10. Introduction to Learning; Nearest Neighbors Video Lecture | Artificial Intelligence: A Fundamental Guide - AI & ML

PROF.
PATRICK WINSTON: Well that's
the Kodo Drummers.
They're a group of about 30 or
40 Japanese people who live in
a village on some island off
the coast of Japan, and
preserve traditional
Japanese music.
It's an unusual semi
communal group.
They generally run about 10
kilometers before breakfast,
which is served at 5:00 AM.
Strange group.
Wouldn't miss a concert for
the world, although they,
alas, don't seem to be
coming down to the
Boston area very soon.
If you go to a concert
from the Kodo
Drummers--and you should--
and if you're no longer young,
you'll want to bring earplugs.
Because, as we humans get
older the dynamic range
control in our inner ear tends
to be less effective.
So that's why a person of my age
might find some piece of
music excruciatingly loud,
whereas you'll
think it's just fine.
Because you have better
automatic gain control.
Just like in any kind of
communication device there's a
control on how intense
the sound gets.
Ah, but I go off on a sidebar.
Many of you have looked
at me in astonishment
as I drink my coffee.
And you have undoubtedly have
been saying to yourself, you
know, Winston doesn't look like
a professional athlete,
but he seemed to have no trouble
drinking his coffee.
So today's material is going
to be pretty easy.
So I want to give you the side
problem of thinking about how
it's possible for somebody
to do that.
How is it possible?
How would you make a computer
program that could reach out
and drink a cup of coffee, if
it wanted a cup of coffee?
So that's one puzzle I'd
like you to work on.
There's another puzzle, too.
And that puzzle concerns
diet drinks.
This is a so-called Diet Coke.
Yeah, it's ripe.
If you take a Diet Coke and ask
yourself, what would a dog
think a Diet Coke is for?
That's another puzzle that you
can work on while we go
through the material
of the day.
So this is our first lecture
on learning, and I want to
spend a minute or two in the
beginning talking about the
lay of the land.
And then we'll race through
some material on nearest
neighbor learning.
And then we'll finish up
with the advertised
discussion of sleep.
Because I know many of you think
that because your MIT
students you're pretty tough,
and you don't need
to sleep and stuff.
And we need to address that
question before it's too late
in the semester to get
back on track.
All right.
So here's the story.
Now the way we're going
to look at learning is
there are two kinds.
There's this kind, and
there's that kind.
And we're going to talk a little
bit about both kinds.
The kind of the right is
learning based on observations
of regularity.
And computers are particularly
good at this stuff.
And amongst the things that
we'll talk about in connection
with regularity based learning
are today's topic, which is
nearest neighbors.
Then a little bit downstream
we'll talk about neural nets.
And then somewhere near the end
of the segment, we'll talk
about boosting.
And these ideas come from
all over the place.
In particular, the stuff we're
talking about today, nearest
neighbors, is the stuff of which
the field of pattern
recognition--
it's the stuff of which pattern
recognition journals
are filled.
This stuff has been around
a long time.
Does that mean it's not good?
I hope not, because that would
mean that everything you
learned in 1801 is not good,
because the same course was
taught 1910.
So it has been around a while,
but it's extremely useful.
And it's the first thing to try
when you have a learning
problem, because it's
the simplest thing.
And you always want to try the
simplest thing before you try
something more complex that
you will be less likely to
understand.
So that's nearest neighbors
and pattern recognitions.
And the custodians of knowledge
about neural nets,
well this is sort of an attempt
to mimic biology.
And I'll cast a lot of calumny
on that when we get down there
to talk about it.
And finally, this is the gift
of the theoreticians.
So we in AI have invented some
stuff, we've borrowed some
stuff, we've stolen some stuff,
we've championed some
stuff, and we've improved
some stuff.
That's why our discussion of
learning will reach around all
of these topics.
So that's regularity
based learning.
And you can think of
this as the branch
of bulldozer computing.
