Placeholder Image

字幕表 動画を再生する

  • BENEDICT BROWN: This is CS50.

  • I'm David Malan.

  • And you can tell because I'm wearing all black.

  • [LAUGHTER]

  • So, when I'm not being David Malan, I'm Benedict Brown.

  • I'm a lecturer here in the computer science department.

  • And it's really my pleasure to welcome you all to CS50 at Yale.

  • [SCATTERED CHEERING]

  • [LAUGHTER]

  • And we've been doing this-- this is now our fourth year.

  • And it was great the first year.

  • And it's been getting better and better every year since.

  • So I'm really thrilled to be here.

  • This is my second year with it.

  • And we're looking forward to a fantastic year.

  • You will see me throughout the semester, sometimes in the lab for office hours,

  • sometimes when you come see me in my office for office hours,

  • which I encourage you all to do.

  • I also work a lot behind the scenes with the organization

  • of the class with the staff and supporting the staff,

  • making sure that they are in a position to help you and work with you

  • in sections and in office hours.

  • And the other person who is really working overtime

  • all semester long on the course here at Yale

  • is Natalie Melo, who I would like to bring up to introduce herself.

  • And then she'll also introduce you to many of our core staff that are here.

  • And Harvard's David Malan will also be making an appearance, I believe.

  • NATALIE MELO: [CHUCKLES]

  • Hi, everyone.

  • [APPLAUSE]

  • Oh!

  • [LAUGHS]

  • [APPLAUSE CONTINUES]

  • Hi, everyone!

  • Welcome!

  • Happy Thursday!

  • Woo-hoo1 [LAUGHS]

  • [SCATTERED CHEERING]

  • I'm Natalie Melo.

  • I'm the course manager for CS50 at Yale.

  • You'll see me a lot at office hours.

  • I might have my own office hours by appointment this semester.

  • But you'll see me around quite a bit.

  • So when you see me, I hope you smile.

  • [LAUGHS] But I'd like to introduce my amazing staff members.

  • So come on up.

  • [CHUCKLES] Woo-hoo!

  • [APPLAUSE]

  • So we have a full team out here ready to help you out,

  • help you learn computer science--

  • people from all different backgrounds, some not even

  • majoring in computer science.

  • They only took CS50, and they still wanted to help out.

  • So feel free if you see any of them around campus--

  • say hi.

  • Wave to them.

  • If they see you, and they remember your face,

  • they'll obviously wave to you, as well.

  • So, yeah-- let's give a round of applause to our staff.

  • [APPLAUSE AND CHEERING]

  • And now I'd like to hand it off to Professor Malan.

  • DAVID MALAN: [LAUGHS] OK.

  • NATALIE MELO: OK.

  • DAVID MALAN: [LAUGHING] OK.

  • [APPLAUSE]

  • (WHISPERING) OK.

  • [APPLAUSE CONTINUES]

  • Thanks, guys.

  • OK. (WHISPERING) We're good.

  • [LAUGHTER]

  • (LAUGHING) Thanks.

  • (WHISPERING) Thanks.

  • All right.

  • So this is, indeed, CS50--

  • An Introduction to the Intellectual Enterprises

  • of Computer Science and the Art of Programming.

  • And what's perhaps striking is that so many of the folks who just stepped up

  • here alongside Natalie and Benedict were exactly where you are seated here

  • just last year.

  • And so, let's get one detail out of the way.

  • 68% of you have never--

  • if last year is any indication-- studied computer science before.

  • So if you're in this classroom right now,

  • and you're feeling, [SUCKING IN AIR] I'm not so much a computer person,

  • or why am I shopping this class?

  • Odds are, the person to the left of you and maybe even to the right of you

  • is feeling that exact same thing.

  • And if I can say, back in my day, when I first got to college, even

  • I shied away from computer science, even though it's kind of in vogue

  • these days.

  • Certainly, I daresay, it's still a field to beware.

  • An courses like this and others are a bit daunting, I think,

  • if you don't think of yourself as a computer person.

  • And you certainly use computers and mobile phones and all that everyday,

  • but you don't really know what's going on.

  • And god forbid, something goes wrong, it's

  • not really in your wheelhouse to fix.

  • And so ultimately in this class, it's about building

  • comfort and confidence, skills, and a foundation in computer science.

  • And we'll see what that looks like today.

  • And let me start with this message that you'll see in the course's syllabus

  • as well, that what ultimately matters in this course truly is just this,

  • "is not so much where you end up relative to your classmates,

  • but where you, in week 11, and up relative to yourself in week 0."

  • As we'll soon see, computer scientists and programmers

  • tend to start counting from 0.

  • And it's about a 12-week semester.

  • And so by week 11--

  • the 12th-- will you hopefully feel quite the delta versus where you feel today.

  • So what is, then, computer science?

  • And what are we going to get out of this?

  • So I dare say, we could distill it-- maybe oversimplify it-- as just this--

  • inputs and outputs, right.

  • It is problem solving, but using computation, using computers,

  • using hardware, using thought to actually solve those problems.

  • And to solve problems, we take inputs.

  • Now what might the inputs to a problem be?

  • I might want to take attendance and start counting.

  • So the people in this room are the inputs.

  • And the output is the total number of people.

  • Or you have some ingredients in the kitchen.

  • And the goal is to produce dinner.

  • And so your inputs are those ingredients, and the output, of course,

  • is dinner.

  • So that's really what problem solving is.

  • And inside of this so-called black box is where computer science really fits.

  • And giving you the tools and the ideas with which you

  • can take inputs and produce outputs that are of interest to you.

  • But to do that, we have to represent inputs and outputs in some way.

  • Right, I'm using English words right now.

  • And if you're taking math classes, you're

  • probably using math symbols and numbers.

  • If you're taking chemistry classes, you might have yet other symbols

  • to play with, as well.

  • But so we have to represent inputs in this class

  • and in this world of computer science in some way.

  • So how might I represent information?

  • If I'm doing something like attendance--

  • 1, 2, 3.

  • It's not uncommon to do something old school, like, 1, 2, 3, 4.

  • And then we have little tricks just to make it a little more compact--

  • 5, 6, 7, 8, 9, 10.

  • So we can just use hash marks.

  • And I could do that on my hands, too-- not for the whole class.

  • But how high can I count with just one hand?

  • You say five, right?

  • 1, 2, 3, 4, 5.

  • Not really a trick question.

  • But I daresay, I can actually count higher than that on this hand, right.

  • I'm pretty naively just treating each of my fingers

  • at the moment like hash marks on the board.

  • They're just straight lines representing people in this room.

  • But I'm not really permuting them or combining my fingers

  • in any interesting ways.

  • They're just down or up, but from left to right, or from right to left,

  • in your case.

  • So how might I be more clever?

  • Well, what if this is just 0?

  • And what if this is 1?

  • And what if this is 2?

  • So just one finger up still-- so not one, but two.

  • What if this is 3?

  • What if this-- offensively-- is 4?

  • [LAUGHTER]

  • (CHUCKLING) What if this is 5?

  • 5, 5.

  • And 6, and 7.

  • In other words, if I actually take into account

  • the ordering in which I have my fingers up,

  • I can actually count up notably higher.

  • Off the top of one's head, anyone want to ballpark

  • how high I can count on one hand?

  • 30-- yeah, 31, actually.

  • And we'll come back to why that is before long.

  • But 31-- that's pretty painful to even imagine or physically do.

  • But if you do kind of permute your fingers up and down

  • in different patterns, we can do even better than just 5 people on one hand.

  • So it turns out that computers aren't all that dissimilar to how

  • they represent information, because we, in our human world,

  • tend to use numbers like 1 and 2 and 3, and we immediately see that and think,

  • probably, 123.

  • But why is that?

  • All you see on the screen, technically, are just like three symbols, right?

  • They might be typed.

  • They might be drawn or painted.

  • But these are three symbols, or glyphs, if you will, that just have

  • meaning that we humans ascribe to them.

  • And we know that that is 123 because it's been ingrained in us.

  • But we know this because we've had an alphabet for quite some time that's

  • actually pretty expressive, right.

  • I grew up knowing 0s and 1s and 2, 3, 4, 5, 6, 7, 8, 9--

  • technically called the decimal system, "dec" meaning 10--

  • 10 because there's 10 digits in this alphabet,

  • so to speak, for representing numbers.

  • But it turns out computers are more simple than this.

