PATRICK WINSTON: Welcome
to 6034. I don't know if I can deal
with this microphone. We'll see what happens. It's going to be a good year. We've got [INAUDIBLE] a bunch
of interesting people. It's always interesting to see
what people named their children two decades ago. And I find they were overwhelmed
with Emilys. And there are not too many
Peters, Pauls, and Marys, but enough to call forth a suitable
song at some point. We have lots of Jesses
of both genders. We have a [INAUDIBLE] of both genders. And we have a Duncan,
where's Duncan? There you are, Duncan. You've changed your hairstyle. I want to assure use that the
Thane of Cawdor is not taking the course this semester. What I'm going to do is tell
you about artificial intelligence today, and what
this subject is about. There's been about a 10% percent
turnover in the roster in the last 24 hours. I expect another 10% turnover
in the next 24 hours, too. So I know many of you are
sightseers, wanting to know if this is something
you want to do. So I'm going to tell you about
what we're going to do this semester, and what you'll know
when you get out of here. I'm going to walk you through
this outline. I'm going to start by talking
about what artificial intelligence is, and
why we do it. And then I'll give you a little
bit of the history of artificial intelligence, and
conclude with some of the covenants by which we
run the course. One of which is no
laptops, please. I'll explain why we have these
covenants at the end. So what is it? Well, it must have something
to do with thinking. So let's start up here, a
definition of artificial intelligence, by saying that
it's about thinking, whatever that is. My definition of artificial
intelligence has to be rather broad. So we're going to say it's
not only about thinking. It's also about perception,
and it's about action. And if this were a philosophy
class, then I'd stop right there and just say, in this
subject we're going to talk about problems involving
thinking, perception, and action. But this is not a philosophy
class. This a Course six class. It's an engineering
school class. It's an MIT class. So we need more than that. And therefore we're going to
talk about models that are targeted at thinking,
perception, and action. And this should not be strange
to you, because model making is what MIT is about. You run into someone at a bar,
or relative asks you what you do at MIT, the right knee jerk
reaction is to say, we learned how to build models. That's what we do at MIT. We build the models using
differential equations. We build models using
probabilities. We build models using physical
and computational simulations. Whatever we do, we
build models. Even in humanities class, MIT
approach is to make models that we can use to explain the
past, predict the future, understand the subject,
and control the world. That's what MIT is about. And that's what this subject
is about, too. And now, our models are
models of thinking. So you might say,
if I take this classic will I get smarter? And the answer is yes. You will get smarter. Because you'll have better
models of your own thinking, not just the subject matter of
the subject, but better models of your own thinking. So models targeted at thinking, perception, and action. We know that's not quite enough,
because in order to have a model, you have to
have representation. So let's say that artificial
intelligence is about representations that support
the making of models to facilitate an understanding
of thinking, perception, and action. Now you might say to me, well
what's a representation? And what good can it do? So I'd like to take a brief
moment to tell you about gyroscopes. Many of you have friends in
mechanical engineering. One of the best ways embarrass
them is to say here's a bicycle wheel. And if I spin it, and blow hard
on it right here, on the edge of the wheel, is
going to turn over this way or this way? I guarantee that what they will
do is they'll put their hand in an arthritic posture
called the right hand screw rule, aptly named because people
who use it tend to get the right answer about
50% of the time. But we're never going to make
that mistake again. Because we're electrical
engineers, not mechanical engineers. And we know about
representation. What we're going to do is we're
going to think about it a little bit. And we're going to use some
duct tape to help us think about just one piece
of the wheel. So I want you to just think
about that piece of the wheel as the wheel comes flying
over the top, and I blow on it like that. What's going to happen
to that one piece? It's going to go off
that way, right? And the next piece is going
to go off that way too. So when it comes over, it
has to go that way. Let me do some ground f
here just to be sure. It's very powerful feeling. Try it. We need a demonstration. I don't anybody think that
I'm cheating, here. So let's just twist it
one way or the other. So that's powerful
pull, isn't it. Alex is now never going to get
