1
00:00:01,720 --> 00:00:07,520
Speaker 1: Al Zone Media.

2
00:00:06,120 --> 00:00:10,040
Speaker 2: Sweet Baby, Jiminy Christmas, Welcome back to It Could Happen

3
00:00:10,119 --> 00:00:14,120
Speaker 2: Here a podcast. It's normally about all of the sad

4
00:00:14,160 --> 00:00:18,400
Speaker 2: and horrifying and violent and dangerous and sometimes inspiring things

5
00:00:18,480 --> 00:00:21,720
Speaker 2: happening around the world. But this week, well, today is

6
00:00:21,760 --> 00:00:27,560
Speaker 2: about something different. Today We're talking about ces finally, for

7
00:00:27,680 --> 00:00:29,400
Speaker 2: those of you who don't know or who are new

8
00:00:29,440 --> 00:00:32,960
Speaker 2: to the show. Every year in Las Vegas, Nevada, a

9
00:00:33,159 --> 00:00:35,920
Speaker 2: bunch of the world's big tech companies come together for

10
00:00:36,000 --> 00:00:39,519
Speaker 2: the Consumer Electronics Show, where they present their visions for

11
00:00:39,600 --> 00:00:42,240
Speaker 2: the future, the new products that will be coming out

12
00:00:42,280 --> 00:00:44,080
Speaker 2: that year, and stuff that will be coming out and

13
00:00:44,159 --> 00:00:47,199
Speaker 2: used to come that's less developed, and the whole industry

14
00:00:47,240 --> 00:00:50,879
Speaker 2: talks about itself, and Garrison and I show up and

15
00:00:52,000 --> 00:00:54,920
Speaker 2: largely just kind of let it wash over us like

16
00:00:55,840 --> 00:01:02,480
Speaker 2: a warming tide of lukewarm garbage water, fairy lukewarmy, very lukewarm,

17
00:01:02,600 --> 00:01:06,280
Speaker 2: and it smells like someone did not clean their fridge

18
00:01:06,280 --> 00:01:08,759
Speaker 2: out often enough before putting it into the trash.

19
00:01:08,840 --> 00:01:13,560
Speaker 3: That was the feeling of showstoppers tonight, the media only

20
00:01:14,400 --> 00:01:16,680
Speaker 3: presentation on the finest products of CEES.

21
00:01:16,880 --> 00:01:19,160
Speaker 2: Yeah, yeah, why don't we start? So, I mean I mean,

22
00:01:19,520 --> 00:01:22,320
Speaker 2: there's two different things that are interesting about CEES broadly.

23
00:01:22,360 --> 00:01:24,760
Speaker 2: One of them is people bring gadgets that are not

24
00:01:24,880 --> 00:01:26,960
Speaker 2: out yet that it will be coming out this year

25
00:01:27,080 --> 00:01:29,520
Speaker 2: or coming out soon, and you can actually test them

26
00:01:29,520 --> 00:01:33,160
Speaker 2: and use them and see how technology is progressing, and

27
00:01:33,200 --> 00:01:35,880
Speaker 2: that can be kind of fun. The downside of that

28
00:01:36,040 --> 00:01:39,479
Speaker 2: is that people also bring gadgets that are crap. Right,

29
00:01:39,680 --> 00:01:41,560
Speaker 2: some guy has a vision for a way to like,

30
00:01:42,120 --> 00:01:44,919
Speaker 2: you know, there's not a good way for blind people

31
00:01:45,000 --> 00:01:48,440
Speaker 2: to use the pogo stick while watching Netflix, and so

32
00:01:48,560 --> 00:01:51,080
Speaker 2: I have created this product, right, Or like, there's not

33
00:01:51,200 --> 00:01:54,360
Speaker 2: a good way for children to test their blood alcohol

34
00:01:54,480 --> 00:01:57,120
Speaker 2: level before getting behind the wheel of a jeep Grand Cherokee,

35
00:01:57,200 --> 00:01:59,320
Speaker 2: and I have invented the device to make it about

36
00:01:59,400 --> 00:02:03,840
Speaker 2: things that, like I have no conceivable audience or utilization. Right.

37
00:02:04,080 --> 00:02:06,520
Speaker 2: That's the other side of the gadget part of CEES.

38
00:02:06,600 --> 00:02:08,519
Speaker 2: And then outside of that you get a hint at

39
00:02:08,560 --> 00:02:10,920
Speaker 2: like there's all these panels where people from the industry

40
00:02:10,919 --> 00:02:13,400
Speaker 2: come to talk about the major trends in technology, how

41
00:02:13,400 --> 00:02:15,720
Speaker 2: things are developing, and what they see as the future.

42
00:02:15,760 --> 00:02:18,440
Speaker 2: And so there's both here's what they're going to try

43
00:02:18,480 --> 00:02:20,640
Speaker 2: to sell us and here's the devices that might change

44
00:02:20,680 --> 00:02:23,200
Speaker 2: the way we live. And also here's how a bunch

45
00:02:23,240 --> 00:02:25,960
Speaker 2: of the richest, sometimes craziest people in the country you're

46
00:02:26,000 --> 00:02:28,320
Speaker 2: talking about the future. Those are the two things that

47
00:02:28,400 --> 00:02:31,440
Speaker 2: happen at CES and Garrison. You wanted to talk about

48
00:02:31,440 --> 00:02:35,280
Speaker 2: the first the gadgets. The gadgets, the gadgets one as

49
00:02:35,320 --> 00:02:37,080
Speaker 2: you went to the gadget show tonight. I spent my

50
00:02:37,240 --> 00:02:38,320
Speaker 2: entire day in panels.

51
00:02:38,800 --> 00:02:40,480
Speaker 4: Yeah, I mean I did most of the panels in

52
00:02:40,520 --> 00:02:40,799
Speaker 4: the day.

53
00:02:40,800 --> 00:02:43,400
Speaker 3: I didn't really got to watch the show floor on

54
00:02:43,440 --> 00:02:46,359
Speaker 3: the first day, which is which is Tuesday. So instead

55
00:02:46,360 --> 00:02:49,120
Speaker 3: of doing the show floor, I went to show stoppers

56
00:02:49,120 --> 00:02:53,960
Speaker 3: at the Bellagio, which is this presentation of Usually usually

57
00:02:54,440 --> 00:02:56,880
Speaker 3: you know, a collection of gadgets that have won CES

58
00:02:56,960 --> 00:03:01,120
Speaker 3: Innovation Awards, which are on display or for journalists and

59
00:03:01,200 --> 00:03:04,240
Speaker 3: media you can talk to the people behind them choicetoppers.

60
00:03:04,280 --> 00:03:06,520
Speaker 3: This year was a little different. It took place like.

61
00:03:06,520 --> 00:03:08,400
Speaker 4: A in a in like a different venue. Haul was

62
00:03:08,520 --> 00:03:12,600
Speaker 4: smaller than the past few showstoppers years, and I would

63
00:03:12,639 --> 00:03:15,160
Speaker 4: say about forty percent of it with smart glasses.

64
00:03:16,160 --> 00:03:19,280
Speaker 2: Yeah, there's usually like a big product that is like

65
00:03:19,360 --> 00:03:22,400
Speaker 2: this product category is the hot thing. This year, we've

66
00:03:22,520 --> 00:03:25,399
Speaker 2: tried all smart glasses every year, it is that they've

67
00:03:25,400 --> 00:03:26,200
Speaker 2: always had them.

68
00:03:26,280 --> 00:03:27,040
Speaker 4: Every year that.

69
00:03:27,160 --> 00:03:29,639
Speaker 3: We've been doing this, we've done smart glasses, and they've

70
00:03:29,639 --> 00:03:32,400
Speaker 3: always kind of been the same. Maybe the resolution on

71
00:03:32,480 --> 00:03:36,440
Speaker 3: like the text better, like the glasses get a little

72
00:03:36,440 --> 00:03:38,800
Speaker 3: bit smaller, and then this year, yeah, the glasses were

73
00:03:38,800 --> 00:03:42,360
Speaker 3: generally smaller, but for all practical purposes function about the same.

74
00:03:42,640 --> 00:03:44,720
Speaker 3: But there was but ten different smart glasses. Most of

75
00:03:44,760 --> 00:03:47,880
Speaker 3: them could do some kind of like transcription service, could

76
00:03:47,920 --> 00:03:50,040
Speaker 3: have some kind of heads up display. One of them

77
00:03:50,080 --> 00:03:52,280
Speaker 3: was just audio only. It was like an audio audio

78
00:03:52,320 --> 00:03:55,240
Speaker 3: transcriptions like it like it listens to someone else speaking,

79
00:03:55,280 --> 00:03:57,800
Speaker 3: in this case Chinese, and it would translates to me

80
00:03:58,160 --> 00:04:02,080
Speaker 3: to American. Yeah, yeah, translate to American via sound. It

81
00:04:02,120 --> 00:04:04,240
Speaker 3: had speakers. It had speakers, and like the actual you know,

82
00:04:04,280 --> 00:04:07,040
Speaker 3: like the arm of the glasses, the delay was long

83
00:04:07,160 --> 00:04:09,160
Speaker 3: enough that it was you couldn't really keep a conversation

84
00:04:09,200 --> 00:04:13,320
Speaker 3: of a normal speed like the visual translations, which you

85
00:04:13,360 --> 00:04:15,760
Speaker 3: can't actually kind of just talk in full time. But

86
00:04:15,840 --> 00:04:18,880
Speaker 3: the audio only ones were like a smaller profile. The

87
00:04:18,960 --> 00:04:22,600
Speaker 3: visual ones weren't necessarily bulkier, but you can definitely see that.

88
00:04:22,560 --> 00:04:23,920
Speaker 4: There's more hardware inside them.

89
00:04:24,000 --> 00:04:26,080
Speaker 3: Yeah, the thing that I have seen this year which

90
00:04:26,120 --> 00:04:30,200
Speaker 3: is newer, er, maybe not totally new, but incorporating smart

91
00:04:30,200 --> 00:04:34,240
Speaker 3: glasses technology into other types of eyewear, so like swim goggles,

92
00:04:34,440 --> 00:04:39,040
Speaker 3: ski goggles, like outdoor sports stuff. So if you're you know,

93
00:04:39,279 --> 00:04:42,600
Speaker 3: swimming or you're diving and you can't really use your

94
00:04:42,600 --> 00:04:45,120
Speaker 3: phone underwater, you have there's there's a heads up that

95
00:04:45,240 --> 00:04:48,120
Speaker 3: there's a heads up display in like your like Scoopa goggles.

96
00:04:48,480 --> 00:04:51,240
Speaker 4: So that that's that's a new, a newish thing that

97
00:04:51,279 --> 00:04:51,760
Speaker 4: I've seen.

98
00:04:52,279 --> 00:04:56,160
Speaker 3: I've seen like you know, biking glasses, skiing, snowboarding, so

99
00:04:56,279 --> 00:04:59,680
Speaker 3: that that's the kind of one one slight change. But

100
00:04:59,839 --> 00:05:02,480
Speaker 3: in besides that, it's basically five different smart glasses which

101
00:05:02,520 --> 00:05:05,880
Speaker 3: are for all pactal purposes identical right to each other.

102
00:05:06,160 --> 00:05:09,240
Speaker 2: Yeah, I mean, and that I think is kind of

103
00:05:09,240 --> 00:05:11,760
Speaker 2: one of the things that I've watched happen over the

104
00:05:12,440 --> 00:05:15,240
Speaker 2: fifteen years almost that I've been going to cess or

105
00:05:15,240 --> 00:05:18,760
Speaker 2: cess whichever is more accurate, which is, you know, when

106
00:05:18,800 --> 00:05:21,240
Speaker 2: I first started coming, the smartphone era was new, and

107
00:05:21,279 --> 00:05:23,600
Speaker 2: then we had like the tablet era after that, and

108
00:05:23,640 --> 00:05:26,040
Speaker 2: so there was a lot of like you would have

109
00:05:26,120 --> 00:05:29,240
Speaker 2: dozens of manufacturers making different devices, and every year there

110
00:05:29,279 --> 00:05:32,280
Speaker 2: were very different capability. For the first few years, smartphones

111
00:05:32,320 --> 00:05:35,640
Speaker 2: were out advanced very rapidly, and that was really exciting,

112
00:05:35,680 --> 00:05:39,719
Speaker 2: and the conventionally thrived on that as the number of

113
00:05:39,760 --> 00:05:42,680
Speaker 2: new device categories of winnowed down and the difference like

114
00:05:42,760 --> 00:05:45,159
Speaker 2: I'm not excited when I get a phone anymore now

115
00:05:45,240 --> 00:05:45,920
Speaker 2: there's anyone I.

