top of page
Поиск
  • Фото автораТимофей Милорадович

Listening on Artificial Intelligence

Обновлено: 31 мая 2022 г.


JOHN BRACAGLIA: My name is John Bacaglia.

And I'm a Googler working in YouTube operations.

I also lead a group called the Singularity Network,

an internal organization focused on discussions

and rationality in artificial intelligence.

I'm pleased to be here today with Mr. John Searle.

As a brief introduction, John Searle

is the Slusser Professor of Philosophy at the University

of California-Berkeley.

He is widely noted for his contributions

to the philosophy of language, philosophy of mind,

and social philosophy.

John has received the Jean Nicod Prize, the National Humanities

Medal in the Mind and Brain prize for his work.

Among his noble concepts is the Chinese room argument

against strong artificial intelligence.

John Searle, everyone.

[APPLAUSE]

JOHN SEARLE: Thank you Thank you.


Many thanks.

It's great to be back at Google.

It is a university outside of a university.

And sometimes, I think, this is what a university

ought really to look like.

Anyway, it's just terrific to be here.

And I'm going to talk about some-- well,

I'm going to talk about a whole lot of stuff.

But, basically, I want to start with talking

about the significance of technological advances.

And America, especially, but everybody, really,

is inclined to just celebrate the advances.

If they got a self-driving car who the hell

cares about whether or not it's conscious.

But I'm going to say there are a lot of things that

matter for certain purposes about the understanding

of the technology.

And that's really what I'm going to talk about.

Now to begin with, I have to make a couple rather

boring distinctions because you won't really

understand contemporary intellectual life if you don't

understand these distinctions.

In our culture, there's a big deal about objectivity

and subjectivity.

We strive for an objective science.

The problem is that these notions are systematically

ambiguous in a way that produces intellectual catastrophes.

They're ambiguous between a sense, which is epistemic,

where epistemic means having to do with knowledge--

epistemic-- and a sense, which is ontological,

where ontological means having to do with existence.

I hate using a lot of fancy polysyllabic words.

And I'll try to keep them to a minimum.

But I need these two, epistemic and ontological.

Now the problem with objectivity and subjectivity

is that they're systematically ambiguous--

I'll just abbreviate subjectivity--

between an epistemic sense and an ontological sense.

Epistemically, the distinction is between types

of knowledge claims.

If I say, Rembrandt died in 1606, well-- no,

he didn't die then.

He was born then.

I'd say Rembrandt was born in 1606.

That is to say, it's a matter of objective fact.

That's epistemically objective.

But if I say Rembrandt is the greatest

painter that ever lived, well, that's a matter of opinion.

That is epistemically subject.

So we have epistemic objectivity and subjectivity.

Underlying that is a distinction in modes of existence.

Lots of things exist regardless of what anybody thinks.

Mountains, molecules, and tectonic plates

have a mode of existence that is ontologically objective.

But pains and pickles and itches,

they only exist insofar as they are experienced by a subject.

They are ontologically subjective.

So I want everybody to get that distinction

because it's very important because-- well,

for a lot of reasons, but one is lots of phenomena that

are ontologically subjective admit of an account which

is epistemically objective.

I first got interested in this kind of stuff.

I thought, well, why don't these brain guys solve

the problem of consciousness.

And I went over UCSF to their neurobiology gang

and told them, why the hell don't

you guys figure out how the brain causes consciousness?

What am I paying you to do?

And their reaction was, look, we're doing science.

Science is objective.

And you, yourself, admit that consciousness is subjective.

So there can't be a science of consciousness.

Now you'll all recognize that's a fallacy of ambiguity.

Science is indeed epistemically objective

because we strive for claims that

can be established as true or false,

independent of the attitudes of the makers

and interpreters of the claim.

But epistemic objectivity of the theory

does not preclude an epistemically objective account

of a domain that's ontologically subjective.

I promised you I wouldn't use too many big words,

but anyway there are a few.

The point is this.

You can have an epistemically objective science

of consciousness, even though consciousness

is ontologically subjective.

Now that's going to be important.

And there's another distinction.

Since not everybody can see this,

I'm going to erase as I go along.

There's another distinction which is crucial.

And that's between phenomena that are observer-independent.


And there I'm thinking of mountains and molecules

and tectonic plates, how they exist regardless

of what anybody thinks.

But the world is full of stuff that

matters to us that is observer-relative.

It only exists relative to observers and users.

So, for example, the piece of paper in my wallet is money.

But the fact that makes it money is not a fact of its chemistry.

It's a fact about the attitudes that we have toward it.

So money is observer-relative.

Money, property, government, marriage, universities, Google,

cocktail parties, and summer vacations

are all observer-relative.


And that has to be distinguished from observer-independent.

And notice now, all observer-relative phenomenon

are created by human consciousness.

Hence, they contain an element of ontological subjectivity.

But you already know that you can have,

in some cases, an epistemically objective

science of a domain that is observer-relative.

That's why you can have an objective science of economics

even though the phenomena studied by economics

is, in general, observer-relative,

and hence contains an element of ontological subjectivity.

Economists tend to forget that.

They tend to think that economics

is kind of like physics, only it's harder.


When I studied economics, I was appalled.

We learned that marginal cost equals marginal revenue

in the same tone of voice that in physics we

learned that force equals mass times acceleration.

They're totally different because the stuff in economics

is all observer-relative and contains an element

of ontological subjectivity.

And when the subjectivity changes-- ffft--

the whole thing collapses.

That was discovered in 2008.

This is not a lecture about economics.

I want you to keep all that in mind.

Now that's important because a lot of the phenomena that

are studied in cognitive science,

particularly phenomena of intelligence, cognition,

memory, thought, perception, and all the rest of it

have two different senses.

They have one sense, which is observer-independent,

and another sense, which is observer-relative.

And, consequently, we have to be very careful

that we don't confuse those senses because many

of the crucial concepts in cognitive science

have as their reference phenomena

that are observer-relative and not observer-independent.

I'm going to get to that.

OK, everybody up with us so far?

I want everything to sound so obvious you

think, why does this guy bore us with these platitudes?

Why doesn't he say something controversial?


Now I'm going to go and talk about

some intellectual history.

Many years ago, before any of you were born,

a new discipline was born.

It was called cognitive science.

And it was founded by a whole bunch of us

who got sick of behaviorism in psychology, effectively.

That was the reason for it.

And the Sloan Foundation used to fly us around to lecture,

mostly to each other.

But anyway, that's all right.