the gyroscope wrong, because he's got the right
representation. So much of what you're going to
accumulate in this subject is a suite of representations
that will help you to build programs that are intelligent. But I want to give you a second
example, one a little bit more computational. But one of which was very
familiar to you by the time you went to first grade,
in most cases. It's the problem of the farmer,
the fox, the goose, and the grain. There's a river, a leaky rowboat
that can only carry the farmer, and one of
his four possessions. So what's the right representation for this problem? It might be a picture
of the farmer. It might be a poem about the
situation, perhaps a haiku. We know that those are not
the right representation. Somehow, we get the sense that
the right representation most involve something about the
location of the participants in this scenario. So we might draw a picture
that looks like this. There's the scenario, and
here in glorious green, representing our algae infested
rivers is the river. And here's the farmer, the fox,
the goose, and the grain. An initial situation. Now there are other situations
like this one, for example. We have the river, and
the farmer, and the goose is on that side. And the fox and the grain
is on that side. And we know that the farmer can
execute a movement from one situation to another. So now we're getting somewhere
where with the problem. This is at MIT approach
to the farmer, fox, goose, and grain problem. It might have stumped you when
you were a little kid. How many such situations
are there? What do you think, Tanya? It looks to me like all four of
individuals can be on one side or the other. So for every position the farmer
can be, each of the other things can be on either
side of the river. So it would be two to the fourth
she says aggressively and without hesitation. Yes, two to the fourth,
16 possibilities. So we could actually draw
out the entire graph. It's small enough. There's another position over
here with the farmer, fox, goose, and grain. And in fact that's
the one we want. And if we draw out the entire
graph, it looks like this. This is a graph of the
situations and the allowed connections between them. Why are there not 16? Because the other-- how many have I got? Four? 10? The others are situations in
which somebody gets eaten. So we don't want to go to
any of those places. So having got the
representation, something magical has happened. We've got our constraints
exposed. And that's why we build
representations. That's whey you algebra in high
school, because algebraic notation exposes the constraints
that make it possible to actually figure out
how many customers you get for the number of advertisements
you place in the newspaper. So artificial intelligence is
about constraints exposed by representations that support
models targeted to thinking-- actually there's one
more thing, too. Not quite done. Because after all, in the end,
we have to build programs. So it's about algorithms enabled
by constraints exposed by representations that model
targeted thinking, perception, and action. So these algorithms, or we might
call them just as well procedures, or we might call
them just as well methods, whatever you like. These are the stuff of what
artificial intelligence is about-- methods, algorithms,
representations. I'd like to give you
one more example. It's something we call, in
artificial intelligence, generated test. And it's such a simple idea,
you'll never hear it again in this subject. But it's an idea you need to
add to your repertoire of problem solving methods,
techniques, procedures, and algorithms. So here's how it works. Maybe I can explain to best by
starting off with an example. Here's a tree leaf I picked
off a tree on the way over to class. I hope it's not the last
of the species. What is it, what kind of tree? I don't know. I never did learn my trees,
or my colors, or my multiplication tables. So I have to go back to this
book, the Audubon Society Field Guide to North
American Trees. And how would I solve
the problem? It's pretty simple. I just turn the pages one at a
time, until I find something that looks like this leaf. And then I discover it's a
sycamore, or something. MIT's full of them. So when I do that, I do
something very intuitive, very natural, something you
do all the time. But we're going to
give it a name. We're going to call it
generate and test. And generate and test method
consists of generating some possible solutions, feeding
them into a box that tests them, and then out the other
side comes mostly failures. But every once in a while we
get something that succeeds and pleases us. That's what I did
with the leaf. But now you have
a name for it. Once you have a name
for something, you get power over it. You can start to
talk about it. So I can say, if you're doing
a generate and test approach to a problem, you better build
a generator with certain properties that make
