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  • 00:03

    [APPLAUSE]
    [APPLAUSE]

  • 00:10

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

  • 00:14

    "Hold That Thought" show.
    "Hold That Thought" show.

  • 00:16

    I'm Marlyn McGrath from the admissions office accompanied
    I'm Marlyn McGrath from the admissions office accompanied

  • 00:19

    by four stars on our faculty who volunteered
    by four stars on our faculty who volunteered

  • 00:23

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

  • 00:28

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

  • 00:32

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

  • 00:35

    What Do People Do All Day?
    What Do People Do All Day?

  • 00:37

    This is a version of that.
    This is a version of that.

  • 00:39

    It's also, by the way--
    It's also, by the way--

  • 00:41

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

  • 00:44

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

  • 00:49

    to figure out who you are.
    to figure out who you are.

  • 00:51

    Who is this person?
    Who is this person?

  • 00:53

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

  • 00:57

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

  • 01:00

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

  • 01:03

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

  • 01:08

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

  • 01:11

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

  • 01:15

    You've got other great choices.
    You've got other great choices.

  • 01:17

    You're not going to make a mistake.
    You're not going to make a mistake.

  • 01:19

    You never have.
    You never have.

  • 01:20

    You never told us you did.
    You never told us you did.

  • 01:22

    [LAUGHTER]
    [LAUGHTER]

  • 01:23

    This is gravy.
    This is gravy.

  • 01:24

    You're not going to make a mistake.
    You're not going to make a mistake.

  • 01:25

    Harvard's a great place.
    Harvard's a great place.

  • 01:26

    So are a lot of other wonderful places.
    So are a lot of other wonderful places.

  • 01:28

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

  • 01:31

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

  • 01:34

    And so our strategy for this is what we want
    And so our strategy for this is what we want

  • 01:36

    is for you to want to come to Harvard.
    is for you to want to come to Harvard.

  • 01:38

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

  • 01:42

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

  • 01:45

    would conclude, at the end of the weekend,
    would conclude, at the end of the weekend,

  • 01:47

    that Harvard would be a lot of fun.
    that Harvard would be a lot of fun.

  • 01:50

    And so much talent is represented in this room,
    And so much talent is represented in this room,

  • 01:53

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

  • 01:55

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

  • 01:58

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

  • 02:02

    Things you have not yet thought about.
    Things you have not yet thought about.

  • 02:05

    Things that won't have occurred to you.
    Things that won't have occurred to you.

  • 02:07

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

  • 02:11

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

  • 02:15

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

  • 02:18

    fun, pleasure, and satisfaction.
    fun, pleasure, and satisfaction.

  • 02:20

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

  • 02:24

    But both to amuse you and to confuse you,
    But both to amuse you and to confuse you,

  • 02:27

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

  • 02:30

    to amuse you and confuse you.
    to amuse you and confuse you.

  • 02:33

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

  • 02:35

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

  • 02:38

    You still got time.
    You still got time.

  • 02:39

    [CHUCKLING]
    [CHUCKLING]

  • 02:40

    Our faculty colleagues will can you glimpses anyway
    Our faculty colleagues will can you glimpses anyway

  • 02:44

    of what they do all day.
    of what they do all day.

  • 02:46

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

  • 02:49

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

  • 02:51

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

  • 02:58

    which would be by Professor Robert Lue, whose
    which would be by Professor Robert Lue, whose

  • 03:03

    talk will be called "Solving Global Challenges Through Collective
    talk will be called "Solving Global Challenges Through Collective

  • 03:07

    Learning."
    Learning."

  • 03:08

    Well, who is he?
    Well, who is he?

  • 03:10

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

  • 03:12

    of molecular and cellular biology.
    of molecular and cellular biology.

  • 03:15

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

  • 03:19

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

  • 03:24

    Hold that thought.
    Hold that thought.

  • 03:25

    HarvardX.
    HarvardX.

  • 03:25

    Lots of online learning.
    Lots of online learning.

  • 03:27

    He went to high school--
    He went to high school--

  • 03:29

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

  • 03:32

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

  • 03:35

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

  • 03:42

    He's very well-known also--
    He's very well-known also--

  • 03:44

    hold this idea too, because none of these people
    hold this idea too, because none of these people

  • 03:46

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

  • 03:50

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

  • 03:54

    with cellular biology.
    with cellular biology.

  • 03:57

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

  • 04:00

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

  • 04:03

    Challenges Through Collective Learning."
    Challenges Through Collective Learning."

  • 04:05

    [APPLAUSE]
    [APPLAUSE]

  • 04:14

    ROBERT LUE: Thanks, Marlyn.
    ROBERT LUE: Thanks, Marlyn.

  • 04:23

    So let me add my words of welcome.
    So let me add my words of welcome.

  • 04:26

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

  • 04:30

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

  • 04:33

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

  • 04:37

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

  • 04:42

    about the kinds of learning experiences that
    about the kinds of learning experiences that

  • 04:45

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

  • 04:50

    that one might imagine.
    that one might imagine.

  • 04:52

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

  • 04:56

    things that you will experience here.
    things that you will experience here.

  • 04:59

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

  • 05:01

    while what you experience here will be absolutely
    while what you experience here will be absolutely

  • 05:04

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

  • 05:10

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

  • 05:15

    else learns thousands of miles away from you.
    else learns thousands of miles away from you.

  • 05:19

    So I'm a cell biologist.
    So I'm a cell biologist.

  • 05:20

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

  • 05:25

    of personalized learning at scale.
    of personalized learning at scale.

  • 05:28

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

  • 05:31

    And how can this sort of challenge really change
    And how can this sort of challenge really change

  • 05:35

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

  • 05:41

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

  • 05:45

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

  • 05:49

    massive open online courses.
    massive open online courses.

  • 05:53

    I suspect that some of you have even taken
    I suspect that some of you have even taken

  • 05:56

    some massive open online courses, perhaps from Harvard
    some massive open online courses, perhaps from Harvard

  • 05:59

    as well, from HarvardX.
    as well, from HarvardX.

  • 06:01

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

  • 06:06

    to develop a platform called edX.
    to develop a platform called edX.

  • 06:09

    The notion was that we really wanted to share broadly
    The notion was that we really wanted to share broadly

  • 06:12

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

  • 06:17

    but to make it much more accessible.
    but to make it much more accessible.

  • 06:21

    But what did we do?
    But what did we do?

  • 06:22

    We made courses.
    We made courses.

  • 06:24

    Things that were 10 weeks long.
    Things that were 10 weeks long.

  • 06:27

    12 weeks long.
    12 weeks long.

  • 06:28

    8 weeks long.
    8 weeks long.

  • 06:29

    6 weeks long.
    6 weeks long.

  • 06:31

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

  • 06:36

    which is through a course.
    which is through a course.

  • 06:39

    So fast forward to now.
    So fast forward to now.

  • 06:41

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

  • 06:45

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

  • 06:48

    Don't get me wrong.
    Don't get me wrong.

  • 06:50

    You will have amazing courses here.
    You will have amazing courses here.

  • 06:52

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

  • 06:59

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

  • 07:04

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

  • 07:09

    how do we do this?
    how do we do this?

  • 07:10

    What platform do we have?
    What platform do we have?

  • 07:13

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

  • 07:19

    online is that we're open-source.
    online is that we're open-source.

  • 07:23

    We're free.
    We're free.

  • 07:24

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

  • 07:29

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

  • 07:32

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

  • 07:38

    have engaged with the platform around the world.
    have engaged with the platform around the world.

  • 07:43

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

  • 07:50

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

  • 07:55

    A whole variety of organizations use the platform.
    A whole variety of organizations use the platform.

  • 08:00

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

  • 08:04

    And so what this means is that we are currently
    And so what this means is that we are currently

  • 08:07

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

  • 08:12

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

  • 08:15

    to figure out how I feel about Harvard.
    to figure out how I feel about Harvard.

  • 08:17

    I'm looking inside.
    I'm looking inside.

  • 08:18

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

  • 08:21

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

  • 08:26

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

  • 08:30

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

  • 08:36

    platform--
    platform--

  • 08:37

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

  • 08:40

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

  • 08:43

    And what makes it next generation is that if you
    And what makes it next generation is that if you

  • 08:46

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

  • 08:51

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

  • 08:54

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

  • 09:00

    Probably hundreds of millions of dollars have been spent.
    Probably hundreds of millions of dollars have been spent.

  • 09:05

    And what you have are a multitude of courses
    And what you have are a multitude of courses

  • 09:08

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

  • 09:15

    all of those things.
    all of those things.

  • 09:17

    But all of them are locked in courses.
    But all of them are locked in courses.

  • 09:22

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

  • 09:25

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

  • 09:31

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

  • 09:37

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

  • 09:43

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

  • 09:49

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

  • 09:53

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

  • 10:01

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

  • 10:06

    get to pick what you want from it.
    get to pick what you want from it.

  • 10:09

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

  • 10:14

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

  • 10:19

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

  • 10:22

    in a sequence of your own choosing.
    in a sequence of your own choosing.

  • 10:25

    You can then add your own stuff to it.
    You can then add your own stuff to it.

  • 10:28

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

  • 10:33

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

  • 10:38

    or that's local to you.
    or that's local to you.

  • 10:40

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

  • 10:44

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

  • 10:49

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

  • 10:53

    We all know that learning depends on narrative,
    We all know that learning depends on narrative,

  • 10:57

    and being able to tell a story.
    and being able to tell a story.

  • 10:59

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

  • 11:05

    that lets you tell that story.
    that lets you tell that story.

  • 11:09

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

  • 11:15

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

  • 11:20

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

  • 11:25

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

  • 11:32

    We share the outcome of what we have learned.
    We share the outcome of what we have learned.

  • 11:35

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

  • 11:39

    you figure it out--
    you figure it out--

  • 11:40

    I know you've all done this--
    I know you've all done this--

  • 11:42

    and you end up with something at the end.
    and you end up with something at the end.

  • 11:44

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

  • 11:49

    of those things.
    of those things.

  • 11:51

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

  • 11:56

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

  • 12:00

    Learning is not just the product.
    Learning is not just the product.

  • 12:02

    Learning is also the process.
    Learning is also the process.

  • 12:05

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

  • 12:08

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

  • 12:12

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

  • 12:18

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

  • 12:24

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

  • 12:28

    some day to do something great.
    some day to do something great.

  • 12:31

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

  • 12:34

    others have learned to do something.
    others have learned to do something.

  • 12:38

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

  • 12:45

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

  • 12:50

    individuals that are interested in doing something--
    individuals that are interested in doing something--

  • 12:54

    to make a difference, to build challenges,
    to make a difference, to build challenges,

  • 12:57

    to address challenges in some way--
    to address challenges in some way--

  • 13:00

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

  • 13:06

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

  • 13:10

    And that these pathways, as we call them,
    And that these pathways, as we call them,

  • 13:13

    are something that an individual can share,
    are something that an individual can share,

  • 13:16

    a high school teacher can share with her class,
    a high school teacher can share with her class,

  • 13:19

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

  • 13:23

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

  • 13:28

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

  • 13:33

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

  • 13:39

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

  • 13:44

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

  • 13:48

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

  • 13:54

    to allow them to address challenges.
    to allow them to address challenges.

  • 13:57

    So right now, 50 undergraduates are working
    So right now, 50 undergraduates are working

  • 14:01

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

  • 14:06

    graduate students.
    graduate students.

  • 14:08

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

  • 14:13

    thinking of students as recipients, but you're
    thinking of students as recipients, but you're

  • 14:16

    agents in building the possibilities that we
    agents in building the possibilities that we

  • 14:20

    hope to make available to the world.
    hope to make available to the world.