Because, when doing these kinds
of things, a computer's
processing information like a
bulldozer processes gravel.
Now that's not necessarily a
good model for all the kinds
of learning that humans do.
And after all, learning is one
of the things that we think
characterizes human
intelligence.
So if we were to build models
of it and understand that we
have to go down this
other branch, too.
And down this other branch we
find learning ideas that are
based on constraint.
And let's call this the
human-like side of the picture.
And we'll talk about ideas
that enable, for example,
one-shot learning, where you
learn something definite from
each experience.
And we'll talk about explanation
based learning.
By the way, do you learn
by self explanation?
I think so.
I had an advisee once, who got
nothing but A's and F's.
And I said, what are the
subjects that you get A's in?
And why don't you get A's
in all of your subjects?
And he said, oh, I get A's in
the subjects when I convince
myself the material is true.
So the learning was a byproduct
of self explanation,
an important kind of learning.
But alas, that's downstream.
And what we're going to talk
about today is this path
through the tree, nearest
neighbor learning.
And here's how it works,
in general.
Here's just a general picture
of what we're talking about.
When you think of pattern
recognition, or nearest
neighbor based learning,
you've got some sort of
mechanism that generates
a vector of features.
So we'll call this the
feature detector.
And out comes a vector
of values.
And that vector of values
goes into a
comparator of some sort.
And that comparator compares
the feature vector with
feature vectors coming from a
library of possibilities.
And by finding the closest
match the comparator
determines what some
object is.
It does recognition.
So let me demonstrate that with
these electrical covers.
Suppose they arrived on an
assembly line and some robot
wants to sort them.
How would it go about
doing that?
Well it could easily
use the nearest
neighbor sorting mechanism.
So how would that work?
Well here's how if would work.
You would make some
measurements.
And it we'll just make some
measurements in two
dimensions.
And one of those measurements
might be the total area,
including the area
of the holes of
these electrical covers.
Just so you can follow what I'm
doing without craning your
neck, let me see if I can find
the electrical covers.
Yes, there they are.
So we've got one big blank
one, and several others.
So we might also measure
the hole area.
And this one here, this guy
here, this big white one has
no hole area, and its got the
maximum amount of total area.
So it will find itself
at that point in
this space of features.
Then we've got the guy
here, with room for
four sockets in it.
That's got the maximum amount
of hole area, as well as the
maximum amount of area.
So it will be right straight
up, maybe up here.
Then we have, in addition to
those two, a blank cover, like
this, that's got about 1/2 the
total area that any cover can
have, so we'll put
it right here.
And finally, we've got one
more of these guys.
Oh yes, this one.
1/2 the hole area, and
1/2 the total area.
So I don't know, let's see.
Where will that go?
Maybe about right here.
So now our robot is looking on
the assembly line and it sees
something coming along, and
it measures the area.
And of course, there's noise.
There's manufacturing
variability.
So it won't be precisely
on top of anything.
But suppose it's right there.
Well it doesn't take any
genius human, human or
computer, to figure out that
this must be one of those guys
with maximum area and
maximum hole area.
But now let's ask some
other questions.
Where would
[TAPPING ON CHALK BOARD], what
would that be?
Or what would this be?
[TAPPING ON CHALK BOARD],
and so on.
Well we have to figure out
what those newly viewed
objects are closest to in order
to do an identification.
But that's easy.
We just calculate the distance
to all of those standard,
platonic, ideal descriptions
of things, and we find out
which is nearest.
But in general, it's a little
easier to think about
producing some boundaries
between these various idealize
places, so that we can just
say, well which area
is the object in?
And then we'll know
instantaneously to what
category it belongs.
So if we only had two, like the
purple one and the yellow
one, it would be easy.
Because, we would just construct
a line between the
two, with a line between the
purple and yellow as a
perpendicular bisector.
And so drawing it out instead of
talking about it, if there
were only two, that would
be the boundary line.
Anything south of the dotted
line would be purple, and
anything north would
be yellow.
And now we can do this with
all the points, right?
