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  • [APPLAUSE]

  • MARLYN MCGRATH: Welcome back to Sanders Theatre for this afternoon's show,

  • "Hold That Thought" show.

  • I'm Marlyn McGrath from the admissions office accompanied

  • by four stars on our faculty who volunteered

  • because they're eager to welcome you to Harvard and to entertain you.

  • Some of you-- students, anyway-- might know the wonderful Richard Scarry

  • book for toddlers, if you can remember that far back,

  • What Do People Do All Day?

  • This is a version of that.

  • It's also, by the way--

  • I should note-- a version of the thing that the admissions committee does.

  • We figure we spend a lot of weeks, months, fall, winter, trying

  • to figure out who you are.

  • Who is this person?

  • You get some chance to see who some of the other people at Harvard

  • are today-- the faculty who are responsible, really,

  • for the whole program that you would experience if you came.

  • You already know already, I hope, that no one here-- no one in my staff,

  • no one in our faculty, et cetera, is trying to-- "no one" is a strong word,

  • but anyway, no one is trying to pressure you into choosing Harvard.

  • You've got other great choices.

  • You're not going to make a mistake.

  • You never have.

  • You never told us you did.

  • [LAUGHTER]

  • This is gravy.

  • You're not going to make a mistake.

  • Harvard's a great place.

  • So are a lot of other wonderful places.

  • You would not be thinking about them if they were not.

  • But of course, we really, really want you to come.

  • And so our strategy for this is what we want

  • is for you to want to come to Harvard.

  • That's our-- we think-- much nicer segue into this.

  • And what we think that ought to mean is that you

  • would conclude, at the end of the weekend,

  • that Harvard would be a lot of fun.

  • And so much talent is represented in this room,

  • it's fairly daunting, actually, to stand up here

  • in front of all of the talented people in this room, who we all

  • hope you'll use those talents in new and unanticipated ways.

  • Things you have not yet thought about.

  • Things that won't have occurred to you.

  • Things that you might, along the way in college, think of.

  • And that means finding out what will give you fun, actually.

  • I can't say that loudly enough, so I won't try, but give you

  • fun, pleasure, and satisfaction.

  • Don't assume you know that now as you enter college, Harvard or otherwise.

  • But both to amuse you and to confuse you,

  • which is the very, very Harvard thing to do--

  • to amuse you and confuse you.

  • If you like that, Harvard is a great choice for you.

  • If you don't like amusement and confusion, think.

  • You still got time.

  • [CHUCKLING]

  • Our faculty colleagues will can you glimpses anyway

  • of what they do all day.

  • And some glimpses, I think, of who they are anyway.

  • And we hope you'll have fun watching them have fun.

  • So without further ado, I will introduce the first act,

  • which would be by Professor Robert Lue, whose

  • talk will be called "Solving Global Challenges Through Collective

  • Learning."

  • Well, who is he?

  • He is, among other things, professor of the practice

  • of molecular and cellular biology.

  • He's the faculty director of the Bok Center for Teaching and Learning.

  • And he's the faculty director of the Harvard Allston Education Portal.

  • Hold that thought.

  • HarvardX.

  • Lots of online learning.

  • He went to high school--

  • I try to remember high school's for everybody, key thing here--

  • at St. George's College in Kingston, Jamaica.

  • His PhD is from Harvard, and he's taught our undergraduate courses since 1988.

  • He's very well-known also--

  • hold this idea too, because none of these people

  • has ever stayed in his or her lane intellectually--

  • he's also known for his passion for art, and merging that interest

  • with cellular biology.

  • So without further ado, having said I would not do this without further ado,

  • you can now hear from Professor Lue, "Solving Global

  • Challenges Through Collective Learning."

  • [APPLAUSE]

  • ROBERT LUE: Thanks, Marlyn.

  • So let me add my words of welcome.

  • I'm sure that you have been welcomed more times than you can count.

  • But I must welcome you to Harvard, and your thinking

  • and your experiencing of what a Harvard life might be like.

  • But what I'd like to do is perhaps help us think a little bit differently

  • about the kinds of learning experiences that

  • is possible in a setting like Harvard, and also, in any setting

  • that one might imagine.

  • So you've probably heard a lot already about Harvard courses, concentrations,

  • things that you will experience here.

  • But what I would argue is that, without question,

  • while what you experience here will be absolutely

  • critical to your own learning, we now live in a world where what you learn

  • can indeed be something that can be a major contribution to what someone

  • else learns thousands of miles away from you.

  • So I'm a cell biologist.

  • But for a number of years, I've been very interested in this challenge

  • of personalized learning at scale.

  • And what is the role of a university like Harvard in doing this?

  • And how can this sort of challenge really change

  • how you think about your own time here at an institution like Harvard?

  • So as some of you may know, in 2012, 2011,

  • there was a lot of discussion around what we called MOOCs--

  • massive open online courses.

  • I suspect that some of you have even taken

  • some massive open online courses, perhaps from Harvard

  • as well, from HarvardX.

  • But one of the critical aspects of this is that Harvard partnered with MIT

  • to develop a platform called edX.

  • The notion was that we really wanted to share broadly

  • with the world learning content from top universities around the world,

  • but to make it much more accessible.

  • But what did we do?

  • We made courses.

  • Things that were 10 weeks long.

  • 12 weeks long.

  • 8 weeks long.

  • 6 weeks long.

  • So we started off with a traditional notion of how you learn,

  • which is through a course.

  • So fast forward to now.

  • After I founded and built HarvardX, what we now realize

  • is that, in fact, courses are incredibly important.

  • Don't get me wrong.

  • You will have amazing courses here.

  • But there are other ways in which you can learn that give you more agency--

  • the ability to personalize in ways that perhaps we didn't have before.

  • So if we want to make personalized learning more available,

  • how do we do this?

  • What platform do we have?

  • Well, one of the critical aspects of edX compared to any other course platform

  • online is that we're open-source.

  • We're free.

  • So what that means is that there's something called Open edX.

  • And you see a bunch of numbers and words there.

  • Open edX and edX together now accounts for roughly 60 million learners

  • have engaged with the platform around the world.

  • There are more than 1,300 organizations, ranging from universities like Harvard

  • to Amnesty International, the World Economic Forum, Microsoft, Google.

  • A whole variety of organizations use the platform.

  • All countries have been touched and have access to the platform.

  • And so what this means is that we are currently

  • the largest open-source learning platform in the world.

  • So you're probably thinking, well, I'm trying

  • to figure out how I feel about Harvard.

  • I'm looking inside.

  • Well, what I'm going to try to urge you to do

  • is to, at the same time that you're looking inside, look outside as well,

  • and what you might be able to do in that regard.

  • So what we have done is that we are now building the next generation of the edX

  • platform--

  • once again free, once again open-source--

  • in a project that I'm hearing called LabXchange.

  • And what makes it next generation is that if you

  • think about the amount of learning content out there--

  • and I know that you have seen a lot of things--

  • literally tens of millions of individual assets have been created.

  • Probably hundreds of millions of dollars have been spent.

  • And what you have are a multitude of courses

  • that have videos, that have text, infographics, simulations, animations,

  • all of those things.

  • But all of them are locked in courses.

  • And so you need to decide, OK, if this is what I want,

  • I need to jump in, somehow find it, take what I want, and then jump back out.

  • Or, do I have time to spend 12 weeks doing something online?

  • What LabXchange has done is completely re-architect the core of the edX

  • platform so that now everything is combined into a common repository where

  • the course is no longer the unit size, but any learning

  • asset can be searched for, found, and utilized for your own purposes.

  • So that imagine this remarkable library, and a library where you now

  • get to pick what you want from it.

  • From a course at Harvard, a course at MIT, a course at Stanford,

  • or some kind of open educational resource from Amnesty International,

  • you can now bring it all together and put it together

  • in a sequence of your own choosing.

  • You can then add your own stuff to it.

  • So let's say you're interested in studying the impact of changing water

  • quality on a particular organism that's important to you,

  • or that's local to you.

  • You can take your own research, your own data that you might have gathered,

  • and you can add this to what we call a pathway.

  • Now, just putting stuff together doesn't tell a story.

  • We all know that learning depends on narrative,

  • and being able to tell a story.

  • So what the Xchange does is allow you to add sort of interstitial material

  • that lets you tell that story.

  • So this allows you to personalize learning experiences for yourself.

  • But this also allows you to personalize learning experiences for others.

  • And this is where the collective learning at scale occurs.

  • We are accustomed to sharing the products of our learning at best.

  • We share the outcome of what we have learned.

  • You want to make something, you want to do something, you put things together,

  • you figure it out--

  • I know you've all done this--

  • and you end up with something at the end.

  • It might be a physical product, an intellectual idea, a proposal-- any

  • of those things.

  • And if you're lucky, maybe you can share that with the world.

  • But how often do we get to share how we got there?

  • Learning is not just the product.

  • Learning is also the process.

  • So for the first time, what we'll be able to do

  • is take what you have brought together, take the narrative

  • that you have created to do something, and now you can share that.

  • We all stand on the shoulders of others, and we all hope--

  • I think-- that others will stand on our shoulders

  • some day to do something great.