We were called Sloan Rangers.

And I was invited to lecture to the Artificial Intelligence Lab

at Yale.

And I thought, well, christ, I don't know anything

about artificial intelligence.

So I went out and bought a book written by the guys at Yale.

And I remember thinking, $16.95 plus tax-- money wasted.

But it turned out I was wrong.

They had in there a theory about how computers could understand.

And the idea was that you give the computer a story.

And then you ask the computer questions about the story.

And the computer would give the correct answer to the questions

even though the answer was not contained in the story.

A typical story.

A guy goes into a restaurant and orders a hamburger.

When they brought him the hamburger,

it was burned to a crisp.

The guy stormed out of the restaurant

and didn't even pay his bill.

Question, did the guy eat the hamburger?

Well, all of you computers know the answer to that.

No, the guy didn't eat the hamburger.

And I won't tell you the story where the answer is yes.

It's equally boring.

Now, the point was this proves that the computer really

understands the story.

So there I was on my way to New Haven on United Airlines

at 30,000 feet.

And I thought, well, hell, they could give me

these stories in Chinese.

And I could follow the computer program for answering stories.

And I don't understand a word of the story.

And I thought, well, that's an objection

they must have thought of.

And besides that won't keep me going

for a whole week in New Haven.

Well, it turned out they hadn't thought of it.

And everybody was convinced I was wrong.

But interestingly they all had different reasons

for thinking I was wrong.

And the argument has gone on longer than a week.

It's gone on for 35 years.

I mean, how often do I have to refute these guys?

But anyway, let's go through it.

The way the argument goes in its simplest version

is I am locked in a room full of Chinese-- well,

they're boxes full of Chinese symbols and a rule book

in English for manipulating the symbols.

Unknown to me, the boxes are called a database,

and the rule book is called a program.

In coming in the room, I get Chinese symbols.

Unknown to me, those are questions.

I look up what I'm supposed to do.

And after I shuffle a lot of symbols,

I give back other symbols.

And those are answers to the questions.

Now we will suppose-- I hope your bored

with this, because I am.

I mean, I've told this story many times.

We will suppose that they get so good at writing the program,

I get so good at shuffling the symbols,

that my answers are indistinguishable

from a native Chinese speaker.

I pass the Turing test for understanding Chinese.

All the same, I don't understand a word of Chinese.

And there's no way in the Chinese room

that I could come to understand Chinese because all I am

is a computer system.

And the rules I operate are a computer program.

And-- and this is the important point--

the program is purely syntactical.

It is defined entirely as a set of operations

over syntactical elements.

To put it slightly more technically,

the notion same implemented program

defines an equivalence class that

is specified completely independently of any physics

and, in particular, independent of the physics

of its realization.

The bottom line is if I don't understand

the questions and the answers on the basis of implementing

the program, then neither does any other digital computer

on that basis because no computer

has anything that I don't have.

Computers are purely syntactical devices.

Their operations are defined syntactically.

And human intelligence requires more than syntax.

It requires a semantics.

It requires an understanding of what's going on.

You can see this if you contrast my behavior in English

with my behavior in Chinese.

They ask me questions in English.

And I give answers in English.

They say, what's the longest river in the United States?

And I say, well, it's the Mississippi,

or the Mississippi-Missouri, depending

on if you count that as one river.

They ask me in Chinese, what's the longest river in China?

I don't know what the question is or what it means.

All I got are Chinese symbols.

But I look up what I'm supposed to do with that symbol,

and I give back an answer, which is the right answer.

It says, it's the Yangtze.

That's the longest river in China.

I don't know any of that.

I'm just a computer.

So the bottom line is that the implemented computer program

by itself is never going to be sufficient

for human understanding because human understanding has

more than syntax.

It has a semantics.

There are two fundamental principles

that underlie the Chinese room argument.

And both of them seem to me obviously true.

You can state each in four words.

Syntax is not semantics.

And simulation is not duplication.

You can simulate-- you're going to have

plenty of time for questions.

How much time we got, by the way?

I want to--

JOHN BRACAGLIA: We'll leave time for questions at the end.

JOHN SEARLE: I want everybody that

has a question to have a chance to ask the question.

Anyway, that's the famous Chinese room argument.

And it takes about five minutes to explain it.

Now you'd be amazed at the responses I got.

They were absolutely breathtaking

in their preposterousness.

Now let me give you some answers.

A favorite answer was this.

You were there in a room.

You had all those symbols.

You had a box.

You probably had scratch paper on which to work.

Now, it wasn't you that understood.

You're just a CPU, they would say with contempt, the Central

Processing Unit.

I didn't know what any of these words meant in those days.

CPU, it's the system that understands.

And when I first heard this, I mean,

the room understands Chinese, I said to the guy.

And he said, yes, the room understands Chinese.

Well, it's a desperate answer.

And I admire courage.

But it's got a problem.

And that is the reason I don't understand

is I can't get from the syntax to the semantics.

But the room can't either.

How does the room get from the syntax of the computer

program of the input symbols to the semantics

of the understanding of the symbols?

There's no way the room can get there

because that would require some consciousness

in the room in addition to my consciousness.

And there is no such consciousness.

Anyway, that was one of many answers.

One of my favorites was this.

This was in a public debate.

A guy said to me, but suppose we ask you,

do you understand Chinese?

And suppose you say, yes, I understand Chinese.

Well?

Well, OK, let's try that and see how far we get.

I get a question that looks like this.


Now, this will be in a dialect of Chinese some of you

won't recognize.

Unknown to me, that symbol means,

do you understand Chinese?

I look up what I'm supposed to do.

And I give them back a symbol that's

in the same dialect of Chinese.

And it looks like that.

And that says, why do you guys ask me such dumb questions?

Can't you see that I understand Chinese?


I could go on with the other responses and objections,

but I think they're all equally feeble.

The bottom line is there's a logical truth.

And that is that the implemented computer program

is defined syntactically.

And that's not a weakness.

That's the power.

The power of the syntactical definition of computation

is you can implement it on electronic machines that

can perform literally millions of computations

in a very small amount of time.

I'm not sure I believe this, but it always

says it in the textbooks, that Deep Blue

can do 250 million computations in a second.

OK, I take their word for it.

So it's not a weakness of computers.

Now, another argument I sometimes got

was, well, in programs, we often have

a section called the semantics of natural understanding

programs.

And that's right.

But, of course, what they do is they put in more computer

implementation.

They put in more syntax.

Now, so far, so good.

