. There are typically only about 10 primitive processes such as these that a computer can carry out - there are different sets of processes, all of which are equivalent they can perform the same kinds of manipulation. I noted earlier that it can be misleading to say that a computer works by manipulating or processing data, or information. A useful analogy: computation involves the manipulation of shapes or syntax , rather than items with semantic properties. This is a meaningful sequence of words.

In performing this manipulation, you were aware of the meanings of a the individual words, and 2 of the sentences they compose, and 3 the meaning of the sentence I uttered, the command I gave. You were operating at the semantic level.

Artificial intelligence

But I could easily create a machine which would produce the same effect, i. All it does is:. This is what computers do: they perform certain simple mechanical operations on items with certain distinctive shapes.


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  • It works on a syntactic rather than a semantic level. It works with letters forms, shapes rather than meanings. Does this mean that AI is doomed from the start? Not necessarily. The claim made is this: genuine intelligence, perhaps meaning and understanding, depend upon patterns being manipulated in the right sort of way. Given enough complexity, semantics emerges from syntax.

    In fact, computers were named after people : the people who spend their time in banks and offices doing arithmetic and book-keeping were called computers. Given this, it may seem surprising that earlier pioneers such as Turing were even then speculating that computers could one day be intelligent, given the right program, and that maybe human intelligence was really just computation. Take a simple argument:. You might think you need to be pretty smart or at least be a little bit smart to recognize that the conclusion follows from the premises.

    Any reasonable person would conclude 3 given the premises. But in fact, this reasoning is easily mechanizable. This is a simple example of logical reasoning or logical argument. The conclusion is bound to be true if the premises are true. The argument is valid because of its structure or form , not because of the contents of the individual sentences or words. What we have here is a semantically evaluable process which has been reduced to a purely mechanical sequence of steps.

    We know that long and complex arguments can be dealt with in the same manner. Any argument to which formal logic can be applied can be handled in a purely mechanical manner. That is, given the premises, what follows from these premises the consequences can be generated mechanically. It consists of a set of mechanical rules for manipulating symbols, or inscriptions.

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    And as such, it can be handled by a machine which works at the purely syntactic level. The important point: because the rules are syntactic, they can be mechanized. Reasoning involves logic, and so to this extent, reasoning can be mechanized. Beyond any doubt, the most important thing that has happened in cognitive science was Turing's invention of the notion of mechanical rationality. Here's a quick, very informal, introduction.

    It's a remarkable fact that you can tell, just by looking at it, that any sentence of the syntactic form P and Q 'John swims and Mary drinks', as it might be is true only if P and Q are both true. This really is remarkable since, after all, it's what they mean, together with how the non-linguistic world is, that decide whether P or Q is itself true. This line of thought is often summarised by saying that some inferences are rational in virtue of the syntax of the sentences that enter into them; metaphorically, in virtue of the 'shapes' of these sentences. Turing noted that, wherever an inference is formal in this sense, a machine can be made to execute the inference.

    This is because, although machines are awful at figuring out what's going on in the world, you can make them so that they are quite good at detecting and responding to syntactic relations among sentences. Give it an argument that depends just on the syntax of the sentences that it is couched in and the machine will accept the argument if and only if it is valid. To that extent, you can build a rational machine. Thus, in chrysalis, the computer and all its works.

    Thus, too, the idea that some, at least, of what makes minds rational is their ability to perform computations on thoughts; where thoughts, like sentences, are assumed to be syntactically structured and where 'computations' means formal operations in the manner of Turing. It's this theory that Pinker has in mind when he claims that 'thinking is a kind of computation'. It has proved to be a simply terrific idea.

    A philosophical question — not a technical one.

    Like Truth, Beauty and Virtue, rationality is a normative notion; the computational theory of mind is the first time in all of intellectual history that a science has been made out of one of those. If God were to stop the show now and ask us what we've discovered about how we think, Turing's theory of computation is far the best thing that we could offer.

