19. Architectures: GPS; SOAR; Subsumption; Society of Mind Video Lecture | Artificial Intelligence: A Fundamental Guide - AI & ML

PROFESSOR: [INAUDIBLE].
"Thus Spake Zarathustra"
was made famous
and popular by 2001.
And that is music played at this
magic moment when some
primate suddenly gets an idea,
presumably one of our ancestors.
So how do we explain all that?
We've got all of the ingredients
on the table.
And today I want to talk about
various ways of putting those
ingredients together.
So we talked about
representations, we've talked
about methods, and today we're
going to talk about
architectures.
And by the end of the class
you'll know how to put one of
those things together.
Actually, no one knows
how to put one of
those things together.
But what you will know is about
some alternatives for
putting those things together
so as to make something that
is arguably intelligent in
the same way we are.
So that is our agenda
for today.
We'll also talk a little
bit more about stories.
I think it was in 2007 when
the Estonians moved a war
memorial from the center
of Tallinn off to
a Russian war cemetery.
Prior to that time the Estonians
had been building up
their national computer networks
because they thought
that computation was the wave
of the future-- networks and
all of that.
Shortly after the movement of
that war memorial, someone
brought the Estonian national
network down--
a cyber attack.
It was widely believed
to be the Russians.
There's a large Russian
ethnic population in
Estonia to start with.
And the movement of
that war memorial
irritated the Russians.
And so everybody everybody
thinks that they did it.
But you know what?
No computer can understand
the story I just told.
They can revel through all of
the worldwide web finding
information that's relevant to
that, but no computer can
understand the story I just
told, except one.
You'll see a demonstration
of that later on today.
So by the way, if you're
interested in understanding
the nature of intelligence, this
is, of course, the most
important lecture
of the semester.
And I should tell you a little
bit about what we're going to
do on Wednesday.
Because for some reason the day
before Thanksgiving tends
to be a lecture that's
lightly populated,
except in this class.
Because I'm going to talk
about the artificial
intelligence business and what
can be learned from it about
how to avoid going broke when
you start your company.
So for many of you that will be
the most important lecture
of the semester.
It all started back in the
dawn age of artificial
intelligence.
And really, it all started at
Carnegie Mellon, sad to say.
Because the people at Carnegie
Mellon, notably Newell and
Simon, were the first to think
about sort of a general
purpose way of putting things
together so as to build a
structure or architecture in
which particular intelligent
systems could be built.
So their idea was called the
general problem solver.
A long name for a simple idea.
And the simple idea is that you
start your life out in a
current state, call it C.
And you want to get
to some goal state.
Call it S. And a way you do that
is you measure somehow
the symbolic difference between
where you are and
where you want to be.
So that's the difference.
We'll call that difference D.
And when you observe that
difference that's enough, they
say, in this general approach
to problem solving.
For you to select some operation
that will move you
from your current state
to some new state, an
intermediate state.
Call it I. So I, or that
operator, O, is determined by
the difference, D.
And then, of course, the next
thing to do is to measure the
difference between that
intermediate state and the
state you want to be in, and
choose some operator that's
relevant to reducing
that state.
So we'll call that D2, and
we'll call this O2.
And D2 is what leads you
to 02, and so it goes.
So that's the idea.
And that's often called
means-ends analysis.
Why?
Because the end that you want
to achieve is being in that
final state, S. And the means
is that operator, O. So you
have some notion of where you
want to be and the difference
of where you are and where
you want to be.
And you pick an operator so as
to reduce that difference.
So this is all very abstract.
Let's exercise it in solving a
problem that you will all be
faced with here in
a day or two.
That is, for many of you--
most Of you, I hope-- the
problem of going home.
So here you are.
You're at MIT.
And where you want to be
is over here, at home.
So you measure the difference
between MIT and home.
And for many of you it's further
than you can go by car
and not so far that you
can't go at all.
So what you do is, you say,
well, the right operator is
taking an airplane.
So there is the operator,
take an airplane.
And this is the difference, D.
And the difference, D, being
sufficiently large, you
take the plane.
Trouble is, if you happen
to be sitting here in
[? 10-250 ?]
there's no way you can take an
airplane, because they don't
fit in here.
So you've got another
problem, and that is
to get to the airplane.
So the distance between here
and Logan is such that the
right way to do that is
to take the MBTA.