And I think if that's all there was to say,

I've said all of that before.

But now I want to go on to something much more

interesting.

And here goes with that.

Now how we doing?

I'm not-- everybody seems to understand

there's going to be plenty of time for questions.

I insist on a good question period.

So let me take a drink of water, and we

go to the next step, which I think is more important.


A lot of people thought, well, look,

maybe the computer doesn't understand Chinese,

but all the same, it does information processing.

And it does, after all, do computation.

That's what we define the machine to do.

And I had to review a couple of books recently.

One book said that we live in a new age,

the age of information.

And in a wonderful outburst, the author

said everything is information.

Now that ought to worry us if everything is information.

And I read another book.

This was an optimistic book.

I reviewed-- this for "The New York Review of Books"--

a less optimistic book by a guy who said computers are now

so smart they're almost as smart as we are.

And pretty soon, they'll be just as smart as we are.

And then I don't have to tell this audience the next step.

They'll be much smarter than we are.

And then look out because they might get

sick of being oppressed by us.

And they might simply rise up and overthrow us all.

And this, the author said modestly--

I guess this is how you sell books--

he said this may be the greatest challenge that humanity

has ever faced, the upcoming revolt

of super-smart computers.

Now, I want to say both of these claims are silly.

I mean, I'm speaking shorthand here.

There'll be plenty of chance to answer me.

And I want to say briefly why.

The notion of intelligence has two different senses.

It has an observer-independent sense

where it identifies something that is psychologically real.

So I am more intelligent than my dog Tarski.

Now, Tarski's pretty smart, I agree.

But overall, I'm smarter than Tarski.

I've had four dogs, by they way--

Frege, Russell, Ludwig, and Tarski.


And Tarski, he's a Bernese mountain dog.

I'm sorry I didn't bring him along,

but he's too big for the car.

Now, he's very smart.

But he does have intelligence in the same sense that I do.

Only he happens to have somewhat less than I do.

Now, my computer is also intelligent.

And it also processes information.

But-- and this is the key point-- it's observer-relative.

The only sense in which the computer has intelligence

is not in an intrinsic, but it's in an observer-relative sense.

We can interpret its operations in such a way

that we can make-- now, watch this terminology-- we can make

epistemically objective claims of intelligence

even though the intelligence in question

is entirely in the eye of the beholder.

This was brought home forcefully to me

when I read in the newspapers that IBM had designed

a computer program which could beat the world's leading chess

player.

And in the same sense in which Kasparov beat Karpov

so we were told Deep Blue beat Kasparov.

Now that ought to worry us because for Karpov and Kasparov

to play chess, they both have to be conscious

that they're playing chess.

They both have to know such things

as I opened with pawn to king four,

and my queen is threatened on the left-hand side

of the board.

But now notice, Deep Blue knows none on that

because it doesn't know anything.

You can make epistemically objective claims

about Deep Blue.

It made such and such a move.

But the attributions of intelligent chess playing,

this move or that move, it's all observer-relative.

None of it is intrinsic.

In the intrinsic sense in which I have more intelligence

than my dog, my computer has zero intelligence-- absolutely

none at all.

It's a very complex electronic circuit

that we have designed to behave as if it were thinking,

as if it were intelligent.

But in the strict sense, in the observer-independent sense

in which you and I have intelligence,

there is zero intelligence in the computer.

It's all observer-relative.

And what goes for intelligence goes

for all of the key notions in cognitive science.

The notions of intelligence, memory, perception,

decision-making, rationality-- all those

have two different senses, a sense

where they identify psychologically real phenomena

of the sort that goes on in you and me and the sort

where they identify observer-relative phenomena.

But in the intrinsic sense in which

you and I have intelligence, the machinery we're talking about

has zero intelligence.

It's no question of its having more or less.

It's not in the same line of business.

All of the intelligence is in the eye of the beholder.

It's all observer-relative.

Now, you might say-- and I would say-- so, for most purposes,

it makes no difference at all.

I mean, if you can design a car that can drive itself,

who cares if it's conscious or not?

Who cares if it literally has any intelligence?

And I agree.

For most purposes, it doesn't matter.

For practical purposes, it doesn't matter whether or not

you have the observer-independent

or the observer-relative sense.

The only point where it matters, if you

think there's some psychological significance to the attribution

of intelligence to machinery which

has no intrinsic intelligence.

Now, notice the intelligence by which we--

the mental processes by which we attribute intelligence

to the computer require consciousness.

So the attribution of observer-relativity

is done by conscious agents.

But the consciousness is not itself observer-relative.

The consciousness that creates the observer-relative phenomena

is not itself observer-relative.

But now let's get to the crunch line then.

If information is systematically ambiguous

between an intrinsic sense, in which you and I have

information, and an observer-relative sense,

in which the computer has information,

what about computation?

After all, computation, that must surely

be intrinsic to the computer.

That's what we designed and built the damn things to do,

was computation.

But, of course, the same distinction applies.

And I want to take a drink of water

and think about history for a moment.

When I first read Alan Turing's article,

it was called "Computing Machinery and Intelligence."

Now why didn't he call it "Computers and Intelligence"?

Well, you all know the answer.

In those days, "computer" meant "person who computes."

A computer is like a runner or a piano player.

It's some human who does the operation.

Nowadays nobody would think that because the word

has changed its meaning.

Or, rather, it's acquired the systematic ambiguity

between the observer-relative sense

and the observer-independent sense.

Now we think that a computer names

a type of machinery, not a human being who actually carries out

computation.

But the same distinction that we've been applying,

the same distinction that we discovered

in all these other cases, that applies

to computation in the literal or observer-independent

sense in which I will now do a simple computation.

I will do a computation using the addition function.

And here's how it goes.

It's not a very big deal.

One plus one equals two.

Now, the sense in which I carried out a computation

is absolutely intrinsic and observer-independent.

I don't care what anybody says about me.

If the experts say, well, you weren't really computing.

No, I was.

I consciously did a computation.

When my pocket calculator does the same operation,

the operation is entirely observer-relative.

Intrinsically all that goes on is a set of electronic state

transitions that we have designed so that we

can interpret computationally.

And, again, to repeat, for most purposes, it doesn't matter.

When it matters is when people say,

well, we've created this race of mechanical intelligences.

And they might rise up and overthrow us.

Or they attribute some other equally implausible

psychological interpretation to the machinery.

In commercial computers, the computation

is observer-relative.

Now notice, you all know that doesn't mean

it's epistemically subjective.

