1. Introduction and Scope Video Lecture | Artificial Intelligence: A Fundamental Guide - AI & ML

hi welcome to 603 for I don't know if I
can deal with this microphone but we'll
see what happens I it's going to be a
good year we've got your a bunch of
interesting people it's always
interesting to see what people name
their children two decades ago and I
find that were overwhelmed with Emily's
and they're not too many Peters Paul's
and Mary's but enough to call forth a
suitable song at some point we have lots
of Jesse's of both them genders we have
a yangyang of both genders and we have a
Duncan where's Duncan
there you are Duncan you've changed your
hairstyle I want to assure you 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 the subject is about
there's been about a 10% turnover in the
in the in the roster in the last 24
hours and I expect another 10% turnover
in the next 24 hours too so I know many
of you are sightseers wanting to want 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 but 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 is a it's of 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 in action and this should not
be strange to you because model making
is what MIT is about if you run into
someone at a bar or relative ask you
what you do at MIT the right knee-jerk
reaction to say we learn how to build
models that's what we do at MIT
we build models using differential
equations we build models using
probabilities we build models using
physical and computational simulations
but 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 well and now
our models or models of thinking so you
might say well if I take this class will
I get smarter and the answer is yes you
will get smarter because you have better
models of your own thinking not just the
subject matter of the subject the better
models of your own thinking
so models targeted at thinking
perception and action but you know
that's not quite enough because in order
to have a model you not to have
representation so let's say that our
official intelligence is about
representations
that support the making of models that
facilitate an understanding of thinking
perception and actually now you might
say to me well what's the representation
and what good can I do so I'd like to
take a brief moment to tell you about
gyroscopes now many of you have friends
in mechanical engineering one of the
best ways to 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 it gonna turn over
this way or this way now I guarantee
that what they will do is they will put
their hand in an arthritic posture call
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 and 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 truth here just to be sure it's a
very powerful fear can I try what time
we did a demonstration I want don't want
anybody to think that I'm cheating here
so that's just just twist it one way or
the other so that's powerful pole isn't
it
alex is uh now never going to get the
gyroscopes 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 want a little bit
more computational but one of which was
very familiar to you by the time you
went to first grade most cases it's the
problem of the farmer the Fox the goose
of 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 but we know
that those are not the right
representation somehow we get the sense
that the right representation must
involve something about the location of
the participants in the scenario so we
might draw a picture that looks like
this there's the scenario
and here in glorious green representing
our algae and festive rivers is the
river and here's the farmer the Fox the
goose and the grain in the initial
situation now of course there are other
situations like this one for example we
have the river and the farmer and the
goose is on that side 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 with a problem this is
an MIT approach to the farmer Fox goose
and grain problem that might have
stumped you when you were a little kid
how many such situations are there what
do you think Tanya looks to me like all
four 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'd be two
to the fourth she says aggressively and
without hesitation yeah it's to the four
sixteen possibilities so we could
actually draw out the entire graph it's
small enough there's another position
over here for the farmer Fox goose and
grain and in fact that's the one we want
and if we draw out the entire graph that
looks like this this is a graph of
situations and the allowed connections
between them why are there not 16 well
because the other how many of I got for
10 the others are situations in which
somebody gets eaten so we don't want to
go any of those places
so having got the representation right
something magical has happened we got
our constraints exposed and that's why
we build representations that's why you
do 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 at thinking oh actually
there's one more thing to I'm not quite
done because after all in the end we
have to build programs so it's about
it's about algorithms enabled by
constraints exposed by representations
that my model targeted thinking
perception in 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 an
artificial intelligence generated test
and it's such a simple idea I'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 and procedures and algorithms
so here's how it works
oh maybe I can explain it best by
starting off with an example here's a
tree leaf I picked off a tree on the way
over to class god I hope it's not the
last of the species what is it what kind
of tree well 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 in North American trees and
how would I solve the problem that's
pretty simple I just turned the pages
one of the time until I find something
it looks like this leaf and then I
discover it's a sycamore or something
MIT is full of them so when I do that I
do something very intuitive and very
natural something you do all the time
but we're gonna give it a name we're
gonna call it generate and test and a
generating test method consists of
generating some possible solutions
feeding them into a box that test 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 do with a leaf but now
you have a name for it and once we have
a name for something let's have a name
for something to get power over it you
can start to talk about it so I can say
if you're doing a generating 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 informal 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 it
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'll be
some in every class Rumpelstiltskin
principle says that once you can name
something you get power over it do 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 to