  • And they actually don't have access, necessarily, to all of these numbers.

  • They actually-- anyone know what alphabet they actually do use?

  • Yeah, 0s and 1s, otherwise known as binary--

  • "bi" implying 2, because it has just 2 digits at its disposal--

  • 0 and 1.

  • But how in the world using just 0 and 1 can you count to 2,

  • let alone 3 or 4 or 31 or much higher than that?

  • Well, it turns out, it's actually pretty familiar,

  • if you think back to grade school, how you might have first learned math.

  • Odds are, if you're like me, that you saw number like 123--

  • and it is 123 and not just 1-2-3, because this

  • was the so-called 1's place, this was the so-called 10's place,

  • this was the so-called 100's place.

  • And then, none of us do this anymore, but this would be 100 times

  • 1, plus 10 times 2, plus 1 times 3.

  • And that, of course, is 100, plus 20, plus 3.

  • And then we get, mathematically, 123.

  • And even though it's obviously the same pattern of symbols,

  • it now has some mathematical meaning because we

  • ascribe meaning to these columns.

  • Well, it turns out computers work in fundamentally the same way.

  • But they just don't have 2s and 3s and 4s and 9s.

  • They just have 0s and 1s.

  • So they have to be a little more clever how

  • they use those 0s and 1s-- those binary digits, or "bits."

  • "Binary digits" is where the word "bits" comes from.

  • And so they just change what these columns mean.

  • And so instead of having 1, 10, and 100, which it turns out are powers of 10--

  • it's technically 10 to the 0, which gives you 1.

  • 10 to the 1 gives you 10.

  • 10 to the 2 gives you 100, and then 1,000, and 10,000, and so forth.

  • Computers actually use powers of not 10, but you might guess--

  • 2s, because they only have 2 digits in their alphabet.

  • So this is the 1's place, and this would be the 2's place,

  • and this would be the 4's place, for instance.

  • And now I only have 0s and 1s at my disposal.

  • So how do I represent the number we humans know is the number 0?

  • Well, if I need to put a number in these columns, I'm just going to put a 0.

  • Or without fundamentally changing the meaning, I can actually do three 0s.

  • But, just as in the human world, we don't need, necessarily,

  • all of those left-most 0s.

  • We just need the one.

  • How do I represent 1 in binary using just 0s and 1s, if I have three of them

  • at my disposal?

  • 0, 0, 1.

  • Why?

  • Well, I need zero 4s, zero 2s, but I do need one 1 to represent the number 1.

  • And here now, can you see why my fingers went up in the order they did?

  • How do I represent the number 2 in binary?

  • With what pattern?

  • Yeah, 0, 1, 0.

  • And again, if that's not obvious, that's fine.

  • You can just literally do the math.

  • Well, I don't need a 4 to represent the number 2.

  • I do need a 2.

  • But and once I have a 2, I don't need any more numbers.

  • So I can have zero 1s.

  • So here's where we began.

  • This with 0, all fingers down.

  • This was 1.

  • This was 2.

  • And 3 is going to be what on the board now?

  • 0, 1, 1.

  • I just need to go from 2 to 3.

  • And then when I almost made a gesture, that

  • was because my middle finger could have been up, and my other two fingers

  • were down.

  • And if we continue this story, how high can you count with 3 bits,

  • or, equivalently, 3 fingers?

  • What's the biggest decimal number you can represent?

  • 7, because if you just put a 1 in each of these columns,

  • that gives me a 4 plus a 2 plus 1, which gives me the number we humans know

  • has 7.

  • So that's it.

  • Like, all this talk for all these years that you

  • might have gleaned that computers only speak binary or 0s and 1s--

  • it's still fundamentally the same thing that we all learned in grade school.

  • And now, just take for granted, it's just computers only

  • have 0s and 1s at their disposal, but they treat them a little bit

  • differently.

  • And what's nice is that we're doing this in chalk

  • and we're doing this in fingers, but there's

  • a very nice mapping between 0s and 1s to the physical world.

  • All of us who have phones that need to be charged at night,

  • or all of us that have laptops that are plugged in now or at night or in class,

  • are plugging them into the wall.

  • And even if you're not an electrical engineer,

  • you probably generally know that there's like electrons flowing in

  • and out of things to charge things up.

  • So there's some kind of input-- a physical or electronic input--

  • that's either there and plugged in, or it's not.

  • And we can actually simulate this with lights.

  • The lights are all on right now.

  • So I daresay, this room, SSS114 represents a 1.

  • It's just on.

  • But if I go ahead and hit a button, which in the real world

  • is sometimes literally labeled "0," if you've ever noticed, now it's off.

  • This room is representing a 0.

  • So this is kind of a dramatic way of representing

  • 0s and 1s with all of these lights, but we can do it on a simpler form.

  • Like all of us have phones.

  • And those phones have probably built into software little flashlights

  • these days.

  • So if I just tap this button, my phone here is now representing,

  • arguably, the number 1.

  • And if I turn it off, it's representing 0.

  • So using one phone or one flash or really one switch

  • that I'm throwing, digitally, by touching it,

  • I can turn a light bulb on and off or represent a 1 or a 0.

  • And can I borrow someone's phone?

  • Yeah, OK.

  • That helps.

  • You don't have to unlock it for me though.

  • [CHUCKLES] So if I go ahead now, and now I

  • have two bits, two phones, or two switches

  • at my disposal, what am I representing?

  • I think 0, 0.

  • The light bulbs aren't off.

  • If I go ahead and turn on this one, what am I now representing?

  • 1.

  • And now?

  • 3, good.

  • And now, if I turn this one off, too.

  • So thank you so much for volunteering.

  • And so if, now, I just had more switches, and in turn more light bulbs,

  • I could keep counting higher and higher.

  • So what's actually inside of a computer?

  • It's just many, many-- billions, even-- of tiny little switches these days.

  • And those switches happen to be called transistors.

  • So if you've ever heard that word, it's just

  • like a light switch, or the switch on a lot bulb,

  • that either is turning something on or turning something off.

  • And as soon as you have that ability to turn something on, like a 1,

  • or turn something off, like a 0, you can clearly count as high as you want,

  • so long as you have more and more of those switches.

  • I have just three, or I have just five, but we can certainly throw hardware

  • at it by adding more and more.

  • So let me pause there for just a second because that was a bit of a mouthful.

  • Are there any questions on what we've now defined as binary?

  • And recall that the reason we got into the weeds

  • here was we need a way to represent information inputs,

  • so we just need some kind of pattern or symbology.

  • All right.

  • So that's all fine and good that we can represent numbers.

  • But what would you also like that your computer do?

  • Back in the day, a computer was, by definition, really just a calculator

  • doing mathematical things.

  • But of course, these days we use computers and phones for so much more.

  • What might you also want to represent?

  • Well, what about just English messages, or any language for that matter?

  • In English, I might want to represent the letter A.

  • Well, if we had been focusing on math and numbers here

  • and patterns, how do we make this leap conceptually now

  • to represent letters, so you can send text messages and emails

  • and write essays and the like?

  • How might we solve this problem?

  • Because again, the only physical input is, like, my power cord into the wall.

  • Yeah?

  • AUDIENCE: A is the first letter of the alphabet,

  • so you could represent it as 1.

  • DAVID MALAN: Yeah.

  • So that's pretty good intuition.

  • A is the first letter of the English alphabet,

  • so why don't we just start numbering those letters?

  • So if A is 1, sure.

  • And capital A is 1.

  • Capital B, presumably, would be 2.

  • And C would be 3, and so forth.

  • And that's actually spot on.

  • That's exactly what computers do.

  • They actually give themselves a little bit of breathing room,

  • so it's not actually starting at 1.

  • Actually-- weirdly-- starts at 65.

  • But then, the answer is exactly the same.

  • B is 66.

  • C is 67, and so forth.

  • And so just some years ago, decades ago, a bunch of humans in a room

  • essentially needed to decide that, hey, everyone, can

  • we all agree that capital A shall be represented in a computer as 65.

  • Now, in turn, 65 is represented how?

  • Well as a pattern of 0s and 1s.

  • But that's fine.

  • We don't need to keep talking about 0s and 1s

  • if we can just kind of talk at a higher level,

  • like our familiar decimal numbers.

  • So C is going to be 67.

  • And D is going to be 68, and so forth.

  • And then there's a whole mapping, so to speak,

  • between lowercase letters and numbers, as well.