So we can figure out-- oh could
you, Pierre, could you
just close the lap top please?
So if we want to do this with
all these guys it would go
something like this--
I better get rid of these
dotted x's before
they confuse me.
Let's see, if these were the
only two points, then we would
want to construct a
perpendicular bisector between
the line joining them.
And if these two were the only
points, I would want to
construct this perpendicular
bisector.
And if these two were the only
points, I would want to
construct a perpendicular
bisector.
And if these two points were
the only ones involved I'd
want to construct--
oh, you see what I'm doing?
I'm constructing perpendicular
bisectors, and those are
exactly the lines that
I need in order to
divide up this space.
And it's going to divide
up like this.
And I won't say we'll give you
a problem like this on an
examination, but we have every
year in the past ten.
To divide up a space
and produce--
something we would like
to give a name.
You know, Rumpelstiltskin
effect, when you have a name
you get power over it.
So we're going to call these
decision boundaries.
OK so those are the simple
decision boundaries, produced
in a sample space,
by a simple idea.
But there is a little bit
more to say about this.
Because, I've talked about this
as if we're trying to
identify something.
There's another way of thinking
about it that's
extremely important.
And that is this.
Suppose I come in with a brand
new cover, never before seen.
And I only measure, well
let's say I only
measure the hole area.
And the hole area
has that value.
What is the most likely
total area?
Well I don't know.
But there's a kind of weak
principle of, if something is
similar in some respects,
it's likely to be
similar in other respects.
So I'm going to guess, if you
hold a knife to my throat and
back me into a corner, that it's
total area is going to be
something like that orange
cover whole, total area.
So this is a contrived example,
and I don't make too
much of it.
But I do want to make
a lot of that first
principal, over there.
And that is the idea that, if
something is similar in some
respects, it's likely to be
similar in other respects.
Because that's what most
of education is about.
Fairy tales, legal
cases, medical
cases, business cases--
if you can see that there are
similar in some respects to a
situation you've got now, then
it's likely that they're going
to be similar in other
respects, as well.
So when we're learning, we're
not just learning to recognize
a category, we're learning
because we're attempting to
apply some kind of precedent.
That's the story on that.
Well that's a simple idea but
does it have any application?
The answer is sure.
Here's an example.
My second example, the example
of cell identification.
Suppose you have some
white blood cells,
what might you do?
You might measure the total
area of the cell.
And not the hole area, but
maybe the nucleus area.
And maybe you might measure four
or five other things, and
put this thing in a high
dimensional space.
You can still measure
the nearness in a
high dimensional space.
So you can use the
idea to do that.
It works pretty well.
A friend of mine once started a
company based on this idea.
He got wiped out, of course,
but it wasn't his fault.
What happened is that somebody
invented a better stain and it
became much easier
to just do the
recognition by brute force.
So let's see, that's
two examples.
the introductory example of the
holes of the electrical
covers, and the example
of cells.
And what I want to do now is
show you how the idea can
reappear in disguised forms in
areas where you might not
expect to see it.
So consider the following
problem.
You have a collection of
articles from magazines.
And you're interested in
learning something about how
to address a particular
question.
How do you go about finding the
articles that are relevant
to your question?
So this is a puzzle that has
been studied for decades by
people interested in information
retrieval.
And here's the simple
way to do it.
I'm going to illustrate, once
again, in just two dimensions.
But it has to be applied in
many, many dimensions.
The idea is you count up the
words in the articles in your
library, and you compare the
word counts to the word counts
in your probing question.
So you might be interested
in 100 words.
I'm only going to write two on
the board for illustration.
So we're going to think about
articles from two magazines.
Well first of all, what words
are we going to use?
One word is going to be hack,
and that will include all
derivatives of hack-- hacker,
hacking, and so on.
And the other word is going
to be computer.
And so it would not be
surprising for you to see that
articles from Wired Magazine
might appear
in places like this.
They would involve lots of uses
of the word computer, and
lots of uses of the word hack.