motor stuff and how it helps us think
what is it no one knows it's an egg
something right it's an aglet very good
so what you have the name you can start
to talk about it you can say you know
the purpose of an aglet is pretty much
like the whipping on the end of a rope
keeps the thing from unwinding now you
have a place to hang that knowledge so
we'll be 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 generating test today but it's a
trivial idea the word trivial is a is a
word I would like you to purge from your
vocabulary because it's a very dangerous
label and 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 what's the most
simple ideas in artificial intelligence
often the most powerful now we could
teach an artificial intelligence course
to you that would be so full of
mathematics that would make the course
eighteen professor gag but those ideas
would be merely retort ously complicated
and gratuitously mathematical and
gratuitously not simple simple ideas are
often the most powerful so where are we
so far talked about definition talked
about an example of a method showing you
a representation and perhaps also talked
about the first idea - you've got the
representation right you're often almost
done because with this representation we
can immediately see that there are just
two solutions to this problem something
that we wouldn't have wouldn't have
occurred to us while were little kids
and didn't think to draw the state
diagram we know there's still one more
thing in in in in in in 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 solve that problem with your
eyes so I'd like to reinforce that by
giving you a little puzzle let's see
who's here
I don't see Kombe but I'll bet he's from
Africa is anyone from Africa
no one's from Africa no well so much the
better
no because 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 no 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 nut is a miracle
now without understanding that miracle
will never have a full understanding of
the nature of intelligence but that kind
of problem solving is the kind of comb
solving I wish we could teach you a lot
about well we can't teach you about
stuff we don't understand we can all
give you Europe's for that all right so
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 toolkit
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 talked about in this subject if
you're scientists there's a somewhat
different motivation but at the mountain
studying the same sorts of things you're
scientist you're interested in what it
is that that enables us to build an
account 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 shared 95% of our DNA
with chimpanzees then it went up to 98
thank God it stopped about there and
then it actually went back a little bit
I think we're back down to 94
so how about if we talk a little bit now
about the history of nei 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 a hundred 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
could 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 I 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 so 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 the Turing
wrote his paper in 1950 and that was the
first milestone after a lady Lovelace's
comment in 1842
and then the modern era really began
with the paper written by Marvin Minsky
in 1960 titled steps toward artificial
intelligence and it wasn't long after
the Jim Slagel a nearly buying graduate
student wrote a program that did
symbolic integration not adding up area
under a curve but doing symbolic
integration just like you learned to do
in high school when you were 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
could write one yourself and we're going
to reach way back in time to look at
that program because in one day it
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
accusation and this was the dawn age in
here so 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 oh well 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 imagine your
demonstration it's just reading it right
to prefer a demonstration okay let's see
if we can demonstrate it okay
all right
[Music]
it's left over from like a humming
fashion debate of a couple of years ago
how do you spell hamantaschen anybody
know ch yeah
sure hope that's right well it doesn't
matter something interesting will come
okay your choice
teal Burton house teal
[Applause]
so that's that's dawn a ji and and no
one ever took that stuff seriously
except that it was a it was a it was a
fun your 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 to do
geometric analogy problems of the kind
you find on intelligence tests you have
the answer to this a is to be as C as to
what they're to be to I guess what's the
second best answer and the theories of
the program that solved these problems
are pretty much identical to what you
just what you just figured out in the
first case you deleted the inside
character or the inside the inside
figure and in the second case 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 forgotten a is to be as C
as two three 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 maybe a series of can you
see the saw blades sticking up if you if
you go through the reversal programs
were written that could learn from a
small number of examples many people
think of computer learning is involving
meeting some neural net to submission
with thousands of trials programs are
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'll
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's actually
where your problem actually is they just
give you a broad-spectrum antibiotic
I'll kill everything but this late dart
age system the so called Meissen system
was the was the system that lost that
launched a thousand companies because
people started building expert systems
built on that technology here's one that
you don't know you used or that was used
in 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 half a million dollars a
day at jet fuel by being a little
smarter about how to park them so that's
an example of an expert system it does a
little bit of good for a lot of people
there's deep blue that takes us to the
next stage beyond the the the age of
expert systems and the business age
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
introducing programs from those ages as
we go through the subject there's 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'd like to
call it the 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 of 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 pronounced the
system to imagine something okay I only
imagine the amount of ball falls into it
okay imagine a man runs into a woman you
say it does the best it can if it
