1
00:00:00,000 --> 00:00:00,500


2
00:00:00,500 --> 00:00:03,416
[APPLAUSE]

3
00:00:03,416 --> 00:00:09,329


4
00:00:09,329 --> 00:00:10,745
MARLYN E. MCGRATH: Good afternoon.

5
00:00:10,745 --> 00:00:14,930
I'm Marlyn McGrath, and I want to,
again, thank you all for joining us,

6
00:00:14,930 --> 00:00:21,470
welcome you to Visitas Thinks
Big, which draws great crowds.

7
00:00:21,470 --> 00:00:25,730
Let me tell you what we're
about to do, though, the problem

8
00:00:25,730 --> 00:00:28,130
tells you a little bit about
what we're going to do.

9
00:00:28,130 --> 00:00:29,921
Let me tell you what
we're not going to do.

10
00:00:29,921 --> 00:00:33,620
This is not an info session,
not an information session.

11
00:00:33,620 --> 00:00:36,350
This is not the moment
when you're going to learn

12
00:00:36,350 --> 00:00:41,330
which courses you would have to
take to do statistics and classics.

13
00:00:41,330 --> 00:00:43,680
There will be time for that tomorrow.

14
00:00:43,680 --> 00:00:45,170
You'll all have a chance--

15
00:00:45,170 --> 00:00:47,330
if you're staying over,
and I hope you are--

16
00:00:47,330 --> 00:00:50,880
to meet with faculty and students who
are concentrating in various areas.

17
00:00:50,880 --> 00:00:52,880
And you'll get whatever
information you actually

18
00:00:52,880 --> 00:00:57,110
want about the curricular
offerings, the support

19
00:00:57,110 --> 00:01:01,771
offerings in the way of museums,
and practice rooms, and laboratories

20
00:01:01,771 --> 00:01:02,270
and so on.

21
00:01:02,270 --> 00:01:04,819
Lots of information will
come at you tomorrow.

22
00:01:04,819 --> 00:01:06,230
This isn't now.

23
00:01:06,230 --> 00:01:09,980
This is a Sunday afternoon
entertainment program.

24
00:01:09,980 --> 00:01:13,130
This is performance.

25
00:01:13,130 --> 00:01:16,850
I'm of the belief-- and some of
you have heard me say this before--

26
00:01:16,850 --> 00:01:22,310
that one of the many problems with the
admissions process is that you may--

27
00:01:22,310 --> 00:01:27,500
if you're a successful candidate at
colleges as you have been, apparently.

28
00:01:27,500 --> 00:01:31,960
It's very easy to imprison
yourself in a persona.

29
00:01:31,960 --> 00:01:35,437
You think of yourself as the
geologist who does rock climbing

30
00:01:35,437 --> 00:01:38,270
and plays the violin or whatever
it is that we were so impressed by,

31
00:01:38,270 --> 00:01:39,650
and that's terrific.

32
00:01:39,650 --> 00:01:43,600
And we assume it's all true
and so on, and it's wonderful

33
00:01:43,600 --> 00:01:46,880
that all of that stuff
need not imprison you

34
00:01:46,880 --> 00:01:50,420
in that person who was admitted
to Harvard and many other places,

35
00:01:50,420 --> 00:01:51,860
I'm sure.

36
00:01:51,860 --> 00:01:54,620
We will not pull your
admission away from you

37
00:01:54,620 --> 00:01:59,600
because you turn out to be the
geologist who turned into a musician.

38
00:01:59,600 --> 00:02:02,450
This is your life, your freedom.

39
00:02:02,450 --> 00:02:04,760
That's part of what a
liberal education means.

40
00:02:04,760 --> 00:02:08,000
Therefore, we want to dazzle you
today with many things, of course,

41
00:02:08,000 --> 00:02:10,850
with Harvard in general because
we would love you to come,

42
00:02:10,850 --> 00:02:14,570
but we also want you to get a
sense of what we really are like.

43
00:02:14,570 --> 00:02:18,530
If Harvard or whatever college you
enroll in doesn't change your mind

44
00:02:18,530 --> 00:02:22,370
or at least open it, you
may have wasted your time,

45
00:02:22,370 --> 00:02:25,481
and we hope to rattle your cage
a little bit if you come here.

46
00:02:25,481 --> 00:02:28,730
I don't know how many of you remember--
there may be some parents who remember

47
00:02:28,730 --> 00:02:30,020
it better in the room--

48
00:02:30,020 --> 00:02:32,630
a wonderful masterpiece
of a children's book--

49
00:02:32,630 --> 00:02:35,300
at least one of my
favorites for my children--

50
00:02:35,300 --> 00:02:39,980
by Richard Scarry called,
What Do People Do All Day.

51
00:02:39,980 --> 00:02:45,080
Well, this is our presentation of
that there are five fabulous faculty

52
00:02:45,080 --> 00:02:48,890
members who will tell you not what
the requirements are for concentrating

53
00:02:48,890 --> 00:02:52,340
in their field or how you can get
to know their colleagues better,

54
00:02:52,340 --> 00:02:55,460
but what they do all day, what
kinds of things excite them.

55
00:02:55,460 --> 00:03:00,080
Really, why they're here, why they
teach, why they love universities.

56
00:03:00,080 --> 00:03:03,860
So our first speaker
is Robin Kelsey, who

57
00:03:03,860 --> 00:03:07,730
is the Shirley Carter Burden
Professor of Photography

58
00:03:07,730 --> 00:03:12,680
and the Dean of Arts and
Humanities at Harvard.

59
00:03:12,680 --> 00:03:15,520
He has many things he does
and many distinctions,

60
00:03:15,520 --> 00:03:17,270
but one you should
know about is that he's

61
00:03:17,270 --> 00:03:20,420
the co-chair of the Harvard
University Committee on the Arts.

62
00:03:20,420 --> 00:03:23,930
Also, he's affiliated with
Kirkland House-- one of our 12

63
00:03:23,930 --> 00:03:30,250
residential houses, more about that
later in different parts of these days

64
00:03:30,250 --> 00:03:31,250
here.

65
00:03:31,250 --> 00:03:34,640
He got his PhD at the History of
Art and Architecture from Harvard,

66
00:03:34,640 --> 00:03:36,000
but he's really a Yaley.

67
00:03:36,000 --> 00:03:39,510
He went to that great
institution in Connecticut.

68
00:03:39,510 --> 00:03:43,220
He also, interestingly enough, has
a JD from the Yale Law School--

69
00:03:43,220 --> 00:03:46,810
also that fine place in Connecticut.

70
00:03:46,810 --> 00:03:50,702
But he writes books with many
wonderful titles such as,

71
00:03:50,702 --> 00:03:52,910
The Photography and the Art
of Chance, which was just

72
00:03:52,910 --> 00:03:56,180
published by the Harvard Press in 2015.

73
00:03:56,180 --> 00:04:00,090
So without further ado or further
introduction, I'll introduce Robin.

74
00:04:00,090 --> 00:04:00,590
Thank you

75
00:04:00,590 --> 00:04:03,978
[APPLAUSE]

76
00:04:03,978 --> 00:04:11,250


77
00:04:11,250 --> 00:04:18,260
ROBIN KELSEY: Good afternoon,
Visitas Thinks Big.

78
00:04:18,260 --> 00:04:23,390
When I was young and my father
said, hey, what's the big idea?

79
00:04:23,390 --> 00:04:28,880
He didn't want a big idea,
he wanted an apology.

80
00:04:28,880 --> 00:04:34,390
But I'll try to give you a big
idea today without an apology,

81
00:04:34,390 --> 00:04:38,280
and I'm going to start with
this morning when you got up.

82
00:04:38,280 --> 00:04:41,930
And I suspect within a few
seconds, maybe minutes,

83
00:04:41,930 --> 00:04:45,620
you checked your favorite
electronic device.

84
00:04:45,620 --> 00:04:51,500
Maybe you went on Facebook, Twitter,
posted something on Instagram,

85
00:04:51,500 --> 00:04:56,660
or maybe you used some apps that your
parents and I have never heard of.

86
00:04:56,660 --> 00:05:02,300
Your habitual uses of social
media probably feel automatic,

87
00:05:02,300 --> 00:05:07,550
but it's worth recalling that
every time you get on these apps,

88
00:05:07,550 --> 00:05:11,240
every time you use social
media, you are affirming

89
00:05:11,240 --> 00:05:15,080
your belonging in a virtual community.

90
00:05:15,080 --> 00:05:19,700
Whether that's Facebook
friends or Twitter followers,

91
00:05:19,700 --> 00:05:23,690
you are asserting your belonging
in a virtual community.

92
00:05:23,690 --> 00:05:29,120
A virtual community that is held
together electronically by servers,

93
00:05:29,120 --> 00:05:33,580
apps, and so forth, but
virtual communities that

94
00:05:33,580 --> 00:05:39,260
are held within you most
vividly in your imagination.

95
00:05:39,260 --> 00:05:41,870
These are imagined communities.

96
00:05:41,870 --> 00:05:44,570


97
00:05:44,570 --> 00:05:46,880
I Stole my title.

98
00:05:46,880 --> 00:05:50,600
I Stole it from a book by
Benedict Anderson, a scholar who

99
00:05:50,600 --> 00:05:53,810
passed away a couple of years ago.

100
00:05:53,810 --> 00:05:58,490
One of Benedict Anderson's
big ideas was that people

101
00:05:58,490 --> 00:06:05,210
tended to overlook the obvious fact
that the modern nation is itself

102
00:06:05,210 --> 00:06:07,900
an imagined community.

103
00:06:07,900 --> 00:06:11,830
Americans may be willing
to die for America,

104
00:06:11,830 --> 00:06:18,660
but they actually know only a
tiny fraction of other Americans.

105
00:06:18,660 --> 00:06:26,080
Their belonging to this community of
Americans involves the imagination.

106
00:06:26,080 --> 00:06:29,050
Another insight that
Anderson had is that these

107
00:06:29,050 --> 00:06:33,640
imagined communities
called nations are finite,

108
00:06:33,640 --> 00:06:39,460
and they are surrounded by other
imagined communities called nations

109
00:06:39,460 --> 00:06:45,190
so that these imagined communities
are defined oppositionally.

110
00:06:45,190 --> 00:06:52,130
That is to say, if you are Brazilian,
that means quite precisely,

111
00:06:52,130 --> 00:06:57,410
especially when it comes to soccer,
that you are not Argentinian.

112
00:06:57,410 --> 00:07:02,540
And if you are Canadian, as my
Canadian friends constantly remind me,

113
00:07:02,540 --> 00:07:07,830
it means precisely that
you are not an American.

114
00:07:07,830 --> 00:07:10,620
Benedict Anderson spend a
lot of his career thinking

115
00:07:10,620 --> 00:07:16,560
deeply about why it is in the
modern period there emerged these

116
00:07:16,560 --> 00:07:20,440
imagined communities called nations.

117
00:07:20,440 --> 00:07:26,800
Another of his big ideas was that
certain technologies and their uses

118
00:07:26,800 --> 00:07:31,330
gave rise to and supported
these imagined communities.

119
00:07:31,330 --> 00:07:37,180
In particular, Anderson associated
the rise of the modern nation

120
00:07:37,180 --> 00:07:41,790
with the emergence of what
he called print capitalism.

121
00:07:41,790 --> 00:07:48,480
When people in the 19th or 20th century
sat down to read the morning paper,

122
00:07:48,480 --> 00:07:54,030
that act may have seemed very
solitary and private, but in fact,

123
00:07:54,030 --> 00:08:01,530
that act asserted a belonging to an
imagined community of people who were

124
00:08:01,530 --> 00:08:04,470
doing the same thing that morning--

125
00:08:04,470 --> 00:08:10,140
reading the paper, digesting
matters of common concern.

126
00:08:10,140 --> 00:08:16,700
And with this synchrony, an
imagined community formed.

127
00:08:16,700 --> 00:08:22,160
And what Anderson argued is that
without this capacity through print

128
00:08:22,160 --> 00:08:27,170
to disseminate this news
so readily across a nation,

129
00:08:27,170 --> 00:08:30,610
the modern nation state
would not have emerged.

130
00:08:30,610 --> 00:08:34,900
One corollary of this insight
is that these nation states

131
00:08:34,900 --> 00:08:37,900
are bound linguistically.

132
00:08:37,900 --> 00:08:39,309
There are German newspapers.

133
00:08:39,309 --> 00:08:40,750
There are Portuguese newspapers.

134
00:08:40,750 --> 00:08:44,320
There are newspapers in Mandarin, right?

135
00:08:44,320 --> 00:08:49,840
And these languages are part
of the national self-definition

136
00:08:49,840 --> 00:08:53,090
of these imagined communities.

137
00:08:53,090 --> 00:08:56,360
We can build upon Anderson
to talk about the role

138
00:08:56,360 --> 00:09:00,170
that other technologies
have played in the formation

139
00:09:00,170 --> 00:09:03,920
and sustenance of imagined communities.

140
00:09:03,920 --> 00:09:09,020
One technology near and dear
to my heart is photography.

141
00:09:09,020 --> 00:09:14,090
Alongside print media,
photography has played a key role

142
00:09:14,090 --> 00:09:17,510
in the development of
imagined communities.

143
00:09:17,510 --> 00:09:24,110
I show you here on the left two album
pages from a 19th century photography

144
00:09:24,110 --> 00:09:24,660
album.

145
00:09:24,660 --> 00:09:28,430
These are small photographs
on little pieces of cardboard.

146
00:09:28,430 --> 00:09:29,870
They recalled carte de visite.

147
00:09:29,870 --> 00:09:32,150
They were extraordinarily popular--

148
00:09:32,150 --> 00:09:35,660
produced in the millions in
the middle of the 19th century.

149
00:09:35,660 --> 00:09:40,700
The figures you see on these cards
are all dressed in Norwegian costumes,

150
00:09:40,700 --> 00:09:43,610
and I used this example
to show that photography

151
00:09:43,610 --> 00:09:49,070
could reinforce the notion of a
national identity, such as Anderson

152
00:09:49,070 --> 00:09:50,930
was concerned about.

153
00:09:50,930 --> 00:09:54,090
On the right, you see a
very different album page.

154
00:09:54,090 --> 00:09:59,000
This one from a Victorian English
album in which the maker of the album

155
00:09:59,000 --> 00:10:02,780
has cut out people from
different photographs

156
00:10:02,780 --> 00:10:09,710
and pasted them together fancifully
in this imagined scene in a parlor.

157
00:10:09,710 --> 00:10:12,710
And what this album
page can remind us is

158
00:10:12,710 --> 00:10:19,040
that photographs tend to circulate
very promiscuously and therefore

159
00:10:19,040 --> 00:10:23,000
able to overcome some
of the rigid boundaries

160
00:10:23,000 --> 00:10:27,430
that the album pages on
the left would suggest.

161
00:10:27,430 --> 00:10:31,180
Now photography, as an instrument
of imagined communities,

162
00:10:31,180 --> 00:10:34,870
had some very particular
characteristics.

163
00:10:34,870 --> 00:10:38,350
For one thing, it was
not based on language.

164
00:10:38,350 --> 00:10:43,750
And one of the aspects of photography
that captivated the social imagination

165
00:10:43,750 --> 00:10:47,650
when photography was
first introduced in 1839

166
00:10:47,650 --> 00:10:52,270
was precisely this potential
for a kind of universality--

167
00:10:52,270 --> 00:10:57,580
that everyone sitting before the
camera would register in the same way

168
00:10:57,580 --> 00:11:02,380
and that photographs were legible to
people who spoke different languages

169
00:11:02,380 --> 00:11:05,690
and came from different cultures.

170
00:11:05,690 --> 00:11:08,380
So for these reasons,
photography was thought

171
00:11:08,380 --> 00:11:12,280
to be particularly suited
to a time in which there

172
00:11:12,280 --> 00:11:17,190
was great democratic fervor, as
there was in the 19th century.

173
00:11:17,190 --> 00:11:24,610


174
00:11:24,610 --> 00:11:25,920
Here we go.

175
00:11:25,920 --> 00:11:28,830
I wanted to show you some
quotations to give you

176
00:11:28,830 --> 00:11:35,460
a flavor of the various people who,
in the early years of photography,

177
00:11:35,460 --> 00:11:39,870
articulated this remarkable
quality of the medium

178
00:11:39,870 --> 00:11:44,490
that whereas, in painting and
drawing, the making of the picture

179
00:11:44,490 --> 00:11:48,510
tended to be mediated by all
kinds of conventions such

180
00:11:48,510 --> 00:11:53,970
that a painter would render the
nobility in an idealized fashion

181
00:11:53,970 --> 00:11:57,630
but tend to caricature the
poor and the downtrodden--

182
00:11:57,630 --> 00:12:02,850
But that photography treated
everyone more or less the same.

183
00:12:02,850 --> 00:12:06,030
And this was a remarkable
principle that writers

184
00:12:06,030 --> 00:12:10,290
ranging from the
transcendentalist Emerson

185
00:12:10,290 --> 00:12:17,750
to the abolitionist
Frederick Douglass noted.

186
00:12:17,750 --> 00:12:21,980
This universalist
potential of photography

187
00:12:21,980 --> 00:12:24,890
has surfaced time and
again in its history.

188
00:12:24,890 --> 00:12:29,270
After the second World War when
the emergence of the atomic bomb

189
00:12:29,270 --> 00:12:32,720
raised the specter of national
differences and strife

190
00:12:32,720 --> 00:12:37,100
resulting in the
destruction of the planet,

191
00:12:37,100 --> 00:12:42,620
hopes were placed in photography
to be an agent of the creation

192
00:12:42,620 --> 00:12:46,850
of an imagined global community.