And I pay a lot of money so that Apple

will make a piece of machinery that will implement programs

that my earlier computers were not intelligent enough

to implement.

Notice the observer-relative attribution

of intelligence here.

So it's absolutely harmless unless you

think there's some psychological significance.

Now what is lacking, of course, in the machinery, which

we have in human beings which makes

the difference between the observer

relativity of the computation in the commercial computer

and the intrinsic or observer independent computation

that I have just performed on the blackboard, what's lacking

is consciousness.

All observer-relative phenomena are

created by human and animal consciousness.

But the human and animal consciousness

that creates them is not itself observer-relative.

So there's an intrinsic mental phenomena, the consciousness

of the agent, which creates the observer-relative phenomena,

or interprets the mechanical system in an observer

relative fashion.

But the consciousness that creates observer relativity

is not itself observer-relative.

It's intrinsic.

Now, I wanted to save plenty of time for discussion.

So let me catch my breath and then give

a kind of summary of the main thrust of what

I've been arguing.

And one of things I haven't emphasized

but I want to emphasize now, and that

is most of the apparatus, the conceptual apparatus,

we have for discussing these issues is totally obsolete.

The difference between the mental and the physical,

the difference between the social and the individual,

and the distinction between those features which

can be identified in an observer-relative fashion,

such as computation, and those which

can be identified in an observer-independent fashion,

such as computation.

We're confused by the vocabulary which doesn't make

the matters sufficiently clear.

And I'm going to end this discussion

by going through some of the elements of the vocabulary.

Now, let me have a drink of water and catch my breath.


Let's start with that old question,

could a machine think?

Well, I said the vocabulary was obsolete.

And the vocabulary of humans and machines

is already obsolete because if by machine is

meant a physical system capable of performing

certain functions, then we're all machines.

I'm a machine.

You're a machine.

And my guess is only machines could think.

Why?

Well that's the next step.

Thinking is a biological process created in the brain

by certain quite complex, but insufficiently understood

neurobiological processes.

So in order to think, you've got to have a brain,

or you've got to have something with equivalent causal powers

to the brain.

We might figure out a way to do it in some other medium.

We don't know enough about how the brain does it.

So we don't know how to create it artificially.

So could a machine think?

Human beings are machines.

Yes, but could you make an artificial machine

that could think?

Why not?

It's like an artificial heart.

The question, can you build an artificial brain that

can think, is like the question, can you

build an artificial heart that pumps blood.

We know how the heart does it, so we

know how to do it artificially.

We don't know how the brain does it, so we have no idea.

Let me repeat this.

We have no idea how to create a thinking

machine because we don't know how the brain does it.

All we can do is a simulation using

some sort of formal system.

But that's not the real thing.

You don't create thinking that way,

whereas the artificial heart really does pump blood.

So we had two questions.

Could a machine think?

And could an artificially-made machine think?

Answer the question one is obviously yes.

Answer to question two is, we don't know yet,

but there's no obstacle in principle.

Does everybody see that?

Building an artificial brain is like building

an artificial heart.

The only thing is no one has begun to try it.

They haven't begun to try it because they have no idea how

the actual brain does it.

So they don't know how to imitate actual brains.

Well, OK, but could you build an artificial brain that

could think out of some completely different materials,

out of something that had nothing

to do with nucleo-proteins, had nothing to

with neurons and neurotransmitters

and all the rest of it.

And the answer is, again, we don't know.

That seems to me an open question.

If we knew how the brain did it, we

might be able to define-- I mean,

be able to design machines that could do it using

some completely different biochemistry in a way

that the artificial heart doesn't use

muscle tissue to pump blood.

You don't need muscle tissue to pump blood.

And maybe you don't need brain tissue to create consciousness.

We just are ignorant.

But notice there's no obstacle in principle.

The problem is no one has begun to think about how you would

build a thinking machine, how you'd build a thinking

machine out of some material other than neurons

because they haven't begun to think about how we might

duplicate and not merely simulate

what the brain actually does.

So the question, could a machine think,

could an artificial machine think,

could an artificial machine made out

of some completely different materials,

could those machines think?

And now the next question is the obvious one.

Well, how about a computer?

Could a computer think?

Now, you have to be careful here.

Because if a computer is defined as anything that can carry out

computations, well, I just did.

This is a computation.

So I'm a computer.

And so are all of you.

Any conscious agent capable of carrying out

that simple computation is capable, is both a computer

and capable of thinking.

So my guess is-- and I didn't have a chance

to develop this idea-- is that not only can computers

think-- you and me-- but my guess

is that anything capable of thinking

would have to be capable of carrying out

simple computations.

But now what is the status of computation?

Well, the key element here is the one I've already mentioned.

Computation has two senses, an observer-independent sense

and an observer-relative sense.

In the observer-relative sense, anything

is a computer if you can ascribe a computational interpretation.

Watch.

I'll show you a very simple computer.

That computer just computed a well-known function.

s equals one-half gt squared.

And if you had a good-enough watch,

you could actually time and figure out

how far the damn thing fell.

Everybody sees.

It's elementary mathematics.

So if this is a computer, then anything

is a computer because being a computer

in the observer-relative sense is not

an intrinsic feature of an object,

but a feature of our interpretation of the physics

of the phenomenon.

In the old Chinese room days, when

I had to debate these guys, at one point, I'd take my pen,

slam it on a table, and say that is a digital computer.

It just happens to have a boring computer program.

The program says stay there.

The point is nobody ever called me on this

because it's obviously right.

It satisfies a textbook definition.

You know, in the early days, they

tried to snow me with a whole lot of technical razzmatazz.

"You've left out the distinction between the virtual machine

and the non-virtual machine" or "you've

left out the transducers."

You see, I didn't know what the hell a transducer was,

a virtual machine.

But it takes about five minutes to learn those things.

Anyway, so now we get to the crucial question in this.

If computers can think, man-made computers

can think, machines can think, what about computation?

Does computation name a machine, a thinking process?

That is, is computation, as defined by Alan Turing,

is that itself sufficient for thinking?

And you now know the answer to that.

In the observer-relative sense, the answer is no.

Computation is not a fact of nature.

It's a fact of our interpretation.

And insofar as we can create artificial machines that

carry out computations, the computation by itself

is never going to be sufficient for thinking

or any other cognitive process because the computation is

defined purely formally or syntactically.

Turing machines are not to be found in nature.

They're to be found in our interpretations of nature.