doesn't have a good memory of what these
situations actually involved but having
imagined the scenic and then having
having imagined the scene that can then
read the answers using its visual
apparatus on the scene that on the scene
of that it imagined so it's just like
what you did with Africa
well now it's working with its own
visual memory using visual programs
I know these looks like these look like
slugs but they're actually distinguished
professors it always does the best it
can it's the it's the best thing they
can do so that concludes our discussion
of the of the history and a little bit
of a provide you with a little bit of
glimpse of what we're going to look at
is the semester unfolds yes Chris has a
small database of videos based on their
content so if you have a if you say
imagine that a student gave a ball to
another student it imagines that if you
say now did the others does the other
student have the ball now the student
take the ball it can answer those
questions because it can review the same
video and see the tape as well as the
give in the same video so now we have to
think about why we ought to be a little
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 build up missing
about the future is because all of our
friends have been on the march and our
friends include the cognitive
psychologists the dementia belle-isle
psychologists the linguists sometimes
the philosophers and especially the
paleo anthropologists 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 - other than
that you wouldn't be able to tell the
difference especially at MIT and it's a
the then the ensuing 150 thousand years
was a period in which we humans didn't
actually amount to much but somehow
shortly before fifty thousand 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
but this leads us to speculate on what
it was that happened fifty thousand
years ago and paleoanthropologists noam
chomsky a lot of people reached similar
conclusions and that conclusion is if I
could upload Chomsky so use the voice of
authority
it seems that shortly before fifty
thousand 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 it'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 money 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 through its 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 you you know it goes away a lot
you're going to be bent over you're
going to stagger you know that 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
marshaling the resources of the
perceptual apparatus going down and
that's where we're going to finish the
subject this semester but 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 and yeah confusing first of
all we have four kinds of activities in
the course
oops
and each of these has a different
purpose
so I did the lectures and the lecturers
are supposed to be an hour about
introducing the material and the big
picture they're about powerful ideas
they're about 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 learn 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 my role as I see it in
giving these lectures recitations are
for my twisting and expanding on the
material and providing a venue that's
small enough for discussion mega
recitations are unusual component of the
course they're taught at the same hour
on Fridays marks after my graduate
student will be teaching those and those
are wrapped around past quiz problems
and Mark will show you how to work them
so it's a very important component to to
the subject and finally the tutorials
are about helping helping you with the
homework so you might say to me well do
I really need to go to class and I'd
like to say that the answer is only if
you'd 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 was that that information was
there's a little suspect for two reasons
one of which is we ask people the
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 what at how I feel about
the people up there in the upper left
hand corner there one or two people who
are 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 have a CH
habitually and all the subjects you take
at MIT that you would resign and go
somewhere else and let somebody else
take your slot because you're not
benefiting from the powerful ideas and
the other kinds of things that involve
interaction with the 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 lies
well 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
[Music]
like everybody else we started off with
a the 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 that's this number right
here and what score would you get if you
had a good understanding the material
that's that score and what happens if
you're down here is that you're falling
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 5 if
you're in this range down here there's a
sharp drop off to 4 and if you're in
this range down here there's a sharp
fall-off to 3 so that means that if
you're in the middle of one of those
plateaus there's no point in arguing
with this it's not going to do you any
good we have these boundaries where we
think the performance breakpoints are
and you say well that seems a little
harsh there might be a few point blah
blah blah blah blah and start arguing
but then we will come back with a second
major innovation we have in this and the
course and that is that your grade is
calculated in several parts part one is
the max of your grade on Q one and Part
one of the final so in other words you
get two shots at everything so if you
give 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 in
those two pieces and now you say to me I
have 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'll be a lot of
time pressure if you thought to do all
of the final 0:05 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 buy in
student roles but that's roughly how it
works and you can read about more of the
details and the FAQ on the subject home
page so now we're almost done I just
want to talk a little bit about how
we're trying 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 where you need to schedule you and
dude 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 an recitations and a mega rest
eight ordinary recitations at a mega
recitation this week so pay attention
otherwise you're going to be stranded in
a classroom with nothing to do we are
not going to have any regular
recitations this week are we having
regular recitation this week job 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
religious holiday on Friday and so we
will be putting a lot of resources
online so that you can get that review
in another way now we probably will have
a Python review on Friday and we ask
that you look at our homepage 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 your
form we're through
you