193
00:12:46,850 --> 00:12:49,520
This universality of
photography was thought

194
00:12:49,520 --> 00:12:54,320
to have the capacity to transcend
national boundaries and differences.

195
00:12:54,320 --> 00:12:59,360
And in 1955, Edward Steichen, a
curator at the Museum of Modern Art,

196
00:12:59,360 --> 00:13:03,440
put on a very famous show
called The Family of Man

197
00:13:03,440 --> 00:13:06,680
in which he tried to make
a case that we are bonded

198
00:13:06,680 --> 00:13:09,890
across cultures and
across these differences

199
00:13:09,890 --> 00:13:13,310
by certain universals of human culture--

200
00:13:13,310 --> 00:13:18,620
whether that be eating together,
or dancing, or singing.

201
00:13:18,620 --> 00:13:21,440
And it's crucial that
for Steichen, photography

202
00:13:21,440 --> 00:13:26,780
was the medium that would convey
that universalism because it itself

203
00:13:26,780 --> 00:13:31,370
was a universal medium.

204
00:13:31,370 --> 00:13:34,130
Now the problem is that
technologies rarely

205
00:13:34,130 --> 00:13:39,470
fulfilled their utopian potential,
and the same is true of photography.

206
00:13:39,470 --> 00:13:44,630
Although, many writers, and curators,
and thinkers have noted the possibility

207
00:13:44,630 --> 00:13:47,810
that photography could bond
people in this fashion.

208
00:13:47,810 --> 00:13:50,750
In point of fact, photography
has been used time

209
00:13:50,750 --> 00:13:54,080
and again to create
imagined communities that

210
00:13:54,080 --> 00:13:59,390
are based on difference,
subjugation, and subordination.

211
00:13:59,390 --> 00:14:02,440
So to just give you three
examples, on the left,

212
00:14:02,440 --> 00:14:05,510
this is an early mugshot
from the late 19th century

213
00:14:05,510 --> 00:14:10,280
to remind us that an imagined
community of law abiding citizens

214
00:14:10,280 --> 00:14:14,660
has been generated against those
who are considered outlaws.

215
00:14:14,660 --> 00:14:18,050
Or the middle photograph,
a long duration photograph

216
00:14:18,050 --> 00:14:21,800
showing the motion of
a worker to be used

217
00:14:21,800 --> 00:14:25,880
to try to increase the mechanical
efficiency of workers on the assembly

218
00:14:25,880 --> 00:14:26,660
line--

219
00:14:26,660 --> 00:14:33,440
defining an imagined community of
managers who would define themselves

220
00:14:33,440 --> 00:14:35,150
against a working class.

221
00:14:35,150 --> 00:14:37,940
Or on the right, a
photograph that can remind us

222
00:14:37,940 --> 00:14:42,290
of the complicity of
photography in colonialism,

223
00:14:42,290 --> 00:14:46,940
and the defining of a certain imagined
community as belonging to the civilized

224
00:14:46,940 --> 00:14:51,500
against those who are
allegedly less civilized.

225
00:14:51,500 --> 00:14:56,210
The history of photography, as I have
sketched it out in this brief time,

226
00:14:56,210 --> 00:15:00,200
feels very resident
to me today where were

227
00:15:00,200 --> 00:15:05,690
undergoing this seismic shift from
print capitalism to screen capitalism.

228
00:15:05,690 --> 00:15:10,790
And where we have a technology
called the internet which

229
00:15:10,790 --> 00:15:17,870
came with all kinds of utopian dreams,
but very recently has evidently

230
00:15:17,870 --> 00:15:25,070
shown a propensity for a kind of echo
chamber fragmentation pitting one

231
00:15:25,070 --> 00:15:27,410
imagined community against another.

232
00:15:27,410 --> 00:15:31,140
And to remind us of that, I
just put in the upper right

233
00:15:31,140 --> 00:15:35,950
an announcement of the release of
Barack Obama's birth certificate--

234
00:15:35,950 --> 00:15:39,650
a very sober, every
day document that you

235
00:15:39,650 --> 00:15:46,010
think might be a kind of object of
consensus, but in point of fact,

236
00:15:46,010 --> 00:15:51,590
is viewed so divisively by these
different imagined communities.

237
00:15:51,590 --> 00:15:56,630
And I leave you with the idea
that one of the great challenges

238
00:15:56,630 --> 00:16:00,800
of your generation is going
to be to help us figure out

239
00:16:00,800 --> 00:16:05,630
how to foster and sustain the
imagined communities that we need

240
00:16:05,630 --> 00:16:10,700
and how to connect them to a
reality that stubbornly resides

241
00:16:10,700 --> 00:16:12,830
outside our digital dreams.

242
00:16:12,830 --> 00:16:13,710
Thank you very much

243
00:16:13,710 --> 00:16:17,133
[APPLAUSE]

244
00:16:17,133 --> 00:16:33,752


245
00:16:33,752 --> 00:16:34,960
MARLYN E. MCGRATH: Thank you.

246
00:16:34,960 --> 00:16:37,930
That was really-- that
was terrific, thank you.

247
00:16:37,930 --> 00:16:41,620
Elena Kramer is our next speaker.

248
00:16:41,620 --> 00:16:46,180
And she is the Bussey Professor of
Organismic and Evolutionary Biology,

249
00:16:46,180 --> 00:16:50,470
and she's also the chair of the
Organismic and Evolutionary Biology

250
00:16:50,470 --> 00:16:51,850
Department.

251
00:16:51,850 --> 00:16:53,980
She also has been
named a Harvard College

252
00:16:53,980 --> 00:16:58,000
professor which speaks to her
devotion to teaching undergraduates.

253
00:16:58,000 --> 00:17:00,310
That's generally true
of our faculty but worth

254
00:17:00,310 --> 00:17:03,490
noting that she happens
to have that recognition.

255
00:17:03,490 --> 00:17:06,220
She's also what we call a member
of the Senior Common Room.

256
00:17:06,220 --> 00:17:09,940
In other words, she's affiliated with
Leverett House, another one of our 12

257
00:17:09,940 --> 00:17:12,079
undergraduate houses.

258
00:17:12,079 --> 00:17:15,300
She has her PhD from Yale--

259
00:17:15,300 --> 00:17:17,980
that great place in Connecticut--

260
00:17:17,980 --> 00:17:19,750
in her science.

261
00:17:19,750 --> 00:17:23,619
Her lab at Harvard does
something quite fascinating.

262
00:17:23,619 --> 00:17:28,780
Not what I, a non-scientist, think first
when I think of what scientists-- even

263
00:17:28,780 --> 00:17:30,310
life scientists-- do.

264
00:17:30,310 --> 00:17:32,440
She's interested
particularly in how flowers

265
00:17:32,440 --> 00:17:35,440
have changed over the
course of evolution,

266
00:17:35,440 --> 00:17:38,980
and she explores topics like
gene lineage, evolution,

267
00:17:38,980 --> 00:17:41,680
and the diversification
of petals and fruits.

268
00:17:41,680 --> 00:17:46,141
There's more than a little aesthetics
in this, and with further ado, Lena.

269
00:17:46,141 --> 00:17:46,640
Thank you.

270
00:17:46,640 --> 00:17:49,070
[APPLAUSE]

271
00:17:49,070 --> 00:17:53,415


272
00:17:53,415 --> 00:17:54,415
ELENA KRAMER: All right.

273
00:17:54,415 --> 00:17:57,150
Well, thank you so much
for coming out here today.

274
00:17:57,150 --> 00:17:59,270
It's a thrill to be here with you.

275
00:17:59,270 --> 00:18:02,270
So, as you just heard,
I'm a plant biologist.

276
00:18:02,270 --> 00:18:06,180
I study the evolution of genetic
programs that control the development,

277
00:18:06,180 --> 00:18:08,010
particularly of flowers.

278
00:18:08,010 --> 00:18:11,270
Now, I have to admit, I've sat
next to a lot of people on planes

279
00:18:11,270 --> 00:18:13,550
and told them that I
was a plant biologist.

280
00:18:13,550 --> 00:18:19,430
And often, their reaction is, help
me find out why my fern is dying,

281
00:18:19,430 --> 00:18:20,930
or something like that.

282
00:18:20,930 --> 00:18:23,180
I don't keep things alive.

283
00:18:23,180 --> 00:18:25,070
This is the problem.

284
00:18:25,070 --> 00:18:27,950
I like to grind them up in little
test tubes and things like that,

285
00:18:27,950 --> 00:18:30,110
so don't ask me how to
keep something alive.

286
00:18:30,110 --> 00:18:35,030
But we talk in plant biology
about plant blindness.

287
00:18:35,030 --> 00:18:37,130
People often don't
appreciate the importance

288
00:18:37,130 --> 00:18:39,410
of plants in our everyday
life, but I think

289
00:18:39,410 --> 00:18:43,760
we can all agree that flowers are
very compelling and charismatic.

290
00:18:43,760 --> 00:18:47,780
As a human civilization, we've had
a fascination with flowers, really,

291
00:18:47,780 --> 00:18:48,980
from the beginning.

292
00:18:48,980 --> 00:18:51,950
And what we're really trying
to understand in our lab

293
00:18:51,950 --> 00:18:55,400
is how we have, over
the course of evolution,

294
00:18:55,400 --> 00:18:59,120
generated this enormous
diversity of floral morphology,

295
00:18:59,120 --> 00:19:03,110
and then, particularly, how changes
in the genetic programs that

296
00:19:03,110 --> 00:19:08,360
control these developmental processes
has generated the change in morphology.

297
00:19:08,360 --> 00:19:12,164
And in order to do that,
we work at the intersection

298
00:19:12,164 --> 00:19:13,580
of sort of three different fields.

299
00:19:13,580 --> 00:19:15,390
We ask different types of questions.

300
00:19:15,390 --> 00:19:19,250
So for instance, we're very
interested in development.

301
00:19:19,250 --> 00:19:22,730
Very specifically, what are the
patterns of cell division and cell

302
00:19:22,730 --> 00:19:25,490
expansion, the duration,
their localization,

303
00:19:25,490 --> 00:19:29,180
and how do these different
patterns generate the morphology

304
00:19:29,180 --> 00:19:30,380
that we're interested in?

305
00:19:30,380 --> 00:19:35,190
So we ask very specific questions
about how morphology changes over time.

306
00:19:35,190 --> 00:19:38,960
And then we overlay this information
with information about the genes.

307
00:19:38,960 --> 00:19:42,170
How do different genes interact
with each other on a local scale,

308
00:19:42,170 --> 00:19:44,390
on a global scale, on the genomic scale?

309
00:19:44,390 --> 00:19:48,920
And ask how these genetic programs
control the developmental processes.

310
00:19:48,920 --> 00:19:50,960
And then we take all of
those questions and we

311
00:19:50,960 --> 00:19:53,480
put them in an evolutionary
context, and say,

312
00:19:53,480 --> 00:19:56,990
well, when we ask all these
questions in one species then

313
00:19:56,990 --> 00:20:00,470
we compare them to a different species,
what aspects of these processes

314
00:20:00,470 --> 00:20:02,660
are the same, what
aspects are different,

315
00:20:02,660 --> 00:20:06,830
and can we understand how
those differences arise?

316
00:20:06,830 --> 00:20:08,720
So when we put all
those things together,

317
00:20:08,720 --> 00:20:12,860
that's a field called evo-devo, or
evolutionary developmental biology.

318
00:20:12,860 --> 00:20:17,000
So we're essentially asking how
morphological diversity is generated

319
00:20:17,000 --> 00:20:20,100
over time at a molecular level.

320
00:20:20,100 --> 00:20:23,660
So, why don't we use
plants as a model system?

321
00:20:23,660 --> 00:20:26,600
So if you'll let me proselytize
for a moment about why plants

322
00:20:26,600 --> 00:20:28,340
are such a wonderful model system.

323
00:20:28,340 --> 00:20:29,960
I'm a developmental biologist.

324
00:20:29,960 --> 00:20:32,540
If I was an animal
developmental biologist,

325
00:20:32,540 --> 00:20:38,360
I would only be focused on a very narrow
window of that organism's life-- what

326
00:20:38,360 --> 00:20:43,650
we typically call embryogenesis, from
fertilization to some type of hatching.

327
00:20:43,650 --> 00:20:48,260
But in plants, the fantastic thing
about plants is that if they are alive,

328
00:20:48,260 --> 00:20:50,450
they are undergoing development.

329
00:20:50,450 --> 00:20:53,720
If we look at this plant
embryo, you can immediately

330
00:20:53,720 --> 00:20:58,310
recognize that while, yes, there was
a developmental process that took this

331
00:20:58,310 --> 00:21:03,020
from a single cell to
this embryo, this is not

332
00:21:03,020 --> 00:21:06,330
the entirety of the body of a plant.

333
00:21:06,330 --> 00:21:10,820
So this enormous oak tree
started out as this tiny embryo,

334
00:21:10,820 --> 00:21:14,030
and it is in a constant
state of organogenesis

335
00:21:14,030 --> 00:21:17,570
and producing new organs in
order to generate its body.

336
00:21:17,570 --> 00:21:18,400
And all around us--

337
00:21:18,400 --> 00:21:20,150
I mean, we finally got
to go outside today

338
00:21:20,150 --> 00:21:22,340
and enjoy a little bit of nice weather--

339
00:21:22,340 --> 00:21:26,090
you can see trees all over campus,
things coming up from the ground,

340
00:21:26,090 --> 00:21:28,820
these are plants that are
undergoing organogenesis even

341
00:21:28,820 --> 00:21:31,370
in what we would consider
their adult body.

342
00:21:31,370 --> 00:21:35,220
And plants can do that because
all throughout their body,

343
00:21:35,220 --> 00:21:38,730
they have reservoirs of
undifferentiated cells--

344
00:21:38,730 --> 00:21:39,560
stem cells.

345
00:21:39,560 --> 00:21:42,374
Now, my animal colleagues get
really excited about stem cells.

346
00:21:42,374 --> 00:21:43,790
Stem cells are nothing for plants.

347
00:21:43,790 --> 00:21:46,510
They've got stem cells to
spare, and their stem cells

348
00:21:46,510 --> 00:21:50,330
are arranged in these specific
regions called meristems.

349
00:21:50,330 --> 00:21:53,300
They're domes of undifferentiated cells.

350
00:21:53,300 --> 00:21:56,302
So when I tell you that I want
to understand floral development,

351
00:21:56,302 --> 00:21:57,260
this is where we start.

352
00:21:57,260 --> 00:22:00,260
We start with a dome of
undifferentiated cells,

353
00:22:00,260 --> 00:22:04,220
and we look at how that set
of cells changes over time

354
00:22:04,220 --> 00:22:06,750
to generate floral morphology.

355
00:22:06,750 --> 00:22:08,550
So, this is our big problem.

356
00:22:08,550 --> 00:22:12,050
We have about 350,000
species of flowering plants

357
00:22:12,050 --> 00:22:15,950
that have arisen over the
past 150 million years.

358
00:22:15,950 --> 00:22:17,120
That's a big problem.

359
00:22:17,120 --> 00:22:19,640
I don't try-- I'm not
looking at that scale, right?

360
00:22:19,640 --> 00:22:23,000
You have to start with something
tractable, and what we work with

361
00:22:23,000 --> 00:22:27,080
is a model system called Aquilegia--
that you may know as Columbine.

362
00:22:27,080 --> 00:22:29,960
A lot of you may have this
in your yards at home.

363
00:22:29,960 --> 00:22:33,567
It is what we call a recently
radiated species flock.

364
00:22:33,567 --> 00:22:35,150
So let me explain to you what that is.

365
00:22:35,150 --> 00:22:37,250
Why is Aquilegia a good model system?

366
00:22:37,250 --> 00:22:40,340
So obviously, it's very
pretty, but it's also

367
00:22:40,340 --> 00:22:43,010
very tractable from a
molecular standpoint.

368
00:22:43,010 --> 00:22:46,460
It has a relatively small genome,
about 300 million base pairs,

369
00:22:46,460 --> 00:22:48,860
and you'll take my word for
it that that's manageable.

370
00:22:48,860 --> 00:22:51,110
We have a fully sequenced genome.

371
00:22:51,110 --> 00:22:53,480
We also have a lot of
functional tools that

372
00:22:53,480 --> 00:22:57,890
allow us to trick the plant into
doing things that we want it to do.

373
00:22:57,890 --> 00:23:01,460
It also has a wonderful array
of morphological novelty.

374
00:23:01,460 --> 00:23:04,070
So, I mentioned that this
is a radiated species flock.

375
00:23:04,070 --> 00:23:05,070
What am I talking about?

376
00:23:05,070 --> 00:23:08,870
I'm talking about that when we study
the evolutionary history of this genus,

377
00:23:08,870 --> 00:23:13,550
when we reconstruct its phylogeny, and
understand how the species are related

378
00:23:13,550 --> 00:23:18,530
to each other, we see that these species
have radiated over a period of one

379
00:23:18,530 --> 00:23:21,240
to six million years,
which is relatively recent.

380
00:23:21,240 --> 00:23:24,950
And in fact, the North American
radiation that's pictured here

381
00:23:24,950 --> 00:23:26,720
is less than a million years.

382
00:23:26,720 --> 00:23:29,550
So these are very
closely related species.