And now for the sake of
illustration, the second
magazine from which we are going
to draw articles is Town
and Country.
It's a very tony magazine, and
the people who read out Town
and Country tend to be
social parasites.
And they still use
the word hack.
Because you can talk about
hacking, there's some sort of
specialize term of art in
dealing with horses.
So all the Town and Country
articles would be likely to be
down here somewhere.
And maybe they would be one like
that when they talk about
hiring some computer expert to
keep track of the results so
the weekly hunt, or something.
And now, in you come
with your probe.
And of course your probe
question is going to be
relatively small.
It's not going to have
a lot of words in it.
So here's your here's
your probe question.
Here's your unknown.
Which article's going
to be closest?
Which articles are going
to be closest?
Well, alas, all those Town and
Country articles are closest.
So you can't use the nearest
neighbor idea, it would seem.
Anybody got a suggestion
for how we might
get out of this dilemma?
Yes, Christopher.
CHRISTOPHER: If you're looking
for word counts and you want
to include some terms of
computer, then wouldn't you
want to use that as a threshold,
rather than the
nearest neighbor?
PROF.
PATRICK WINSTON: I don't
know, it's a good idea.
It might work, who knows.
Doug?
DOUG: Instead of using decision
boundaries that are
perpendicular bisectors, if you
treated Wired and Town and
Country as sort of this
like, [INAUDIBLE]
targets.
And they would look like some
[? great radial ?], here.
I guess, some radius
around curves.
If it's within a certain
radius then--
PROF.
PATRICK WINSTON: Yes?
[? SPEAKER 1: Are we, ?]
necessarily, have it done with
some sort of a
[? politidy distance ?]
metric?
PROF.
PATRICK WINSTON:
Oh, here we go.
We're not going to use any
[? politidy distance ?]
metric.
We're going to use some
other metric.
SPEAKER 1: Like alogrithmic,
or whatnot?
PROF.
PATRICK WINSTON: Well,
algorithmic,
gees, I don't know.
[LAUGHTER]
PROF.
PATRICK WINSTON: Let
me give you a hint.
Let me give you a hint.
There are all those articles up
there, out there, and out
there, just for example.
And here are the Town and
Country articles.
They're out there, and out
there, for example.
And now our unknown
is out there.
Anybody got an idea now?
Hey Brett, what do you think?
BRETT: So you sort of
want the ratio.
Or in this case, you can
take the angle--
PROF.
PATRICK WINSTON: Let's be-- ah,
there we go, we're getting
a little more sophisticated.
The angle between what?
BRETT: The angle between
the vectors.
PROF.
PATRICK WINSTON: The vectors.
Good.
So we're going to use
a different metric.
What we're going to do is,
we're going to forget
including a distance, and we're
going to measure the
angle between the vectors.
So the angle between the
vectors, well let's actually
measure the cosine of the angle
between the vectors.
Let's see how we can
calculate that.
So we'll take the cosine of the
angle between the vectors,
we'll call it theta.
That's going to be equal to the
sum of the unknown values
times the article values.
Those are just the values
in various dimensions.
And then we'll divide that
by the magnitude
of the other vectors.
So we'll divide by the magnitude
of u, and we'll
divide by the magnitude of the
art vector to the article.
So that's just the dot
product right?
That's a very fast
computation.
So with a very fast computation
you can see if
these things are going to be
in the same direction.
By the way, if this vector here
is actually identical to
one of those articles, what
will the value be?
Well then a cosine will be 0 and
we'll get the maximum die
of the cosine, which is 1.
Yeah, that will do it.
So if we use any of the articles
to probe the article
space, they'll find themselves,
which is a good
thing to have a mechanism do.
OK.
So that's just the dot product
of those two vectors.
And it works like a charm.
It's not the most sophisticated
way of doing
these things.
There are hairy ways.
You can get a Ph.D. by doing
this sort of stuff in some new
and sophisticated way.
But this is a simple way.