116
00:05:45,880 --> 00:05:48,000
Speaker 3: Know, because it's like no, usually I'm actually kind of

117
00:05:48,200 --> 00:05:49,120
Speaker 3: yeah more sad.

118
00:05:49,240 --> 00:05:49,440
Speaker 2: Yeah.

119
00:05:49,520 --> 00:05:49,680
Speaker 3: Yeah.

120
00:05:49,800 --> 00:05:51,240
Speaker 2: The only thing that's exciting is like, well, my old

121
00:05:51,279 --> 00:05:52,960
Speaker 2: phone was literally not working anymore.

122
00:05:53,040 --> 00:05:55,840
Speaker 4: Yeah the phone work, the battery has been completely destroyed.

123
00:05:55,880 --> 00:05:57,800
Speaker 2: Yeah the battery works now or whatever. But it's not

124
00:05:57,839 --> 00:06:01,119
Speaker 2: like the cameras are not ce changes better. Generally, nothing

125
00:06:01,240 --> 00:06:03,880
Speaker 2: is like you're not getting a lot more out of

126
00:06:03,920 --> 00:06:06,400
Speaker 2: it than you used to. And the same mystruy of

127
00:06:06,440 --> 00:06:09,480
Speaker 2: like laptops, i mean, graphics cards just because of the

128
00:06:10,480 --> 00:06:13,280
Speaker 2: data center crunch, like that's not nearly as exciting a

129
00:06:13,320 --> 00:06:15,760
Speaker 2: technology category for consumers as it used to be. So

130
00:06:16,279 --> 00:06:20,000
Speaker 2: this stuff is just like less less sexy, and yeah,

131
00:06:20,320 --> 00:06:21,960
Speaker 2: it just kind of shows that we're at a point

132
00:06:21,960 --> 00:06:23,960
Speaker 2: where kind of one of the only spaces where they

133
00:06:24,000 --> 00:06:26,039
Speaker 2: are still making improvements and where there's a lot of

134
00:06:26,040 --> 00:06:27,960
Speaker 2: competition in the market is smart glasses.

135
00:06:28,160 --> 00:06:31,000
Speaker 3: Yeah, I mean that's like the wearables category in general, Yeah,

136
00:06:31,000 --> 00:06:35,159
Speaker 3: which was mentioned. I went to the Consumer Technology Association

137
00:06:35,440 --> 00:06:38,800
Speaker 3: like keynote panel this morning, which is the group that

138
00:06:38,880 --> 00:06:42,400
Speaker 3: puts on cees, and they mentioned only only a few products,

139
00:06:42,440 --> 00:06:45,560
Speaker 3: but one of them we're smart glasses. And then also

140
00:06:45,640 --> 00:06:49,360
Speaker 3: like wearables in general, like AI powered wearables and now

141
00:06:49,400 --> 00:06:53,680
Speaker 3: like wearable technology. You know, it's like smart watches, rings, necklaces,

142
00:06:53,720 --> 00:06:56,160
Speaker 3: whatever are going to make like a big comeback now

143
00:06:56,200 --> 00:06:59,039
Speaker 3: that now that AI is a lot is a lot

144
00:06:59,080 --> 00:07:02,920
Speaker 3: more intelligent, and it used to be in particular at

145
00:07:02,920 --> 00:07:06,080
Speaker 3: the CEES Like big keynote Tuesday morning, you mentioned a

146
00:07:06,120 --> 00:07:09,880
Speaker 3: persona smart tutor glasses glasses to help you, you know,

147
00:07:09,960 --> 00:07:12,480
Speaker 3: well learning. I haven't tried to be able to check

148
00:07:12,480 --> 00:07:14,400
Speaker 3: out the product yet, but they kind of remind me

149
00:07:14,440 --> 00:07:17,960
Speaker 3: of some of the concept behind those cleuely glasses that

150
00:07:18,000 --> 00:07:20,520
Speaker 3: you may have seen on social media, the.

151
00:07:20,480 --> 00:07:21,640
Speaker 4: Glasses that help you like cheat.

152
00:07:21,960 --> 00:07:25,600
Speaker 2: Yeah, but also somebody who's like we should embrace people cheating.

153
00:07:25,280 --> 00:07:28,280
Speaker 3: And but cheat just in a conversation, it seems like

154
00:07:28,280 --> 00:07:32,080
Speaker 3: that product isn't necessarily as real as uh what the

155
00:07:32,200 --> 00:07:33,720
Speaker 3: video might make it out to be.

156
00:07:33,880 --> 00:07:37,120
Speaker 2: People whose company was based on lying didn't make a

157
00:07:37,160 --> 00:07:37,840
Speaker 2: real product.

158
00:07:38,000 --> 00:07:40,880
Speaker 3: But well, walking through your Eureka Park today, it's funny.

159
00:07:40,880 --> 00:07:43,840
Speaker 3: I also saw this this product in one of like

160
00:07:43,880 --> 00:07:45,920
Speaker 3: the National Pavilions I think it was the one of

161
00:07:45,960 --> 00:07:49,880
Speaker 3: like the japan Tech Pavilions. AI powered tool to help

162
00:07:50,160 --> 00:07:52,680
Speaker 3: to help prevent cheating while test taking. So you have

163
00:07:53,000 --> 00:07:56,080
Speaker 3: AI powered tools that will monitor you to make sure

164
00:07:56,120 --> 00:07:56,840
Speaker 3: you're not cheating.

165
00:07:56,840 --> 00:07:58,960
Speaker 4: Will you use an AI powered tool that helps.

166
00:07:58,760 --> 00:08:00,880
Speaker 2: Cheat cheat it? Yeah? Better at school?

167
00:08:00,960 --> 00:08:03,520
Speaker 3: Yeah, that's kind of just a good representation of kind

168
00:08:03,560 --> 00:08:05,520
Speaker 3: of where where this whole industry is at at the moment.

169
00:08:05,800 --> 00:08:06,240
Speaker 2: Yeah.

170
00:08:06,480 --> 00:08:08,280
Speaker 3: In some ways, I think, you know, this is probably

171
00:08:08,280 --> 00:08:12,240
Speaker 3: what year three of AI being you know, the big thing,

172
00:08:12,280 --> 00:08:14,960
Speaker 3: whether that's unwearables, you know, where that's smart glasses, whether

173
00:08:14,960 --> 00:08:18,360
Speaker 3: that's you know, a generative AI, whether that's AI. You know,

174
00:08:19,240 --> 00:08:22,600
Speaker 3: but it's been but a has been been, like you know,

175
00:08:22,880 --> 00:08:26,520
Speaker 3: the added of property for for everything, and some of

176
00:08:26,560 --> 00:08:28,720
Speaker 3: that might be starting to kind of tucker out or

177
00:08:28,760 --> 00:08:33,320
Speaker 3: at least the they've taken the victory lap. And there's

178
00:08:33,360 --> 00:08:35,600
Speaker 3: there's a certain like you know, like cultural victory that

179
00:08:35,600 --> 00:08:38,160
Speaker 3: that they're resting on whether they're starting to put some

180
00:08:38,240 --> 00:08:41,640
Speaker 3: of their eggs in other baskets now, which certainly wasn't

181
00:08:41,640 --> 00:08:42,480
Speaker 3: the case last year.

182
00:08:42,960 --> 00:08:46,080
Speaker 2: No, and I I got a sense I attended six

183
00:08:46,120 --> 00:08:50,240
Speaker 2: panels today, congratulations. It was a mix of like advertising people,

184
00:08:51,000 --> 00:08:56,000
Speaker 2: entertainment associated people, some journalism associated people, and in robotics,

185
00:08:56,000 --> 00:08:57,920
Speaker 2: people in robotics talking about a lot of a lot

186
00:08:57,920 --> 00:08:59,960
Speaker 2: of robodies. Yeah, what they saw was the future of AI,

187
00:09:00,640 --> 00:09:03,760
Speaker 2: and there was a lot of focus first on AI

188
00:09:03,880 --> 00:09:05,760
Speaker 2: is not going to be taking jobs as much as

189
00:09:05,760 --> 00:09:08,560
Speaker 2: it's going to be augmenting jobs, right, Although you would

190
00:09:08,559 --> 00:09:10,480
Speaker 2: get the occasional person be like, I's going to take

191
00:09:10,520 --> 00:09:12,200
Speaker 2: a lot of jobs, I got.

192
00:09:12,120 --> 00:09:14,600
Speaker 3: People saying that it's it's only going to take jobs

193
00:09:14,600 --> 00:09:17,240
Speaker 3: if if you don't know how to incorporate AI into

194
00:09:17,280 --> 00:09:20,320
Speaker 3: your workfloce. And that's the argument at the moment right now. Yeah,

195
00:09:21,160 --> 00:09:23,480
Speaker 3: if you're not using AI, you're a greater risk of

196
00:09:24,120 --> 00:09:24,800
Speaker 3: you losing So.

197
00:09:24,800 --> 00:09:28,360
Speaker 2: You better get on it, right, start start learning it. Yes, Yes.

198
00:09:28,520 --> 00:09:30,680
Speaker 2: And then the other thing is that there was a

199
00:09:30,679 --> 00:09:32,920
Speaker 2: lot of like it's there to help or take away

200
00:09:32,960 --> 00:09:36,120
Speaker 2: unpleasant tasks from workers, but really emphasizing the it's not

201
00:09:36,240 --> 00:09:38,960
Speaker 2: your enemy thing, you don't need to be scared. And

202
00:09:39,000 --> 00:09:42,680
Speaker 2: I quoted like half of these panels, people would quote

203
00:09:42,679 --> 00:09:46,320
Speaker 2: statistics about low user trust and AI and the fact

204
00:09:46,360 --> 00:09:50,960
Speaker 2: that people are generally not super comfortable with this technology

205
00:09:50,960 --> 00:09:53,600
Speaker 2: even if they use it in parts of their work

206
00:09:53,640 --> 00:09:56,640
Speaker 2: life right or daily life. And so what I saw

207
00:09:56,679 --> 00:09:58,360
Speaker 2: from that, when I interpret from that, is that there

208
00:09:58,400 --> 00:10:00,360
Speaker 2: is internal concern that like that that's one of the

209
00:10:00,400 --> 00:10:02,200
Speaker 2: things that could screw the pooch on this is that

210
00:10:02,240 --> 00:10:05,600
Speaker 2: people are not really sure they like this stuff, and

211
00:10:06,000 --> 00:10:10,400
Speaker 2: so there's this impulse to kind of cover the softer

212
00:10:10,520 --> 00:10:13,720
Speaker 2: and fuzzier sides of it that I didn't see in

213
00:10:13,840 --> 00:10:16,720
Speaker 2: previous years. Yeah, and I think is really focusing on

214
00:10:17,559 --> 00:10:20,520
Speaker 2: this is just making things you already like better, as

215
00:10:20,520 --> 00:10:23,960
Speaker 2: opposed to this is a revolution that's completely changing life.

216
00:10:24,679 --> 00:10:28,719
Speaker 3: And to the extent where AI was fremous revolutionary, it

217
00:10:28,760 --> 00:10:32,679
Speaker 3: was specifically trying to ground it in like physical applications,

218
00:10:32,720 --> 00:10:36,000
Speaker 3: as opposed to this more general kind of like spectral

219
00:10:36,120 --> 00:10:38,199
Speaker 3: like AI hype that we've seen the past few years,

220
00:10:38,240 --> 00:10:40,760
Speaker 3: which is in specifically around like generative AI right where

221
00:10:40,760 --> 00:10:42,520
Speaker 3: it's like this like kind of vague thing that we

222
00:10:42,640 --> 00:10:46,560
Speaker 3: like gestured to there's more specific applications for AI being

223
00:10:46,600 --> 00:10:48,960
Speaker 3: talked about right now, and they talked talked about like

224
00:10:49,240 --> 00:10:55,920
Speaker 3: AI assisted manufacturing simulations like digital twins of factories, shipyards,

225
00:10:55,960 --> 00:10:59,000
Speaker 3: power plants, a lot of digital twin talk building you know,

226
00:10:59,080 --> 00:11:03,000
Speaker 3: a digital replica of like everything you know of society,

227
00:11:03,040 --> 00:11:06,400
Speaker 3: to like run these simulations to both make AIS smarter,

228
00:11:06,800 --> 00:11:10,040
Speaker 3: to generate new solutions outside of the limitations of a

229
00:11:10,120 --> 00:11:13,680
Speaker 3: language model, also find you know, potential problems in you know,

230
00:11:13,800 --> 00:11:15,280
Speaker 3: when you build these things physically.