And that's determined because
you're working on this
difference reduction right
here, the difference from
being at MIT and being
at the airport.
So that difference dictates
that you take the MBTA.
So you see, you're re-cursing.
But you know there are no MBTA
cars in here either.
So there's still a difference
like so.
And that difference dictates
that you walk.
So you've got D1, D2, and D3.
And by the time you've excised
the operators relevant to
those three differences,
you're at Logan.
Then you take the airplane, you
get over to your hometown,
and your faced with the smaller
difference of getting
from that airport to where
you actually want to go.
So that's the general problem-
solver idea.
It was such an exciting
idea at the time.
It was such an exciting idea
at the time because people
would say to themselves, ah!
This is a general purpose
problem solver, so we can set
it onto the problem of making
itself smarter.
And so there was a kind of
imagined chain reaction that
would take place.
And the developers of this
architecture warned the public
that within 10 years-- that
is to say, by about 1970--
computers would be generally
as smart as people.
And a lot of people made fun of
them for that prediction.
But it was actually scientists
attempting to be responsible.
Because they thought something,
a quite serious
dislocation was coming along,
and that people should know
that it was coming.
And so they felt it was their
responsibility in that age of
scientific responsibility
to warn the public.
It didn't turn out that way,
because the problem of
collecting the differences and
finding the operators, that's
outside the scope of
the architecture.
So this is the problem that has
to be solved by a human
before this architecture
can be used.
You have to have identified the
differences that you might
encounter and the operators that
you might use, and build
this table which relates
the two together.
So maybe that one, that
one, some off-diagonal
elements, and so on.
But building that table turned
out to be a hard job.
So not surprisingly,
the idea evolved.
And eventually the folks at
Carnegie who developed the
general problem solver--
most notably Newell
and his students--
developed a newer, fresher, more
elaborate architecture
called SOAR.
And here's how SOAR works.
First of all, what
does SOAR mean?
It doesn't mean anything.
It used to mean State
Operator And Result.
But for some reason the
proponents of the SOAR
architecture decided they don't
like that acronym, and
have asserted that SOAR is
merely a label that shouldn't
be thought of as an acronym.
In any event, SOAR consists
of various parts.
It has a long-term memory.
It has a short-term memory.
And it has connections to the
outside world, maybe a vision
system and an action system.
But most of the activity of
the SOAR problem-solving
architecture takes place
in a short-term memory.
So you can view the contents
of the long-term memory as
shuttling in and out of
short-term memory.
So you can see right away that
this mechanism, this
architecture, is heavily
influenced by certain
cognitive psychology experiments
having to do with
how much you can hold in your
short-term memory--
nonsense syllables and all that
sort of thing that was
popular back in those days.
So this was an architecture
devised primarily by
psychologists.
And it had amongst its features
a short-term memory
and a long-term memory.
So that's part 1 of
this architecture.
So what's in the long-term
memory?
Well, assertions and rules,
AKA productions.
A production being the Carnegie
vernacular for rule.
It's just the rule-based stuff
like you saw on almost the
first day of class.
So the whole thing is a gigantic
rule-based system
with assertions and rules the
shuttle back and forth from
long-term memory into short-term
memory where
processing takes place.
The third thing that comes to
mind when you think of SOAR
architecture is they had an
elaborate preference system.
You recall that when we talked
about rule-based systems
there's always a question of
what do you do when more than
one rule would work?
You have to have some way
of breaking those ties.
The SOAR architecture
has an elaborate
subsystem for doing that.
But I said that these are the
first three things you think,
and maybe that's not right.
Because the next thing you
think about is perhaps a
better thing to identify with
the SOAR architecture.
And that's the idea
of problem spaces.
And that's the idea that if
you're going to solve a
problem you have to develop a
space and do a search through
that space.
Just like we did when we talked
about how we can get
from here to home.
There's a space of places,
that's our problem space.
We can do a search through that
space to find a way to
get from one place to another.
That's the sort of thing that
SOAR is focused on.
Finally, the fifth element that
you tend to think about
when you think about SOAR
is the idea of universal
subgoaling.
And that's the idea that
whenever you can't think of
want to do next, that becomes
your next problem that
deserves it's own problem
space and its own set of
differences and operators,
and rules and assertions.
