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思想 vs. 机器：图灵测试与中文房间
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Do curso por The University of Edinburgh
哲学导论（中文版）
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心智，大脑和电脑 - Suilin Lavelle
如果你正在阅读此内容，则你已有思想。 但是，什么是思想？拥有思想的意义是什么？ 我们应该将思想理解为一些以某种特定方式行事的意愿，还是大脑中类似电脑硬件上运行的程序的物质？ 在本模块中，我们将依次了解近代哲学是如何选择接受各个观点的，以及他们接受这些观点的原因，我们还将考虑这些观点面临的问题和前景。
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Dr. Dave WardLecturer in Philosophy
University of Edinburgh
Professor Duncan PritchardProfessor of Philosophy
University of Edinburgh
Professor Michela MassimiFull Professor
Philosophy
Dr. Suilin LavelleLecturer in Philosophy
University of Edinburgh
Dr. Matthew ChrismanReader in Philosophy
Philosophy
Dr. Allan Hazlett
Dr. Alasdair RichmondDr.
Philosophy
In 1950, Alan Turing took on this question.
He thought to himself that the question,
can machines think, is just hopelessly vague.
It's really difficult for us to get our head around.
And maybe a better question to ask was, when can a machine be mistaken for
a real thinking person?
So Turing proposed the following thought experiment.
He says, imagine that you're in a room, and you're facing a barrier.
And behind the barrier there's a human, and there's a computer.
And you can ask the human and the computer questions, but
you don't know which one's which.
You don't know which is the human, and you don't know which is the computer.
And your task is to work out which is the human and which is the computer
just from the answers you get from the questions that you ask.
Now the questions that you can ask can be about anything.
So you could ask, where's the best place to buy wallpaper?
How do you feel about the current government?
Do you like ducks?
And you take the answers that you get.
And Turing says when we get to the point that we cannot decide which is the human
and which the machine, or if our decision about which is which becomes arbitrary,
then we have managed to build a machine that's capable of mentality,
a machine that can think.
It can think because it's been able to trick another human being.
So this is an organism, or rather a machine that's reached the appropriate
level of functional complexity to count as having a mind.
Now if you want to see a fun cultural reference to this test,
you can look at Blade Runner where Harrison Ford's character is sent to find
the replicants in a human society.
So you have this human society, and
then there are robots in this society which look and act a lot like humans.
And it's Harrison Ford's job to weed them out.
So he applies something like the Turing test.
He asks the machines and the humans lots of different questions to try and
work out whether what he's talking to is a replicant,
a robot that's pretending to be a human, or a human.
So how good is the Turing test for testing whether a machine can think?
Here are some problems that philosophers have leveled at it.
First of all, it's language-based, so all the testing is for
an intelligence that can communicate via language.
We couldn't, for instance, check for
animal intelligence if those animals couldn't speak languages.
The second problem with the Turing test that people have raised
is that perhaps it's too anthropocentric because what we're testing it for
is whether a machine can be mistaken for being a human being.
Well that means we're testing for
human intelligence, and surely it seems very chauvinistic to
think that the only intelligence worth studying is human intelligence.
There could be lots of different forms of intelligence out there,
which could be realized by a machine, but
which we're not allowing an opportunity to shine through using the Turing Test.
The third problem with the Turing Test is that it doesn't take into account
the inner states of the machine.
So take for example, a machine that's doing a sum.
And you asked it, what's 2 plus 8?
Now you could have a machine that does some sort of calculation.
It thinks 2 plus 8, adds the two things together, and gives you the answer, 10.
But you could have a machine that's programmed in a certain way that when it
receives the input 2 plus 8, it rattles through its files.
And comes to a file which says 2 plus 8 on it, pulls it out, opens it up, and
it says 10, and it throws out the answer, 10.
Now, in the first instance,
we want to say that that machine is doing some kind of computation.
It's going through some kind of thought process.
Whereas the second machine is just retrieving a file.
Now if it were possible to build a machine that could pass Turing's test just using
a file system, then surely this is a bit worrying,
because we don't seem to have a thought processes.
We just have a machine that can retrieve lots of different files and
spit out the answers.
Now this isn't necessarily a killer flaw to Turing's plan.
First of all you could argue that a machine that just pulled
out files according to the questions which it was asked
probably wouldn't be able to pass the test.
It would have to have such a gigantic file store and such a huge database searching
system that it just wouldn't be the type of thing that could pass the test.
Secondly, any kind of machine that could pass a Turing test,
perhaps you don't want to go all the way and say that it has mentality,
that it can have thought processes, that it can have mental states.
