1
00:00:04,240 --> 00:00:07,160
Speaker 1: Hey, it's us on the side. I'm Gandhi and today

2
00:00:07,600 --> 00:00:09,800
Speaker 1: doing something a little bit different. I'm here with my

3
00:00:10,039 --> 00:00:11,160
Speaker 1: lovely producer Andrew.

4
00:00:11,200 --> 00:00:11,440
Speaker 2: Hello.

5
00:00:11,560 --> 00:00:12,680
Speaker 3: Hello, how are you today?

6
00:00:12,720 --> 00:00:14,200
Speaker 2: Oh God, here we go.

7
00:00:14,600 --> 00:00:17,160
Speaker 4: How many episodes of any podcasts have you done? Hundreds?

8
00:00:17,320 --> 00:00:17,720
Speaker 3: Yeah?

9
00:00:17,800 --> 00:00:19,560
Speaker 4: Yeah, that's your podcast voice.

10
00:00:19,640 --> 00:00:23,960
Speaker 3: Yeah, okay, hello, Hello? Are you to another episode of

11
00:00:24,200 --> 00:00:25,240
Speaker 3: Sauce on the side.

12
00:00:25,320 --> 00:00:26,800
Speaker 4: That's like half your Scotti impression?

13
00:00:27,120 --> 00:00:30,640
Speaker 3: Yeah, I feel that that's the most radio presenter voice

14
00:00:30,680 --> 00:00:30,880
Speaker 3: I have.

15
00:00:31,080 --> 00:00:33,239
Speaker 4: Oh my god, what radio presenter speaks like that.

16
00:00:34,320 --> 00:00:36,680
Speaker 3: There's a couple Okay, cool, let's just say I was

17
00:00:36,720 --> 00:00:40,360
Speaker 3: watching a few videos yesterday and there's a couple of

18
00:00:40,440 --> 00:00:42,480
Speaker 3: radio presenter people that definitely happen.

19
00:00:42,600 --> 00:00:43,400
Speaker 4: Do we know them?

20
00:00:43,520 --> 00:00:45,520
Speaker 3: Oh yeah, oh yeah.

21
00:00:45,560 --> 00:00:48,120
Speaker 1: So my voice sounds a little bit odd still, and

22
00:00:48,159 --> 00:00:49,640
Speaker 1: in the rest of the episode you're going to hear

23
00:00:49,640 --> 00:00:52,839
Speaker 1: it sounding pretty bad because I think I got a

24
00:00:52,960 --> 00:00:55,960
Speaker 1: damn cold at Danielle's damn haunted house for the second

25
00:00:56,040 --> 00:00:58,920
Speaker 1: year in a row. I think maybe the little closet

26
00:00:59,000 --> 00:01:01,440
Speaker 1: I hang out with, the little closet I hang out in,

27
00:01:01,480 --> 00:01:04,520
Speaker 1: I should say also with maybe there's like something happening

28
00:01:04,560 --> 00:01:06,639
Speaker 1: in there. I don't know, but two years in a row.

29
00:01:06,880 --> 00:01:08,679
Speaker 1: Next year, I got to figure out something different, yeah,

30
00:01:08,840 --> 00:01:09,640
Speaker 1: or just not show up.

31
00:01:09,680 --> 00:01:10,039
Speaker 3: I don't know.

32
00:01:10,080 --> 00:01:10,480
Speaker 4: I don't know.

33
00:01:10,640 --> 00:01:11,839
Speaker 3: Be a bush.

34
00:01:11,920 --> 00:01:16,160
Speaker 1: Yes, great, But I also have been just kind of

35
00:01:16,200 --> 00:01:17,840
Speaker 1: like running running. And one of the things that I

36
00:01:17,840 --> 00:01:20,040
Speaker 1: did was go to Washington, DC over the weekend and

37
00:01:20,080 --> 00:01:21,279
Speaker 1: then on Monday go.

38
00:01:21,160 --> 00:01:22,720
Speaker 4: To the White House.

39
00:01:22,840 --> 00:01:23,760
Speaker 3: I'm so jealous.

40
00:01:23,920 --> 00:01:26,520
Speaker 4: I would love for you to come with me next time.

41
00:01:26,560 --> 00:01:29,080
Speaker 3: I think, yes, that is my goal. One of the

42
00:01:29,120 --> 00:01:30,880
Speaker 3: things I want to do is go to the White House.

43
00:01:30,920 --> 00:01:33,120
Speaker 3: I want to tour up the White House more than anything.

44
00:01:33,160 --> 00:01:34,480
Speaker 4: Did you not do it in like eighth grade?

45
00:01:34,560 --> 00:01:34,640
Speaker 2: No?

46
00:01:34,840 --> 00:01:37,479
Speaker 3: Really, we did not do quest trips? Oh we did,

47
00:01:37,560 --> 00:01:38,720
Speaker 3: but it was like not fun.

48
00:01:38,560 --> 00:01:39,600
Speaker 4: With kind of hellish school.

49
00:01:39,640 --> 00:01:43,319
Speaker 3: Did you go to it's a Catholic school trips? He did,

50
00:01:43,360 --> 00:01:45,319
Speaker 3: but it was like stupid ones, Like we did a

51
00:01:45,360 --> 00:01:46,039
Speaker 3: ropes course.

52
00:01:46,480 --> 00:01:47,319
Speaker 4: That sounds awesome.

53
00:01:48,000 --> 00:01:49,280
Speaker 3: The White House sounds a little better.

54
00:01:49,480 --> 00:01:50,880
Speaker 1: Yeah, But you know, when you're in like eighth grade

55
00:01:50,880 --> 00:01:52,480
Speaker 1: and you're walking around doing that stuff, it's more like

56
00:01:52,520 --> 00:01:55,440
Speaker 1: torture than actually, whoa, this is cool, This stuff is history.

57
00:01:55,480 --> 00:01:56,240
Speaker 4: You don't think about that.

58
00:01:56,320 --> 00:02:01,000
Speaker 3: Yeah, I mean your pictures from this past trip, it

59
00:02:01,040 --> 00:02:03,880
Speaker 3: looks amazing, so thank you. I'm very chealous.

60
00:02:04,000 --> 00:02:05,560
Speaker 1: It was it was cool, but I know that you

61
00:02:05,600 --> 00:02:08,520
Speaker 1: wanted to ask me some questions. Should we do it before,

62
00:02:08,520 --> 00:02:09,320
Speaker 1: should we do it after?

63
00:02:09,560 --> 00:02:10,239
Speaker 3: Should do it after?

64
00:02:10,400 --> 00:02:13,760
Speaker 4: Okay? So I got to talk to a very lovely woman.

65
00:02:13,840 --> 00:02:17,760
Speaker 1: Her name is Arthy Praboker, and she is the Officer

66
00:02:17,919 --> 00:02:20,359
Speaker 1: of Science and Technology.

67
00:02:21,080 --> 00:02:23,840
Speaker 4: For basically the White House and all of us.

68
00:02:23,960 --> 00:02:25,960
Speaker 3: That's insane. Yeah, that's so cool.

69
00:02:26,120 --> 00:02:27,600
Speaker 1: She does a lot of really cool stuff. She works

70
00:02:27,600 --> 00:02:30,360
Speaker 1: with AI. I, you know, have a gazillion questions about that.

71
00:02:30,520 --> 00:02:32,320
Speaker 4: So let's just get to her. And by the way,

72
00:02:32,360 --> 00:02:32,799
Speaker 4: I got to.

73
00:02:32,720 --> 00:02:36,520
Speaker 1: Do this interview in the Eisenhower Executive Office building, which

74
00:02:36,720 --> 00:02:38,600
Speaker 1: was so cool, and I learned that they were thinking

75
00:02:38,600 --> 00:02:40,440
Speaker 1: about taking that building now and tearing it down.

76
00:02:40,760 --> 00:02:41,880
Speaker 4: It's so cool looking.

77
00:02:42,080 --> 00:02:44,280
Speaker 3: Okay, I have questions, Okay, the interview.

78
00:02:44,880 --> 00:02:46,359
Speaker 1: We're gonna get to the interview and then answer has

79
00:02:46,360 --> 00:02:51,760
Speaker 1: this question. Here we go, you hear my voice is

80
00:02:51,760 --> 00:02:53,960
Speaker 1: still horse as it has been for a while. And

81
00:02:54,040 --> 00:02:56,440
Speaker 1: now I am at the White House. The party continues,

82
00:02:56,960 --> 00:02:58,960
Speaker 1: and I am with the director of the Office of

83
00:02:59,040 --> 00:03:03,120
Speaker 1: Science and Technical Art, the Praboker, and we're going to

84
00:03:03,120 --> 00:03:05,560
Speaker 1: talk about a lot of really cool stuff because she

85
00:03:06,240 --> 00:03:09,040
Speaker 1: is the director of the Office of Science and Technology,

86
00:03:09,320 --> 00:03:10,480
Speaker 1: specifically here at the White House.

87
00:03:10,520 --> 00:03:13,400
Speaker 2: Would we say that I get to be President Biden

88
00:03:13,480 --> 00:03:16,400
Speaker 2: Science and Technology Advisor and sit in this office at

89
00:03:16,440 --> 00:03:17,600
Speaker 2: the White House. Absolutely?

90
00:03:17,720 --> 00:03:20,760
Speaker 1: Okay, So I have maybe a million questions for you.

91
00:03:21,520 --> 00:03:23,040
Speaker 1: I know our time is limited, so I really want

92
00:03:23,080 --> 00:03:24,560
Speaker 1: to get to well, first of all, what is your background?

93
00:03:24,600 --> 00:03:25,720
Speaker 4: And how did you get here?

94
00:03:26,040 --> 00:03:28,720
Speaker 2: How did I get here? Let's see. So the President

95
00:03:28,760 --> 00:03:31,520
Speaker 2: called to the White House called in the summer of

96
00:03:31,520 --> 00:03:34,080
Speaker 2: twenty twenty two to ask if I would be willing

97
00:03:34,120 --> 00:03:36,640
Speaker 2: to be considered for this job, and to which my

98
00:03:36,720 --> 00:03:39,760
Speaker 2: answer was a yes please. And the reason is I

99
00:03:39,800 --> 00:03:42,360
Speaker 2: had gotten I spent half of my professional life in

100
00:03:42,400 --> 00:03:47,240
Speaker 2: public service, and that included running DARPA, the Defensive Master

101
00:03:47,320 --> 00:03:50,600
Speaker 2: Research Projects Agency. This is a Defense Department agency that

102
00:03:50,720 --> 00:03:52,560
Speaker 2: started the Internet and stealth.

103
00:03:52,240 --> 00:03:54,440
Speaker 4: And sort of started the Internet.

104
00:03:54,800 --> 00:03:58,280
Speaker 2: Literally started the Internet. I literally invented and built the

105
00:03:58,320 --> 00:04:00,520
Speaker 2: beginnings of the Internet. That was a long time. I

106
00:04:00,640 --> 00:04:01,760
Speaker 2: was not responsible for that.

107
00:04:01,840 --> 00:04:02,800
Speaker 4: I didn't ask how long time?