  • 14:23

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

  • 14:28

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

  • 14:37

    in government, in economics will have the opportunity
    in government, in economics will have the opportunity

  • 14:41

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

  • 14:48

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

  • 14:54

    on your shoulders when the time comes.
    on your shoulders when the time comes.

  • 14:58

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

  • 15:03

    How do we connect these kinds of things?
    How do we connect these kinds of things?

  • 15:05

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

  • 15:10

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

  • 15:14

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

  • 15:22

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

  • 15:26

    It's focused on biology and social innovation.
    It's focused on biology and social innovation.

  • 15:29

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

  • 15:33

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

  • 15:36

    is bring Harvard students and French students
    is bring Harvard students and French students

  • 15:39

    from Sciences Po and the University of Paris
    from Sciences Po and the University of Paris

  • 15:42

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

  • 15:48

    in an urban setting can be better.
    in an urban setting can be better.

  • 15:52

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

  • 15:56

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

  • 15:59

    [CHUCKLING]
    [CHUCKLING]

  • 16:02

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

  • 16:05

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

  • 16:08

    is remarkably rewarding for everyone involved, I think.
    is remarkably rewarding for everyone involved, I think.

  • 16:13

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

  • 16:18

    contradictory cities.
    contradictory cities.

  • 16:20

    It is a museum city.
    It is a museum city.

  • 16:22

    It is beautiful.
    It is beautiful.

  • 16:24

    It's a tourist destination.
    It's a tourist destination.

  • 16:26

    It is also profoundly unequal.
    It is also profoundly unequal.

  • 16:29

    It is in turmoil.
    It is in turmoil.

  • 16:30

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

  • 16:34

    just how in turmoil it is.
    just how in turmoil it is.

  • 16:36

    So it presents a setting that in some ways
    So it presents a setting that in some ways

  • 16:39

    is so contradictory and so complex.
    is so contradictory and so complex.

  • 16:43

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

  • 16:49

    in a particular place?
    in a particular place?

  • 16:51

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

  • 16:54

    with both cities being together.
    with both cities being together.

  • 16:57

    So we have done this now for four years.
    So we have done this now for four years.

  • 17:00

    There are close to 50 design plans.
    There are close to 50 design plans.

  • 17:04

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

  • 17:09

    have come from this.
    have come from this.

  • 17:12

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

  • 17:16

    One I will talk briefly about is BubbleBox.
    One I will talk briefly about is BubbleBox.

  • 17:20

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

  • 17:24

    students.
    students.

  • 17:26

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

  • 17:32

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

  • 17:37

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

  • 17:41

    displaced from where they set their tents up, how
    displaced from where they set their tents up, how

  • 17:45

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

  • 17:52

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

  • 17:57

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

  • 18:02

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

  • 18:06

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

  • 18:10

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

  • 18:15

    this will go where the need is greatest.
    this will go where the need is greatest.

  • 18:20

    How do you fund this?
    How do you fund this?

  • 18:21

    You fund it by actually renting BubbleBox
    You fund it by actually renting BubbleBox

  • 18:26

    to large music concerts in Europe and elsewhere.
    to large music concerts in Europe and elsewhere.

  • 18:31

    So the government of Jordan is building BubbleBox now,
    So the government of Jordan is building BubbleBox now,

  • 18:35

    and the team won the Paris Talent 2024 international competition
    and the team won the Paris Talent 2024 international competition

  • 18:41

    for innovation.
    for innovation.

  • 18:43

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

  • 18:47

    So BubbleBox is in process.
    So BubbleBox is in process.

  • 18:49

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

  • 18:54

    to do something like this.
    to do something like this.

  • 18:56

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

  • 19:00

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

  • 19:05

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

  • 19:12

    to really make the world a better place.
    to really make the world a better place.

  • 19:15

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

  • 19:20

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

  • 19:25

    and partnerships.
    and partnerships.

  • 19:27

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

  • 19:32

    because there are 17 of them that articulate key challenges
    because there are 17 of them that articulate key challenges

  • 19:38

    that the world needs to face.
    that the world needs to face.

  • 19:40

    We have a decade to meet these challenges.
    We have a decade to meet these challenges.

  • 19:44

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

  • 19:50

    And our hope is that more and more Harvard students
    And our hope is that more and more Harvard students

  • 19:54

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

  • 19:59

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

  • 20:06

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

  • 20:12

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

  • 20:19

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

  • 20:27

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

  • 20:32

    You'll get ahead.
    You'll get ahead.

  • 20:33

    That is your advantage.
    That is your advantage.

  • 20:36

    Those days are over.
    Those days are over.

  • 20:40

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

  • 20:44

    What is critical is the flow of knowledge.
    What is critical is the flow of knowledge.

  • 20:48

    It's not holding everything to yourself.
    It's not holding everything to yourself.

  • 20:51

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

  • 20:58

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

  • 21:05

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

  • 21:10

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

  • 21:15

    but is networking and really making a difference,
    but is networking and really making a difference,

  • 21:19

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

  • 21:24

    a better place.
    a better place.

  • 21:27

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

  • 21:30

    [APPLAUSE]
    [APPLAUSE]

  • 21:51

    MARLYN MCGRATH: Rob, thank you.
    MARLYN MCGRATH: Rob, thank you.

  • 21:52

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

  • 21:56

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

  • 21:59

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

  • 22:02

    Now I have the pleasure of introducing our colleague Melissa
    Now I have the pleasure of introducing our colleague Melissa

  • 22:04

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

  • 22:09

    She's an experimental particle physicist who
    She's an experimental particle physicist who

  • 22:12

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

  • 22:18

    I hope I said that all right.
    I hope I said that all right.

  • 22:21

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

  • 22:25

    starting from high school, at least.
    starting from high school, at least.

  • 22:27

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

  • 22:32

    Hold that thought too.
    Hold that thought too.

  • 22:34

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

  • 22:39

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

  • 22:42

    to London to attend the Lycée Francais de Londre.
    to London to attend the Lycée Francais de Londre.

  • 22:47

    She has no high school diploma.
    She has no high school diploma.

  • 22:49

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

  • 22:52

    It turns out that she has an honorary high school
    It turns out that she has an honorary high school

  • 22:54

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

  • 22:57

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

  • 23:00

    She does have a Bachelor of Science from the University
    She does have a Bachelor of Science from the University

  • 23:02

    of Toronto and a doctorate from Stanford,
    of Toronto and a doctorate from Stanford,

  • 23:05

    entirely accredited place in the West Coast.
    entirely accredited place in the West Coast.

  • 23:08

    [LAUGHTER]
    [LAUGHTER]

  • 23:09

    She's worked at Lawrence Berkeley Lab.
    She's worked at Lawrence Berkeley Lab.

  • 23:11

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

  • 23:16

    turns up in the newspaper and we gasp.
    turns up in the newspaper and we gasp.

  • 23:19

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

  • 23:23

    I'm sure there are stories there.
    I'm sure there are stories there.

  • 23:24

    This is not the topic of today.
    This is not the topic of today.

  • 23:26

    She was part of the teams that discovered
    She was part of the teams that discovered

  • 23:28

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

  • 23:34

    She will speak to us.
    She will speak to us.

  • 23:35

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

  • 23:40

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

  • 23:43

    So I give you Melissa.
    So I give you Melissa.

  • 23:45

    [APPLAUSE]
    [APPLAUSE]

  • 23:55

    MELISSA FRANKLIN: Hi.
    MELISSA FRANKLIN: Hi.

  • 23:56

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

  • 23:58

    [CHUCKLING]
    [CHUCKLING]

  • 23:59

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

  • 24:03

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

  • 24:07

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

  • 24:09

    Hi.
    Hi.

  • 24:10

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

  • 24:13

    [CHUCKLING]
    [CHUCKLING]

  • 24:16

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

  • 24:20

    You don't have to--
    You don't have to--

  • 24:21

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

  • 24:24

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

  • 24:27

    [CHUCKLING]
    [CHUCKLING]

  • 24:29

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

  • 24:35

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

  • 24:42

    in measuring the universe with nothing in it.
    in measuring the universe with nothing in it.

  • 24:44

    So I guess I should get the clicker.
    So I guess I should get the clicker.

  • 24:46

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

  • 24:51

    at all.
    at all.

  • 24:53

    It's stuff.
    It's stuff.

  • 24:54

    I get that out of my universe.
    I get that out of my universe.

  • 24:56

    Now, here's an atom.
    Now, here's an atom.

  • 24:57

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

  • 25:01

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

  • 25:05

    which I'm sure you know.
    which I'm sure you know.

  • 25:06

    And I'm interested in the quarks.
    And I'm interested in the quarks.

  • 25:08

    I really like quarks.
    I really like quarks.

  • 25:09

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

  • 25:13

    Here is my world.
    Here is my world.

  • 25:16

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

  • 25:20

    You would look at the quarks.
    You would look at the quarks.

  • 25:21

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

  • 25:25

    and all the leptons--
    and all the leptons--

  • 25:27

    electrons, et cetera, the neutrinos--
    electrons, et cetera, the neutrinos--

  • 25:30

    and all the forces that hold all those particles
    and all the forces that hold all those particles

  • 25:34

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

  • 25:40

    [CHUCKLING]
    [CHUCKLING]

  • 25:42

    Here's what you would find.
    Here's what you would find.

  • 25:44

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

  • 25:48

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

  • 25:52

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

  • 25:55

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

  • 26:00

    You can't find one.
    You can't find one.

  • 26:02

    It's over.
    It's over.

  • 26:03

    [CHUCKLING]
    [CHUCKLING]

  • 26:05

    There's only six.
    There's only six.

  • 26:08

    So I was on that team.
    So I was on that team.

  • 26:10

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

  • 26:13

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

  • 26:17

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

  • 26:21

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

  • 26:24

    Those are all the particles and the forces.
    Those are all the particles and the forces.

  • 26:27

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

  • 26:31

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

  • 26:36

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

  • 26:39

    But people like me don't really--
    But people like me don't really--

  • 26:42

    it doesn't fit inside my head.
    it doesn't fit inside my head.

  • 26:43

    I like reading it aloud.
    I like reading it aloud.

  • 26:46

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

  • 26:50

    It's fun with friends.
    It's fun with friends.

  • 26:53

    It's very fun.
    It's very fun.

  • 26:54

    There must be a game.
    There must be a game.

  • 26:55

    It's not a drinking game.
    It's not a drinking game.

  • 26:58

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

  • 27:02

    So here's the thing.
    So here's the thing.

  • 27:03

    For each of these terms in this equation--
    For each of these terms in this equation--

  • 27:07

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

  • 27:10

    And this is a Feynman diagram.
    And this is a Feynman diagram.

  • 27:11

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

  • 27:15

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

  • 27:20

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

  • 27:23

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

  • 27:26

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

  • 27:32

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

  • 27:39

    These are just heavier particles.
    These are just heavier particles.

  • 27:40

    And we say, oh.
    And we say, oh.

  • 27:41

    Ha.
    Ha.

  • 27:42

    I can write this down.
    I can write this down.

  • 27:43

    Can I measure it?
    Can I measure it?

  • 27:44

    So that's sort of my life.
    So that's sort of my life.

  • 27:46

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

  • 27:50

    Now, for the people interested in archeology,
    Now, for the people interested in archeology,

  • 27:53

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

  • 27:59

    after everything happens, probably, you'll
    after everything happens, probably, you'll

  • 28:03

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

  • 28:07

    And you'll probably understand them.
    And you'll probably understand them.

  • 28:10

    Could be sooner than 1,000 years.
    Could be sooner than 1,000 years.

  • 28:12

    It could be-- OK.
    It could be-- OK.

  • 28:14

    But I'm just saying.
    But I'm just saying.

  • 28:15

    I'm just saying.
    I'm just saying.

  • 28:16

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

  • 28:19

    and don't just not think about it.
    and don't just not think about it.