231
00:11:15,760 --> 00:11:17,240
Speaker 2: Yeah, that was something that was brought up during the

232
00:11:17,320 --> 00:11:20,280
Speaker 2: robotics panel, which was talking about how to like take

233
00:11:21,120 --> 00:11:23,760
Speaker 2: the machine learning technology and other things that are generally

234
00:11:23,760 --> 00:11:26,439
Speaker 2: grouped under AI and apply it in the physical world.

235
00:11:26,800 --> 00:11:30,240
Speaker 3: Yeah, manufacturing, I've had done so much right in just

236
00:11:30,320 --> 00:11:33,079
Speaker 3: one day. I probably I've heard the word manufacturing more

237
00:11:33,080 --> 00:11:36,959
Speaker 3: today than I have most in every cees I've been

238
00:11:37,000 --> 00:11:39,160
Speaker 3: to previously, combined.

239
00:11:38,760 --> 00:11:41,960
Speaker 2: And I think consciously more focused on industrial applications than

240
00:11:42,000 --> 00:11:45,440
Speaker 2: on consumer technology because there's not that much new to

241
00:11:45,480 --> 00:11:48,480
Speaker 2: give the consumer right and they are also I think

242
00:11:48,520 --> 00:11:51,720
Speaker 2: starting to recognize that you can get people using chat,

243
00:11:51,760 --> 00:11:54,840
Speaker 2: GPT and the like, but they're mostly not using it,

244
00:11:54,880 --> 00:11:56,920
Speaker 2: and the data backs this up. People are mostly using

245
00:11:56,920 --> 00:11:59,640
Speaker 2: it at work and for school and gin z a lot.

246
00:12:00,440 --> 00:12:02,400
Speaker 2: There's a lot of people who are doing like their

247
00:12:02,440 --> 00:12:05,000
Speaker 2: research on like what to buy and whatnot through using

248
00:12:05,080 --> 00:12:07,960
Speaker 2: chat GPT. But there's not a lot that you can

249
00:12:08,000 --> 00:12:12,439
Speaker 2: sell people in CES because it's an app and there's

250
00:12:12,480 --> 00:12:15,679
Speaker 2: not a ton of different devices for it. People are

251
00:12:15,720 --> 00:12:17,520
Speaker 2: using it on their phone, they're using it on their computer,

252
00:12:17,640 --> 00:12:19,280
Speaker 2: but like none of the new phones and computers are

253
00:12:19,320 --> 00:12:22,520
Speaker 2: marketly better at using chat GPT or another you know,

254
00:12:22,640 --> 00:12:24,800
Speaker 2: chatbot thing than any of the others. So there's not

255
00:12:24,880 --> 00:12:28,640
Speaker 2: a lot that's sexy in just that at CES. So

256
00:12:28,679 --> 00:12:32,520
Speaker 2: I think I have seen this conscience reforming around people

257
00:12:32,600 --> 00:12:35,360
Speaker 2: in manufacturing and people who are like thinking of the

258
00:12:35,400 --> 00:12:38,760
Speaker 2: concerns of like I have a pair like an exoskeleton

259
00:12:38,800 --> 00:12:40,720
Speaker 2: to test this week. That's seeing a lot of its

260
00:12:40,720 --> 00:12:44,920
Speaker 2: business in folks who are like doing like Amazon type

261
00:12:44,960 --> 00:12:47,640
Speaker 2: jobs right loading and unloading packages and whatnot all day long,

262
00:12:47,800 --> 00:12:50,439
Speaker 2: you know. And I do see a conscious reforming there,

263
00:12:50,480 --> 00:12:52,680
Speaker 2: which I think is kind of evidence of like there's

264
00:12:52,679 --> 00:12:54,800
Speaker 2: almost an admission that like, yeah, we don't really have

265
00:12:54,840 --> 00:12:59,360
Speaker 2: that much to hand consumers anymore. On a yearly.

266
00:12:59,120 --> 00:13:10,480
Speaker 5: Basis speaking of handing things to consumers ads.

267
00:13:15,280 --> 00:13:18,120
Speaker 2: So the first panel I went to of the day

268
00:13:18,520 --> 00:13:21,640
Speaker 2: was about the funnel, which, as I understand it is

269
00:13:21,679 --> 00:13:25,120
Speaker 2: just kind of like the way in which people have

270
00:13:25,280 --> 00:13:29,920
Speaker 2: traditionally engaged with like media, gotten advertised to and then

271
00:13:30,040 --> 00:13:32,320
Speaker 2: like gone to stores and bought stuff like the funnel

272
00:13:32,360 --> 00:13:35,560
Speaker 2: by which you like make a customer, and how that's

273
00:13:35,600 --> 00:13:38,520
Speaker 2: been completely blown up now, right, and AI is like

274
00:13:38,559 --> 00:13:42,080
Speaker 2: a further massive disruption because people are not like people

275
00:13:42,160 --> 00:13:45,240
Speaker 2: are increasingly, especially very young people, which was putted out

276
00:13:45,240 --> 00:13:47,720
Speaker 2: in a number of these are like buying stuff that

277
00:13:47,840 --> 00:13:50,679
Speaker 2: a chatbot recommends them, right, And so a lot of

278
00:13:50,720 --> 00:13:53,840
Speaker 2: marketing is being seen as being done through how do

279
00:13:53,920 --> 00:13:56,800
Speaker 2: you get the chatbot to talk about you a certain way?

280
00:13:56,840 --> 00:14:00,200
Speaker 2: What is the SEO of getting chat ebt? Oh, it's interesting,

281
00:14:00,240 --> 00:14:01,560
Speaker 2: like right, there's a lot of time I.

282
00:14:01,760 --> 00:14:04,560
Speaker 3: Thought, as someone who's not a regular chatbot user, which

283
00:14:04,559 --> 00:14:06,400
Speaker 3: I'm sure must people here am I, which chastis me

284
00:14:06,480 --> 00:14:09,680
Speaker 3: for we're not maximizing my productivity.

285
00:14:09,240 --> 00:14:11,080
Speaker 2: And meaning to get on to you for that garrison.

286
00:14:12,400 --> 00:14:14,720
Speaker 3: There's not a regular chat about user. I've never thought

287
00:14:14,720 --> 00:14:16,600
Speaker 3: of that before. Of yeah, I mean, like I know

288
00:14:16,640 --> 00:14:19,360
Speaker 3: people use these chatbots as a replacement for search engines,

289
00:14:19,400 --> 00:14:21,520
Speaker 3: But the idea of like trying to you know, evaluate

290
00:14:21,720 --> 00:14:25,240
Speaker 3: purchases is uh. I mean, I guess that makes sense now,

291
00:14:25,280 --> 00:14:26,400
Speaker 3: but I've never put that together.

292
00:14:26,600 --> 00:14:28,920
Speaker 2: Yeah, And the only because there was a lot of

293
00:14:29,080 --> 00:14:32,720
Speaker 2: talking about like how AI is helping advertisers, how it's

294
00:14:33,160 --> 00:14:36,520
Speaker 2: making advertisements, how like it's helping in the process of that,

295
00:14:36,640 --> 00:14:39,520
Speaker 2: And there was a focus in all the panels about

296
00:14:39,560 --> 00:14:42,080
Speaker 2: that on how like, well, it's just augmenting the humans.

297
00:14:42,600 --> 00:14:46,280
Speaker 2: But the only specific examples given were the McDonald's and

298
00:14:46,560 --> 00:14:50,360
Speaker 2: Coca Cola AI generated ads, which were both disasters. I

299
00:14:50,400 --> 00:14:53,800
Speaker 2: mean the McDonald's one in the Netherlands got removed. Yeah,

300
00:14:53,880 --> 00:14:54,760
Speaker 2: they people so bad.

301
00:14:54,840 --> 00:14:57,400
Speaker 4: They withdrew the attic so ugly.

302
00:14:57,440 --> 00:14:59,600
Speaker 2: Deed. Yeah, I don't know. Coke did do it twice.

303
00:15:00,040 --> 00:15:02,440
Speaker 2: Maybe they consider it a win. But everything I saw

304
00:15:02,520 --> 00:15:04,400
Speaker 2: was very negative. I didn't see a lot of positive

305
00:15:04,400 --> 00:15:08,520
Speaker 2: feedback on Coca Cola visavi. They're weird. AI holidays are coming.

306
00:15:08,320 --> 00:15:10,600
Speaker 3: At It's like people who don't know it's AI think

307
00:15:10,840 --> 00:15:13,080
Speaker 3: feel very neutral about it. Yeah, people that do we

308
00:15:13,160 --> 00:15:15,840
Speaker 3: know it's AI, I think generally have negative reactions.

309
00:15:15,920 --> 00:15:17,720
Speaker 2: I think if you look at it, it's pretty clear.

310
00:15:17,720 --> 00:15:19,840
Speaker 3: But anyway, I mean, it less clear if maybe you're

311
00:15:19,880 --> 00:15:22,000
Speaker 3: like a sixty year old watches.

312
00:15:21,720 --> 00:15:25,160
Speaker 2: You know, exact or in between stuff, and that's all

313
00:15:25,360 --> 00:15:25,960
Speaker 2: good enough.

314
00:15:26,280 --> 00:15:29,360
Speaker 4: Grandpa, don't go Internet, right, Yeah, he doesn't know.

315
00:15:29,480 --> 00:15:31,920
Speaker 2: He might notice some of those those fucking h polar

316
00:15:31,960 --> 00:15:34,480
Speaker 2: bears have the wrong number of pause. But yeah, so,

317
00:15:34,600 --> 00:15:35,960
Speaker 2: like there was some talk of that and the other.

318
00:15:36,040 --> 00:15:38,480
Speaker 2: The only specific example they gave of like an AI

319
00:15:39,120 --> 00:15:43,520
Speaker 2: enhanced strategy was Allegra. The people who own like the

320
00:15:43,600 --> 00:15:46,240
Speaker 2: medicine had like a new non drowsy formula or they

321
00:15:46,280 --> 00:15:48,600
Speaker 2: just wanted to highlight that it was non drowsy. So

322
00:15:48,640 --> 00:15:53,320
Speaker 2: they basically had a bunch of like seeded the stuff

323
00:15:53,360 --> 00:15:56,200
Speaker 2: that chatbot that like open Ai was scored that chatbots

324
00:15:56,200 --> 00:15:59,720
Speaker 2: were scraping, yeah, with content about how Allegra makes it

325
00:15:59,760 --> 00:16:03,680
Speaker 2: is on drowsy and about how like competing similar medications

326
00:16:03,680 --> 00:16:05,800
Speaker 2: make you drowsy, I mean, so that it would get

327
00:16:05,840 --> 00:16:10,480
Speaker 2: mentioned in like and they talked about They called it

328
00:16:10,560 --> 00:16:12,960
Speaker 2: like model hacking, I think was the exact term used.

329
00:16:13,440 --> 00:16:15,120
Speaker 2: And that was the only thing that was the only

330
00:16:15,160 --> 00:16:17,600
Speaker 2: specific example that I can't know any of this works too,

331
00:16:17,680 --> 00:16:19,760
Speaker 2: Like everyone else was just talking in vague terms about

332
00:16:19,760 --> 00:16:23,640
Speaker 2: like and we've really seen our teams creativity sore or whatever.

333
00:16:23,800 --> 00:16:26,640
Speaker 3: That's interesting, you know because the way that I probably

334
00:16:26,720 --> 00:16:29,200
Speaker 3: use or exposed to AI the most is like on

335
00:16:29,240 --> 00:16:31,280
Speaker 3: like Google Search now, which has you know, it's it's

336
00:16:31,280 --> 00:16:34,560
Speaker 3: like AI like summaries instead of like actual search results.