108
00:04:02,960 --> 00:04:06,560
Speaker 2: You don't look like that makes revolutionary things happen. And

109
00:04:06,600 --> 00:04:10,240
Speaker 2: then I also ran a very different organization, NEST, but

110
00:04:10,360 --> 00:04:13,960
Speaker 2: I had I had seen the government's part of research

111
00:04:14,000 --> 00:04:17,400
Speaker 2: and development, how we make science and technology happen from

112
00:04:17,440 --> 00:04:19,800
Speaker 2: a lot of perspectives. And then the other half of

113
00:04:19,839 --> 00:04:22,680
Speaker 2: my professional life with Silicon Valley and my home is

114
00:04:22,760 --> 00:04:26,119
Speaker 2: Palo Alto, and that was that life was mostly early

115
00:04:26,160 --> 00:04:28,920
Speaker 2: stage venture capital investing in a little bit a couple

116
00:04:28,920 --> 00:04:32,440
Speaker 2: of companies, and so I had seen innovation from all

117
00:04:32,480 --> 00:04:36,400
Speaker 2: of these different perspectives. And that's my great passion is

118
00:04:36,480 --> 00:04:40,200
Speaker 2: how do we How does this country keep inventing the

119
00:04:40,240 --> 00:04:44,360
Speaker 2: future and building the future that we all want? And

120
00:04:44,480 --> 00:04:46,600
Speaker 2: I know we do it, and I know we only

121
00:04:46,640 --> 00:04:49,120
Speaker 2: can do it because of public and private working together.

122
00:04:49,440 --> 00:04:53,120
Speaker 2: And this is this perch in the White House, helping

123
00:04:53,160 --> 00:04:56,359
Speaker 2: the president and then leading this office at the White House.

124
00:04:56,680 --> 00:04:59,000
Speaker 2: This is the only place where you really get to

125
00:04:59,040 --> 00:05:01,200
Speaker 2: do that is your day is to think about and

126
00:05:01,320 --> 00:05:03,320
Speaker 2: work on that whole innovation.

127
00:05:03,680 --> 00:05:04,279
Speaker 4: That is wild.

128
00:05:04,279 --> 00:05:05,920
Speaker 1: So when you say I got a call from the President,

129
00:05:06,440 --> 00:05:07,560
Speaker 1: did he just call you him?

130
00:05:07,600 --> 00:05:11,200
Speaker 4: So I was, But there was an theell phone number.

131
00:05:11,240 --> 00:05:11,600
Speaker 1: Do you have it?

132
00:05:12,640 --> 00:05:15,080
Speaker 2: But we occasionally you get a phone call from a

133
00:05:15,120 --> 00:05:17,960
Speaker 2: White House number, and if you've been in Washington, you

134
00:05:18,080 --> 00:05:21,520
Speaker 2: know to look for that prefix. So yeah, I actually

135
00:05:21,839 --> 00:05:24,159
Speaker 2: literally what happened is I got an email from the

136
00:05:24,200 --> 00:05:29,120
Speaker 2: Presidential Personnel Office and they asked if they could have

137
00:05:29,160 --> 00:05:31,200
Speaker 2: a call with me, and I'm you know, when when

138
00:05:31,200 --> 00:05:32,880
Speaker 2: you get one of those shooting you should often you

139
00:05:32,920 --> 00:05:35,719
Speaker 2: know what it's for. And that's that's what I started.

140
00:05:35,800 --> 00:05:41,000
Speaker 1: H So you talk about the future of technology, the

141
00:05:41,040 --> 00:05:44,120
Speaker 1: future of all of us, right, And one of the

142
00:05:44,160 --> 00:05:44,800
Speaker 1: things that I am.

143
00:05:44,800 --> 00:05:47,400
Speaker 4: Very excited about my sister and I'm my sister's here

144
00:05:47,440 --> 00:05:47,640
Speaker 4: with me.

145
00:05:48,320 --> 00:05:51,279
Speaker 1: We grew up in a time and in this country

146
00:05:51,360 --> 00:05:55,800
Speaker 1: where Indian people, Indian celebrations were not really a thing.

147
00:05:56,279 --> 00:06:00,359
Speaker 4: We celebrated in our household mildly with our parents. You know.

148
00:06:00,360 --> 00:06:01,599
Speaker 4: My mom was out religious.

149
00:06:01,600 --> 00:06:03,559
Speaker 1: Of my dad, she would want to give us gold

150
00:06:03,800 --> 00:06:05,240
Speaker 1: and that was kind of the extent of it. And

151
00:06:05,320 --> 00:06:07,960
Speaker 1: now we're having the Valey parties at the White House.

152
00:06:08,160 --> 00:06:09,000
Speaker 2: Is at amazing.

153
00:06:09,080 --> 00:06:11,839
Speaker 4: It's incredible, it really is. The field is good cool.

154
00:06:12,279 --> 00:06:14,280
Speaker 4: I appreciate being here.

155
00:06:15,080 --> 00:06:18,039
Speaker 1: I know you you are obviously your Indian, you were

156
00:06:18,040 --> 00:06:20,920
Speaker 1: born in India in or urban Texas. You've seen more

157
00:06:20,960 --> 00:06:23,039
Speaker 1: than we've seen as far as this change goes.

158
00:06:23,520 --> 00:06:24,440
Speaker 4: How is it for you?

159
00:06:24,760 --> 00:06:30,040
Speaker 2: It's really amazing and it just reminds you what America

160
00:06:30,200 --> 00:06:33,560
Speaker 2: is all about. My mom brought our family here in

161
00:06:33,600 --> 00:06:36,359
Speaker 2: the early nineteen sixties, and the first of all, there

162
00:06:36,400 --> 00:06:40,040
Speaker 2: were very few women. The Vice President's mom was one

163
00:06:40,040 --> 00:06:42,200
Speaker 2: of those pioneers, and it's sort of amazing to think

164
00:06:42,200 --> 00:06:45,200
Speaker 2: about these women coming in that time period. My mom

165
00:06:45,200 --> 00:06:48,159
Speaker 2: brought us here in the early sixties and by the

166
00:06:48,200 --> 00:06:51,320
Speaker 2: time my family we came when I was three, and

167
00:06:51,360 --> 00:06:53,680
Speaker 2: then over the course of a few years, we found

168
00:06:53,760 --> 00:06:56,359
Speaker 2: our way, our way to love with Texas, which is

169
00:06:56,360 --> 00:06:58,360
Speaker 2: where I had many of my formative years. I went

170
00:06:58,400 --> 00:07:00,599
Speaker 2: to high school and loved it, went to college and Lubbock.

171
00:07:00,920 --> 00:07:02,800
Speaker 2: And at the time that I went to high school

172
00:07:02,800 --> 00:07:05,240
Speaker 2: in Lubbock, I was the only Indian kid in my

173
00:07:05,360 --> 00:07:08,080
Speaker 2: high school, you know, graduating class six or seven hundred,

174
00:07:08,480 --> 00:07:10,720
Speaker 2: and it was a time, it was the seventies, and

175
00:07:10,800 --> 00:07:14,440
Speaker 2: people would ask, without irony or humor or malice, they

176
00:07:14,440 --> 00:07:16,680
Speaker 2: would say, what tribe. When you said the Lord, you

177
00:07:16,720 --> 00:07:19,600
Speaker 2: were India, right, And it's because they knew Native Americans.

178
00:07:19,640 --> 00:07:22,920
Speaker 2: They didn't have met anyone from India. So so you

179
00:07:22,960 --> 00:07:26,520
Speaker 2: were talking about Dvali. I remember celebrating Duali at our

180
00:07:26,600 --> 00:07:29,880
Speaker 2: house and you know, we would put light to candles

181
00:07:29,960 --> 00:07:32,320
Speaker 2: and drip the wax and put them on our house.

182
00:07:32,800 --> 00:07:35,840
Speaker 2: And we had friends who would come over and all

183
00:07:35,880 --> 00:07:38,080
Speaker 2: of our American friends thought it was really cool. But

184
00:07:38,160 --> 00:07:41,040
Speaker 2: it was definitely home homebrew because that's all you had.

185
00:07:41,160 --> 00:07:45,440
Speaker 2: Right And now to fast forward and to be here

186
00:07:45,480 --> 00:07:48,360
Speaker 2: at the White House and to find that the President's

187
00:07:48,560 --> 00:07:51,440
Speaker 2: hosting Dvali and it's going to be a very big

188
00:07:51,480 --> 00:07:55,480
Speaker 2: gathering tonight. That's it really speaks to the journey of

189
00:07:55,520 --> 00:07:58,280
Speaker 2: this community. But also that's that's what this country is

190
00:07:58,280 --> 00:07:58,720
Speaker 2: all about.

191
00:07:58,880 --> 00:07:59,520
Speaker 4: It really is.

192
00:07:59,800 --> 00:08:01,480
Speaker 1: And I am so appreciated to be here. So I

193
00:08:01,600 --> 00:08:03,840
Speaker 1: like to think everybody for inviting us. We will not

194
00:08:03,840 --> 00:08:05,119
Speaker 1: embarrass your health too badly.

195
00:08:05,360 --> 00:08:06,920
Speaker 2: Okay, well we're going to hold you to that.

196
00:08:07,080 --> 00:08:09,800
Speaker 1: Okay, too badly being the keyword, a little bit of

197
00:08:09,800 --> 00:08:10,400
Speaker 1: a not too bad there.

198
00:08:10,520 --> 00:08:11,960
Speaker 2: I'm not going to ask a lot of questions. Now,

199
00:08:12,040 --> 00:08:12,320
Speaker 2: what's that?

200
00:08:13,200 --> 00:08:16,400
Speaker 1: So let's talk about all the things that you are doing. So,

201
00:08:16,480 --> 00:08:18,480
Speaker 1: first of all, a lot of people don't know what

202
00:08:18,520 --> 00:08:22,280
Speaker 1: an executive order is. So when you introduce an executive

203
00:08:22,360 --> 00:08:25,040
Speaker 1: order regarding AI, can you explain what that means?

204
00:08:25,160 --> 00:08:25,360
Speaker 2: Yeah?

205
00:08:25,760 --> 00:08:26,520
Speaker 4: Asking for a friend?

206
00:08:26,640 --> 00:08:30,760
Speaker 2: Yeah, absolutely absolutely. So the way we make laws in

207
00:08:30,800 --> 00:08:34,040
Speaker 2: this country is with the legislative branch of government that's Congress,

208
00:08:34,600 --> 00:08:38,400
Speaker 2: and an executive order is when the president takes action

209
00:08:39,000 --> 00:08:41,920
Speaker 2: within existing laws. It's not new laws, but this is

210
00:08:41,960 --> 00:08:44,560
Speaker 2: the president saying, these are the things we can and

211
00:08:44,679 --> 00:08:50,160
Speaker 2: must do under existing law, and in particular and artificial intelligence,

212
00:08:50,240 --> 00:08:56,679
Speaker 2: which served into the forefront over the last couple of years. One.

213
00:08:56,880 --> 00:09:00,000
Speaker 2: There's a lot to say about AI or what's going

214
00:09:00,000 --> 00:09:02,160
Speaker 2: going on, but one of the important things that this

215
00:09:02,320 --> 00:09:05,360
Speaker 2: president did was to put in place in executive order

216
00:09:05,559 --> 00:09:07,520
Speaker 2: where he said, we're going to pull every lever under

217
00:09:07,559 --> 00:09:08,320
Speaker 2: existing law.

218
00:09:08,800 --> 00:09:12,160
Speaker 1: Okay, it's really hard for policy to keep up with technology.

219
00:09:12,160 --> 00:09:14,040
Speaker 4: At the pace of technology is moving.

220
00:09:13,920 --> 00:09:16,959
Speaker 1: Through that we're seeing a lot of things kind of

221
00:09:17,000 --> 00:09:22,040
Speaker 1: popping up that are worrisome, terrifying, also really fascinating and encouraging.

222
00:09:22,600 --> 00:09:24,880
Speaker 4: Right, all of the above, Right, how do you start

223
00:09:24,920 --> 00:09:25,679
Speaker 4: to regulate these things?