  • What they are doesn't really matter--

  • just that there is an agreed upon standard

  • is actually what's important here.

  • So it turns out this standard, this mapping that humans came up with,

  • which is called ASCII--

  • American Standard Code for Information Interchange.

  • Not interesting what it stands for, but we all agreed some time ago.

  • But it turns out that ASCII actually has some limitations.

  • It has some limitations, in that you can only express so many letters.

  • Here, for instance, is a summary of just some of the letters we might represent.

  • And just to hammer this home, if this is the mapping,

  • as you were proposed here, albeit starting at 65,

  • and you received on a computer somehow some series of bits--

  • 0s and 1s-- somehow, through Wi-Fi or ethernet or whatever-- and the bits

  • you received represent the decimal numbers

  • 72, 73, and then 33, what message did you just

  • received from someone on the internet?

  • 72, 73, 33.

  • Yeah?

  • AUDIENCE: Hi.

  • DAVID MALAN: Hi, so 72 is H. 73 is I. 33, it's actually not on the board.

  • Anyone want to guess what it is?

  • So, well said.

  • It is an exclamation point.

  • So "HI!" is indeed correct.

  • And that's because we just agreed some time ago to have that mapping.

  • So 72, 73 is HI, followed by, if you actually

  • looked at what the dot, dot, dot meant in an online reference,

  • you would see that, indeed, it represents an exclamation point.

  • Meanwhile, though, underneath the hood, so to speak, like actually

  • in the computer's memory--

  • and more on memory in the weeks to come--

  • some pattern of switches, effectively, is actually

  • storing that pattern of 0s and 1s.

  • There's a bunch of light bulbs on.

  • There's a bunch of light bulbs off.

  • And they're in groups.

  • And it turns out that computers usually use groups of eight

  • because it's convenient for today's purposes-- eight bits-- eight 0s or 1s.

  • You don't have to write all of them if the leading things to the left

  • are 0s, so we don't bother typically, but computers typically use eight bits.

  • So if you were to represent, in ASCII, any letter of the alphabet,

  • you would have 1, 2, 3, 4, 5, 6, 7 bits, historically.

  • But it turns out, for convenience, it's often represented as eight.

  • And there's something called extended ASCII, but the point is, it's finite.

  • There's only a fixed number of bits, or 0s or 1s,

  • used to represent every letter of the alphabet.

  • So what's the implication of that?

  • If you're only using eight bits-- and we don't

  • have to get too into the weeds of math or arithmetic--

  • but if you only have eight bits or eight fingers,

  • how many different combinations of fingers up and down

  • can you come up with?

  • AUDIENCE: 255.

  • DAVID MALAN: 200-- well, a total pattern's 256.

  • And why is that?

  • Well, if each of these sort of hangman placeholders is either a 0 or 1,

  • there's two options here, times two more options here, times two, times two,

  • times two.

  • And if you just multiply out two by itself eight times,

  • you actually do get 256.

  • So with just eight bits, you can count from 0 all the way up to 255

  • if, again, you start counting at 0.

  • So it's finite.

  • It sounds like a lot, though.

  • But if you consider a typical keyboard, say a US keyboard,

  • you'll notice that there's actually a lot of symbols not

  • on our American keyboards, at least.

  • And indeed, if you're from abroad or you speak another language

  • and you have a different type of Mac or PC keyboard,

  • there's probably symbols you use every day with family or friends that

  • just aren't on the keyboards that you might see in the computer lab

  • or on most American's keys.

  • And, indeed, ASCII was pretty biased-- like, American Standard Code

  • for Information Interchange.

  • Hey, the operative word there was "American."

  • And so what, apparently, can we not represent with a typical keyboard, let

  • alone ASCII?

  • What kind of symbols, if you speak other languages?

  • Yeah?

  • AUDIENCE: A Chinese character.

  • DAVID MALAN: Yeah, like a Chinese character, which is not obvious,

  • certainly on this type of keyboard.

  • Yeah?

  • AUDIENCE: Upside-down question mark.

  • DAVID MALAN: Upside-down question mark that you might use in Spanish,

  • for instance, or another language.

  • Yeah?

  • What's that?

  • AUDIENCE: Accents.

  • DAVID MALAN: Accents on A's and E's and vowels and other characters, as well.

  • So we're just scratching the surface, right.

  • There's a lot of characters you might see in your own life

  • or in any language you've studied that just don't fit that model.

  • So just instinctively then, what is the solution?

  • There's a problem.

  • ASCII is not expressive enough.

  • So what's the solution to representing accents and other characters?

  • AUDIENCE: Use more bits.

  • DAVID MALAN: Yeah, use more bits, right.

  • And computer scientists might say, throw hardware at the problem.

  • And it's kind of true, literally, like just use more light bulbs,

  • use more switches, use more memory.

  • But indeed, that's the solution.

  • So along came something called Unicode some years ago,

  • whereby you can actually use not just one byte or eight bits--

  • so eight bits is a byte.

  • It's just a convenient term to describe eight bits like the ones on the board.

  • And with Unicode, a successor to ASCII, can you

  • use one or two or three or four bytes?

  • And no one pointed this out, but I just flew past this.

  • Besides being able to represent things like accents on a keyboard,

  • turns out that these are very much in vogue these days.

  • Right, most of us in this room probably use emojis,

  • perhaps use them a little too much.

  • But what is it?

  • You're actually not sending images, per se.

  • Well, you might be.

  • If you're downloading GIFs or animations or whatnot,

  • those might be proper images.

  • But if you're just sending emojis from the built-in Android or iOS or Mac

  • or PC keyboards, you're actually sending a keyboard signal, so to speak.

  • It's not just eight bits.

  • It's more.

  • Because on my American keyboard, I don't typically

  • see keys labeled with all of these faces.

  • But Unicode increases exponentially just how many symbols, accents,

  • and upside-down question marks, and other punctuation-- and, it turns out,

  • these fun things like emoji that you can actually

  • represent inside of a computer.

  • As an aside, if you've ever seen or you will eventually

  • see the expression, UTF-8, this is just the name

  • for the very specific standard.

  • And we'll come back to this toward the end of the semester

  • when we start using this.

  • So does anyone want to guess what the decimal number is

  • being used underneath the hood of a computer,

  • so to speak, to represent this, which, according to Apple

  • is the most popular iOS emoji by far?

  • I couldn't find a statistic for Android.

  • I mean, actually, out of curiosity, how many people have recently

  • sent this emoji?

  • OK.

  • So I guess the data holds out.

  • Proof by example.

  • So anyone want to guess what the value is?

  • Is it 1?

  • Is it 65, 64?

  • Is it 255, 256?

  • Well, turns out there's a hell of a lot of emoji these days.

  • It is number 128514.

  • That is the decimal number that represents--

  • what is it called?

  • Smiling face with joy, or crying face of joy--

  • something like that.

  • And there's a lot of other emojis, too.

  • And they have different skin tones and the like.

  • So that just involves using more bits--

  • more 0s and 1s.

  • So this is only to say that we have had, in the past, and we have had now

  • and in the future, like, a way of representing information.

  • And we just evolved over time.

  • And in fact, this is the pattern of bits.

  • If you send a text message or an iMessage right

  • now to someone with that emoji, you're not sending a picture, per se.

  • You're sending, effectively, this pattern of bits to their phone.

  • And their phone, upon receiving that sequence of bits,

  • realizes, oh, that sequence of bits represents-- the number 128514.

  • Let me display to the user the 128514th emoji, if you will,

  • that it knows how to display.

  • Well, that's a bit of an exaggeration.

  • This is just how many there actually are.

  • So we've got numbers that we can represent from binary--

  • just 0s and 1s and electricity.

  • We've got letters, and even things like emojis.

  • But what else?

  • Well, it's super common, of course, to use computers to send actual images

  • or to look at photographs, take photographs, look

  • at pictures on websites.

  • And so here is just a single dot, enlarged for the projector's sake.

  • And we're going to call it a pixel.

  • A pixel is just a single dot that has some color.

  • How, in a computer, might I go about representing colors now?

  • Again, if you unwind the story, all we've got

  • is like electricity coming out of the wall and switches

  • to turn it on and off.

  • What could we do?

  • How would you go about defining a color?

  • Yeah?

  • AUDIENCE: You would have three different colors for RGB.