337
00:16:34,800 --> 00:16:36,840
Speaker 4: But those are based on like which you.

338
00:16:36,760 --> 00:16:39,160
Speaker 2: Can type minus AI in with the search results if

339
00:16:39,160 --> 00:16:40,240
Speaker 2: we want to cut that stuff out.

340
00:16:40,440 --> 00:16:43,600
Speaker 3: But those AI results are you know, pulling from certain

341
00:16:43,600 --> 00:16:46,360
Speaker 3: like articles which which they'll link to. So yeah, I

342
00:16:46,360 --> 00:16:47,920
Speaker 3: guys said, if I was trying to design it like

343
00:16:47,920 --> 00:16:51,560
Speaker 3: an AI marketing strategy, I would I would either you know,

344
00:16:51,920 --> 00:16:55,600
Speaker 3: pay publications to mention my product in more articles or

345
00:16:56,400 --> 00:16:58,760
Speaker 3: find find out find other ways to to to influence

346
00:16:58,800 --> 00:17:02,080
Speaker 3: mentions of of my product in like written media that

347
00:17:02,240 --> 00:17:04,600
Speaker 3: then would be used as like training data for AI.

348
00:17:05,280 --> 00:17:07,520
Speaker 3: And yeah, I guess there there can be a whole

349
00:17:07,600 --> 00:17:11,760
Speaker 3: you know, search engine optimization model acking is I guess.

350
00:17:12,000 --> 00:17:16,200
Speaker 2: Model optimism or yeah, like product opposite optimization for a model.

351
00:17:16,240 --> 00:17:19,200
Speaker 2: I guess that's funny, but yeah, like the So the

352
00:17:19,680 --> 00:17:22,000
Speaker 2: first talk that I went to was about the funnel

353
00:17:22,080 --> 00:17:23,840
Speaker 2: or whatever, and one of the people speaking. There was

354
00:17:23,880 --> 00:17:27,359
Speaker 2: the CMO, the chief marketing officer of Intuit, which is

355
00:17:27,400 --> 00:17:30,040
Speaker 2: the company that owns Turbo tax right like, it's one

356
00:17:30,040 --> 00:17:33,680
Speaker 2: of the big we do your taxes companies out there,

357
00:17:33,680 --> 00:17:37,000
Speaker 2: and also a lobbyer in terms of stopping any sort

358
00:17:37,000 --> 00:17:38,399
Speaker 2: of a form of make it's that you don't need

359
00:17:38,400 --> 00:17:40,520
Speaker 2: to do your own other countries.

360
00:17:40,520 --> 00:17:42,760
Speaker 4: Credit score monitoring, yes, a whole bunch of.

361
00:17:42,720 --> 00:17:44,840
Speaker 2: Stuff, all that kind of stuff. So this this guy,

362
00:17:44,880 --> 00:17:47,720
Speaker 2: the CMO of the company, Thomas Renize, was part of

363
00:17:47,720 --> 00:17:50,680
Speaker 2: this the end of the funnel speech, and he made

364
00:17:50,680 --> 00:17:52,280
Speaker 2: a couple of comments that I took note of. One

365
00:17:52,359 --> 00:17:55,360
Speaker 2: is product is brand and brand is product full stop.

366
00:17:55,800 --> 00:17:57,879
Speaker 2: So the more people you can make experience your product,

367
00:17:58,000 --> 00:18:01,359
Speaker 2: that's the best selling point and value of right which

368
00:18:01,600 --> 00:18:03,199
Speaker 2: it was just interesting to me in terms of the

369
00:18:03,520 --> 00:18:05,479
Speaker 2: into it as a company that has lobbied to make

370
00:18:05,520 --> 00:18:08,280
Speaker 2: it impossible for like any reform that would allow people

371
00:18:08,320 --> 00:18:12,000
Speaker 2: to not need a third party to do there they're taxes.

372
00:18:12,359 --> 00:18:14,520
Speaker 2: But also this idea that like product is brand and

373
00:18:14,560 --> 00:18:17,919
Speaker 2: brand is product isn't true of a lot of companies.

374
00:18:17,960 --> 00:18:20,639
Speaker 2: Like if you think about like, for example, like a

375
00:18:20,680 --> 00:18:23,080
Speaker 2: lot of the different soft drinks are all owned by

376
00:18:23,119 --> 00:18:26,159
Speaker 2: one company, but they're fundamentally different like products and have

377
00:18:26,240 --> 00:18:29,600
Speaker 2: an often cases like a different user base, and it's

378
00:18:29,760 --> 00:18:31,920
Speaker 2: very like, it's a very tech when your product is

379
00:18:31,960 --> 00:18:35,040
Speaker 2: a concept, Like you can't do your own taxes because

380
00:18:35,040 --> 00:18:36,840
Speaker 2: it's a pain in the ass, but the government doesn't

381
00:18:36,880 --> 00:18:38,880
Speaker 2: do it for you because we lobby to make that illegal.

382
00:18:39,359 --> 00:18:39,439
Speaker 3: Like.

383
00:18:40,080 --> 00:18:42,399
Speaker 2: I found that interesting and it kind of got me

384
00:18:43,040 --> 00:18:46,679
Speaker 2: angry at Thomas at the start of this. And I

385
00:18:46,880 --> 00:18:50,479
Speaker 2: was particularly interested in one of the things he brought up,

386
00:18:50,480 --> 00:18:52,520
Speaker 2: which is that he talked about the one hundred million

387
00:18:52,560 --> 00:18:55,040
Speaker 2: dollars that into It is putting into open Ai, and

388
00:18:55,080 --> 00:18:56,879
Speaker 2: they're putting this into open Ai as part of a

389
00:18:56,960 --> 00:19:00,800
Speaker 2: multi year partnership. And I want to qu vote from

390
00:19:00,920 --> 00:19:05,800
Speaker 2: an article in the website Araptus which is discussing this

391
00:19:05,920 --> 00:19:08,440
Speaker 2: exact thing that I found useful when I was formulating

392
00:19:08,440 --> 00:19:11,359
Speaker 2: my question for Thomas. The contract was to embed AM

393
00:19:11,400 --> 00:19:14,840
Speaker 2: models directly into QuickBooks, TurboTax, and credit Karma. The promise

394
00:19:14,880 --> 00:19:18,840
Speaker 2: AI assistants that can generate invoices, provide tax extments, recommend loans,

395
00:19:19,119 --> 00:19:22,760
Speaker 2: and help you make informed financial decisions. Right. That may

396
00:19:22,800 --> 00:19:25,960
Speaker 2: sound like kind of like a basic move, like's what's

397
00:19:26,160 --> 00:19:30,080
Speaker 2: so sketchy about just integrating like an AI chatbot to

398
00:19:30,080 --> 00:19:32,679
Speaker 2: make it easier to use your tax software. It can

399
00:19:32,720 --> 00:19:36,680
Speaker 2: be complicated and hard to use as anyway. But kind

400
00:19:36,680 --> 00:19:39,639
Speaker 2: of the necessary part of this is if you are

401
00:19:39,800 --> 00:19:42,680
Speaker 2: if you're doing this, if you're integrating all of these

402
00:19:42,720 --> 00:19:46,920
Speaker 2: different tax and credit programs into an AI model, you're

403
00:19:46,960 --> 00:19:50,600
Speaker 2: giving that AI model access to people's financial data in

404
00:19:50,680 --> 00:19:55,000
Speaker 2: a tremendous amount of detail, right, And all of these

405
00:19:55,040 --> 00:19:58,760
Speaker 2: AI models have a massive shared vulnerability, which is a

406
00:19:58,840 --> 00:20:03,120
Speaker 2: vernability to something called called prompt injection, right. And that's when,

407
00:20:03,119 --> 00:20:05,840
Speaker 2: for example, say someone is a customer of a tax

408
00:20:05,880 --> 00:20:08,359
Speaker 2: prepared that uses one of into its products to prepare

409
00:20:08,359 --> 00:20:11,160
Speaker 2: taxes for its customers, and this person sends an invoice

410
00:20:11,160 --> 00:20:14,879
Speaker 2: into the company that has hidden text in it that

411
00:20:15,000 --> 00:20:17,399
Speaker 2: is a command to the language model that will be

412
00:20:17,480 --> 00:20:22,800
Speaker 2: scraping this and uploading it to basically open up and

413
00:20:22,880 --> 00:20:26,080
Speaker 2: send over a bunch of customer data to a specific source.

414
00:20:26,640 --> 00:20:29,400
Speaker 2: That's a thing that you can do. It's called prompt injection,

415
00:20:29,960 --> 00:20:32,440
Speaker 2: and there's not really a way to counter it. There's

416
00:20:32,480 --> 00:20:36,240
Speaker 2: not like a proven comprehensive defense against this sort of thing.

417
00:20:36,960 --> 00:20:40,240
Speaker 2: And so there's this massive vulnerability, and this was first

418
00:20:40,240 --> 00:20:42,760
Speaker 2: brought up in an article on the website. I cited

419
00:20:43,040 --> 00:20:47,080
Speaker 2: a raptus by Chris Black, who's a security researcher an expert,

420
00:20:47,480 --> 00:20:50,359
Speaker 2: and I want to read a quote from his article

421
00:20:50,440 --> 00:20:53,680
Speaker 2: about this. There are no proven comprehensive defenses against prompt

422
00:20:53,720 --> 00:20:57,080
Speaker 2: injection when not if an AI powered financial tool leaks

423
00:20:57,080 --> 00:20:59,960
Speaker 2: customer data through a prompt injection attack, who is liable

424
00:21:00,080 --> 00:21:02,679
Speaker 2: the company using quick books into it open Ai? The

425
00:21:02,760 --> 00:21:06,080
Speaker 2: regulations weren't written for this scenario. So I decided to

426
00:21:06,080 --> 00:21:09,200
Speaker 2: ask that question of Thomas, being like the chief marketing officer,

427
00:21:09,240 --> 00:21:12,159
Speaker 2: I figured, well he should have some answer to like

428
00:21:12,480 --> 00:21:15,359
Speaker 2: what do you have, what sort of security measures do

429
00:21:15,440 --> 00:21:17,800
Speaker 2: you have to mitigate the risk of a prompt injection attack?

430
00:21:18,480 --> 00:21:20,960
Speaker 2: And who do you see as being responsible? If you

431
00:21:21,040 --> 00:21:24,679
Speaker 2: are the ones providing customer data to open ai and

432
00:21:24,720 --> 00:21:26,800
Speaker 2: their tool gets it by a prompt injection attack, are

433
00:21:26,840 --> 00:21:29,320
Speaker 2: you responsible? Is open ai is a third party that

434
00:21:29,400 --> 00:21:31,760
Speaker 2: might be using your products? And he had no answer

435
00:21:31,760 --> 00:21:33,960
Speaker 2: to this. He like, his only answer when we were

436
00:21:33,960 --> 00:21:36,560
Speaker 2: on stage was like, we're talking with open ai about it,

437
00:21:36,600 --> 00:21:39,720
Speaker 2: which like, well, you're already in the process of collaborating

438
00:21:39,760 --> 00:21:40,120
Speaker 2: with them.

439
00:21:40,680 --> 00:21:44,159
Speaker 6: Yeah, I have a question for Thomas. I was kind

440
00:21:44,200 --> 00:21:47,199
Speaker 6: of concerned when reading about into it assists that a

441
00:21:47,600 --> 00:21:50,560
Speaker 6: open AI is going to have read and write access

442
00:21:50,680 --> 00:21:55,720
Speaker 6: to quite a lot of financial information from users, which

443
00:21:55,920 --> 00:21:58,760
Speaker 6: opens up a vulnerability for a prompt injection. Right, you

444
00:21:58,840 --> 00:22:01,080
Speaker 6: have the possibility that people who can hide things and

445
00:22:01,119 --> 00:22:04,640
Speaker 6: invoices that are then being uploaded that will cause the

446
00:22:04,680 --> 00:22:09,439
Speaker 6: AI to provide the malicious user with financial details for

447
00:22:09,640 --> 00:22:14,240
Speaker 6: individuals or corporations. And I guess my primary question here

448
00:22:14,400 --> 00:22:18,880
Speaker 6: this seems like a major liability issue when somebody's information

449
00:22:19,000 --> 00:22:21,639
Speaker 6: gets rerouted to a place that's not supposed to go

450
00:22:21,720 --> 00:22:22,800
Speaker 6: to a malicious actor.