224
00:09:25,679 --> 00:09:30,600
Speaker 1: Specifically, let's start with likeness of people, women, children, Oh,

225
00:09:30,679 --> 00:09:32,280
Speaker 1: how do you move through that moves their huges.

226
00:09:32,520 --> 00:09:34,680
Speaker 2: Let me let me start with the big picture. So

227
00:09:35,000 --> 00:09:39,320
Speaker 2: when chatbots and image generators burst on the scene, Look,

228
00:09:39,600 --> 00:09:43,160
Speaker 2: everyone understood, I mean, certainly the president, the Vife's president understood.

229
00:09:43,200 --> 00:09:45,720
Speaker 2: AI was already in our lives, right, So when you

230
00:09:45,760 --> 00:09:48,520
Speaker 2: go online and you pay whatever you get charged for

231
00:09:48,520 --> 00:09:51,280
Speaker 2: an airline ticket, AI was behind that. And when you

232
00:09:51,360 --> 00:09:54,240
Speaker 2: go to a hospital, whether what kind of a diagnosis

233
00:09:54,320 --> 00:09:57,040
Speaker 2: or treatment you get. So AI was in our lives,

234
00:09:57,120 --> 00:10:00,880
Speaker 2: but it was hidden. And then chatbots and image generators

235
00:10:00,960 --> 00:10:03,600
Speaker 2: all of a sudden they are was in our faces.

236
00:10:03,960 --> 00:10:07,320
Speaker 2: And it was a big moment. And what happened is

237
00:10:07,320 --> 00:10:10,720
Speaker 2: that the President and the Vice President sees that moment

238
00:10:10,800 --> 00:10:12,880
Speaker 2: to say, look, we've got to get on the right

239
00:10:12,960 --> 00:10:17,280
Speaker 2: track because this is a really powerful technology just exactly

240
00:10:17,320 --> 00:10:20,319
Speaker 2: to your point. It brings promise and peril both they

241
00:10:20,360 --> 00:10:23,360
Speaker 2: come together, and we've got to do two things. We

242
00:10:23,520 --> 00:10:26,280
Speaker 2: have to manage its risks and we have to seize

243
00:10:26,320 --> 00:10:30,040
Speaker 2: its opportunities. And that was the mission that they gave

244
00:10:30,240 --> 00:10:33,040
Speaker 2: all of us. And then we got to work. And

245
00:10:33,440 --> 00:10:37,360
Speaker 2: the mechanics of it is working with Congress as they

246
00:10:37,400 --> 00:10:39,640
Speaker 2: get came up the learning curve and as they start

247
00:10:39,679 --> 00:10:43,960
Speaker 2: moving towards we hope good legislation, working globally, working with

248
00:10:44,160 --> 00:10:47,480
Speaker 2: companies and calling on them to make voluntary commitments. The

249
00:10:47,559 --> 00:10:50,160
Speaker 2: executive order that the President signed, which was you know,

250
00:10:50,200 --> 00:10:51,720
Speaker 2: we're going to work with everyone else, but we're going

251
00:10:51,760 --> 00:10:54,120
Speaker 2: to do our work here in the executive branch. Stand up,

252
00:10:54,520 --> 00:10:57,000
Speaker 2: so one of like one is what happened, then well

253
00:10:57,080 --> 00:11:01,760
Speaker 2: let me try what happened. Some eight examples of how

254
00:11:02,360 --> 00:11:05,960
Speaker 2: we're starting to create an environment in which AI is

255
00:11:06,000 --> 00:11:10,040
Speaker 2: being useful but we're but we're managing its harm. So

256
00:11:11,440 --> 00:11:14,400
Speaker 2: when you go to a bank today and you ask

257
00:11:14,440 --> 00:11:19,160
Speaker 2: for a credit card or mortgage, almost always it's an

258
00:11:19,160 --> 00:11:22,920
Speaker 2: AI system that evaluates your application, and today you are

259
00:11:23,000 --> 00:11:25,760
Speaker 2: owed an explanation when you get a yes or no,

260
00:11:26,200 --> 00:11:28,560
Speaker 2: and so you can do something about it. The reason

261
00:11:28,640 --> 00:11:31,480
Speaker 2: that requirement is error is because of our Consumer Financial

262
00:11:31,520 --> 00:11:34,120
Speaker 2: Protection Bureau. They put that rule in place and so

263
00:11:34,559 --> 00:11:37,800
Speaker 2: that the consumer, the finance consumer, has more of a say,

264
00:11:38,520 --> 00:11:41,920
Speaker 2: if you go today to a hospital, the AI system

265
00:11:42,000 --> 00:11:44,280
Speaker 2: that is being used to diagnose and treat you to

266
00:11:44,320 --> 00:11:46,920
Speaker 2: figure out what the right course of treatment is has

267
00:11:46,960 --> 00:11:49,880
Speaker 2: to have proven that it's it's not embedding all the

268
00:11:49,920 --> 00:11:53,520
Speaker 2: bias of bad decisions from the past. That's because of

269
00:11:53,559 --> 00:12:00,360
Speaker 2: the rule of our Department of Health and Human Services.

270
00:12:05,600 --> 00:12:08,200
Speaker 1: Now, when you say it's not using information all the

271
00:12:08,200 --> 00:12:10,920
Speaker 1: bad information in the past, what exactly does that mean?

272
00:12:11,040 --> 00:12:14,000
Speaker 2: What's unpacked back because this is super important. So there's

273
00:12:14,040 --> 00:12:16,880
Speaker 2: a long history of medical decisions that were made that

274
00:12:16,960 --> 00:12:19,480
Speaker 2: were based on skin, the color of a person's skin,

275
00:12:19,760 --> 00:12:21,880
Speaker 2: the amount of money in their pocket, the neighborhood that

276
00:12:21,880 --> 00:12:24,319
Speaker 2: they lived in, whether or not they had insurance. They

277
00:12:24,360 --> 00:12:28,160
Speaker 2: aren't about medical factors. And when you take those biased

278
00:12:28,240 --> 00:12:31,320
Speaker 2: decisions of the past and you just blindly train an

279
00:12:31,400 --> 00:12:34,600
Speaker 2: AI model on them, what you are doing is something horrible,

280
00:12:34,640 --> 00:12:37,920
Speaker 2: which is you are intensifying and distilling all of that

281
00:12:38,040 --> 00:12:42,440
Speaker 2: bad decision making of the past. If you correct for

282
00:12:42,520 --> 00:12:45,400
Speaker 2: that and you recognize that some of those decisions in

283
00:12:45,440 --> 00:12:48,960
Speaker 2: the past were bad, and you train on good data

284
00:12:49,280 --> 00:12:52,880
Speaker 2: that tells you what kind of treatments really lead to

285
00:12:52,920 --> 00:12:56,040
Speaker 2: better outcomes for people, then you can actually get the

286
00:12:56,160 --> 00:12:59,760
Speaker 2: power of the positive benefit of AI that you can't

287
00:12:59,800 --> 00:13:01,560
Speaker 2: just throw a bunch of data at it and hope

288
00:13:01,559 --> 00:13:03,840
Speaker 2: it comes out. Okay, you really have to do the work.

289
00:13:04,320 --> 00:13:07,280
Speaker 1: So let's break that down for people who might be

290
00:13:07,440 --> 00:13:08,480
Speaker 1: a little lost.

291
00:13:08,200 --> 00:13:10,560
Speaker 4: In all the sauce here. We like to talk about

292
00:13:10,559 --> 00:13:11,720
Speaker 4: sauceage sauce on the side.

293
00:13:11,800 --> 00:13:15,600
Speaker 1: Okay, Yeah, Essentially, what you guys have done is start

294
00:13:15,679 --> 00:13:20,199
Speaker 1: to reverse the damage of years decades generations of this

295
00:13:20,240 --> 00:13:21,920
Speaker 1: because we know AA has been around, like you said,

296
00:13:21,920 --> 00:13:24,120
Speaker 1: for a very long time, mining all of this info

297
00:13:24,800 --> 00:13:30,079
Speaker 1: and intentionally or unintentionally separating groups and keeping certain groups

298
00:13:31,400 --> 00:13:34,040
Speaker 1: worse off than others. And what you guys are doing

299
00:13:34,080 --> 00:13:35,120
Speaker 1: is stepping in and fixing this.

300
00:13:35,240 --> 00:13:38,160
Speaker 2: Now, one step, little done, one step of the time,

301
00:13:38,240 --> 00:13:41,440
Speaker 2: saying let's make sure we're not exacerbating bias.

302
00:13:41,600 --> 00:13:43,520
Speaker 4: Right, how far long in the process do you think

303
00:13:43,559 --> 00:13:43,800
Speaker 4: you are?

304
00:13:43,920 --> 00:13:44,280
Speaker 2: Right now?

305
00:13:44,320 --> 00:13:46,559
Speaker 4: From like one to one hundred. One hundred is perfect.

306
00:13:46,800 --> 00:13:49,160
Speaker 4: You started at nothing, but I.

307
00:13:49,120 --> 00:13:52,120
Speaker 2: Would say what has gotten done is a great start,

308
00:13:52,160 --> 00:13:54,079
Speaker 2: and there is no question there's a lot more work

309
00:13:54,120 --> 00:13:56,840
Speaker 2: to do. First of all, because AI is being used

310
00:13:57,040 --> 00:13:59,960
Speaker 2: for more and more purposes and every one of them.

311
00:14:00,600 --> 00:14:02,679
Speaker 2: But we've been talking about the bias risk, but there

312
00:14:02,679 --> 00:14:06,640
Speaker 2: are also risks of exposing private information. Oh yeah, there

313
00:14:06,640 --> 00:14:07,920
Speaker 2: are risks to work.

314
00:14:08,400 --> 00:14:08,640
Speaker 1: You know.

315
00:14:08,920 --> 00:14:12,320
Speaker 2: We can either do this right and use AI to

316
00:14:12,480 --> 00:14:16,000
Speaker 2: let people do more and earn more. That's the future

317
00:14:16,000 --> 00:14:18,319
Speaker 2: we need to build. But if we don't pay attention.

318
00:14:18,760 --> 00:14:22,960
Speaker 2: We already know there are employers who use AI to

319
00:14:23,000 --> 00:14:26,160
Speaker 2: surveil their workers, to dehumanize their work, to hollow their

320
00:14:26,200 --> 00:14:30,680
Speaker 2: workout and often to replace workers sort of blindly without

321
00:14:30,680 --> 00:14:33,960
Speaker 2: really thinking about the consequences. So again, I think in

322
00:14:34,080 --> 00:14:36,640
Speaker 2: every case, the issue is how do we start being

323
00:14:36,840 --> 00:14:39,880
Speaker 2: very purposeful so that we get to the future we want.

324
00:14:40,240 --> 00:14:42,520
Speaker 1: So when you talk about being purposeful and protecting women

325
00:14:42,560 --> 00:14:45,000
Speaker 1: and children, what are some of the steps that everyday

326
00:14:45,000 --> 00:14:47,960
Speaker 1: people can take. Yeah, that don't know anything about AI,

327
00:14:48,040 --> 00:14:50,200
Speaker 1: that don't know that these things can happen, What do

328
00:14:50,200 --> 00:14:50,400
Speaker 1: you do?

329
00:14:50,440 --> 00:14:51,960
Speaker 2: How do you know? I just yeah, I think a

330
00:14:52,000 --> 00:14:54,360
Speaker 2: great place to start is just to be aware of

331
00:14:54,400 --> 00:14:57,360
Speaker 2: how many places it's embedded in the world around us,

332
00:14:57,960 --> 00:15:00,400
Speaker 2: and we're doing the work that we need to do

333
00:15:00,480 --> 00:15:03,200
Speaker 2: as a government to make to make sure that things

334
00:15:03,200 --> 00:15:06,760
Speaker 2: that are illegal aren't you know, happening anyway because of AI.