Now, let me add, a lot of people think, ah,

this debate has something to do with technology

or there'll be advances in technology.

I think that technology's wonderful.

And I welcome it.

And I see no limits to the possibilities of technology.

My aim is this talk is simply to get across,

you shouldn't misunderstand the philosophical, psychological,

and, indeed, scientific implication of the technology.

Thank you very much.

[APPLAUSE]


JOHN BRACAGLIA: Thank you, John.

JOHN SEARLE: I'm sorry I talk so fast,

but I want to leave plenty of time for questions.

JOHN BRACAGLIA: We'll start with one question from Mr. Ray

Kurzweil.

RAY KURZWEIL: Is this on?

[INTERPOSING VOICES]


RAY KURZWEIL: Well, thanks, John.

I'm one of those guys you've been debating this issue for 18

years, I think.


And I would praise the Chinese room for its longevity

because it does really get at the apparent absurdity

that some deterministic process like computation

could possibly be responsible for something like thinking.

And you point out the distinction

of thinking between its effects and the subjective states,

which is a synonym for consciousness.

So I quoted you here in my book "Singularity is Near,"

at the equivalence of neurons and even brains with machines.

So then I took your argument why a machine and a computer

could not truly understand what it's doing and simply

substituted human brain for computers,

since you said they were equivalent,

and neurotransmitter concentrations and related

mechanisms for formal symbols, since basically

neurotransmitter concentrations, it's

just a mechanistic concept.

And so you wrote, with those substitutions,

the human brain succeeds by manipulating

neurotransmitter concentrations and other related mechanisms.

The neurotransmitter concentrations

and related mechanisms themselves

are quite meaningless.

They only have the meaning we have attached to them.

The human brain knows nothing of this.

It just shuffles the neurotransmitter concentrations

and related mechanisms.

Therefore, the human brain cannot have true understanding.

So--

[LAUGHTER]

JOHN SEARLE: There's something interesting variations, again,

on my original.

RAY KURZWEIL: But the point I'd like to make,

and that I'd be interested in your addressing,

is the nature of consciousness because, I mean,

you said today, and you wrote, the essential thing

is to recognize that consciousness

is a biological processes like digestion, lactation,

photosynthesis, or mitosis.

We know that brains cause consciousness

with specific biological mechanisms.

But how do we know that a brain is conscious?

How do you know that I'm conscious?

And how do you--

JOHN SEARLE: [INAUDIBLE]

RAY KURZWEIL: And how do we know if a computer was conscious?

We don't have a computer today that

seems conscious, that's convincing in its responses.

But my prediction is we will.

We can argue about the time frame.

And when we do, how do we know if it's conscious of

it just seems conscious?

How do we measure that?

JOHN SEARLE: Well, there are two questions here.

One is, if you do a substitution of words that I didn't use

and the words I did use, can you get these observed results?

And, of course, you can do that.

That's a well-known technique of politicians.

But that wasn't the claim.

What is the difference between the computer and the brain?

In one sentence, the brain is a causal mechanism

that produces consciousness by a certain rather complex

and still imperfectly understood neurobiological processes.

But those are quite specific to a certain electrochemistry.

We just don't know the details.

But we don't know if you mess around in the synaptic cleft,

you're going to get weird effects.

How does cocaine work?

Well, it isn't because it's got a peculiar computational

capacity.

Because it messes with the capacity

of the postsynaptic receptors to reabsorb quite specific

neurotransmitters, norepinephrine--

what are the other two?

God, I'm flunking the exam here.

Dopamine.

Gaba is the third.

Anyway, the brain, like the stomach or any other organ,

is a specific causal mechanism.

And it functions on specific biochemical principles.

The problem of the computer is it

has nothing to do with the specifics

of the implementation.

Any implementation will do provided

it's sufficient to carry out the steps in the program.

Programs are purely formal or syntactical.

The brain is not.

The brain is a specific biological organ

that operates on specific principles.

And to create a conscious machine,

we've got to know how to duplicate the causal powers

of those principles.

Now, the computer doesn't in that way

work as a causal mechanism producing higher level

features.

Rather, computation names an abstract mathematical process

that we have found ways to implement in specific hardware.

But the hardware is not essential to the computation.

Any system that can carry out the computation

will be equivalent.

Now, the second question is about how do

you know about consciousness.

Well, think about real life.

How do I know my dog Tarski is conscious

and this thing here, my smartphone, is not conscious?

I don't have any doubts about either one.

I can tell that Tarski is conscious not

on behavioristic grounds.

People say, well, it's because he behaves like a human being.

He doesn't.

See, human beings I know when they

see me don't rush up and lick my hands and wag their tails.

They just don't.

My friends don't do that.

But Tarski does.

I can see that Tarski is conscious

because he's got a machinery that's

relatively similar to my own.

Those are his eyes.

These are his ears.

This is his skin.

He has mechanisms that mediate the input stimuli to the output

behavior that are relatively similar to human mechanisms.

This is why I'm completely confident that Tarski's

conscious.

I don't know anything about fleas and termites.

You know, your typical termite's got 100,000 neurons.

Is that enough?

Well, I lose 100,000 on a big weekend.

So I don't know if that's enough for consciousness.

But that's a factual question.

I'll leave that to the experts.

But as far as human beings are concerned

there isn't any question that everybody in this room

is conscious.

I mean, maybe that guy over there is falling asleep,

but there's no question about what the general-- it's not

even a theory that I hold.

It's a background presupposition.

The way I assume that the floor is solid,

I simply take it for granted that everybody's conscious.

If forced to justify it, I could.

Now, there's always a problem about the details

of other minds.

Of course, I know you're conscious.

But are you suffering the angst of post-industrial man

under late capitalism?

Well, I have a lot of friends who claim they do.

And they think I'm philistine because I don't.

But that's tougher.

We'd have to have a conversation about that.

But for consciousness, it's not a real problem

in a real-life case.

AUDIENCE: So you've said that we haven't

begun to understand how brains work or build

comparable machines.

But imagine in the future we do.

So we can run a simulation, as you put it, of a brain.

And then we interface it with reality

through motor output, sensory input.

What's the difference between that

and a brain, which you say you know

is producing consciousness?

In

JOHN SEARLE: In some cases, there's no difference at all.

And the difference doesn't matter.

If you've got a machine-- I hope you

guys are, in fact, building it because the newspapers

say you are.

If you've got a program that'll drive

my car without a conscious driver, that's great.

I think that's wonderful.

The question is not, what can the technology do?