generators good. For example, they should
not be redundant. They shouldn't give you the
same solution twice. They should be informable. They should be able to absorb
information such as, this is a deciduous tree. Don't bother looking
at the conifers. So once you have a name for
something, you can start talking about. And that vocabulary
gives you power. So we call this the
Rumpelstiltskin Principle perhaps The first of our
powerful ideas for the day. This subject is full
of powerful ideas. There will be some
in every class. Rumpelstiltskin Principle says
that once you can name something, you get
power over it. You know what that little
thing is on the end of your shoelace? It's interesting. She's gesturing like mad. That's something we'll talk
about later, too-- motor stuff, and how
it helps us think. What is it? No one knows? It's an ag something, right? It's an aglet, very good. So once you have the name, you
can start to talk about. You can say the purpose of an
aglet is pretty much like the whipping on the end of a rope. It keeps the thing
from unwinding. Now you have a place to
hang that knowledge. So we're talking about this
frequently from now into the rest of the semester,
the power of being able to name things. Symbolic labels give us
power over concepts. While we're here I should also
say that this is a very simple idea, generate and test. And you might be tempted to
say to someone, we learned about generate and test today. But it's a trivial idea. The word trivial is a word I
would like you to purge from your vocabulary, because it's
a very dangerous label. The reason it's dangerous is
because there's a difference between trivial and simple. What is it? What's the difference between
labeling something as trivial and calling it simple? Yes? Exactly so. He says that simple can be
powerful, and trivial makes it sound like it's not only simple,
but of little worth. So many MIT people miss
opportunities, because they have a tendency to think that
ideas aren't important unless they're complicated. But the most simple ideas in
artificial intelligence are often the most powerful. We could teach an artificial
intelligence course to you that would be so full of
mathematics it would make a Course 18 professor gag. But those ideas would be
merely gratuitously complicated, and gratuitously
mathematical, and gratuitously not simple. Simple ideas are often
the most powerful. So where are we so far? We talked about the
definition. We talked about an example
of a method. Showed you a representation, and
perhaps also talked about the first idea, too. You've got the representation
right, you're often almost done. Because with this
representation, they can immediately see that there are
just two solutions to this problem, something that wouldn't
have occurred to us when we were little kids, and
didn't think to draw the [? state ?] diagram. There's still one more thing. In the past, and in other
places, artificial intelligence is often taught
as purely about reasoning. But we solve problems with
our eyes, as well as our symbolic apparatus. And you solved that problem
with your eyes. So I like to reinforce that by
giving you a little puzzle. Let's see, who's here? I don't see [? Kambe, ?] but
I'll bet he's from Africa. Is anyone from Africa? No one's from Africa? No? Well so much the better-- because they would know the
answer to the puzzle. Here's the puzzle. How many countries in Africa
does the Equator cross? Would anybody be willing
to stake their life on their answer? Probably not. Well, now let me repeat
the question. How many countries in Africa
does the Equator cross? Yeah, six. What happened is a miracle. The miracle is that I have
communicated with you through language, and your language
system commanded your visual system to execute a program that
involves scanning across that line, counting as you go. And then your vision system
came back to your language system and said, six. And that is a miracle. And without understanding that
miracle, we'll never have a full understanding of the
nature of intelligence. But that kind of problem solving
is the kind of problem solving I wish we could teach
you a lot about it. But we can't teach you about
stuff we don't understand. We [INAUDIBLE] for that. That's a little bit about the
definition and some examples. What's it for? We can deal with that
very quickly. If we're engineers, it's for
building smarter programs. It's about building a tool kit
of representations and methods that make it possible to
build smarter programs. And you will find, these days,
that you can't build a big system without having embedded
in it somewhere the ideas that we talk about in the subject. If you're a scientist, there's
a somewhat different motivation. But it amounts to studying
the same sorts of things. If you're a scientist, you're
interested in what it is that enables us to build a
computational account of intelligence. That's the part that I do. But most this subject is going