It works pretty well.
And you don't have to strain
yourself, much,
to implement it.
So that's cool.
That's an example where
we have a very
non-standard metric.
Now let's see, what
else can we do?
How about a robotic
arm control?
Here we go.
We're going to just
have a simple arm.
And what we want to do is, we
want to get this arm to move
that ball along some trajectory
at a speed,
velocity, and acceleration
that we have determined.
So we've got two
problems here.
Well let's see, we've got two
problems because, first of
all, we've got angles,
theta 1 and theta 2.
It's a 2 degree of 3 of arm, so
there are only two angles.
So the first problem we have
is the kinematic problem of
translating the (x,y)-cordinates
of the ball,
the desired ones, into the
theta 1, theta 2 space.
That's simple kinematic
problem.
No f equals ma there.
It Doesn't involve forces,
or time, or
acceleration, anything.
Pretty simple.
But then we've got the problem
of getting it to go along that
trajectory with positions,
speeds, and
accelerations that we desire.
And now you say to me, well I've
got 801, I can do that.
And that's true, you can.
Because, it's Newtonian
mechanics.
All you have to do is
solve the equations.
There are the equations.
Good luck.
Why are they so complicated?
Well because of the complicated
geometry.
You notice we've got some
products of theta 1 and theta
2 in there, somewhere,
I think?
You've got theta 2's.
I see an acceleration squared.
And yeah, there's a theta 1
dot times a theta 2 dot.
A velocity times a velocity.
Where the hell did
that come from?
I mean it's supposed to
be f equals ma, right?
Those are Coriolis forces,
because of
the complicated geometry.
OK.
So you hire Berthold Horn, or
somebody, to work these
equations out for you.
And he comes up with something
like this.
And you try it out and
it doesn't work.
Why doesn't it work?
It's Newtonian mechanics,
I said.
It doesn't work because we
forgot to tell Berthold that
there's friction in
all the joints.
And we forgot to tell him that
they've worn a little bit
since yesterday.
And we forgot that the
measurements we make on the
lab table are not
quite precise.
So people try to do this.
It just doesn't work.
As soon as you get a ball of a
different weight you have to
start over.
It's gross.
So I don't know.
I can do this sort of thing
effortlessly, and I couldn't
begin to solve those
equations.
So let's see.
What we're going to do is we're
going to forget about
the problem for a minute.
And we're going to talk
about building
ourselves a gigantic table.
And here's what's going
to be on the table.
Theta 1, theta 2, theta 3,
oops, there are only two.
So that's theta 1 again,
but it's the
velocity, angular velocity.
And then we have the
accelerations.
So we're going to have a big
table of these things.
And what we're going to
do, is we're going to
give this arm a childhood.
And we're going to write down
all the combinations we ever
see, every 100 milliseconds,
or something.
And the arm is just going to
wave around like a kid does in
the cradle.
And then, we're not
quite done.
Because there are two other
things we're going to record.
Can you guess what they are?
There are going to be the torque
on the first motor, and
the torque on the
second motor.
And so now, we've got a whole
bunch of those records.
The question is, what do
we got to do with it?
Well here's what we're
going to do it.
We're going to divide this
trajectory that we're hoping
to achieve, up into
little pieces.
And there's a little piece.
And in that little
piece nothing is
going to change much.
There's going to be an
acceleration, velocity, position.
And so we can look those
up in the table that
we made in the childhood.
And we'll look around and find
the closest match, and this
will be the set of values for
the positions, velocities, and
accelerations that are
associated with that
particular movement.
And guess what we can do now?
We can say, in the past, the
torques associated with that
particular little piece of
movement lie right there.
So we can just look it up.
Now this method was thought
up and rejected, because
computers weren't
powerful enough.
And then, this is the age
of recycling, right?
So the idea got recycled when
computers got strong enough.
And it works pretty well,
for things like this.
But you might say to me, well
can it do the stuff that we
humans can do, like this?
And the answer is, let's look.