383
00:23:29,550 --> 00:23:31,874
That means that they're
also interfertile,

384
00:23:31,874 --> 00:23:34,790
and that's something that makes the
geneticists sit up and take notice

385
00:23:34,790 --> 00:23:37,310
because we can cross these species.

386
00:23:37,310 --> 00:23:41,990
Even species that look quite different
from one another, we can hybridize them

387
00:23:41,990 --> 00:23:47,480
and then look at how their traits
segregate in later generations,

388
00:23:47,480 --> 00:23:51,920
and map the molecular changes that
are responsible for the differences

389
00:23:51,920 --> 00:23:53,130
in morphology.

390
00:23:53,130 --> 00:23:56,240
So this means that we have traditional
developmental genetic tools,

391
00:23:56,240 --> 00:23:59,000
and we also have a lot of
evolutionary genetic tools

392
00:23:59,000 --> 00:24:01,840
that make it a good model system.

393
00:24:01,840 --> 00:24:03,750
OK, so let's step back a second.

394
00:24:03,750 --> 00:24:06,220
I'm going to tell you a little
bit about floral morphology

395
00:24:06,220 --> 00:24:07,290
and floral development.

396
00:24:07,290 --> 00:24:10,430
So, this is what you usually think
of when you think about a flower.

397
00:24:10,430 --> 00:24:13,580
You may have a little bit of plant
biology, maybe in elementary school,

398
00:24:13,580 --> 00:24:16,640
and they told you that there are four
different kinds of floral organs,

399
00:24:16,640 --> 00:24:17,180
right?

400
00:24:17,180 --> 00:24:19,700
So you have sepals that
are the productive organs,

401
00:24:19,700 --> 00:24:23,540
the attractive petals that are
involved in manipulating pollinators,

402
00:24:23,540 --> 00:24:26,420
the male stamens, and
the female carpels.

403
00:24:26,420 --> 00:24:29,570
And these organs are arranged
in what we call whorls--

404
00:24:29,570 --> 00:24:31,230
they're concentric circles.

405
00:24:31,230 --> 00:24:34,460
So moving, you get sepals,
petals, stamens and carpels.

406
00:24:34,460 --> 00:24:37,250
And about 30 years ago,
molecular biologists

407
00:24:37,250 --> 00:24:39,680
working in a number of
different model systems

408
00:24:39,680 --> 00:24:44,280
defined a genetic program
called the ABC Model,

409
00:24:44,280 --> 00:24:49,100
and this is a model that tells us
how each of these floral organs

410
00:24:49,100 --> 00:24:55,267
figures out during development what
flavor of origin it wants to be.

411
00:24:55,267 --> 00:24:57,350
So for instance, there are
three different classes

412
00:24:57,350 --> 00:25:00,920
of genes, the a, b, and c-class genes.

413
00:25:00,920 --> 00:25:03,050
If you are primordium
in the first whorl,

414
00:25:03,050 --> 00:25:07,130
you're only exposed to A function,
and that tells you to be a sepal.

415
00:25:07,130 --> 00:25:11,420
In the second whorl, you have A and B
function that tells you to be a petal.

416
00:25:11,420 --> 00:25:16,670
Stamens are exposed to B and C
together and then carpels to C alone.

417
00:25:16,670 --> 00:25:19,550
So this is what we call
a combinatorial code.

418
00:25:19,550 --> 00:25:24,260
Each little position in the floral
meristem has a different set of genes,

419
00:25:24,260 --> 00:25:27,480
and that tells the organ
what kind of organ to be.

420
00:25:27,480 --> 00:25:29,670
And the way that they
came up with this program

421
00:25:29,670 --> 00:25:32,930
was by examining mutants, and
developmental geneticists,

422
00:25:32,930 --> 00:25:33,920
we love mutants.

423
00:25:33,920 --> 00:25:35,300
We make mutants all the time.

424
00:25:35,300 --> 00:25:39,590
We like to knock out gene function
and then infer from the way

425
00:25:39,590 --> 00:25:40,640
the plant looks--

426
00:25:40,640 --> 00:25:42,260
or any other organism--

427
00:25:42,260 --> 00:25:45,410
the way it looks what the
function of that gene must be.

428
00:25:45,410 --> 00:25:49,850
And the genes that comprise the ABC
program have something in common

429
00:25:49,850 --> 00:25:52,790
that when you knock
them out with mutation,

430
00:25:52,790 --> 00:25:55,670
you dramatically change
floral morphology.

431
00:25:55,670 --> 00:26:01,520
You transform the identity of one
organ into the identity of another.

432
00:26:01,520 --> 00:26:05,750
And so here, for instance, if
you eliminate this B-class gene,

433
00:26:05,750 --> 00:26:09,560
then all you have are A and C
functions in the floral meristem.

434
00:26:09,560 --> 00:26:13,550
And the petals turn into sepals,
and the stamens turn into carpels.

435
00:26:13,550 --> 00:26:16,880
And this is a complete
transformation, so this

436
00:26:16,880 --> 00:26:20,720
highlights how minor genetic
changes can have really

437
00:26:20,720 --> 00:26:24,210
profound effects on morphology.

438
00:26:24,210 --> 00:26:26,330
So these are the kinds
of genetic programs

439
00:26:26,330 --> 00:26:29,880
that we think about when we
think about floral evolution.

440
00:26:29,880 --> 00:26:32,720
OK, so how are we applying
this kind of model

441
00:26:32,720 --> 00:26:36,350
in the context of our
model system, Aquilegia?

442
00:26:36,350 --> 00:26:39,890
So there's a lot of interesting
things about Aquilegia flowers.

443
00:26:39,890 --> 00:26:43,730
These guys, up here,
those are the sepals.

444
00:26:43,730 --> 00:26:46,490
And usually, you think about sepals
as being green and protective,

445
00:26:46,490 --> 00:26:49,550
but these are not just green and
protective, they're bright red,

446
00:26:49,550 --> 00:26:51,920
or sometimes blue, or white, or yellow.

447
00:26:51,920 --> 00:26:55,580
They're what we call petaloid, which
just means that they're showing.

448
00:26:55,580 --> 00:26:57,240
So this is an interesting question.

449
00:26:57,240 --> 00:27:01,130
Here, we have the ecological
function of pollinator attraction

450
00:27:01,130 --> 00:27:04,640
that has been transferred from
the inner whorl of the flower

451
00:27:04,640 --> 00:27:06,500
to the outer whorl of the flower.

452
00:27:06,500 --> 00:27:10,370
So how does that happen, and what's
going on at the molecular level

453
00:27:10,370 --> 00:27:14,180
in order to make these
sepals bright and showy?

454
00:27:14,180 --> 00:27:19,250
Another really interesting aspect of the
Aquilegia flower is this nectar spur.

455
00:27:19,250 --> 00:27:23,900
So the true petals in the second whorl
have this long, tubular structure

456
00:27:23,900 --> 00:27:28,130
that terminates in a nectary, and
you can just imagine all the things

457
00:27:28,130 --> 00:27:32,630
that Aquilegia is doing to manipulate
pollinators to make them go all

458
00:27:32,630 --> 00:27:34,490
the way down here and get the nectar.

459
00:27:34,490 --> 00:27:37,190
So there's all kinds of things
that we can study here related

460
00:27:37,190 --> 00:27:39,410
to how the spurs first evolved.

461
00:27:39,410 --> 00:27:41,720
They're what we call
a key innovation that

462
00:27:41,720 --> 00:27:44,030
were important for the
radiation of the genus.

463
00:27:44,030 --> 00:27:47,630
And what is the genetic basis of
their diversification in morphology?

464
00:27:47,630 --> 00:27:50,940
They get longer and shorter, and
they're curved into different shapes.

465
00:27:50,940 --> 00:27:54,965
So we're studying all these different
aspects across species of Aquilegia.

466
00:27:54,965 --> 00:27:57,340
And then one of my favorite
things about Aquilegia flower

467
00:27:57,340 --> 00:27:59,380
that I didn't even
realize until I started

468
00:27:59,380 --> 00:28:03,820
dissecting them is that they actually
have five types of floral organs.

469
00:28:03,820 --> 00:28:07,840
They have this fifth type of organ
called the staminodium, which

470
00:28:07,840 --> 00:28:11,440
is a sterile organ positioned
between the male stamens

471
00:28:11,440 --> 00:28:12,970
and the female carpels.

472
00:28:12,970 --> 00:28:15,160
So, how the heck does
that-- how does that work?

473
00:28:15,160 --> 00:28:17,710
The ABC Model gives us
a very elegant mechanism

474
00:28:17,710 --> 00:28:20,260
for giving us four types
of floral organs, what

475
00:28:20,260 --> 00:28:23,800
happens when we intercalate
a fifth type of floral organ

476
00:28:23,800 --> 00:28:25,780
into that genetic program?

477
00:28:25,780 --> 00:28:28,480
So we've been studying
all of these questions.

478
00:28:28,480 --> 00:28:30,789
Another one that I'm
going to show you today

479
00:28:30,789 --> 00:28:32,830
is, we've been interested
in this question about,

480
00:28:32,830 --> 00:28:36,100
how does the flower know when to stop?

481
00:28:36,100 --> 00:28:37,660
You think about a flower.

482
00:28:37,660 --> 00:28:39,835
Once it makes those
carpels, then it stops.

483
00:28:39,835 --> 00:28:43,090
It doesn't make any other floral
organs, and that's actually

484
00:28:43,090 --> 00:28:45,850
a very carefully
controlled genetic process.

485
00:28:45,850 --> 00:28:48,250
In most of the flowers
you might think of,

486
00:28:48,250 --> 00:28:52,840
there may only be one whorl of
stamens like in this Snapdragon.

487
00:28:52,840 --> 00:28:55,090
But here, we're looking
down on an Aquilegia flower,

488
00:28:55,090 --> 00:28:58,240
you can see that there are
many whorls of stamens,

489
00:28:58,240 --> 00:29:02,170
so how does the meristem decide
how many whorls it's going to make?

490
00:29:02,170 --> 00:29:04,750
And to investigate this,
we started by actually

491
00:29:04,750 --> 00:29:08,200
working with these
C-class genes because this

492
00:29:08,200 --> 00:29:09,700
makes sense when you think about it.

493
00:29:09,700 --> 00:29:12,970
It turns out, the C-class
genes confer the identity

494
00:29:12,970 --> 00:29:17,860
of the stamens and the carpels, but
in conferring identity of the carpels,

495
00:29:17,860 --> 00:29:20,560
they tell the floral meristem to stop.

496
00:29:20,560 --> 00:29:23,800
So, what we want to ask is,
what happens when we knock out

497
00:29:23,800 --> 00:29:25,930
the function of these genes?

498
00:29:25,930 --> 00:29:28,780
And in Aquilegia, we
use a molecular tool

499
00:29:28,780 --> 00:29:32,290
to trick the plant into
silencing our gene of interest.

500
00:29:32,290 --> 00:29:36,100
And when we do that-- here's a wild
type Aquilegia flower, and here's

501
00:29:36,100 --> 00:29:40,360
a flower where we have used this
trick to silence the C-class gene.

502
00:29:40,360 --> 00:29:43,810
And you can already see it's looking
a little weird, but as it develops,

503
00:29:43,810 --> 00:29:47,410
you see that the stamens are
completely replaced by petals.

504
00:29:47,410 --> 00:29:51,100
And the staminodium, that
novel organ, and the carpels

505
00:29:51,100 --> 00:29:54,790
are both replaced by sepals.

506
00:29:54,790 --> 00:29:57,700
And when we look inside the flower,
you may not be able to see this,

507
00:29:57,700 --> 00:30:00,460
but there are actually
dozens and dozens of whorls

508
00:30:00,460 --> 00:30:04,090
of organs here, so it's
clear that this gene also

509
00:30:04,090 --> 00:30:06,760
controls floral meristem termination.

510
00:30:06,760 --> 00:30:09,550
Now, this was actually just
the first step in this project.

511
00:30:09,550 --> 00:30:13,960
We were trying to confirm that this
gene functions the same way in Aquilegia

512
00:30:13,960 --> 00:30:15,850
that it functions in other species.

513
00:30:15,850 --> 00:30:19,620
We expected it to be conserved
because, in fact, all around you,

514
00:30:19,620 --> 00:30:21,460
you see mutants.

515
00:30:21,460 --> 00:30:24,120
These are all mutant varieties.

516
00:30:24,120 --> 00:30:27,130
So here's a rose, a
daffodil, and a peony.

517
00:30:27,130 --> 00:30:29,920
Where on the left, you
see the wild type flower

518
00:30:29,920 --> 00:30:34,150
that has a single whorl of petals
and many whorls of stamens.

519
00:30:34,150 --> 00:30:37,240
And on the right, you
see an Agonis mutant

520
00:30:37,240 --> 00:30:40,270
that has the transformation
of stamen identity

521
00:30:40,270 --> 00:30:44,590
into petal identity and the
indeterminancy of the floral meristem.

522
00:30:44,590 --> 00:30:46,869
So actually, you're looking
at mutants all the time,

523
00:30:46,869 --> 00:30:48,160
you may just not have known it.

524
00:30:48,160 --> 00:30:51,190
So homeotic or transformative
mutants have always

525
00:30:51,190 --> 00:30:53,980
been very unpopular in horticulture.

526
00:30:53,980 --> 00:30:57,430
So, I hope I've convinced you that
evo-devo is an interesting field.

527
00:30:57,430 --> 00:31:00,477
If you're not totally taken
by plant biology as I am,

528
00:31:00,477 --> 00:31:02,810
you might be interested in
some of my other colleagues--

529
00:31:02,810 --> 00:31:07,150
Mansi Srivastava, who studies these
fascinating little flatworms that

530
00:31:07,150 --> 00:31:10,960
have the capacity to regenerate
their entire bodies from just

531
00:31:10,960 --> 00:31:12,700
small fragments of their body.

532
00:31:12,700 --> 00:31:14,740
They're actually thought to be immortal.

533
00:31:14,740 --> 00:31:17,470
My colleague, Cassandra
Extavour, who studies

534
00:31:17,470 --> 00:31:19,640
the evolution of the
genetic pathways that

535
00:31:19,640 --> 00:31:24,190
control germ line specification, which
is incredibly important for evolution

536
00:31:24,190 --> 00:31:25,300
and development.

537
00:31:25,300 --> 00:31:28,000
Cliff Tabin over at the
medical school studies

538
00:31:28,000 --> 00:31:31,930
the diversification of vertebrates
and particularly the vertebral column

539
00:31:31,930 --> 00:31:32,530
itself.

540
00:31:32,530 --> 00:31:34,690
Why don't snakes have any limbs?

541
00:31:34,690 --> 00:31:37,420
Why do ostriches have such long necks?

542
00:31:37,420 --> 00:31:41,380
My colleague Terry Capellini over
in Human Evolutionary Biology

543
00:31:41,380 --> 00:31:45,040
is actually using evo-devo approaches
to understand the evolution

544
00:31:45,040 --> 00:31:46,960
of the skeleton in humans.

545
00:31:46,960 --> 00:31:51,130
So he uses mouse models a lot, but
what he's ultimately interested in

546
00:31:51,130 --> 00:31:53,980
is actually human skeletal evolution.

547
00:31:53,980 --> 00:31:57,432
So that's everything I want to tell
you about evo-devo, so thank you.

548
00:31:57,432 --> 00:32:00,408
[APPLAUSE]

549
00:32:00,408 --> 00:32:17,780


550
00:32:17,780 --> 00:32:20,720
MARLYN E. MCGRATH: Roland
Fryer is our next speaker.

551
00:32:20,720 --> 00:32:25,040
He is currently the Henry Lee
Professor of Economics at Harvard

552
00:32:25,040 --> 00:32:30,740
and Faculty Director of the
Education Innovation Laboratory.

553
00:32:30,740 --> 00:32:36,260
He has, among our faculty members,
one of the more interesting life

554
00:32:36,260 --> 00:32:38,090
academic stories.

555
00:32:38,090 --> 00:32:43,820
He went to UT Austin as an undergraduate
on an athletic scholarship.

556
00:32:43,820 --> 00:32:46,680
He did not do that forever.

557
00:32:46,680 --> 00:32:49,640
Eventually, he got a PhD
in economics and so on.

558
00:32:49,640 --> 00:32:54,260
Along the way, he's been awarded many
of the most prestigious and somehow

559
00:32:54,260 --> 00:32:57,530
dazzling awards like a
MacArthur Genius Fellowship,

560
00:32:57,530 --> 00:33:03,080
and he won the John Bates Clark
Medal for the greatest, that

561
00:33:03,080 --> 00:33:05,690
year, under 40 economists and so on.

562
00:33:05,690 --> 00:33:09,920
His work focuses on education
in inequality and race,

563
00:33:09,920 --> 00:33:13,820
and it draws an economic
theory and empirical evidence

564
00:33:13,820 --> 00:33:16,250
in all other contextual elements.

565
00:33:16,250 --> 00:33:19,670
He did note somewhere when he
was speaking about his career

566
00:33:19,670 --> 00:33:23,000
that one element of his
diverse view of the world

567
00:33:23,000 --> 00:33:27,260
is that he had a job along the
way before he became a Henry Lee

568
00:33:27,260 --> 00:33:34,640
professor working at McDonald's
in drive-thru, not in corporate.

569
00:33:34,640 --> 00:33:39,590
But he's seen all of these worlds,
and he studies some aspects of that.

570
00:33:39,590 --> 00:33:40,502
Thank you.

