[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?
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.
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]