  • Now, there's an opportunity to stand on how

  • others have learned to do something.

  • So it's both the process as well as what the outcome might be.

  • So what this allows us to do now for the first time is give a platform where

  • individuals that are interested in doing something--

  • to make a difference, to build challenges,

  • to address challenges in some way--

  • can now figure out what materials they need, utilize them, and share

  • not just the outcome of their ideas, but what they learned.

  • And that these pathways, as we call them,

  • are something that an individual can share,

  • a high school teacher can share with her class,

  • a college professor can share with his or her class.

  • It is now a situation where we have opened up and cracked open

  • the process of getting to where we need to go.

  • So the world is a better place now in many ways

  • than it was 20 years ago, 50 years ago, 10 years ago.

  • But challenges remain, as I don't need to tell you.

  • This is an opportunity for us to connect individuals across the world

  • to allow them to address challenges.

  • So right now, 50 undergraduates are working

  • with me building LabXchange, building content for LabXchange with another 30

  • graduate students.

  • This is one of those places where we are not only

  • thinking of students as recipients, but you're

  • agents in building the possibilities that we

  • hope to make available to the world.

  • And the notion is that, in time, every single student that

  • does a fantastic summer research project in biology, in physics, in visual art,

  • in government, in economics will have the opportunity

  • to put together how they got there, and to share what they created.

  • All tagged, all searchable, all findable so that someone can stand

  • on your shoulders when the time comes.

  • So these nodes, as we sometimes call them, are really important.

  • How do we connect these kinds of things?

  • And so one thing we've done is to try to create an example of what

  • is an innovation node that will take advantage of the platform

  • to share ideas and proposals for a better world with the world?

  • So there is a summer program that I run in Paris called The Biopolis.

  • It's focused on biology and social innovation.

  • And I won't go into all the details of what it does,

  • but what it does in part, in its simplest form,

  • is bring Harvard students and French students

  • from Sciences Po and the University of Paris

  • to use Paris as a laboratory to really interrogate ways in which life

  • in an urban setting can be better.

  • The first time I suggested this program, colleagues teased me and said,

  • you just want to spend a bunch of weeks in Paris.

  • [CHUCKLING]

  • I'm like, well, you try having 48 students with you.

  • That's not exactly a vacation-- even though it

  • is remarkably rewarding for everyone involved, I think.

  • But what is important here is that Paris is one of-- in some ways-- the most

  • contradictory cities.

  • It is a museum city.

  • It is beautiful.

  • It's a tourist destination.

  • It is also profoundly unequal.

  • It is in turmoil.

  • And I think now we understand, with the yellow vest movement,

  • just how in turmoil it is.

  • So it presents a setting that in some ways

  • is so contradictory and so complex.

  • What better laboratory do we have for students to work on making lives better

  • in a particular place?

  • The version of this in Boston will be launching quite soon

  • with both cities being together.

  • So we have done this now for four years.

  • There are close to 50 design plans.

  • And many of these plans-- so there are at least eight start-ups

  • have come from this.

  • And a multitude of awards for the proposals have happened.

  • One I will talk briefly about is BubbleBox.

  • BubbleBox was developed by a team of Harvard students and Sciences Po

  • students.

  • And what BubbleBox does is ask the question, in a city like Paris where

  • refugee encampments are not allowed, where they are all ad hoc,

  • where they have to move from place to place because they are frequently

  • displaced from where they set their tents up, how

  • do you deal with issues of hygiene, showering, laundry, all of that?

  • So the team came up with an idea to take a shipping container,

  • convert it into a truck that's entirely self-contained--

  • water tanks, solar panels, a shower loop, laundry.

  • All of it is contained in this box that is self-powered.

  • And instead of thinking about building a center where the refugees go,

  • this will go where the need is greatest.

  • How do you fund this?

  • You fund it by actually renting BubbleBox

  • to large music concerts in Europe and elsewhere.

  • So the government of Jordan is building BubbleBox now,

  • and the team won the Paris Talent 2024 international competition

  • for innovation.

  • So they won more than 30,000 euros to actually build this.

  • So BubbleBox is in process.

  • This is the kind of thing where you come here to make a difference,

  • to do something like this.

  • You have a way of connecting with others to make this happen,

  • and we really want to facilitate that for you as much as possible.

  • So the hope is that you will contribute to a growing core of resources

  • to really make the world a better place.

  • That The Biopolis focuses, for example, on the Sustainable Development Goals

  • from the United Nations, particularly good health and well-being, education

  • and partnerships.

  • But if you haven't looked at the SDGs before, I recommend you do,

  • because there are 17 of them that articulate key challenges

  • that the world needs to face.

  • We have a decade to meet these challenges.

  • The goal from the UN is to meet them by 2030 as best as we can.

  • And our hope is that more and more Harvard students

  • can partner with others around the world to build new ideas,

  • share what they're doing, and bring many more concerned minds into the dialogue

  • and into the build of what we need to make the world a better place.

  • So in the past, quite often, both individuals and organizations

  • competed and got ahead based on building the best silo.

  • If you had the best knowledge silo, you're more competitive.

  • You'll get ahead.

  • That is your advantage.

  • Those days are over.

  • We no longer live in a world of knowledge silos.

  • What is critical is the flow of knowledge.

  • It's not holding everything to yourself.

  • It's connecting with others where you are, but also across the world.

  • So our hope for all of you is that we will provide you with the opportunity

  • to not just be here, but to connect with the world

  • to do things that is not simply broadcasting to the world,

  • but is networking and really making a difference,

  • both in your own development, but also in solutions to make the world

  • a better place.

  • So welcome to Harvard once again, and thank you.

  • [APPLAUSE]

  • MARLYN MCGRATH: Rob, thank you.

  • In our ongoing variety show, we will now have something completely different.

  • As we always do, one thing is always different from another,

  • so this is a shift gears, as you'll do each time.

  • Now I have the pleasure of introducing our colleague Melissa

  • Franklin from the physics department, Mallinckrodt Professor of Physics.

  • She's an experimental particle physicist who

  • studies proton-proton collisions produced by the Large Hadron Collider.

  • I hope I said that all right.

  • I told you that I would try to remind you or tell you who people were

  • starting from high school, at least.

  • Melissa went to Jarvis Collegiate in Toronto for grade 9.

  • Hold that thought too.

  • She was one of the first 100 students at a free school held in the basement

  • of the YMCA, where she spent a couple of schools before decamping and going

  • to London to attend the Lycée Francais de Londre.

  • She has no high school diploma.

  • We don't actually require a high school diploma.

  • It turns out that she has an honorary high school

  • diploma-- as I gather-- from the Science High School in Worcester.

  • There are many paths to being a particle physicist and many other things.

  • She does have a Bachelor of Science from the University

  • of Toronto and a doctorate from Stanford,

  • entirely accredited place in the West Coast.

  • [LAUGHTER]

  • She's worked at Lawrence Berkeley Lab.

  • She's worked at lots of places in an incredible exciting work that always

  • turns up in the newspaper and we gasp.

  • She is the first woman to earn tenure in the Harvard Physics Department.

  • I'm sure there are stories there.

  • This is not the topic of today.

  • She was part of the teams that discovered

  • the top quark at the Fermilab and Higgs boson at CERN.

  • She will speak to us.

  • Her title-- and you, by the way, also have equipment for this event--

  • is "Measuring a Universe with Nothing in It."

  • So I give you Melissa.

  • [APPLAUSE]

  • MELISSA FRANKLIN: Hi.

  • You know, they don't usually let me up here.

  • [CHUCKLING]

  • But when they do, there's people sending paper airplanes at me

  • during the Ig Nobel Prize ceremony, which takes place every year,

  • and I'm sure some of you will attend.

  • Hi.

  • I can't see you, but I know you're young.

  • [CHUCKLING]

  • You have some glasses, and those are sort of diffraction grating glasses.

  • You don't have to--

  • I just want to say, if you get bored with what I'm saying,

  • just start looking up there, because it's really just very, very relaxing.

  • [CHUCKLING]

  • But later, we're going to actually use them for a demo.

  • But to begin with, I just want to tell you, I'm very interested in the vacuum,

  • in measuring the universe with nothing in it.

  • So I guess I should get the clicker.

  • So this stuff-- the apple, all that virus, I'm not interested in that

  • at all.

  • It's stuff.

  • I get that out of my universe.

  • Now, here's an atom.

  • The atom has a nucleus, and it has electrons.

  • And the nucleus is made up of protons and neutrons, which have quarks inside,

  • which I'm sure you know.

  • And I'm interested in the quarks.

  • I really like quarks.

  • But I'd like to have the universe without any atoms in it.

  • Here is my world.

  • So if you think about me, my name is Melissa.

  • You would look at the quarks.

  • All the quarks that exist in the universe that make up all the matter,

  • and all the leptons--

  • electrons, et cetera, the neutrinos--

  • and all the forces that hold all those particles

  • together to make matter, and black holes, and stuff.

  • [CHUCKLING]

  • Here's what you would find.

  • And unfortunately, I'm really old, but--

  • I was not a part of finding the charm quark, the c quark.