So this is a training
phase, it's
going through its childhood.
You see what's happening
is this.
The initial table won't
be very good.
But that's OK.
Because there are only a small
number of things that it's
important for you to
be able to do.
So when you try those
things it's still
writing into the table.
So the next time you try that
particular motion, it's going
to be better at it, because
its got better stuff to
interpolate [? amongst ?]
in that table.
So that's why this thing is
getting better and better as
it goes on.
That's as good as I was doing.
Pretty good, don't you think?
There's just one thing I want
to show at the end of this
clip just for fun.
Maybe you've seen some
old Zorro movies?
So here's a little set up where
this thing has learned
to use a lash.
So here's the lash, and there's
a candle down there.
So watch this.
Pretty good, don't you think?
So how fast does the learning
take place?
Let me go back to that other
slides and show you.
So here's some graphs to show
you how fast goes, boom.
That gives you the curves of how
well the robot arm can go
along a straight line, after
no practice with just some
stuff recorded in the memory.
And then with a couple of
practice runs do give it
better values amongst which
to interpolate.
So I think that's pretty cool.
So simple, but yet
so effective.
But you still might say, well,
I don't know, it might be
something that can be done
in special cases.
I wonder if old Winston uses
something like that when he
drinks his coffee?
Well we' ought to
do the numbers
and see if it's possible.
But I don't want to
use coffee, it's
the baseball season.
We're approaching the
World Series.
We might as well talk about
professional athletes.
So let's suppose that this
is a baseball pitcher.
And I want to know how much
memory I'll need to record a
whole lot of pitches.
Is there a good pitcher
these days?
The Red Socks suck so I
don't do Red Socks.
Clay Buchholz, I guess.
I don't know, some pitcher.
And what we're going to do, is
we're going to say for each of
these little segments
were going to record
100 bytes per joint.
And we've got joints
all over the place.
I don't know how many are
involved in doing a baseball
pitch, but let's just say
we have had 100 joints.
And then we have to divide
the pitch up
into a bunch of segments.
So let's just say for sake
of argument that
there are 100 segments.
And how many pitches does a
pitcher throw in a day?
What?
SPEAKER 2: In a day?
PROF.
PATRICK WINSTON:
In a day, yeah.
This, we all know,
is about 100.
Everybody knows that
they take them out
after about 100 pitches.
So what I want to know is how
much memory we need to record
all the pitches a pitcher
pitches in his career.
So we still have to work on
this little bit more.
How many days a year does
a pitcher pitch?
Well, they've got winter ball,
and that sort of thing, so
let's just approximate
it as 100.
I don't know, some of these may
be a little high, some of
the others may be a low.
And of course, the career--
just to make things easy--
is 100 years.
So that's one, two, three,
four, five, six.
So we have 10 to
the 12th bytes.
Is that the hopelessly
big to store in here?
CHRISTOPHER: 10 to 100
[INAUDIBLE] or
just 100 times throwing?
PROF.
PATRICK WINSTON: 100
pitches in a day--
Christopher's asking
some detail--
and what we're gong to do
is we're going to record
everything there is to know
about one pitch, and then
we're going to see how
many pitches, he
pitches in his lifetime.
And we're going to
record all that.
Trust me.
Trust me.
OK. so we want to know if this
is actually a practical scale.
And this, by the way, is
cocktail conversation, who
knows, right?
But it's useful to work out
these numbers, and know some
of these numbers.
So the question we have
to ask is, how much
computation is in there?
And the first question relevant
to that is, how many
neurons do we have
in our brain?
Volunteer?
Neuroscience?
No one to volunteer?
All right.
Well this is a number you should
know, because this is
what you've got in there.
There are 10 to the 10th neurons
in the brain, of which
10 to the 11th are in the
cerebellum, alone.
What the devil do
I mean by that?
I mean that your cerebellum is
so full of neurons that it
dwarfs the rest of the brain.
So if you exclude the
cerebellum, you've got about
10 to 10th neurons.