to be about the other part, the part that makes it possible
for you to build smarter programs. And some of it will be about
what it is that makes us different from the chimpanzees
with whom we share an enormous fraction of our DNA. It used to be thought that we
share 95% of our DNA with chimpanzees. Then it went up to 98. Thank God it stopped
about there. Then it actually went
back a little bit. I think we're back down to 94. How about if we talk a little
bit now about the history of AI, so we can see how we got
to where we are today? This will also be a history of
AI that tells you a little bit about what you'll learn
in this course. It all started with Lady
Lovelace, the world's first programmer, Who wrote programs
about 100 years before there were computers to run them. But it's interesting that even
in 1842, people were hassling her about whether computers
could get really smart. And she said, "The analytical
engine has no pretensions to originate anything. It can do whatever we know how
to order it to perform." Screwball idea that persists
to this day. Nevertheless, that was
the origin of it all. That was the beginning
of the discussions. And then nothing much happened
until about 1950, when Alan Turing wrote his famous
paper, which introduced the Turing test. Of course, Alan Turing had
previously won the Second World War by breaking the German
code, the Ultra Code, for which the British government
rewarded him by driving him to suicide, because
he happened to be homosexual. But Turing wrote his paper in
1950, and that was the first milestone after Lady Lovelace's
comment in 1842. And then the modern era really
began with a paper written by Marvin Minsky in 1960, titled
"Steps Toward Artificial Intelligence." And it wasn't
a long after that Jim [? Slagle, ?] a nearly blind graduate student,
wrote a program that did symbolic integration. Not adding up area under a
curve, but doing symbolic integration just like you learn
to do in high school when you're a freshman. Now on Monday, we're going to
talk about this program. And you're going to understand
exactly how it works. And you can write
one yourself. And we're going to reach way
back in time to look at that program because, in one day
discussing it, talking about it, will be in itself a
miniature artificial intelligence course. Because it's so rich with
important ideas. So that's the dawn age,
early dawn age. This was the age of speculation,
and this was the dawn age in here. So in that early dawn age , the
integration program took the world by storm. Because not everybody knows
how to do integration. And someone, everyone, thought
that if we can do integration today, the rest of intelligence
will be figured out tomorrow. Too bad for our side it didn't
work out that way. Here's another dawn age
program, the Eliza [? thing ?]. But I imagine you'd prefer
a demonstration to just reading it, right? Do you prefer a demonstration? Let's see if we can
demonstrate it. This is left over from a
hamentashen debate of a couple of years ago. How do you spell hamentashen,
anybody know? I sure hope that's right. It doesn't matter. Something interesting
will come. OK, your choice. Teal? Burton House? Teal. So that's dawn age AI. And no one ever took that stuff
seriously, except that it was a fun [INAUDIBLE] project level thing to work
out some matching programs, and so on. The integration program
was serious. This one wasn't. This was serious, programs that
do geometric analogy, problems of the kind you find
on intelligence tests. Do you have the answer
to this? A is to B as C is to what? That would be 2, I guess. What's the second best answer? And the theories of the program
that solve these problems are pretty much
identical to what you just figured out. In the first case you deleted
the inside figure. And the second case is, the
reason you got four is because you deleted the outside part
and grew the inside part. There's another one. I think this was the hardest one
it got, or the easiest one it didn't get. I've forgotten. A is to B as C is to 3. In the late dawn age, we began
to turn our attention from purely symbolic reasoning to
thinking a little bit about perceptual apparatus. And programs were written that
could figure out the nature of shapes and forms,
such as that. And it's interesting that those
programs had the same kind of difficulty with
this that you do. Because now, having deleted
all the edges, everything becomes ambiguous. And it may be a series of
platforms, or it may be a series of-- can you see the saw blade
sticking up if you go through the reversal? Programs were written that
could learn from a small number of examples. Many people think of computer
learning as involving leading some neural net to submission
with thousands of trials. Programs were written in the
early dawn age that learned that an arch is something that
has to have the flat part on top, and the two sides can't
touch, and the top may or may not be a wedge. In the late dawn age, though,