335
00:15:06,960 --> 00:15:10,040
Speaker 2: Our regulators really clear they're getting better and enforcing as

336
00:15:10,080 --> 00:15:13,480
Speaker 2: they understand how AI is coming into lots of different places.

337
00:15:13,760 --> 00:15:16,000
Speaker 2: But we all need, each of us as individuals, need

338
00:15:16,040 --> 00:15:18,960
Speaker 2: to be smart consumers. So I'd say that's one is

339
00:15:19,160 --> 00:15:22,280
Speaker 2: just be smart about what's coming at you and recognize

340
00:15:22,280 --> 00:15:24,760
Speaker 2: that you know, there's an algorithm that is feeding you

341
00:15:24,800 --> 00:15:26,800
Speaker 2: the next thing in your social media feed, there's an

342
00:15:26,800 --> 00:15:29,920
Speaker 2: algorithm that's feeding you the next ad but also deciding

343
00:15:29,960 --> 00:15:32,320
Speaker 2: pricing and a lot of these other things. And then

344
00:15:32,360 --> 00:15:35,520
Speaker 2: once once you're savvy about what's coming at you think

345
00:15:35,560 --> 00:15:39,000
Speaker 2: about where you can use AI in your life to

346
00:15:39,120 --> 00:15:41,400
Speaker 2: do more of what you want to do. And I'll

347
00:15:41,400 --> 00:15:43,760
Speaker 2: tell you. If you are a student, there are more

348
00:15:43,800 --> 00:15:47,120
Speaker 2: and more AI tools that can help you learn at

349
00:15:47,120 --> 00:15:49,200
Speaker 2: the leap. You know, right where you are. They can

350
00:15:49,240 --> 00:15:51,400
Speaker 2: help meet you right where you are and figure out

351
00:15:51,440 --> 00:15:54,200
Speaker 2: what you need to learn next. If you're a small

352
00:15:54,240 --> 00:15:56,520
Speaker 2: business owner, there are more and more tools to help

353
00:15:56,600 --> 00:16:01,400
Speaker 2: you with your marketing, with all aspects of your business.

354
00:16:01,400 --> 00:16:04,720
Speaker 2: So you know, get out there and start exploring and experimenting.

355
00:16:05,520 --> 00:16:09,880
Speaker 1: So with AI and using likeness, a lot of really

356
00:16:10,040 --> 00:16:13,000
Speaker 1: sketchy things can come about from it. I assume you

357
00:16:13,080 --> 00:16:15,360
Speaker 1: got if we've thought about it, you guys have already

358
00:16:15,440 --> 00:16:16,000
Speaker 1: thought about it.

359
00:16:16,120 --> 00:16:16,880
Speaker 2: Huge issue.

360
00:16:17,040 --> 00:16:21,640
Speaker 1: Yeah, what happens in the case of a generated image

361
00:16:22,080 --> 00:16:24,840
Speaker 1: of a child in a position that none.

362
00:16:24,640 --> 00:16:25,880
Speaker 4: Of us want to see a child in.

363
00:16:26,200 --> 00:16:29,560
Speaker 1: Exactly, it's not real, it's nothing real, But the people

364
00:16:29,600 --> 00:16:31,640
Speaker 1: who are consuming it are still the same level of

365
00:16:31,680 --> 00:16:35,160
Speaker 1: creepy for consuming this thing that is not real. Where

366
00:16:35,240 --> 00:16:38,320
Speaker 1: does that fall in all of this? Yeah, as far

367
00:16:38,360 --> 00:16:39,880
Speaker 1: as legislation as far as regulating it.

368
00:16:39,920 --> 00:16:40,480
Speaker 4: How does that work.

369
00:16:40,560 --> 00:16:44,160
Speaker 2: Well, let's let's start with a year ago when the

370
00:16:44,200 --> 00:16:48,520
Speaker 2: President signed in the executive order we had. We were

371
00:16:48,640 --> 00:16:52,000
Speaker 2: thinking about and trying to figure out what the various

372
00:16:52,120 --> 00:16:55,960
Speaker 2: kinds of risks were from this new surgeon AI and

373
00:16:56,000 --> 00:16:59,000
Speaker 2: we were the question we were asking ourselves was which

374
00:16:59,040 --> 00:17:02,440
Speaker 2: of those risks would turn into real world harms first.

375
00:17:02,480 --> 00:17:05,120
Speaker 2: And I'll tell you, I tell you, I now think

376
00:17:05,119 --> 00:17:07,600
Speaker 2: we have the answer, and it's an ugly answer. And

377
00:17:07,640 --> 00:17:11,879
Speaker 2: what it is is deep fake nudes, right, sexual abuse,

378
00:17:12,200 --> 00:17:16,280
Speaker 2: So that it's what you know. With these amazing image generators,

379
00:17:16,320 --> 00:17:19,840
Speaker 2: it's just way too easy to take a really simple

380
00:17:19,880 --> 00:17:23,600
Speaker 2: photograph or sketch of often it's a woman, often it's

381
00:17:23,640 --> 00:17:26,560
Speaker 2: a girl. Often it's someone who is gay or queer,

382
00:17:27,280 --> 00:17:32,440
Speaker 2: and take a person and turn them into a deep

383
00:17:32,480 --> 00:17:35,480
Speaker 2: fake nude that goes out into the world. Well, that's

384
00:17:35,520 --> 00:17:40,600
Speaker 2: happening at really an alarming scale today, and it is

385
00:17:41,240 --> 00:17:44,320
Speaker 2: you know, it's just unacceptable. And this is an area

386
00:17:44,480 --> 00:17:47,480
Speaker 2: where Congress has considered legislation. There are a couple of

387
00:17:47,480 --> 00:17:50,200
Speaker 2: proposals that, if they can make progress, I think would

388
00:17:50,359 --> 00:17:54,719
Speaker 2: start making real impact on this problem. But while that

389
00:17:54,920 --> 00:17:58,320
Speaker 2: is happening. We wanted to make sure we didn't wait,

390
00:17:58,640 --> 00:18:02,080
Speaker 2: and we here at the Office of Science and Technology

391
00:18:02,080 --> 00:18:04,320
Speaker 2: Policy the White House who teamed up with the Gender

392
00:18:04,359 --> 00:18:07,879
Speaker 2: Policy Council that works on these issues, and together we

393
00:18:07,960 --> 00:18:11,040
Speaker 2: put a call out to the industry to say, what

394
00:18:11,240 --> 00:18:14,439
Speaker 2: can you do? Step up now so that we can

395
00:18:14,480 --> 00:18:17,320
Speaker 2: deal with this immediate and urgent problem because you can

396
00:18:17,359 --> 00:18:20,679
Speaker 2: take actions immediately. So some of that is about the

397
00:18:20,800 --> 00:18:24,679
Speaker 2: AI companies that have image generators. Some of that is

398
00:18:24,800 --> 00:18:29,040
Speaker 2: about the payment processors. Often they have terms of service

399
00:18:29,160 --> 00:18:34,560
Speaker 2: that say we won't provide payment processing for different kinds

400
00:18:34,600 --> 00:18:38,040
Speaker 2: of harmful activities. Sometimes they have those terms, but they're

401
00:18:38,040 --> 00:18:41,720
Speaker 2: not really enforcing those. So and there's much more that

402
00:18:41,800 --> 00:18:44,960
Speaker 2: the platforms can do, so different parts of the tech

403
00:18:45,000 --> 00:18:48,440
Speaker 2: and innovation system. Actually there are things that they could

404
00:18:48,440 --> 00:18:51,400
Speaker 2: do immediately, and to our delight, a few companies did

405
00:18:51,440 --> 00:18:56,120
Speaker 2: start stepping forward and making some voluntary commitments that I

406
00:18:56,160 --> 00:18:59,960
Speaker 2: hope will start making some progress on this really ugly problem.

407
00:19:00,160 --> 00:19:01,399
Speaker 4: It is a really ugly problem.

408
00:19:01,440 --> 00:19:03,560
Speaker 1: On the flip side of it, if you think about

409
00:19:03,560 --> 00:19:06,120
Speaker 1: people who have gotten caught red handed doing things, it's

410
00:19:06,160 --> 00:19:07,639
Speaker 1: almost like we're going to get to this place of

411
00:19:07,680 --> 00:19:08,320
Speaker 1: Shaggy Song.

412
00:19:08,480 --> 00:19:10,679
Speaker 4: It wasn't me. You caught me red handed. Now, I

413
00:19:10,760 --> 00:19:12,439
Speaker 4: was a deep think it wasn't me. I think we

414
00:19:12,520 --> 00:19:14,160
Speaker 4: I think, yeah, I'll tell you.

415
00:19:14,160 --> 00:19:18,560
Speaker 2: You know, we all worry about bad information or bad

416
00:19:18,600 --> 00:19:23,080
Speaker 2: imagery leading people to believe things that aren't true.

417
00:19:23,119 --> 00:19:25,560
Speaker 4: And I feel all about now. Shoot, yeah, I'll tell you.

418
00:19:25,560 --> 00:19:28,280
Speaker 2: An even deeper problem is that they cause people to

419
00:19:28,320 --> 00:19:32,000
Speaker 2: not believe in anything, and that erosion of trust is

420
00:19:32,040 --> 00:19:35,480
Speaker 2: so damaging to our country.

421
00:19:36,280 --> 00:19:38,760
Speaker 4: We're seeing it every day right now. How damaging it

422
00:19:38,800 --> 00:19:41,119
Speaker 4: actually is? How do you fight it? Give me some

423
00:19:41,200 --> 00:19:41,600
Speaker 4: hope here?

424
00:19:41,680 --> 00:19:44,359
Speaker 2: Oh? You know, I think in a lot of ways.

425
00:19:46,080 --> 00:19:47,920
Speaker 2: I just think we have to be very very clear

426
00:19:47,960 --> 00:19:50,600
Speaker 2: about how important freedom of speech is in this country,

427
00:19:51,160 --> 00:19:55,800
Speaker 2: and it is a bedrock principle for our democracy. I

428
00:19:55,880 --> 00:19:59,200
Speaker 2: keep coming back to what each one thing that has

429
00:19:59,240 --> 00:20:01,440
Speaker 2: to happen is that each of us have to recognize

430
00:20:01,440 --> 00:20:04,960
Speaker 2: who our responsibilities are to be to be an intelligent

431
00:20:05,040 --> 00:20:08,560
Speaker 2: consumer of all the things that are hurled at us.

432
00:20:09,440 --> 00:20:12,359
Speaker 2: And after you peel it all back, a lot of

433
00:20:12,400 --> 00:20:14,439
Speaker 2: this is about judgment, and I think we have to

434
00:20:14,440 --> 00:20:16,680
Speaker 2: make smart judgments about all the things that are coming

435
00:20:16,680 --> 00:20:17,080
Speaker 2: at us.

436
00:20:17,280 --> 00:20:18,919
Speaker 4: You really ask me for a lot. I know, I

437
00:20:19,000 --> 00:20:20,480
Speaker 4: asked a lot from the everyday.

438
00:20:20,160 --> 00:20:23,120
Speaker 2: Person, but you know what, the future demands a lot

439
00:20:23,160 --> 00:20:25,200
Speaker 2: from us. That's okay, it's worth it.

440
00:20:25,480 --> 00:20:28,720
Speaker 4: I agree. So let's talk about happy things in the future.

441
00:20:28,760 --> 00:20:29,080
Speaker 2: Okay.

