Visitas Thinks Big 2019 – Harvard University

Visitas Thinks Big 2019 – Harvard University


[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]

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