  • And I was not a part of finding the bottom quark, but almost.

  • But after 25 years of trying, I was on the team that found the last quark.

  • You can't find one.

  • It's over.

  • [CHUCKLING]

  • There's only six.

  • So I was on that team.

  • And then I was also on the team recently that discovered the Higgs.

  • And I wanted to tell you what I'm interested in,

  • and why we were looking for the Higgs, and what it meant to me.

  • So here is what's called the standard model.

  • Those are all the particles and the forces.

  • And if you're a theorist, and you have soft skin and stuff--

  • I'm an experimentalist-- you would write this equation down, and you would say,

  • this is the standard model, and this describes the universe.

  • But people like me don't really--

  • it doesn't fit inside my head.

  • I like reading it aloud.

  • When you go home, you could try reading equations aloud.

  • It's fun with friends.

  • It's very fun.

  • There must be a game.

  • It's not a drinking game.

  • It's more of a just good fun game.

  • So here's the thing.

  • For each of these terms in this equation--

  • the way experimentalists like to think about it is a diagram.

  • And this is a Feynman diagram.

  • There's a guy called Feynman, and this is his diagram.

  • And a diagram takes one of the terms in that equation and says,

  • let's see what it looks like if we're human.

  • And so here, for instance, time is going along to the right.

  • And what it's showing is matter and antimatter electrons come together,

  • annihilate into light, which then turns into antimatter and matter muons.

  • These are just heavier particles.

  • And we say, oh.

  • Ha.

  • I can write this down.

  • Can I measure it?

  • So that's sort of my life.

  • I can write down every possible diagram like this and try and measure it.

  • Now, for the people interested in archeology,

  • you might want to understand Feynman diagrams, because 1,000 years from now,

  • after everything happens, probably, you'll

  • find diagrams like this, just sort of like hieroglyphs.

  • And you'll probably understand them.

  • Could be sooner than 1,000 years.

  • It could be-- OK.

  • But I'm just saying.

  • I'm just saying.

  • People who are interested in linguistics or stuff like that, just look at that,

  • and don't just not think about it.

  • OK, here is me.

  • When you're in science, you have a lot of thoughts about yourself,

  • who you are.

  • Here's the top quark on my shoe.

  • That's me.

  • But as an experimentalist, I can make me a line drawing,

  • and it has just as much information.

  • So this is the real me on the left, and before children, and the right me.

  • [CHUCKLING]

  • The me that-- it's the spiritual.

  • For those interested in religious studies, this is the spiritual me.

  • So I want to describe the vacuum.

  • I want to describe the world with nothing in it.

  • I take everything out.

  • Is there something there?

  • I'll give you a hint.

  • Yes.

  • But it's kind of an interesting idea.

  • And if you're a literature person, you will

  • see that Samuel Beckett thought about this a lot.

  • Samuel Beckett starts with two people and nothing else--

  • Waiting for Godot.

  • And then he goes to Murphy, which is just a guy

  • strapped to a chair sitting alone.

  • And then The Unnameable, which is nobody, really.

  • So in literature, we discuss this idea of the vacuum.

  • And the Samuel Beckett, if you haven't read him, then you can start tomorrow.

  • And so if I want to understand the vacuum-- so there's nothing there--

  • what do I do?

  • So I want to tell you one thing.

  • And if this is the only thing that you remember, it's this.

  • The ground state doesn't talk to us.

  • So what do I mean?

  • The lowest energy state of anything doesn't say anything to us.

  • It doesn't reveal what it is.

  • And I want to do a demo with my friend Daniel Davis to show that.

  • So do we understand the ground state?

  • The lowest energy state is just there, like a lump sitting on a chair.

  • And you can't tell anything about that lump.

  • So to begin with, put on your glasses, and pull down the house lights,

  • and rock and roll.

  • So what we're going to show--

  • so these glasses are diffraction grating glasses, and they will act like a prism

  • and separate all the colors that are coming out.

  • So right now, what you should see from an incandescent light

  • is a spectrum of the rainbow.

  • Do you guys see it?

  • Look a little to the right or to the left.

  • AUDIENCE: Yes.

  • MELISSA FRANKLIN: Yeah?

  • OK.

  • Now, next to it, we have something which is just hydrogen gas.

  • Hydrogen gas, normally, you can't see anything.

  • Now what do you see?

  • Do you see two lines, or three?

  • AUDIENCE: Three.

  • MELISSA FRANKLIN: OK.

  • So what we're doing is we're exciting the atom because we're putting

  • an electrical current through it.

  • So I'm just saying, I don't want to just look

  • at hydrogen. I want to put electrical current through it.

  • And then I can see its nature.

  • I can see about its structure by looking at those lines.

  • And then if I look at the next one down, I'm

  • going to put an electric current through helium.

  • Isn't it beautiful?

  • Do you see the lines?

  • Is anyone thinking, I don't know what you're talking about?

  • [CHUCKLING]

  • No?

  • So helium is a different atom.

  • So you can see the structure of helium by the light it gives off.

  • And the final one is neon.

  • AUDIENCE: Whoa.

  • MELISSA FRANKLIN: [CHUCKLES]

  • I love this.

  • I love demos.

  • Daniel also loves demos.

  • OK.

  • Thank you.

  • OK.

  • So you're saying, what does that got to do with anything?

  • Not really anything.

  • Doesn't really have anything.

  • [APPLAUSE]

  • OK.

  • It doesn't have anything to do with anything, but here's the thing.

  • I want to understand the vacuum, but I'm going to have to excite it, OK?

  • If I want to understand the structure of the vacuum,

  • I'm going to have to excite it.

  • So there was this guy called--

  • this is a theorist guy, those are the cute ones--

  • called Peter Higgs.

  • And he solved this theoretical problem.

  • And in order to solve the problem, he had

  • to introduce something called the Higgs field.

  • So let me just say, this is how we understand the Higgs field.

  • Remember the Lagrangian?

  • Remember that equation?

  • If to that equation of the standard model

  • you add what I'm going to call a Higgs field, and I'll tell you what it is,

  • and you put it through a machine, what you will come out

  • is a Higgs boson, which is a particle.

  • And then all the particles in the universe will have mass,

  • and everybody will be happy.

  • But the problem is, this is what a theorist would draw,

  • but I'm the person who has to build that machine.

  • So that machine takes the Higgs field and puts an electric current

  • through it.

  • So what's a field?

  • Is this too boring?

  • Are we boring?

  • No, we're not boring.

  • OK.

  • So this is a wind map of America.

  • And at every point there, it shows the strength of the wind by how white

  • it is, and the direction.

  • So at every point in the world, you can imagine a field tells you

  • the strength and the direction.

  • So if it's a gravitational field, it should tell you

  • how fast you should fall, and in what direction.

  • So imagine that I have--

  • so let's go back one step.

  • So this is the wind field.

  • If I want to excite the wind field somehow,

  • I would get something like a tornado.

  • So an excitation of the wind field would be an amazing amount of energy in wind,

  • like a tornado.

  • So what I want to do is I want to take the Higgs field, which I can't see.

  • And the Higgs field has no direction.

  • And it has no size, so you cannot feel it in any way.

  • I want to take that, and I want to make a tornado.

  • And then I want to--

  • that's my whole life.

  • [CHUCKLING]

  • Actually, it doesn't seem as important as the last speaker.

  • So when--

  • [CHUCKLING]

  • I was thinking, I shouldn't even come up here, really, because--

  • but then I thought, OK.

  • OK, Melissa, it's going to be fine.

  • And I knew that my friend Daniel was here.

  • OK.

  • So here's what we want to do.

  • In order to make an excitation of this field--

  • and I don't even know if it's there--

  • I just need a whole bunch of energy in a very short amount of time.

  • And so what I do is I take a lot of protons,

  • and I collide them together at very high energies,

  • and I'm putting a huge amount of energy into a tiny little space

  • in a tiny little time.

  • And I use my theory that I learned from going to college--

  • I did go to college.

  • [CHUCKLING]

  • I didn't get a physics degree, though.

  • I just want you to know that.

  • Although it might say that my CV.

  • [LAUGHTER]

  • What I want to do is I want to take that Feynmann dagger,

  • and I run it right down the diagram that can actually

  • make a Higgs boson by making all this energy in a really small place.

  • And I say, oh, yeah, I can draw this, because the theorists say I can.

  • And then I just have the LHC--

  • the Large Hadron Collider-- and I just push the button, and this happens.

  • Protons collide.

  • And so what's really happening--

  • I'm walking around a lot.

  • So what's really happening is that about 100 billion protons hit 100 billion

  • protons every 25 nanoseconds.

  • So nano is small.

  • [CHUCKLING]

  • Yeah, it's really small.

  • Every 25 nanoseconds.

  • So 25 nanoseconds is like the amount of time it takes light to go 25 feet.

  • I do that.

  • Protons are going to collide.

  • The quarks inside the protons are going to collide.

  • I can make my Higgs boson one time out of every 10 to the something or other.

  • 10 to the 10 trillion.

  • 10 trillion.

  • I sound like that guy in the bad, bad movie.