  • 28:22

    OK, here is me.
    OK, here is me.

  • 28:24

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

  • 28:27

    who you are.
    who you are.

  • 28:28

    Here's the top quark on my shoe.
    Here's the top quark on my shoe.

  • 28:31

    That's me.
    That's me.

  • 28:32

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

  • 28:36

    and it has just as much information.
    and it has just as much information.

  • 28:38

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

  • 28:45

    [CHUCKLING]
    [CHUCKLING]

  • 28:46

    The me that-- it's the spiritual.
    The me that-- it's the spiritual.

  • 28:49

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

  • 28:55

    So I want to describe the vacuum.
    So I want to describe the vacuum.

  • 28:58

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

  • 29:00

    I take everything out.
    I take everything out.

  • 29:02

    Is there something there?
    Is there something there?

  • 29:05

    I'll give you a hint.
    I'll give you a hint.

  • 29:06

    Yes.
    Yes.

  • 29:07

    But it's kind of an interesting idea.
    But it's kind of an interesting idea.

  • 29:09

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

  • 29:12

    see that Samuel Beckett thought about this a lot.
    see that Samuel Beckett thought about this a lot.

  • 29:16

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

  • 29:21

    Waiting for Godot.
    Waiting for Godot.

  • 29:22

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

  • 29:26

    strapped to a chair sitting alone.
    strapped to a chair sitting alone.

  • 29:29

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

  • 29:33

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

  • 29:37

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

  • 29:44

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

  • 29:49

    what do I do?
    what do I do?

  • 29:51

    So I want to tell you one thing.
    So I want to tell you one thing.

  • 29:53

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

  • 29:59

    The ground state doesn't talk to us.
    The ground state doesn't talk to us.

  • 30:01

    So what do I mean?
    So what do I mean?

  • 30:02

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

  • 30:05

    It doesn't reveal what it is.
    It doesn't reveal what it is.

  • 30:08

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

  • 30:14

    So do we understand the ground state?
    So do we understand the ground state?

  • 30:16

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

  • 30:20

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

  • 30:23

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

  • 30:29

    and rock and roll.
    and rock and roll.

  • 30:31

    So what we're going to show--
    So what we're going to show--

  • 30:33

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

  • 30:38

    and separate all the colors that are coming out.
    and separate all the colors that are coming out.

  • 30:41

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

  • 30:45

    is a spectrum of the rainbow.
    is a spectrum of the rainbow.

  • 30:47

    Do you guys see it?
    Do you guys see it?

  • 30:48

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

  • 30:49

    AUDIENCE: Yes.
    AUDIENCE: Yes.

  • 30:49

    MELISSA FRANKLIN: Yeah?
    MELISSA FRANKLIN: Yeah?

  • 30:51

    OK.
    OK.

  • 30:51

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

  • 30:56

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

  • 30:59

    Now what do you see?
    Now what do you see?

  • 31:00

    Do you see two lines, or three?
    Do you see two lines, or three?

  • 31:02

    AUDIENCE: Three.
    AUDIENCE: Three.

  • 31:03

    MELISSA FRANKLIN: OK.
    MELISSA FRANKLIN: OK.

  • 31:04

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

  • 31:09

    an electrical current through it.
    an electrical current through it.

  • 31:10

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

  • 31:12

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

  • 31:15

    And then I can see its nature.
    And then I can see its nature.

  • 31:18

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

  • 31:21

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

  • 31:24

    going to put an electric current through helium.
    going to put an electric current through helium.

  • 31:28

    Isn't it beautiful?
    Isn't it beautiful?

  • 31:29

    Do you see the lines?
    Do you see the lines?

  • 31:30

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

  • 31:33

    [CHUCKLING]
    [CHUCKLING]

  • 31:34

    No?
    No?

  • 31:36

    So helium is a different atom.
    So helium is a different atom.

  • 31:38

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

  • 31:42

    And the final one is neon.
    And the final one is neon.

  • 31:44

    AUDIENCE: Whoa.
    AUDIENCE: Whoa.

  • 31:46

    MELISSA FRANKLIN: [CHUCKLES]
    MELISSA FRANKLIN: [CHUCKLES]

  • 31:50

    I love this.
    I love this.

  • 31:51

    I love demos.
    I love demos.

  • 31:53

    Daniel also loves demos.
    Daniel also loves demos.

  • 31:54

    OK.
    OK.

  • 31:55

    Thank you.
    Thank you.

  • 31:56

    OK.
    OK.

  • 31:57

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

  • 31:59

    Not really anything.
    Not really anything.

  • 32:01

    Doesn't really have anything.
    Doesn't really have anything.

  • 32:03

    [APPLAUSE]
    [APPLAUSE]

  • 32:08

    OK.
    OK.

  • 32:09

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

  • 32:12

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

  • 32:16

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

  • 32:19

    I'm going to have to excite it.
    I'm going to have to excite it.

  • 32:19

    So there was this guy called--
    So there was this guy called--

  • 32:21

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

  • 32:24

    called Peter Higgs.
    called Peter Higgs.

  • 32:26

    And he solved this theoretical problem.
    And he solved this theoretical problem.

  • 32:28

    And in order to solve the problem, he had
    And in order to solve the problem, he had

  • 32:30

    to introduce something called the Higgs field.
    to introduce something called the Higgs field.

  • 32:33

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

  • 32:39

    Remember the Lagrangian?
    Remember the Lagrangian?

  • 32:40

    Remember that equation?
    Remember that equation?

  • 32:42

    If to that equation of the standard model
    If to that equation of the standard model

  • 32:45

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

  • 32:50

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

  • 32:53

    is a Higgs boson, which is a particle.
    is a Higgs boson, which is a particle.

  • 32:55

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

  • 32:58

    and everybody will be happy.
    and everybody will be happy.

  • 32:59

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

  • 33:02

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

  • 33:07

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

  • 33:11

    through it.
    through it.

  • 33:11

    So what's a field?
    So what's a field?

  • 33:13

    Is this too boring?
    Is this too boring?

  • 33:14

    Are we boring?
    Are we boring?

  • 33:16

    No, we're not boring.
    No, we're not boring.

  • 33:16

    OK.
    OK.

  • 33:17

    So this is a wind map of America.
    So this is a wind map of America.

  • 33:21

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

  • 33:27

    it is, and the direction.
    it is, and the direction.

  • 33:29

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

  • 33:34

    the strength and the direction.
    the strength and the direction.

  • 33:37

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

  • 33:39

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

  • 33:42

    So imagine that I have--
    So imagine that I have--

  • 33:45

    so let's go back one step.
    so let's go back one step.

  • 33:48

    So this is the wind field.
    So this is the wind field.

  • 33:50

    If I want to excite the wind field somehow,
    If I want to excite the wind field somehow,

  • 33:53

    I would get something like a tornado.
    I would get something like a tornado.

  • 33:55

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

  • 34:01

    like a tornado.
    like a tornado.

  • 34:02

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

  • 34:06

    And the Higgs field has no direction.
    And the Higgs field has no direction.

  • 34:08

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

  • 34:14

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

  • 34:19

    And then I want to--
    And then I want to--

  • 34:20

    that's my whole life.
    that's my whole life.

  • 34:22

    [CHUCKLING]
    [CHUCKLING]

  • 34:23

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

  • 34:27

    So when--
    So when--

  • 34:28

    [CHUCKLING]
    [CHUCKLING]

  • 34:30

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

  • 34:34

    but then I thought, OK.
    but then I thought, OK.

  • 34:37

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

  • 34:39

    And I knew that my friend Daniel was here.
    And I knew that my friend Daniel was here.

  • 34:41

    OK.
    OK.

  • 34:42

    So here's what we want to do.
    So here's what we want to do.

  • 34:47

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

  • 34:50

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

  • 34:52

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

  • 34:56

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

  • 35:04

    and I collide them together at very high energies,
    and I collide them together at very high energies,

  • 35:07

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

  • 35:11

    in a tiny little time.
    in a tiny little time.

  • 35:12

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

  • 35:19

    I did go to college.
    I did go to college.

  • 35:20

    [CHUCKLING]
    [CHUCKLING]

  • 35:21

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

  • 35:24

    I just want you to know that.
    I just want you to know that.

  • 35:27

    Although it might say that my CV.
    Although it might say that my CV.

  • 35:29

    [LAUGHTER]
    [LAUGHTER]

  • 35:30

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

  • 35:33

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

  • 35:36

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

  • 35:41

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

  • 35:44

    And then I just have the LHC--
    And then I just have the LHC--

  • 35:45

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

  • 35:51

    Protons collide.
    Protons collide.

  • 35:52

    And so what's really happening--
    And so what's really happening--

  • 35:54

    I'm walking around a lot.
    I'm walking around a lot.

  • 35:55

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

  • 36:03

    protons every 25 nanoseconds.
    protons every 25 nanoseconds.

  • 36:06

    So nano is small.
    So nano is small.

  • 36:07

    [CHUCKLING]
    [CHUCKLING]

  • 36:10

    Yeah, it's really small.
    Yeah, it's really small.

  • 36:12

    Every 25 nanoseconds.
    Every 25 nanoseconds.

  • 36:14

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

  • 36:21

    I do that.
    I do that.

  • 36:22

    Protons are going to collide.
    Protons are going to collide.

  • 36:24

    The quarks inside the protons are going to collide.
    The quarks inside the protons are going to collide.

  • 36:27

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

  • 36:34

    10 to the 10 trillion.
    10 to the 10 trillion.

  • 36:39

    10 trillion.
    10 trillion.

  • 36:40

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

  • 36:44

    Anyway--
    Anyway--

  • 36:44

    [LAUGHTER]
    [LAUGHTER]

  • 36:46

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

  • 36:49

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

  • 36:55

    and I actually got a boson out.
    and I actually got a boson out.

  • 36:57

    There must be a field there, right?
    There must be a field there, right?

  • 36:59

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

  • 37:03

    in Switzerland.
    in Switzerland.

  • 37:05

    And then hire maybe--
    And then hire maybe--

  • 37:07

    I don't know-- 20,000 people.
    I don't know-- 20,000 people.

  • 37:10

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

  • 37:13

    comes out of these proton collisions.
    comes out of these proton collisions.

  • 37:15

    And this is the detector.
    And this is the detector.

  • 37:16

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

  • 37:19

    [CHUCKLING]
    [CHUCKLING]

  • 37:21

    So you have to--
    So you have to--

  • 37:23

    obviously, French people are the same size.
    obviously, French people are the same size.

  • 37:25

    But--
    But--

  • 37:26

    [CHUCKLING]
    [CHUCKLING]

  • 37:27

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

  • 37:29

    you actually sometimes get a little--
    you actually sometimes get a little--

  • 37:32

    you should go to the bathroom first.
    you should go to the bathroom first.

  • 37:33

    Anyway, it's very, very tall.
    Anyway, it's very, very tall.

  • 37:36

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

  • 37:40

    Anyhow, we built this detector very fast.
    Anyhow, we built this detector very fast.

  • 37:41

    Sorry.
    Sorry.

  • 37:42

    I know that-- and this comes out.
    I know that-- and this comes out.

  • 37:45

    All of a sudden, protons, quarks collide.
    All of a sudden, protons, quarks collide.

  • 37:48

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

  • 37:51

    is just figuring out what happened.
    is just figuring out what happened.

  • 37:53

    What happened?
    What happened?

  • 37:54

    What happened?
    What happened?

  • 37:55

    OK.
    OK.

  • 37:55

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

  • 38:03

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

  • 38:06

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

  • 38:10

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

  • 38:15

    naturally.
    naturally.

  • 38:16

    So don't trust yourself.
    So don't trust yourself.

  • 38:18

    So what we do is we blind ourselves.
    So what we do is we blind ourselves.