But maybe what we should instead say is that look, if a machine can pass a Turing
test, then it must be worthwhile looking to see how it's structured.
What kind of thing could pass the Turing test?
Because surely that in itself is going to give us an insight into what's
required for human mentality.
So coming back to the thought about whether minds are really like computers,
imagine that we did build a computer that could pass the Turing test.
Would this properly count as having a mind?
The philosopher John Searle had some very interesting things to say about this.
He introduces the following thought experiment.
He says imagine that you are in a room, and
there are two slots entering the room, one that says I and one that says O.
And what happens is you get symbols on bits of paper that come
through the I slot.
Now in your room you have a book, and in the book is a list of algorithms about
what to do depending on the type of symbol you receive.
So if you receive a symbol,
it might say if you receive a symbol of type A, put out a symbol of type B.
So you have a steady stream of symbols coming in, and
you look up in your book what you should be putting out again.
And let's imagine that you just have a whole stack of these
symbols available to you in the room.
So you receive a symbol,
you check in your book what you should put through the output box, the O box.
You pick up a symbol and put it back through the O box.
Now imagine that, unknown to you, these symbols are actually Chinese,
and the person who's feeding in the symbols is a Chinese speaker who's
asking you questions.
So what's happening is that you're receiving questions, and
you're actually giving out answers.
And the rule book is as such that you're actually giving out really
quite coherent answers.
In fact you're realizing something like a machine that would pass Turing's test.
So the Chinese speaker outside really believes that she is conversing
with another Chinese speaker.
Now, the big question that Searle asks is well I,
the person inside the box or the person inside the room, don't understand Chinese.
So if I weren't able to converse with this Chinese speaker
in a way that seems perfectly coherent to the person outside,
I myself have no understanding of what I'm doing.
It doesn't make sense to say I understand Chinese because I don't.
All I'm doing is looking up rules in this logbook.
And Searle points out that this is just how computers work.
Computers receive inputs, and then their program is like the logbook.
The program tells them what sort of output they should give.
They check up or they run through a list of rules and
see that given a particular input, they should give a particular output.
But Searle's point is that that machine
can never itself be said to understand Chinese.
That machine isn't thinking.
That machine is just putting on a very good simulation of a thinker, a simulation
that's so good that it could fool someone on the outside, but a simulation that
can't possibly count as thinking itself, because there's no understanding involved.
And this is one of the most important questions that's been leveled at
the computational metaphor of the mind.
We're saying minds are like computers, that minds take inputs, manipulate them,
and send out outputs.
Then where does the meaning come in?
This ties back to one of our opening questions, the aboutness of thoughts.
We want to say that our thoughts are about things,
that our thoughts have meaning, that we can think about things.
And yet what we have on this computational view of the mind
is a system that has no understanding at all.
There's no meaning involved.
The system just receives inputs and sends out outputs.
At no point do we have any understanding.
What's made really salient by Searle's thought experience is that computers
only work on syntactic properties of symbols.
What does this mean?
Well imagine you have a square with a line through it, and that's a symbol.
And let's just say that that symbol is Chinese for
the question, do you like ducks?
And you're the person inside the Chinese room, and you get the symbol in.
Now all you have to work on is the syntactic properties of the symbols.
The syntactic property is just the square with a line.
It's the shape of the symbol, if you like.
So you look at the shape of the symbol, and
you get out an answer depending on what the shape of the symbol is.
Now the important thing about computers is that all they ever operate on
are the shapes of symbols.
Computers don't need to understand what those symbols stand for.
The computer doesn't need to understand that a square with a line through it means
the question, do you like ducks?
All it needs to be able to do is run its program, so
that it knows what the correct output to give is there.
But this is misleading because what's very important for
human thought are the semantic properties.
Semantic properties are what that symbol stands for.
So the square with a line through it stands for
the question, do you like ducks?
What it stands for, the meaning, is known as the semantic property of that symbol.
As humans, we can look at that symbol and provided we understand the code,
we know that it stands for the question, do you like ducks?
But a computer doesn't need to know that.
A computer just needs to know how it ought to change that symbol and
give a particular output.
This is Searle's point.
He says that a system, no matter how functionally complex it is,
even if it can pass something like the Turing test, that only ever works on
the syntactic properties of symbols, the shapes that they get,
will never be a proper thinking system because it will never have the aboutness.
It will never be able to understand the semantic value of those symbols.
And intuitively, in order to have a thinking system, you have to have a system
that understands the semantic properties of symbols as well as the syntactic ones.
That's how we get the aboutness of thought.
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