571
00:33:40,502 --> 00:33:43,454
[APPLAUSE]

572
00:33:43,454 --> 00:33:48,482


573
00:33:48,482 --> 00:33:49,690
ROLAND FRYER: Good afternoon.

574
00:33:49,690 --> 00:33:52,240
What I'm most proud of actually
is that at my advanced age,

575
00:33:52,240 --> 00:33:55,198
I still spend two hours a morning
working out with the Harvard football

576
00:33:55,198 --> 00:33:58,960
team, so that's the most proud I am.

577
00:33:58,960 --> 00:34:02,694
How many of you actually
want to change the world?

578
00:34:02,694 --> 00:34:04,110
It's OK, you can raise your hands.

579
00:34:04,110 --> 00:34:05,401
You don't have to be like this.

580
00:34:05,401 --> 00:34:08,250
OK, how are you going
to change the world?

581
00:34:08,250 --> 00:34:08,750
You.

582
00:34:08,750 --> 00:34:13,070


583
00:34:13,070 --> 00:34:14,940
Well, but that was rude, you're right.

584
00:34:14,940 --> 00:34:17,110
I'm Roland, how are you
going to change the world?

585
00:34:17,110 --> 00:34:19,984


586
00:34:19,984 --> 00:34:21,900
AUDIENCE: I'm Hassad [INAUDIBLE].

587
00:34:21,900 --> 00:34:26,219
I want to change the world
by being an attorney.

588
00:34:26,219 --> 00:34:27,290
ROLAND FRYER: Awesome.

589
00:34:27,290 --> 00:34:29,706
You guys went simultaneously
on the hand, so I'll go both.

590
00:34:29,706 --> 00:34:30,944
What about you?

591
00:34:30,944 --> 00:34:33,570
AUDIENCE: I want to be an astronaut
and learn more about space.

592
00:34:33,570 --> 00:34:35,694
ROLAND FRYER: Astronaut
and learn more about space.

593
00:34:35,694 --> 00:34:38,800
Sorry, the lights are super in my
face, so I can't really see upstairs.

594
00:34:38,800 --> 00:34:42,530
But anyone upstairs want
to change the world?

595
00:34:42,530 --> 00:34:44,389
There we go.

596
00:34:44,389 --> 00:34:47,348
AUDIENCE: I want to build robots that
save people in natural disasters.

597
00:34:47,348 --> 00:34:48,639
ROLAND FRYER: Wow, that's cool.

598
00:34:48,639 --> 00:34:50,060
Build robots-- that's amazing.

599
00:34:50,060 --> 00:34:53,040


600
00:34:53,040 --> 00:34:57,490
A million years ago when I was your
age, I also wanted to change the world.

601
00:34:57,490 --> 00:34:58,750
I wanted to go to the NFL.

602
00:34:58,750 --> 00:35:01,300
Let me rephrase that, I
wanted to change my world.

603
00:35:01,300 --> 00:35:03,900


604
00:35:03,900 --> 00:35:06,560
Maybe that could have some
spill-overs to other people.

605
00:35:06,560 --> 00:35:07,984
And I was interested two things.

606
00:35:07,984 --> 00:35:09,400
I wanted to wear t-shirts to work.

607
00:35:09,400 --> 00:35:11,100
I thought that would be cool.

608
00:35:11,100 --> 00:35:13,920
And I wanted to have
a room full of shoes.

609
00:35:13,920 --> 00:35:16,155
I don't know what the
second one is about.

610
00:35:16,155 --> 00:35:18,390
And to be clear, I've
accomplished both of them,

611
00:35:18,390 --> 00:35:22,690
but I really wanted
a room full of shoes.

612
00:35:22,690 --> 00:35:27,930
Now, what I do for a living is that
I raise lots of private resources

613
00:35:27,930 --> 00:35:32,957
to run programs in communities
across the United States.

614
00:35:32,957 --> 00:35:35,540
And I'm an economist, but I'm
interested in racial inequality.

615
00:35:35,540 --> 00:35:37,309
That's like a little
bit of a weird thing.

616
00:35:37,309 --> 00:35:39,600
That's like an oxymoron like
jumbo shrimp or something.

617
00:35:39,600 --> 00:35:40,920
That's weird, right?

618
00:35:40,920 --> 00:35:45,210
Economists-- we're really
careful and analytical,

619
00:35:45,210 --> 00:35:49,590
but caring about other people's
not typically our thing.

620
00:35:49,590 --> 00:35:50,260
But what I do--

621
00:35:50,260 --> 00:35:52,020
I love the economic method.

622
00:35:52,020 --> 00:35:54,150
I really love-- like when
I was in graduate school,

623
00:35:54,150 --> 00:36:00,360
I got totally fascinated with the tools
like the separating hyperplane theorem.

624
00:36:00,360 --> 00:36:04,470
I was just blown away with the
beautiful mathematics of economics,

625
00:36:04,470 --> 00:36:07,710
and I thought it was being
used to study problems

626
00:36:07,710 --> 00:36:13,440
that were not as interesting to me
like the optimal cake eating problem.

627
00:36:13,440 --> 00:36:14,850
You see my size.

628
00:36:14,850 --> 00:36:16,546
I'm 6' 2", 215--

629
00:36:16,546 --> 00:36:17,420
I eat the whole cake.

630
00:36:17,420 --> 00:36:20,040


631
00:36:20,040 --> 00:36:23,580
But I thought maybe these things can
be used on problems that I deeply

632
00:36:23,580 --> 00:36:28,170
care about like why all the kids in
my neighborhood-- not a lot of them

633
00:36:28,170 --> 00:36:32,610
went to college even though I
thought they had great talent, why

634
00:36:32,610 --> 00:36:36,900
many of my friends ended up
incarcerated at young ages

635
00:36:36,900 --> 00:36:40,660
even though I thought there
was a lot of potential.

636
00:36:40,660 --> 00:36:43,680
And so what I do is I go around
and we raise a lot of dollars.

637
00:36:43,680 --> 00:36:46,513
We've probably raised a couple
hundred million dollars at this point

638
00:36:46,513 --> 00:36:49,410
over the last 10 years
to run experiments.

639
00:36:49,410 --> 00:36:51,120
That's the economist in me, right?

640
00:36:51,120 --> 00:36:54,730
I don't tie my shoes without
a treatment and control group.

641
00:36:54,730 --> 00:37:00,330
But to run experiments so that we
can really understand what works

642
00:37:00,330 --> 00:37:07,080
and what doesn't with the idea that
we want to lower inequality in America

643
00:37:07,080 --> 00:37:11,437
by expanding opportunity.

644
00:37:11,437 --> 00:37:13,270
There's a thing here
somewhere, isn't there.

645
00:37:13,270 --> 00:37:15,436
So I'm going to show you a
project I've been working

646
00:37:15,436 --> 00:37:18,510
on for the last year and a half.

647
00:37:18,510 --> 00:37:21,185
It hasn't made me the
most popular, and that's

648
00:37:21,185 --> 00:37:23,560
what happens sometimes when
you're dealing with big data.

649
00:37:23,560 --> 00:37:28,290
Sometimes, you get answers that aren't
what you expected when you started.

650
00:37:28,290 --> 00:37:32,520
Like many of you, I was
really and I am really

651
00:37:32,520 --> 00:37:37,740
concerned with what's going
on between race and police

652
00:37:37,740 --> 00:37:39,790
in communities across America.

653
00:37:39,790 --> 00:37:41,670
We had started with--

654
00:37:41,670 --> 00:37:43,710
I should say it started with.

655
00:37:43,710 --> 00:37:49,950
It became more salient for us
with Michael Brown in Ferguson,

656
00:37:49,950 --> 00:37:55,740
Eric Garner in New York City, Laquan
McDonald in Chicago, I could go on.

657
00:37:55,740 --> 00:37:58,590
And I was frustrated by
this, to be honest with you.

658
00:37:58,590 --> 00:38:01,620
I was tired of looking at
my TV at night and seeing

659
00:38:01,620 --> 00:38:06,120
the protests and silly debates.

660
00:38:06,120 --> 00:38:10,050
They weren't silly in their content,
they were silly in their execution

661
00:38:10,050 --> 00:38:13,150
about what was actually going on.

662
00:38:13,150 --> 00:38:19,480
And as you might imagine, I'm
kind of too weak to be protesting.

663
00:38:19,480 --> 00:38:23,320
I mean, it's like, hot out there, so
I decided maybe what I should do--

664
00:38:23,320 --> 00:38:25,904


665
00:38:25,904 --> 00:38:27,070
my skin's too fair for that.

666
00:38:27,070 --> 00:38:29,990


667
00:38:29,990 --> 00:38:32,980
But what I can do is go into
our lab and roll up our sleeves,

668
00:38:32,980 --> 00:38:35,470
and see if we can
actually make progress.

669
00:38:35,470 --> 00:38:42,490
OK, and so the key thing was like
how do you make progress on something

670
00:38:42,490 --> 00:38:45,550
that is so deeply emotional?

671
00:38:45,550 --> 00:38:48,670
People on both sides of this issue
are deeply emotional about it

672
00:38:48,670 --> 00:38:50,260
and rightly so.

673
00:38:50,260 --> 00:38:51,550
So here's what I did.

674
00:38:51,550 --> 00:38:53,750
I'll just tell you the full story.

675
00:38:53,750 --> 00:38:59,140
I called up Bud Riley, who turns out
to be the police chief of the Harvard

676
00:38:59,140 --> 00:39:00,970
University Police.

677
00:39:00,970 --> 00:39:02,440
And I said, hey, bud.

678
00:39:02,440 --> 00:39:07,060
I'm interested in race and police,
but I've got to be honest with you.

679
00:39:07,060 --> 00:39:10,750
I spent my youth running
from dudes like you.

680
00:39:10,750 --> 00:39:16,060
So I don't really like you all
that well, but I'd like to learn.

681
00:39:16,060 --> 00:39:18,430
And Bud said, I got
just the thing for you.

682
00:39:18,430 --> 00:39:19,450
OK, true story.

683
00:39:19,450 --> 00:39:22,450
So Bud says, why don't you come over
to my office tomorrow at 3 o'clock,

684
00:39:22,450 --> 00:39:23,741
and I've got something for you.

685
00:39:23,741 --> 00:39:27,430
I said, OK, Bud, but you know my
grandmother always taught me not

686
00:39:27,430 --> 00:39:28,870
to just go hang out with police.

687
00:39:28,870 --> 00:39:29,869
This seems like a setup.

688
00:39:29,869 --> 00:39:33,610


689
00:39:33,610 --> 00:39:35,800
So I brought one of my
white students with me,

690
00:39:35,800 --> 00:39:38,300
and I was like, come on
with me-- you can film this.

691
00:39:38,300 --> 00:39:40,770
OK, that's actually not true.

692
00:39:40,770 --> 00:39:42,530
That's not true.

693
00:39:42,530 --> 00:39:44,925
That part's not true.

694
00:39:44,925 --> 00:39:47,300
If I had thought about it
first, it would have been true,

695
00:39:47,300 --> 00:39:50,470
but it's not true.

696
00:39:50,470 --> 00:39:51,160
I risked it.

697
00:39:51,160 --> 00:39:54,150
I went and had coffee
with Bud in the afternoon,

698
00:39:54,150 --> 00:39:55,649
and Bud took me to the simulator.

699
00:39:55,649 --> 00:39:57,940
So I went into the police
simulator where they actually

700
00:39:57,940 --> 00:40:01,560
train police officers because
I thought this was easy.

701
00:40:01,560 --> 00:40:04,159
Because I got this fancy
European wife who's

702
00:40:04,159 --> 00:40:06,950
like, why don't they just shoot
him in the leg, and I'm like, well,

703
00:40:06,950 --> 00:40:07,300
I don't know.

704
00:40:07,300 --> 00:40:08,800
Why do they shoot him in the leg?

705
00:40:08,800 --> 00:40:16,090
So I go over and he puts on the utility
belt like I'm going deep in here.

706
00:40:16,090 --> 00:40:18,210
Like, now, I'm in the sociology.

707
00:40:18,210 --> 00:40:22,690
I got the utility belt on, I've got
my pistol on, I've got my nightlight

708
00:40:22,690 --> 00:40:26,170
or whatever police have,
I got that stuff on,

709
00:40:26,170 --> 00:40:30,910
and I'm in this kind of simulator
where bad guys are jumping out.

710
00:40:30,910 --> 00:40:33,080
And you've got to decide who to shoot.

711
00:40:33,080 --> 00:40:35,569
OK, so my first interaction--

712
00:40:35,569 --> 00:40:38,110
I only have 10 minutes, so I
can't bore you with all of them.

713
00:40:38,110 --> 00:40:40,610
Bottom line is I'm the worst
police officer ever imaginable.

714
00:40:40,610 --> 00:40:45,610
But the first guy
comes up-- true story--

715
00:40:45,610 --> 00:40:52,810
and it's a mid-forties, maybe
50, white gentleman, long hair.

716
00:40:52,810 --> 00:40:57,730
And I'm going to a suburban pool party
because there had been complaints.

717
00:40:57,730 --> 00:41:00,370
So I get there and
this person is clearly

718
00:41:00,370 --> 00:41:03,940
inebriated and is a little handsy.

719
00:41:03,940 --> 00:41:06,070
You know, he's, why
are you bothering me?

720
00:41:06,070 --> 00:41:07,860
What are you doing?

721
00:41:07,860 --> 00:41:11,470
And so I get a little nervous
even though I'm in the simulator.

722
00:41:11,470 --> 00:41:14,790
And the simulator has got some
artificial intelligence parts to it,

723
00:41:14,790 --> 00:41:18,460
so as I talk him down using
de-escalation techniques,

724
00:41:18,460 --> 00:41:22,450
this image on the screen
is supposed to relax.

725
00:41:22,450 --> 00:41:27,910
So I say to him, sir, I'm
just doing my patrols here.

726
00:41:27,910 --> 00:41:29,890
You've got to relax.

727
00:41:29,890 --> 00:41:32,380
He says, well, you're
always bothering me.

728
00:41:32,380 --> 00:41:33,839
I said, sir--

729
00:41:33,839 --> 00:41:35,130
I'm talking to the screen now--

730
00:41:35,130 --> 00:41:36,070
you've got to relax.

731
00:41:36,070 --> 00:41:39,830
And he put his hands in his pocket,
and I decide that's enough for me.

732
00:41:39,830 --> 00:41:42,840
So I pull my gun on the guy.

733
00:41:42,840 --> 00:41:47,736
Bud turns on the lights,
Professor, what are you doing?

734
00:41:47,736 --> 00:41:49,360
I was like, isn't that what you all do?

735
00:41:49,360 --> 00:41:53,040


736
00:41:53,040 --> 00:41:56,215
Could have fooled me.

737
00:41:56,215 --> 00:42:00,520
And so we go through this training
and I have to say in all seriousness,

738
00:42:00,520 --> 00:42:01,200
it was--

739
00:42:01,200 --> 00:42:04,720
not just the training,
but this whole experience

740
00:42:04,720 --> 00:42:08,110
I think I learned it was
the most important lessons

741
00:42:08,110 --> 00:42:11,410
I learned in life since my
grandmother taught me how to read.

742
00:42:11,410 --> 00:42:15,430
It turns out, police
have a really tough job.

743
00:42:15,430 --> 00:42:21,244
And so my next thing was I learned
how to shoot police issued weapons

744
00:42:21,244 --> 00:42:24,160
just because I wanted to understand,
and then actually embedded myself

745
00:42:24,160 --> 00:42:26,940
in the Camden Police Department.

746
00:42:26,940 --> 00:42:28,850
Now, I know the police
chief here in Boston,

747
00:42:28,850 --> 00:42:31,807
and I highly recommend anyone
do a ride along with police just

748
00:42:31,807 --> 00:42:33,390
to understand their side of the story.

749
00:42:33,390 --> 00:42:36,440
I do not recommend riding
along embedding yourself

750
00:42:36,440 --> 00:42:38,910
in Camden, New Jersey.

751
00:42:38,910 --> 00:42:42,420
OK, it was a crazy experience.

752
00:42:42,420 --> 00:42:45,240
We were responding to 911 calls.

753
00:42:45,240 --> 00:42:49,350
We were responding to shots fired.

754
00:42:49,350 --> 00:42:54,890
Now all of us thinkers, if
there are some shots over here,

755
00:42:54,890 --> 00:42:59,150
our way to respond is to go
over here, but it turns out,

756
00:42:59,150 --> 00:43:01,400
when you're in the police
car and they start shooting,

757
00:43:01,400 --> 00:43:04,540
you've got to go in there.

758
00:43:04,540 --> 00:43:07,920
And it was an unbelievable experience.

759
00:43:07,920 --> 00:43:11,590
And in part because I've got
to say, I'm not proud of it,

760
00:43:11,590 --> 00:43:15,270
but I would say it
took five to six hours

761
00:43:15,270 --> 00:43:21,530
to get pretty desensitized because
you're always seeing people in crisis.

762
00:43:21,530 --> 00:43:24,690
And that's not a part of policing
that I ever thought about.

763
00:43:24,690 --> 00:43:30,330
You show up to a Wendy's and
someone has threatened the cashier,

764
00:43:30,330 --> 00:43:34,140
and you show up three hours late
because the Wendy's threatening thing

765
00:43:34,140 --> 00:43:36,720
was way down on your list
of things to show up for.