  • 38:21

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

  • 38:25

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

  • 38:27

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

  • 38:31

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

  • 38:37

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

  • 38:39

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

  • 38:43

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

  • 38:46

    And we go, wow.
    And we go, wow.

  • 38:48

    This is not exciting.
    This is not exciting.

  • 38:50

    [CHUCKLING]
    [CHUCKLING]

  • 38:50

    OK.
    OK.

  • 38:51

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

  • 38:53

    OK.
    OK.

  • 38:54

    Let's just talk about this.
    Let's just talk about this.

  • 38:55

    My team is 3,000 people.
    My team is 3,000 people.

  • 38:57

    It's not my team.
    It's not my team.

  • 38:58

    I'm not the boss.
    I'm not the boss.

  • 39:00

    Otherwise, I wouldn't-- yeah.
    Otherwise, I wouldn't-- yeah.

  • 39:02

    [LAUGHTER]
    [LAUGHTER]

  • 39:05

    Yeah.
    Yeah.

  • 39:06

    I'd probably-- yeah.
    I'd probably-- yeah.

  • 39:09

    My team is 3,000.
    My team is 3,000.

  • 39:10

    There's another experiment that's 3000.
    There's another experiment that's 3000.

  • 39:12

    You gotta check each other.
    You gotta check each other.

  • 39:14

    That's about the whole Harvard undergraduate class.
    That's about the whole Harvard undergraduate class.

  • 39:17

    Imagine that everybody in the whole class--
    Imagine that everybody in the whole class--

  • 39:20

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

  • 39:23

    were all working on the same project.
    were all working on the same project.

  • 39:26

    That would be weird.
    That would be weird.

  • 39:29

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

  • 39:33

    And this is how I feel afterwards.
    And this is how I feel afterwards.

  • 39:34

    [CHUCKLING]
    [CHUCKLING]

  • 39:36

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

  • 39:40

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

  • 39:44

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

  • 39:51

    And a lot of my colleagues feel weird also.
    And a lot of my colleagues feel weird also.

  • 39:54

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

  • 39:58

    Should I stop?
    Should I stop?

  • 39:58

    Because I think-- no?
    Because I think-- no?

  • 40:00

    It's OK?
    It's OK?

  • 40:01

    AUDIENCE: Keep going.
    AUDIENCE: Keep going.

  • 40:02

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

  • 40:05

    It's a weird thing to want to do.
    It's a weird thing to want to do.

  • 40:07

    It's very specific.
    It's very specific.

  • 40:10

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

  • 40:16

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

  • 40:20

    Has anyone ever heard of it?
    Has anyone ever heard of it?

  • 40:24

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

  • 40:31

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

  • 40:34

    A guy called O'Donnell.
    A guy called O'Donnell.

  • 40:38

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

  • 40:48

    I thought that was kind of interesting.
    I thought that was kind of interesting.

  • 40:50

    He didn't make them up.
    He didn't make them up.

  • 40:51

    He just wrote them down.
    He just wrote them down.

  • 40:53

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

  • 41:00

    Live and stuff.
    Live and stuff.

  • 41:02

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

  • 41:07

    is the overarching idea of physics?
    is the overarching idea of physics?

  • 41:13

    If we put all the things we know together,
    If we put all the things we know together,

  • 41:15

    what do we find as an overarching idea?
    what do we find as an overarching idea?

  • 41:17

    So what is the overarching idea here?
    So what is the overarching idea here?

  • 41:19

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

  • 41:25

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

  • 41:27

    going to come up with the answer.
    going to come up with the answer.

  • 41:30

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

  • 41:35

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

  • 41:40

    You guys have watched TV.
    You guys have watched TV.

  • 41:42

    [CHUCKLING]
    [CHUCKLING]

  • 41:43

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

  • 41:49

    So what is the idea here?
    So what is the idea here?

  • 41:51

    Why are these funny?
    Why are these funny?

  • 41:53

    And Walt Disney says this.
    And Walt Disney says this.

  • 41:56

    [VIDEO PLAYBACK]
    [VIDEO PLAYBACK]

  • 41:56

    [END PLAYBACK]
    [END PLAYBACK]

  • 41:57

    Oh.
    Oh.

  • 41:58

    Walt Disney.
    Walt Disney.

  • 42:00

    [VIDEO PLAYBACK]
    [VIDEO PLAYBACK]

  • 42:01

    - Impossible cartoon actions will seem plausible
    - Impossible cartoon actions will seem plausible

  • 42:04

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

  • 42:09

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

  • 42:12

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

  • 42:16

    but it has some basis in fact.
    but it has some basis in fact.

  • 42:19

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

  • 42:23

    here.
    here.

  • 42:24

    That is the spinal column.
    That is the spinal column.

  • 42:28

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

  • 42:33

    [BELL RINGING]
    [BELL RINGING]

  • 42:39

    [END PLAYBACK]
    [END PLAYBACK]

  • 42:40

    MELISSA FRANKLIN: All right.
    MELISSA FRANKLIN: All right.

  • 42:40

    OK.
    OK.

  • 42:41

    So this is really interesting.
    So this is really interesting.

  • 42:43

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

  • 42:47

    And that's what makes it funny.
    And that's what makes it funny.

  • 42:49

    So I was trying to think of physics.
    So I was trying to think of physics.

  • 42:51

    Real physics.
    Real physics.

  • 42:52

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

  • 42:55

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

  • 43:02

    But what we do is incredibly implausible.
    But what we do is incredibly implausible.

  • 43:05

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

  • 43:10

    and how we measure what it looks like.
    and how we measure what it looks like.

  • 43:13

    But "particle physics is the unbelievable in pursuit
    But "particle physics is the unbelievable in pursuit

  • 43:16

    of the unimaginable.
    of the unimaginable.

  • 43:17

    To pinpoint the smallest fragments of the universe,
    To pinpoint the smallest fragments of the universe,

  • 43:19

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

  • 43:21

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

  • 43:24

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

  • 43:26

    So we're looking for the implausible possible.
    So we're looking for the implausible possible.

  • 43:29

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

  • 43:32

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

  • 43:39

    to help us figure out the next puzzle.
    to help us figure out the next puzzle.

  • 43:41

    Thanks.
    Thanks.

  • 43:42

    [APPLAUSE]
    [APPLAUSE]

  • 44:05

    MARLYN MCGRATH: Thank you.
    MARLYN MCGRATH: Thank you.

  • 44:08

    In our pursuit of one different thing after another,
    In our pursuit of one different thing after another,

  • 44:11

    here is another different thing.
    here is another different thing.

  • 44:13

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

  • 44:18

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

  • 44:22

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

  • 44:25

    one of his specialties.
    one of his specialties.

  • 44:27

    And he does a lot of other things.
    And he does a lot of other things.

  • 44:29

    I won't list them.
    I won't list them.

  • 44:30

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

  • 44:33

    for the Center for the Environment.
    for the Center for the Environment.

  • 44:35

    A lot of things are connected at Harvard.
    A lot of things are connected at Harvard.

  • 44:37

    I think you're figuring that out.
    I think you're figuring that out.

  • 44:39

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

  • 44:42

    He went to Marshall University High School
    He went to Marshall University High School

  • 44:44

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

  • 44:48

    have no new graduates from there, I assume.
    have no new graduates from there, I assume.

  • 44:51

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

  • 44:56

    in Connecticut, and a PhD from Harvard.
    in Connecticut, and a PhD from Harvard.

  • 44:58

    He has a JD from Yale Law School.
    He has a JD from Yale Law School.

  • 45:01

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

  • 45:04

    and so that's a terrific extra thing.
    and so that's a terrific extra thing.

  • 45:07

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

  • 45:10

    and he has not either.
    and he has not either.

  • 45:12

    He's been on our faculty since 2001.
    He's been on our faculty since 2001.

  • 45:14

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

  • 45:18

    and he teaches lots of other things as well.
    and he teaches lots of other things as well.

  • 45:20

    But that's not the subject of today.
    But that's not the subject of today.

  • 45:23

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

  • 45:25

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

  • 45:28

    No reason why they should correspond exactly.
    No reason why they should correspond exactly.

  • 45:30

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

  • 45:34

    So without any further ado.
    So without any further ado.

  • 45:37

    [APPLAUSE]
    [APPLAUSE]

  • 45:46

    ROBIN KELSEY: Good afternoon.
    ROBIN KELSEY: Good afternoon.

  • 45:48

    Good afternoon!
    Good afternoon!

  • 45:48

    AUDIENCE: Good afternoon.
    AUDIENCE: Good afternoon.

  • 45:49

    ROBIN KELSEY: Thank you.
    ROBIN KELSEY: Thank you.

  • 45:52

    I needed that.
    I needed that.

  • 45:52

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

  • 45:56

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

  • 46:00

    I love Melissa Franklin.
    I love Melissa Franklin.

  • 46:02

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

  • 46:04

    I want to come to Harvard and study physics.
    I want to come to Harvard and study physics.

  • 46:06

    But you can't all study physics because we
    But you can't all study physics because we

  • 46:09

    don't have that many physics faculty.
    don't have that many physics faculty.

  • 46:11

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

  • 46:15

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

  • 46:19

    [CHUCKLING]
    [CHUCKLING]

  • 46:22

    No, it's true.
    No, it's true.

  • 46:23

    It's really a matter of scale.
    It's really a matter of scale.

  • 46:25

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

  • 46:28

    or when they're really tiny.
    or when they're really tiny.

  • 46:30

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

  • 46:33

    where things get very deadly serious.
    where things get very deadly serious.

  • 46:35

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

  • 46:37

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

  • 46:41

    and you can laugh.
    and you can laugh.

  • 46:42

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

  • 46:45

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

  • 46:50

    So-- [CHUCKLES] yeah.
    So-- [CHUCKLES] yeah.

  • 46:52

    Notes are very precious.
    Notes are very precious.

  • 46:53

    OK.
    OK.

  • 46:54

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

  • 47:02

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

  • 47:06

    Harvard is a great university, in my view,
    Harvard is a great university, in my view,

  • 47:10

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

  • 47:15

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

  • 47:22

    And the questions I'm going to pose today
    And the questions I'm going to pose today

  • 47:23

    are about the future of cultural space.
    are about the future of cultural space.

  • 47:28

    Now, what do I mean by cultural space?
    Now, what do I mean by cultural space?

  • 47:30

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

  • 47:37

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

  • 47:43

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

  • 47:51

    To experience human creativity together.
    To experience human creativity together.

  • 47:58

    These spaces are incredibly important in our civic life.
    These spaces are incredibly important in our civic life.

  • 48:04

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

  • 48:08

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

  • 48:16

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

  • 48:24

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

  • 48:32

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

  • 48:38

    Public Library, Disney Hall--
    Public Library, Disney Hall--

  • 48:42

    I thought of Disney Hall because of Walt Disney,
    I thought of Disney Hall because of Walt Disney,

  • 48:45

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

  • 48:48

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

  • 48:56

    But our local movie theater, our town public library
    But our local movie theater, our town public library

  • 49:03

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

  • 49:09

    These places where we gather and we attend to
    These places where we gather and we attend to

  • 49:13

    and honor human creativity, human efforts
    and honor human creativity, human efforts

  • 49:18

    to find meaning, beauty, empathy, and understanding
    to find meaning, beauty, empathy, and understanding

  • 49:23

    are really essential to our humanity.
    are really essential to our humanity.

  • 49:28

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

  • 49:35

    I grew up in Minneapolis, Minnesota.
    I grew up in Minneapolis, Minnesota.

  • 49:37

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

  • 49:41

    Don't let that fool you.
    Don't let that fool you.