My daddy was an electrical engineer for AT&T.

And his biggest disappointment was

I decided to be a philosopher, for God's sake,

instead of going to Bell Labs and MIT as he had hoped.

So I have no problem with the success of the technology.

The question is, what does it mean?

Of course, if you've got a machine

that can drive a car as well as I,

or probably better than I can, then so much

the better for the machinery.

The question is, what is the philosophical psychological

scientific significance of that?

And if you think, well, that means you've created

consciousness, you have not.

You have to have more to create consciousness.

And for a whole lot of things, consciousness

matters desperately.

In this case of this book that I reviewed,

where the guy said, well, they got machines

that are going to rise up and overthrow us all,

it's not a serious possibility because the machines

have no consciousness.

They have no conscious psychological state.

It's about like saying the shoes might get up out of the closet

and walk all over us.

After all, we've been walking on them for centuries,

why don't they strike back?

It is not a real-life worry.

Yeah?

AUDIENCE: The difference that I'm interested in-- sorry,

the similarity I'm interested in is not necessarily

the output or the outcome of the system,

but rather, that is, it has the internal causal similarity

to the brain that you mentioned.

JOHN SEARLE: Yeah, that's a factual question.

The question is, to what extent are the processes that

go on in the computer isomorphic to processes

that go on in the brain?

As far as we know, not very much.

I mean, the chess-playing programs

were a good example of this.

In the early days of AI, they tried

to interview great chess players and find out what their thought

processes were and get them to try

to duplicate that on computers.

Well, we now know how Deep Blue worked.

Deep Blue can calculate 250 million chess positions

in one second.

See, chess is a trivial game from a games theoretical point

of view because you have perfect information.

And you have a finite number of possibilities.

So there are x number of possibilities

of responding to a move and x number

of possibilities for that move.

It's interesting to us because of the exponential problem.

And it's very hard to program computers that

can go very many steps in the exponents, but IBM did.

It's of no psychological interest.

And to their credit, the people in AI

did not claim it as a great victory for-- at least the ones

I know didn't claim it as a victory for AI

because they could see it had nothing

to do with human cognition.

So my guess is it's an interesting philosophical

question-- or psychological question-- to what extent

the actual processes in the brain

mirror a computational simulation.

And, of course, to some respect, they do.

That's why computational simulations

are interesting in all sorts of fields

and not just in psychology, because you

can simulate all sorts of processes that are going on.

But that's not strong AI.

Strong AI says the simulation isn't just a simulation.

It's a duplication.

And that we can refute.

AUDIENCE: Could you prove to me that you understand English?

JOHN SEARLE: Yeah, I wouldn't bother.

(SPEAKING WITH BRITISH ACCENT) When I was in Oxford,

many people doubted that I did.


I happened to be in a rather snobbish college called

Christ Church.

And, of course, I don't speak English.

I never pretended to.

I speak a dialect of American, which makes many English people

shudder at the thought.


AUDIENCE: So you've said you understand English,

but how do I know you're not just a computer program?

JOHN SEARLE: Well, it's the same question as Ray's.

And the answer is all sorts of ways.

You know, if it got to a crunch, you might ask me.

Now I might give a dishonest answer.

Or I might give an honest answer.

But there's one route that you don't want to go.

And that's the epistemic route.

The epistemic route says, well, you

have as much evidence that the computer is conscious

as that we have that you are conscious.

No, not really.

I mean, I could go into some detail

about what it is about people's physical structure that

make them capable of producing consciousness.

You don't have to have a fancy theory.

I don't need a fancy theory of neurobiology

to say those are your eyes.

You spoke through your mouth.


The question was an expression of a conscious intention

to ask a question.

Believe me, if you are a locally produced machine,

Google is further along than I thought.

But clearly, you're not.


JOHN BRACAGLIA: We're going to take a question from the Dory.

JOHN SEARLE: Is he next?

JOHN BRACAGLIA: We had some people--

AUDIENCE: Almost.

JOHN BRACAGLIA: We had some people

submit questions ahead of time.

JOHN SEARLE: OK.

JOHN BRACAGLIA: So we're going to read those as well.

JOHN SEARLE: OK.

All right.

Right.

JOHN BRACAGLIA: So the first question from the Dory

is, what is the definition of consciousness you've been using

for the duration of this talk?

JOHN SEARLE: OK.

Here goes.

JOHN BRACAGLIA: Please be as specific as possible.

JOHN SEARLE: It is typically said that consciousness

is hard to define.

I think it's rather easy to define.

We don't have a scientific definition

because we don't have a scientific theory.

The commonsense definition of any term

will identify the target of the investigation.

Water is a clear, colorless, tasteless liquid.

And it comes in bottles like this.

That's the commonsense definition.

You do science and you discover it's H2O.

Well, with consciousness, we're in

the clear, colorless, liquid, tasteless sense.

But here it is.

Consciousness consists of all those states

of feeling or sentience or awareness

that begin in the morning when you awake

from a dreamless sleep.

And they go on all day long until you fall asleep

again or otherwise become, as they would say, unconscious.

On this definition, dreams are a form of consciousness.

The secret, the essence, of consciousness

is that for any conscious state, there's

something it feels like to be in that conscious state.

Now, for that reason, consciousness always

has a subjective ontology.

Remember, I gave you that subjective-objective bit.

It always has a subjective ontology.

That's the working definition of consciousness.

And that's the one that's actually

used by neurobiological investigators trying to figure

out how the brain does it.

That's what you're trying to figure out.

How does the brain produce that?

How does it exist in the brain?

How does it function?

AUDIENCE: I'd like to propose a stronger

bound on your observation that we do not

know how to build a thinking machine today.

Even if we knew how to build it, because, I mean,

our thinking machine was built by the process of evolution,

I'd like to propose-- well, what do you

think about stating that, actually, we

may not have the time?

And that it actually may not matter.

The reason we may not have the time is the probabilities that

need to happen, like the asteroid falling and wiping

the dinosaurs and whatnot, may not happen in the universe

that we live been.

But if you subscribe to the parallel universes theory,

then there is some artificial consciousness somewhere else.

JOHN SEARLE: Yeah.

OK, about we may not have the time, well, I'm in a hurry.

But I think we ought to try as hard as we can.

It's true.

Maybe some things are beyond our capacity

to solve in the life of human beings on Earth.

But let's get busy and try.

There was a period when people said, well, we'll never

really understand life.

And while we don't fully understand it,

but we're pretty far along.