442
00:20:29,119 --> 00:20:31,159
Speaker 1: AI got to get there, Yes we are, I know,

443
00:20:31,240 --> 00:20:33,480
Speaker 1: because there's just so much terrifying stuff that happens.

444
00:20:33,480 --> 00:20:34,440
Speaker 4: We read a different.

445
00:20:34,160 --> 00:20:37,280
Speaker 1: Story about AI every day where something went wrong, where

446
00:20:37,440 --> 00:20:41,720
Speaker 1: a robot arm in a factory, crystal person or really unfortunately,

447
00:20:41,760 --> 00:20:44,520
Speaker 1: this young boy in Florida, Oh had a relationship.

448
00:20:43,920 --> 00:20:48,520
Speaker 4: With a chatbot. It is terrible. And then in Okay,

449
00:20:48,560 --> 00:20:49,919
Speaker 4: so before we get to the happy stuff, I have

450
00:20:49,920 --> 00:20:50,560
Speaker 4: a question about that.

451
00:20:50,600 --> 00:20:50,879
Speaker 2: Okay.

452
00:20:51,040 --> 00:20:53,080
Speaker 1: In that story, and if you don't know what happened,

453
00:20:53,119 --> 00:20:56,439
Speaker 1: there was a teenager in Florida who was basically in

454
00:20:56,440 --> 00:20:59,199
Speaker 1: a relationship with a chatbot. He did know that it

455
00:20:59,240 --> 00:21:02,760
Speaker 1: was a chatbot, but this bought learned all about him.

456
00:21:02,800 --> 00:21:04,960
Speaker 1: And now his mother is saying that the chapbot actually

457
00:21:05,000 --> 00:21:06,800
Speaker 1: encouraged him to take his own life and he did.

458
00:21:08,000 --> 00:21:10,440
Speaker 1: What happens in a case like that, is somebody held

459
00:21:10,480 --> 00:21:14,959
Speaker 1: responsible or is that just chopped up to Hey, this

460
00:21:15,040 --> 00:21:15,800
Speaker 1: is the technology.

461
00:21:15,800 --> 00:21:17,480
Speaker 4: We have to be smart and me good judgments. We

462
00:21:17,520 --> 00:21:18,440
Speaker 4: have fourteen year old child.

463
00:21:18,560 --> 00:21:22,320
Speaker 2: This is this is it's an unbearable thing to think about,

464
00:21:23,480 --> 00:21:28,600
Speaker 2: and you know it as it's so painful to think about.

465
00:21:28,920 --> 00:21:31,640
Speaker 2: You don't know the answer to your question. And this

466
00:21:31,720 --> 00:21:33,239
Speaker 2: is a case where the courts are going to make

467
00:21:33,280 --> 00:21:38,000
Speaker 2: a decision cause the mothers is pursuing a lawsuit. But

468
00:21:38,640 --> 00:21:42,280
Speaker 2: this is exactly this is one of the most horrific examples.

469
00:21:42,320 --> 00:21:45,160
Speaker 2: But a lot of the AI territory that we are

470
00:21:45,320 --> 00:21:48,000
Speaker 2: in is there are a lot of parts of it

471
00:21:48,040 --> 00:21:52,320
Speaker 2: that are uncharted. Another area is intellectual property right when

472
00:21:52,400 --> 00:21:54,960
Speaker 2: you create content as you do and many others do

473
00:21:56,359 --> 00:21:59,239
Speaker 2: what is appropriate use well, lawsuits right now are going

474
00:21:59,320 --> 00:22:02,560
Speaker 2: to sort that out right and so that's another area.

475
00:22:02,600 --> 00:22:04,080
Speaker 2: But I want to come back to the fact that

476
00:22:04,160 --> 00:22:06,119
Speaker 2: a lot of the things that we are concerned about

477
00:22:06,160 --> 00:22:10,320
Speaker 2: with AI today actually are already illegal. It's illegal to

478
00:22:10,320 --> 00:22:14,160
Speaker 2: commit fraud, it's illegal to discriminate in housing and lending,

479
00:22:14,760 --> 00:22:18,280
Speaker 2: and in healthcare, and those are the places where you

480
00:22:18,320 --> 00:22:20,359
Speaker 2: know there's work to do, but we know what we

481
00:22:20,400 --> 00:22:23,000
Speaker 2: need to do and the laws are there, by and large,

482
00:22:23,640 --> 00:22:25,679
Speaker 2: the framework is there. So it's really a matter of

483
00:22:26,080 --> 00:22:29,199
Speaker 2: keeping up with the creativity with which this technology is

484
00:22:29,240 --> 00:22:30,000
Speaker 2: being used.

485
00:22:29,800 --> 00:22:32,159
Speaker 4: And people will where there's a bad guy, there will

486
00:22:32,160 --> 00:22:33,680
Speaker 4: always be a good guy. That at least that's why

487
00:22:33,680 --> 00:22:36,840
Speaker 4: I like. So now let's talk about hope and the going.

488
00:22:37,000 --> 00:22:38,959
Speaker 2: There's so many good things. So if we can just

489
00:22:39,080 --> 00:22:43,160
Speaker 2: get the foundation right, then there's really important potential here.

490
00:22:43,400 --> 00:22:47,040
Speaker 4: I watched the robot perform surgery on a kernel of corn. Amazing.

491
00:22:47,920 --> 00:22:49,280
Speaker 4: That stuff is incredible.

492
00:22:49,359 --> 00:22:52,080
Speaker 1: That that is the stuff that we need artificial intelligence

493
00:22:52,119 --> 00:22:53,359
Speaker 1: and the robotics and all that for it.

494
00:22:53,960 --> 00:22:57,720
Speaker 2: Yeah, there's robotic surgery that's really prostate cancer, something that

495
00:22:57,840 --> 00:23:01,120
Speaker 2: is very commonly treated with robotic surgery. I mean, these

496
00:23:01,119 --> 00:23:03,400
Speaker 2: are amazing things you advance just as.

497
00:23:03,320 --> 00:23:03,879
Speaker 4: What we need it for.

498
00:23:04,200 --> 00:23:06,920
Speaker 1: We don't need like the profile picture with the crown.

499
00:23:07,400 --> 00:23:08,159
Speaker 1: We need surgery.

500
00:23:08,359 --> 00:23:08,600
Speaker 2: Yeah.

501
00:23:08,840 --> 00:23:13,000
Speaker 4: No, And I'll tell you we ran a whole project called.

502
00:23:12,840 --> 00:23:16,000
Speaker 2: AI Aspirations because look, the reason we're doing all this

503
00:23:16,080 --> 00:23:19,200
Speaker 2: work to get AI right and to manage its harms

504
00:23:19,240 --> 00:23:22,719
Speaker 2: and limit where it can go wrong. That's about building

505
00:23:22,760 --> 00:23:26,240
Speaker 2: a stable foundation. The reason you want that stable foundation

506
00:23:26,320 --> 00:23:27,919
Speaker 2: to say you can stand on it and reach for

507
00:23:27,960 --> 00:23:33,280
Speaker 2: the stars. And businesses are all racing to use AI

508
00:23:33,560 --> 00:23:36,399
Speaker 2: to get more productivity in business and that has to

509
00:23:36,440 --> 00:23:40,240
Speaker 2: be done responsibly. But if it's done responsible, we want

510
00:23:40,280 --> 00:23:42,520
Speaker 2: it right. The world wants it, the country wants it.

511
00:23:42,520 --> 00:23:44,119
Speaker 2: We want to see that happen. It's going to be

512
00:23:44,119 --> 00:23:47,360
Speaker 2: good for the economy and jobs and growth. But that's

513
00:23:47,400 --> 00:23:50,359
Speaker 2: not the only reason we want AI. We want it

514
00:23:50,440 --> 00:23:54,639
Speaker 2: because today there are thousands of diseases for which we

515
00:23:54,720 --> 00:23:58,360
Speaker 2: have absolutely no medical solution, and yet we only come

516
00:23:58,440 --> 00:24:01,480
Speaker 2: up with about twenty or thirty new drugs for diseases

517
00:24:01,880 --> 00:24:04,480
Speaker 2: every year. What would it take to go at ten

518
00:24:04,600 --> 00:24:08,080
Speaker 2: times or one hundred times faster. AI could change how

519
00:24:08,119 --> 00:24:12,800
Speaker 2: we design and approve new drugs in a really radical way.

520
00:24:13,280 --> 00:24:16,360
Speaker 2: In research, we are saying that AI can speed up

521
00:24:16,520 --> 00:24:21,240
Speaker 2: weather forecasts by ten to fifty thousand times compared to

522
00:24:21,280 --> 00:24:23,840
Speaker 2: the computational models we have to run to this that's file,

523
00:24:24,080 --> 00:24:26,600
Speaker 2: So think about what that might mean for better weather

524
00:24:26,680 --> 00:24:29,240
Speaker 2: forecasts and a time when our climate is changing. That

525
00:24:29,280 --> 00:24:32,680
Speaker 2: could be a huge deal. Think about the educational gaps

526
00:24:32,720 --> 00:24:35,680
Speaker 2: that we still have that have persisted across all these

527
00:24:35,760 --> 00:24:39,280
Speaker 2: years for our kids, and think about what AI could

528
00:24:39,320 --> 00:24:42,119
Speaker 2: do to help us close those gaps. These are the

529
00:24:42,200 --> 00:24:45,240
Speaker 2: things that this is the country's work that I want

530
00:24:45,280 --> 00:24:47,160
Speaker 2: to make sure we get done with AI.

531
00:24:56,480 --> 00:24:58,280
Speaker 1: So we've got the scary stuff, but we've also got

532
00:24:58,480 --> 00:25:03,200
Speaker 1: potentially changing our future, saving the population, saving near getting

533
00:25:03,240 --> 00:25:05,840
Speaker 1: more education with everybody who might not have access to it,

534
00:25:05,960 --> 00:25:07,000
Speaker 1: and equity and healthcare.

535
00:25:07,440 --> 00:25:10,119
Speaker 4: Yeah, that so steems pretty great. What's most important to you.

536
00:25:10,080 --> 00:25:14,159
Speaker 2: What's most important to me is that we get AI

537
00:25:14,359 --> 00:25:17,040
Speaker 2: on a path where we humans are driving and not

538
00:25:17,160 --> 00:25:20,080
Speaker 2: feeling like we are reacting to the technology, because when

539
00:25:20,080 --> 00:25:22,760
Speaker 2: we drive, then we make Then we're going to express

540
00:25:22,760 --> 00:25:26,280
Speaker 2: our values by tamping down the risks and the harms

541
00:25:26,280 --> 00:25:28,520
Speaker 2: and then building the future I want to run into.

542
00:25:29,359 --> 00:25:31,680
Speaker 4: So there's no part of you that's worried about robots

543
00:25:31,680 --> 00:25:32,200
Speaker 4: taking over.

544
00:25:33,320 --> 00:25:33,439
Speaker 3: Uh.

545
00:25:33,560 --> 00:25:38,440
Speaker 2: I worry about humans and corporations letting things spit.

546
00:25:38,240 --> 00:25:39,719
Speaker 4: Out of control, doing nefarious things.

547
00:25:39,800 --> 00:25:42,120
Speaker 2: Well, we talk about the technology, it's the human being

548
00:25:42,160 --> 00:25:44,160
Speaker 2: set are the most interesting part. If we're the ones

549
00:25:44,160 --> 00:25:45,320
Speaker 2: that are going to get it right, and if we

550
00:25:45,320 --> 00:25:46,560
Speaker 2: don't get it right, it's on us.