  • Anyway--

  • [LAUGHTER]

  • If I can do this, and I can do it like for two years,

  • I can probably get enough Higgs bosons that I can say, I excited the field

  • and I actually got a boson out.

  • There must be a field there, right?

  • And so all I have to do is build a 27-kilometer accelerator

  • in Switzerland.

  • And then hire maybe--

  • I don't know-- 20,000 people.

  • And then I have to build a detector to see what

  • comes out of these proton collisions.

  • And this is the detector.

  • And you'd think those people are really small, but they're French.

  • [CHUCKLING]

  • So you have to--

  • obviously, French people are the same size.

  • But--

  • [CHUCKLING]

  • --the point is, when you're working on this detector,

  • you actually sometimes get a little--

  • you should go to the bathroom first.

  • Anyway, it's very, very tall.

  • It's very tall, so when you're working up at the top, it's a little scary.

  • Anyhow, we built this detector very fast.

  • Sorry.

  • I know that-- and this comes out.

  • All of a sudden, protons, quarks collide.

  • Whole bunch of stuff comes out, and our whole lives for the next five years

  • is just figuring out what happened.

  • What happened?

  • What happened?

  • OK.

  • So we waited two years of taking data every 25 nanoseconds.

  • And we weren't allowed to look at the data.

  • And the reason is, if you're going to be studying psychology,

  • then you know that [INAUDIBLE] said that humans are very bad at statistics

  • naturally.

  • So don't trust yourself.

  • So what we do is we blind ourselves.

  • We don't actually-- we don't look at anything.

  • We don't look at the data for two years.

  • And then all of a sudden, one day, we make a plot.

  • And we make a plot of the mass of the Higgs boson,

  • or what we think it might be, and the number of events,

  • and we see something-- the red thing there--

  • that wouldn't be there if there wasn't the Higgs boson.

  • And we go, wow.

  • This is not exciting.

  • [CHUCKLING]

  • OK.

  • But you're saying, wow, that's not exciting.

  • OK.

  • Let's just talk about this.

  • My team is 3,000 people.

  • It's not my team.

  • I'm not the boss.

  • Otherwise, I wouldn't-- yeah.

  • [LAUGHTER]

  • Yeah.

  • I'd probably-- yeah.

  • My team is 3,000.

  • There's another experiment that's 3000.

  • You gotta check each other.

  • That's about the whole Harvard undergraduate class.

  • Imagine that everybody in the whole class--

  • like not just 1, 2, 3, 4, all of you--

  • were all working on the same project.

  • That would be weird.

  • It's a lot of people, so I don't even know who I am, unfortunately.

  • And this is how I feel afterwards.

  • [CHUCKLING]

  • Now I know everywhere in the universe-- everywhere in the universe--

  • there's a Higgs field that I can't touch.

  • But I know it's there intellectually, so I kind of feel weird as I'm walking.

  • And a lot of my colleagues feel weird also.

  • So I just wanted to tell you two more things.

  • Should I stop?

  • Because I think-- no?

  • It's OK?

  • AUDIENCE: Keep going.

  • MELISSA FRANKLIN: So you're thinking, that's a weird thing to do, Melissa.

  • It's a weird thing to want to do.

  • It's very specific.

  • But I kind of wanted to tell you what the whole project was of physics.

  • So it turns out that Harvard has a thing called the Harvard Lampoon.

  • Has anyone ever heard of it?

  • It's the humor magazine, and various other things.

  • And there was a guy many, many years ago.

  • A guy called O'Donnell.

  • And he decided that he wanted to write down the laws of cartoon physics.

  • I thought that was kind of interesting.

  • He didn't make them up.

  • He just wrote them down.

  • He turned out to end up writing for David Letterman and Saturday Night

  • Live and stuff.

  • But what's interesting to me about his laws of cartoon physics are, what

  • is the overarching idea of physics?

  • If we put all the things we know together,

  • what do we find as an overarching idea?

  • So what is the overarching idea here?

  • Well, the first law is gravity doesn't work until you look down.

  • So I'm going to show you three laws, and then we're

  • going to come up with the answer.

  • As speed increases, objects can be in more than one place at the same time.

  • And an anvil always falls more slowly than any person.

  • You guys have watched TV.

  • [CHUCKLING]

  • A lot of Harvard students haven't, but just pretend you have.

  • So what is the idea here?

  • Why are these funny?

  • And Walt Disney says this.

  • [VIDEO PLAYBACK]

  • [END PLAYBACK]

  • Oh.

  • Walt Disney.

  • [VIDEO PLAYBACK]

  • - Impossible cartoon actions will seem plausible

  • if the viewer feels the action he's watching has some factual basis.

  • For example, the idea that only the cow's tail

  • could ring a bell hanging on her neck may seem far-fetched,

  • but it has some basis in fact.

  • There is an anatomical connection between the bell here and the tail

  • here.

  • That is the spinal column.

  • And so it seems entirely plausible that pulling her tail would ring the bell.

  • [BELL RINGING]

  • [END PLAYBACK]

  • MELISSA FRANKLIN: All right.

  • OK.

  • So this is really interesting.

  • So what Walt Disney says is, it has to be plausible but impossible.

  • And that's what makes it funny.

  • So I was trying to think of physics.

  • Real physics.

  • What do real physics, and particularly particle physics do?

  • And so we're more interested in the possible, I'd have to say, in science.

  • But what we do is incredibly implausible.

  • What I just talked about was me describing to you spacetime,

  • and how we measure what it looks like.

  • But "particle physics is the unbelievable in pursuit

  • of the unimaginable.

  • To pinpoint the smallest fragments of the universe,

  • you have to build the biggest machine in the world.

  • To recreate the first millionths of a second of creation,

  • you have to focus energy on an awesome scale."

  • So we're looking for the implausible possible.

  • And for instance, this summer, five undergraduates are coming to CERN--

  • which is the place where the Large Hadron Collider is--

  • to help us figure out the next puzzle.

  • Thanks.

  • [APPLAUSE]

  • MARLYN MCGRATH: Thank you.

  • In our pursuit of one different thing after another,

  • here is another different thing.

  • Robin Kelsey is professor of history of art and architecture.

  • He's the dean of Arts and Humanities, actually, at Harvard.

  • He's the Shirley Carter Burden Professor of Photography--

  • one of his specialties.

  • And he does a lot of other things.

  • I won't list them.

  • But he is, among those other things, a faculty associate

  • for the Center for the Environment.

  • A lot of things are connected at Harvard.

  • I think you're figuring that out.

  • He's also a member of the Kirkland House Senior Common Room.

  • He went to Marshall University High School

  • in Minneapolis, which closed in 1982, so today, we

  • have no new graduates from there, I assume.

  • He has a BA in art history from Yale, another fine accredited place

  • in Connecticut, and a PhD from Harvard.

  • He has a JD from Yale Law School.

  • And I've come to understand that you can never have enough lawyers,

  • and so that's a terrific extra thing.

  • Again, I told you that none of these people has stayed in one lane,

  • and he has not either.

  • He's been on our faculty since 2001.

  • He has a wonderful course called The Art of Looking,

  • and he teaches lots of other things as well.

  • But that's not the subject of today.

  • The subject of today, he calls it-- remember,

  • there's perhaps some distance between titles and talk.

  • No reason why they should correspond exactly.

  • But he wishes to speak about the future of cultural space.

  • So without any further ado.

  • [APPLAUSE]

  • ROBIN KELSEY: Good afternoon.

  • Good afternoon!

  • AUDIENCE: Good afternoon.

  • ROBIN KELSEY: Thank you.

  • I needed that.

  • I never teach at 2:00 PM because it's my nap time,

  • so now you've got me all charged up.

  • I love Melissa Franklin.

  • If I were sitting where you are, I would be thinking,

  • I want to come to Harvard and study physics.

  • But you can't all study physics because we

  • don't have that many physics faculty.

  • So some of you are going to have to study the arts and humanities.

  • And the arts and humanities aren't as funny as physics.

  • [CHUCKLING]

  • No, it's true.

  • It's really a matter of scale.

  • Things are very funny when they're cosmically scaled,

  • or when they're really tiny.

  • But we sit there at the scale of Samuel Beckett,

  • where things get very deadly serious.

  • So if at any point, I get too serious, just

  • think of one of the hundreds of funny things that Melissa said,

  • and you can laugh.

  • One of the reasons we're not funny is we have notes.

  • We use notes which are not funny, but they're very, very precious.

  • So-- [CHUCKLES] yeah.

  • Notes are very precious.

  • OK.

  • So today, I am not going to be offering you any answers to important questions.

  • In fact, I'm just going to pose a few questions.

  • Harvard is a great university, in my view,

  • not because it has all the answers, but because the people here

  • ask important questions, and they work together on coming up with answers.

  • And the questions I'm going to pose today

  • are about the future of cultural space.

  • Now, what do I mean by cultural space?

  • I mean the museum, the library, the concert hall, the theater,

  • the movie theater, the dance center, the public park.

  • I mean those spaces in which we gather to experience culture.

  • To experience human creativity together.

  • These spaces are incredibly important in our civic life.