I mean, the old debate between the mechanists

and the vitalists, that doesn't make any sense to us anymore.

So we made a lot of progress.

There was another half to your question.

AUDIENCE: It may not matter because all universe--

JOHN SEARLE: Oh, yeah.

Maybe conscious doesn't matter.

Well, it's where I live.

It matters to me.

AUDIENCE: Philosophically speaking.

JOHN SEARLE: Yeah, but the point is

there are a lot of things that may or may not

matter which are desperately important to us--

democracy and sex and literature and good food

and all that kind of stuff.

Maybe it doesn't matter to somebody,

but all those things matter to me in varying degrees.

AUDIENCE: Your artificial heart analogy that you mentioned.

I think you included the idea that it's possible,

just like with the artificial heart,

that we use different materials and different approaches

to simulate a heart and, in some ways,

go beyond just-- come closer to duplication,

that we might, in theory, be able to do the same thing

with an artificial brain.

I'm wondering if you think it's possible

that going down the path just trying

to do a simulation of a brain accidentally creates

a consciousness or accidentally creates duplication,

even if we don't intend to do it with exact same means

as a brain is made.

JOHN SEARLE: I would say to believe in that, you

have to believe in miracles.

You have to-- now think about it.

We can do computer simulations of just about anything

you can describe precisely.

You do a computer simulation of digestion.

And you could get a computer model

that does a perfect model of digesting pizza.

For all I know, maybe somebody in this building has done it.

But once you've done that, you don't rush out and buy a pizza

and stuff it in the computer because it

isn't going to digest a pizza.

What it gives you is a picture or a model

or a mathematical diagram.

And I have no objection to that.

But if my life depended on figuring out

how the brain produces consciousness,

I would use the computer the way you

use a computer in any branch of biology.

It's very useful for figuring out

the implications of your axioms, for figuring out

the possible experiments that you could design.

But somehow or other that the idea

that the computer simulation of cognitive behavior

might provide the key to the biochemistry,

well, it's not out of the question,

it's just not plausible.

JOHN BRACAGLIA: Humans are easily fooled and frequently

overestimate the intelligence of machines.

Can you propose a better test of general intelligence

than the Turing test, one that is less likely to relate

false positives?

JOHN SEARLE: Well, you all know my answer

to that is the first step is to distinguish

between genuine intrinsic observer-independent

intelligence and observer-relative intelligence.

And observer-relative intelligence

is always in the eye of the beholder.

And anything will have the intelligence

that you're able to attribute to it.

I just attributed a great deal of intelligence to this object

because it can compute a function,

s equals one-half squared.

Now this object has prodigious intelligence

because it discriminates one hair from-- I

won't demonstrate it, but in any-- take

my word for it that it does, even in a head that's

sparse with hair.

So because intelligence is observer-relative,

you have to tell me the criteria by which we're

going to judge it.

And the problem with the Turing test--

well, it's got all sorts of problems,

but the basic problem is that both the input and the output

are what they are only relative to our interpretation.

You have to interpret this as a question.

And you have to interpret that as an answer.

One bottom line of my whole discussion

today is that the Turing test fails.

It doesn't give you a test of intelligence.

AUDIENCE: So you seem to take it as an article of faith

that we are conscious, that your dog is conscious,

and that that consciousness comes

from biological material, the likes of which we can't really

understand.

But forgive me for saying this, that

makes you sound like an intelligent design theorist who

says that because evolution and everything

in this creative universe that exists

is so complex, that it couldn't have

evolved from inert material.

So somewhere between an amoeba and your dog,

there must not be consciousness.

And I'm not sure where you would draw that line.

And so if consciousness in human beings

is emergent, or even in your dog at some point

in the evolutionary scale, why couldn't it

emerge from a computation system that's

sufficiently distributed, networked, and has the ability

to perform many calculations and maybe is even hooked

into biologic systems?

JOHN SEARLE: Well, about could it emerge, miracles are always

possible.

How do you know that you don't have

chemical processes that will turn this

into a conscious comb?

How do I know that?

Well, it's not a serious possibility.

I mean, the mechanisms by which consciousness

is created in the brain are quite specific.

And remember, this is the key point.

Any system that creates consciousness

has to duplicate those causal powers.

That's like saying, you don't have to have feathers in order

to have a flying machine, but you have to duplicate

and not merely simulate the causal power of the bird

to overcome the force of gravity in the Earth's atmosphere.

And that's what airplanes do.

They duplicate causal powers.

They use the same principle, Bernoulli's principle,

to overcome the force of gravity.

But the idea that somehow or other you might do it

just by doing a simulation of certain formal structures

of input-output mechanisms, of input-output functions,

well, miracles are always possible.

But it doesn't seem likely.

That's not the way evolution works.

AUDIENCE: But machines can improve themselves.

And you're making the case for why an amoeba could never

develop into your dog over a sufficiently long period

of time and have consciousness.

JOHN SEARLE: No, I didn't make that case.

No, I didn't make that case.

[INTERPOSING VOICES]


JOHN SEARLE: Amoeba don't have it.

AUDIENCE: You're refuting that consciousness

could emerge from a sufficiently complex computation system.

JOHN SEARLE: Complexity is always observer-relative.

If you talk about complexity, you

have to talk about the metric.

What is the metric by which you calculate complexity?

I think complexity is probably irrelevant.

It might turn out that the mechanism is simple.

There's nothing in my account that

says a computer could never become conscious.

Of course, we're all conscious computers, as I said.

And the point about the amoeba is not

that amoebas can't evolve into much more complex organisms.

Maybe that's what happened.

But the amoeba as it stands-- a single-celled organism--

that doesn't have enough machinery to duplicate

the causal powers of the brain.

I am not doing a science fiction project to say, well,

there can never be an artificially created

consciousness by people busy designing computer programs.

Of course, I'm not saying that's logically impossible.

I'm just saying it's not an intelligent project.

If you're thinking about your life depends

on building a machine that creates consciousness,

you don't sit down your console and start programming things

in some programming language.

It's the wrong way to go about it.

AUDIENCE: If we gave you a disassembly of Google Translate

and had you implement the Chinese room experiment,

either it would take you thousands

of years to run all the assembly instructions on pen and paper,

or else you'd end up decompiling it into English

and heavily optimizing it in that form.

And in the process, you'd come to learn a lot

about the relationships between the different variables

and subroutines.

So who's to say that an understanding of Chinese

wouldn't emerge from that?

JOHN SEARLE: Well, OK, I love this kind of question.