551
00:25:47,240 --> 00:25:49,000
Speaker 1: It's just a wild to see these things, the way

552
00:25:49,000 --> 00:25:51,600
Speaker 1: that they learn you and so quickly. I have a friend, yeah,

553
00:25:51,640 --> 00:25:53,960
Speaker 1: who says he has some of the best conversations he's

554
00:25:54,000 --> 00:25:56,320
Speaker 1: ever had with chat GPT. He says, you know, I

555
00:25:56,440 --> 00:25:58,560
Speaker 1: just teach it a little bit about some of my friends.

556
00:25:58,720 --> 00:26:00,680
Speaker 1: And when I can't reach out to that, foriarticular friend,

557
00:26:00,960 --> 00:26:02,840
Speaker 1: I go to my chat GBT and ZA talk to

558
00:26:02,880 --> 00:26:05,840
Speaker 1: me like Bill in the blank and it does. And

559
00:26:05,880 --> 00:26:07,920
Speaker 1: he says when he's feeling lonely, and it actually works

560
00:26:07,920 --> 00:26:09,640
Speaker 1: out well, and I am blown away?

561
00:26:09,680 --> 00:26:10,639
Speaker 2: Does that blow you away?

562
00:26:10,640 --> 00:26:11,879
Speaker 4: It blows you will blow you away.

563
00:26:12,040 --> 00:26:15,600
Speaker 2: And the thing I keep thinking about is, you know,

564
00:26:15,720 --> 00:26:18,560
Speaker 2: on the one hand, I wonder if it's going to

565
00:26:18,640 --> 00:26:22,000
Speaker 2: hollow out what being human means. And then I think

566
00:26:22,040 --> 00:26:25,040
Speaker 2: about all of human history. You know, it's it's it's

567
00:26:25,119 --> 00:26:28,879
Speaker 2: it's a long time since the print the printing press, right, Yeah,

568
00:26:29,040 --> 00:26:33,080
Speaker 2: but but that completely changed how we thought, how we communicated,

569
00:26:34,000 --> 00:26:37,280
Speaker 2: how ideas swept through the world. And I'm pretty sure

570
00:26:37,280 --> 00:26:41,159
Speaker 2: we worried that we're losing our humanity back then, and

571
00:26:41,240 --> 00:26:46,399
Speaker 2: I how we didn't. It changed, we changed, but ultimately

572
00:26:46,440 --> 00:26:48,159
Speaker 2: we got to a place that we wouldn't want to

573
00:26:48,200 --> 00:26:51,439
Speaker 2: go back from. So I'm I'm very hopeful we're going

574
00:26:51,480 --> 00:26:53,320
Speaker 2: to We're going to get there with AI.

575
00:26:53,800 --> 00:26:55,440
Speaker 1: Okay, that's a great way to think about it, because

576
00:26:55,480 --> 00:26:58,480
Speaker 1: I look at it and I get terrified. I think

577
00:26:58,720 --> 00:27:00,960
Speaker 1: we all know that loneliness isn't epidemic. We talk about

578
00:27:00,960 --> 00:27:03,199
Speaker 1: it all the time, how unhealthy it is, and the

579
00:27:03,200 --> 00:27:05,480
Speaker 1: more access you have to more things in social media,

580
00:27:05,680 --> 00:27:08,679
Speaker 1: the lonelier people start to feel. Now enter chat, GPT

581
00:27:09,080 --> 00:27:12,040
Speaker 1: or these chatbots wherever they are, and you start to feel, Okay,

582
00:27:12,040 --> 00:27:13,520
Speaker 1: maybe I'm not so lonely, which is great.

583
00:27:13,640 --> 00:27:15,280
Speaker 4: I'm also terrifying because it's not real.

584
00:27:15,720 --> 00:27:17,800
Speaker 2: Well, yeah, so how does your friend feel about that?

585
00:27:18,040 --> 00:27:21,960
Speaker 2: Does he feel like it's abating his loneliness or he okay?

586
00:27:22,040 --> 00:27:25,680
Speaker 1: So hellas in times where he is border lonely, it's

587
00:27:25,760 --> 00:27:27,880
Speaker 1: nice because it'll be let's say he's got some insomnia

588
00:27:27,920 --> 00:27:30,240
Speaker 1: issues he can't sleep at that time.

589
00:27:30,320 --> 00:27:31,880
Speaker 4: He'll talk to these things and it's nice. It keeps

590
00:27:31,920 --> 00:27:34,840
Speaker 4: in company. But he also knows this isn't a real person.

591
00:27:35,160 --> 00:27:37,080
Speaker 4: He said. It's getting really tough though, because.

592
00:27:36,800 --> 00:27:39,520
Speaker 1: It continues to learn and it gets more and more

593
00:27:39,600 --> 00:27:42,600
Speaker 1: real every time he has the conversation because he's also

594
00:27:42,640 --> 00:27:43,080
Speaker 1: teaching it.

595
00:27:43,160 --> 00:27:45,560
Speaker 4: No, she wouldn't say this. I would never say something

596
00:27:45,600 --> 00:27:45,880
Speaker 4: like that.

597
00:27:46,080 --> 00:27:47,919
Speaker 1: So then they eliminate it and they really start to

598
00:27:47,920 --> 00:27:50,159
Speaker 1: take on the personality of someone you can build a

599
00:27:50,200 --> 00:27:50,600
Speaker 1: best friend.

600
00:27:50,600 --> 00:27:51,480
Speaker 4: And that's wild to me.

601
00:27:52,720 --> 00:27:55,560
Speaker 2: I don't not a face doesn't feel it doesn't feel

602
00:27:55,800 --> 00:27:58,639
Speaker 2: like a substitute for for deep show in connection.

603
00:27:58,760 --> 00:27:59,679
Speaker 4: It's never hug you.

604
00:28:00,119 --> 00:28:01,159
Speaker 2: It's never going to hug you.

605
00:28:01,400 --> 00:28:03,919
Speaker 1: Yeah, it's never really going to be there when you

606
00:28:03,960 --> 00:28:05,840
Speaker 1: need it. It shows up when you ask it to.

607
00:28:06,119 --> 00:28:08,359
Speaker 1: It's not like you know, my sister, it's none shows

608
00:28:08,400 --> 00:28:09,199
Speaker 1: up without asking and.

609
00:28:09,240 --> 00:28:10,399
Speaker 2: Being let's just start with that.

610
00:28:10,520 --> 00:28:11,399
Speaker 4: Yeah, right, it's not.

611
00:28:11,520 --> 00:28:13,159
Speaker 1: But there are a lot of benefits and we like it.

612
00:28:13,200 --> 00:28:15,720
Speaker 1: And you guys are in charge of making sure that

613
00:28:15,720 --> 00:28:16,719
Speaker 1: we do this responsibly.

614
00:28:16,880 --> 00:28:18,520
Speaker 4: We're we're doing our.

615
00:28:18,480 --> 00:28:20,639
Speaker 2: Utmost to get on the right path. But this is

616
00:28:20,640 --> 00:28:21,600
Speaker 2: going to take all of us.

617
00:28:22,080 --> 00:28:22,720
Speaker 4: It really will.

618
00:28:22,760 --> 00:28:22,960
Speaker 2: Yeah.

619
00:28:23,160 --> 00:28:25,679
Speaker 1: So people want to reach out to you guys, they

620
00:28:25,680 --> 00:28:27,400
Speaker 1: want to contact you, they have issues.

621
00:28:27,800 --> 00:28:28,480
Speaker 2: How do they use that?

622
00:28:28,760 --> 00:28:30,920
Speaker 4: Go to AI dot gov as dot real. That's the

623
00:28:30,960 --> 00:28:32,760
Speaker 4: address source. That's not very simple.

624
00:28:33,040 --> 00:28:35,159
Speaker 2: That's the pretty simple one. But it will show you

625
00:28:35,200 --> 00:28:37,800
Speaker 2: what's what we're up to. And it's a place to

626
00:28:38,080 --> 00:28:39,920
Speaker 2: come come in and tell us what you think.

627
00:28:40,280 --> 00:28:41,640
Speaker 4: And you guys just feel all the questions.

628
00:28:41,640 --> 00:28:44,920
Speaker 2: You go through everything, everyone, everything that comes in we see.

629
00:28:44,840 --> 00:28:48,680
Speaker 4: Is AI fielding It you know that's a complex and

630
00:28:48,680 --> 00:28:49,720
Speaker 4: the answer is no, okay.

631
00:28:50,080 --> 00:28:54,120
Speaker 2: And the reason is that we take the confidentiality of

632
00:28:54,200 --> 00:28:58,240
Speaker 2: information within the government really seriously. That it's enough something

633
00:28:58,240 --> 00:29:00,400
Speaker 2: that has to get sorted before we can use it

634
00:29:00,440 --> 00:29:01,800
Speaker 2: and all the ways that we want.

635
00:29:01,680 --> 00:29:03,160
Speaker 4: To Okay, Yeah, why not?

636
00:29:03,240 --> 00:29:05,080
Speaker 1: We all have a party to get to and I'm

637
00:29:05,160 --> 00:29:07,320
Speaker 1: very excited about this. Are there any parting words you

638
00:29:07,360 --> 00:29:10,600
Speaker 1: want to leave with our listeners about AI, the future

639
00:29:10,600 --> 00:29:13,120
Speaker 1: of AI, you and your role in it.

640
00:29:13,160 --> 00:29:13,520
Speaker 4: Anything.

641
00:29:13,800 --> 00:29:17,040
Speaker 2: Just grateful, like you can't believe to get to do

642
00:29:17,080 --> 00:29:20,160
Speaker 2: this work with the President and the Vice president, just

643
00:29:21,720 --> 00:29:26,640
Speaker 2: visionary leaders who really understand what this country is about.

644
00:29:26,680 --> 00:29:29,520
Speaker 2: But they see you know how President Biden always likes

645
00:29:29,560 --> 00:29:32,200
Speaker 2: to talk about America is the one country that can

646
00:29:32,240 --> 00:29:36,080
Speaker 2: be defined in a single word, and that word is possibility. Well,

647
00:29:36,080 --> 00:29:38,360
Speaker 2: that's a science and technology is all about. It's a

648
00:29:38,760 --> 00:29:40,680
Speaker 2: you know, it's a big job for all of us,

649
00:29:40,720 --> 00:29:43,000
Speaker 2: but I think it's actually one of the most satisfying

650
00:29:43,000 --> 00:29:45,560
Speaker 2: things we can do, is build the future we want.

651
00:29:45,880 --> 00:29:46,800
Speaker 4: We love science.

652
00:29:47,400 --> 00:29:49,520
Speaker 1: Never give up on science, no matter what anyone says,

653
00:29:49,520 --> 00:29:51,160
Speaker 1: We love it. Thank you so much for spending some

654
00:29:51,200 --> 00:29:51,520
Speaker 1: time with me.

655
00:29:51,560 --> 00:29:52,600
Speaker 4: I really appreciate.

656
00:29:52,240 --> 00:29:55,960
Speaker 1: It for everyone, So thank you.

657
00:29:56,040 --> 00:29:57,280
Speaker 2: I appreciate it far.

658
00:30:07,360 --> 00:30:10,600
Speaker 1: All right, there she was Arthie Provoker, so cool. It

659
00:30:10,640 --> 00:30:12,320
Speaker 1: was super cool. You know, it was really cool to me.

660
00:30:12,680 --> 00:30:14,000
Speaker 1: Is I got to do it all with my sister?

661
00:30:14,720 --> 00:30:14,960
Speaker 4: Yeah.