the most important thing, perhaps, was what you look at
with me on Wednesday next. It's a rule-based
expert systems. And a program was written at
Stanford that did diagnosis of bacterial infections
of the blood. It turned out to do it better
than most doctors, most general practitioners. It was never used,
curiously enough. Because nobody cares what your
problem actually is. They just give you a broad
spectrum antibiotic that'll kill everything. But this late dawn age system,
the so-called [INAUDIBLE] system, was the system that
launched a thousand companies, because people started building
expert systems built on that technology. Here's that you don't know
you used, or that was used on your behalf. If you go through, for example,
the Atlanta airport, your airplane is parked by a
rule-based expert system that knows how to park aircraft
effectively. It saves Delta Airlines about
to $0.5 million a day of jet fuel by being all smarter
about how to park them. So that's an example of an
expert system that does a little bit of good for
a lot of people. There's Deep Blue. That takes us to the next stage
beyond the age of expert systems, and the business age. It takes us into this age
here, which I call the bulldozer age, because this is
the time when people began to see that we had at our disposal unlimited amounts of computing. And frequently you can
substitute computing for intelligence. So no one would say that Deep
Blue does anything like what a human chess master does. But nevertheless, Deep Blue,
by processing data like a bulldozer processes gravel,
was able to beat the world champion. So what's the right way? That's the age we're
in right now. I will of course be inducing
programs for those ages as we go through the subject. There is a question of what
age we're in right now. And it's always dangerous
to name an age when you're in it, I guess. I like to call at the age
of the right way. And this is an age when we begin
to realize that that definition up there is actually
a little incomplete, because much of our intelligence
has to do not with thinking, perception, and
action acting separately, but with loops that tie all
those together. We had one example
with Africa. Here's another example drawn
from a program that has been under development, and continues
to be, in my laboratory. We're going to ask the system
to imagine something. SYSTEM: OK. I will imagine that a ball
falls into a bowl. OK. I will imagine that a man
runs into a woman. PATRICK WINSTON: You see, it
does the best that it can if it doesn't have a good memory
of what these situations actually involve. But having imagined the
scene it can then-- SYSTEM: Yes. I have learned from experience
that contact between a man and a woman appeared because a
man runs into a woman. PATRICK WINSTON: Having imagined
the scene, it can then read the answers using its
visual apparatus on the scene that it imagined. So just like what you did with
Africa, only now it's working with its own visual memory,
using visual programs. SYSTEM: OK. I will imagine that a man
gives a ball to a man. PATRICK WINSTON: I know this
looks like slugs, but they're actually distinguished
professors. It always does the
best it can. SYSTEM: OK. I will imagine that
a man flies. PATRICK WINSTON: It's the
best that it can do. So that concludes our discussion
of the history. And I've provided you with a
little bit of a glimpse of what we're going to look at
as the semester unfolds. Yes, Chris? CHRIS: Is it actually a
demonstration of something? Does it have a large
database of videos? PATRICK WINSTON: No, it has
a small database videos. CHRIS: But it's intelligently
picking among them based on-- PATRICK WINSTON: Based
on their content. So if you say imagine that a
student gave a ball to another student, it imagines that. You say, now does the other
student have the ball? Does the other student
take the ball? It can answer those questions
because it can review the same video and see the take as well
as the give in the same video. So now we have to think about
why we ought to be optimistic about the future. Because we've had a long history
here, and we haven't solved the problem. But one reason why we can feel
optimistic about future is because all of our friends
have been on the march. And our friends include the
cognitive psychologists, the [? developmental ?] psychologists, the linguists,
sometimes the philosophers, and especially the
paleoanthropologists. Because it is becoming
increasingly clear why we're actually different from the
chimpanzees, and how we got to be that way. The high school idea is that
we evolved through slow, gradual, and continuous
improvement. But that doesn't seem to
be the way it happened. There are some characteristics
of our species that are informative when it comes to
guiding the activities of people like me. And here's what the
story seems to be from the fossil record. First of all, we humans have
been around for maybe 200,000 years in our present