  • DAVID MALAN: Yeah

  • AUDIENCE: Then, I guess that you'll send 0s and 1s for red, 0s and 1s

  • for green, 0s and 1s for blue.

  • DAVID MALAN: Exactly.

  • That's spot on.

  • And there's actually two different answers.

  • There's different ways you can combine colors.

  • But that's actually the most common one.

  • RGB, if you've ever heard that acronym, it stands for red, green blue.

  • And if you also regress a little bit, if you think back

  • to grade school or primary school or whenever we all played with like finger

  • paints, right, odds are you combined paints,

  • which is a little different from combining waves of light.

  • But with different colors of paint, can you obviously

  • make, well, as I recall, just like brown.

  • By just combining everything makes sort of brown by combining things.

  • But if you think about colors being represented ultimately

  • as some combination of red and green and blue, or RGB as you proposed,

  • you can imagine taking those colors and overlapping them and seeing

  • what color actually emerges.

  • And so if we call this RGB, each of those colors

  • uses one byte or eight bits, so which is to say,

  • you pick a number between 0 and 255--

  • because, again, that's how high we can count

  • using a byte, which is eight bits.

  • So you use eight bits to represent red, eight bits to represent green,

  • eight bits to represent blue.

  • So if you have the value 0, 0, 0, that's no red, no green, no blue.

  • And it turns out, that's how you represent black, is just 0, 0, 0.

  • But if you throw big values at red and green and blue--

  • and this is where it diverges from the paint metaphor--

  • then you have lots of red, lots of green, lots of blue.

  • And it turns out that makes white--

  • 255, 255, 255.

  • And we'll come back to this.

  • Because when we actually introduce web programming later in the semester

  • and you actually visually create web pages that have colors in them,

  • the onus will be on you to decide, well, what color

  • do I want to make my text or the background?

  • And in the web, even though you can just say, make this page red

  • or make this text blue, you can also specify more precisely--

  • give me this much red, this much green, this much blue.

  • And the browser will display exactly that for you.

  • So if here we have three swatches of color red, green, and blue

  • and we actually take 72 amount of red, 73 amount of green, 33 amount of blue--

  • and then if you're just kind of animate it in your mind's eye,

  • overlapping all three of those colors, that

  • is how we end up getting this murky shade of yellow here.

  • Underwhelming, perhaps.

  • But we can make a lot of other colors, as well.

  • But notice the resemblance of those values to something we saw earlier.

  • What numbers that I just propose?

  • Yeah, it's also HI!

  • And so, somehow, HI!

  • is represented by computers as yellow.

  • So it turns out, context matters.

  • Like, if you're using a text messaging application or Microsoft Word or Google

  • Docs or whatever, and your computer sees the pattern 72, 73, 33, odds are,

  • because of that context and how that program is written,

  • it's going to display to you H-I-!.

  • But if you open up another program--

  • Photoshop, for instance, if you've heard of it.

  • Even if you've never used it, it's like a photo editing program.

  • Well, odds are, if that program sees the same pattern of bits--

  • 72, 73, 33, among others, it's going to say, oh, I should show the user not HI!

  • but rather one yellow pixel-- probably among many others

  • if you're indeed editing a photograph in it.

  • So it's just context that actually matters.

  • So it turns out you can see this if you just zoom

  • in on some of the things we see here.

  • And you might need to do this with special software

  • because most phones won't let you zoom infinitely.

  • But here is just a copy of that same laughing-with-joy emoji.

  • And if I zoom in a little bit, it still looks pretty good.

  • If I zoom in a little bit more, starting to get a little

  • pixelated, so to speak-- a little blocking.

  • If I really punch in on it, now you can actually

  • see what is going on with this image.

  • It turns out that an image is just a whole bunch of dots or pixels--

  • thousands of them-- tens of thousands of them, depending how big the image is.

  • And each of those pixels or dots just has some color associated with it.

  • Each of those colors is 3 bytes or 24 bits.

  • So just ballpark, if you had an image like this,

  • how would you go about estimating how big the file is?

  • If this is an emoji and you know that every pixel is 24 bits,

  • what kind of intuition would you use to estimate how big this emoji is

  • on disk, so to speak?

  • How big of a file it would be to actually send a copy of?

  • Yeah?

  • Oh, sir?

  • AUDIENCE: Multiply the number of pixels with--

  • DAVID MALAN: That's it-- multiply the number of pixels.

  • I mean, literally, if I pointed at them--

  • 1, 2, 3, 4, 5, 6-- count them all up, all of those squares,

  • and multiply by that value.

  • Of course, we're zoomed in so there's even more of them, but that's it.

  • So all this stuff with which you might be

  • sort of familiar on a day-to-day basis, it's actually not

  • even all that complex, so long as you start so-called first principles

  • and you understand from the ground floor up how you solve one problem,

  • and from there go about solving others.

  • Now some of you with iPhones might be familiar with animojis too-- the sort

  • of silly feature on iPhone X or your friends' phones that

  • allows you to do Snapchat-style things on iPhones, that

  • actually creates animations or videos.

  • And I will admit that Natalie and I have, in the past,

  • spent far too much time over dinner like playing with animojis,

  • though it was only once and only once.

  • But that footage is somewhere.

  • But that results in video files, essentially.

  • But what is a video?

  • Whether in the context of animojis or just movies on TV or movies on Netflix

  • or movies in the theater, what is a video?

  • Yeah, it's just a sequence of images flying past the screen, quickly.

  • If you've ever heard of 24 frames per second or 30 frames per second or FPS--

  • it doesn't matter if you haven't-- that's all a movie is.

  • And back in the day, they called them "moving pictures"

  • because they were just pictures, again and again and again,

  • flying in front of your face so quickly that it looks like animation.

  • But each of the frames in a movie-- and you

  • can see it, if you just pause a movie or a video, you're seeing an image.

  • And it might even be a little blurry because

  • of compression and fancy techniques humans use these days to save space.

  • But it's just an image-- one of 24, one of 30 or so, for that given second.

  • So we can consider where we started the story, like electricity from the wall.

  • Then we had 0s and 1s that we can represent digitally,

  • so to speak, as binary.

  • Then we have letters of the alphabet, ASCII.

  • From there, we can go to colors if we want.

  • From colors, we get images.

  • From images, we get movies.

  • And all of that, by simply deciding how to represent

  • the inputs to some problem.

  • And the outputs, presumably, come in that same format.

  • Whatever the problem is you're solving--

  • you're outputting an image, a video, a number, a string of text--

  • that ultimately being how you solve a problem.

  • So let me pause here, too, see if there are any questions.

  • All right, well-- yeah?

  • In the back.

  • AUDIENCE: How do you know where [INAUDIBLE] rather,

  • where a string breaks?

  • Because you can't just put a 0 in if you, like, close it.

  • DAVID MALAN: Where a string breaks?

  • AUDIENCE: Yeah, where a binary string-- like, where it ends,

  • where a number ends.

  • So that would be 72,000, or, well, just a string of 0s and 1s

  • that somewhere ends rather than eight bits.

  • DAVID MALAN: Ah, really good question.

  • And I only alluded to this earlier.

  • Humans do tend to use units of eight bits.

  • And so, unlike schemes like Morse code, if you're familiar,

  • where there are disparate lengths based on the character,

  • in ASCII and in Unicode, you were always using eight bits at a time, or maybe

  • 16, or 24, or 32.

  • So the boundaries are always at an eight-bit mark.

  • The ninth bit onward represents the next byte.

  • But there, too, it's context-sensitive.

  • Because in some programs, you might interpret four bytes in a row-- byte,

  • byte, byte, byte--

  • as four separate characters.

  • Or you might interpret it as a really big value for a really big emoji.

  • And it's totally context-sensitive.

  • The software, the program you're using, has to know how to interpret that data.

  • And so when I mentioned, we'll see things like UTF-8 and these fancy terms

  • later in the semester, because when you're making web pages

  • and you're writing software, you sometimes have to decide,

  • I'm just going to get from my users a string of bits from the internet.

  • How do I interpret it?

  • So it depends.

  • Other questions on inputs?

  • Yeah?

  • AUDIENCE: Why use the binary system instead of the decimal system?

  • DAVID MALAN: Oh, why use the binary system instead of the decimal system?

  • Let me toss that back to someone for just someone's intuition.

  • Why do you think?

  • Like, why did we solve a problem in just a different way here?

  • Yeah.

  • AUDIENCE: The computer's only outputs [INAUDIBLE]..