451
00:22:23,520 --> 00:22:24,600
Speaker 2: Who's responsible.

452
00:22:25,240 --> 00:22:29,680
Speaker 7: You're taking the value of integrity and protecting our customer

453
00:22:29,760 --> 00:22:33,600
Speaker 7: data privaly seriously over our entire lives. And that's what

454
00:22:33,840 --> 00:22:36,320
Speaker 7: forty years as a company and meeting in the software

455
00:22:36,359 --> 00:22:39,160
Speaker 7: space for financial services. So this is not something we're

456
00:22:39,160 --> 00:22:42,200
Speaker 7: about to use it in any way in the new

457
00:22:42,240 --> 00:22:44,040
Speaker 7: age of AI. In fact, it has to get even

458
00:22:44,119 --> 00:22:47,439
Speaker 7: more and more level down of protecting people's information and

459
00:22:47,520 --> 00:22:50,440
Speaker 7: security of edforation. So that is something that we are

460
00:22:50,600 --> 00:22:53,080
Speaker 7: already in the conversations with open AI have a nation

461
00:22:53,200 --> 00:22:56,080
Speaker 7: about that group, no matter where we're starving.

462
00:22:57,480 --> 00:22:59,679
Speaker 2: And when I kind of cornered him afterwards, he didn't

463
00:22:59,680 --> 00:23:01,840
Speaker 2: have like his eventual follow up answers like I don't

464
00:23:01,880 --> 00:23:03,560
Speaker 2: know that kind of stuff, and like you are the

465
00:23:03,640 --> 00:23:05,320
Speaker 2: chief marketing officer.

466
00:23:05,280 --> 00:23:06,680
Speaker 8: Thank you again for answering my question.

467
00:23:06,840 --> 00:23:07,680
Speaker 9: Sure, your question.

468
00:23:08,000 --> 00:23:11,680
Speaker 8: I'm still really concerned about the danger of prompt injection

469
00:23:11,800 --> 00:23:16,560
Speaker 8: attacks revealing financial data, and it doesn't still sound like

470
00:23:16,640 --> 00:23:19,080
Speaker 8: there's an understanding of who will be liable.

471
00:23:20,840 --> 00:23:22,160
Speaker 2: To answer that question for you.

472
00:23:22,200 --> 00:23:23,600
Speaker 6: So I mean, like I can tell you that we're

473
00:23:23,680 --> 00:23:26,640
Speaker 6: coming into our security and privacy and we are doing.

474
00:23:26,480 --> 00:23:28,879
Speaker 2: Everything we can to protect that. I feel a lot

475
00:23:28,920 --> 00:23:30,359
Speaker 2: writing on it, as you might imagine.

476
00:23:30,400 --> 00:23:33,240
Speaker 8: Well, yeah, it's every digital security expert I've talked to

477
00:23:33,359 --> 00:23:36,200
Speaker 8: says it's a matter of when, not if, that there

478
00:23:36,320 --> 00:23:38,680
Speaker 8: is financial data revealed.

479
00:23:38,280 --> 00:23:41,960
Speaker 2: By these attacks. It seems like understanding.

480
00:23:43,440 --> 00:23:44,920
Speaker 8: I'm just I'm not going to be the expert to

481
00:23:44,920 --> 00:23:46,160
Speaker 8: get into the details on that.

482
00:23:46,320 --> 00:23:52,280
Speaker 2: Okay, Yeah, A key part of marketing this should be

483
00:23:52,280 --> 00:23:55,280
Speaker 2: being able to tell people what kind of safety precautions

484
00:23:55,320 --> 00:23:58,439
Speaker 2: are being taken with their data. And the fact that

485
00:23:58,480 --> 00:24:00,840
Speaker 2: he didn't and clearly had never thought about any of

486
00:24:00,880 --> 00:24:02,400
Speaker 2: this stuff and I had a couple of different people

487
00:24:02,400 --> 00:24:05,120
Speaker 2: come up to me afterwards and like be like, Wow,

488
00:24:05,160 --> 00:24:07,480
Speaker 2: that was a really good question. And I was like, well,

489
00:24:07,960 --> 00:24:11,480
Speaker 2: why hasn't this been asked before? Like why is this

490
00:24:11,520 --> 00:24:13,119
Speaker 2: a thing where like some guy's blogging about it and

491
00:24:13,160 --> 00:24:14,560
Speaker 2: I'm asking you about it and you don't have an

492
00:24:14,600 --> 00:24:17,000
Speaker 2: answer to it, and you're the CEO of one of

493
00:24:17,080 --> 00:24:20,200
Speaker 2: like the largest tax prep the largest tax prep company

494
00:24:20,760 --> 00:24:24,480
Speaker 2: in the country. Like it's just it's emblematic of how

495
00:24:24,840 --> 00:24:30,000
Speaker 2: careless everyone adjacent to this industry is, which personal data

496
00:24:30,400 --> 00:24:33,600
Speaker 2: with the safety of people, end of society as a

497
00:24:33,640 --> 00:24:36,000
Speaker 2: result of like what their products are doing, Like there's

498
00:24:36,119 --> 00:24:39,800
Speaker 2: absolutely no consideration given the harms of any of this shit.

499
00:24:39,920 --> 00:24:42,720
Speaker 2: And it's it's the most consistently dispiriting part of showing

500
00:24:42,760 --> 00:24:45,840
Speaker 2: up at CES. Well what is right?

501
00:24:46,119 --> 00:24:47,040
Speaker 4: What a fun story?

502
00:24:47,119 --> 00:25:01,639
Speaker 2: That is? All right? We're back. So one of the

503
00:25:01,680 --> 00:25:03,920
Speaker 2: other things that's been a major topic on the panels

504
00:25:03,920 --> 00:25:05,679
Speaker 2: I went to and is generally a big thing at

505
00:25:05,720 --> 00:25:09,040
Speaker 2: CES this year and in tech this year is agentic

506
00:25:09,080 --> 00:25:12,240
Speaker 2: AI or agents. Right, the idea that you have an

507
00:25:12,240 --> 00:25:14,720
Speaker 2: AI that you can send off to like book a

508
00:25:14,720 --> 00:25:16,600
Speaker 2: flight for you, and it doesn't just like find a

509
00:25:16,640 --> 00:25:18,639
Speaker 2: flight that it searches for and be like, hey, this

510
00:25:18,720 --> 00:25:21,000
Speaker 2: looks good. It like actually books it for you and

511
00:25:21,359 --> 00:25:24,080
Speaker 2: handles all of that. Right. This has been one of

512
00:25:24,119 --> 00:25:26,600
Speaker 2: the big promises of AI, not just for like flights,

513
00:25:26,600 --> 00:25:29,160
Speaker 2: but that you can have like an actual digital assistant

514
00:25:29,160 --> 00:25:31,800
Speaker 2: that persistently remembers all of your shit and can book

515
00:25:31,800 --> 00:25:34,879
Speaker 2: stuff for you and handle like the pain in the

516
00:25:34,920 --> 00:25:37,440
Speaker 2: ass nitty gritty. If you say, like, hey, I need

517
00:25:37,480 --> 00:25:39,920
Speaker 2: you to find a restaurant within like this four block

518
00:25:40,040 --> 00:25:44,480
Speaker 2: radius that has seven seats open at eight pm and

519
00:25:44,840 --> 00:25:47,760
Speaker 2: abides by these dietary restrictions, you kind of just have

520
00:25:47,800 --> 00:25:50,600
Speaker 2: to slog through figuring that out right now. And the

521
00:25:50,680 --> 00:25:53,240
Speaker 2: idea is an agent can do that for you, and

522
00:25:53,320 --> 00:25:56,440
Speaker 2: currently none of them can, right. This is a thing

523
00:25:56,520 --> 00:26:00,720
Speaker 2: that is changing, like the performance of different agents are

524
00:26:00,840 --> 00:26:04,520
Speaker 2: changing over time. But it is still very unclear. If

525
00:26:04,560 --> 00:26:06,399
Speaker 2: you're not somebod who's fully bought into the kool aid,

526
00:26:06,440 --> 00:26:09,200
Speaker 2: I'll say it's very unclear where these things will top

527
00:26:09,280 --> 00:26:12,080
Speaker 2: out at. And there was a good article in futurism

528
00:26:12,320 --> 00:26:14,480
Speaker 2: recently and I want to quote from it right now.

529
00:26:14,800 --> 00:26:17,560
Speaker 2: Researchers at Carnegie Mellen University found earlier this year that

530
00:26:17,640 --> 00:26:20,040
Speaker 2: even the best performing AI agent, which was Google's gym

531
00:26:20,040 --> 00:26:21,920
Speaker 2: and I two point five pro at the time, failed

532
00:26:21,960 --> 00:26:25,119
Speaker 2: to complete real world office tasks seventy percent of the time.

533
00:26:25,800 --> 00:26:27,840
Speaker 2: And this is there's been a bunch of articles in

534
00:26:27,840 --> 00:26:30,120
Speaker 2: the last couple of months about like, why didn't because

535
00:26:30,119 --> 00:26:31,720
Speaker 2: twenty twenty five was supposed to be the year of

536
00:26:31,760 --> 00:26:34,480
Speaker 2: agent to AAI and how they're saying, well, twenty twenty six,

537
00:26:34,520 --> 00:26:36,560
Speaker 2: it's going to be the year of agenta AAI. Not

538
00:26:36,640 --> 00:26:38,960
Speaker 2: because none of this stuff works, and in fact enough

539
00:26:39,000 --> 00:26:41,680
Speaker 2: does that there's a number of viable businesses in it.

540
00:26:41,680 --> 00:26:44,320
Speaker 2: It's not nothing, but it does not work as well

541
00:26:44,359 --> 00:26:46,239
Speaker 2: as they said it would be working right now, and

542
00:26:46,280 --> 00:26:49,280
Speaker 2: it consequently has not been adopted merely as widely as

543
00:26:49,520 --> 00:26:52,520
Speaker 2: was expected even this time last year. Right there's an

544
00:26:52,600 --> 00:26:55,159
Speaker 2: article in hr dive half of gen Z chat GPT

545
00:26:55,359 --> 00:26:57,800
Speaker 2: users say they view it as a coworker. Survey shows

546
00:26:58,119 --> 00:26:59,959
Speaker 2: that sites the survey of about eighty six hundred four

547
00:27:00,160 --> 00:27:03,119
Speaker 2: time US workers, which found that about eleven percent of

548
00:27:03,160 --> 00:27:06,520
Speaker 2: those who responded said they use chat GPT regularly, including

549
00:27:06,560 --> 00:27:09,359
Speaker 2: about twenty one percent of gen Z workers, which is

550
00:27:09,400 --> 00:27:12,199
Speaker 2: significantly lower. You can find depending on who you go to.

551
00:27:12,240 --> 00:27:14,320
Speaker 2: And the stat I've seen bandied about was that like

552
00:27:15,320 --> 00:27:18,840
Speaker 2: fifty seven percent of gen z people use chat GPT

553
00:27:19,320 --> 00:27:22,560
Speaker 2: on a daily basis for like work, and like more

554
00:27:22,560 --> 00:27:25,240
Speaker 2: than half used it as like their primary source recommendations

555
00:27:25,280 --> 00:27:27,880
Speaker 2: like what stuff to buy. I don't know, like which

556
00:27:27,880 --> 00:27:30,080
Speaker 2: set of numbers is accurate. There's a lot of different

557
00:27:30,080 --> 00:27:33,160
Speaker 2: posters giving data, right, but kind of no matter who

558
00:27:33,200 --> 00:27:36,720
Speaker 2: you look at, the evidence suggests that the year that

559
00:27:36,760 --> 00:27:38,680
Speaker 2: was supposed to be the year of agentic AI did

560
00:27:38,680 --> 00:27:41,080
Speaker 2: not turn it into a normal thing, right, It's still

561
00:27:41,160 --> 00:27:44,120
Speaker 2: lagging behind expectation. So that's kind of what we're seeing

562
00:27:44,160 --> 00:27:47,560
Speaker 2: at CES is a lot of people trying to, like, well,

563
00:27:47,640 --> 00:27:50,399
Speaker 2: let's bring back kind of the same agentic shit we

564
00:27:50,440 --> 00:27:53,040
Speaker 2: had last year slightly improved and see if it catches on,

565
00:27:53,160 --> 00:27:54,879
Speaker 2: maybe this year it'll hit maturity.