anatomical form. If someone walked through the
door right now from 200,000 years ago, I imagine they
would be dirty, but other than that-- probably naked, too-- other than that, you wouldn't
be able to tell the difference, especially at MIT. And so the ensuing 150,000 years
was a period in which we humans didn't actually
amount to much. But somehow, shortly before
50,000 years ago, some small group of us developed a
capability that separated us from all other species. It was an accident
of evolution. And these accidents may or may
not happen, but it happened to produce us. It's also the case that we
probably necked down as a species to a few thousand, or
maybe even a few hundred individuals, something which
made these accidental changes, accidental evolutionary
products, more capable of sticking. This leads us to speculate on
what it was that happened 50,000 years ago. And paleoanthropologists, Noam
Chomsky, a lot of people reached similar conclusions. And that conclusion is-- I'll quote Chomsky. He's the voice of authority. "It seems that shortly before
50,000 years ago, some small group of us acquired the ability
to take two concepts, and combine them to make a
third concept, without disturbing the original two
concepts, without limit." And from a perspective of an AI
person like me, what Chomsky seems to be saying is, we
learned how to begin to describe things, in a way
that was intimately connected with language. And that, in the end, is what
separates us from the chimpanzees. So you might say, well let's
just study language. No, you can't do that, because
we think with our eyes. So language does two things. Number one, it enables us
to make descriptions. Descriptions enable us
to tell stories. And storytelling and story
understanding is what all of education is about. That's going up. And going down enables us to
marshal the resources of our perceptual systems, and even
command our perceptual systems to imagine things we've
never seen. So here's an example. Imagine running down
the street with a full bucket of water. What happens? Your leg gets wet. The water sloshes out. You'll never find that fact
anywhere on the web. You've probably never been told
that that's what happens when you run down the street
with a full bucket of water. But you easily imagine this
scenario, and you know what's going to happen. There was internal imagination
simulation. We're never going to understand
human intelligence until we can understand that. Here's another example. Imagine running down
the street with a full bucket of nickels? What happens? Nickels weigh a lot. You're going to be bent over. You're going to stagger. But nobody ever told you that. You won't find it anywhere
on the web. So language is at the center of
things because it enables storytelling going up, and
marshalling the resources of the perceptual apparatus,
going down. And that's where we're going
to finish the subject the semester, by trying to
understand more about that phenomenon. So that concludes everything
I wanted to say about the material and the subject. Now I want to turn my attention
a little bit to how we are going to operate
the subject. Because there are many
characteristics of the subject that are confusing. First of all, we have
four kinds of activities in the course. And each of these has
a different purpose. So I did the lectures. And the lectures are supposed
to be an hour about introducing the material
and the big picture. They're about powerful ideas. They're about the experience
side of the course. Let me step aside and
make a remark. MIT is about two things. It's about skill building,
and it's about big ideas. So you can build a skill at
home, or at Dartmouth, or at Harvard, or Princeton, or all
those kinds of places. But the experience you
can only get at MIT. I know everybody there is
to know in artificial intelligence. I can tell you about
how they think. I can tell you about
how I think. And that's something
you're not going to get any other place. So that's my role, as I see it,
in giving these lectures. Recitations are four buttressing
and expanding on the material, and providing a
venue that's small enough for discussion. Mega recitations are
[? a usual ?] components of the course. They're taught at the same
hour on Fridays. Mark Seifter, my graduate
student, will be teaching those. And those are wrapped around
past quiz problems. And Mark will show you
how to work them. It's very important component
to the subject. And finally the tutorials
are about helping you with the homework. So you might say to me,
well, do I really need to go to class? I like to say that the answer
is, only if you like to pass the subject. But you are MIT students. And MIT people always like
to look at the data. So this is a scattergram we
made after the subject was taught last fall, which shows
the relationship between attendance at lectures and the
grades awarded in the course. And if you're not sure what
