The Chinese Room Argument - PowerPoint Presentation

Slide1

The Chinese Room ArgumentSlide2

The language of thoughtSlide3

The Language of ThoughtSlide4

The Language of Thought

If the mind has representational states, then there is some format the representations are in.

One idea is that the format is a language that is a lot like a computer language for an electronic computer or a natural, spoken human language: the language of thought (sometimes: “Mentalese”).Slide5

The Necker CubeSlide6

The Language of Thought

The idea would be that when you think “dogs hate cats,” there are discrete ‘words’ of the language of thought, DOGS, HATE, CATS. These are your ideas. The thought is a ‘sentence’ that is made out of those ideas:

DOGS HATE CATSSlide7

Systematicity

You can use those same ideas in different combinations:

CATS HATE DOGS

The LOT hypothesis thus predicts mental systematicity: that people who can think that cats hate dogs can think that dogs hate cats.Slide8

Systematicity

Thought is systematic := For any thought T containing a concept (idea) C, and any concept C* of the

same category

as C: anyone who can think T(C) can think T(C*).

Categories: concepts that represent individuals (“names”), concepts that represent properties (“adjectives,” “intransitive verbs”) concepts that represent logical relations (“connectives”), etc.Slide9

Systematicity

Sometimes Fodor just says:

Thought is systematic := anyone who can think

aRb

can think

bRa

.Slide10

The Argument from Systematicity

If the LOT hypothesis is true, then thought should be systematic.

It seems like thought is systematic.

The best explanation of the systematicity of thought is that LOT is true.Slide11

Compositionality

A representational system is compositional := what complex representations represent is determined completely by what their basic symbols represent.Slide12

Basic Symbol

A basic symbol is just a symbol that has no meaningful parts. Classic example ‘cattle’ contains the part ‘cat,’ but that part of it has no meaning in the expression ‘cattle.’Slide13

RUNS

FROM POLICE

MICHAELSlide14

Novel Utterance

“Yesterday, on my way to the plastic cow hat factory, I witnessed on two separate occasions police selling cupcakes out of empty space shuttles that had been painted in red and blue stripes.”Slide15

Compositionality and Natural Language

Many linguists think that the only way we can understand an infinite number of different sentences with different meanings is if those sentences are compositional.

This way we can learn a finite number of meanings (for individual words) and use those to calculate the meanings for all the more complicated expressions (like sentences).Slide16

Productivity

A representational system is productive := that system contains an infinite number of representations with an infinite number of distinct meanings.Slide17

The Argument from Productivity

Thought appears to be productive. We can think a potential infinitude of different things. There will be no point at which humans have “thought all the thoughts.”

If thought occurs in a language, we can use a compositional meaning theory to assign meanings to each thought on the basis of the meanings of their simple parts (concepts).Slide18

The Argument from Productivity

Therefore, the best explanation for the productivity of thought is that thought involves a language-like representational medium, and has a compositional semantics. LOT is true.Slide19

Scumbag Analytic PhilosopherSlide20

Computing and intelligenceSlide21

The Turing Test

Turing didn’t just discover the theory of computation, he also proposed a test for deciding whether a machine could think.Slide22

The Imitation GameSlide23

Chatterbots

ELIZA, 1966

http://nlp-addiction.com/eliza

/

(Joseph

Weizenbaum

, creator)Slide24

Simon & Newell

The heuristic search hypothesis says:

“The

solutions

to problems

are represented as symbol

structures. A

physical symbol system exercises its

intelligence in

problem solving by search--that is,

by generating

and progressively modifying

symbol structures

until it produces a solution structure

.” (Computer Science as Empirical Enquiry, 1976)Slide25

Efficient SearchSlide26

The chinese

roomSlide27

Searle

Professor of Philosophy at UC Berkeley

Jean

Nicod

Prize (2000)

National Humanities Medal (2004)

Mind and Brain Prize (2006)Slide28

Searle

Doesn’t know any Chinese language.

Never heard of China.

Never seen a Chinese character.

Doesn’t even know that there are languages other than English.Slide29

Searle’s New Job

Searle takes a job. He’s told that he works for a company that makes funny squiggles for decorations.

Currently, they need to update their squiggles, so Searle’s job is to receive “input” squiggles, and update them to the new squiggles.Slide30

Searle’s Room

37Slide31

Searle’s Room

37Slide32

Step 1: Find Rulebook #37

37Slide33

Step 2: Find Instructions for this Squiggle.

37

98Slide34

Step 3: Copy Down New Squiggles

37Slide35

Step 5: Update Blackboard

98Slide36

The Room From Outside

This guy is so smart!Slide37

What’s Going On?

Searle is “running the program” of a real Chinese speaker’s mind.

The states on the blackboard correspond to different states that speaker could be in: tired, hungry, in a hurry, bored…

Each volume contains what that speaker would say, given the state he’s in, in response to any question.Slide38

The Argument

According to CTM, all the mechanisms underlying human cognitive abilities and functions are computational.

So the cognitive ability to understand Chinese is a computational process realized by a program in the brain.

Therefore, someone like Searle in his room could realize this same program and thus understand Chinese.Slide39

The Argument

BUT, obviously, Searle in his room does not understand Chinese. He doesn’t know what any of the characters mean, or even that they have meanings.

Therefore, the computational theory of mind is false.Slide40

The Systems Reply

One standard reply to the Chinese room argument is the “Systems Reply.”

This reply concedes that Searle doesn’t understand Chinese, but maintains that the entire room, with Searle as its CPU, does understand Chinese.Slide41

Searle’s Response

Searle argues that in theory, he could just memorize all the rules, and get rid of the rest of the system. Now the entire system = Searle, but Searle still does not understand Chinese.Slide42

Understanding and Action

One thing that supports Searle’s response is the fact that if you hold up a sign saying “you’re going to get hit by that bus!” (in Chinese), Searle can write down an appropriate Chinese response (“

Ahhh

!”), but what he won’t do is

jump out of the way

.Slide43

The Robot Reply

The robot reply says that in order for the system to understand Chinese, it has to appropriately control behavior.

If told he’s going to get hit by a bus, Searle has to jump out of the way. If told his mother is a dog, he has to get angry. If told a funny joke, he has to laugh.Slide44

The Robot Reply

So, on the robot reply, mere computers can never understand a language, only computers controlling robot bodies (in an appropriate manner) can understand a language.

If you build a computer-controlled robot that behaved exactly like a native Chinese speaker, then it would in fact understand Chinese.Slide45

Searle’s Response

37Slide46

Searle’s Response

37Slide47

Last Word

Fodor counters Searle’s response as follows: whether any computer’s internal states actually represent the outside world or not depends on how those states are connected with action and experience.

Searle has shown one way that is not the right connection: him in a room. But he has not proven that no such

connections exist.