766
00:43:36,720 --> 00:43:39,160
And you get there and you
get yelled at for 30 minutes,

767
00:43:39,160 --> 00:43:42,159
and then you end up saying something
nonsensical like, if they come back

768
00:43:42,159 --> 00:43:43,439
and threaten again, call us.

769
00:43:43,439 --> 00:43:44,605
Give us a three hour window.

770
00:43:44,605 --> 00:43:48,290


771
00:43:48,290 --> 00:43:51,920
It was incredible, and so I did all this
because I wanted to do a few things.

772
00:43:51,920 --> 00:43:54,560
One-- I'm going to show
some data as I go--

773
00:43:54,560 --> 00:43:57,700
I wanted to learn how to talk cop.

774
00:43:57,700 --> 00:43:59,420
That was important.

775
00:43:59,420 --> 00:44:02,960
I wanted to build trust
with police departments,

776
00:44:02,960 --> 00:44:09,010
and most importantly for an
economist, I needed some data.

777
00:44:09,010 --> 00:44:13,740
And so what we've done
is we have collected now

778
00:44:13,740 --> 00:44:19,200
in places like New York City, 6 million
data observations over the last 15

779
00:44:19,200 --> 00:44:22,020
years on use of force
that ranges anywhere

780
00:44:22,020 --> 00:44:27,300
from policemen putting their hands
on you to hitting you with a baton.

781
00:44:27,300 --> 00:44:31,260
We've also worked with lots of cities--

782
00:44:31,260 --> 00:44:40,980
Houston, Arlington, Austin, most
counties in Florida, Boston, Camden--

783
00:44:40,980 --> 00:44:46,180
to collect data on officer-involved
shootings because it's not enough,

784
00:44:46,180 --> 00:44:48,930
and you guys know this from
high school statistics.

785
00:44:48,930 --> 00:44:56,970
It's not enough to just look at
numbers of people who are being shot.

786
00:44:56,970 --> 00:44:59,810
That's also awful, but that's
not enough statistically.

787
00:44:59,810 --> 00:45:03,840
What you really want to understand
is in a similar situation,

788
00:45:03,840 --> 00:45:08,700
does race impact the probability of
a police officer pulling the trigger?

789
00:45:08,700 --> 00:45:10,860
That's a very different question.

790
00:45:10,860 --> 00:45:15,480
In that question, the burden of
the data is strong because you need

791
00:45:15,480 --> 00:45:19,500
to understand all of the police
shootings that could have happened

792
00:45:19,500 --> 00:45:21,860
but didn't.

793
00:45:21,860 --> 00:45:24,970
That's far more complicated.

794
00:45:24,970 --> 00:45:33,730
In fact, just in one city alone, we
spent 50 minutes per observation,

795
00:45:33,730 --> 00:45:36,040
hand-typing them in
the police department

796
00:45:36,040 --> 00:45:38,350
because they didn't trust
us to leave with the data.

797
00:45:38,350 --> 00:45:44,060
We had to pay a sergeant $50 an
hour to watch us type in the data.

798
00:45:44,060 --> 00:45:47,115
OK, and we typed in
roughly 2,000 observations.

799
00:45:47,115 --> 00:45:48,875
It took us nearly a year.

800
00:45:48,875 --> 00:45:49,750
Here's what we found.

801
00:45:49,750 --> 00:45:53,080


802
00:45:53,080 --> 00:45:59,110
This is the odds ratio for blacks
versus whites, so anything over one

803
00:45:59,110 --> 00:46:01,150
shows a racial difference.

804
00:46:01,150 --> 00:46:05,150
On the X-axis here, I have
the amount of force used.

805
00:46:05,150 --> 00:46:09,850
Yeah, so one is hands, seven is baton.

806
00:46:09,850 --> 00:46:11,470
This is the same for Hispanics.

807
00:46:11,470 --> 00:46:13,600
On the Y-axis is the odds ratio.

808
00:46:13,600 --> 00:46:16,480
Anything over one means
that Hispanics are

809
00:46:16,480 --> 00:46:22,090
more likely to have that use of force,
and on the X-axis, it's the same.

810
00:46:22,090 --> 00:46:24,110
These are uses of force.

811
00:46:24,110 --> 00:46:27,620
OK, now this is important
because a couple of reasons.

812
00:46:27,620 --> 00:46:31,480
One, we usually don't have
data on uses of force.

813
00:46:31,480 --> 00:46:34,299
Particularly like, do you
put your hands on someone?

814
00:46:34,299 --> 00:46:36,840
We even have data that I can't
show you today because of time

815
00:46:36,840 --> 00:46:40,880
where I know just if the
police officer shouted at you.

816
00:46:40,880 --> 00:46:46,130
OK, and so what you find here
is something pretty interesting.

817
00:46:46,130 --> 00:46:50,780
When it comes to any
force being used, blacks

818
00:46:50,780 --> 00:46:54,080
are 53% more likely
to have any force used

819
00:46:54,080 --> 00:46:58,100
on them in any interaction with police.

820
00:46:58,100 --> 00:47:01,810
Now, this is reported by the police.

821
00:47:01,810 --> 00:47:04,747
So maybe this is an
underestimate, maybe it's not,

822
00:47:04,747 --> 00:47:06,080
but it's reported by the police.

823
00:47:06,080 --> 00:47:07,871
This is not people
complaining on a survey.

824
00:47:07,871 --> 00:47:11,740
This is administrative
data from the police.

825
00:47:11,740 --> 00:47:16,450
As the use of force increases,
the racial difference

826
00:47:16,450 --> 00:47:20,880
remains pretty constant at about 25%.

827
00:47:20,880 --> 00:47:24,200
Here's a pretty
interesting fact, even when

828
00:47:24,200 --> 00:47:28,400
you look at individuals who are
perfectly compliant as reported

829
00:47:28,400 --> 00:47:32,330
by the police, perfectly compliant--

830
00:47:32,330 --> 00:47:35,960
not arrested, they
don't have contraband,

831
00:47:35,960 --> 00:47:44,000
there is nothing in the record
to show that any ill was done--

832
00:47:44,000 --> 00:47:47,150
the racial differences in whether or
not force was used in that interaction

833
00:47:47,150 --> 00:47:48,050
is roughly 24%.

834
00:47:48,050 --> 00:47:50,830


835
00:47:50,830 --> 00:47:56,000
So there are large racial differences
between Hispanics and African-Americans

836
00:47:56,000 --> 00:48:01,090
in the US on lower level uses of force
that I cannot explain with the data,

837
00:48:01,090 --> 00:48:04,870
and many, many police departments as
I've talked to them about this over

838
00:48:04,870 --> 00:48:07,690
the last few months agree with this.

839
00:48:07,690 --> 00:48:08,850
They understand.

840
00:48:08,850 --> 00:48:12,040
Some police departments-- sometimes
when you get into more rural areas to be

841
00:48:12,040 --> 00:48:12,915
very blunt about it--

842
00:48:12,915 --> 00:48:16,030


843
00:48:16,030 --> 00:48:20,320
they don't want to debate with these
data, but the vast majority of them

844
00:48:20,320 --> 00:48:25,920
understand that there may be
an issue on the use of force.

845
00:48:25,920 --> 00:48:28,500
What's interesting, however,
is that when you actually

846
00:48:28,500 --> 00:48:33,130
look at police shootings, we
find no racial differences.

847
00:48:33,130 --> 00:48:37,010
We find racial differences
in the numbers.

848
00:48:37,010 --> 00:48:41,870
More African-Americans are
shot by police than whites,

849
00:48:41,870 --> 00:48:46,290
but not once you actually take
more context into account.

850
00:48:46,290 --> 00:48:49,430
OK, so this is where we went to
places like Houston and others

851
00:48:49,430 --> 00:48:54,991
and collected very, very detailed
data on racial differences

852
00:48:54,991 --> 00:48:56,240
in officer-involved shootings.

853
00:48:56,240 --> 00:49:01,490
Now, here's a very important
caveat, we don't have every city.

854
00:49:01,490 --> 00:49:02,570
We don't have Chicago.

855
00:49:02,570 --> 00:49:03,590
I'd love Chicago.

856
00:49:03,590 --> 00:49:04,829
We don't have Chicago.

857
00:49:04,829 --> 00:49:05,870
They won't give it to me.

858
00:49:05,870 --> 00:49:09,560
I've asked many times.

859
00:49:09,560 --> 00:49:15,680
After we released this paper,
myself and a couple of the folks

860
00:49:15,680 --> 00:49:18,320
who started the Black
Lives Matter movement

861
00:49:18,320 --> 00:49:21,860
met with President Obama for
five hours in the White House.

862
00:49:21,860 --> 00:49:25,164
Five hours and after it was over--

863
00:49:25,164 --> 00:49:28,330
and that's a long time to meet with the
president, and frankly I had to pee.

864
00:49:28,330 --> 00:49:29,121
And what do you do?

865
00:49:29,121 --> 00:49:33,620
Like, Mr. President-- this was one
of the big issues in that meeting.

866
00:49:33,620 --> 00:49:38,290
I was sitting there like, Mr.
President, that's really interesting.

867
00:49:38,290 --> 00:49:41,785
Finally, I-- this is
true, ask Loretta Lynch.

868
00:49:41,785 --> 00:49:44,410
I have a little handwritten note
that I wrote to Loretta Lynch.

869
00:49:44,410 --> 00:49:45,850
Like, this is the first
interaction you have with her,

870
00:49:45,850 --> 00:49:47,370
you want to be really
good and bold and like,

871
00:49:47,370 --> 00:49:49,411
this is the thing my
grandmother prepared me for.

872
00:49:49,411 --> 00:49:52,070
And my little notes
says, where do I pee?

873
00:49:52,070 --> 00:49:58,587
Anyway, so we met and we debated
this a lot, and I asked him--

874
00:49:58,587 --> 00:50:00,670
I said, Mr. President, is
there anything the White

875
00:50:00,670 --> 00:50:02,090
House can do to further this research?

876
00:50:02,090 --> 00:50:03,798
I said, you can get
me data from Chicago.

877
00:50:03,798 --> 00:50:07,739
He said, is there anything
else the White House can do?

878
00:50:07,739 --> 00:50:08,530
So we don't see it.

879
00:50:08,530 --> 00:50:12,040
We don't see it whether
it's on the shootings,

880
00:50:12,040 --> 00:50:18,430
so we look at similar situations in
the data and calculate the times where

881
00:50:18,430 --> 00:50:20,440
shooting could have
taken place but didn't.

882
00:50:20,440 --> 00:50:21,760
We don't see it there.

883
00:50:21,760 --> 00:50:24,970
Houston has a really interesting thing
because on the strong arm in Houston,

884
00:50:24,970 --> 00:50:25,930
there is a pistol.

885
00:50:25,930 --> 00:50:29,654
On the weak arm, there's
a Taser, so in economics

886
00:50:29,654 --> 00:50:31,320
lingo, that's a discrete choice problem.

887
00:50:31,320 --> 00:50:32,740
You either shoot or taser.

888
00:50:32,740 --> 00:50:35,530
We actually don't see any
racial differences on Tasers.

889
00:50:35,530 --> 00:50:40,740
We don't see any racial differences
on any city in which we have data.

890
00:50:40,740 --> 00:50:43,680
Again, with the caveat
being, they gave us the data.

891
00:50:43,680 --> 00:50:44,190
Now.

892
00:50:44,190 --> 00:50:46,490
Why is this important?

893
00:50:46,490 --> 00:50:49,310
It's important because
you've heard several times,

894
00:50:49,310 --> 00:50:53,330
you have great choices, awesome.

895
00:50:53,330 --> 00:50:57,890
So do a lot of us, but the key thing
that's awesome about being here--

896
00:50:57,890 --> 00:51:00,790
I've been here for 15
years, and I will always

897
00:51:00,790 --> 00:51:03,610
stay here as long as ideas
are my only constraint.

898
00:51:03,610 --> 00:51:05,980
And that's what's so
wonderful about this place.

899
00:51:05,980 --> 00:51:09,190
Ideas truly are your
only constraint, and so--

900
00:51:09,190 --> 00:51:10,180
and data.

901
00:51:10,180 --> 00:51:13,740
Ideas are really your only
constraint, and I really

902
00:51:13,740 --> 00:51:18,180
believe that this is the future
for understanding racial inequality

903
00:51:18,180 --> 00:51:21,390
in America and around the world.

904
00:51:21,390 --> 00:51:26,220
It's using the passion
that fueled our parents,

905
00:51:26,220 --> 00:51:29,100
and our grandparents, and
our great grandparents,

906
00:51:29,100 --> 00:51:36,240
but using the tools of computer
science, and economics,

907
00:51:36,240 --> 00:51:38,880
and artificial intelligence,
and those disciplines

908
00:51:38,880 --> 00:51:43,800
who have been set silent when it
comes to racial inequality in America

909
00:51:43,800 --> 00:51:45,480
to truly solve these problems.

910
00:51:45,480 --> 00:51:47,310
We actually need you.

911
00:51:47,310 --> 00:51:52,060
Your generation thinks about
race differently than mine,

912
00:51:52,060 --> 00:51:57,140
and we need your insights
and your mental capabilities

913
00:51:57,140 --> 00:52:04,190
to finally put this old problem to
bed through data, through analysis,

914
00:52:04,190 --> 00:52:05,580
and through objective thinking.

915
00:52:05,580 --> 00:52:06,914
Thanks, enjoy your visit.

916
00:52:06,914 --> 00:52:09,878
[APPLAUSE]

917
00:52:09,878 --> 00:52:24,582


918
00:52:24,582 --> 00:52:25,790
MARLYN E. MCGRATH: Thank you.

919
00:52:25,790 --> 00:52:29,120


920
00:52:29,120 --> 00:52:31,670
Another person is about
to speak to us who has

921
00:52:31,670 --> 00:52:34,970
another extraordinarily varied history.

922
00:52:34,970 --> 00:52:38,450
I don't want to focus right
in on the plebeian stuff,

923
00:52:38,450 --> 00:52:41,390
but I do know that she
raised five children

924
00:52:41,390 --> 00:52:45,170
while she was a graduate student
getting her PhD in history.

925
00:52:45,170 --> 00:52:47,240
But she actually was born in the west.

926
00:52:47,240 --> 00:52:49,270
She comes east from Idaho.

927
00:52:49,270 --> 00:52:54,440
She was born in a small town in Idaho,
and went to the University of Utah.

928
00:52:54,440 --> 00:53:00,770
This is Laurel Thatcher Ulrich, one of
Harvard's 25 very esteemed university

929
00:53:00,770 --> 00:53:01,310
professors.

930
00:53:01,310 --> 00:53:04,490
That means that she can teach anywhere
at any time, I think, on any topic

931
00:53:04,490 --> 00:53:07,310
actually, so we'll see
what she does for us today.

932
00:53:07,310 --> 00:53:12,200
Her work has crossed so many boundaries
of disciplines, which I think

933
00:53:12,200 --> 00:53:15,200
is one of the hallmarks actually
of everybody speaking to us today.

934
00:53:15,200 --> 00:53:20,750
It's one of the hallmarks of
the new university as it were.

935
00:53:20,750 --> 00:53:23,860
One of her books, A Midwife's Tale,
which some of you may have read,

936
00:53:23,860 --> 00:53:28,790
won the Pulitzer Prize for history
and the Bancroft Prize for history.

937
00:53:28,790 --> 00:53:33,290
She's also winner of the
Harvard College Professorship

938
00:53:33,290 --> 00:53:36,650
in recognition of her excellence and
interest in undergraduate teaching.

939
00:53:36,650 --> 00:53:39,530


940
00:53:39,530 --> 00:53:43,900
She really focuses on the
history of early America,

941
00:53:43,900 --> 00:53:46,400
but she teaches a very,
very popular course.

942
00:53:46,400 --> 00:53:49,010
This may have something to
with what she says to us today

943
00:53:49,010 --> 00:53:53,570
at Harvard called Tangible Things,
promoting an understanding of history

944
00:53:53,570 --> 00:53:56,240
through material objects.

945
00:53:56,240 --> 00:54:01,520
She is also affiliated with one of
the residential houses, Dunster House,

946
00:54:01,520 --> 00:54:03,740
and her latest book, which
is currently I think still

947
00:54:03,740 --> 00:54:06,660
in press, A House Full of Females--

948
00:54:06,660 --> 00:54:09,740
Mormon diaries, 1830 the 1870.

949
00:54:09,740 --> 00:54:10,240
Laurel.

950
00:54:10,240 --> 00:54:13,168
[APPLAUSE]

951
00:54:13,168 --> 00:54:22,940


952
00:54:22,940 --> 00:54:24,890
LAUREL THATCHER ULRICH: Hello.

953
00:54:24,890 --> 00:54:28,280
Well, my goodness, aren't I lucky?

954
00:54:28,280 --> 00:54:32,090
I got to follow all
these fabulous talks,

955
00:54:32,090 --> 00:54:38,590
and I'm up here to talk
about improbable things.

956
00:54:38,590 --> 00:54:45,670
Hmm, and I even have two
titles to remind you.

957
00:54:45,670 --> 00:54:49,940
I am really interested in
the common and the ordinary,

958
00:54:49,940 --> 00:54:55,730
and I have a challenge here to convince
you that there's some value in that.

959
00:54:55,730 --> 00:55:05,690
When somebody asks me to think big,
I have an inclination to start small.

960
00:55:05,690 --> 00:55:07,790
I'm a historian.

961
00:55:07,790 --> 00:55:10,220
History is all around us.

962
00:55:10,220 --> 00:55:15,950
I hope you can feel it in this
building that we're in today.