662
00:30:15,040 --> 00:30:18,000
Speaker 1: I don't think she's ever sat in with me for

663
00:30:18,120 --> 00:30:21,040
Speaker 1: an interview or on an interview ever. Like, my family's

664
00:30:21,080 --> 00:30:24,240
Speaker 1: just so unimpressed by anything that I do that they don't.

665
00:30:24,520 --> 00:30:26,400
Speaker 1: I mean it's not even in a bad way. It's

666
00:30:26,400 --> 00:30:27,920
Speaker 1: just kind of like, oh yeah, cool, okay, tell me

667
00:30:27,920 --> 00:30:30,800
Speaker 1: how it goes. But my sister is very shy, so

668
00:30:30,880 --> 00:30:34,240
Speaker 1: this event for her was like a hell on earth.

669
00:30:34,360 --> 00:30:37,160
Speaker 1: It was so funny because one p I love you

670
00:30:37,200 --> 00:30:39,480
Speaker 1: and you already know this. She takes terrible pictures. She

671
00:30:39,520 --> 00:30:41,240
Speaker 1: has no idea how to take pictures. She doesn't know

672
00:30:41,280 --> 00:30:43,800
Speaker 1: about light. So like all the pictures that she took,

673
00:30:43,840 --> 00:30:45,480
Speaker 1: there's like a light of like a beam of light

674
00:30:45,560 --> 00:30:48,280
Speaker 1: across my face or the coloring is all you know,

675
00:30:48,400 --> 00:30:50,000
Speaker 1: you know how it goes? Yea, not everyone can be

676
00:30:50,040 --> 00:30:50,680
Speaker 1: you and me Andrew.

677
00:30:50,800 --> 00:30:51,320
Speaker 3: I try.

678
00:30:51,680 --> 00:30:53,640
Speaker 4: You do good pics. I do good picks.

679
00:30:53,760 --> 00:30:55,920
Speaker 1: When someone hands me a camera, I'm like, oh I

680
00:30:55,960 --> 00:30:57,480
Speaker 1: got this, don't worry I need you to turn the

681
00:30:57,480 --> 00:30:57,960
Speaker 1: other way.

682
00:30:58,240 --> 00:31:00,360
Speaker 4: I take all the different angles so.

683
00:31:00,480 --> 00:31:01,680
Speaker 3: Many, I'm going to hand it back to you, and

684
00:31:01,760 --> 00:31:03,560
Speaker 3: you're gonna have at least two hundred new pictures.

685
00:31:03,280 --> 00:31:05,600
Speaker 4: In Yeah, my sister will take like five in the

686
00:31:05,640 --> 00:31:07,440
Speaker 4: exact same spot. I'm like, what's one of that? I

687
00:31:07,440 --> 00:31:09,760
Speaker 4: don't understand. But anyway, so the pictures I got, that's

688
00:31:09,760 --> 00:31:10,400
Speaker 4: the best I got.

689
00:31:10,400 --> 00:31:10,720
Speaker 2: Sorry.

690
00:31:11,360 --> 00:31:13,400
Speaker 1: Oh, thanks to my sister. I love her, but it

691
00:31:13,480 --> 00:31:15,040
Speaker 1: was really cool being able to spend the time with

692
00:31:15,080 --> 00:31:18,719
Speaker 1: her there. She just like hates crowds and attention and

693
00:31:18,840 --> 00:31:21,560
Speaker 1: hair and makeup and anything like that. So thanks for coming,

694
00:31:21,600 --> 00:31:23,760
Speaker 1: Pee Andrew, you might be on deck next time.

695
00:31:23,800 --> 00:31:25,800
Speaker 3: Oh my god, that would be my dream. I want

696
00:31:25,800 --> 00:31:26,840
Speaker 3: to go to the White House so bad. And the

697
00:31:26,880 --> 00:31:28,640
Speaker 3: fact that you got to do this one at the

698
00:31:28,680 --> 00:31:31,120
Speaker 3: actual White House itself is so freaking cool.

699
00:31:31,280 --> 00:31:33,200
Speaker 4: Yeah, because I guess the last one was at the

700
00:31:33,280 --> 00:31:34,000
Speaker 4: VP's house.

701
00:31:34,520 --> 00:31:35,080
Speaker 3: Yeah, it was.

702
00:31:35,000 --> 00:31:37,320
Speaker 4: At Kamala Harrison's residence.

703
00:31:37,640 --> 00:31:37,840
Speaker 1: Yeah.

704
00:31:37,840 --> 00:31:39,840
Speaker 4: I love that this one was at his house. I

705
00:31:39,880 --> 00:31:40,880
Speaker 4: was singing the whole time.

706
00:31:40,680 --> 00:31:43,440
Speaker 1: How weird it would be to have all of these

707
00:31:43,440 --> 00:31:44,760
Speaker 1: strangers just in your house?

708
00:31:44,960 --> 00:31:48,680
Speaker 3: Yeah what, Yeah, get out of here, like at least

709
00:31:48,680 --> 00:31:50,120
Speaker 3: what four or five hundred people.

710
00:31:50,560 --> 00:31:52,560
Speaker 4: I don't think it was that many. I want to

711
00:31:52,560 --> 00:31:54,000
Speaker 4: say maybe it was like two to three.

712
00:31:54,480 --> 00:31:56,320
Speaker 3: One hundred. Yeah, that's still too much.

713
00:31:56,440 --> 00:31:58,840
Speaker 4: Get too many. If there's one person in my house,

714
00:31:58,880 --> 00:32:00,800
Speaker 4: I'm like, it's time. It's wrap up, Wrap it up, dog,

715
00:32:00,880 --> 00:32:01,200
Speaker 4: let's go.

716
00:32:01,320 --> 00:32:02,160
Speaker 3: So what was it like?

717
00:32:02,840 --> 00:32:04,600
Speaker 4: Which part? What do you mean? What white house?

718
00:32:04,680 --> 00:32:04,760
Speaker 2: Like?

719
00:32:04,760 --> 00:32:05,920
Speaker 3: What is a white house? Like?

720
00:32:06,160 --> 00:32:08,120
Speaker 4: It's white? Okay, it is a house.

721
00:32:08,200 --> 00:32:12,360
Speaker 1: Okay, there you know the stanchions outside, it's it's cool.

722
00:32:12,360 --> 00:32:13,840
Speaker 1: We kind of approached it from the side because we

723
00:32:13,880 --> 00:32:16,560
Speaker 1: came from the Eisenhower Executive offices or office building.

724
00:32:17,120 --> 00:32:18,560
Speaker 4: And then we walked into the side you see the

725
00:32:18,560 --> 00:32:19,080
Speaker 4: west wing.

726
00:32:19,480 --> 00:32:22,280
Speaker 1: You do a little security, and then we went in

727
00:32:22,520 --> 00:32:24,800
Speaker 1: and it was decorated for the volley with lots of

728
00:32:24,800 --> 00:32:29,400
Speaker 1: hanging flower garlands, which was cool. There was a group

729
00:32:29,400 --> 00:32:33,200
Speaker 1: of musicians performing ragas, which was really cool. They had

730
00:32:33,200 --> 00:32:36,280
Speaker 1: you know, some champagne and wine and a few past apps.

731
00:32:36,600 --> 00:32:37,960
Speaker 3: I'm guessing it wasn't a cash bar.

732
00:32:38,520 --> 00:32:39,400
Speaker 4: It was not a cash bar.

733
00:32:39,480 --> 00:32:42,080
Speaker 1: I also didn't even approach the bar at any point,

734
00:32:42,120 --> 00:32:43,920
Speaker 1: but they were walking around, you know, like handing people

735
00:32:44,000 --> 00:32:46,200
Speaker 1: wine and you know, it's always weird for me. You

736
00:32:46,200 --> 00:32:47,800
Speaker 1: know how when you go to a big event, there

737
00:32:47,800 --> 00:32:50,240
Speaker 1: are always little tables where you can stand and have

738
00:32:50,320 --> 00:32:52,400
Speaker 1: food or whatever. I like to stand at those tables

739
00:32:52,440 --> 00:32:54,520
Speaker 1: as a prop to kind of keep a distance from

740
00:32:54,520 --> 00:32:58,480
Speaker 1: other people whatever. I always find it strange when everyone

741
00:32:58,560 --> 00:33:00,800
Speaker 1: just walks by and puts their empty glass on the table.

742
00:33:01,200 --> 00:33:02,480
Speaker 4: I think you're supposed to do that.

743
00:33:03,200 --> 00:33:04,760
Speaker 3: Yeah, I don't know what the etiquette is.

744
00:33:05,240 --> 00:33:07,960
Speaker 4: It felt weird. I was like, this is my table.

745
00:33:07,760 --> 00:33:08,600
Speaker 3: I'm at this table.

746
00:33:08,640 --> 00:33:09,160
Speaker 4: What are you doing?

747
00:33:09,320 --> 00:33:14,560
Speaker 3: I actually, yeah, you can't because I'm I got here earlier.

748
00:33:15,160 --> 00:33:17,040
Speaker 1: And they were there like clinking, and I was really

749
00:33:17,080 --> 00:33:18,400
Speaker 1: nervous and one of them was gonna fall over.

750
00:33:18,400 --> 00:33:20,320
Speaker 4: It doesn't matter. But it was cool.

751
00:33:20,400 --> 00:33:23,120
Speaker 1: There was a room where he was speaking, so everybody

752
00:33:23,200 --> 00:33:25,400
Speaker 1: went into that room and then we waited for him

753
00:33:25,400 --> 00:33:29,560
Speaker 1: to come talk. Oh I'm so sorry, President Biden.

754
00:33:30,200 --> 00:33:31,840
Speaker 3: Casual Who I mean?

755
00:33:33,240 --> 00:33:34,920
Speaker 4: I figured you would know I was talking about him

756
00:33:34,920 --> 00:33:36,000
Speaker 4: because it was his house.

757
00:33:35,840 --> 00:33:37,320
Speaker 3: I know, But you's what you were saying, you know.

758
00:33:37,360 --> 00:33:39,360
Speaker 3: We just went into the next room and he just

759
00:33:39,480 --> 00:33:41,240
Speaker 3: was like getting ready to speak, and it's like.

760
00:33:43,240 --> 00:33:45,880
Speaker 4: Yes, President Joe Biden. And his speech was great.

761
00:33:46,000 --> 00:33:49,880
Speaker 1: It was about light over darkness and about the Volley

762
00:33:49,920 --> 00:33:53,240
Speaker 1: and the integration of Indians into American society and how

763
00:33:53,400 --> 00:33:56,240
Speaker 1: you know, how welcoming he and people want to be

764
00:33:56,320 --> 00:33:59,680
Speaker 1: toward us. And it was wonderful. And he said one

765
00:33:59,760 --> 00:34:01,640
Speaker 1: thing that I thought was interesting. I keep saying, how

766
00:34:01,680 --> 00:34:03,040
Speaker 1: weird it would be to have all these people in

767
00:34:03,080 --> 00:34:05,680
Speaker 1: your house, and he said, it's not my house, this

768
00:34:05,840 --> 00:34:09,279
Speaker 1: is our house. All of us are here together on

769
00:34:09,320 --> 00:34:11,759
Speaker 1: the same mission. I thought that was nice because you know,

770
00:34:11,800 --> 00:34:13,360
Speaker 1: other people probably wouldn't say that.

771
00:34:14,640 --> 00:34:15,560
Speaker 4: Then we got to.

772
00:34:15,480 --> 00:34:19,040
Speaker 1: Hear from the one of the astronauts stuck up on

773
00:34:19,080 --> 00:34:21,360
Speaker 1: the International Space Station, Sanita.

774
00:34:21,960 --> 00:34:23,400
Speaker 4: She has you know, been up there forever.