566
00:27:55,160 --> 00:27:58,960
Speaker 3: Right Yeah, No, I mean we've been hearing agentic stuff

567
00:27:58,960 --> 00:28:01,520
Speaker 3: every once in a while, not as much as last year.

568
00:28:02,000 --> 00:28:03,639
Speaker 3: It's one of those like salt pepper words that they

569
00:28:03,680 --> 00:28:07,160
Speaker 3: throw in the second batch of panels that I attended

570
00:28:07,160 --> 00:28:11,040
Speaker 3: after the keynote, which I should mention as soon as

571
00:28:11,080 --> 00:28:15,240
Speaker 3: I walked into the keynote at eight thirty am. The

572
00:28:15,280 --> 00:28:18,520
Speaker 3: first thing, the very first thing I heard from Gary

573
00:28:18,560 --> 00:28:21,440
Speaker 3: Shapiro's well, one of the heads the Consumer Technology Association

574
00:28:22,119 --> 00:28:24,199
Speaker 3: was a six to seven joke. But whether this is

575
00:28:24,240 --> 00:28:29,440
Speaker 3: your first CEES or your fifteenth on my case, you.

576
00:28:29,480 --> 00:28:34,359
Speaker 5: Belong to number, was that sixty or seventy?

577
00:28:34,480 --> 00:28:35,680
Speaker 4: You did a six to seven joke?

578
00:28:35,800 --> 00:28:40,920
Speaker 3: Already, very first thing, Wow, eight great, great, thirty am.

579
00:28:41,000 --> 00:28:42,080
Speaker 4: As soon as I walk in.

580
00:28:42,320 --> 00:28:44,760
Speaker 3: It's because I walked in maybe like five minutes late,

581
00:28:45,280 --> 00:28:49,800
Speaker 3: but very first words. So that's that's good's that kind

582
00:28:49,800 --> 00:28:52,440
Speaker 3: of sets the tone for a lot of a lot

583
00:28:52,440 --> 00:28:55,200
Speaker 3: of that panel. But then I went to a few

584
00:28:55,520 --> 00:29:00,320
Speaker 3: panels in Eureka Park about like AI, governance, some like

585
00:29:00,600 --> 00:29:05,520
Speaker 3: governments working working with AI, A lot of stuff, mostly

586
00:29:05,840 --> 00:29:09,240
Speaker 3: about like the challenge of governments keeping up with innovation.

587
00:29:09,480 --> 00:29:12,840
Speaker 3: How you know, too much regulation restricts these companies from

588
00:29:12,880 --> 00:29:18,120
Speaker 3: doing real regulation. The Secretary of State of Austria had

589
00:29:18,160 --> 00:29:23,920
Speaker 3: a really good quote about how data protections inhibit innovation.

590
00:29:24,680 --> 00:29:26,680
Speaker 10: One of the things that you are saying today is

591
00:29:26,720 --> 00:29:29,320
Speaker 10: that some of the people, some of the citizens, have

592
00:29:29,560 --> 00:29:30,880
Speaker 10: this fear about AI.

593
00:29:31,360 --> 00:29:34,480
Speaker 9: So how do you feel it in Austria.

594
00:29:35,000 --> 00:29:36,680
Speaker 4: I think you mentioned very very well.

595
00:29:36,800 --> 00:29:41,160
Speaker 10: It's all about building trust, taking the field trust for

596
00:29:41,320 --> 00:29:45,360
Speaker 10: FEI that's the most important thing. And of course data

597
00:29:45,360 --> 00:29:48,959
Speaker 10: protection is very huge. But on the other side, between

598
00:29:49,080 --> 00:29:53,560
Speaker 10: data protection and innovation, you need to find the middle

599
00:29:53,600 --> 00:29:56,920
Speaker 10: way because sometimes data protection.

600
00:29:56,680 --> 00:29:57,880
Speaker 4: Is not good for innovation.

601
00:29:58,720 --> 00:30:02,200
Speaker 3: On a similar notice, the into it the turbo tax

602
00:30:02,280 --> 00:30:05,480
Speaker 3: thing of data protection is mainly getting in the way

603
00:30:05,520 --> 00:30:08,600
Speaker 3: of trying to actually make make real social progress, which

604
00:30:08,640 --> 00:30:11,640
Speaker 3: will carry with it some degree of risk. The second

605
00:30:11,680 --> 00:30:15,240
Speaker 3: one of these AI governance panels was like these EU

606
00:30:15,680 --> 00:30:21,520
Speaker 3: ambassadors to the US from Estonia and Luxembourg talking about

607
00:30:21,880 --> 00:30:25,959
Speaker 3: like Reaganomics, basically for thirty minutes, talking about how much

608
00:30:26,000 --> 00:30:28,760
Speaker 3: they love Ronald Reigen great.

609
00:30:28,520 --> 00:30:33,959
Speaker 9: Whenos Soniavo certainly subscribed to the this statement that Reagan

610
00:30:34,000 --> 00:30:38,240
Speaker 9: once made that the fuel most horrific words in English

611
00:30:38,560 --> 00:30:42,480
Speaker 9: are the ones saying that a I'm from the government

612
00:30:42,520 --> 00:30:43,760
Speaker 9: and I'm here to help.

613
00:30:43,560 --> 00:30:48,080
Speaker 3: You, specifically in trying to make sure that governments are

614
00:30:48,080 --> 00:30:51,240
Speaker 3: able to keep up with technology. And the previous panel

615
00:30:51,280 --> 00:30:54,200
Speaker 3: with the Austrian Secretary of State was about the challenges

616
00:30:54,240 --> 00:30:57,000
Speaker 3: of trying to convince the citizens of these countries to

617
00:30:57,600 --> 00:31:02,000
Speaker 3: adopt AI and adopt in general like digitalization and specifically

618
00:31:02,000 --> 00:31:04,320
Speaker 3: with like digital ideas, and how how there's like you know,

619
00:31:04,480 --> 00:31:06,239
Speaker 3: maybe like twenty to thirty percent of people who are

620
00:31:06,280 --> 00:31:09,360
Speaker 3: very resistant and the challenge of like making making sure

621
00:31:09,360 --> 00:31:11,080
Speaker 3: that like this gets framed is not as like a

622
00:31:11,120 --> 00:31:14,080
Speaker 3: product or like a like a project for technology, but

623
00:31:14,120 --> 00:31:16,360
Speaker 3: as like a society wide push.

624
00:31:16,800 --> 00:31:17,520
Speaker 2: Yeah.

625
00:31:17,560 --> 00:31:19,920
Speaker 3: But besides that, these these panels were honestly a little

626
00:31:19,960 --> 00:31:23,280
Speaker 3: bit sleepy as well as the state of the Creator

627
00:31:23,320 --> 00:31:24,200
Speaker 3: Economy panel.

628
00:31:24,240 --> 00:31:24,920
Speaker 2: Oh how's it doing?

629
00:31:25,720 --> 00:31:29,440
Speaker 3: You know what, It's both it's both in its adolescence

630
00:31:29,520 --> 00:31:30,840
Speaker 3: but also reach maturity.

631
00:31:30,920 --> 00:31:31,240
Speaker 2: Wow.

632
00:31:31,640 --> 00:31:34,160
Speaker 3: And they said, you know, it's hard to pick both.

633
00:31:34,240 --> 00:31:36,040
Speaker 3: It's hard for too for something to be two things

634
00:31:36,080 --> 00:31:38,280
Speaker 3: at once, but in this case it is.

635
00:31:41,160 --> 00:31:43,640
Speaker 4: But they talked about how creators are more.

636
00:31:43,640 --> 00:31:46,440
Speaker 3: Enabled to use brand deals, including you know, brand deals

637
00:31:46,480 --> 00:31:50,200
Speaker 3: to enabled with like a backlog of older content. You

638
00:31:50,200 --> 00:31:53,600
Speaker 3: can remove brand deals from older content and replace them

639
00:31:53,640 --> 00:31:56,400
Speaker 3: with current brand deals using a new future from YouTube.

640
00:31:56,520 --> 00:31:59,640
Speaker 3: Right there was there's a guy from YouTube at the panel. Sure, yeah,

641
00:31:59,640 --> 00:32:02,160
Speaker 3: I'm sure who's very excited. But it was mostly about how,

642
00:32:02,360 --> 00:32:05,719
Speaker 3: you know, new ways to use influencers to market your product,

643
00:32:06,080 --> 00:32:08,880
Speaker 3: and that was the extent of what the creator economy

644
00:32:08,920 --> 00:32:09,400
Speaker 3: really meant.

645
00:32:09,560 --> 00:32:11,680
Speaker 2: And that's all any of these people have any idea

646
00:32:11,720 --> 00:32:15,320
Speaker 2: on is like we can inject ads into AI they

647
00:32:15,360 --> 00:32:18,280
Speaker 2: trust AI so they'll buy the products, or we can

648
00:32:18,320 --> 00:32:22,000
Speaker 2: inject ads into influencers they trusts. That was the thing,

649
00:32:22,240 --> 00:32:24,600
Speaker 2: Like none of these people they dress it up with

650
00:32:24,640 --> 00:32:27,000
Speaker 2: all sorts of fancy language, but it's and most of

651
00:32:27,040 --> 00:32:29,840
Speaker 2: these panels you mentioned, like there's a lot of bullshit

652
00:32:29,920 --> 00:32:31,800
Speaker 2: every now and then you get some like good moments

653
00:32:31,840 --> 00:32:34,400
Speaker 2: so you get to like question an asshole. But it's

654
00:32:34,400 --> 00:32:37,680
Speaker 2: mostly bullshit, but it's occasionally worth it for moments like

655
00:32:38,080 --> 00:32:40,160
Speaker 2: when I was on the agentic AI cutting through the

656
00:32:40,240 --> 00:32:44,200
Speaker 2: hype panel, Jay Patasol, who's the principal analyst at Forrester,

657
00:32:44,720 --> 00:32:48,440
Speaker 2: started speaking and he said something beautiful, Garrison, and I'm

658
00:32:48,720 --> 00:32:51,560
Speaker 2: this is not an exact but it's pretty close. We

659
00:32:51,600 --> 00:32:54,600
Speaker 2: have a new audience. We are speaking to machines. We

660
00:32:54,680 --> 00:32:57,400
Speaker 2: are through the looking glass. We are building content for engines.

661
00:32:57,440 --> 00:32:59,840
Speaker 2: We are building websites to be scraped so that an

662
00:33:00,360 --> 00:33:03,760
Speaker 2: can understand what you wanted to understand about your brand. Yep, yep, yeah,

663
00:33:03,880 --> 00:33:06,480
Speaker 2: that gets it. That's what these people see the Internet

664
00:33:06,520 --> 00:33:08,880
Speaker 2: as they see it as like everything before this was

665
00:33:08,920 --> 00:33:12,440
Speaker 2: a mistake or was. What the Internet was for was

666
00:33:12,520 --> 00:33:17,200
Speaker 2: a place for brands to feed information into machines that

667
00:33:17,240 --> 00:33:20,720
Speaker 2: then spoon feed the information directly into customers who trust

668
00:33:20,720 --> 00:33:23,560
Speaker 2: it like little lambs. That's what they want the Internet

669
00:33:23,600 --> 00:33:25,840
Speaker 2: to be, and that's what they believe they've gotten to.

670
00:33:25,960 --> 00:33:28,320
Speaker 2: That's what AI. That's the promise of AI.