    All this may not seem so remarkable. If so, then the fact that it strikes many people as a remarkable advance just goes to show how little understanding of the nature of thought and reasoning we used to have. We are now in a better position to see how semantics might emerge from syntax: if the shape-shuffling is of the right form and complexity, then the symbols become meaningful, in virtue of being manipulated in the right ways. Again we have the point that the rational i. To put it another way: meaning is use.

    Use a symbol in the right way, and it becomes a genuine symbol: i. It ceases to be a meaningless shape. Semantics emerges from or consists in, syntax. Some people would say that syntax is necessary for meaning, but not sufficient: to actually be meaningful, the symbols have to be able to enter into the right sorts of causal relationship with the world.

    The key point is that for the classical cognitivist, syntax plays an essential role in the generation of meaning, and may be sufficient. The general thesis, then, is this: intelligence and rationality are products of pattern-shuffling. Weak Symbol-System thesis : a computer with the right program would be intelligent; i.

    Symbol-shuffling capabilities of the right kind are sufficient for intelligence. Strong Symbol-System thesis : symbol-shuffling capabilities of the right kind are necessary and sufficient for intelligence. The only way to be intelligent is by being a pattern-manipulator. How can we decide whether either of these theses is true? There are a number of possibilities.

    The most obvious are these:. If we could find a symbol-system which manifested human-levels of reason and intelligence, then we would know the Weak Thesis is true. Are there any non-empirical or philosophical arguments which can be brought to bear? An a priori proof of either the Weak or the Strong theses is harder to envisage. One possible route would be this:. Some people think a thesis in mathematical logic - the Church-Turing Thesis - proves this.

    It might be thought that we have a quick and easy refutation of the Strong Thesis:. We are intelligent, and we owe our intelligence to our complex brains; but our brains are not symbol shuffling computers. But is this the case? There are several points to note. But this is irrelevant. We saw earlier that primitive processors are multiply realizable; they can be made of anything. There is no central processing unit working away on registers of binary bit-strings. Minds and Computers introduces readers to these issues by offering an engaging, coherent, and highly approachable interdisciplinary introduction to the Philosophy of Artificial Intelligence.

    Readers are presented with introductory material from each of the disciplines which constitute Cognitive Science: Philosophy, Neuroscience, Psychology, Computer Science, and Linguistics. Throughout, readers are encouraged to consider the implications of this disparate and wide-ranging material for the possibility of developing machines with minds. See All Customer Reviews. Shop Textbooks. Add to Wishlist. USD Sign in to Purchase Instantly.

    Spara som favorit. Could a computer have a mind? What kind of machine would this be? Exactly what do we mean by 'mind' anyway? The notion of the 'intelligent' machine, whilst continuing to feature in numerous entertaining and frightening fictions, has also been the focus of a serious and dedicated research tradition.

    Minds and Computers: An Introduction to the Philosophy of Artificial Intelligence Minds and Computers: An Introduction to the Philosophy of Artificial Intelligence
    Minds and Computers: An Introduction to the Philosophy of Artificial Intelligence Minds and Computers: An Introduction to the Philosophy of Artificial Intelligence
    Minds and Computers: An Introduction to the Philosophy of Artificial Intelligence Minds and Computers: An Introduction to the Philosophy of Artificial Intelligence
    Minds and Computers: An Introduction to the Philosophy of Artificial Intelligence Minds and Computers: An Introduction to the Philosophy of Artificial Intelligence
    Minds and Computers: An Introduction to the Philosophy of Artificial Intelligence Minds and Computers: An Introduction to the Philosophy of Artificial Intelligence
    Minds and Computers: An Introduction to the Philosophy of Artificial Intelligence Minds and Computers: An Introduction to the Philosophy of Artificial Intelligence
    Minds and Computers: An Introduction to the Philosophy of Artificial Intelligence Minds and Computers: An Introduction to the Philosophy of Artificial Intelligence
    Minds and Computers: An Introduction to the Philosophy of Artificial Intelligence Minds and Computers: An Introduction to the Philosophy of Artificial Intelligence
    Minds and Computers: An Introduction to the Philosophy of Artificial Intelligence

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