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Summary
In this introductory lecture to MIT's course on artificial intelligence, the speaker sets the stage for the semester by exploring the foundations and future of AI. The lecture covers several aspects of artificial intelligence, including its definition as a field encompassing thinking, perception, and action. The speaker discusses the historical context of AI, notable developments, and essential principles such as model building and representation. Throughout the lecture, there's an emphasis on the fun and power of AI concepts, metaphors, and examples. Students are encouraged to engage with the course through lectures, recitations, and interactive components. Optimism about the future of AI is supported by interdisciplinary advancements and the unique human abilities that differentiate us from other species.
Highlights
AI covers thinking, perception, and action—it's more than just replicating human thought. 📚
Model making is a huge part of MIT's approach, applying across disciplines. 🎨
Corny but true: AI helps us build smarter programs by understanding models and constraints. 📖
Simple ideas in AI, like generating test methods, are powerful. Simplicity does not mean triviality! 🧠
Active participation in lectures and activities correlates with better grades—engagement matters! 📈
Key Takeaways
Artificial Intelligence is not just about machines mimicking human thinking but includes perception and action. 🤖
Model building is core to understanding and advancing AI. At MIT, it's all about creating models to predict and understand the world. 🛠️
Representation is key in AI—it determines how effectively a problem can be solved by exposing constraints. 🔍
Simple ideas in AI can be incredibly powerful; don't underestimate concepts because they're easy to grasp. 💡
Engagement with the course through attendance and interaction enhances understanding of AI principles. 🎓
Overview
In kicking off the semester, the course invites students to dive into the expansive field of artificial intelligence. It's not just about complex equations and machine learning; it's about understanding how we perceive, act, and think. With the introduction of models and representations, learners get a peek into the world where philosophy meets engineering.
The journey through AI's history shines a light on iconic milestones, from Lady Lovelace to modern-day AI evolution. Students revisit age-old problems with fresh perspectives, revealing how AI developments have borrowed and innovated upon historical foundations. These stories aren't mere history lessons—they're building blocks for future tech advancements.
By setting expectations for active class engagement and promising an interdisciplinary approach, the course aims to enrich students' educational experience. With insights into game-changing AI methods and human cognition, this class is not just another course; it's a pathway to greater intellectual curiosity and potential breakthroughs in AI understanding.
1. Introduction and Scope Transcription
00:00 - 00:30 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
00:30 - 01:00 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
01:00 - 01:30 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
01:30 - 02:00 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
02:00 - 02:30 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
02:30 - 03:00 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
03:00 - 03:30 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
03:30 - 04:00 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
04:00 - 04:30 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
04:30 - 05:00 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
05:00 - 05:30 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
05:30 - 06:00 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
06:00 - 06:30 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
06:30 - 07:00 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
07:00 - 07:30 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
07:30 - 08:00 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
08:00 - 08:30 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
08:30 - 09:00 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
09:00 - 09:30 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
09:30 - 10:00 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
10:00 - 10:30 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
10:30 - 11:00 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
11:00 - 11:30 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
11:30 - 12:00 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
12:00 - 12:30 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
12:30 - 13:00 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
13:00 - 13:30 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
13:30 - 14:00 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
14:00 - 14:30 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
14:30 - 15:00 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
15:00 - 15:30 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
15:30 - 16:00 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
16:00 - 16:30 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
16:30 - 17:00 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
17:00 - 17:30 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
17:30 - 18:00 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
18:00 - 18:30 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
18:30 - 19:00 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
19:00 - 19:30 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
19:30 - 20:00 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
20:00 - 20:30 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
20:30 - 21:00 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
21:00 - 21:30 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
21:30 - 22:00 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
22:00 - 22:30 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
22:30 - 23:00 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
23:00 - 23:30 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
23:30 - 24:00 all right [Music]
24:00 - 24:30 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
24:30 - 25:00 [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
25:00 - 25:30 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
25:30 - 26:00 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
26:00 - 26:30 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
26:30 - 27:00 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
27:00 - 27:30 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
27:30 - 28:00 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
28:00 - 28:30 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
28:30 - 29:00 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
29:00 - 29:30 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
29:30 - 30:00 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
30:00 - 30:30 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
30:30 - 31:00 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
31:00 - 31:30 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
31:30 - 32:00 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
32:00 - 32:30 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
32:30 - 33:00 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
33:00 - 33:30 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
33:30 - 34:00 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
34:00 - 34:30 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
34:30 - 35:00 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
35:00 - 35:30 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
35:30 - 36:00 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
36:00 - 36:30 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
36:30 - 37:00 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
37:00 - 37:30 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
37:30 - 38:00 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
38:00 - 38:30 oops and each of these has a different purpose
38:30 - 39:00 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
39:00 - 39:30 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
39:30 - 40:00 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
40:00 - 40:30 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
40:30 - 41:00 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
41:00 - 41:30 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
41:30 - 42:00 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
42:00 - 42:30 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
42:30 - 43:00 [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
43:00 - 43:30 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
43:30 - 44:00 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
44:00 - 44:30 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
44:30 - 45:00 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
45:00 - 45:30 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
45:30 - 46:00 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
46:00 - 46:30 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
46:30 - 47:00 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