  • 49:43

    There was no university--
    There was no university--

  • 49:45

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

  • 49:48

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

  • 49:52

    But I was very, very fortunate in having parents
    But I was very, very fortunate in having parents

  • 49:55

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

  • 50:00

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

  • 50:06

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

  • 50:09

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

  • 50:13

    are there any people here from Minnesota?
    are there any people here from Minnesota?

  • 50:15

    AUDIENCE: Woo!
    AUDIENCE: Woo!

  • 50:15

    ROBIN KELSEY: Yeah?
    ROBIN KELSEY: Yeah?

  • 50:16

    All right.
    All right.

  • 50:17

    Good.
    Good.

  • 50:19

    All right.
    All right.

  • 50:20

    Yeah.
    Yeah.

  • 50:21

    The theater in Minneapolis, from the flagship
    The theater in Minneapolis, from the flagship

  • 50:23

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

  • 50:26

    Theater in the Cedar Riverside neighborhood,
    Theater in the Cedar Riverside neighborhood,

  • 50:29

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

  • 50:34

    So this is where this issue of cultural space
    So this is where this issue of cultural space

  • 50:37

    has particular significance to me.
    has particular significance to me.

  • 50:42

    Here.
    Here.

  • 50:43

    This is the clicker.
    This is the clicker.

  • 50:45

    Yes?
    Yes?

  • 50:46

    No?
    No?

  • 50:46

    MARLYN MCGRATH: Try the other one.
    MARLYN MCGRATH: Try the other one.

  • 50:47

    ROBIN KELSEY: What other one?
    ROBIN KELSEY: What other one?

  • 50:48

    The duck?
    The duck?

  • 50:49

    MARLYN MCGRATH: No.
    MARLYN MCGRATH: No.

  • 50:50

    ROBIN KELSEY: Oh.
    ROBIN KELSEY: Oh.

  • 50:51

    This.
    This.

  • 50:52

    This?
    This?

  • 50:52

    Oh, OK.
    Oh, OK.

  • 50:53

    Good.
    Good.

  • 50:54

    All right.
    All right.

  • 50:55

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

  • 51:05

    And one reason is probably obvious to you,
    And one reason is probably obvious to you,

  • 51:08

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

  • 51:14

    Those in charge of our libraries are wondering,
    Those in charge of our libraries are wondering,

  • 51:18

    what is a library when our smartphone can bring us
    what is a library when our smartphone can bring us

  • 51:26

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

  • 51:31

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

  • 51:37

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

  • 51:43

    and shows are streaming into our homes?
    and shows are streaming into our homes?

  • 51:50

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

  • 51:56

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

  • 52:00

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

  • 52:03

    unique experiences to visitors, patterns of usage
    unique experiences to visitors, patterns of usage

  • 52:09

    are changing radically in this digital moment.
    are changing radically in this digital moment.

  • 52:13

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

  • 52:19

    have very much changed the experience of art museums.
    have very much changed the experience of art museums.

  • 52:24

    And museum directors and staff are scrambling
    And museum directors and staff are scrambling

  • 52:27

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

  • 52:33

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

  • 52:38

    and even new museum spaces are being designed
    and even new museum spaces are being designed

  • 52:42

    to accommodate the making of selfies.
    to accommodate the making of selfies.

  • 52:49

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

  • 52:52

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

  • 52:57

    that they can make the culinary offerings more Instagrammable.
    that they can make the culinary offerings more Instagrammable.

  • 53:01

    [CHUCKLING]
    [CHUCKLING]

  • 53:03

    No, I kid you not.
    No, I kid you not.

  • 53:04

    I kid you not.
    I kid you not.

  • 53:08

    In addition, cultural tastes and desires are changing.
    In addition, cultural tastes and desires are changing.

  • 53:12

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

  • 53:17

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

  • 53:22

    which is fabulous--
    which is fabulous--

  • 53:23

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

  • 53:28

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

  • 53:33

    was unheard of 30 years ago.
    was unheard of 30 years ago.

  • 53:35

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

  • 53:39

    So attention spans.
    So attention spans.

  • 53:40

    Demands for interactivity are changing when
    Demands for interactivity are changing when

  • 53:44

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

  • 53:50

    and with their interactivity.
    and with their interactivity.

  • 53:52

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

  • 53:57

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

  • 54:02

    span to go to the opera and so forth.
    span to go to the opera and so forth.

  • 54:04

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

  • 54:07

    But nonetheless, these are important considerations.
    But nonetheless, these are important considerations.

  • 54:11

    There is also the exceedingly important issue of inclusion.
    There is also the exceedingly important issue of inclusion.

  • 54:17

    Whose culture gets exalted?
    Whose culture gets exalted?

  • 54:20

    Who gets invited and welcomed into our cultural spaces?
    Who gets invited and welcomed into our cultural spaces?

  • 54:25

    Who can afford to buy a ticket?
    Who can afford to buy a ticket?

  • 54:28

    Many of us are deeply concerned with the urgency
    Many of us are deeply concerned with the urgency

  • 54:32

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

  • 54:36

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

  • 54:42

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

  • 54:47

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

  • 54:52

    involves white men and women.
    involves white men and women.

  • 54:55

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

  • 55:00

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

  • 55:06

    to the mainstream of Broadway.
    to the mainstream of Broadway.

  • 55:07

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

  • 55:12

    you can't possibly attend without considerable sacrifice.
    you can't possibly attend without considerable sacrifice.

  • 55:19

    So these challenges are formidable.
    So these challenges are formidable.

  • 55:23

    And they have led me to become very interested
    And they have led me to become very interested

  • 55:28

    in the future of cultural space.
    in the future of cultural space.

  • 55:30

    How do we address these challenges?
    How do we address these challenges?

  • 55:32

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

  • 55:37

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

  • 55:42

    gasp-- an administrator.
    gasp-- an administrator.

  • 55:45

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

  • 55:47

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

  • 55:53

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

  • 55:55

    but that's kind of accidental.
    but that's kind of accidental.

  • 55:58

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

  • 56:03

    But I have been brought as an administrator at Harvard
    But I have been brought as an administrator at Harvard

  • 56:07

    as someone who serves on all too many committees.
    as someone who serves on all too many committees.

  • 56:10

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

  • 56:15

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

  • 56:18

    lab across the river officially opening in September,
    lab across the river officially opening in September,

  • 56:21

    but it's already being used.
    but it's already being used.

  • 56:23

    A fabulous new facility for experimentation
    A fabulous new facility for experimentation

  • 56:25

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

  • 56:33

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

  • 56:36

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

  • 56:41

    and for art history.
    and for art history.

  • 56:42

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

  • 56:45

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

  • 56:49

    across the river.
    across the river.

  • 56:51

    And this is incredibly exciting work.
    And this is incredibly exciting work.

  • 56:53

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

  • 56:56

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

  • 57:01

    to revitalize its cultural spaces.
    to revitalize its cultural spaces.

  • 57:04

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

  • 57:10

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

  • 57:14

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

  • 57:20

    of our time.
    of our time.

  • 57:22

    Now, why do I say it is vital?
    Now, why do I say it is vital?

  • 57:27

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

  • 57:31

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

  • 57:38

    or a group of data points.
    or a group of data points.

  • 57:40

    It is really important that we are bound together
    It is really important that we are bound together

  • 57:45

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

  • 57:50

    and value of cultural difference.
    and value of cultural difference.

  • 57:54

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

  • 57:58

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

  • 58:03

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

  • 58:07

    into places to experience one another's humanity,
    into places to experience one another's humanity,

  • 58:11

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

  • 58:16

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

  • 58:21

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

  • 58:26

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

  • 58:31

    And on this occasion, I accidentally had a conversation
    And on this occasion, I accidentally had a conversation

  • 58:36

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

  • 58:40

    School of Design.
    School of Design.

  • 58:41

    Turns out he was thinking about these same questions
    Turns out he was thinking about these same questions

  • 58:44

    about the future of cultural space.
    about the future of cultural space.

  • 58:47

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

  • 58:55

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

  • 58:59

    And one of the great things about universities
    And one of the great things about universities

  • 59:02

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

  • 59:07

    And that engine is called the classroom.
    And that engine is called the classroom.

  • 59:10

    So this fall, rather belatedly, Jerold and I
    So this fall, rather belatedly, Jerold and I

  • 59:14

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

  • 59:19

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

  • 59:23

    and fortunately, it was approved.
    and fortunately, it was approved.

  • 59:25

    So we taught it this spring.
    So we taught it this spring.

  • 59:27

    It was a course we limited to about 30 students
    It was a course we limited to about 30 students

  • 59:29

    because it was really an experiment, and we
    because it was really an experiment, and we

  • 59:31

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

  • 59:35

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

  • 59:38

    One week, the library.
    One week, the library.

  • 59:40

    Another week, the museum.
    Another week, the museum.

  • 59:41

    Another week, the public park.
    Another week, the public park.

  • 59:43

    And each week, we brought in a leading expert
    And each week, we brought in a leading expert

  • 59:48

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

  • 59:57

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

  • 01:00:04

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

  • 01:00:07

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

  • 01:00:13

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

  • 01:00:16

    around the wheels that move this enormous skin backward
    around the wheels that move this enormous skin backward

  • 01:00:21

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

  • 01:00:24

    or you can have an exterior space.
    or you can have an exterior space.

  • 01:00:29

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

  • 01:00:35

    come and talk about public parks as cultural spaces,
    come and talk about public parks as cultural spaces,

  • 01:00:40

    and the art projects that he is overseeing.
    and the art projects that he is overseeing.

  • 01:00:44

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

  • 01:00:49

    International Film Festival, come to talk
    International Film Festival, come to talk

  • 01:00:51

    about the future of the movie theater.
    about the future of the movie theater.

  • 01:00:55

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

  • 01:01:00

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

  • 01:01:06

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

  • 01:01:10

    And so these practitioners would come.
    And so these practitioners would come.

  • 01:01:13

    They would speak for about 30, 40 minutes.
    They would speak for about 30, 40 minutes.

  • 01:01:15

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

  • 01:01:19

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

  • 01:01:26

    design these spaces for the future?
    design these spaces for the future?

  • 01:01:28

    And teaching this class has been exhilarating.
    And teaching this class has been exhilarating.

  • 01:01:31

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

  • 01:01:35

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

  • 01:01:39

    at pretty much any other institution.
    at pretty much any other institution.

  • 01:01:40

    Maybe Yale could pull this off.
    Maybe Yale could pull this off.

  • 01:01:43

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

  • 01:01:48

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

  • 01:01:50

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

  • 01:01:53

    to talk to our class?
    to talk to our class?

  • 01:01:55

    And Jerold said, this is Harvard.
    And Jerold said, this is Harvard.

  • 01:01:57

    She'll come.
    She'll come.

  • 01:01:59

    And what's great is that--
    And what's great is that--

  • 01:02:02

    [APPLAUSE]
    [APPLAUSE]

  • 01:02:07

    I mean, it's a little crazy.
    I mean, it's a little crazy.

  • 01:02:08

    We're so lucky.
    We're so lucky.

  • 01:02:10

    We are so fortunate.
    We are so fortunate.

  • 01:02:11

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

  • 01:02:15

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

  • 01:02:18

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

  • 01:02:22

    that brings in the most incredible people.
    that brings in the most incredible people.

  • 01:02:25

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

  • 01:02:28

    and they come.
    and they come.

  • 01:02:29

    So we've just been doing this together.
    So we've just been doing this together.

  • 01:02:31

    It's been incredible.
    It's been incredible.

  • 01:02:32

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

  • 01:02:38

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

  • 01:02:46

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

  • 01:02:49

    We are going to be writing it up.
    We are going to be writing it up.

  • 01:02:51

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

  • 01:02:54

    and building an archive.
    and building an archive.