671
00:33:28,600 --> 00:33:30,480
Speaker 3: The promise of AI is that this isn't just the

672
00:33:30,520 --> 00:33:33,080
Speaker 3: Internet anymore. This can actually just be the physical world

673
00:33:33,120 --> 00:33:35,200
Speaker 3: as well. And this is something that was talked about

674
00:33:35,320 --> 00:33:40,280
Speaker 3: during the cees CTA keynote Tuesday morning. Specifically, with the

675
00:33:40,280 --> 00:33:43,440
Speaker 3: birth of AI wearables, each of these wearables is able

676
00:33:43,480 --> 00:33:46,680
Speaker 3: to now collect information about the physical world and as

677
00:33:46,720 --> 00:33:49,240
Speaker 3: talng as you have, you know, adequate data sharing, AI

678
00:33:49,280 --> 00:33:52,560
Speaker 3: is able to gain so much more knowledge about how

679
00:33:52,600 --> 00:33:55,239
Speaker 3: the quote unquote real world operates. And this is going

680
00:33:55,280 --> 00:33:58,440
Speaker 3: to make you know, all of the processes of AI

681
00:33:58,680 --> 00:34:01,520
Speaker 3: stronger in the future as that learns more about what

682
00:34:01,600 --> 00:34:05,960
Speaker 3: this world actually is. Yeah, and beyond the promise of

683
00:34:06,000 --> 00:34:10,760
Speaker 3: wearables to improve someone's life, this is the real project

684
00:34:11,040 --> 00:34:13,640
Speaker 3: is strengthening AI through the use of these wearables.

685
00:34:13,200 --> 00:34:15,680
Speaker 4: It's not actually about the consumer experience.

686
00:34:15,360 --> 00:34:17,120
Speaker 2: It's about providing data to this machine.

687
00:34:17,160 --> 00:34:21,640
Speaker 3: It's this like larger, larger, very like existential thing at

688
00:34:21,719 --> 00:34:24,399
Speaker 3: least for these executives or like that. That's the thing

689
00:34:24,440 --> 00:34:26,719
Speaker 3: that they are really emphasizing despite this being called the

690
00:34:26,760 --> 00:34:28,200
Speaker 3: Consumer Electronics Showcase.

691
00:34:28,320 --> 00:34:30,359
Speaker 2: And I think again, the best thing I can give

692
00:34:30,400 --> 00:34:33,200
Speaker 2: you into how fundamentally as much money as there is

693
00:34:33,239 --> 00:34:35,319
Speaker 2: behind this and as many grand words as they dress

694
00:34:35,360 --> 00:34:39,920
Speaker 2: it up, and how intellectually bankrupt this whole tech movement

695
00:34:40,080 --> 00:34:44,360
Speaker 2: is is that. The third panel that I went to,

696
00:34:44,880 --> 00:34:47,720
Speaker 2: which is about AI and creativity. One of the people

697
00:34:47,719 --> 00:34:50,719
Speaker 2: on it was Jesse Damasek, who works for Diagio, which

698
00:34:50,760 --> 00:34:52,320
Speaker 2: is like a company that imports all of your favorite

699
00:34:52,360 --> 00:34:55,080
Speaker 2: whiskeys from Europe, right like they sell all of the

700
00:34:55,120 --> 00:34:57,800
Speaker 2: different like Scottish whiskies that have to get like imported

701
00:34:57,840 --> 00:35:01,120
Speaker 2: and sold over here. And he was talking about they

702
00:35:01,120 --> 00:35:04,040
Speaker 2: were talking about some of the specific examples they had

703
00:35:04,239 --> 00:35:07,440
Speaker 2: of like how AI has been used in advertising campaigns,

704
00:35:08,120 --> 00:35:12,080
Speaker 2: and his exact statement was you can leverage an artist

705
00:35:12,160 --> 00:35:14,960
Speaker 2: and create infinite examples of their work, but which he

706
00:35:15,000 --> 00:35:16,920
Speaker 2: means you can find an artist that you like, sign

707
00:35:17,000 --> 00:35:19,120
Speaker 2: a deal with them, and then have AI created infinite

708
00:35:19,160 --> 00:35:22,319
Speaker 2: examples in their style, and so I camped afterwards and

709
00:35:22,360 --> 00:35:24,680
Speaker 2: I was like, what were you specifically referring to, Like,

710
00:35:24,719 --> 00:35:26,640
Speaker 2: how is this actually work as a product? And the

711
00:35:26,680 --> 00:35:28,360
Speaker 2: thing that he pointed out is that they have a

712
00:35:28,400 --> 00:35:32,239
Speaker 2: couple of whiskey brands that they have done. You go

713
00:35:32,400 --> 00:35:35,560
Speaker 2: in and you order a bottle and it's printed on site,

714
00:35:35,640 --> 00:35:38,000
Speaker 2: and it uses AI to make an example in the

715
00:35:38,080 --> 00:35:41,479
Speaker 2: style of this existing artist that they likes work that's

716
00:35:41,600 --> 00:35:45,920
Speaker 2: unique for you. Uh huh. And he said it's been

717
00:35:45,960 --> 00:35:49,839
Speaker 2: successful for them. Is it trillions of dollars? Three trillion dollars?

718
00:35:49,880 --> 00:35:49,920
Speaker 3: No?

719
00:35:49,960 --> 00:35:51,480
Speaker 2: I mean, these are these are things that like, yeah,

720
00:35:51,480 --> 00:35:53,600
Speaker 2: I guess I can see that maybe selling some Is

721
00:35:53,640 --> 00:35:58,040
Speaker 2: it selling better than any other like branded whiskey than

722
00:35:58,080 --> 00:36:00,440
Speaker 2: any other, like you know, because Whiskey Company, these big

723
00:36:00,440 --> 00:36:02,920
Speaker 2: ones will come out with like here's this edition every

724
00:36:03,000 --> 00:36:05,439
Speaker 2: year or whatever, they'll have one special limited edition one.

725
00:36:05,760 --> 00:36:07,840
Speaker 2: Is it selling better than that? We don't have that data,

726
00:36:07,920 --> 00:36:09,520
Speaker 2: but it was it's one of those is like that's

727
00:36:09,600 --> 00:36:10,080
Speaker 2: the idea.

728
00:36:10,200 --> 00:36:10,439
Speaker 3: Huh.

729
00:36:10,440 --> 00:36:13,680
Speaker 2: That's like we're talking about, like AI is supercharging creativity

730
00:36:13,680 --> 00:36:17,160
Speaker 2: and letting us like think bolder and more creatively than

731
00:36:17,280 --> 00:36:19,720
Speaker 2: we've ever thought before. And there were so many lines

732
00:36:20,440 --> 00:36:25,040
Speaker 2: in this fucking panel about like how we were like

733
00:36:25,160 --> 00:36:28,319
Speaker 2: hyper charging what human beings can be and do, and

734
00:36:28,400 --> 00:36:30,719
Speaker 2: like everyone should be really excited about what all this

735
00:36:30,760 --> 00:36:33,080
Speaker 2: means for the future. One of the panelists that my

736
00:36:33,200 --> 00:36:35,960
Speaker 2: best advice for you is let a thousand flowers bloom.

737
00:36:36,640 --> 00:36:38,200
Speaker 2: I'm sorry that was in the panel right before, but

738
00:36:38,239 --> 00:36:40,440
Speaker 2: it's still in all of this, Like it's all that

739
00:36:40,480 --> 00:36:43,440
Speaker 2: seems it's all the same kind of shit that bleeds together,

740
00:36:43,480 --> 00:36:45,680
Speaker 2: and it's like, Okay, what are your ideas? Well, we're

741
00:36:45,719 --> 00:36:48,800
Speaker 2: having a legrick kind of lie to manipulated an engine,

742
00:36:49,200 --> 00:36:53,520
Speaker 2: and we've got the custom printed bottles for your whiskey.

743
00:36:53,760 --> 00:36:58,279
Speaker 3: Well, you know, speaking of AI unleashing creativity. The last

744
00:36:58,320 --> 00:37:01,960
Speaker 3: thing I'll talk about this episode is the worst booth

745
00:37:02,520 --> 00:37:05,680
Speaker 3: at Showstoppers, which this year is kind of impressive because, yeah,

746
00:37:05,680 --> 00:37:09,000
Speaker 3: that's hard. It's mostly smart glasses and like three different

747
00:37:09,040 --> 00:37:11,800
Speaker 3: pool cleaners. Yeah, and then some random software stuff and

748
00:37:11,840 --> 00:37:13,719
Speaker 3: then a few things we saw last year. Sure, the

749
00:37:13,760 --> 00:37:18,000
Speaker 3: worst worst booth, Robert. You you write books.

750
00:37:17,760 --> 00:37:19,080
Speaker 2: Right, I have in the past.

751
00:37:19,600 --> 00:37:22,160
Speaker 3: What if in the future, what if I told you

752
00:37:22,760 --> 00:37:26,359
Speaker 3: that you could write three books in less than twenty

753
00:37:26,440 --> 00:37:27,040
Speaker 3: four hours.

754
00:37:27,080 --> 00:37:30,000
Speaker 2: God, thank you Garrison as.

755
00:37:29,840 --> 00:37:32,040
Speaker 4: A writer without using cocaine.

756
00:37:32,080 --> 00:37:35,320
Speaker 2: There's well, okay, now i'd say you're a liar.

757
00:37:35,480 --> 00:37:36,800
Speaker 4: That's see a little bit harder.

758
00:37:36,920 --> 00:37:39,080
Speaker 2: Yeah, but I thought you were trying to sell me

759
00:37:39,120 --> 00:37:41,000
Speaker 2: some blow. And I was going to say when we

760
00:37:41,040 --> 00:37:41,839
Speaker 2: turned the mic off.

761
00:37:42,200 --> 00:37:46,080
Speaker 3: With the power of AI, you can write three books

762
00:37:46,120 --> 00:37:48,080
Speaker 3: in six to twenty four hours.

763
00:37:48,560 --> 00:37:52,040
Speaker 2: Wow, that's almost as fast as Stephen King Winny was

764
00:37:52,040 --> 00:37:53,919
Speaker 2: son co. Yeah, there you get not quite.

765
00:37:54,280 --> 00:37:55,640
Speaker 4: So there's this table.

766
00:37:55,920 --> 00:37:58,560
Speaker 3: There was the least abouting table definitely all showstoppers because

767
00:37:58,560 --> 00:38:01,280
Speaker 3: it was it was filled with books with I will.

768
00:38:01,120 --> 00:38:04,839
Speaker 4: Show you the covers here. They all look like this.

769
00:38:06,000 --> 00:38:09,080
Speaker 2: They all oh yeah, no those I mean I'm seeing

770
00:38:09,160 --> 00:38:10,080
Speaker 2: blue and orange.

771
00:38:10,239 --> 00:38:13,040
Speaker 4: It's colors in it's AI generated images.

772
00:38:13,160 --> 00:38:15,480
Speaker 3: Like yeah, it's like every movie poster now in like

773
00:38:15,520 --> 00:38:19,400
Speaker 3: there's no art style behind it. It's very generic and

774
00:38:19,440 --> 00:38:23,040
Speaker 3: they have like, you know, like the most generic font

775
00:38:23,280 --> 00:38:25,600
Speaker 3: for the title, all in the same placement with some

776
00:38:25,719 --> 00:38:26,720
Speaker 3: author's name at the bottom.

777
00:38:26,960 --> 00:38:27,880
Speaker 2: They're very sleepy.

778
00:38:27,920 --> 00:38:30,040
Speaker 3: You could you can find pictures of these covers if

779
00:38:30,080 --> 00:38:34,440
Speaker 3: you Google or bing or you know, maybe chat gpt

780
00:38:35,200 --> 00:38:37,280
Speaker 3: write three books in twenty four hours.

781
00:38:37,400 --> 00:38:38,680
Speaker 4: You can see you can see the cover.

782
00:38:38,840 --> 00:38:41,400
Speaker 2: Finally, I've always wanted to write three books, Garrison.

783
00:38:41,560 --> 00:38:45,800
Speaker 3: So what this is is an app that will help

784
00:38:45,880 --> 00:38:48,359
Speaker 3: you write these books is not going to do it

785
00:38:48,400 --> 00:38:50,600
Speaker 3: all for you. You still need to come up with

786
00:38:50,640 --> 00:38:52,400
Speaker 3: the general idea of the story.

787
00:38:52,560 --> 00:38:55,480
Speaker 4: The hard stuff and the characters really the difficult. The

788
00:38:55,520 --> 00:38:57,160
Speaker 4: world building is always the hardest part.

789
00:38:57,239 --> 00:38:58,880
Speaker 2: Everyone says most of the work on a book is

790
00:38:58,880 --> 00:38:59,920
Speaker 2: done the first six hours.

791
00:39:00,400 --> 00:39:03,160
Speaker 3: The world building is the really hard part. The easy

792
00:39:03,239 --> 00:39:05,719
Speaker 3: part is just getting all those words down. Yeah, so

793
00:39:06,320 --> 00:39:08,920
Speaker 3: you need to create create some characters. Now, could you

794
00:39:09,000 --> 00:39:11,360
Speaker 3: just have some other AI service create these characters?