963
00:55:15,950 --> 00:55:21,740
It's meant to intimidate you, it's
meant to overpower you, and I can tell,

964
00:55:21,740 --> 00:55:26,240
some of you at least
can feel that sense.

965
00:55:26,240 --> 00:55:35,000
But we can get ahold of lives and
ahold of our position in the world.

966
00:55:35,000 --> 00:55:41,960
If we can exercise a lot more
curiosity about ordinary things,

967
00:55:41,960 --> 00:55:46,160
including the things sitting
right in front of us, and so

968
00:55:46,160 --> 00:55:51,350
I'm going to try to convince
you of that a little bit today.

969
00:55:51,350 --> 00:55:55,170
What did you have for
breakfast this weekend?

970
00:55:55,170 --> 00:56:02,330
Did they feed you a very strange
thing called a Veritaffle?

971
00:56:02,330 --> 00:56:04,940
Did anyone get a Veritaffle?

972
00:56:04,940 --> 00:56:06,050
No?

973
00:56:06,050 --> 00:56:06,860
Yes?

974
00:56:06,860 --> 00:56:08,660
Yes?

975
00:56:08,660 --> 00:56:12,380
Oh, if you didn't get
a Veritaffle this time,

976
00:56:12,380 --> 00:56:15,390
you'll get one when you come back.

977
00:56:15,390 --> 00:56:17,600
What is a Veritaffle?

978
00:56:17,600 --> 00:56:21,600
It's one of the strangest
things on this campus.

979
00:56:21,600 --> 00:56:26,360
It's actually a waffle
produced on, I think, generally

980
00:56:26,360 --> 00:56:29,900
on Sunday mornings in
the Harvard dining halls

981
00:56:29,900 --> 00:56:36,701
that is impressed with
the official Harvard seal.

982
00:56:36,701 --> 00:56:39,770
The Harvard seal?

983
00:56:39,770 --> 00:56:47,060
It is three open books
arranged in a kind of triangle

984
00:56:47,060 --> 00:56:56,060
and one syllable of the three syllable
word Veritas embossed upon on it.

985
00:56:56,060 --> 00:57:00,014
Veritas, which is a Latin word for

986
00:57:00,014 --> 00:57:00,680
AUDIENCE: Truth.

987
00:57:00,680 --> 00:57:04,120
LAUREL THATCHER ULRICH:
Truth, you've already got it.

988
00:57:04,120 --> 00:57:09,040
OK, what's going on here?

989
00:57:09,040 --> 00:57:10,780
What are they trying to do?

990
00:57:10,780 --> 00:57:15,380
Are they trying to get you to think big?

991
00:57:15,380 --> 00:57:22,280
Is that what goes on in the dining hall
as you pour syrup on your Veritaffle?

992
00:57:22,280 --> 00:57:26,180
Well, Veritas is all over the place.

993
00:57:26,180 --> 00:57:29,300
When you walk out of
the auditorium today

994
00:57:29,300 --> 00:57:35,060
and you're in what's called the
transept, at each end of the transept

995
00:57:35,060 --> 00:57:39,640
is a stained glass window, and
you will find the word Veritas

996
00:57:39,640 --> 00:57:44,090
in both of those windows in
slightly different forms.

997
00:57:44,090 --> 00:57:48,980
I'd like to suggest a little
game, as you go around campus,

998
00:57:48,980 --> 00:57:54,440
looking for various manifestations
of this word to Veritas.

999
00:57:54,440 --> 00:58:00,080
They show up on windows,
on t-shirts, on banners.

1000
00:58:00,080 --> 00:58:05,620
Here, we are in the
entrance to Widener Library.

1001
00:58:05,620 --> 00:58:08,690
Here is a really fabulous
example that you're not

1002
00:58:08,690 --> 00:58:12,260
going to find walking around
campus, but if you're lucky enough

1003
00:58:12,260 --> 00:58:16,490
to go into the University
Archives and ask,

1004
00:58:16,490 --> 00:58:24,880
they might show you a Harvard flag
emblazoned with the symbol Veritas that

1005
00:58:24,880 --> 00:58:36,410
went many, many times circumnavigated
the earth in 1991 in the space shuttle

1006
00:58:36,410 --> 00:58:38,090
Atlantis.

1007
00:58:38,090 --> 00:58:42,410
So Veritas has gotten around a bit.

1008
00:58:42,410 --> 00:58:45,680
Where did it come from?

1009
00:58:45,680 --> 00:58:48,780
I think as you see this
symbol over of a campus,

1010
00:58:48,780 --> 00:58:51,860
you think nothing could
be more stable, nothing

1011
00:58:51,860 --> 00:58:57,710
could be more ancient
than the Harvard seal.

1012
00:58:57,710 --> 00:59:01,730
And in fact, if you went
into the Harvard Archives,

1013
00:59:01,730 --> 00:59:09,720
you would find in an early college
book this inscribed in 1643,

1014
00:59:09,720 --> 00:59:16,140
someone drew the outline of the
shield and in a crude kind of way,

1015
00:59:16,140 --> 00:59:21,830
outlined and designed the Harvard
seal with the word for truth.

1016
00:59:21,830 --> 00:59:25,190


1017
00:59:25,190 --> 00:59:29,750
Unfortunately, a few historians have
been punking around in the archives.

1018
00:59:29,750 --> 00:59:31,940
This is a legitimate document--

1019
00:59:31,940 --> 00:59:33,530
it's real.

1020
00:59:33,530 --> 00:59:37,850
But somebody designed
the symbol for Veritas,

1021
00:59:37,850 --> 00:59:40,880
but the college didn't want it.

1022
00:59:40,880 --> 00:59:42,530
They didn't like it.

1023
00:59:42,530 --> 00:59:48,050
They turned it down, and they preferred
something more specific to the Puritan

1024
00:59:48,050 --> 00:59:50,990
origins of Harvard.

1025
00:59:50,990 --> 00:59:57,710
And they gave it a Latin
term Christi Gloriam--

1026
00:59:57,710 --> 01:00:02,690
to the glory of Christ, and
that symbol in various forms,

1027
01:00:02,690 --> 01:00:07,100
different Latin incarnations
about Christ in the church,

1028
01:00:07,100 --> 01:00:08,540
to the glory of Christ.

1029
01:00:08,540 --> 01:00:12,620
The ultimate purpose of
Harvard College initially

1030
01:00:12,620 --> 01:00:16,040
was to train a learned ministry.

1031
01:00:16,040 --> 01:00:20,810
In various incarnations, that
version of the Harvard seal

1032
01:00:20,810 --> 01:00:27,290
lasted through the administration
of President Josiah Quincy, who

1033
01:00:27,290 --> 01:00:36,350
actually found the old document from
1643, and it continued until the 1880s.

1034
01:00:36,350 --> 01:00:42,170
Long, long time after the
origins of the universities,

1035
01:00:42,170 --> 01:00:46,370
and it was there in time to show
up on the base of the John Harvard

1036
01:00:46,370 --> 01:00:49,770
statue, which you have all seen.

1037
01:00:49,770 --> 01:00:52,850
So, what was going on here?

1038
01:00:52,850 --> 01:00:55,730
What was this fight about?

1039
01:00:55,730 --> 01:01:01,070
This was about what kind of
university Harvard was going to be,

1040
01:01:01,070 --> 01:01:06,680
and those who eventually went
out and reduced the shield

1041
01:01:06,680 --> 01:01:13,370
to its old 1643 version wanted
a kind of universal commitment

1042
01:01:13,370 --> 01:01:20,340
to truth rather than the ratification
of the sectarian original organization

1043
01:01:20,340 --> 01:01:21,980
of Harvard.

1044
01:01:21,980 --> 01:01:25,670
Now, why does this matter?

1045
01:01:25,670 --> 01:01:30,440
I'm not sure that the people
who come up to the John Harvard

1046
01:01:30,440 --> 01:01:39,230
statue, which was installed in
1884, care this isn't John Harvard.

1047
01:01:39,230 --> 01:01:41,510
Nobody knows what he looks like.

1048
01:01:41,510 --> 01:01:44,000
I mean, this is a
historical fact that Harvard

1049
01:01:44,000 --> 01:01:49,070
took its name from the donation
of books from John Harvard,

1050
01:01:49,070 --> 01:01:55,100
but nobody seemed to notice that
and create this statue of Harvard

1051
01:01:55,100 --> 01:01:59,900
until almost 250 years later.

1052
01:01:59,900 --> 01:02:02,150
People don't care about that.

1053
01:02:02,150 --> 01:02:09,940
This is about the present,
like the Veritaffle.

1054
01:02:09,940 --> 01:02:12,730
It's not about the past.

1055
01:02:12,730 --> 01:02:15,220
It's about the present.

1056
01:02:15,220 --> 01:02:20,810
Is it about a Harvard brand,
or is it about something

1057
01:02:20,810 --> 01:02:24,920
more enduring and
something more significant

1058
01:02:24,920 --> 01:02:28,070
about a commitment to an idea?

1059
01:02:28,070 --> 01:02:30,980
So what the Veritaffle teaches me--

1060
01:02:30,980 --> 01:02:35,600
and it's a theme I
hammer on in my courses--

1061
01:02:35,600 --> 01:02:38,540
is history is not the old and moldy.

1062
01:02:38,540 --> 01:02:41,990
History is not the past.

1063
01:02:41,990 --> 01:02:46,300
History is the study
of how things change.

1064
01:02:46,300 --> 01:02:49,230
You want to change the world?

1065
01:02:49,230 --> 01:02:54,540
You really want to know how people
have changed things in the past.

1066
01:02:54,540 --> 01:02:59,440
History is not about the
veneration of the past.

1067
01:02:59,440 --> 01:03:01,950
It's about understanding it.

1068
01:03:01,950 --> 01:03:07,620
And history remains contentious as you
know if you noticed in the newspapers

1069
01:03:07,620 --> 01:03:15,300
the controversy over the seal of
Harvard Law School, which was recently,

1070
01:03:15,300 --> 01:03:20,370
with the approval of the corporation,
done away with because it carried

1071
01:03:20,370 --> 01:03:23,260
a symbol about slavery.

1072
01:03:23,260 --> 01:03:28,320
It's a very complicated story
that I won't go in here today,

1073
01:03:28,320 --> 01:03:31,860
but history is controversial.

1074
01:03:31,860 --> 01:03:38,250
And history is above all, a
conversation between present and past,

1075
01:03:38,250 --> 01:03:43,440
about what matters, about who
we are, what kind of boundaries

1076
01:03:43,440 --> 01:03:46,875
we establish between one another.

1077
01:03:46,875 --> 01:03:49,800


1078
01:03:49,800 --> 01:03:52,560
This project, Tangible
Things, that I have

1079
01:03:52,560 --> 01:03:57,210
engaged in with a number of colleagues
for many years, one of the things we've

1080
01:03:57,210 --> 01:04:00,600
done is dig into archives
and museums at Harvard

1081
01:04:00,600 --> 01:04:05,640
to look for little
stuff, small stuff that

1082
01:04:05,640 --> 01:04:10,420
open up new ways of thinking
about the world around us.

1083
01:04:10,420 --> 01:04:16,260
We found lots of interesting things
in our explorations, but none of us

1084
01:04:16,260 --> 01:04:25,410
were quite prepared to meet
Harvard's 120-year-old tortilla.

1085
01:04:25,410 --> 01:04:28,260
Yes, it's there.

1086
01:04:28,260 --> 01:04:34,500
It's in the Harvard Herbaria and
botanical research libraries-- a place

1087
01:04:34,500 --> 01:04:41,210
that Professor [? Claymore ?] does some
of her path-breaking work is recorded.

1088
01:04:41,210 --> 01:04:45,870
A tortilla, what on
earth is doing there?

1089
01:04:45,870 --> 01:04:51,300
Well, it confronts us with
a past we've forgotten

1090
01:04:51,300 --> 01:04:54,840
and invites us to
confront it, and invites

1091
01:04:54,840 --> 01:04:58,170
us to explore, and to understand.

1092
01:04:58,170 --> 01:05:03,480
It's pretty obvious that if
Harvard is collecting tortillas--

1093
01:05:03,480 --> 01:05:07,620
and when I dug into this
problem, I discovered

1094
01:05:07,620 --> 01:05:14,820
a whole jar of tortillas
that are 139 years old

1095
01:05:14,820 --> 01:05:19,800
collected in Mexico by a
man named Edward Palmer, who

1096
01:05:19,800 --> 01:05:24,600
pioneered a new field
called ethnobotany,

1097
01:05:24,600 --> 01:05:29,850
which was not just about
plants and their development

1098
01:05:29,850 --> 01:05:33,210
but was about how people used plants.

1099
01:05:33,210 --> 01:05:36,930
So he paid a lot of
attention to how women

1100
01:05:36,930 --> 01:05:42,540
in the areas he was researching
in Mexico made their tortillas--

1101
01:05:42,540 --> 01:05:45,870
very interesting
documents that survived,

1102
01:05:45,870 --> 01:05:49,190
and so interestingly
enough, these tortillas

1103
01:05:49,190 --> 01:05:53,370
that were collected by
people who were called

1104
01:05:53,370 --> 01:05:59,970
botanic explorers in the 19th and early
20th century, went out to find plants.

1105
01:05:59,970 --> 01:06:03,230
These ended up-- some in
the anthropology museum,

1106
01:06:03,230 --> 01:06:08,820
some in the botanical
museum, or the herbarium.

1107
01:06:08,820 --> 01:06:13,230
And then something else
interesting happened,

1108
01:06:13,230 --> 01:06:17,280
it wasn't just about the
advancement of science,

1109
01:06:17,280 --> 01:06:23,250
the collection of usable plant
products, or the plants themselves.

1110
01:06:23,250 --> 01:06:29,070
It was about a kind of science
that supported the larger economy

1111
01:06:29,070 --> 01:06:32,640
and found new ways to use plants.

1112
01:06:32,640 --> 01:06:38,100
Harvard has then a
collection in something

1113
01:06:38,100 --> 01:06:44,790
called economic botany that is how
to take a plant and make it useful.

1114
01:06:44,790 --> 01:06:47,670


1115
01:06:47,670 --> 01:06:52,980
A man named Oakes Ames-- the archive
with economic botany is named after

1116
01:06:52,980 --> 01:06:57,150
him, and looking at his papers, I
was just fascinated because it was

1117
01:06:57,150 --> 01:07:03,000
an example of how something that
looks kind of clever and interesting

1118
01:07:03,000 --> 01:07:10,260
in the 1920s becomes a big political
controversy in our own time--

1119
01:07:10,260 --> 01:07:13,080
as he noted that someone
had figured out how

1120
01:07:13,080 --> 01:07:21,030
to make a substitute for maple
syrup out of corn or maize.

1121
01:07:21,030 --> 01:07:26,820
So, what did you have for lunch?

1122
01:07:26,820 --> 01:07:33,630
I have no idea, but I know many of
you have eaten the products produced

1123
01:07:33,630 --> 01:07:37,170
by economic botany.

1124
01:07:37,170 --> 01:07:44,370
And if you start with a tortilla and
begin to move forward in time to a time

1125
01:07:44,370 --> 01:07:50,190
when a tortilla was so exotic that
nobody in Cambridge, Massachusetts

1126
01:07:50,190 --> 01:07:56,220
has ever seen one and therefore
put it in a museum exhibit

1127
01:07:56,220 --> 01:07:59,670
to a time when that's
practically all some of us

1128
01:07:59,670 --> 01:08:03,100
eat, and how did that happen?

1129
01:08:03,100 --> 01:08:10,230
How did that change occur in ordinary
life as manufacturers took over?

1130
01:08:10,230 --> 01:08:14,310
And here's the interesting
thing, food crosses boundaries

1131
01:08:14,310 --> 01:08:20,100
and creates boundaries as
people move from place to place.

1132
01:08:20,100 --> 01:08:29,319
So the Frito Kid, blue eyed and blond,
becomes the Frito Bandito and then it

1133
01:08:29,319 --> 01:08:36,600
sort of pushed off the
screen for something else,

1134
01:08:36,600 --> 01:08:43,850
and we are now in the period of
the perfectly organic, healthy,

1135
01:08:43,850 --> 01:08:50,220
gluten-free, fabulously
[? hand-produced, ?] authentic Mexican

1136
01:08:50,220 --> 01:09:03,000
tortilla made in Western Massachusetts
or perhaps Joe Bravo's fascinating

1137
01:09:03,000 --> 01:09:08,439
tortilla art produced and sold
in galleries in Los Angeles.

1138
01:09:08,439 --> 01:09:11,210


1139
01:09:11,210 --> 01:09:14,630
Food is about history.

1140
01:09:14,630 --> 01:09:22,590
Common things take us beyond our own
lives, not just to foreign places,

1141
01:09:22,590 --> 01:09:29,520
but to lost eras in time, and
food can also remind us, I think,

1142
01:09:29,520 --> 01:09:38,700
that in a world where humble bread,
a tortilla crosses boundaries,

1143
01:09:38,700 --> 01:09:44,219
it can still remain very difficult
for some people to do so.

1144
01:09:44,219 --> 01:09:45,325
Thank you.

1145
01:09:45,325 --> 01:09:48,295
[APPLAUSE]

1146
01:09:48,295 --> 01:10:02,650


1147
01:10:02,650 --> 01:10:06,400
MARLYN E. MCGRATH: Our clean-up
batter, David Malan is one of our own.

1148
01:10:06,400 --> 01:10:08,500
I've known him since he
was an undergraduate here.