  • In fact, our governments-- whether local or national--

  • situate these spaces in the center of our civic geography.

  • They do that because we are anchored as a people by our culture.

  • The most well-known and celebrated of our cultural spaces in America--

  • spaces such as Lincoln Center, the Metropolitan Museum, the New York

  • Public Library, Disney Hall--

  • I thought of Disney Hall because of Walt Disney,

  • but I'm not going to make any jokes about Disney Hall--

  • the Smithsonian, these spaces are touchstones of national identity.

  • But our local movie theater, our town public library

  • are no less central to civic life on a smaller scale.

  • These places where we gather and we attend to

  • and honor human creativity, human efforts

  • to find meaning, beauty, empathy, and understanding

  • are really essential to our humanity.

  • Now, I'm showing you an example of a cultural space that's important to me.

  • I grew up in Minneapolis, Minnesota.

  • Marshall University High School has a kind of elite ring to it.

  • Don't let that fool you.

  • There was no university--

  • except the University of Minnesota, which was nearby--

  • related to my high school, which was distinctly public.

  • But I was very, very fortunate in having parents

  • who took advantage of the cultural riches of Minneapolis and St. Paul,

  • which are extensive, which is a very fortunate thing.

  • And in particular, my parents loved to take me to the theater.

  • And the theater in Minneapolis, from the flagship Guthrie Theater--

  • are there any people here from Minnesota?

  • AUDIENCE: Woo!

  • ROBIN KELSEY: Yeah?

  • All right.

  • Good.

  • All right.

  • Yeah.

  • The theater in Minneapolis, from the flagship

  • Guthrie Theater, to smaller theaters, such as the Mixed Blood

  • Theater in the Cedar Riverside neighborhood,

  • near where I grew up, the Penumbra Theater in St. Paul, really fantastic.

  • So this is where this issue of cultural space

  • has particular significance to me.

  • Here.

  • This is the clicker.

  • Yes?

  • No?

  • MARLYN MCGRATH: Try the other one.

  • ROBIN KELSEY: What other one?

  • The duck?

  • MARLYN MCGRATH: No.

  • ROBIN KELSEY: Oh.

  • This.

  • This?

  • Oh, OK.

  • Good.

  • All right.

  • But today, cultural spaces are under considerable challenge and strain.

  • And one reason is probably obvious to you,

  • which is the rise of digital networks and electronic devices.

  • Those in charge of our libraries are wondering,

  • what is a library when our smartphone can bring us

  • more information and knowledge than thousands of books ever could?

  • Those in charge of our theaters, movie theaters, and other performance venues

  • are wondering, how do we get people to come see our shows when so many films

  • and shows are streaming into our homes?

  • So for many of these cultural spaces, this is an existential threat.

  • But even for our cultural spaces such as the art museum that

  • have an easier time making the case that they are delivering

  • unique experiences to visitors, patterns of usage

  • are changing radically in this digital moment.

  • In particular, the popularity of social media and the selfie

  • have very much changed the experience of art museums.

  • And museum directors and staff are scrambling

  • to negotiate this different way of being in the art museum.

  • Exhibitions are being arranged to accommodate the making of selfies,

  • and even new museum spaces are being designed

  • to accommodate the making of selfies.

  • Restaurants-- which can be cultural spaces in their own right--

  • are thinking about questions of lighting and background, and the extent to which

  • that they can make the culinary offerings more Instagrammable.

  • [CHUCKLING]

  • No, I kid you not.

  • I kid you not.

  • In addition, cultural tastes and desires are changing.

  • Many traditional forms of culture require people to sit still,

  • like you're doing, and pay attention-- as you seem to be doing,

  • which is fabulous--

  • for long periods of time to go see the ballet, or the opera, and so forth.

  • In fact, this particular lecture style-- the kind of TED talk, 10, 15 minutes--

  • was unheard of 30 years ago.

  • You would have had to sit through us going on for an hour.

  • So attention spans.

  • Demands for interactivity are changing when

  • people become more accustomed to these fluid and flickering screens,

  • and with their interactivity.

  • So this is changing demand in cultural spaces as well.

  • Although I'm not saying in this that young people don't have the attention

  • span to go to the opera and so forth.

  • I actually think a lot of that concern has been overblown.

  • But nonetheless, these are important considerations.

  • There is also the exceedingly important issue of inclusion.

  • Whose culture gets exalted?

  • Who gets invited and welcomed into our cultural spaces?

  • Who can afford to buy a ticket?

  • Many of us are deeply concerned with the urgency

  • of making our cultural spaces more welcoming to more people.

  • And I show you a scene from Lin-Manuel Miranda's brilliant musical Hamilton,

  • which is in fact a very complicated emblem for this issue.

  • On the one hand, it tells a historical story that principally

  • involves white men and women.

  • On the other hand, the casts are predominantly people of color.

  • On the one hand, it brings a kind of rap or hip hop sensibility

  • to the mainstream of Broadway.

  • On the other hand, the ticket prices are so high that unless you're wealthy,

  • you can't possibly attend without considerable sacrifice.

  • So these challenges are formidable.

  • And they have led me to become very interested

  • in the future of cultural space.

  • How do we address these challenges?

  • How do we design cultural spaces for the 21st century?

  • I've come to this interest in part through becoming--

  • gasp-- an administrator.

  • Because I'm really trained as a historian of photography.

  • So I'm trained at looking at pictures and considering historical evidence.

  • I have no training in-- well, I have training in law,

  • but that's kind of accidental.

  • I don't have training in architectural design and planning.

  • But I have been brought as an administrator at Harvard

  • as someone who serves on all too many committees.

  • I've brought into teams that have designed new cultural spaces here.

  • So I was part of a team that created a new art

  • lab across the river officially opening in September,

  • but it's already being used.

  • A fabulous new facility for experimentation

  • in the arts where works in progress are shared with various audiences.

  • I was part of a team that renovated one of our museum buildings

  • to add new spaces for art-making, for architectural design,

  • and for art history.

  • And I'm currently part of the team that is

  • working on creating a new home for the American Repertory theater

  • across the river.

  • And this is incredibly exciting work.

  • And I'm incredibly grateful to be a part of it.

  • It has convinced me that it is very important for Harvard

  • to revitalize its cultural spaces.

  • But more important, it has convinced me that the design--

  • and I mean that conceptually as well as architecturally-- the design

  • of cultural spaces is one of the most pressing and vital questions

  • of our time.

  • Now, why do I say it is vital?

  • It's vital because it's vital that, as a people,

  • we are not simply a group of consumers, or a group of users,

  • or a group of data points.

  • It is really important that we are bound together

  • through culture, and through the mutual recognition of the importance

  • and value of cultural difference.

  • And I do not believe, as connected as Rob Lue is going to make us--

  • and I'm sure he's going to make us very connected--

  • I believe we still need to come together bodily, physically,

  • into places to experience one another's humanity,

  • and to experience the power of culture to bring us together.

  • So to my mind, this is an exceedingly important question.

  • Now, when I come across what I think is a really interesting new question,

  • I am reminded again of how great it is to be at Harvard.

  • And on this occasion, I accidentally had a conversation

  • with a colleague-- a professor named Jerold Kayden in the Graduate

  • School of Design.

  • Turns out he was thinking about these same questions

  • about the future of cultural space.

  • And within about an hour scribbling on stray pieces of paper,

  • we decided that we should really work on this problem together.

  • And one of the great things about universities

  • is that they have a tremendous engine of intellectual inquiry.

  • And that engine is called the classroom.

  • So this fall, rather belatedly, Jerold and I

  • put together a general education course on the future of cultural space.

  • We submitted it at the 11th hour, crossed our fingers,

  • and fortunately, it was approved.

  • So we taught it this spring.

  • It was a course we limited to about 30 students

  • because it was really an experiment, and we

  • wanted to create a kind of seminar-like atmosphere.

  • And each week, we thought about a different cultural space.

  • One week, the library.

  • Another week, the museum.

  • Another week, the public park.

  • And each week, we brought in a leading expert

  • in the design or the oversight of such a cultural space.

  • So some of you may know The Shed opened to enormous publicity in New York City.

  • Well, Liz Diller, who was the principal architect of The Shed,

  • came and spoke to our class even as this hubbub was taking place.

  • And she talked about the fact that The Shed was designed

  • around the wheels that move this enormous skin backward

  • and forward so that you can have an enclosed interior space,

  • or you can have an exterior space.

  • We had Mitch Silver, who is the head of the New York City park system

  • come and talk about public parks as cultural spaces,

  • and the art projects that he is overseeing.

  • We had Joana Vicente, who is the new executive director of the Toronto

  • International Film Festival, come to talk

  • about the future of the movie theater.

  • We had Rebecca Robertson, who runs the Park Avenue Armory in New York

  • come and talk about the Armory, which is a regeneration of an obsolete space,

  • which is a type of cultural space that we were very interested in.

  • And so these practitioners would come.

  • They would speak for about 30, 40 minutes.

  • And then for about an hour and a half, they would be grilled by the students

  • and by Jerold and me about, what are we to be thinking about as we

  • design these spaces for the future?