that all means, here's the regression line. So that information is a
little suspect for two reasons, one of which is we
asked people to self report on how many lectures they thought
they attended. And our mechanism for assigning
these numerical grades is a little weird. And there's a third thing, too,
and that is, one must never confuse correlation
with cause. You can think of other
explanations for why that trend line goes up, different
from whether it has something to do with lectures producing
good grades. You might ask how I feel about
the people up there on the other upper left hand corner. There are one or two people who
were near the top of the subject who didn't go
to class at all. And I have mixed feelings
about that. You're adults. It's your call. On the other hand, I wish that
if that's what you do habitually in all the subjects
you take at MIT, that you would resign and go somewhere
else, and let somebody else take their slot. Because you're not benefiting
from the powerful ideas, and the other kinds of things
that involve interaction with faculty. So it can be done. But I don't recommend it. By the way, all of the four
activities that we have here show similar regression lines. But what about that
five point scale? Let me explain how that
works to you. We love to have people
ask us what the class average is on a quiz. Because that's when we get
to use our blank stare. Because we have no idea
what the class average ever is on any quiz. Here's what we do. Like everybody else, we
start off with a score from zero to 100. But then we say to ourselves,
what score would you get if you had a thorough understanding
of the material? And we say, well, for this
particular exam, it's this number right here. And what score would you
get if you had a good understanding of the material? That's that score. And what happens if you're
down here is that you're following off the edge of the
range in which we think you need to do more work. So what we do is, we say that if
you're in this range here-- following MIT convention
with GPAs and stuff, that gets you a five. If you're in this range
down here, there's a sharp drop off to four. If you're in this range
down here, there's a sharp fall off to three. So that means if you're in the
middle of one of those plateaus there's no point
in arguing with this. Because it's not going
to do you any good. We have these boundaries where
we think performance break points are. So you say, well that seems
a little harsh. Blah, blah, blah, blah, blah,
and start arguing. But then we will come back with
a second major innovation we have in the course. That is that your grade is
calculated in several parts. Part one is the max of
your grade on Q1, and part one of the final. So in other words, you get
two shots at everything. So if you have complete glorious
undeniable horrible F on the first quiz, it gets
erased on the final if you do well on that part
of the final. So each quiz has
a corresponding mirror on the final. You get the max of the score you
got on those two pieces. And now you say to me,
I'm an MIT student. I have a lot of guts. I'm only going to
take the final. It has been done. We don't recommend it. And the reason we don't
recommend it is that we don't expect everybody to do
all of the final. So there would be a lot of time
pressure if you had to do all of the final, all five
parts of the final. So we have four quizzes. And the final has a fifth part
because there's some material that we teach you after the last
date on which we can give you a final by institute
rules. But that's roughly
how it works. And you can read about more of
the details in the FAQ on the subject homepage. So now we're almost done. I just want to talk a little bit
about how we're going to communicate with you in the
next few days, while we're getting ourselves organized. So, number one-- if I could ask
the TAs to help me pass these out-- we need to schedule you
into tutorials. So we're going to ask you to
fill out this form, and give it to us before you leave. So you'll be hearing from
us once we do the sort. There's the issue of whether
we're going to have ordinary recitation and a mega recitation
this week. So pay attention. Otherwise, you're going to be
stranded in a classroom with nothing to do. We're not going to have any
regular recitations this week. Are we having regular recitation
this week, [INAUDIBLE]? No. We may, and probably will, have
a mega recitation this week that's devoted to
a Python review. Now we know that there are
many of you who are celebrating a religious holiday
on Friday, and so we will be putting a lot of
resources online so you can get that review in
another way. We probably will have a Python
review on Friday. And we ask that you look at
our home page for further information about that as
the week progresses. So that's all folks. That concludes what we're
going to do today. And as soon as you give us
your form, we're through.
MechE here. Never have heard such a succinct explanation. Bravo.