1149
01:10:08,500 --> 01:10:11,620
This is the place where all
of his degrees come from.

1150
01:10:11,620 --> 01:10:15,670
Today, he is Gordon MacKay
Professor of the practice

1151
01:10:15,670 --> 01:10:20,050
of computer science in the School
of Engineering and Applied Sciences

1152
01:10:20,050 --> 01:10:23,890
and a member of the
faculty of education.

1153
01:10:23,890 --> 01:10:26,650
He teaches-- I'm struggling here--

1154
01:10:26,650 --> 01:10:31,090
he teaches Computer Science 50, which is
the largest course at Harvard College.

1155
01:10:31,090 --> 01:10:34,180
But he has also taught
Computer Science 50

1156
01:10:34,180 --> 01:10:37,660
as it's known here, which was
the largest course at Yale.

1157
01:10:37,660 --> 01:10:41,890
That is his, I think,
principle Yale connection.

1158
01:10:41,890 --> 01:10:43,474
And that's quite a popular course.

1159
01:10:43,474 --> 01:10:44,890
As I say, it's our largest course.

1160
01:10:44,890 --> 01:10:47,800
We don't have a lot of large
courses, but that is a biggie.

1161
01:10:47,800 --> 01:10:51,150
He's done a lot of other things.

1162
01:10:51,150 --> 01:10:54,430
When he went to graduate school, I think
he worked for the Middlesex District

1163
01:10:54,430 --> 01:10:57,670
Attorney office as a
forensic investigator,

1164
01:10:57,670 --> 01:11:00,040
and that has influenced
some of his work since then.

1165
01:11:00,040 --> 01:11:05,890
Today, he does a lot with security
and privacy and digital forensics

1166
01:11:05,890 --> 01:11:06,970
and so on.

1167
01:11:06,970 --> 01:11:10,240
He lived in Mather House where
I think he retains a connection,

1168
01:11:10,240 --> 01:11:15,535
and despite his very young age,
he is the founding father really

1169
01:11:15,535 --> 01:11:18,250
of Visitas Thinks Big
and has always been

1170
01:11:18,250 --> 01:11:22,630
the person who supported its
widespread production values.

1171
01:11:22,630 --> 01:11:25,150
So thank you, David,
again for doing this.

1172
01:11:25,150 --> 01:11:27,640
I should also say because
you told me to and I almost

1173
01:11:27,640 --> 01:11:34,000
forgot, that even though I will urge
you when David is through, I think,

1174
01:11:34,000 --> 01:11:36,640
to go to the Extracurricular
Fair, which is up at Hilles.

1175
01:11:36,640 --> 01:11:40,360
Which is really one of the great--

1176
01:11:40,360 --> 01:11:43,150
Hilles Library, which is
one of the great fairs

1177
01:11:43,150 --> 01:11:45,760
and see Harvard people
doing the things they

1178
01:11:45,760 --> 01:11:47,410
do when they're not in the classroom.

1179
01:11:47,410 --> 01:11:50,670
That's normally at 4
o'clock-- it is at 4 o'clock.

1180
01:11:50,670 --> 01:11:54,239
When you leave here whenever we
finish, which won't be terribly long,

1181
01:11:54,239 --> 01:11:57,280
you will have plenty of time to get
there, so you don't need to rush out.

1182
01:11:57,280 --> 01:11:58,733
You get to hear David.

1183
01:11:58,733 --> 01:12:01,691
[APPLAUSE]

1184
01:12:01,691 --> 01:12:25,787


1185
01:12:25,787 --> 01:12:27,370
DAVID MALAN: I teach computer science.

1186
01:12:27,370 --> 01:12:37,970


1187
01:12:37,970 --> 01:12:39,290
So thank you.

1188
01:12:39,290 --> 01:12:45,930


1189
01:12:45,930 --> 01:12:50,930
So I indeed teach CS50, and I took
this class myself as a freshman.

1190
01:12:50,930 --> 01:12:54,670
It was the fall of 1996,
but I almost did it.

1191
01:12:54,670 --> 01:12:56,790
In fact, I got to campus
and I didn't really

1192
01:12:56,790 --> 01:12:58,610
know what I wanted to do to be honest.

1193
01:12:58,610 --> 01:13:01,660
I kind of gravitated toward,
I think, things I knew.

1194
01:13:01,660 --> 01:13:04,910
Like, I thought back on high school, and
I kind of liked history, really liked

1195
01:13:04,910 --> 01:13:08,170
constitutional law, and
sort of liked English.

1196
01:13:08,170 --> 01:13:11,700
And so I very naturally,
my freshman year 1995,

1197
01:13:11,700 --> 01:13:13,992
gravitated toward things
with which I was familiar.

1198
01:13:13,992 --> 01:13:16,200
And I swear to God, I was
one of these students where

1199
01:13:16,200 --> 01:13:18,741
I went into one of my first
meetings with my freshman proctor

1200
01:13:18,741 --> 01:13:23,220
or RA who lives in the dorms with you,
and I can't believe she humored this,

1201
01:13:23,220 --> 01:13:26,820
I went in with a 32-course
proposal with what I wanted to do

1202
01:13:26,820 --> 01:13:29,370
in that first week or two of college.

1203
01:13:29,370 --> 01:13:33,210
Most of which, I ended up not
actually taking and thank God.

1204
01:13:33,210 --> 01:13:37,080
It wasn't until sophomore year that,
honestly, I finally got up the nerve

1205
01:13:37,080 --> 01:13:38,880
and got the confidence
to sort of veer off

1206
01:13:38,880 --> 01:13:41,280
of that path that just
felt most comfortable,

1207
01:13:41,280 --> 01:13:43,800
and I put my toes in
unfamiliar waters and finally

1208
01:13:43,800 --> 01:13:47,520
got up the nerve to take a class
called CS50 or Introduction to Computer

1209
01:13:47,520 --> 01:13:50,250
Science for concentrators
and non concentrators.

1210
01:13:50,250 --> 01:13:53,220
And that was only because
at the time, the professor

1211
01:13:53,220 --> 01:13:57,150
let me sign up for pass/fail because
I indeed had this fear of failure.

1212
01:13:57,150 --> 01:13:58,939
It was something unfamiliar to me.

1213
01:13:58,939 --> 01:14:01,480
I thought back on high school,
and it was one of those areas,

1214
01:14:01,480 --> 01:14:03,150
one of those kinds of classes to beware.

1215
01:14:03,150 --> 01:14:05,430
Frankly, it didn't even
seem all that enthralling.

1216
01:14:05,430 --> 01:14:08,705
I still remember looking through
the glass window of the computer

1217
01:14:08,705 --> 01:14:10,830
lab in my high school just
seeing all of my friends

1218
01:14:10,830 --> 01:14:13,680
kind of hunkered over computer
terminals, like, typing away.

1219
01:14:13,680 --> 01:14:17,850
It just didn't seem interesting, didn't
seem social, didn't seem germane,

1220
01:14:17,850 --> 01:14:21,120
and so when I finally got
to campus and put my toes

1221
01:14:21,120 --> 01:14:23,430
in these waters in shop
CS50, did I realize

1222
01:14:23,430 --> 01:14:27,960
that this whole field CS was actually
more familiar to me than I thought.

1223
01:14:27,960 --> 01:14:32,859
And the reality is that it opens
your eyes to intuitions and ideas

1224
01:14:32,859 --> 01:14:34,900
that you yourself might
already be familiar with,

1225
01:14:34,900 --> 01:14:38,160
you just kind of need to learn how
to harness it and think about it.

1226
01:14:38,160 --> 01:14:41,340
And indeed, most all of us have these
kinds of devices in our pockets,

1227
01:14:41,340 --> 01:14:44,220
or laptops, or desktops in our
homes, and for the most part,

1228
01:14:44,220 --> 01:14:46,425
we only do what people let
us do with them, right?

1229
01:14:46,425 --> 01:14:48,300
We download an app or
some piece of software,

1230
01:14:48,300 --> 01:14:50,730
and we use that because someone
else has made it for us.

1231
01:14:50,730 --> 01:14:53,600
And yet, with this same machine
and with this same device can

1232
01:14:53,600 --> 01:14:56,910
I actually build things, and create,
and solve problems of my own.

1233
01:14:56,910 --> 01:14:59,670
And they don't even need
to be all that unfamiliar,

1234
01:14:59,670 --> 01:15:03,900
so this is an old school problem like
looking someone up in a phone book.

1235
01:15:03,900 --> 01:15:06,960
If we imagine this is a really big
whitepages with like 1,000 pages,

1236
01:15:06,960 --> 01:15:09,427
and I'm looking for
someone like Mike Smith,

1237
01:15:09,427 --> 01:15:11,760
this isn't all that different
from what happens nowadays

1238
01:15:11,760 --> 01:15:14,970
on our iPhones and Android devices
where if you pull up your contacts app,

1239
01:15:14,970 --> 01:15:17,100
you probably see all of your
friends and family alphabetically

1240
01:15:17,100 --> 01:15:18,440
by first name or last name.

1241
01:15:18,440 --> 01:15:21,150
And that's essentially what we
have in this technology here,

1242
01:15:21,150 --> 01:15:22,904
and now if I wanted
to find Mike Smith, I

1243
01:15:22,904 --> 01:15:26,070
could sort of start at the beginning
of this problem, look down at the page,

1244
01:15:26,070 --> 01:15:28,920
and if he's not there, which
indeed he's not in the A section,

1245
01:15:28,920 --> 01:15:33,220
I might turn a page, turn a page,
turn a page and not see Mike Smith.

1246
01:15:33,220 --> 01:15:37,020
And so I continue proceeding
one more page at a time.

1247
01:15:37,020 --> 01:15:39,750
This is incredibly tedious,
it's incredibly slow,

1248
01:15:39,750 --> 01:15:43,500
but it is correct because if
Mike Smith is in this phone book,

1249
01:15:43,500 --> 01:15:45,007
I'll eventually reach him.

1250
01:15:45,007 --> 01:15:46,590
Now, of course, I could optimize this.

1251
01:15:46,590 --> 01:15:48,381
I could think a little
harder about it, how

1252
01:15:48,381 --> 01:15:51,990
to solve this problem not just
correctly but well, efficiently,

1253
01:15:51,990 --> 01:15:56,677
and I could start to do something
like 2, 4, 6, 8, 10, 12, and so forth.

1254
01:15:56,677 --> 01:15:59,010
That's going to get me through
the problem twice as fast

1255
01:15:59,010 --> 01:16:02,550
and find like twice as fast,
but it's potentially flawed.

1256
01:16:02,550 --> 01:16:03,510
There's a flaw, right?

1257
01:16:03,510 --> 01:16:06,254
If Mike accidentally ends up
sandwiched between two pages.

1258
01:16:06,254 --> 01:16:07,920
So I might at least have to double back.

1259
01:16:07,920 --> 01:16:09,900
If maybe I hit the T
section in the book,

1260
01:16:09,900 --> 01:16:11,640
I might have to double
back one page just

1261
01:16:11,640 --> 01:16:13,835
to double check that I
didn't blow past him,

1262
01:16:13,835 --> 01:16:16,710
but that too is not something any
of us are going to do in this room.

1263
01:16:16,710 --> 01:16:18,900
All of us already have
the intuition for sort

1264
01:16:18,900 --> 01:16:22,200
of opening this problem to the middle,
looking down, realizing, oh, I'm

1265
01:16:22,200 --> 01:16:24,809
in the M section, so
Mike must clearly be

1266
01:16:24,809 --> 01:16:26,350
in the right half of this phone book.

1267
01:16:26,350 --> 01:16:32,400
And so we can tear the problem in
half, throw, if I may, half of it away.

1268
01:16:32,400 --> 01:16:34,410
And now what's interesting
about this problem

1269
01:16:34,410 --> 01:16:38,340
is that it's fundamentally the same,
but it's gone from 1,000 pages to 500.

1270
01:16:38,340 --> 01:16:41,130
But the algorithm, the
step by step instructions

1271
01:16:41,130 --> 01:16:44,940
that I can now apply myself are the
same-- go roughly to the middle,

1272
01:16:44,940 --> 01:16:47,261
look down, and I say, oh,
I'm now in the T section,

1273
01:16:47,261 --> 01:16:48,510
so I've gone a little too far.

1274
01:16:48,510 --> 01:16:52,860
And so I can again tear the problem in
half, throw that half of the problem

1275
01:16:52,860 --> 01:16:54,700
away, and now I've got 250 pages.

1276
01:16:54,700 --> 01:16:58,470
And if I repeat down to 125 and
again and again and again ultimately

1277
01:16:58,470 --> 01:17:04,530
theoretically we end up with just one
page on which Mike either is or is not.

1278
01:17:04,530 --> 01:17:06,540
And what I realized
early on is that that

1279
01:17:06,540 --> 01:17:08,190
is what computer science actually is.

1280
01:17:08,190 --> 01:17:09,540
It's about problem solving.

1281
01:17:09,540 --> 01:17:13,320
And we might bring to bear computers
and programming on those problems,

1282
01:17:13,320 --> 01:17:18,600
but those computers are really just
tools that we use and means to an end.

1283
01:17:18,600 --> 01:17:22,200
Computer science is not itself about
programming but about problem solving,

1284
01:17:22,200 --> 01:17:27,180
and yet it took me years until graduate
school when I was back here again

1285
01:17:27,180 --> 01:17:30,900
and this time focusing not
just in computer science

1286
01:17:30,900 --> 01:17:32,940
but dabbling in an elective.

1287
01:17:32,940 --> 01:17:38,754
I enrolled as an auditor in Anthropology
1010 Introduction to Archeology.

1288
01:17:38,754 --> 01:17:41,670
I admittedly had this sort of mid-grad
school crisis where I wondered,

1289
01:17:41,670 --> 01:17:43,590
why am I becoming a computer scientist?

1290
01:17:43,590 --> 01:17:45,730
Archeology is a lot more
interesting, a lot more

1291
01:17:45,730 --> 01:17:52,610
fun or so I thought sort of halfway
through my thesis work or aspirations.

1292
01:17:52,610 --> 01:17:56,080
But what archeology opened my
eyes to was the interconnections

1293
01:17:56,080 --> 01:17:57,697
of computer science with other fields.

1294
01:17:57,697 --> 01:17:59,530
In fact, it's such a
simple thing to imagine

1295
01:17:59,530 --> 01:18:01,809
going on Google Maps or
Google Earth these days.

1296
01:18:01,809 --> 01:18:03,850
For instance, to pull up
something like the place

1297
01:18:03,850 --> 01:18:08,350
we're currently in, searching for
something like Sanders Theatre and then

1298
01:18:08,350 --> 01:18:13,630
thanks to technology, being flown
down to where we are right now.

1299
01:18:13,630 --> 01:18:16,390
But in anthro 1010,
I had the opportunity

1300
01:18:16,390 --> 01:18:19,930
to see my professors actually leverage
this technology in a much more

1301
01:18:19,930 --> 01:18:22,960
compelling academic way
whereby we might have searched

1302
01:18:22,960 --> 01:18:26,470
for the great pyramids zooming
halfway across the world,

1303
01:18:26,470 --> 01:18:29,020
zooming down pretty low
thanks to satellite imagery,

1304
01:18:29,020 --> 01:18:32,380
and actually be able to see not
just well-known artifacts like this

1305
01:18:32,380 --> 01:18:36,070
but archaeological digs from which
my professor that semester had just

1306
01:18:36,070 --> 01:18:36,910
come back.

1307
01:18:36,910 --> 01:18:38,740
And he was able to give
us this aerial view

1308
01:18:38,740 --> 01:18:42,580
and really open our eyes thanks to
software and thanks to computer science

1309
01:18:42,580 --> 01:18:46,970
and its outgrowths to actually
exploring yet other problems still.

1310
01:18:46,970 --> 01:18:50,020
And so it's not just CS plus archeology.

1311
01:18:50,020 --> 01:18:54,130
I also realize quickly that I could
apply CS to frosh IM, so rewind

1312
01:18:54,130 --> 01:18:54,770
a few years.

1313
01:18:54,770 --> 01:18:57,730
Freshman intramural sports for some
time didn't even have a website.

1314
01:18:57,730 --> 01:19:00,940
Back in the day, we signed up for
sports and walked across the yard,

1315
01:19:00,940 --> 01:19:03,370
slid them under the doors of
the proctors, and registered.

1316
01:19:03,370 --> 01:19:08,470
Well, I instead discovered that I
could actually apply my newfound skills

1317
01:19:08,470 --> 01:19:11,950
and savvy from CS50, CS51,
and follow on courses

1318
01:19:11,950 --> 01:19:14,200
and actually solve that
problem with software,

1319
01:19:14,200 --> 01:19:16,900
but more generally did we
have this sort of opportunity

1320
01:19:16,900 --> 01:19:19,060
to combine CS with other things--

1321
01:19:19,060 --> 01:19:21,490
CS plus X, if you will,
where X, a variable,

1322
01:19:21,490 --> 01:19:26,500
might be the arts, or business, or
engineering, humanities, law, medicine,

1323
01:19:26,500 --> 01:19:28,540
sciences, applied
sciences, social sciences,

1324
01:19:28,540 --> 01:19:30,650
or any number of other fields.

1325
01:19:30,650 --> 01:19:34,210
In fact, at the end of this particular
course, this introductory course CS50,

1326
01:19:34,210 --> 01:19:37,930
do we end the semester with a
campus-wide celebration of what

1327
01:19:37,930 --> 01:19:41,230
the students in that class achieve
over the course of just one semester--

1328
01:19:41,230 --> 01:19:44,350
students who were just like
you sitting here some year ago.