And there about 10 to
the 11th neurons in
the cerebellum, alone.
What's the cerebellum for?
Motor control.
Interesting.
So we're a little short.
Oh, but we forget, that's just
the number of neurons.
We have to count up the
number of synapses.
Because conceivably, we might
be able to adjust those
synapses, right?
So how many synapses
does a neuron have?
The answer is, it depends.
But the ones in the
cerebellum--
I should be pointing back
there, I guess--
10 to the 5th.
So if we add all that up
we have 10 to the 16th.
No problem.
It's just that existence proves
that you don't have to
worry too much about
having storage.
So maybe our cerebellum
functions, in some way, as a
gigantic table.
And that's maybe how we learn
motor skills, by filling up
that table as we run around
emerging from the cradle,
learning how to manipulate
ourselves as we go on.
So that's the story
on arm control.
Now all this is pretty
straightforward, easy to
understand.
And of course, there
are some problems.
Problem number one, what
if the space of
samples looks like this?
[TAPPING ON CHALK BOARD]
What's going to happen
in that case?
Well what's going to happen
in that case is that the--
let's see, which values are
going to be more important?
The x values, right?
The y values are spread out
all over the place.
So you'd like the spread of
the data to sort of be the
same in all the dimensions.
So is there anything we
can do to arrange
for that to be true?
Sure, we can just normalize
the data.
So we can borrow from our
statistics course and say,
well, let's see, we're
interested in x.
And we know that the variance
of x is equal to 1 over n
times the sum of the values,
minus the mean value squared.
That's a measure of how much
the data spreads out.
So now, instead of using x, we
can use x prime, which is
equal to x over sigma.
What's the variance of
that going to be?
x over sigma sub x.
Anybody see, instantaneously,
what the variance of
that's going be?
Or do we have to work it out?
It's going to be 1, Work
out the algebra for me.
It's obvious, it's simple.
Just substitute x prime into
this formula for variance, and
do the algebraic high
school manipulation.
And you'll see that the variance
turns out not to be
of this new variable, this
transformed variable you want.
So that problem, the non
uniformity problem, the spread
problem, is easy to handle.
What about that other problem?
No cake without flour?
What if it turns out
that the data--
you have two dimensions and the
answer, actually, doesn't
depend on y at all.
What will happen?
Then you're often going to get
screwy results, because it'll
be measuring a distance
that is merely
confusing the answer.
So problem number two is the
what matters problem.
Write it down, what matters.
Problem number three is, what
if the answer doesn't depend
on the data at all?
Then you've got the trying to
build a cake without flour.
Once somebody asked me--
a classmate of mine, who went
on to become an important
executive in an important credit
card company-- asked me
if we could use artificial
intelligence to determine when
somebody was going
to go bankrupt?
And the answer was, no.
Because the data available was
data that was independent of
that question.
So he was trying to make a cake
without flour, and you
can't do that.
So that concludes what
I want to say
about nearest neighbors.
No I want to talk a little
bit about sleep.
Over there on that left-side
branch, now disappeared, we
talked about the human
side of learning.
And I said something
about one-shot, an
escalation based learning.
And what that means is,
you don't learn
without problem solving.
And the question is, how is
problem solving related to how
much sleep you get?
And to answer questions like
that, of course, you want to
go to the people who are the
custodians of the kind of
knowledge you are
interested in.
And so you would say, who are
the custodians of knowledge
about how much sleep you need?
And what happens if
you don't get it?
And the answer is the
United States Army.
Because they're extremely
interested in what happens
when you cross 10 or 12 times
zones, and have no sleep, and
have to perform.
So they're very interested
in that question.
And they got even more
interested after the first
Gulf War, which was the
most studied war in
history, up to that time.
Because, there were after action
reports they were full
of examples like this.
The US Forces, in a certain part
of the battlefield, and
drawn up for the night.
And those are Bradley fighting
vehicles, there, and back here
Abrams tanks.
And they're all just kind
of settling down for
good night's sleep.