  • 01:02:56

    And we hope to build a center of research
    And we hope to build a center of research

  • 01:02:58

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

  • 01:03:03

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

  • 01:03:06

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

  • 01:03:08

    [APPLAUSE]
    [APPLAUSE]

  • 01:03:30

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

  • 01:03:33

    from each other.
    from each other.

  • 01:03:34

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

  • 01:03:38

    Remember, that was one of the questions.
    Remember, that was one of the questions.

  • 01:03:40

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

  • 01:03:43

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

  • 01:03:46

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

  • 01:03:49

    But he's one of our own.
    But he's one of our own.

  • 01:03:51

    So he is an example--
    So he is an example--

  • 01:03:52

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

  • 01:03:55

    and spend your life at Harvard.
    and spend your life at Harvard.

  • 01:03:58

    His title now is Gordon McKay Professor of the Practice
    His title now is Gordon McKay Professor of the Practice

  • 01:04:01

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

  • 01:04:06

    Sciences.
    Sciences.

  • 01:04:06

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

  • 01:04:09

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

  • 01:04:13

    Common Room.
    Common Room.

  • 01:04:13

    He was in Mather House as an undergraduate.
    He was in Mather House as an undergraduate.

  • 01:04:16

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

  • 01:04:20

    at Brunswick school in Connecticut.
    at Brunswick school in Connecticut.

  • 01:04:22

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

  • 01:04:24

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

  • 01:04:28

    1999 was his college year.
    1999 was his college year.

  • 01:04:30

    College years are the ones that matter.
    College years are the ones that matter.

  • 01:04:32

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

  • 01:04:35

    most recent degree-- in 2007.
    most recent degree-- in 2007.

  • 01:04:38

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

  • 01:04:43

    which is "A Taste of CS50."
    which is "A Taste of CS50."

  • 01:04:46

    Teaches a course called Computer Science 50, CS50:
    Teaches a course called Computer Science 50, CS50:

  • 01:04:50

    Introduction to Computer Science.
    Introduction to Computer Science.

  • 01:04:51

    It is a very large course at Harvard.
    It is a very large course at Harvard.

  • 01:04:53

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

  • 01:04:58

    That course, oddly enough, franchise-style,
    That course, oddly enough, franchise-style,

  • 01:05:02

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

  • 01:05:05

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

  • 01:05:08

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

  • 01:05:11

    He teaches variants of it too.
    He teaches variants of it too.

  • 01:05:13

    CS50 for Lawyers, CS50 for MBAs.
    CS50 for Lawyers, CS50 for MBAs.

  • 01:05:16

    What you want, you can get.
    What you want, you can get.

  • 01:05:18

    In his spare time--
    In his spare time--

  • 01:05:20

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

  • 01:05:22

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

  • 01:05:27

    as a forensic investigator.
    as a forensic investigator.

  • 01:05:29

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

  • 01:05:34

    His research interests won't surprise you--
    His research interests won't surprise you--

  • 01:05:37

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

  • 01:05:42

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

  • 01:05:44

    [APPLAUSE]
    [APPLAUSE]

  • 01:06:04

    DAVID MALAN: Thank you to Marlyn.
    DAVID MALAN: Thank you to Marlyn.

  • 01:06:05

    So I was actually just back in Connecticut
    So I was actually just back in Connecticut

  • 01:06:07

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

  • 01:06:10

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

  • 01:06:14

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

  • 01:06:17

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

  • 01:06:19

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

  • 01:06:22

    Because I still remember wandering around the hallways
    Because I still remember wandering around the hallways

  • 01:06:24

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

  • 01:06:27

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

  • 01:06:29

    that I spent no time in.
    that I spent no time in.

  • 01:06:31

    And that was the computer science lab.
    And that was the computer science lab.

  • 01:06:33

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

  • 01:06:36

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

  • 01:06:40

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

  • 01:06:43

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

  • 01:06:46

    whatever those were.
    whatever those were.

  • 01:06:48

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

  • 01:06:50

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

  • 01:06:53

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

  • 01:06:55

    And so I focused on history, and English,
    And so I focused on history, and English,

  • 01:06:58

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

  • 01:07:01

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

  • 01:07:04

    stuck with where I was comfortable.
    stuck with where I was comfortable.

  • 01:07:06

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

  • 01:07:09

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

  • 01:07:13

    and know way more than me.
    and know way more than me.

  • 01:07:14

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

  • 01:07:17

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

  • 01:07:20

    And it had this alluring reputation.
    And it had this alluring reputation.

  • 01:07:22

    There were a lot of students in it.
    There were a lot of students in it.

  • 01:07:24

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

  • 01:07:26

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

  • 01:07:28

    And I felt like I was behind.
    And I felt like I was behind.

  • 01:07:29

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

  • 01:07:33

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

  • 01:07:37

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

  • 01:07:41

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

  • 01:07:45

    so to speak--
    so to speak--

  • 01:07:45

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

  • 01:07:50

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

  • 01:07:52

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

  • 01:07:56

    So no harm to the GPA.
    So no harm to the GPA.

  • 01:07:57

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

  • 01:08:01

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

  • 01:08:06

    that homework can actually be fun.
    that homework can actually be fun.

  • 01:08:08

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

  • 01:08:10

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

  • 01:08:14

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

  • 01:08:18

    The problem set or homework assignment had just
    The problem set or homework assignment had just

  • 01:08:20

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

  • 01:08:22

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

  • 01:08:28

    assignments.
    assignments.

  • 01:08:28

    Because I finally realized what it was.
    Because I finally realized what it was.

  • 01:08:30

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

  • 01:08:34

    It really is just about problem-solving.
    It really is just about problem-solving.

  • 01:08:36

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

  • 01:08:39

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

  • 01:08:42

    to be more creative in my extracurriculars.
    to be more creative in my extracurriculars.

  • 01:08:44

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

  • 01:08:47

    to now make processes more easily accessible
    to now make processes more easily accessible

  • 01:08:50

    on campus, like the intramural sports program.
    on campus, like the intramural sports program.

  • 01:08:52

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

  • 01:08:54

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

  • 01:08:59

    problem-solving really is kind of a picture
    problem-solving really is kind of a picture

  • 01:09:02

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

  • 01:09:04

    is to achieve some outputs.
    is to achieve some outputs.

  • 01:09:06

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

  • 01:09:08

    And programming, and a lot of the particulars
    And programming, and a lot of the particulars

  • 01:09:10

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

  • 01:09:14

    dives into this very simple idea.
    dives into this very simple idea.

  • 01:09:16

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

  • 01:09:19

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

  • 01:09:22

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

  • 01:09:25

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

  • 01:09:27

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

  • 01:09:30

    to get those outputs.
    to get those outputs.

  • 01:09:31

    And therein lies the problem-solving.
    And therein lies the problem-solving.

  • 01:09:33

    And so how do you go about representing information?
    And so how do you go about representing information?

  • 01:09:36

    Well, I could represent information--
    Well, I could represent information--

  • 01:09:39

    all I need is some kind of input.
    all I need is some kind of input.

  • 01:09:41

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

  • 01:09:43

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

  • 01:09:46

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

  • 01:09:49

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

  • 01:09:52

    And if you unplug it, it's off.
    And if you unplug it, it's off.

  • 01:09:54

    If you plug it in, it's on.
    If you plug it in, it's on.

  • 01:09:56

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

  • 01:09:58

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

  • 01:10:03

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

  • 01:10:06

    AUDIENCE: Binary.
    AUDIENCE: Binary.

  • 01:10:06

    DAVID MALAN: Yeah, binary.
    DAVID MALAN: Yeah, binary.

  • 01:10:07

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

  • 01:10:11

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

  • 01:10:14

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

  • 01:10:17

    because the simplest thing to do electrically
    because the simplest thing to do electrically

  • 01:10:19

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

  • 01:10:22

    1 or 0.
    1 or 0.

  • 01:10:23

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

  • 01:10:26

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

  • 01:10:31

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

  • 01:10:35

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

  • 01:10:38

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

  • 01:10:41

    This has some power.
    This has some power.

  • 01:10:41

    It happens to have a battery.
    It happens to have a battery.

  • 01:10:43

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

  • 01:10:45

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

  • 01:10:47

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

  • 01:10:50

    known in computing as a transistor.
    known in computing as a transistor.

  • 01:10:51

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

  • 01:10:55

    that just turn things on and off.
    that just turn things on and off.

  • 01:10:57

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

  • 01:11:00

    That's not all that interesting.
    That's not all that interesting.

  • 01:11:02

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

  • 01:11:05

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

  • 01:11:07

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

  • 01:11:10

    and maybe start doing things a little more methodically.
    and maybe start doing things a little more methodically.

  • 01:11:13

    So I could do 1, 2, 3.
    So I could do 1, 2, 3.

  • 01:11:15

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

  • 01:11:18

    But that would seem to be it as well.
    But that would seem to be it as well.

  • 01:11:19

    But no.
    But no.

  • 01:11:20

    Computers are a little smarter than that,
    Computers are a little smarter than that,

  • 01:11:21

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

  • 01:11:25

    So this now, I'll claim is 0.
    So this now, I'll claim is 0.

  • 01:11:27

    All three of these light bulbs are off.
    All three of these light bulbs are off.

  • 01:11:29

    Let me turn on this one on, thereby representing
    Let me turn on this one on, thereby representing

  • 01:11:32

    what I'm going to call a 1.
    what I'm going to call a 1.

  • 01:11:33

    But you know what?
    But you know what?

  • 01:11:34

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

  • 01:11:37

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

  • 01:11:40

    And you know what?
    And you know what?

  • 01:11:40

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

  • 01:11:44

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

  • 01:11:47

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

  • 01:11:50

    AUDIENCE: 4.
    AUDIENCE: 4.

  • 01:11:51

    DAVID MALAN: 4.
    DAVID MALAN: 4.

  • 01:11:52

    This happens to be 6.
    This happens to be 6.

  • 01:11:53

    This is now 7.
    This is now 7.

  • 01:11:54

    Why?
    Why?

  • 01:11:55

    How did I choose those particular patterns?
    How did I choose those particular patterns?

  • 01:11:57

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

  • 01:11:59

    are probably really familiar with.
    are probably really familiar with.

  • 01:12:01

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

  • 01:12:05

    something on the screen quite simply--
    something on the screen quite simply--

  • 01:12:08

    like this pattern of symbols.
    like this pattern of symbols.

  • 01:12:09

    1, 2, 3.
    1, 2, 3.

  • 01:12:11

    This is, of course, 123.
    This is, of course, 123.

  • 01:12:13

    But why?
    But why?

  • 01:12:14

    Because all of us just pretty instantaneously did
    Because all of us just pretty instantaneously did

  • 01:12:16

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

  • 01:12:20

    this is the hundreds place.
    this is the hundreds place.

  • 01:12:22

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

  • 01:12:24

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

  • 01:12:30

    you 100 plus 20 plus 3, or 123.
    you 100 plus 20 plus 3, or 123.

  • 01:12:34

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

  • 01:12:38

    but now these symbols--
    but now these symbols--

  • 01:12:39

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

  • 01:12:42

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

  • 01:12:49

    So computers are actually fundamentally the same thing.
    So computers are actually fundamentally the same thing.

  • 01:12:52

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

  • 01:12:57

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

  • 01:13:02

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

  • 01:13:06

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

  • 01:13:11

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

  • 01:13:14

    can probably guess that in the world of computers,
    can probably guess that in the world of computers,

  • 01:13:17

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

  • 01:13:21

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

  • 01:13:25

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

  • 01:13:28

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

  • 01:13:33

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

  • 01:13:36

    why is this 2?
    why is this 2?

  • 01:13:37

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

  • 01:13:41

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

  • 01:13:43

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

  • 01:13:46

    but powers of 2.
    but powers of 2.