1329
01:19:44,350 --> 01:19:48,370
In fact, in CS50, 53% of the students
this past year were first-year,

1330
01:19:48,370 --> 01:19:51,520
62% of those are among
those they describe as less

1331
01:19:51,520 --> 01:19:54,040
comfortable with the idea
of even being in a CS class,

1332
01:19:54,040 --> 01:19:59,230
not unlike myself some years ago,
68% had never even taken a CS course,

1333
01:19:59,230 --> 01:20:01,690
and yet at the end of the
semester at this so-called CS50

1334
01:20:01,690 --> 01:20:04,160
fair are expressions like
this all around the room

1335
01:20:04,160 --> 01:20:06,250
where all several hundred
students in the class

1336
01:20:06,250 --> 01:20:07,999
come together with a
few thousand students

1337
01:20:07,999 --> 01:20:12,190
and faculty and staff across campus to
celebrate in exactly what students have

1338
01:20:12,190 --> 01:20:16,150
accomplished by a bit of new exposure
to a field that they might not remain.

1339
01:20:16,150 --> 01:20:19,480
And indeed, the majority of students
in CS50 and in Introductory Computer

1340
01:20:19,480 --> 01:20:22,960
Science don't stay within the CS but
take these ideas and new skills back

1341
01:20:22,960 --> 01:20:26,319
to problems of their own domain
in a number of those other fields.

1342
01:20:26,319 --> 01:20:29,110
In fact, just to give you a glimpse
of some of this past year where

1343
01:20:29,110 --> 01:20:33,780
CS plus X was in fact personified
in some of our very students,

1344
01:20:33,780 --> 01:20:37,060
Lyra and Sarah here
implemented an iPhone app

1345
01:20:37,060 --> 01:20:41,165
that actually enabled people who are
color blind to effectively see colors

1346
01:20:41,165 --> 01:20:43,540
by taking a photograph of
something and then telling them

1347
01:20:43,540 --> 01:20:46,450
what the actual color is thanks
to the camera on the phone.

1348
01:20:46,450 --> 01:20:48,940
Nick here implemented software
that used image recognition

1349
01:20:48,940 --> 01:20:52,567
to take printed old school music like
this, run it through software that

1350
01:20:52,567 --> 01:20:55,150
figured out what those notes
were so that he could then change

1351
01:20:55,150 --> 01:20:58,630
it to be targeted at one
instrument instead of the other

1352
01:20:58,630 --> 01:20:59,830
for which it was designed.

1353
01:20:59,830 --> 01:21:02,500
Allison and Rita actually
implemented a site

1354
01:21:02,500 --> 01:21:04,510
for Harvard student
agencies on campus here

1355
01:21:04,510 --> 01:21:07,270
to facilitate the pairing
of Harvard students

1356
01:21:07,270 --> 01:21:09,550
with high school students
for tutoring opportunities.

1357
01:21:09,550 --> 01:21:12,502
Angela and Isabel, virtual
postering an event board website

1358
01:21:12,502 --> 01:21:14,710
so that students could
publicize and bring themselves

1359
01:21:14,710 --> 01:21:16,090
together across campus.

1360
01:21:16,090 --> 01:21:18,490
Stephen and Stuti here,
a stock simulator iPhone

1361
01:21:18,490 --> 01:21:22,510
app so that you could actually
simulate buys and sells of stocks.

1362
01:21:22,510 --> 01:21:24,670
Chris and Enxhi here,
an app that actually

1363
01:21:24,670 --> 01:21:27,490
allows you to compose a
reading list for yourself

1364
01:21:27,490 --> 01:21:32,260
by scanning ISBNs and other such
decodes in the back of a book.

1365
01:21:32,260 --> 01:21:34,960
And then lastly, folks like
Lucas here, who actually you

1366
01:21:34,960 --> 01:21:38,410
can see him talking into an Amazon
Alexa at top right commanding

1367
01:21:38,410 --> 01:21:41,320
his computer to do something,
so it's a car simulating

1368
01:21:41,320 --> 01:21:44,444
the sorts of things that are very
much in trend in industry today.

1369
01:21:44,444 --> 01:21:46,360
And then Billy, and Sam,
and Victor here, they

1370
01:21:46,360 --> 01:21:48,880
wrote a website that
allows you to mimic text.

1371
01:21:48,880 --> 01:21:53,080
So you read in and scan in text from
well-known dignitaries, or politicians,

1372
01:21:53,080 --> 01:21:55,030
or authors, and you can
generate text that's

1373
01:21:55,030 --> 01:21:57,790
very much reminiscent
of and statistically

1374
01:21:57,790 --> 01:22:01,630
equivalent to the kind of language those
humans might have written themselves.

1375
01:22:01,630 --> 01:22:05,050
Jeanette, and Ken, and
Mary here implementing

1376
01:22:05,050 --> 01:22:10,060
a website that allowed students to
actually improve their bill of health

1377
01:22:10,060 --> 01:22:13,660
by actually dropping the Raman and
getting recommendations for food.

1378
01:22:13,660 --> 01:22:16,180
Alice and Ian, anonymous chat
servers for peer counselors.

1379
01:22:16,180 --> 01:22:18,340
Luke, one of our students
at Yale, visualizations

1380
01:22:18,340 --> 01:22:22,060
of NBA player's missed or hit shots.

1381
01:22:22,060 --> 01:22:24,220
From the court, Mai-linh
and Maria, a website

1382
01:22:24,220 --> 01:22:27,910
that allowed them to actually
stop themselves from using

1383
01:22:27,910 --> 01:22:30,550
websites like Facebook and
other such distractions

1384
01:22:30,550 --> 01:22:33,349
so that they could actually
focus on their own studies.

1385
01:22:33,349 --> 01:22:35,140
And some of our students
even did something

1386
01:22:35,140 --> 01:22:37,540
like this using virtual reality--

1387
01:22:37,540 --> 01:22:41,690
the ability to put on headsets like
this these days and be able to look up,

1388
01:22:41,690 --> 01:22:44,770
down, left, right and
actually see a virtual space

1389
01:22:44,770 --> 01:22:47,980
that isn't physically here but is
recorded effectively or created

1390
01:22:47,980 --> 01:22:52,600
in software so that you can explore
other areas beyond your present tense.

1391
01:22:52,600 --> 01:22:56,230
And indeed, this is what we ourselves
in CS50 focused on exploring this year,

1392
01:22:56,230 --> 01:22:58,660
CS plus X where X is
education considering

1393
01:22:58,660 --> 01:23:01,540
how we might bring to bear this
technology to bring high school

1394
01:23:01,540 --> 01:23:04,750
students, adult learners from around
the world to a place like this

1395
01:23:04,750 --> 01:23:08,410
that we're all fortunate to
enjoy and experience and have

1396
01:23:08,410 --> 01:23:13,627
as a place of learning and yet bring it
to others virtually around the world.

1397
01:23:13,627 --> 01:23:16,210
In fact, this is a clip from our
first lecture this past year,

1398
01:23:16,210 --> 01:23:18,490
and if you can see, at the
top there is a special camera

1399
01:23:18,490 --> 01:23:20,823
that we put right in the
middle of Sanders Theatre here.

1400
01:23:20,823 --> 01:23:23,890
And it's got eight lenses that look
up, down, left, right, forward, back,

1401
01:23:23,890 --> 01:23:26,680
and via these eight lenses do we
end up getting essentially eight

1402
01:23:26,680 --> 01:23:31,370
videos that thanks to CS50's team
behind the scenes, we stitched together.

1403
01:23:31,370 --> 01:23:33,310
So all of these
individual circles you see

1404
01:23:33,310 --> 01:23:37,270
become just part of a sphere to
create this virtual world around,

1405
01:23:37,270 --> 01:23:39,370
and so long as you have
a headset like this

1406
01:23:39,370 --> 01:23:41,560
or another headset like
this Google Cardboard can

1407
01:23:41,560 --> 01:23:45,250
you actually achieve this
and experience this yourself.

1408
01:23:45,250 --> 01:23:48,480
In fact, we ourselves in CS50
and our team of TFs, and CAs,

1409
01:23:48,480 --> 01:23:52,390
and producers decided to go beyond
our own comfort zone this past year

1410
01:23:52,390 --> 01:23:56,850
and define X in this case to be
an opportunity where X is dance

1411
01:23:56,850 --> 01:24:00,880
and X is music, and thanks to CS50's
own, Lauren Scully and our production

1412
01:24:00,880 --> 01:24:02,920
team, thanks to some of
our friends on campus,

1413
01:24:02,920 --> 01:24:05,730
the Harvard Veritones, one of
the acapella groups on campus,

1414
01:24:05,730 --> 01:24:08,650
did we head up to the Harvard
dance studio just a few weeks ago,

1415
01:24:08,650 --> 01:24:12,790
build a 360 degree set like
this, plant that special camera

1416
01:24:12,790 --> 01:24:16,720
right in the middle, and capture
the experience of song and dance

1417
01:24:16,720 --> 01:24:18,080
in this virtual space.

1418
01:24:18,080 --> 01:24:20,290
In fact, I think we have just a minute.

1419
01:24:20,290 --> 01:24:23,530
This is perhaps best explained through
experience and not through retelling.

1420
01:24:23,530 --> 01:24:28,270
Do we have a moment for one
volunteer from the audience?

1421
01:24:28,270 --> 01:24:29,722
OK, come on up.

1422
01:24:29,722 --> 01:24:30,430
What's your name?

1423
01:24:30,430 --> 01:24:31,450
JESSICA: Jessica.

1424
01:24:31,450 --> 01:24:32,325
DAVID MALAN: Jessica?

1425
01:24:32,325 --> 01:24:35,440
Jessica, come on up, and as you
see, this young man here also

1426
01:24:35,440 --> 01:24:36,280
jumped up on stage.

1427
01:24:36,280 --> 01:24:40,000
This is Connor who was where you
sat two years ago at Visitas.

1428
01:24:40,000 --> 01:24:44,860
This was him in that same first
lecture and he's joined CS50's team

1429
01:24:44,860 --> 01:24:46,780
since to help us bring this together.

1430
01:24:46,780 --> 01:24:48,662
Jessica, nice to meet you.

1431
01:24:48,662 --> 01:24:50,370
So Connor's going to
get you set up here,

1432
01:24:50,370 --> 01:24:52,270
and the one disclaimer
is that you have to be--

1433
01:24:52,270 --> 01:24:53,769
and I should have said this before--

1434
01:24:53,769 --> 01:24:58,120
you have to, one, be comfortable
appearing here on camera, and two,

1435
01:24:58,120 --> 01:24:59,890
do you like surprises, too?

1436
01:24:59,890 --> 01:25:00,730
JESSICA: I guess so.

1437
01:25:00,730 --> 01:25:01,729
DAVID MALAN: I guess so.

1438
01:25:01,729 --> 01:25:04,480
OK, we'll take that, and
so what you're about to see

1439
01:25:04,480 --> 01:25:09,510
is just a minute clip from what we
think is the first ever collegiate

1440
01:25:09,510 --> 01:25:14,050
virtual reality recording of an acapella
performance by the same group on campus

1441
01:25:14,050 --> 01:25:19,720
featuring the Veritones of Niya Avery.

1442
01:25:19,720 --> 01:25:24,510
And in three dimensions here, as I
switch our screen over, will Jessica,

1443
01:25:24,510 --> 01:25:28,060
our brave volunteer, be able to
look up, down, left, or right.

1444
01:25:28,060 --> 01:25:32,080
And if we could dim the lights at this
point, will you see on the overhead,

1445
01:25:32,080 --> 01:25:36,280
thanks to the wire she's connected
to, what she's experiencing.

1446
01:25:36,280 --> 01:25:38,090
And we can increase the volume, too.

1447
01:25:38,090 --> 01:25:38,931
And there's Niya.

1448
01:25:38,931 --> 01:25:47,600


1449
01:25:47,600 --> 01:25:49,790
So you'll notice that
she looks up and down.

1450
01:25:49,790 --> 01:25:52,848
You're seeing exactly what she's
saying in this dance studio.

1451
01:25:52,848 --> 01:25:55,842
[MUSIC PLAYING]

1452
01:25:55,842 --> 01:26:29,275


1453
01:26:29,275 --> 01:26:30,273
JESSICA: Oh, woah.

1454
01:26:30,273 --> 01:26:32,820


1455
01:26:32,820 --> 01:26:40,350
[SINGING] I tried hard to make you
want me, but we're not supposed to be.

1456
01:26:40,350 --> 01:26:47,979
And the truth will always haunt
me even though it set me free.

1457
01:26:47,979 --> 01:26:50,802
You haunted me, taunted
me all in my brain.

1458
01:26:50,802 --> 01:26:53,468
Turn off the light and now all
that remains fills me with doubt,

1459
01:26:53,468 --> 01:26:56,462
and I'm shouting your name out loud.

1460
01:26:56,462 --> 01:26:58,955
Why do you want to put
me through the pain?

1461
01:26:58,955 --> 01:27:00,454
I get the feeling I'll never escape.

1462
01:27:00,454 --> 01:27:05,444
I can't hide away from the shame of you.

1463
01:27:05,444 --> 01:27:11,432
Tears on the ground, tears on my
pillow, you won't bring me down.

1464
01:27:11,432 --> 01:27:14,925
I'll get over you.

1465
01:27:14,925 --> 01:27:22,410
This is to get me through,
and I'll get over you.

1466
01:27:22,410 --> 01:27:24,406
I'll get over you.

1467
01:27:24,406 --> 01:27:30,394


1468
01:27:30,394 --> 01:27:32,390
I'll get over you.

1469
01:27:32,390 --> 01:27:35,883


1470
01:27:35,883 --> 01:27:39,875
When did you lose your emotion?

1471
01:27:39,875 --> 01:27:43,867
When did you become so cruel?

1472
01:27:43,867 --> 01:27:52,350
And if you want to cut me open, it
says a thousand words about you.

1473
01:27:52,350 --> 01:28:00,334
In time, I know you'll leave
me like a distant memory.

1474
01:28:00,334 --> 01:28:09,316
I know love can be so easy
if I start by loving me.

1475
01:28:09,316 --> 01:28:11,308
You haunted me, taunted
me all in my brain.

1476
01:28:11,308 --> 01:28:13,807
Turn out the light and all that
remains fills me with doubt,

1477
01:28:13,807 --> 01:28:17,300
and I'm shouting your name out loud.

1478
01:28:17,300 --> 01:28:19,795
Why do you want to put
me through the pain?

1479
01:28:19,795 --> 01:28:21,791
I get the feeling I'll never escape.

1480
01:28:21,791 --> 01:28:26,282
I can't hide away from the shame of you.

1481
01:28:26,282 --> 01:28:30,773
Tears on the ground, tears on my pillow.

1482
01:28:30,773 --> 01:28:34,765
You won't bring me down,
and I'll get over you.

1483
01:28:34,765 --> 01:28:38,258


1484
01:28:38,258 --> 01:28:40,254
I'll get over you.

1485
01:28:40,254 --> 01:28:43,248


1486
01:28:43,248 --> 01:28:47,240
Tears on the ground, rain at my window.

1487
01:28:47,240 --> 01:28:52,729
The pain washes out,
and I'll get over you.

1488
01:28:52,729 --> 01:28:59,715
These tears will get me
through, and I'll get over you.

1489
01:28:59,715 --> 01:29:04,705


1490
01:29:04,705 --> 01:29:06,701
I'll get over you.

1491
01:29:06,701 --> 01:29:16,182


1492
01:29:16,182 --> 01:29:18,178
I'll get over you.

1493
01:29:18,178 --> 01:29:20,174
I'll get over.

1494
01:29:20,174 --> 01:29:24,166
I don't need you to call me tonight.

1495
01:29:24,166 --> 01:29:28,158
I don't need you to
see if I'm all right.

1496
01:29:28,158 --> 01:29:30,653
You left me, so leave me, I'm fine.

1497
01:29:30,653 --> 01:29:36,641
I'll be here getting on with my life.

1498
01:29:36,641 --> 01:29:43,128
Tears on the ground, tears on my
pillow, you won't bring me down.

1499
01:29:43,128 --> 01:29:45,124
I'll get over you.

1500
01:29:45,124 --> 01:29:48,118
The rain on my window,
the pain washes off.

1501
01:29:48,118 --> 01:29:50,423
I'll get over you.

1502
01:29:50,423 --> 01:29:53,874
I'll get over you.

1503
01:29:53,874 --> 01:29:56,832
[APPLAUSE]

1504
01:29:56,832 --> 01:30:13,544


1505
01:30:13,544 --> 01:30:16,210
MARLYN E. MCGRATH: Thank you to
everybody who took part in this.

1506
01:30:16,210 --> 01:30:18,502
That means everybody in
the audience as well.

1507
01:30:18,502 --> 01:30:19,960
This has been a great event for us.

1508
01:30:19,960 --> 01:30:23,990
Thank you for giving us this piece
of your beautiful Sunday afternoon.

1509
01:30:23,990 --> 01:30:26,530
I hope you enjoy the
rest of your stay, hope

1510
01:30:26,530 --> 01:30:29,900
you have a wonderful time tomorrow,
hope you have great fun tonight.

1511
01:30:29,900 --> 01:30:32,810
I hope enjoy the fair up at
Hilles if that's what you do.

1512
01:30:32,810 --> 01:30:34,660
Thank you.