They've been up for about 36
hours straight, by the way.
When, much to their amazement,
across their field-of-view
came a column of
Iraqi vehicles.
And both sides were enormously
surprised.
A firefight broke out.
The lead vehicle, over
here, on the Iraqi
side caught on fire.
So these guys, in the Bradley
fighting vehicles, went around
to investigate, whereupon, these
guys started blasting
away, in acts of fratricidal
fire.
And the interesting thing is
that all these folks here
swore in the after action
reports that they were firing
straight ahead.
And what happened was their
ability to put ordnance on
target was not impaired
at all.
But their idea of where the
target was, what the target
was, whether it was a target,
was all screwed up.
So this led to a lot of
experiments in which people
were sleep deprived.
And by the way, you think
you're a tough
MIT student, right?
These are Army Rangers.
It doesn't get any tougher
than this, believe me.
So here's one of the
experiments that was performed.
In those days they had
what they called
fire control teams.
And their job is to take
information from an observer,
over here, about a target,
over here.
And tell the artillery, over
here, where to fire.
So they kept some of
these folks up
for 36 hours straight.
And after 36 hours they all
said, we're doing great.
And at that time they were
bringing fire down on
hospitals, mosques, churches,
schools, and themselves.
Because, they couldn't do the
calculations anymore, after 36
hours without sleep.
And now you say to me, well I'm
a MIT student, I want to
see the data.
So let's have a look
at the data.
OK.
So there it goes.
That's what happens to you after
72 hours without sleep.
These are simple things to do.
Very simple calculations you
have to do in your head, like
adding numbers, spelling words,
and things like that.
So after 72 hours without
sleep, your performance
relative to what you were at
the beginning is about 30%.
So loss of sleep destroys
ability.
[BELL RINGING]
Sleep loss accumulates.
So you say, well I need
eight hours of sleep--
and what you need, by
the way, varies--
but I'm going to get by was
seven hours of sleep.
So after 20 days of one hour's
worth of sleep deprivation,
you're down about 25%.
If you say, well I need eight
hours of sleep, but I'm going
to have to get by with just six,
after 20 days of that,
you're down to about 25% of
your original capability.
So you might say, well
does caffeine help?
Or naps, naps in this case.
And the answer is, yes,
a little bit.
Some people argue that you get
the more affect out of the
sleep that you do get if
you divide it into two.
Winston Churchill always
took a three
hour nap in the afternoon.
He said that way he got a day
and a half's worth of work out
of every day.
He got the full amount
of sleep.
But he divided it
into two pieces.
Here's the caffeine one.
So caffeine does help.
And now you say, well, shoot,
I think I'm going to take it
kind of easy this semester.
And I'll just work hard during
the week before finals.
Maybe I won't even bother
sleeping for the 24 hours
before the 6034 final.
That's OK.
Well let's see what
will happen.
So let's work the numbers.
Here is 24 hours.
And that's where your
effectiveness is after 24 hours.
Now let's go over to the same
amount of effectiveness on the
blood alcohol curve.
And it's about the level
at which you
would be legally drunk.
So I guess what we ought to do
is to check everybody as they
come in for the 6034 final, and
arrest you if you've been
24 hours without sleep.
And not let you take any finals
again, for a year.
So if you do all that, you
might as well get drunk.
And now we have one thing
left to do today.
And that is address the original
question of, why it
is that the dogs and cats in the
world think that the diet
drink makes people fat?
What's the answer?
It's because only fat guys
like me drink this crap.
So since the dogs and cats don't
have the ability to tell
themselves stories, don't have
that capacity to string
together events into narratives,
they don't have
any way of saying, well this is
a consequence of desiring
not to be fat.
Not a consequence
of being fat.
They don't have that story.
And so what they're doing is
something you have to be very
careful about.
And that thing you have to be
very careful about is the
confusion of correlation
with cause.
They see the correlation, but
they don't understand the
cause, so that's why they
make a mistake.