  • And teaching this class has been exhilarating.

  • I have to say, I'm sure you have many choices of places to go,

  • but I don't think that you can teach this course

  • at pretty much any other institution.

  • Maybe Yale could pull this off.

  • But it is incredible, when you invite people to come to Harvard, who comes.

  • I mean, I said to Jerald, do you really think

  • Liz Diller is going to come within two weeks of the opening of The Shed

  • to talk to our class?

  • And Jerold said, this is Harvard.

  • She'll come.

  • And what's great is that--

  • [APPLAUSE]

  • I mean, it's a little crazy.

  • We're so lucky.

  • We are so fortunate.

  • And Jerold actually knew this because he sat where you sat once.

  • He was a Harvard undergraduate, and he started a program called Learning

  • from Performers, which continues to this day in the Office of the Arts

  • that brings in the most incredible people.

  • So he learned as an undergraduate, you invite people to Harvard,

  • and they come.

  • So we've just been doing this together.

  • It's been incredible.

  • And what we've learned is that there are key issues, dilemmas,

  • conundra around the designing of spaces for a culture of the future.

  • And we are so excited to be working on this project.

  • We are going to be writing it up.

  • We are going to be continuing to work with some of the students in the class

  • and building an archive.

  • And we hope to build a center of research

  • at Harvard to make sure that we start sharing this information

  • and opening the conversation around the future of cultural space.

  • Thanks so much for listening, and please come to Harvard.

  • [APPLAUSE]

  • MARLYN MCGRATH: I promised you a succession of totally different things

  • from each other.

  • And the last totally different thing, I'll start with who are you anyway?

  • Remember, that was one of the questions.

  • David Malan, our next speaker, next faculty member,

  • is an example of-- he is one of our own.

  • I've actually known him since he was undergraduate, and there's a story too.

  • But he's one of our own.

  • So he is an example--

  • among many other things-- of what happens if you just go to Harvard

  • and spend your life at Harvard.

  • His title now is Gordon McKay Professor of the Practice

  • of Computer Science in the John Paulson School of Engineering and Applied

  • Sciences.

  • But he's also a member of the faculty of education

  • and the Graduate School of Education, member of the Mather House Senior

  • Common Room.

  • He was in Mather House as an undergraduate.

  • He went to high school, and I think graduated from high school,

  • at Brunswick school in Connecticut.

  • There's a lot of Connecticut in this program, but anyway.

  • He earned, as I said, all his three degrees from Harvard College.

  • 1999 was his college year.

  • College years are the ones that matter.

  • And he's been teaching at Harvard since he got his last degree, his PhD--

  • most recent degree-- in 2007.

  • He teaches-- and this is the name of the title of his talk today,

  • which is "A Taste of CS50."

  • Teaches a course called Computer Science 50, CS50:

  • Introduction to Computer Science.

  • It is a very large course at Harvard.

  • We had 763 in the course in this past fall.

  • That course, oddly enough, franchise-style,

  • has been from time to time the largest course at Yale,

  • and it's again a large course at Yale this year.

  • We're very mutual in many things around here, apparently.

  • He teaches variants of it too.

  • CS50 for Lawyers, CS50 for MBAs.

  • What you want, you can get.

  • In his spare time--

  • it's hard for me to imagine David has any spare time,

  • but he has worked part-time for the Middlesex District Attorney Office

  • as a forensic investigator.

  • And he's still, from time to time, a volunteer EMT.

  • His research interests won't surprise you--

  • are cybersecurity, computer science education, and digital forensics.

  • So here is David Malan, one of our own.

  • [APPLAUSE]

  • DAVID MALAN: Thank you to Marlyn.

  • So I was actually just back in Connecticut

  • at my high school for the first time in years recently,

  • and chatting with some of my successors about where I made my way in life,

  • and what I really didn't do, actually, in high school.

  • In fact, I gave a talk about all of the studies

  • that I didn't discover when I was back in high school.

  • Because I still remember wandering around the hallways

  • when I was last there, looking in on various classrooms

  • where I'd spent a lot of time, that there was one in particular

  • that I spent no time in.

  • And that was the computer science lab.

  • I still vaguely remember peeking through the glass of the window

  • when some of my friends were taking their introductory computer science

  • classes, but I had no interest in it, honestly.

  • I just assumed it was all about programming, and like C++ or Java,

  • whatever those were.

  • But it just didn't seem all that interesting to me.

  • And any time I did look in, all my friends had their heads down,

  • typing away, doing whatever it was they were doing.

  • And so I focused on history, and English,

  • and constitutional law was my favorite class in high school.

  • And so when I got to Harvard some years later, I kind of just

  • stuck with where I was comfortable.

  • I felt like, well, I hadn't studied CS in high school,

  • so all the other students who are taking CS here surely have a leg up

  • and know way more than me.

  • So I figured, ah, I thought of it too late.

  • And there was this core CS50 my first year here.

  • And it had this alluring reputation.

  • There were a lot of students in it.

  • But it really didn't seem like it was for me.

  • I wasn't really a computer person in that way.

  • And I felt like I was behind.

  • I didn't want to hurt my GPA by taking something so unfamiliar to me.

  • And so I stayed within my comfort zone, and I took more history, and government

  • classes, and I declared my major to be-- or concentration to be government.

  • And it wasn't until sophomore year when I finally got up the nerve to shop,

  • so to speak--

  • Sit in on a class before you officially register-- this class called CS50.

  • And I only got up the nerve to register for it

  • officially because the professor at the time let me sign up for pass-fail.

  • So no harm to the GPA.

  • I was really able to explore really well beyond my comfort zone.

  • And honestly, within weeks, I realized for the first time in like 18 years

  • that homework can actually be fun.

  • And if you find the field that's of interest to you, whether it's

  • CS or anything else, by exploring things that you're not familiar with right

  • now, you might have the same experience I did of going home on a Friday night.

  • The problem set or homework assignment had just

  • been released at like 7:00 PM every Friday night.

  • And I would spend the entire evening on my laptop working on CS50's programming

  • assignments.

  • Because I finally realized what it was.

  • And programming itself is not the ends of a course like this.

  • It really is just about problem-solving.

  • And so quickly did I realize, wow, I can use these kinds of ideas

  • to go solve problems in other courses, to be more efficient,

  • to be more creative in my extracurriculars.

  • I realized, wow, I can now build some application

  • to now make processes more easily accessible

  • on campus, like the intramural sports program.

  • I was able to overhaul just with a little bit of computer science.

  • And if we distill today what took me all too long to discover,

  • problem-solving really is kind of a picture

  • like this, where you have some inputs, and the goal

  • is to achieve some outputs.

  • And that, in some sense, really is computer science.

  • And programming, and a lot of the particulars

  • that you learn in the classroom, are really just deeper

  • dives into this very simple idea.

  • But how do you get to that point of actually solving problems?

  • Well, I eventually realized that you needed to do two things.

  • One, you needed to represent these inputs and these outputs.

  • That is, we just all have to agree how to do it.

  • And then you actually have to do something with those inputs

  • to get those outputs.

  • And therein lies the problem-solving.

  • And so how do you go about representing information?

  • Well, I could represent information--

  • all I need is some kind of input.

  • And here's the power cord to my laptop.

  • And honestly, even if you have no idea how your computer works,

  • odds are, you appreciate that this is pretty integral,

  • having somehow electricity, some physical input come into the computer.

  • And if you unplug it, it's off.

  • If you plug it in, it's on.

  • And batteries, of course, can persist this here too.

  • But off and on maps really cleanly to what you all probably generally know

  • to be true of computers, in that they only speak what language?

  • AUDIENCE: Binary.

  • DAVID MALAN: Yeah, binary.

  • "Bi" meaning two, mapping to this concept of off and on,

  • or as a computer scientist would say, 0 or 1.

  • That's why we have 0s and 1s at the end of the day,

  • because the simplest thing to do electrically

  • is to either turn the power on or turn the power off.

  • 1 or 0.

  • We could have called it A and B, but we call it 1 and 0.

  • But if all you have in a computer is the ability to turn it on or turn it off,

  • or to store some value-- kind of like a light switch goes on or off--

  • how can you possibly do anything interesting or solve problems?

  • Well, let's just consider like a simple light bulb here.

  • This has some power.

  • It happens to have a battery.

  • And if this thing is off, we'll just call it a 0.

  • And if this thing is on, we'll call it a 1.

  • So now we have a single switch, or what's

  • known in computing as a transistor.

  • In fact, inside of your computer are lots and lots-- millions of transistors

  • that just turn things on and off.

  • Well, if I have just one of these, I can only do 0 or 1.

  • That's not all that interesting.

  • That would seem to give us two problems total to solve.

  • So how can we count higher than just 0 or 1?

  • Well, I might take two of these, or three of these,

  • and maybe start doing things a little more methodically.

  • So I could do 1, 2, 3.

  • So now I can clearly count as high as three.

  • But that would seem to be it as well.

  • But no.

  • Computers are a little smarter than that,

  • and we can actually adopt patterns of on and off.

  • So this now, I'll claim is 0.

  • All three of these light bulbs are off.