All right.

Now, let me say, of course, when I did the original thought

experiment, anybody will point out to you if you actually

were carrying out the steps in a program

for answering questions in Chinese,

well, we'd be around for several million years.

OK, I take their word for it.

I'm not a programmer, but I assume

it would take an enormous amount of time.

But the point of the argument is not the example.

The example is designed to illustrate

the point of the argument.

The point of the argument can be given

in the following derivation.

Programs are formal or syntactical.

That's axiom number one.

That's all there is to the program.

To put it slightly more pretentiously,

the notion same implemented program

defines an equivalence class specified entirely

formally or syntactically.

But minds have a semantics, and-- and this

is the whole point of the example-- the syntax by itself

is not sufficient for the semantics.

That's the point of the example.

The Chinese room is designed to illustrate axiom three, that

just having the steps in the program is not by itself

sufficient for a semantics.

And minds have a semantics.

Now, it follows from those that if the computer is defined

in terms of its program operations,

syntactical operations, then the program operations,

the computer operations by themselves

are never sufficient for understanding

because they lack a semantics.

But, of course, I'm not saying, well, you

could not build a machine that was both a computer

and had semantics.

We are such machines.

AUDIENCE: You couldn't verify experimentally

what the difference might be between semantics

and what would emerge from thousands

of years of experience with a given syntactical program.

JOHN SEARLE: I think you can-- I don't inherit this.

He does.

I think you don't want to go the epistemic route.

You don't want to say, well, look

you can't tell the difference between the thinking machine

and the non-thinking machine.

The reason that's the wrong route to go

is we now have overwhelming evidence

of what sorts of mechanisms produce what

sorts of cognition.

When I first got interested in the brain,

I went out and bought all the textbooks.

By the way, if you want to learn a subject,

that's the way to do it.

Go buy all the freshman textbooks

because they're easy to understand.

One of these textbooks, it said cats have different color

vision from ours.

Their visual experiences are different from ours.

And I thought, christ, have these guys been cats?

Have the other cats mind problem?

Do they know what it's like to be a cat?

And the answer is, of course, they know completely

what's the cat's color vision is because they can

look at the color receptors.

And cats do have different color vision from ours

because they have different color receptors.

I forget the difference.

You can look them up in any textbook.

But if in real life we're completely

confident that my dog can hear parts of the auditory spectrum

that I can't hear.

He can hear the higher frequencies that I can't hear.

And cats have a different color vision from mine

because we can see what the apparatus is.

We got another question?

You're on.

JOHN BRACAGLIA: This will be our final question.

JOHN SEARLE: OK.

I'm prepared to go all afternoon.

I love this kind of crap.

AUDIENCE: So at the beginning of your talk,

you mentioned an anecdote about neuroscientists

not being interested in consciousness.

And, of course, by this time, a number of neuroscientists

have studied it.

And so they'll present stimuli that

are near the threshold of perceptibility

and measure the brain responses when it's above or below.

What do you think about that?

Is that on the right track?

What would you do differently?

JOHN SEARLE: No, I think one of the best things that's

happened in my lifetime-- it's getting a rather

long lifetime-- is that there is now

a thriving industry of neuroscientific investigations

of consciousness.

That's how we will get the answer.

When I first got interested in this,

I told you I went over to UCSF and told those guys get busy.

The last thing they wanted to hear

was being nagged by some philosopher, I can tell you.

But one guy said to me-- famous neuroscientist

said-- in my discipline, it's OK to be

interested in consciousness, but get tenure first.

Get tenure first.

Now, there has been a change.

I don't take credit for the change,

but I've certainly been urging it.

You can now get tenure by working on consciousness.

Now, neuroscience has changed, that now there's

a thriving industry in neuroscience

of people who are actually trying to figure out

how the brain does it.

And when they figure that out-- and I don't see any obstacle

to figuring that out-- it will be

an enormous intellectual breakthrough,

when we figure out how exactly does the brain

create consciousness.

AUDIENCE: But in particular, that

approach they're using now-- I use the example of presenting

stimuli that are near the threshold of perceptibility

and looking for neural correlates,

do you think that's going to be fruitful?

What particular questions would you ask to find out?

JOHN SEARLE: I happened to be interested in this crap.

And if you're interested in my views,

I published an article in the "Annual Review of Neuroscience"

with a title "Consciousness."

It's easy to remember.

You can find it on the web.

And what I said is, there are two main lines

of research going on today.

There are guys who take what I call the building block

approach.

And they try to find the neuronal correlate

of particular experiences.

You see a red object.

Or you hear the sound of middle C.

What's the correlate in the brain?

And the idea they have is if you can figure out

how the brain creates the experience of red,

you've cracked the whole problem.

Because it's like DNA.

You don't have to figure out how every phenotype is

caused by DNA.

If you get the general principles, that's enough.

Now, the problem is they're not making much progress

on this what I call the building block approach.

It seems to me a much more fruitful approach

is likely to be think of consciousness as coming

in a unified field.

Think of perception not as creating consciousness,

but as modifying the conscious field.

So when I see the red in this guy's shirt,

it modifies my conscience field.

I now have an experience of red I didn't have before.

Most people-- and the history of science

supports them-- use the building block approach

because most of the history of science

has proceeded atomistically.

You figure out how little things work,

and then you go to big things.

They're not making much progress with consciousness.

And I think the reason is you need to figure out

how the brain creates the conscious field

in the first place because particular experiences,

like the perception of red or the sound of middle C,

those modify that conscious field.

They don't create a conscious field from nothing.

They modify an existing conscious field.

Now, it's much harder to do that because you

have to figure out how large chunks of the brain

create consciousness.

And we don't know that.

The problem is in an MRI, that conscious brain looks a lot

like the unconscious brain.

And there must be some differences there.

But at this point-- and I haven't been working on it.

I've been working on other things.

But I want somebody to tell me exactly what's

the difference between the conscious brain

and the unconscious brain that accounts for consciousness.

We're not there yet.

However, what I'm doing here is neurobiological speculation.

I mean, I'm going to be answered not

by a philosophical argument, but by somebody

who does the hard research of figuring out exactly what are

the mechanisms in the brain the produce consciousness

and exactly how do they work.

JOHN BRACAGLIA: John, it's been an immense, immense honor

to be here with you today.

Thank you so much for your time.

And thank you for talking to Google.

JOHN SEARLE: Well, thank you for having me.

[APPLAUSE]

Comentarios


bottom of page