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思想 vs. 机器：图灵测试与中文房间

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人体生理学导论（中文版) - Introdução à Filosofia (versão chinesa)

Universidade de EdimburgoUniversidade de Edimburgo

4.7 (169 classificações) | 15K alunos inscritos

本课程将为你介绍现代哲学中的一些研究领域。在每个模块中，将由不同的哲学家为你讲述其专业领域内最重要的疑问和问题。首先，我们来了解什么是哲学，哲学有哪些独特的目标与方法以及哲学与其他学科有何不同？之后，我们会在课程剩余的时间内向你简要介绍各种哲学领域的总体情况。你要学习的主题包括：知识论，其中将讨论囊括我们对世界和我们自身的了解以及如何获取这些知识；科学哲学，我们将在此模块中调查科学研究和实践中的基础概念问题；心灵哲学，我们将在此模块中询问物质拥有心灵的意义以及应如何理解和诠释心灵；道德哲学，我们将在此模块中探究道德判断和反应的本质，看它们是旨在得出客观的道德真相，还是仅作为个人或文化偏好，以及，形而上学，我们将在此模块中思考有关事实本质的一些基础概念问题。本慕课 (MOOC) 课程由爱丁堡大学 Eidyn 研究中心领导设计。为配合“哲学导论”的授课，我们为大家倾力推荐一本由 Routledge 出版社出版的名为 "Philosophy for Everyone" 的配套书籍。这本“哲学导论”的课程配套书籍是由爱丁堡大学哲学小组为满足慕课 (MOOC) 学生要求而编写的。"Philosophy for Everyone"一书中包含清晰易懂的章节、章节摘要、词汇表、探究问题、补充阅读推荐以及在线资源指南。 This is a translated version. To join the original English version, Please visit this page https://www.coursera.org/learn/philosophy

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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。

功能主义与功能复杂性4:11

思想 vs. 机器：图灵测试与中文房间11:01

思想 vs. 机器: 关于思想的计算观点面临的问题4:10

拓展讨论4:55

Ministrado por

Dr. Dave Ward
Lecturer in Philosophy
Professor Duncan Pritchard
Professor of Philosophy
Professor Michela Massimi
Full Professor
Dr. Suilin Lavelle
Lecturer in Philosophy
Dr. Matthew Chrisman
Reader in Philosophy
Dr. Allan Hazlett
Dr. Alasdair Richmond
Dr.

Transcrição

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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