  • DAVID MALAN: Exactly.

  • It just maps very conveniently to the physical world.

  • It turns out that just having a switch that's on or off,

  • or electricity that's flowing or not flowing,

  • it's just a very nice unit of measure.

  • It's either doing something or not.

  • And humans built things on top of that.

  • There are systems like ternary, where you actually have three states--

  • sort of one here, one here, and one here.

  • But it turns out that that can be a little more error-prone, especially

  • if there's a bit of noise.

  • And if you do take a class in like electrical engineering

  • or if you've used a battery in the human world recently,

  • you know about things like voltages.

  • Well, generally, voltage is also a very noisy medium.

  • And so you roughly want your 9-volt battery to be close to 9 volts

  • or close to 0 volts when it's dead.

  • You don't really want to in the middle because then your hardware is not

  • going to perform correctly.

  • So it's just convenient, really.

  • Yeah?

  • AUDIENCE: What happens if you were to write a message saying,

  • hi, exclamation point, I'm 65 years old?

  • With a 6 and a 5, right?

  • Like, how does it know within that framework [INAUDIBLE]??

  • DAVID MALAN: A really good question.

  • If you want to send a message that mixes letters and numbers,

  • it turns out that you are technically sending only characters,

  • as we're going to start calling them in a couple of weeks.

  • So this is just a website I knew the URL of-- asciichart.com.

  • It's looked like this for 20 years.

  • And it's provided courtesy of Appropriate Solutions, Inc.

  • for some reason.

  • But this is just a mapping of decimal numbers to the ASCII letters

  • that they accord to.

  • And if you look at the chart, you'll see in the middle--

  • 65 is A, 66 is B. But if you look farther to the left,

  • how would you actually represent in a text message

  • or whatnot, as you propose, HI!

  • I AM 6-5 YEARS OLD--

  • well, how do you represent that 6?

  • As the pattern 54.

  • And 65 is actually the pattern 53.

  • So in the context of sending text, even if the symbols you're sending

  • look like human numbers, they are still represented

  • in this same system called ASCII.

  • Great question.

  • Any others?

  • Yeah, over here.

  • AUDIENCE: How would you do a yellow letter?

  • DAVID MALAN: How would you do a?

  • AUDIENCE: A yellow letter?

  • DAVID MALAN: Oh, really good question.

  • How would you implement a yellow letter?

  • OK, a lot of combinations here.

  • So I to give a simplistic answer, but it totally

  • depends on the piece of software.

  • So a program, such as the ones you will all be writing before long--

  • you would have to decide, well if I want to allow a user

  • to colorize all of the letters in his or her message,

  • well, I minimally need to use 8 bits for every letter he or she is sending.

  • But to represent the color, I might need 8 plus 8--

  • I might need 24 more bits to represent the color

  • of the letter the human is sending.

  • So you know what, I, this programmer, am just going to use 32 bits--

  • 8 for the letter, 24 for the color--

  • to represent every letter that I'm sending in this message.

  • But you can do it any number of other ways.

  • It's totally up to the designer of the software.

  • All right, so from there, if we now stipulate we have a way of representing

  • information-- or more generally, just inputs to problems and the outputs

  • to those problems-- this course, and computer science more fundamentally,

  • is really about what's in between those two--

  • the thought processes, the instructions, the so-called algorithms,

  • step-by-step instructions for solving problems,

  • that fits here inside this proverbial black box.

  • So what might be an algorithm that we want to solve something with?

  • Well, consider something like this.

  • It's a little old school, these days.

  • But this is, or was, a phone book.

  • And it has a whole bunch of names alphabetically from left to right.

  • And it has a whole bunch of phone numbers in it.

  • And even though this is an old-school technology,

  • honestly, if you think about your own iPhone or Android phone or whatnot,

  • and you open up the contacts application, odds are,

  • those contacts are sorted from top to bottom, A to Z,

  • either by first name or last name.

  • So it's pretty much just the digital version of this.

  • So if we can solve this physical problem,

  • we could then apply that same logic as a programmer to solving

  • a problem in an actual Android phone or iPhone or just address book,

  • more generally.

  • So if I want to solve a problem, and here's the input--

  • represented on paper as numbers and names

  • alphabetically from left to right-- and I want to find someone like Mike Smith.

  • And the whole book is alphabetized by last name.

  • Maybe I'll start at the beginning.

  • And I'll look at the first page and look for Mike Smith-- not there.

  • So I keep going.

  • And I keep going.

  • And this is a step-by-step process.

  • Literally, page by page I'm looking-- should be looking-- for Mike Smith.

  • And when I find him, I'll eventually call him.

  • This process, this algorithm, is it correct?

  • Yeah, it's correct, because if Mike's here, I will find him.

  • And I think intuitively we'd all agree that if the problem is

  • to find Mike Smith, this algorithm, though slow,

  • is correct because it will eventually find him.

  • But it's slow.

  • It's inefficient.

  • All right.

  • So from grade school, I also remember counting by 2s, so, 2, 4, 6, 8, 10, 12.

  • All right, I'm going through faster.

  • And I'm being a little sloppy, to be fair.

  • But is this algorithm more efficient?

  • Yeah, I mean, it's literally twice as fast.

  • But is it correct?

  • OK, why not?

  • AUDIENCE: You could skip some of

  • the pages.

  • DAVID MALAN: Yeah, I could get unlucky.

  • And like, once I get to the S's, oh, dammit, like, just by bad coincidence,

  • Mike get sandwiched between two pages.

  • So the algorithm is incorrect.

  • But do I have to just throw it away altogether, or can I fix this?

  • Like, what could you, the human, do?

  • If you're flying through and like, oh, dammit, I passed Mike Smith,

  • do you just give up?

  • Obviously not.

  • You just double back.

  • Right, so there is a fix.

  • So I would argue this algorithm is twice as fast, minus one step.

  • Because if I go ever so slightly too far to hit the T section, for instance,

  • or SN instead of SM, then I might have to double back one page.

  • And that's not bad, right.

  • If this is like a thousand pages, OK, doubling back by one page

  • really just a drop in the bucket.

  • But none of us are going to solve this problem in either of those ways, right.

  • If you're going to solve this problem, what are you going to do these days?

  • Yeah, just go like roughly to the middle where the S is, if it's not obviously

  • labeled on the outside.

  • And OK, I look down.

  • It's roughly in the middle.

  • I'm in the M section.

  • And I know, alphabetically, Mike is to the left--

  • Mike Smith.

  • And so what do I know about this input now?

  • Where is Mike?

  • Yeah, it's on the right--

  • Mike Smith, yeah.

  • So on your left, so, Smith over here.

  • And now, just to be dramatic, we can--

  • [CHUCKLES] dammit.

  • [LAUGHS]

  • [LAUGHTER]

  • Tear the problem-- thank you-- in half.

  • [LAUGHS]

  • [APPLAUSE AND CHEERING]

  • We can tear the problem in half, like throw half of the problem away.

  • And what we're left with, really is the same problem.

  • It's the same problem in the sense that I can

  • use the same logic, the same algorithm.

  • But I've taken a 50% bite out of the problem.

  • And that was way bigger, because the first algorithm took one bite.

  • The second algorithm took two bites.

  • This time it took like 500 bites out of the problem

  • all at once, in one instant.

  • And now I go do the same thing-- roughly in the middle.

  • Darn, I went a little too far.

  • Now I'm in the T section.

  • But again, I can apply the same-- much easier now-- logic,

  • throw that quarter of the problem away, and be left with like 250 pages.

  • And I can repeat and repeat and repeat, until hopefully, logically, I'm

  • left with let's just say--

  • (CHUCKLING) the injury law page--

  • "let us win and fight for you"--

  • that is not Mike Smith, but it is, in fact, one page.

  • And so how quickly did I actually get there?

  • Well, if you imagine starting with like 1,000 pages,

  • the first algorithm might take 1,000 steps at worst.

  • Like, if Mike's all the way at the end or maybe--

  • S isn't in the Z section, so let's say that first one was going

  • to take like 750 steps, give or take.

  • Now the second approach would take maybe 300 steps--

  • 325 steps or whatever, because I'm going twice as fast.

  • But this algorithm, where I started with 1,000 pages

  • and went from 1,000 not to 999 999, and not from 1,000 to 998.