So you start off on a high
level, then you have to solve
problems at a lower level, just
like you did up there
with a general problem solver.
So if you have these two
architectures you can begin to
say, well, what are
they centered on?
And this architecture, this
general problem solver, is
centered on the idea
that everything is
about problem solving.
Because the problem solving
hypothesis--
no one gave it that name.
But that's what it was.
And this architecture
did get its name.
And it was said always, by
Newell, to be based on what he
called the symbol system
hypothesis.
The hypothesis that what we
are as humans is symbol
manipulators.
And we can uncover how that
all works by giving people
crypto-arithmetic problems and
having them talk out loud, by
thinking about what happens
when you try to remember
nonsense syllables, by all that
sort of stuff that was en
vogue in terms of psychology
experiments in the day when
this architecture was
first articulated.
But when you look at
architectures you can sort of
see where they come from and
what their antecedents are.
It has a short-term memory and
a long-term memory, because
Newell and his associates were
cognitive scientists.
It has assertions and rules
and preferences, because
Newell and his associates
were also AI people.
And it has problem spaces and
universal subgoaling because
those are ideas that had been
work out in a more primitive
form already in the general
problem-solver architecture.
So that's a glimpse of
what SOAR looked like
in its early days.
It's been very highly
developed by a
lot of smart people.
So although it's symbol
centered, they've attached to
it things having to do with
emotion and perception, but
generally with the view that
the first thing to do when
faced with this perception is to
get it out of there and get
it into a symbolic form.
That's sort of the bias that the
architecture comes with.
So those are two architectures
that are heavily biased toward
thinking that the important
part of what we
do is problem solving.
But the most important,
perhaps--
at least from an MIT
perspective--
of these problem-solving
oriented ways of thinking
about the world, is Marvin
Minsky's architecture, which
he articulates in his book
"The Emotion Machine."
And Marvin is not just concerned
with problem
solving, but also with
how problem solving
might come in layers.
So let me show you an example
of the sort of problem what
motivates some of Marvin's
thinking.
So you can read that, it's
a short little vignette.
You have no trouble
understanding it, right?
No.
It's not difficult
for us humans.
Awfully tough for a computer.
In part, because the thinking
you need, your ability to
understand that story, requires
you to think on many
levels at the same time.
First of all, there's
a sort of, at the
bottom, instinctive reaction.
You see where there's
instinctive reaction?
That's the part where
she hears a sound
and terns her head.
That's instinct, right?
That's practically built in.
But then what she
sees is a car.
So that's something that
we don't have wired in.
It would be unlikely that we've
evolved in the last 100
years to have an instinctive
appreciation of cars
barrelling down the road.
So the next level in Marvin's
architecture is learned reaction.
So that's the part about
thinking about the car.
Now spread throughout there--
well, let's see where is a
particularly good example.
She decides to sprint
across the road.
So that's where she's
solving a problem.
So that's the deliberative
thinking level.
It doesn't stop there, because
later on she reflects on her
impulsive decision.
So she thinks not only about
stuff that's happening out
there in the world, but she also
thinks about stuff that's
going on in here.
So that's a level which we can
call reflective thinking.
Well, you know, it doesn't stop
there, because she also
considers, in another part of
the story, something about
being uneasy about
arriving late.
So she's not only just thinking
about events that are
going on in her mind right now,
but events that are going
on right now relative
to plans she's made.
Some Marvin calls that the
self-reflecting layer.
But that isn't the whole thing
either, because toward the end
of the story she starts to worry
about what her friends
would think of her.
So there's a kind of reflective
thinking in a more
social context.
So he calls that self-conscious
thinking.
So as the Carnegie folks think,
the SOAR architecture
focuses mostly on problem
solving, Minsky's "Emotion
Machine" book considers not just
thinking, but thinking on
many layers.
And the blocker to doing any of
that can be said to be the
development of common sense,
which computers, alas, have
never had much of.
So this could be said to be
based on the common sense
hypothesis.
And the common sense hypothesis
holds that in order
to do all of that stuff,
you have to have
common sense like people.
And if you have to have common
sense like people, you have to
think about how much of
that is there and how
can we go get it?
And so this spawned a lot of
activity in the media lab
amongst people influenced by
Marvin, having to do with
gathering common sense.