  • 01:13:47

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

  • 01:13:50

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

  • 01:13:52

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

  • 01:13:54

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

  • 01:13:57

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

  • 01:14:00

    is you're interested in.
    is you're interested in.

  • 01:14:01

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

  • 01:14:05

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

  • 01:14:09

    The same reasoning.
    The same reasoning.

  • 01:14:10

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

  • 01:14:15

    0s and 1s?
    0s and 1s?

  • 01:14:16

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

  • 01:14:21

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

  • 01:14:24

    AUDIENCE: [INAUDIBLE]
    AUDIENCE: [INAUDIBLE]

  • 01:14:25

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

  • 01:14:27

    Or really, you need more physical hardware.
    Or really, you need more physical hardware.

  • 01:14:29

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

  • 01:14:31

    or store so much information.
    or store so much information.

  • 01:14:33

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

  • 01:14:36

    to actually store additional information.
    to actually store additional information.

  • 01:14:39

    So that, then, is binary.
    So that, then, is binary.

  • 01:14:40

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

  • 01:14:44

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

  • 01:14:48

    We humans have just all agreed to represent values
    We humans have just all agreed to represent values

  • 01:14:51

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

  • 01:14:56

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

  • 01:14:58

    if all you have are numbers.
    if all you have are numbers.

  • 01:15:00

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

  • 01:15:05

    letters, say of the alphabet?
    letters, say of the alphabet?

  • 01:15:07

    What could we do?
    What could we do?

  • 01:15:08

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

  • 01:15:12

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

  • 01:15:14

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

  • 01:15:20

    AUDIENCE: [INAUDIBLE]
    AUDIENCE: [INAUDIBLE]

  • 01:15:21

    DAVID MALAN: Say again?
    DAVID MALAN: Say again?

  • 01:15:21

    AUDIENCE: Assign number values to letters?
    AUDIENCE: Assign number values to letters?

  • 01:15:23

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

  • 01:15:26

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

  • 01:15:27

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

  • 01:15:30

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

  • 01:15:33

    on how to represent that information.
    on how to represent that information.

  • 01:15:34

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

  • 01:15:36

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

  • 01:15:43

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

  • 01:15:47

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

  • 01:15:50

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

  • 01:15:52

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

  • 01:15:56

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

  • 01:16:03

    This is known as ASCII or Unicode.
    This is known as ASCII or Unicode.

  • 01:16:05

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

  • 01:16:08

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

  • 01:16:12

    just by storing numbers, and those numbers in turn
    just by storing numbers, and those numbers in turn

  • 01:16:14

    are just the result of the computer turning
    are just the result of the computer turning

  • 01:16:16

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

  • 01:16:20

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

  • 01:16:22

    that we can represent colors, and sounds,
    that we can represent colors, and sounds,

  • 01:16:24

    and videos in very similar ways.
    and videos in very similar ways.

  • 01:16:27

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

  • 01:16:30

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

  • 01:16:32

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

  • 01:16:37

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

  • 01:16:39

    Could we get some eight volunteers up on stage?
    Could we get some eight volunteers up on stage?

  • 01:16:41

    OK, 1, 2.
    OK, 1, 2.

  • 01:16:43

    Let me look a little harder.
    Let me look a little harder.

  • 01:16:44

    3, 4.
    3, 4.

  • 01:16:46

    Can I go a little farther?
    Can I go a little farther?

  • 01:16:48

    I see no hands in the back.
    I see no hands in the back.

  • 01:16:49

    OK.
    OK.

  • 01:16:50

    There we go.
    There we go.

  • 01:16:50

    5.
    5.

  • 01:16:52

    6 over there.
    6 over there.

  • 01:16:53

    I see someone pointing at someone else.
    I see someone pointing at someone else.

  • 01:16:54

    Come on, 7.
    Come on, 7.

  • 01:16:55

    And let's go 8, over here.
    And let's go 8, over here.

  • 01:16:57

    Come on down.
    Come on down.

  • 01:16:58

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

  • 01:17:00

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

  • 01:17:04

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

  • 01:17:06

    visibly on the screen.
    visibly on the screen.

  • 01:17:08

    Come on over.
    Come on over.

  • 01:17:09

    What's your name?
    What's your name?

  • 01:17:09

    AUDIENCE: Matt.
    AUDIENCE: Matt.

  • 01:17:09

    DAVID MALAN: Matt.
    DAVID MALAN: Matt.

  • 01:17:10

    Come on over and stand under the 128.
    Come on over and stand under the 128.

  • 01:17:12

    What's your name?
    What's your name?

  • 01:17:13

    AUDIENCE: Mira.
    AUDIENCE: Mira.

  • 01:17:13

    DAVID MALAN: Mira.
    DAVID MALAN: Mira.

  • 01:17:14

    David.
    David.

  • 01:17:14

    AUDIENCE: Hey.
    AUDIENCE: Hey.

  • 01:17:15

    DAVID MALAN: David.
    DAVID MALAN: David.

  • 01:17:15

    Nice to meet you.
    Nice to meet you.

  • 01:17:16

    Hello.
    Hello.

  • 01:17:17

    David.
    David.

  • 01:17:17

    Nice to meet you.
    Nice to meet you.

  • 01:17:19

    AUDIENCE: Anesha.
    AUDIENCE: Anesha.

  • 01:17:20

    DAVID MALAN: Anesha, David.
    DAVID MALAN: Anesha, David.

  • 01:17:21

    And Monica.
    And Monica.

  • 01:17:22

    Nice to meet you.
    Nice to meet you.

  • 01:17:23

    And what was your name?
    And what was your name?

  • 01:17:23

    AUDIENCE: Chris.
    AUDIENCE: Chris.

  • 01:17:24

    DAVID MALAN: Chris.
    DAVID MALAN: Chris.

  • 01:17:24

    Nice to meet you as well.
    Nice to meet you as well.

  • 01:17:25

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

  • 01:17:27

    to each other.
    to each other.

  • 01:17:28

    And you know what?
    And you know what?

  • 01:17:29

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

  • 01:17:32

    that you know what you're vol--
    that you know what you're vol--

  • 01:17:34

    OK, now everyone's hand goes up.
    OK, now everyone's hand goes up.

  • 01:17:35

    OK.
    OK.

  • 01:17:36

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

  • 01:17:39

    Come on down.
    Come on down.

  • 01:17:41

    We'll do this round more quickly.
    We'll do this round more quickly.

  • 01:17:43

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

  • 01:17:47

    What is a byte?
    What is a byte?

  • 01:17:49

    A byte is just 8 bits.
    A byte is just 8 bits.

  • 01:17:50

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

  • 01:17:54

    And notice the terminology here too.
    And notice the terminology here too.

  • 01:17:56

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

  • 01:17:59

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

  • 01:18:01

    And a quick hello to AJ.
    And a quick hello to AJ.

  • 01:18:03

    AUDIENCE: AJ.
    AUDIENCE: AJ.

  • 01:18:03

    DAVID MALAN: David.
    DAVID MALAN: David.

  • 01:18:04

    Jay.
    Jay.

  • 01:18:04

    AUDIENCE: Hi.
    AUDIENCE: Hi.

  • 01:18:04

    DAVID MALAN: David.
    DAVID MALAN: David.

  • 01:18:05

    Nice to meet you.
    Nice to meet you.

  • 01:18:06

    Nice to meet you.
    Nice to meet you.

  • 01:18:07

    Nice to meet you.
    Nice to meet you.

  • 01:18:08

    Nice to meet you.
    Nice to meet you.

  • 01:18:09

    Nice to meet you.
    Nice to meet you.

  • 01:18:10

    AUDIENCE: Bianca.
    AUDIENCE: Bianca.

  • 01:18:10

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

  • 01:18:12

    Here we have our second byte of humans.
    Here we have our second byte of humans.

  • 01:18:14

    And--
    And--

  • 01:18:15

    AUDIENCE: [INAUDIBLE]
    AUDIENCE: [INAUDIBLE]

  • 01:18:15

    DAVID MALAN: What's that?
    DAVID MALAN: What's that?

  • 01:18:16

    AUDIENCE: We have seven right here.
    AUDIENCE: We have seven right here.

  • 01:18:18

    DAVID MALAN: We have a seven right here?
    DAVID MALAN: We have a seven right here?

  • 01:18:20

    1, 2, 3, 4, 5, 6, 7.
    1, 2, 3, 4, 5, 6, 7.

  • 01:18:21

    1, 2, 3, 4, 5, 6, 7, 8.
    1, 2, 3, 4, 5, 6, 7, 8.

  • 01:18:24

    We have a bug.
    We have a bug.

  • 01:18:25

    Here we go.
    Here we go.

  • 01:18:25

    Come on up.
    Come on up.

  • 01:18:26

    Thank you.
    Thank you.

  • 01:18:28

    Thank you very much.
    Thank you very much.

  • 01:18:30

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

  • 01:18:33

    What's your name?
    What's your name?

  • 01:18:34

    AUDIENCE: Helen.
    AUDIENCE: Helen.

  • 01:18:34

    DAVID MALAN: Helen.
    DAVID MALAN: Helen.

  • 01:18:35

    David.
    David.

  • 01:18:35

    Nice to meet you.
    Nice to meet you.

  • 01:18:35

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

  • 01:18:39

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

  • 01:18:42

    Key detail I probably should have mentioned earlier.
    Key detail I probably should have mentioned earlier.

  • 01:18:45

    That's OK if you don't.
    That's OK if you don't.

  • 01:18:47

    That's OK.
    That's OK.

  • 01:18:48

    We're going to recover.
    We're going to recover.

  • 01:18:48

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

  • 01:18:52

    OK.
    OK.

  • 01:18:53

    Let's do this.
    Let's do this.

  • 01:18:54

    OK.
    OK.

  • 01:18:55

    Key detail.
    Key detail.

  • 01:18:56

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

  • 01:18:57

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

  • 01:18:59

    Hang on.
    Hang on.

  • 01:19:00

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

  • 01:19:04

    So you don't have your phone?
    So you don't have your phone?

  • 01:19:06

    Here is a nice iPhone XS.
    Here is a nice iPhone XS.

  • 01:19:09

    OK.
    OK.

  • 01:19:10

    [LAUGHTER]
    [LAUGHTER]

  • 01:19:12

    1, 2, 3, 4.
    1, 2, 3, 4.

  • 01:19:14

    Let's go ahead and turn yours on.
    Let's go ahead and turn yours on.

  • 01:19:15

    Can you swap phones for a moment?
    Can you swap phones for a moment?

  • 01:19:17

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

  • 01:19:20

    on just yet.
    on just yet.

  • 01:19:21

    Could you turn your light bulb on?
    Could you turn your light bulb on?

  • 01:19:22

    And could you turn your light bulb on?
    And could you turn your light bulb on?

  • 01:19:25

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

  • 01:19:27

    So let me step out of the way.
    So let me step out of the way.

  • 01:19:29

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

  • 01:19:34

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

  • 01:19:38

    and lastly, the 1.
    and lastly, the 1.

  • 01:19:40

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

  • 01:19:45

    and turned on those switches in this particular order,
    and turned on those switches in this particular order,

  • 01:19:49

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

  • 01:19:51

    AUDIENCE: Hi.
    AUDIENCE: Hi.

  • 01:19:52

    DAVID MALAN: So it's indeed hi.
    DAVID MALAN: So it's indeed hi.

  • 01:19:53

    Why?
    Why?

  • 01:19:54

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

  • 01:19:59

    and 73 is I.
    and 73 is I.

  • 01:20:00

    Well, let's try one more further.
    Well, let's try one more further.

  • 01:20:02

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

  • 01:20:05

    Two units of eight.
    Two units of eight.