  • I went from 1,000 to 500, to 250, to 125, to 60ish, to 30ish, to 15, to 7,

  • to 3, to 2, to 1.

  • And I'm just kind of rounding to clean it up--

  • to 1, I mean, that was pretty darn fast.

  • And in fact, if we did that perfectly arithmetically correctly,

  • how many times can you divide roughly 1,000 by 2 by 2 by 2?

  • It's actually only about 10 times.

  • So with just 10 dramatic tears of the page, could I find Mike Smith?

  • As opposed to taking maybe 1,000 steps or maybe even taking 500 steps,

  • I whittled it down to 10, by just leveraging the intuition,

  • daresay, that all of us are generally familiar with already.

  • Now as an academic class, let's try to apply some thought process

  • to just what this performance looks like and see if that recurs down the road.

  • But first let's express this a little more precisely.

  • So pseudocode, I claim, is just a way of programming using English.

  • There's no formal definition of this.

  • You just use succinct, clear English that gets your point across.

  • And you might generally number your steps just so

  • that you can refer to it verbally.

  • So here might be the pseudocode for representing that algorithm,

  • if I wanted to repeat it or feed it to like a robot to implement as well.

  • Pick up the phone book.

  • Open to the middle of the phone book.

  • Look at names.

  • And then I have to start making some decisions.

  • If Smith is among names--

  • call Mike.

  • That's a success.

  • That's the output.

  • Else, if Smith is earlier in the book, open to the middle

  • of the left half of the book, as I did.

  • And then go back to step 2, focusing on the middle

  • of the left half at that point.

  • You don't have to technically tear it.

  • Else, if Smith is later in the book, do the same thing but to the right.

  • Else-- and the fourth thing is important--

  • if he's just not there at all and you get down to one page

  • and he's just not on it, you have to do something.

  • You can't just hang or sort of do the spinning beach ball or hourglass

  • like Mac OS or Windows might do if a programmer didn't

  • anticipate a fourth scenario.

  • That forth scenario is, he's just not there.

  • So you should give up.

  • So let's call out some fundamental building blocks here.

  • Everything highlighted in yellow at the moment looked to be verbs in English.

  • They're actions.

  • And moving forward, we're going to start calling these things functions--

  • functionality that a human might have or eventually that a computer might

  • perform on that human's behalf.

  • If we highlight these next words in yellows-- if, else if, else if, else--

  • those are going to be called conditions or branches, the proverbial fork

  • in the road, if you will.

  • We can call it any number of things.

  • It's just a decision point on which you need to make a judgment call.

  • And then there's these, the actual questions

  • you're answering in order to decide to go left, so to speak,

  • or right, or down the middle road.

  • And those questions might be, is Smith among names?

  • Is Smith earlier?

  • Is Smith later in the book?

  • Those, more fancifully, are going to be called Boolean expressions

  • after someone named Boole.

  • But they're just questions that have yes/no or true/false

  • or, heck, 1 or 0 answers, right.

  • It isn't really what we call any of these terms.

  • We can even distill them down to 1s and 0s,

  • and that's great because computers can do exactly that.

  • Lastly something here highlighted in yellow, go back to step 2--

  • that might just be a loop.

  • It's some kind of cycle that we're inducing.

  • And indeed, that's going to be another construct that we have in programming,

  • not using English or pseudocode, but rather using

  • more traditional languages.

  • But before we do that, let me propose this,

  • how do we think about just how fast or slow that algorithm was?

  • Well, we don't actually need to do any kind of actual strong measurements

  • or actually precise measurements.

  • But let me just propose a relationship.

  • So here's a general graph.

  • Let's actually call this like time in some unit of measure-- seconds

  • or whatever.

  • This is number of pages, or more generally, instead

  • of calling this pages, let's just genericize it

  • as the size of the problem.

  • So if this chart represents along the horizontal axis how many pages are

  • in a phone book and the vertical axis represents how much time it takes

  • to find Mike Smith or anyone else-- the performance of the problem--

  • what shape did the first algorithm have?

  • Took me a thousands steps in the worst case.

  • And what's the relationship between the length of the phone book

  • and the amount of time?

  • Yeah, it's just-- it's linear.

  • It's a straight line.

  • Why is that?

  • Well, a handy way of thinking about it, I think,

  • is if like the phone book company, Verizon

  • or whoever, adds one more page to the phone book

  • next year because a bunch of new families move into town,

  • well, that's going to add a page to the phone book.

  • And suppose you're looking for a family whose last name starts with Z,

  • they're all the way at the end.

  • And that first algorithm, you might have to turn 1,001 pages to get to them.

  • So one more page--

  • one more second.

  • And so the relationship is a straight line.

  • One more page-- one more second.

  • One more page-- one more second.

  • And you get a straight line.

  • The second algorithm, where I was flying through the phone book

  • two pages at a time, what's the shape of that algorithm going to look like?

  • Yeah, but steeper, I heard.

  • Yeah, so steeper at least, or lower in the way I'm drawing it here.

  • For instance, if this line is straight up this way,

  • the other algorithm is going to be half as high.

  • Now how do you think about that?

  • Well, for instance, if this is the number of pages in the phone book,

  • the first algorithm might take this many seconds, whatever that is.

  • The other algorithm should take half as long.

  • So I just eyeballed it.

  • Hopefully that point is roughly half as tall as the other point.

  • And that relationship holds no matter how far out you get.

  • And this one's a little less intuitive, but that third and final, daresay,

  • intuitive algorithm-- dividing and conquering the phone book in half

  • and half and half--

  • has a fundamentally different shape that's

  • actually a curve that doesn't ever flatten out,

  • but it almost looks like it is because it starts to grow, so to speak--

  • it starts to rise so terribly slowly as the problem gets really and really big.

  • How do you think about that intuitively?

  • Well if Verizon next year consolidates two towns,

  • like New Haven and another town over, just for whatever bureaucratic reasons,

  • the phone book might go from 1,000 pages to 2,000 pages overnight.

  • But how many steps is that third algorithm actually going to take?

  • Just one additional step.

  • So even if this is 1,000 pages and this is 2,000 pages,

  • it just barely increases-- maybe by one second or one additional page turn.

  • And that holds true the farther and farther out you go.

  • And this is only to say that, even if computer science doesn't necessarily

  • feel like something with which you're all that familiar or necessarily going

  • to be comfortable, it really is about just harnessing

  • some of this intuition and these heuristics

  • that we take for granted, but kind of formalizing them

  • so that you can really and powerfully solve problems that you're actually

  • interested in and not just some of the smaller examples,

  • like counting and representing numbers.

  • Like, the intuition isn't all that far away.

  • But of course, with this phone book or with the contacts in your Android phone

  • or your iPhone, they're sorted already.

  • Right, I made a pretty serious assumption

  • that that phone book was sorted.

  • And in fact, that's perhaps not necessarily fair.

  • Could we ask for one volunteer to come on up here?

  • She's got to be comfortable appearing on camera.

  • But you don't have to say too much.

  • Yeah, what's your name?

  • AUDIENCE: Katarina.

  • DAVID MALAN: Katarina.

  • Come on up.

  • Nice to meet you.

  • Yup, right this way is fine.

  • All right, David.

  • Nice to meet you.

  • AUDIENCE: Nice to meet you.

  • DAVID MALAN: So behind door number one here

  • we have a whole bunch of pieces of paper.

  • And I would like you--

  • there's no prize at the end of this, sadly--

  • but I would like you to find for us the number 50.

  • Nope, that's the number 8.

  • AUDIENCE: Are they in order?

  • Is it--

  • DAVID MALAN: That's the number 42.

  • That's the number 4.

  • 50!

  • All right.

  • Applause for Katarina if we could.

  • [APPLAUSE]

  • OK, just wait one sec.

  • [APPLAUSE CONTINUES]

  • So, why did you ask me if they were in order?

  • AUDIENCE: That way I knew if it would be to the right or the left.

  • DAVID MALAN: Yeah, and so, indeed, they weren't.

  • These numbers are all pretty random in this case.

  • So, she actually did as well as anyone could do with the board.

  • And maybe you could have been gotten lucky,

  • but not by any kind of formal process or algorithm.

  • You just took three, four steps that time

  • because you had no other information to go on.

  • But if we instead do give you that assumption-- now, dramatically,

  • behind door number 2--

  • [LAUGHTER]

  • --we have seven more pieces of paper.