The open mind project, the work
of Henry Lieberman and
others, having to do with the
gathering of common sense from
the world wide web as a way of
populating systems that would
lay the foundation for doing
this kind of layered thinking.
So that is a brief survey of
some mechanisms, some older
than others, but all but GPS--
GPS too.
Let's face it, it's hard to
think of solving any problem
without means-ends analysis
being involved.
So GPS isn't wrong, it's just
not the only tool you need to
think about what to do.
So these are early, and late,
and still-current.
But it's not the only thing
there is, because there have
been reactions against this
problem-solving way of
thinking about the development
of intelligence.
And the most prominent of those
counter currents, of
those alternative ideas, belongs
to Rod Brooks and his
subsumption architecture.
So along about the early--
along about the years
surrounding 1990, Brooks
became upset--
subsumption--
because robots couldn't
do much.
They would turn them on at
night, and then the next
morning they'd come in the
laboratory and they would have
moved 25 feet, nicely avoiding
a table perhaps.
Not doing very much and taking
a long time to do it.
So he had decided that it's
because people were thinking
in the wrong way.
In those days people thought
that the way you build a robot
is you build a vision system,
and then you build a reasoning
system, and then you build
an action system.
And it can do almost nothing,
but it does something.
So you improve the vision
system, and improve the
reasoning system, and improve
the actual system.
And now you've broken it,
because all the stuff you used
to be able to do doesn't
work anymore.
So what's the alternative?
Well, the alternative, as
articulated by Brooks, is to
turn this idea on its side.
So instead of having an
encapsulated vision system, an
encapsulated reasoning system,
and an encapsulated action
system, what you have is layers
that are focused not on
the sensing and the reasoning
and the action, but layers
that are specialized to dealing
with the world.
So in Brook's way of thinking
about things, at the lowest
level you might have a system
that's capable of--
well, before we get to that,
avoiding objects.
And maybe the next level up
is the wandering layer.
And maybe the next level up
after that is explore.
And maybe the next level
up after that is seek.
Now in the old days when people
took 6001 I had no
trouble getting an answer the
question, what does this
remind you of in 6001?
It doesn't remind you of
anything in 6001 since you
haven't taken it.
But it viewed, as a
generalization of a
programming idea, what is
the programming idea?
There are only a few powerful
ideas in program, and this is
a generalization
of one of them.
What is it?
Do you have a name?
Yes, Andrew?
STUDENT: Layers of
abstraction?
PROFESSOR: Layers
of abstraction,
and abstraction barriers.
That nails it pretty well.
Because each of these guys can
have its own vision, action,
and reasoning system.
And if you think of these as
abstraction boundaries, then
when you got this thing working
you don't screw with
it anymore.
You build this layer on top.
And it may reach down in here
from time to time, but it
doesn't fundamentally
change it.
Brooks was inspired in part
by the way our brains are
constructed.
All that old stuff that we share
with pigs is down in
there deep, and we put the
neocortex over it.
So it looks layered in
a way that would make
[? Gerry Sussman ?] proud.
So this then is the way that
Brooks looks at the world, and
it's characterized by a few
features just like SOAR is.
One of those features is
no representation.
So this is a detail that's
probably right at the level
that Brooks was operating, and
very questionable when you get
above the level that Brooks
was operating.
But before I go on, let me say
what the hypothesis is.
The hypothesis is the
creature hypothesis.
It's the hypothesis that once
you can get a machine to act
as smart as an insect, then
the rest will be easy.
Well, how do you get
a creature to
be smart as an insect?
Maybe you don't need
representation.
We focused on representation in
this course, so you can see
there's a little stress---
Next thing is, what do you
do if you don't have a
representation?
Let's see.
Your representation makes
a model possible.
Models make it possible to
predict, to understand, to
explain, and to control.
So if you don't have one what
can you possibly do?
Brooks' answer is, you use the
world instead of a model.
So everything you
do is reactive.
You don't have anything
in your head that is
a map of this room.
But maybe I don't need one
because I can get around that
table by constantly
observing it.
And we don't have to fill
up the memory with that
information, I can
just react to it.
So no representation, use the
world instead of a model, and
the mechanisms in their
purest form are just
finite-state machines.
So with that, Brooks was able to
do things that people were
never able to do before.
And what's the modern
[? instantiation ?]
of this architecture?
Now, according to Brooks, in use
in 5 million homes in the
United States?