  • 01:20:06

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

  • 01:20:10

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

  • 01:20:13

    But suppose that we treated everyone as one
    But suppose that we treated everyone as one

  • 01:20:16

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

  • 01:20:19

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

  • 01:20:23

    but then the 256, 512, 1024.
    but then the 256, 512, 1024.

  • 01:20:26

    All I'm doing is multiplying by 2.
    All I'm doing is multiplying by 2.

  • 01:20:28

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

  • 01:20:31

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

  • 01:20:36

    17 switches or transistors.
    17 switches or transistors.

  • 01:20:38

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

  • 01:20:41

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

  • 01:20:43

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

  • 01:20:46

    Here.
    Here.

  • 01:20:47

    Your flashlight.
    Your flashlight.

  • 01:20:47

    Let me-- that's technically yours.
    Let me-- that's technically yours.

  • 01:20:49

    Can we borrow your phone for a moment?
    Can we borrow your phone for a moment?

  • 01:20:51

    OK.
    OK.

  • 01:20:51

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

  • 01:20:57

    We need to turn yours on.
    We need to turn yours on.

  • 01:20:58

    OK, I'll turn mine on over there.
    OK, I'll turn mine on over there.

  • 01:21:00

    So we need 1, 2.
    So we need 1, 2.

  • 01:21:02

    Can we give you 3, 4 on?
    Can we give you 3, 4 on?

  • 01:21:04

    Can we borrow that?
    Can we borrow that?

  • 01:21:05

    3, 4.
    3, 4.

  • 01:21:06

    Can we-- keep the phones coming.
    Can we-- keep the phones coming.

  • 01:21:08

    [CHUCKLING]
    [CHUCKLING]

  • 01:21:10

    3, 4.
    3, 4.

  • 01:21:10

    So 1, 2, 3, 4.
    So 1, 2, 3, 4.

  • 01:21:11

    And then we skip 1.
    And then we skip 1.

  • 01:21:13

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

  • 01:21:15

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

  • 01:21:19

    So now it's your chance.
    So now it's your chance.

  • 01:21:21

    If a computer were using this many bits--
    If a computer were using this many bits--

  • 01:21:23

    16 bits.
    16 bits.

  • 01:21:24

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

  • 01:21:30

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

  • 01:21:32

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

  • 01:21:40

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

  • 01:21:41

    It's 128,514.
    It's 128,514.

  • 01:21:45

    What might that message say?
    What might that message say?

  • 01:21:50

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

  • 01:21:54

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

  • 01:21:58

    you might have sent this one--
    you might have sent this one--

  • 01:21:59

    Face with Tears of Joy.
    Face with Tears of Joy.

  • 01:22:01

    So that's its official name.
    So that's its official name.

  • 01:22:03

    But it's not an image per se.
    But it's not an image per se.

  • 01:22:04

    It's actually a character.
    It's actually a character.

  • 01:22:07

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

  • 01:22:10

    and that's because computers and humans who
    and that's because computers and humans who

  • 01:22:12

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

  • 01:22:15

    Way more than 16 or 17 bits.
    Way more than 16 or 17 bits.

  • 01:22:17

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

  • 01:22:22

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

  • 01:22:26

    just become kind of a cultural thing that we
    just become kind of a cultural thing that we

  • 01:22:28

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

  • 01:22:31

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

  • 01:22:35

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

  • 01:22:40

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

  • 01:22:43

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

  • 01:22:46

    that represents, rather mundanely, 128,514.
    that represents, rather mundanely, 128,514.

  • 01:22:51

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

  • 01:22:54

    [APPLAUSE]
    [APPLAUSE]

  • 01:22:55

    Let me borrow this.
    Let me borrow this.

  • 01:22:56

    Thank you.
    Thank you.

  • 01:22:59

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

  • 01:23:02

    So we have just one last question to answer.
    So we have just one last question to answer.

  • 01:23:05

    Thank you all so much.
    Thank you all so much.

  • 01:23:07

    We have just one other question to answer,
    We have just one other question to answer,

  • 01:23:09

    which is, if problem-solving ultimately boils down
    which is, if problem-solving ultimately boils down

  • 01:23:12

    to representing inputs and outputs, what is
    to representing inputs and outputs, what is

  • 01:23:15

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

  • 01:23:19

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

  • 01:23:22

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

  • 01:23:26

    So here is an old-school problem where you
    So here is an old-school problem where you

  • 01:23:28

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

  • 01:23:32

    and you want to find someone.
    and you want to find someone.

  • 01:23:33

    And even though this is pretty old-school,
    And even though this is pretty old-school,

  • 01:23:35

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

  • 01:23:38

    that you have in your own iPhone or Android
    that you have in your own iPhone or Android

  • 01:23:40

    phone, or any particular device.
    phone, or any particular device.

  • 01:23:42

    If you scroll through your contacts, odds
    If you scroll through your contacts, odds

  • 01:23:44

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

  • 01:23:47

    So this is just representative of the same problem
    So this is just representative of the same problem

  • 01:23:50

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

  • 01:23:53

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

  • 01:23:57

    last name starting with S--
    last name starting with S--

  • 01:23:58

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

  • 01:24:01

    and do 1, 2, 3, 4.
    and do 1, 2, 3, 4.

  • 01:24:04

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

  • 01:24:07

    And is that algorithm correct?
    And is that algorithm correct?

  • 01:24:08

    Will I find Mike Smith?
    Will I find Mike Smith?

  • 01:24:09

    AUDIENCE: Yes.
    AUDIENCE: Yes.

  • 01:24:10

    DAVID MALAN: Yeah.
    DAVID MALAN: Yeah.

  • 01:24:11

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

  • 01:24:13

    I'll eventually find him.
    I'll eventually find him.

  • 01:24:15

    But I'm not going to do that.
    But I'm not going to do that.

  • 01:24:16

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

  • 01:24:18

    So maybe a little more intelligently or efficiently,
    So maybe a little more intelligently or efficiently,

  • 01:24:20

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

  • 01:24:24

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

  • 01:24:27

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

  • 01:24:30

    AUDIENCE: [INAUDIBLE]
    AUDIENCE: [INAUDIBLE]

  • 01:24:31

    DAVID MALAN: A literal contention.
    DAVID MALAN: A literal contention.

  • 01:24:33

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

  • 01:24:36

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

  • 01:24:39

    through the phone book.
    through the phone book.

  • 01:24:40

    But no big deal.
    But no big deal.

  • 01:24:41

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

  • 01:24:45

    and fix that.
    and fix that.

  • 01:24:46

    But none of us are going to do that.
    But none of us are going to do that.

  • 01:24:48

    What's a typical person going to do?
    What's a typical person going to do?

  • 01:24:49

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

  • 01:24:53

    these days?
    these days?

  • 01:24:54

    AUDIENCE: [INAUDIBLE]
    AUDIENCE: [INAUDIBLE]

  • 01:24:54

    DAVID MALAN: Yeah.
    DAVID MALAN: Yeah.

  • 01:24:55

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

  • 01:24:57

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

  • 01:25:00

    than most letters.
    than most letters.

  • 01:25:01

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

  • 01:25:04

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

  • 01:25:07

    He's definitely this way.
    He's definitely this way.

  • 01:25:08

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

  • 01:25:13

    This is actually not that hard vertically.
    This is actually not that hard vertically.

  • 01:25:16

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

  • 01:25:18

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

  • 01:25:21

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

  • 01:25:25

    And again, down to 125.
    And again, down to 125.

  • 01:25:28

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

  • 01:25:30

    just one or so page.
    just one or so page.

  • 01:25:32

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

  • 01:25:35

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

  • 01:25:40

    can you find Mike Smith in a phone book?
    can you find Mike Smith in a phone book?

  • 01:25:42

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

  • 01:25:46

    by dividing and conquering, dividing and conquering?
    by dividing and conquering, dividing and conquering?

  • 01:25:49

    Whereas the other algorithms might have taken,
    Whereas the other algorithms might have taken,

  • 01:25:51

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

  • 01:25:55

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

  • 01:25:57

    inside this proverbial black box.
    inside this proverbial black box.

  • 01:25:59

    It's the sort of secret sauce.
    It's the sort of secret sauce.

  • 01:26:01

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

  • 01:26:05

    but learn to harness in your own human intuition.
    but learn to harness in your own human intuition.

  • 01:26:08

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

  • 01:26:11

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

  • 01:26:15

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

  • 01:26:19

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

  • 01:26:21

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

  • 01:26:25

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

  • 01:26:27

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

  • 01:26:31

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

  • 01:26:38

    [VIDEO PLAYBACK]
    [VIDEO PLAYBACK]

  • 01:26:38

    [MUSIC - PORTUGAL.
    [MUSIC - PORTUGAL.

  • 01:26:39

    THE MAN - "LIVE IN THE MOMENT"]
    THE MAN - "LIVE IN THE MOMENT"]

  • 01:26:46

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

  • 01:26:54

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

  • 01:27:01

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

  • 01:27:08

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

  • 01:27:14

    Come back Sunday morning.
    Come back Sunday morning.

  • 01:27:17

    Oh my, oh well.
    Oh my, oh well.

  • 01:27:19

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

  • 01:27:23

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

  • 01:27:29

    Come back Sunday morning.
    Come back Sunday morning.

  • 01:27:32

    Got soul to sell.
    Got soul to sell.

  • 01:27:34

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

  • 01:27:42

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

  • 01:27:50

    God only knows we don't need history.
    God only knows we don't need history.

  • 01:27:57

    Your family swinging from the branches of a tree.
    Your family swinging from the branches of a tree.

  • 01:28:05

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

  • 01:28:12

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

  • 01:28:18

    Come back Sunday morning.
    Come back Sunday morning.

  • 01:28:20

    [END PLAYBACK]
    [END PLAYBACK]

  • 01:28:20

    [APPLAUSE]
    [APPLAUSE]

  • 01:28:31

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

  • 01:28:35

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

  • 01:28:38

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

  • 01:28:39

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

  • 01:28:41

    I hope not.
    I hope not.

  • 01:28:42

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

  • 01:28:46

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

  • 01:28:48

    Thank you.
    Thank you.

  • 01:28:49

    [APPLAUSE]
    [APPLAUSE]

All

Visitas Thinks Big 2019 - Harvard University

4,153 views

Video Language:

  • English

Caption Language:

  • English (en)

Accent:

  • English (US)

Speech Time:

97%
  • 1:26:22 / 1:28:59

Speech Rate:

  • 160 wpm - Fast

Category:

  • Entertainment

Tags :

Intro:

[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,

Video Vocabulary

/bēˈkəz/

conjunction

for reason that.

/ˈprōˌɡram/

noun verb

TV show. To write computer code for a piece of software.

/rəˈmembər/

verb

To bring a previous image or idea to your mind.

/kəˈmidē/

noun

Group of people who do or decide something.

/ˌôlˈredē/

adverb

Having happened or been done before this time.

/ˈfakəltē/

noun

Ability to do something with the power of the mind.

/CHo͞oz/

verb

To select; decide between several possibilities.

welcome - welcome

/ˈwelkəm/

adjective exclamation noun verb

gladly received. used to greet someone in polite or friendly way. Friendly greeting to someone who has arrived. greet someone arriving in polite or friendly way.

/ˈvərZHən/

noun verb

An updated form of some software. create new version of.

/ˌen(t)ərˈtān/

other verb

To provide amusement by singing, telling jokes etc.. To provide amusement (e.g. by singing, dancing).

/ədˈmiSHən/

noun other

statement of truth. Taking responsibilities for an error or mistake.

/ˈwəndərfəl/

adjective

Producing feelings of enjoyment or delight.

/ˈpreSHər/

noun verb

continuous physical force exerted on object. To persuade or force someone to do something.

/ˌikˈspirēəns/

noun verb

Knowledge gained by living life, doing new things. To gain knowledge by doing things.