  • But I do tell you this time they're sorted from small

  • to large, like a phone book, like a contacts in a phone,

  • where are you instinctively going to start, probably?

  • AUDIENCE: Probably in the middle.

  • DAVID MALAN: All right, so start in the middle, sure.

  • Because you don't know how small or how big the numbers get.

  • But find me the number 50 with this set of data.

  • (WHISPERING) That's not the middle.

  • AUDIENCE: The middle.

  • DAVID MALAN: [LAUGHS]

  • [LAUGHTER]

  • AUDIENCE: This is the middle?

  • DAVID MALAN: That's a bug, if you will.

  • OK, 16, not 50.

  • But what do you now know?

  • AUDIENCE: That this is to the right.

  • DAVID MALAN: Exactly.

  • We don't have to even bother looking at any of these numbers.

  • So find me 50.

  • AUDIENCE: So in the right.

  • DAVID MALAN: In the middle of the right half.

  • Nope, still 42.

  • But where are you going to go next?

  • AUDIENCE: The only way, so this--

  • DAVID MALAN: And why that way and not this one?

  • AUDIENCE: Because this is going to be smaller.

  • DAVID MALAN: Good, because 42 is smaller than 50, so--

  • excellent.

  • A round of-- [LAUGHS]

  • [APPLAUSE]

  • Congratulations.

  • [APPLAUSE CONTINUES]

  • So, it seems to be a very powerful thing if you're

  • allowed to assume from the get-go that your data is sorted.

  • Well, that's great.

  • But someone how to do that sorting.

  • Right, Verizon sorted the phone book, or Apple or Google sorted your contacts,

  • so that you can then benefit by looking up Mike Smith quickly, or all of us

  • are familiar with like auto-complete these days,

  • where you just start typing characters.

  • It turns out that phone that you're using,

  • when you're searching for someone, is probably using this same algorithm

  • that Katarina did.

  • It looks roughly in the middle of your contacts.

  • And then if you're looking for Smith in your phone,

  • your phone is going to look only at the contacts below or only at the contacts

  • above, so that it can find things much more quickly for you, logarithmically,

  • so to speak--

  • but more on that down the road.

  • But if we need to actually sort the humans,

  • it kind of invites the question, well, that's all fine and good

  • if searching is fast--

  • but how fast is sorting?

  • Right, how did I, before class, or Colton, who lent a hand before class,

  • sort those numbers?

  • And indeed, just to prove that this was not fixed, these numbers--

  • same numbers, it turns out--

  • but they are, in fact, sorted.

  • But they didn't start that way.

  • So actually, why don't we do this?

  • Rather than answer it in the abstract, can we get eight volunteers this time?

  • OK, 1, come on down--

  • 2, 3, 4, 5, 6, 7--

  • no one from this side?

  • Really?

  • OK, 8.

  • Come on down.

  • All right.

  • And if you could just greet Colton over there.

  • He's going to hand you a prop.

  • And go ahead and take a seat wherever you would like.

  • OK, Take a seat wherever.

  • All right.

  • And just to make this a little more personal,

  • should we just fly through the line?

  • Do you want to just say your name and your college and pass it on?

  • AUDIENCE: I'm Victoria.

  • And I'm in Trumbull.

  • DAVID MALAN: Victoria.

  • AUDIENCE: I'm Will.

  • I'm in Pauli Murray.

  • AUDIENCE: Elena, I'm in Hopper.

  • AUDIENCE: Andy, Berklee.

  • AUDIENCE: Adam, Morris.

  • [FAINT CHEER]

  • AUDIENCE: Tieger, Pearson.

  • [SCATTERED CHEERING]

  • AUDIENCE: I'm Valentina.

  • I'm at the School of Management.

  • AUDIENCE: I'm also Victoria.

  • I'm in Ben Franklin.

  • [SCATTERED CHEERING]

  • DAVID MALAN: Excellent.

  • Glad to have you all up here.

  • OK.

  • Oh, key step now-- if you could go put on what you have been given.

  • All right.

  • So hopefully, you did not sort yourselves.

  • Good, because then we'd have very little to talk about.

  • So I see a 7 an 8, a 4, a 3, a 5, a 2, a 6, and 1, all the way at the end.

  • So this invites a question.

  • How do we go about sorting these folks?

  • And now notice, it's actually deliberate that we gave them chairs,

  • because it turns out in a computer, you have stuff called

  • memory-- and more on that before long.

  • But memory is in specific, physical places,

  • just like our humans are in specific, physical places.

  • And for now, we're going to have each of them representing a number.

  • And numbers might take eight bits or more.

  • So each of them represents one or more bytes, but in a very specific location.

  • So I see that you're clearly out of order.

  • So why are they out of order?

  • What does it mean for these humans to be out of order?

  • Like, identify the problem, not just intuitively, but precisely.

  • Yeah, sure.

  • AUDIENCE: We randomly sat down.

  • DAVID MALAN: You randomly sat down.

  • OK, what does "random" mean, though?

  • AUDIENCE: It means you're not actually sorting as you go.

  • Like, you couldn't search and sort, but we just sat down.

  • DAVID MALAN: OK, you just sat down, so you're not sorted.

  • And just could you or someone clarify what would it mean to be sorted?

  • Yeah?

  • AUDIENCE: Like my location doesn't tell you anything relative to me.

  • So like, if we were actually in order, you'd

  • know that anything to the right of me is bigger than me.

  • DAVID MALAN: Good.

  • So what I'm hearing here is, rather than use words with which we're already

  • familiar, like random or such or sorted, now you've

  • given us a more formal definition.

  • Everyone to your left should be smaller, presumably.

  • And everyone to your right should be larger.

  • And because we have a 3 to your left and a 2 to your right,

  • it's not sorted because the 2 should obviously be somewhere to your left.

  • So let's just start at the beginning here.

  • So we see 7 and 8.

  • Are they in order?

  • Are 7 and 8 in order?

  • AUDIENCE: Which way we go, yes.

  • DAVID MALAN: Yeah.

  • The goal is, to be clear-- good qualification--

  • from smallest to largest, as before.

  • So 7 and 8 are in order.

  • I still have some problems ahead of me, but they're in order.

  • And there's really no work to be done here.

  • What about 8 and 4?

  • Are they in order?

  • AUDIENCE: No.

  • DAVID MALAN: No, so would it improve the situation if we had them kindly

  • swap seats?

  • AUDIENCE: Yes.

  • DAVID MALAN: OK, so yeah, if you wouldn't mind, 4 and 8.

  • All right.

  • And now let me keep going.

  • 8 and 3, are they out of order?

  • Yeah, so if you wouldn't mind as well.

  • And let's see, number 8 and 5, if you wouldn't mind, yet again.

  • I'm sorry, you're going to be doing this frequently.

  • 8 and 2.

  • [CHUCKLES] How about 8 and 6?

  • And sure, how about 8 and 1?

  • OK, thank you.

  • So, problem solved, right?

  • AUDIENCE: No.

  • DAVID MALAN: No.

  • But it is better.

  • In what sense is the problem better off now?

  • Yeah, 8 is where she should be all the way at the end.

  • So in some sense, I can kind of sort of ignore, if we could here,

  • number 8, because that problem is solved.

  • So the problem has gotten smaller.

  • It didn't get halved, per se.

  • This problem is not as easily solved as finding Mike Smith in a sorted phone

  • book.

  • But we did solve or chip away at this problem.

  • And honestly, if we repeat this process I

  • think it's going to kind of play itself out, right.

  • Because 7 and 4, if you wouldn't mind swapping.

  • And then 7 and 3, if you wouldn't mind swapping.

  • And 7 and 5, 7 and 2, 7 and 6, 7 and 1--

  • problem's getting better.

  • But notice, I'm using the same algorithm, right.

  • I'm just looking at two people side-by-side.

  • And if they're out of order, swap them.

  • And it's taking me some time.

  • I've got to do it again.

  • But notice, I don't have to go all the way back.

  • I can kind of focus now, if you will, only on these six problems.

  • So 4 and 3, if you could swap.

  • 4 and 5.

  • Oh, oh, oh.

  • That was a bug.

  • OK, you're good.

  • 4 and 5.

  • OK, but 5 and 2-- out of order.

  • 5 and 6-- nope.

  • OK, 6 and 1.

  • 6 and 1-- out of order.

  • And now 6 is in place.

  • And let's go ahead and just kind of fast forward just

  • so we can get the dramatic reveal.