STUDENT: The Roomba?
PROFESSOR: It's the Roomba The
Roomba robot is, by Brooks'
account, approximately
the thirteenth
business plan of iRobot.
And it's the one that made it
big, because the Rumba vacuum
cleaner has been very
successful.
Would you like to see a movie
of its processor?
So this is a film made some time
ago that shows, in some
sense, the summa of
that architecture.
What I want you to imagine very
briefly is a robot that
wanders around in the halls
and rooms of the old
[? Tech Square ?]
clinking the Coke cans.
Okay, you all got an image
of that in your mind?
Because I want you to compare
the image you now have of that
robot that's wandering around
collecting the Coke can, with
the actual movie.
[VIDEO PLAYBACK]
-Herbert, the soda-can
collecting mobile robot.
He was built at the MIT
AI lab in 1989.
Work was done by John Cannell
under the supervision of
Rodney Brooks.
Herbert is a robot controlled
by subsumption architecture.
This is a collection of small
behaviors that influence the
overall activities
of the robot.
There are no centralized
controllers
and no world model.
-Herbert navigates by using a
number of infrared proximity
censors around its body
and basically
following walls and corridors.
It can also look for the can
through a laser light striper.
Right now it's come out of the
door of an office, followed
along the wall, and then its
laser light striper has seen a
can on top of the desk
in front of it.
When this happens the robots
and deploys its arm.
You can see the arm
going out now.
-The arm has a number
of censors itself.
There are fingertip censors, a
break beam in the jaws, and
two infrared proximity sensors
on the front of the hand.
-It grabs cans in a stereotype
fashion.
First, it lowers down to find
a surface somewhere, then it
bounces along the
surface until it
sees the can in front.
It uses the hand-based IRs to
re-center the arm by rotating
the robot's body until the can
comes between the jaws of the
gripper, at which point the
break-beam senses the can.
-After acquiring the can,
Herbert will have tucked the
arm back into its normal
traveling configuration and
attempt to go home.
-Since it has no central
[? arm presentation, ?] it
doesn't have any map of
where it came from.
Instead, it has an algorithm
which uses a magnetic compass
to determine every time it comes
through a door, will it
be able to find the door?
It basically has a policy of
always going north every time
it exits the door.
-So now the can is being
tucked away.
As the robot turns you'll see
a red stripe from the laser
range finder.
And now it's using
the [INAUDIBLE]
IR to navigate back, find the
door, and go through the door
with its prize.
[END VIDEO PLAYBACK]
PROFESSOR: And there, if you
were paying attention, you saw
a little glimpse of John Cannell
who was the student to
develop that system.
So that was a tour de force.
That was a magic moment.
That was when you open
the champagne.
It's not what you expected, of
course, because when I say
imagine a robot wandering around
in [? Tech Square ?]
picking up Coke cans, that
leaves open a huge envelope of
possible hallucinations.
And usually or hallucinations
about these things are--
we imagine things to be more
fluid, more natural, and more
impressive than they
actually are.
But that was impressive, because
no robot came close to
doing anything like
that before.
More to be said about
that during the
business lecture on Wednesday.
So that's the subsumption
architecture.
By the way, maybe at this point
we can say something
about how the other
architectures relate to what
Minsky was talking about.
What's this deliberative
thinking layer correspond to?
That's what SOAR is about,
and maybe GPS.
So what's subsumption about?
It's about stuff down here.
It's about instinctive reaction
and learned reaction.
But shoot, what about Minsky's
other layers?
If we're going to be building
systems that are as smart as
those things then we have to
worry a little bit about that
sort of thing too.
So that brings us to the
genesis architecture.
And now let me give you the
standard caution that should
be early in the presentation
of any academic.
I will sometimes say "I," and
what I mean is "we." And
sometimes I'll say "we," and
what I mean is "they." This
was a system that was developed
mostly by students
of mine who persuaded me, after
a great deal of time,
that they were thinking the
right kinds of thoughts.
But here's how the genesis
architecture works.
As no surprise, given recent
discussions, it's all centered
on language.
And the language part of the
genesis system has two roles,
one of which is to guide, and
marshal, and interact with the
perceptual systems.
And the other is to enable the
description of events.
That's how it works.
So is perception important?
I don't know.
I might ask you a question like,
is there anybody sitting
in the front row wearing
blue jeans?