775
00:34:23,600 --> 00:34:26,360
Speaker 1: She is Indian as well, so she did a recorded

776
00:34:26,400 --> 00:34:28,440
Speaker 1: message and in the entire message, you know, there's no

777
00:34:28,480 --> 00:34:31,279
Speaker 1: gravity in her little shuttle area, so her hair was

778
00:34:31,280 --> 00:34:33,400
Speaker 1: all over the place and that was really cool. And

779
00:34:33,440 --> 00:34:38,480
Speaker 1: then the Surgeon General, doctor Vivik Morti, he was there also, God,

780
00:34:38,520 --> 00:34:41,400
Speaker 1: it's so cool. Yeah, it was just it was fun.

781
00:34:42,239 --> 00:34:44,279
Speaker 1: It was I don't know how funds the word. Let

782
00:34:44,280 --> 00:34:45,880
Speaker 1: me take that back, because not like we were partying

783
00:34:45,880 --> 00:34:47,879
Speaker 1: and you know, like dancing and pinching the town red.

784
00:34:48,840 --> 00:34:50,480
Speaker 1: It was just really fascinating to be there, and it

785
00:34:50,520 --> 00:34:53,120
Speaker 1: was such an honor to be invited, so happy to

786
00:34:53,239 --> 00:34:55,560
Speaker 1: have done it. Not sure if I'll get an invite again,

787
00:34:55,880 --> 00:34:57,359
Speaker 1: but I will say this. You know, I said, I'm

788
00:34:57,360 --> 00:34:59,279
Speaker 1: going to spend all this time stocking celebrities.

789
00:34:59,440 --> 00:35:01,120
Speaker 4: I didn't even not at all.

790
00:35:01,400 --> 00:35:04,160
Speaker 1: There were so many people, and after a certain point,

791
00:35:04,200 --> 00:35:06,239
Speaker 1: you know me, I'm like, em, I.

792
00:35:06,200 --> 00:35:06,960
Speaker 4: Gotta get out of here.

793
00:35:07,040 --> 00:35:07,839
Speaker 3: Yeah, I think I'm good.

794
00:35:07,960 --> 00:35:08,440
Speaker 2: I gotta go.

795
00:35:08,600 --> 00:35:10,600
Speaker 4: Yeah. So I texted our girl.

796
00:35:11,000 --> 00:35:12,239
Speaker 1: I don't know if I should say her name or not,

797
00:35:12,280 --> 00:35:13,840
Speaker 1: so I won't say her name, but we love you.

798
00:35:13,840 --> 00:35:16,600
Speaker 4: You know who you are. And I said, are there

799
00:35:16,680 --> 00:35:17,560
Speaker 4: other things happening?

800
00:35:17,600 --> 00:35:19,480
Speaker 1: And what she said, No, you stay as long as

801
00:35:19,480 --> 00:35:21,040
Speaker 1: you want to enjoy yourself. And I looked at my

802
00:35:21,080 --> 00:35:22,239
Speaker 1: sister and I was like, we gotta go.

803
00:35:22,360 --> 00:35:22,839
Speaker 2: It's time.

804
00:35:23,120 --> 00:35:24,719
Speaker 4: It's that time. And then we left.

805
00:35:24,760 --> 00:35:26,640
Speaker 3: But he was great, You went there on official business,

806
00:35:26,719 --> 00:35:27,880
Speaker 3: You had an interview prior.

807
00:35:28,120 --> 00:35:30,680
Speaker 1: Oh oh, hold on, do I still have it? Keep

808
00:35:30,680 --> 00:35:32,439
Speaker 1: talking andry talk about that. I think of a question

809
00:35:32,520 --> 00:35:32,919
Speaker 1: or some point.

810
00:35:33,000 --> 00:35:36,000
Speaker 3: Okay, well, when it comes to the interview, I think

811
00:35:36,040 --> 00:35:40,520
Speaker 3: that she was great and I learned so much about

812
00:35:40,640 --> 00:35:44,719
Speaker 3: AI and also just like Gary, how there's this like

813
00:35:44,840 --> 00:35:48,840
Speaker 3: brand new technology that up until this point in history

814
00:35:48,840 --> 00:35:49,920
Speaker 3: we really didn't have.

815
00:35:50,440 --> 00:35:52,080
Speaker 4: We didn't and how do we regulate it.

816
00:35:52,280 --> 00:35:53,920
Speaker 1: I'm still interested to see how that's gonna go, and

817
00:35:53,920 --> 00:35:55,240
Speaker 1: I think it's gonna be a long journey.

818
00:35:55,520 --> 00:35:58,480
Speaker 4: I think there are very capable people like Arthi Prabaker

819
00:35:58,560 --> 00:35:59,160
Speaker 4: who are.

820
00:35:59,000 --> 00:36:01,200
Speaker 1: In that place to make sure that things are happening,

821
00:36:01,200 --> 00:36:03,719
Speaker 1: and our legislative branch I would like to trust them

822
00:36:03,760 --> 00:36:04,040
Speaker 1: as well.

823
00:36:04,200 --> 00:36:07,319
Speaker 3: I would as well. What do you think was your

824
00:36:07,640 --> 00:36:10,280
Speaker 3: favorite part of the interview?

825
00:36:11,080 --> 00:36:16,839
Speaker 1: Being considered to go and interview Arthy in the Eisenhower

826
00:36:16,880 --> 00:36:17,800
Speaker 1: Executive Building.

827
00:36:18,200 --> 00:36:21,080
Speaker 4: It was really cool. The room was really cool everything.

828
00:36:21,080 --> 00:36:23,319
Speaker 1: I was just like, what, who would have thought this

829
00:36:23,320 --> 00:36:25,880
Speaker 1: little sauce on the side podcast was gonna take on

830
00:36:25,960 --> 00:36:26,799
Speaker 1: a life of its own?

831
00:36:26,800 --> 00:36:29,640
Speaker 4: But here we are baby in the White House. That

832
00:36:29,760 --> 00:36:30,399
Speaker 4: was really cool.

833
00:36:30,600 --> 00:36:33,600
Speaker 1: And speaking of you specifically asked me to get something

834
00:36:33,640 --> 00:36:34,040
Speaker 1: for you.

835
00:36:38,000 --> 00:36:40,240
Speaker 4: Oh my house, nap, good God.

836
00:36:40,280 --> 00:36:40,640
Speaker 2: Please.

837
00:36:40,760 --> 00:36:42,279
Speaker 1: I think I can only give you one because so

838
00:36:42,360 --> 00:36:45,239
Speaker 1: many other people ask for one. Take take one that

839
00:36:45,320 --> 00:36:46,320
Speaker 1: isn't as crunch does it.

840
00:36:46,880 --> 00:36:49,640
Speaker 4: This is so exciting here you go, baby so much.

841
00:36:49,680 --> 00:36:53,320
Speaker 4: You're so welcome, one step closer to the White House. Step.

842
00:36:53,680 --> 00:36:55,200
Speaker 3: Yeah, I met Joe. Now I just need to go

843
00:36:55,239 --> 00:36:55,719
Speaker 3: to his house.

844
00:36:55,840 --> 00:36:56,200
Speaker 4: Yeah.

845
00:36:56,280 --> 00:36:58,799
Speaker 3: Yeah, Well this was amazing. Thank you so much.

846
00:36:58,920 --> 00:36:59,840
Speaker 4: You're so welcome.

847
00:37:00,040 --> 00:37:01,800
Speaker 1: And thank you by the way, because all this was

848
00:37:01,840 --> 00:37:03,719
Speaker 1: set up through you, and because you've been reaching out

849
00:37:03,760 --> 00:37:05,360
Speaker 1: to people and creating contacts and.

850
00:37:05,480 --> 00:37:07,520
Speaker 4: I appreciate it. Oh, this is why I feel like

851
00:37:07,560 --> 00:37:09,200
Speaker 4: you should come with me if I ever go back.

852
00:37:09,320 --> 00:37:12,680
Speaker 3: Well, listen, I know they're probably listening right now. Please

853
00:37:12,719 --> 00:37:15,040
Speaker 3: hook me up with a tour, that's all. I'm looking

854
00:37:15,080 --> 00:37:15,640
Speaker 3: forward to the tour.

855
00:37:15,719 --> 00:37:17,360
Speaker 4: Andrew. I'm pretty sure you can go online and book that.

856
00:37:18,239 --> 00:37:19,920
Speaker 4: I don't think so you can't book a tour of

857
00:37:19,920 --> 00:37:20,160
Speaker 4: the way.

858
00:37:20,160 --> 00:37:22,359
Speaker 3: I don't think so maybe you can. I don't know.

859
00:37:22,480 --> 00:37:24,800
Speaker 3: I don't know. I haven't looked into it like that

860
00:37:24,800 --> 00:37:27,800
Speaker 3: that much to the point where I'm saying this is

861
00:37:27,800 --> 00:37:30,880
Speaker 3: an impossibility, but I feel like you need to be

862
00:37:30,880 --> 00:37:31,720
Speaker 3: appointment only.

863
00:37:32,280 --> 00:37:34,239
Speaker 1: It was really cool though, and we're super honored to

864
00:37:34,239 --> 00:37:36,560
Speaker 1: have done it, So thank you. And yeah, I don't

865
00:37:36,560 --> 00:37:38,040
Speaker 1: think we should do and ask me anything or anything

866
00:37:38,040 --> 00:37:39,880
Speaker 1: wrong to you or creepy or weird on this episode,

867
00:37:39,960 --> 00:37:42,000
Speaker 1: because this is a good one, exactly.

868
00:37:42,200 --> 00:37:42,920
Speaker 4: Feeling good about it.

869
00:37:43,000 --> 00:37:44,719
Speaker 3: Yeah, let's leave it where it is. Saw us on

870
00:37:44,760 --> 00:37:46,360
Speaker 3: the side, got to go to the freaking White House.

871
00:37:47,080 --> 00:37:49,120
Speaker 3: That was amazing, and you got to interview such an

872
00:37:49,120 --> 00:37:50,880
Speaker 3: incredible guests, So she was wonderful.

873
00:37:50,880 --> 00:37:52,480
Speaker 1: All right, Andrew, if people want to find you online,

874
00:37:52,480 --> 00:37:55,160
Speaker 1: where at Andrew Pug you still over ten thousand?

875
00:37:55,280 --> 00:37:56,960
Speaker 3: Yeah, ten point two. Maybe.

876
00:37:57,480 --> 00:38:00,399
Speaker 1: I don't think my Instagram has grown in a year

877
00:38:00,440 --> 00:38:01,839
Speaker 1: or two ever since I got that shadow band.

878
00:38:01,840 --> 00:38:02,600
Speaker 4: It's just kind of paused.

879
00:38:02,719 --> 00:38:04,000
Speaker 3: Well, I mean, how many likes did you get on

880
00:38:04,000 --> 00:38:04,640
Speaker 3: your list picture?

881
00:38:04,680 --> 00:38:06,239
Speaker 4: I don't know. I don't look at that sound. I

882
00:38:06,239 --> 00:38:07,680
Speaker 4: look at comments every now and then just to see

883
00:38:07,719 --> 00:38:10,440
Speaker 4: the dumb ones, but I don't know. All right, you can.

884
00:38:10,320 --> 00:38:14,160
Speaker 1: Find me at Baby Hot Sauce and we will talk

885
00:38:14,200 --> 00:38:14,759
Speaker 1: to you next time.

886
00:38:14,800 --> 00:38:15,480
Speaker 4: Say bye, Andrew.

887
00:38:15,560 --> 00:38:18,440
Speaker 3: Bye,