  • Let me turn on this one on, thereby representing

  • what I'm going to call a 1.

  • But you know what?

  • Now I'm going to go ahead and claim that that's how a computer would store a 2.

  • It would turn a different light switch on, the second one.

  • And you know what?

  • If it turns the first one back on, this is how a computer stores a 3.

  • And now just take a guess, if I do this--

  • uncomfortably-- what is the computer perhaps now storing?

  • AUDIENCE: 4.

  • DAVID MALAN: 4.

  • This happens to be 6.

  • This is now 7.

  • Why?

  • How did I choose those particular patterns?

  • Well, it turns out this is something that all of us

  • are probably really familiar with.

  • If you think about our grade school understanding of numbers, if I draw

  • something on the screen quite simply--

  • like this pattern of symbols.

  • 1, 2, 3.

  • This is, of course, 123.

  • But why?

  • Because all of us just pretty instantaneously did

  • the mental arithmetic of this being the ones place, this is the tens place,

  • this is the hundreds place.

  • And then what did you probably do in that split second?

  • Well, you did 100 times 1 plus 10 times 2 plus 1 times 3, which of course gives

  • you 100 plus 20 plus 3, or 123.

  • Now, that's a bit of a circular argument because that's kind of where I started,

  • but now these symbols--

  • these curves on the screen, 1, 2, 3--

  • actually have now meaning that we've all agreed represents the human number 123.

  • So computers are actually fundamentally the same thing.

  • And in some sense, they're even simpler than us humans in the following way.

  • If you have the same number of placeholders, and we write down--

  • with great difficulty-- if we write down, say,

  • three places, or three light bulbs, if you will, but doing it now textually,

  • and I write down, for instance, 0, 0, 0, you

  • can probably guess that in the world of computers,

  • if you've got three switches that are all off, this represents the number 0.

  • And if I turn one of these light bulbs on, so to speak, this of course--

  • as before-- is going to be the number that I called 1.

  • Well, if I now do not just change this one, but change this to a 0--

  • and this is where maybe my light bulb patterns got a little non-obvious--

  • why is this 2?

  • Well, it's the same mental arithmetic but just with different places.

  • A computer doesn't use powers of 10, so to speak--

  • 10 to the 0, 10 to the 1, 10 to the 2--

  • but powers of 2.

  • So this is 2 to the 0, or the ones place.

  • This is 2 to the 1, or the twos place.

  • This is 2 to the 2, or the fours place.

  • And so you just need to turn these light bulbs on and off

  • based on this kind of pattern to get whatever number it

  • is you're interested in.

  • So this is 2 because it's 4 times 0 plus 2 times 1 plus 1 times 0.

  • Why is this three when I turned two light bulbs on earlier?

  • The same reasoning.

  • And what's the highest I can count with just three light bulbs, or three

  • 0s and 1s?

  • 7, just because you got a 4 plus a 2 plus a 1, and so forth.

  • And what would happen, then, if I wanted to count as high as 8, would you think?

  • AUDIENCE: [INAUDIBLE]

  • DAVID MALAN: Yeah, you need to add another place.

  • Or really, you need more physical hardware.

  • And this is why your computer can only count so high

  • or store so much information.

  • You need an additional light switch-- or another transistor, if you will--

  • to actually store additional information.

  • So that, then, is binary.

  • If you've just known intuitively computers only speak 0s and 1s, why?

  • Well, that's because they start with electricity as their physical input.

  • We humans have just all agreed to represent values

  • in this way using binary by just having these patterns of 0s and 1s.

  • But that pretty much makes for a very expensive calculator,

  • if all you have are numbers.

  • So how do you get from numbers and from electricity to now,

  • letters, say of the alphabet?

  • What could we do?

  • How do we now enable spreadsheet programs, and word processors,

  • and text messaging, and email clients, and the like?

  • What can we all do if our only input is electricity, or in turn, 0s and 1s?

  • AUDIENCE: [INAUDIBLE]

  • DAVID MALAN: Say again?

  • AUDIENCE: Assign number values to letters?

  • DAVID MALAN: Yeah, we can just assign number values to letters.

  • So you know what we could go ahead and do,

  • and if we want to represent letters of the alphabet,

  • as before, the only goal at hand is to just agree

  • on how to represent that information.

  • So let's pick a few letters of the alphabet.

  • A, B, C, D, E, F, G, H, I. We could just say, you know what?

  • Let's just agree to represent A as 1, and B as 2, and C as 3.

  • Doesn't really matter, so long as we all agree to do that.

  • But it turns out, some years ago, humans decided

  • that A is actually going to be 65, and B is 66,

  • and C is 67, 68, 69, 70, 71, 72, 73, and so forth.

  • This is known as ASCII or Unicode.

  • It's just a system that humans agreed decades ago

  • shall be used by computers to represent letters of the alphabet

  • just by storing numbers, and those numbers in turn

  • are just the result of the computer turning

  • little switches known as transistors on and off in these certain patterns.

  • And let me, with the wave of a hand, assure sure

  • that we can represent colors, and sounds,

  • and videos in very similar ways.

  • But we need to actually just agree on how to do this.

  • So in fact, there's an opportunity here perhaps

  • to write a message in exactly the same way that a computer could.

  • If you could humor me, maybe, with eight volunteers?

  • Could we get some eight volunteers up on stage?

  • OK, 1, 2.

  • Let me look a little harder.

  • 3, 4.

  • Can I go a little farther?

  • I see no hands in the back.

  • OK.

  • There we go.

  • 5.

  • 6 over there.

  • I see someone pointing at someone else.

  • Come on, 7.

  • And let's go 8, over here.

  • Come on down.

  • And I just need you to go ahead, if you could,

  • and stand beneath these placeholders here on the slide, which

  • I've gone ahead and rotated just so that they fit a little more

  • visibly on the screen.

  • Come on over.

  • What's your name?

  • AUDIENCE: Matt.

  • DAVID MALAN: Matt.

  • Come on over and stand under the 128.

  • What's your name?

  • AUDIENCE: Mira.

  • DAVID MALAN: Mira.

  • David.

  • AUDIENCE: Hey.

  • DAVID MALAN: David.

  • Nice to meet you.

  • Hello.

  • David.

  • Nice to meet you.

  • AUDIENCE: Anesha.

  • DAVID MALAN: Anesha, David.

  • And Monica.

  • Nice to meet you.

  • And what was your name?

  • AUDIENCE: Chris.

  • DAVID MALAN: Chris.

  • Nice to meet you as well.

  • So each of these guys is going to have to scooch a little closer

  • to each other.

  • And you know what?

  • If this isn't too much effort, could we actually get eight more volunteers now

  • that you know what you're vol--

  • OK, now everyone's hand goes up.

  • OK.

  • 1, 2, 3, 4, 5, 6, 7, 8, if you could.

  • Come on down.

  • We'll do this round more quickly.

  • And what you'll notice now that we have a bytes' worth of volunteers here.

  • What is a byte?

  • A byte is just 8 bits.

  • It's a more useful unit of measure than just a 0 or 1.

  • And notice the terminology here too.

  • A bit-- a 0 or 1-- is a binary digit.

  • There's the etymology of just that simple phrase.

  • And a quick hello to AJ.

  • AUDIENCE: AJ.

  • DAVID MALAN: David.

  • Jay.

  • AUDIENCE: Hi.

  • DAVID MALAN: David.

  • Nice to meet you.

  • Nice to meet you.

  • Nice to meet you.

  • Nice to meet you.

  • Nice to meet you.

  • AUDIENCE: Bianca.

  • DAVID MALAN: David, and nice to meet you as well.

  • Here we have our second byte of humans.

  • And--

  • AUDIENCE: [INAUDIBLE]

  • DAVID MALAN: What's that?

  • AUDIENCE: We have seven right here.

  • DAVID MALAN: We have a seven right here?

  • 1, 2, 3, 4, 5, 6, 7.

  • 1, 2, 3, 4, 5, 6, 7, 8.

  • We have a bug.

  • Here we go.

  • Come on up.

  • Thank you.

  • Thank you very much.

  • In computer science, that's an off-by-one error.

  • What's your name?

  • AUDIENCE: Helen.

  • DAVID MALAN: Helen.

  • David.

  • Nice to meet you.

  • Go ahead and join, I guess, this group right here in the middle, if you could.

  • So these folks here hopefully do have cell phones on you.

  • Key detail I probably should have mentioned earlier.

  • That's OK if you don't.

  • That's OK.

  • We're going to recover.

  • Whoever doesn't have a cell phone is now going to get a flashlight.

  • OK.

  • Let's do this.

  • OK.

  • Key detail.

  • Sorry, you can go ahead and turn that off.

  • Going to cross my fingers here that we have enough light bulbs.

  • Hang on.

  • Let's go ahead now and turn on, if you could, three light bulbs here.

  • So you don't have your phone?

  • Here is a nice iPhone XS.

  • OK.

  • [LAUGHTER]

  • 1, 2, 3, 4.

  • Let's go ahead and turn yours on.

  • Can you swap phones for a moment?

  • So we have two light bulbs there, and we don't need anyone else's phone

  • on just yet.