And it's hard for you to
resist, under those
circumstances, your eyes from
going over there and answering
the question.
Your eyes answer the question.
No symbol processing system is
involved, except in so far as
my language system has
communicated with their
language system, which drives
your motor system and your
vision system to go over
there and answer the
question for you.
But it's not just the real stuff
that the language system
directs your attention to.
It's also the imagined stuff.
It's been a long semester.
Have I told you the story
about my table saw?
Probably not.
Here's the deal.
I bought a table saw.
It's a wonderful table saw.
I was installing it with
a friend of mine
who's a cabinet maker.
He said, never wear gloves
when you operate the saw.
"Why?" I said.
Before he could answer the
question I figured it out.
Can you figure out why you never
wear gloves when you
operate a table saw?
You know what a table
saw is, right?
It's a table with a spinning
blade in the middle.
And you use it to cut wood.
Why should you never
wear gloves?
Yes?
STUDENT: Well--
STUDENT: --Well, you
know the answer.
Ha, that's not fair.
That's old Brett up there.
He's heard the story
too many times.
Yes, Andrew, you got it.
STUDENT: I've been told
the answer before.
PROFESSOR: You've been
told the answer.
How about somebody who hasn't
been told the answer.
Yes?
STUDENT: Because the gloves
might get caught.
PROFESSOR: Because the glove
might get caught and pull your
hand into the blade.
And then what happens?
It's horrible.
You're hand gets mangled and
your fingers get cut off, and
this happens a lot
to professionals.
It won't actually happen with
that table saw that I bought,
because its play detects flesh
and stops the blade, which
then leads to stopping the blade
and having the blade
retreat into the table in
about two microseconds--
two milliseconds.
So, in general though, it's a
bad idea, and you always have
to suppose that the mechanism
isn't working anyway in order
to use good safety practice.
But here's an example of
something that nobody ever
told you that he was able to
figure out, by imagining what
would happen and reading the
answers off of the scene that
he imagined.
So nobody ever says many of the
things that we know, but
we know them anyway.
Here's another example.
Imagine running down
the street with a
full bucket of water.
What happens?
The water splashes out and
gets your leg wet, right?
You won't find that in
Open Minds database.
Nobody ever said that
over the web.
It's not written
down anywhere.
But you know it.
Because you, we human beings
have the capacity to imagine
perceptual things and read the
answers to questions off of
our imaginations with that
perceptual apparatus.
So that's a very important
connection down there.
And then if you've got the
ability to describe events,
then you've got the ability to
tell and understand stories.
And if you can do that, then you
can start to get a handle
on culture, both macro
and micro.
And by macro culture I mean the
country you grew up in,
the religion you grew up with.
And by micro I mean your
family and personal
experience, and all
shades in between.
So I don't know, what inspires
me and my associates to think
in these terms?
We talked about a little bit of
it last time when I talked
about evolution and the apparent
flowering of our
species about 50,000
years ago, at
which time we got something.
And I believe that
what we got--
and this is the characterization
of this
particular hypothesis--
what we got is the ability
to tell stories
and understand them.
So if we want to label this
representation, it's the label
strong story hypothesis.
So what's the weak
story hypothesis?
The weak story hypothesis
is, this is important.
The strong story hypothesis
is, this is all there is.
But is there any other evidence
of this is really,
really, really important?
So I've queried Krishna here
before the class starts, and
he tells me I haven't told
you about the following
experiment.
This, in my way of thinking, is
the most important series
of experiments ever done in
cognitive psychology,
developmental psychology,
actually.
So here's how we get started.
There's a rectangular room,
if you're a person.
If you're a rat, it's
a rectangular box.
All the walls are
painted white.
Are you with me so far?
So now, in each corner there's
a basket, or cloth, or
something in which or under
which you can put some food.
Now, you put the food there
while the rat watches you.
And then you give the rat a
little spin to disorient it.
All right?
So then, the rest stops
and goes for the food.
And you can keep track of
where the rat goes.
And the rat goes with
approximate equal probability
predominantly to those
two corners.
So I'd have bet you
didn't know that
rats were that smart.
So they understand the
rectangular nature of the room
and they don't go to the
diagonal corners where the
food cannot be.
So are these genius rats?
Or maybe we're just rats
with big brains.
