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.