  • Could you turn your light bulb on?

  • And could you turn your light bulb on?

  • And we need just one light bulb here, if you could turn that on.

  • So let me step out of the way.

  • And you'll see that we have someone in the 64s place whose light is

  • on, in the 8s place, then again in the 64s place and the 8s place,

  • and lastly, the 1.

  • So if a computer indeed had some 16 switches or transistors inside of it

  • and turned on those switches in this particular order,

  • what message are these humans here representing at the moment?

  • AUDIENCE: Hi.

  • DAVID MALAN: So it's indeed hi.

  • Why?

  • Because the mapping we arbitrarily chose but globally decided on is that 72 is H

  • and 73 is I.

  • Well, let's try one more further.

  • At the moment, we're just using two bytes of humans, if you will.

  • Two units of eight.

  • But suppose that we didn't just draw an imaginary line in between them

  • and count only up to the ones place through that 128s place.

  • But suppose that we treated everyone as one

  • much bigger value so that we could count much higher.

  • So now, these humans are taking on the value of a 128s place,

  • but then the 256, 512, 1024.

  • All I'm doing is multiplying by 2.

  • I'm going to need one more volunteer, and I'll take on this role over here.

  • If I were to be at the very end here, I'd now have 17 bits on stage.

  • 17 switches or transistors.

  • Let me go ahead and turn on just some of these, if we could.

  • Most of them, we might have to borrow a couple of phones.

  • Let's go ahead and give-- if you could turn your phone on.

  • Here.

  • Your flashlight.

  • Let me-- that's technically yours.

  • Can we borrow your phone for a moment?

  • OK.

  • Your phone is going over here to the 32,000s place.

  • We need to turn yours on.

  • OK, I'll turn mine on over there.

  • So we need 1, 2.

  • Can we give you 3, 4 on?

  • Can we borrow that?

  • 3, 4.

  • Can we-- keep the phones coming.

  • [CHUCKLING]

  • 3, 4.

  • So 1, 2, 3, 4.

  • And then we skip 1.

  • And then we need you two to be on, if that's OK.

  • And then over here, thankfully, we need just one light bulb on.

  • So now it's your chance.

  • If a computer were using this many bits--

  • 16 bits.

  • And if I stand in place now, 17 bits, where I represent 65,536,

  • and our volunteers all the way on the end represents the number 1,

  • and you do this math, what number are we all representing?

  • OK, no one's going to get this right.

  • It's 128,514.

  • What might that message say?

  • Well, there's not nearly enough clues in mine, but it's actually this.

  • So if you've sent today or recently an email or a text message with an emoji,

  • you might have sent this one--

  • Face with Tears of Joy.

  • So that's its official name.

  • But it's not an image per se.

  • It's actually a character.

  • And in fact, you might know that you have so many emojis these days,

  • and that's because computers and humans who

  • use them have started using way more than 8 bits.

  • Way more than 16 or 17 bits.

  • Sometimes 24 or 32 bits, which gives us so many darn possible permutations

  • of 0s and 1s, or switches being turned on or off, that frankly, it's

  • just become kind of a cultural thing that we

  • have so many darn possibilities, let's start using some of them

  • for more silly reasons, if you will, like emojis.

  • So if you ever receive today or hereafter a face with tears of joy,

  • what your friends have really sent to you is a pattern of 0s

  • and 1s somehow implemented with electricity or wavelengths of light

  • that represents, rather mundanely, 128,514.

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

  • [APPLAUSE]

  • Let me borrow this.

  • Thank you.

  • If you'd like to step off stage, we have a little something for each of you.

  • So we have just one last question to answer.

  • Thank you all so much.

  • We have just one other question to answer,

  • which is, if problem-solving ultimately boils down

  • to representing inputs and outputs, what is

  • the process that we pass those inputs through in order to get those outputs?

  • What is it you learn, ultimately, in a course on computer science?

  • Well, it's perhaps best explained by way of a problem.

  • So here is an old-school problem where you

  • have a whole bunch of names and numbers alphabetically sorted from A through Z,

  • and you want to find someone.

  • And even though this is pretty old-school,

  • it's honestly the same thing as the address book or the contacts app

  • that you have in your own iPhone or Android

  • phone, or any particular device.

  • If you scroll through your contacts, odds

  • are they're A through Z, alphabetized by first name or last name.

  • So this is just representative of the same problem

  • that you and I solve any time we look someone up in our phone.

  • Well, if I want to look up an old friend-- someone like Mike Smith,

  • last name starting with S--

  • I could certainly just start at the beginning of this book

  • and do 1, 2, 3, 4.

  • And that's a step-by-step process, otherwise known as an algorithm.

  • And is that algorithm correct?

  • Will I find Mike Smith?

  • AUDIENCE: Yes.

  • DAVID MALAN: Yeah.

  • I mean, it's a little tedious, and it's a little slow, but if Mike is in here,

  • I'll eventually find him.

  • But I'm not going to do that.

  • I know he's going to be roughly at the end.

  • So maybe a little more intelligently or efficiently,

  • I could do 2, 4, 6, 8, 10, 12, and so forth.

  • It's going to fly me through the phone book twice as fast.

  • And is that algorithm or step-by-step process correct?

  • AUDIENCE: [INAUDIBLE]

  • DAVID MALAN: A literal contention.

  • It's almost correct, except if I get unlucky and might

  • get sandwiched between two pages because I'm a little aggressively flying

  • through the phone book.

  • But no big deal.

  • If I maybe hit the T section, I could maybe double back one or few pages

  • and fix that.

  • But none of us are going to do that.

  • What's a typical person going to do?

  • And really, what's a computer going to do, be it in your phone or a laptop

  • these days?

  • AUDIENCE: [INAUDIBLE]

  • DAVID MALAN: Yeah.

  • It's going to go roughly maybe to the middle, or a little biased

  • toward the right, because you know S is a little alphabetically later

  • than most letters.

  • And I look down, for instance, here, and I see, oh, I'm in the M section.

  • And so I know that Mike is not this way.

  • He's definitely this way.

  • So both metaphorically and literally, can I tear a problem like this in half?

  • This is actually not that hard vertically.

  • I can tear the problem in half, and now I'm

  • left not with 1,000 pages with which I began, but maybe 500.

  • And I can do it again, and whittle myself down to like 250 pages.

  • And again, down to 125.

  • And again and again and again until I'm left with, hopefully,

  • just one or so page.

  • But what's powerful about, honestly, that intuition that odds are you

  • had when you walked in this door is that, in just 10 or so steps,

  • can you find Mike Smith in a phone book?

  • In just 10 or so steps, can iOS or Android find someone in your contacts

  • by dividing and conquering, dividing and conquering?

  • Whereas the other algorithms might have taken,

  • gosh, like 1,000 steps, 500 steps, almost as many pages as there are.

  • And so that's an algorithm, and that's what's

  • inside this proverbial black box.

  • It's the sort of secret sauce.

  • And the idea is that you learn not just to learn along the way,

  • but learn to harness in your own human intuition.

  • And so I wish I had discovered that far earlier for myself,

  • knowing that computer science is not about programming per se.

  • It really is about problem-solving, and just formalizing, and cleaning up

  • your thought process, and introducing you to ideas like this

  • that you can then apply in so many different ways.

  • So that there, say, is just a taste of computer science.

  • Allow me to conclude with a taste of this one course, CS50,

  • by way of the point of view of one of our very own students.

  • [VIDEO PLAYBACK]

  • [MUSIC - PORTUGAL.

  • THE MAN - "LIVE IN THE MOMENT"]

  • - (SINGING) My home is a girl with eyes like wishing wells.

  • I'm not alone, but I'm still lone-- lonely.

  • Those days are done, but I'm still glowing.

  • Ooh, la, la, la, la, la, let's live in the moment.

  • Come back Sunday morning.

  • Oh my, oh well.

  • When you're gone, goodbye, so long, farewell.

  • Ooh, la, la, la, la, la, let's live in the moment.

  • Come back Sunday morning.

  • Got soul to sell.

  • When you're gone, goodbye, so long, farewell.

  • My home is a girl who can't wait for time to tell.

  • God only knows we don't need history.

  • Your family swinging from the branches of a tree.

  • God only knows we don't need ghost stories.

  • Ooh, la, la, la, la, la, let's live in the moment.

  • Come back Sunday morning.

  • [END PLAYBACK]

  • [APPLAUSE]

  • MARLYN MCGRATH: Thank you all for your enthusiasm and your patience today.

  • I hope you have a terrific time this afternoon tonight.

  • I'm afraid we're going to release you with the rain.

  • I actually don't know whether it's still raining.

  • I hope not.

  • But whether or not, we are very honored by your interest at Harvard.

  • Have a great, terrific rest of the long weekend.

  • Thank you.

  • [APPLAUSE]

[APPLAUSE]

字幕與單字

單字即點即查 點擊單字可以查詢單字解釋

A2 初級

VisitasThinks Big 2019 - 哈佛大學。 (Visitas Thinks Big 2019 - Harvard University)

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    林宜悉 發佈於 2021 年 01 月 14 日
影片單字