Because we do the same thing.
So if you repeat this experiment
and replace the rat
with a small child, and then you
put a toy in there instead
of food, and the rat--
not the rat.
The child is usually held in a
parent's arms, usually the
child's mother--
usually because they think that
if they participate in
these experiments up there at
Harvard their kid will get
into Harvard some day.
So the kid goes to a diagonal
corner just like a rat.
And then the next thing you do
is, you try an adult, maybe an
MIT student.
That way you can
use food again.
And you get the same result.
Who could be surprised?
So rats, children, and human
adults, pretty much all the
same with respect to this
experiment, until you paint
one wall blue.
Rats are not colorblind, in
case you're wondering.
Then what happens?
Well, if you pay one wall blue
the rat still goes with equal
probability to the two
diagonal corners.
If you paint one wall blue, the
child still goes to the
two diagonal corners with
approximate equal probability.
It's only us genius human
adults who go
only to that corner.
So this invites a couple
of questions.
One of which is, when does
a child become an adult?
Any ideas?
[INAUDIBLE], what
do you think?
STUDENT: [INAUDIBLE].
PROFESSOR: You can pick a
number greater than 1
and less than 10.
[INAUDIBLE], what
do you think?
STUDENT: Five?
PROFESSOR: It's a pretty
good guess.
Do you have siblings
at that age?
It's a surprise but,
why is it five?
Is it because--
what does it relate to?
Is there any correlate to the
onset of that ability?
You might try everything, as
[INAUDIBLE] does, because
she's extremely careful.
So she's tried gender, she's
tried the onset of language,
the appreciation of music,
handedness, and there's only
one thing that matters.
And that is that the child
becomes adult at that time
when they start to use the word
left and right when they
describe the world.
Now I said that very carefully
because they understand left
and right at an earlier age, but
they only have started to
use the words left and right
when they describe the world
at the time that they begin to
break this symmetry and go to
the correct corner.
Now for the next element of this
I need something to read.
Has anyone got a
textbook handy?
Ah, "China, an Illustrated
History."
Now I need a volunteer.
OK.
Andrew, you want to do this?
So here's what you're
going to do.
You can stay there.
But you need to stand up.
So what I'm going to do is,
I'm going to read you a
passage from this book.
And I want you to say
it back to me at the
same time I read it.
It's as if you're doing
simultaneous translation,
except it's English
to English.
This things got words
I can't pronounce.
OK, are you ready to go?
All right.
"When overwhelmed by the
magnitude of the problems he
tackled, he began to suspect
that others were plotting
against him or secretly
ridiculing him."
Thank you very much.
That's great.
So you see, he could do it.
Some people can't do it.
At least it take a
little practice.
But he did it.
And guess what I've
done to him?
I've reduced his intelligence
to that of a rat.
Because if you do this
experiment with an adult human
who's doing this simultaneous
English to English
translation, they go with equal
probability to the two corners.
So what's happened?
What's happened is
you've jambed
their language processor.
And when their language
processor is jambed they can't
put the blue wall together with
the rectangular shape.
So it seems to be that language
is the mediator of
exactly the combinators you
need in order to build
descriptions.
Because they can't even put
those things together when
their language processor is
jambed by the simultaneous
translation phenomenon.
So that brings us to the two
gold star ideas of the day.
One is, if you want to make
yourself smarter you want to
do those things--
look, listen, draw, and talk.
Because those are the particular
mechanisms that
surround this area down here,
which is the center of what we
do-- which is the center
of our thinking.
So why do you take
notes in class?
Now because you'll ever look at
them before, but because it
forces the engagement
of your linguistic--
of your linguistic, your motor,
and your visual apparatus.
And that makes you smarter,
because it's
exercising that stuff.
The second thing you can say, in
conclusion, especially from
this experiment, is beware
of fast talkers.
Why do you want to be aware
of fast talkers.
It's not because they will
talk you into anything.
It's because that when they talk
fast they're jambing your
language processor and
you can't thing.
That's why you want to be
aware of fast talkers.
Because if they jamb your
language processor you won't
thinking and you'll buy that
car, or you'll buy that drink,
or you'll do any manner of
things that people who want
you to do those things have
learned to do by talking to
jamb your processor.
So that completes what we're
going to do today.
And I'll give you a
demonstration of some of this
stuff on another occasion.