  • What should you look like if we just expedite this algorithm in the end?

  • [LAUGHS]

  • OK, correct.

  • But what was that algorithm?

  • Right, and so there's a difference.

  • Like, all of us have a nice intuition for solving the problem.

  • But if you really sat down and tried to explain

  • to like your younger sibling, who's really young cognitively

  • and just needs to be told exactly what to do

  • or you tell a robot these days exactly what it needs to do,

  • you really need to distill this intuition

  • into something a little more formal.

  • And that was just one way.

  • But it's probably not the only way.

  • In fact, could we do one thing?

  • Could you just randomize yourself?

  • And I'm not technically giving you a definition of that,

  • but odds are we'll just kind of make a mess of the problem

  • and we somehow have an intuition for that too.

  • But just to point out that there's not one solution to a problem.

  • And we won't even touch on all of them today.

  • You know, I claim there's an easier way, or there's at least

  • another way to solve this problem.

  • You know what, I could just look through the numbers--

  • my goal being smallest to largest--

  • let me just find the smallest element and not walk back and forth like I was,

  • swapping things, which felt like it was messy and it worked out.

  • But it just felt like a lot of labor.

  • So, all right, 6 is pretty small so far.

  • 7, you're bigger than that so--

  • oh, 5 is better.

  • Ooh, 2 is really good.

  • 3, 2, 1.

  • OK, I found the small--

  • oh, I should double check.

  • Yep, I found the smallest element.

  • So if you wouldn't mind standing up.

  • Where does she belong?

  • Yeah, all the way over.

  • Yeah, could you?

  • So I can just move my data around.

  • And if number 1 wouldn't mind coming here.

  • Now, I've coincidentally made part of the problem

  • better by moving a bigger number up.

  • But I really just needed to make room.

  • But I could have done that differently.

  • And actually, would you mind swapping back for just a second?

  • I don't have to just summarily evict number 6 to make room.

  • What else could we have done that might have been a little more polite?

  • Or if you imagine being in the theater or a movie.

  • Yeah, I saw this.

  • So if number 1, could you sit up?

  • And then if you guys wouldn't mind, could you just kind of scoot down?

  • This would be another way to do.

  • Which of those ways is better?

  • The first way or the second way?

  • You say first, why?

  • AUDIENCE: It uses less memory.

  • DAVID MALAN: Uses less--

  • well, I still see eight people and eight seats.

  • So I don't think I've added any seats or people.

  • AUDIENCE: The number of operations is less.

  • DAVID MALAN: Operations.

  • The number of operations would seem to be less,

  • because even though you're independent beings,

  • there was just more physical effort going on.

  • And frankly, I think my first instinct of just swapping 1 and 8

  • was just a little more efficient.

  • But it's no more correct than this approach.

  • So again, there's this tension between correctness, perhaps, and efficiency,

  • such to gain a bit more efficiency, you just

  • have to think a little harder about the problem and try to avoid doing work.

  • And so that's kind of the great irony.

  • By pouring more time into writing your code or writing your algorithm,

  • can you over the long run really solve problems and save time significantly?

  • So if we could, a round of applause here for our volunteers.

  • [APPLAUSE]

  • And if you want to go off that way.

  • [APPLAUSE CONTINUES]

  • You're welcome to keep the shirts.

  • We just need the numbers back.

  • And so where are we going to go from here?

  • So again, we can we started the story by talking about data and representation--

  • just 0s and 1s--

  • and all of a sudden we're solving problems algorithmically.

  • It turns out those algorithms have fancy names.

  • The first one, most people call "bubble sort"--

  • "bubble sort" in the sense that the big numbers kind of sort of bubble

  • up from left to right, making their way to their correct position.

  • The other one is called "selection sort,"

  • typically, because what I was doing was selecting the smallest element.

  • And even though we didn't finish that story

  • because it would get pretty tedious pretty quickly,

  • the next step would have been, let me find the number 2

  • and put him or her in place.

  • Then let me find 3, put it in place, and so forth, iteratively,

  • again and again selecting the next smallest element.

  • And thus far, we've done this very physically.

  • We've done this very verbally, very much in pseudocode, if you will,

  • like the text here on the board.

  • And so this class is ultimately going to be about expressing these same ideas,

  • but using languages-- language is called Scratch, which we'll soon see,

  • which is actually a graphical programming

  • language via which you program by dragging and dropping

  • things that look like puzzle pieces that you can only

  • lock together and interconnect and make things like a cat move up,

  • down, left, right, or meow on the screen if it makes logical sense to do so.

  • But then very quickly, we're going to transition

  • to a more traditional but more textual language called C, where you actually

  • type code that is just English-like syntax that's

  • even more succinct and more terse than my pseudocode code before,

  • but it has a formal definition.

  • And it's way smaller than English.

  • So in some sense, even though the logic might take time

  • to get comfortable with and remembering the syntax might take time,

  • there's far fewer words and symbols in a language like C

  • than there is in English or whatever your native language is.

  • So realize that even though it might look like the proverbial Greek to you

  • at first, it will get more familiar quite quickly.

  • From there, we'll introduce languages like Python,

  • with which you might be familiar at least in name-- very much in vogue

  • and allows you to solve problems in the data sciences

  • and medical sciences and arts and humanities.

  • If you have data that you want to manipulate

  • or images that you want to analyze or text that you want to search,

  • we'll introduce JavaScript, a language that's

  • used on browsers typically these days to actually make more interactive user

  • interfaces and make better experiences for users

  • and help him or her actually navigate data or actually access information

  • or content that you want to provide to them.

  • SQL or "Sequel," this is a database language.

  • Once you've got some really juicy data or lots of users, lots of customers,

  • or lots of medical records that you want to search,

  • turns out you can use another language called SQL to actually search that data

  • and store it more efficiently.

  • And so rather than be a course about just one language or one way of solving

  • problems, well, we give you this whole arsenal

  • via which you can actually solve problems using the best tool ultimately

  • for the job.

  • We'll start by writing the simplest of programs next time,

  • whereby you'll literally drag a couple of puzzle pieces--

  • an orange one and a purple one.

  • They'll lock together.

  • You'll click Play.

  • And the cat will meow.

  • And we'll very quickly do more.

  • And a week after, you'll be doing something very similar in spirit,

  • but much more powerfully.

  • And ironically, we're going to take away the menus and the buttons

  • and all the graphical components with which you're

  • very familiar on Macs and PCs, expose you to things like a terminal window,

  • so to speak-- a black and white window that's just text,

  • but at which you can actually start expressing yourself

  • more succinctly, more powerfully.

  • It's just going to be a bit of a learning curve

  • until you get comfortable with what otherwise is an unfamiliar space.

  • In just a moment, we'll adjourn outside where the TAs and staff have

  • a wonderful assortment of cake for you, as is our tradition,

  • so that you can say hello and ask any questions of us.

  • There's no homework this week until next,

  • but we thought we would end with something more visual.

  • I'm going to go ahead and dim the lights for our final 60 seconds

  • here, because even though we focused today on ideas and on problem solving,

  • it turns out, that as Natalie and Benedict introduced,

  • there's a whole team around you and a whole academic and social support

  • structure that we've captured here thanks to last year's students

  • in both New Haven and Cambridge.

  • [MUSIC - P!NK, "WHAT ABOUT US"]

  • SONG: (SINGING) What about us?

  • What about us?

  • La da da.

  • We are searchlights, we can see in the dark.

  • We are rockets, pointed up at the stars.

  • We are billions of beautiful hearts.

  • And you sold us down the river too far.

  • What about us?

  • What about all the times you said you had the answers?

  • What about us?

  • What about all the broken happy every afters?

  • What about us?

  • What about all the plans that ended in disaster?

  • What about us?

  • What about trust?

  • What about us?

  • What about us?

  • What about us?

  • What about us?

  • What about us?

  • [MUSIC CONTINUES]

  • DAVID MALAN: All right.

  • That's it for CS50.

  • We'll see you next week.

  • [CHEERING AND APPLAUSE]

BENEDICT BROWN: This is CS50.

字幕と単語

ワンタップで英和辞典検索 単語をクリックすると、意味が表示されます

A2 初級

CS50 2018 - イェール大学の講義0 - 計算思考 (CS50 2018 - Lecture 0 at Yale - Computational Thinking)

  • 44 0
    小克 に公開 2021 年 01 月 14 日
動画の中の単語