795
00:39:11,400 --> 00:39:11,840
Speaker 2: Maybe?

796
00:39:12,000 --> 00:39:15,160
Speaker 3: But you should write maybe about a thousand words kind

797
00:39:15,200 --> 00:39:17,720
Speaker 3: of like a story Bible type thing or a character

798
00:39:17,880 --> 00:39:21,480
Speaker 3: character outline and a general direction for the story, and

799
00:39:21,520 --> 00:39:26,360
Speaker 3: you feed that into this app and then within hours

800
00:39:26,400 --> 00:39:30,720
Speaker 3: it will generate not just one book, not just two books,

801
00:39:31,640 --> 00:39:35,840
Speaker 3: but a trilogy wow of books. And it's only a trilogy.

802
00:39:35,960 --> 00:39:40,320
Speaker 3: You cannot generate a single book. The only come in trilogy.

803
00:39:40,360 --> 00:39:42,960
Speaker 2: Look, I get it's George Lucas worked the same way, Garrison.

804
00:39:43,280 --> 00:39:46,120
Speaker 2: Look you're telling me that the greatest machine mind and

805
00:39:46,239 --> 00:39:48,479
Speaker 2: history wouldn't think the same as the greatest human mind

806
00:39:48,520 --> 00:39:52,279
Speaker 2: in history. You know, I bet it'll independently create Jiz

807
00:39:52,360 --> 00:39:54,839
Speaker 2: music too. It only comes in trilogies.

808
00:39:57,520 --> 00:40:02,680
Speaker 3: And I now shall read a sample of this writing,

809
00:40:02,680 --> 00:40:04,880
Speaker 3: and like dying read, there was maybe there was maybe

810
00:40:04,920 --> 00:40:07,440
Speaker 3: like five or six different books with many copies of

811
00:40:07,440 --> 00:40:09,680
Speaker 3: the same book on this table, and I flipped through,

812
00:40:09,719 --> 00:40:12,719
Speaker 3: maybe about half reading like a random page every you know,

813
00:40:12,800 --> 00:40:16,880
Speaker 3: every like twenty fifty pages, and it was it was

814
00:40:17,840 --> 00:40:20,400
Speaker 3: it was too boring that I forgot to take pictures

815
00:40:20,719 --> 00:40:23,239
Speaker 3: of these pages because I was just like it was

816
00:40:23,239 --> 00:40:26,200
Speaker 3: a struggle to finish, to finish each page. But luckily

817
00:40:26,239 --> 00:40:28,640
Speaker 3: on their website they do have some sample pages. I

818
00:40:29,239 --> 00:40:31,399
Speaker 3: talked to one of the one of the guys working

819
00:40:31,440 --> 00:40:33,640
Speaker 3: at the booth, and he said that he tried this

820
00:40:33,800 --> 00:40:36,879
Speaker 3: so we're like he found the service and he first thought,

821
00:40:36,880 --> 00:40:39,120
Speaker 3: you know, sure that this can't be any good. And

822
00:40:39,320 --> 00:40:41,480
Speaker 3: when he when he generated his book, he was surprised

823
00:40:41,560 --> 00:40:42,520
Speaker 3: how good the writing is.

824
00:40:42,560 --> 00:40:43,879
Speaker 4: He said that he probably.

825
00:40:43,560 --> 00:40:48,000
Speaker 3: Wouldn't win a Pulletzer or a Hugo, his two awards

826
00:40:48,000 --> 00:40:51,160
Speaker 3: that he named, but he said it was pretty good.

827
00:40:51,360 --> 00:40:51,520
Speaker 7: Right.

828
00:40:53,560 --> 00:40:56,040
Speaker 3: This guy was so far of the most Tim Robinson

829
00:40:56,200 --> 00:41:00,760
Speaker 3: character I met at the cop Yeah, that's great. So Robert,

830
00:41:00,840 --> 00:41:03,680
Speaker 3: you can pick the genre of sample. We have a

831
00:41:03,719 --> 00:41:10,200
Speaker 3: thriller book, a fantasy, mystery, science fiction, romance, or mainstream

832
00:41:10,280 --> 00:41:11,080
Speaker 3: literary fiction.

833
00:41:11,200 --> 00:41:12,560
Speaker 4: What genre do you want?

834
00:41:12,680 --> 00:41:15,480
Speaker 2: I think I want science fiction science fiction because I

835
00:41:15,480 --> 00:41:18,920
Speaker 2: feel like there's the shortest line between parody and legitimate

836
00:41:18,960 --> 00:41:19,719
Speaker 2: within sci fi.

837
00:41:20,680 --> 00:41:24,200
Speaker 3: All right, this is from a book called I don't

838
00:41:24,200 --> 00:41:25,279
Speaker 3: even want to say this one.

839
00:41:26,080 --> 00:41:27,440
Speaker 2: I'm really curious now.

840
00:41:27,880 --> 00:41:30,480
Speaker 4: Palympsit orbit is what I'm going to say.

841
00:41:30,520 --> 00:41:33,400
Speaker 2: Oh my god, they're starting to be Arthur C. Clark.

842
00:41:33,480 --> 00:41:40,000
Speaker 3: It's called the Polympsit Orbit. Chapter one, Desert Signals. Marrow

843
00:41:40,040 --> 00:41:43,080
Speaker 3: woke with the taste of metal in her mouth and

844
00:41:43,120 --> 00:41:46,200
Speaker 3: a pulse in her temples that felt one notch shy

845
00:41:46,320 --> 00:41:49,600
Speaker 3: of a hangover. The ceiling above her was low and white,

846
00:41:49,800 --> 00:41:53,120
Speaker 3: edged with soft events. A monitor over the bed scrolled

847
00:41:53,160 --> 00:41:57,319
Speaker 3: green numbers in a stylized outline of her lungs thin air.

848
00:41:57,760 --> 00:41:58,960
Speaker 2: She remembered.

849
00:42:01,520 --> 00:42:06,360
Speaker 3: The Atacoma sky, somewhere above concrete and glass. Good morning,

850
00:42:06,360 --> 00:42:11,440
Speaker 3: doctor Ellison, A calm baritone. How's the head? She turned

851
00:42:11,480 --> 00:42:14,520
Speaker 3: toward the voice. A man in the doorway wore a

852
00:42:14,560 --> 00:42:17,880
Speaker 3: slate blue clinic jumper and a badge that caught the

853
00:42:17,960 --> 00:42:22,160
Speaker 3: desert light leaking through the polarized glass. Dark curls threaded

854
00:42:22,200 --> 00:42:25,799
Speaker 3: with gray laugh lines that didn't quite match the tiredness

855
00:42:25,840 --> 00:42:26,600
Speaker 3: around his eyes.

856
00:42:26,760 --> 00:42:29,160
Speaker 4: Hire Man, are you good? Go me to keep you?

857
00:42:29,680 --> 00:42:32,600
Speaker 2: I you know, it's it's again. It's like the it's

858
00:42:32,600 --> 00:42:35,440
Speaker 2: an imitation of like a story. Like it's a scene

859
00:42:35,480 --> 00:42:38,960
Speaker 2: and it's a scene with details to describe people. But

860
00:42:39,040 --> 00:42:42,000
Speaker 2: there's not like you would ideally, I would have something

861
00:42:42,040 --> 00:42:43,960
Speaker 2: of an idea of like what the thrust of the

862
00:42:44,000 --> 00:42:47,880
Speaker 2: story is going to be. Like for example, Bilbo Baggins

863
00:42:47,960 --> 00:42:50,239
Speaker 2: was a hobbit who lived in a house underhill or

864
00:42:50,280 --> 00:42:53,080
Speaker 2: something like that. Forget the exact wording of that, but like,

865
00:42:54,480 --> 00:42:57,120
Speaker 2: I you know, it makes sense. It sounds like it

866
00:42:57,400 --> 00:43:01,560
Speaker 2: sounds remarkably like bad No. I don't insult Nano Remo

867
00:43:01,640 --> 00:43:04,799
Speaker 2: writers that much. It just it just it sounds like

868
00:43:04,920 --> 00:43:08,480
Speaker 2: a story that was generated based on a belief that like, well,

869
00:43:08,520 --> 00:43:10,600
Speaker 2: if we can just like describe enough stuff and use

870
00:43:10,680 --> 00:43:14,680
Speaker 2: enough words to describe a scene, then that counts as plot. Yeah,

871
00:43:14,719 --> 00:43:17,399
Speaker 2: I mean in character, which we don't have any of yet.

872
00:43:17,360 --> 00:43:20,880
Speaker 3: It's all of it was this very generic, empty like

873
00:43:21,120 --> 00:43:24,319
Speaker 3: stuff that's very very common. And if you ever have

874
00:43:24,360 --> 00:43:26,359
Speaker 3: to read through a lot of like AI writing, whether

875
00:43:26,440 --> 00:43:28,319
Speaker 3: for work or let's say, you know, you work in

876
00:43:28,360 --> 00:43:30,440
Speaker 3: a college, so you have students submitting this stuff, or

877
00:43:30,440 --> 00:43:33,560
Speaker 3: you for some reason are online and you feel obligated

878
00:43:33,600 --> 00:43:35,360
Speaker 3: to look at the worst parts of the world, like

879
00:43:35,560 --> 00:43:36,279
Speaker 3: what me and Robert do.

880
00:43:36,400 --> 00:43:38,440
Speaker 4: Sometimes this is all feels very familiar.

881
00:43:38,560 --> 00:43:41,520
Speaker 3: I'll read one other like paragraph from a different book,

882
00:43:42,000 --> 00:43:45,520
Speaker 3: a thriller called The Helix Files, Oh Good, obviously part

883
00:43:45,560 --> 00:43:47,880
Speaker 3: of a trilogy, so who knows where these stories go

884
00:43:48,000 --> 00:43:49,280
Speaker 3: over the course of three books.

885
00:43:49,640 --> 00:43:51,800
Speaker 4: Quote the car heater had.

886
00:43:51,680 --> 00:43:55,279
Speaker 3: Died ten minutes ago, cold leaked through the floorboards into

887
00:43:55,320 --> 00:43:59,799
Speaker 3: Helix's boots. Outside the eastern block industrial belt, slid past

888
00:43:59,840 --> 00:44:05,400
Speaker 3: and gray slabs and rusted steel, wet concrete period, diesel period,

889
00:44:06,080 --> 00:44:10,160
Speaker 3: a straight dog nosing tragh heap outside the road first,

890
00:44:10,200 --> 00:44:12,640
Speaker 3: slick with drizzle. So it's it's something that right, Yeah,

891
00:44:12,640 --> 00:44:14,760
Speaker 3: there's a lot of this sort of like quick punchy

892
00:44:14,880 --> 00:44:18,120
Speaker 3: sentences are common in AI writing at the moment, wet concrete,

893
00:44:18,160 --> 00:44:19,960
Speaker 3: you know, with a period. But like a lot of

894
00:44:20,000 --> 00:44:22,759
Speaker 3: this type of stuff you you see you see in

895
00:44:22,800 --> 00:44:24,759
Speaker 3: a AI writing, you have a lot of a lot

896
00:44:24,800 --> 00:44:27,759
Speaker 3: a lot of character and a lot a lot of

897
00:44:27,760 --> 00:44:30,680
Speaker 3: like m dash sentences. As I was flipping through these books,

898
00:44:30,719 --> 00:44:32,319
Speaker 3: I was like, okay, yeah, like I see what they're doing.

899
00:44:32,320 --> 00:44:33,280
Speaker 2: I see yeah.

900
00:44:33,360 --> 00:44:35,480
Speaker 3: But but now, if if you want to write a

901
00:44:35,600 --> 00:44:37,879
Speaker 3: quote unquote write a trilogy of books, you can pay

902
00:44:37,880 --> 00:44:40,560
Speaker 3: the money and and within six hours you will have

903
00:44:40,600 --> 00:44:44,600
Speaker 3: a trilogy. So what really makes me feel optimistic about

904
00:44:44,600 --> 00:44:47,440
Speaker 3: CES is the way that creativity is being democratized.

905
00:44:47,719 --> 00:44:50,600
Speaker 4: It used to be that no ordinary person could write

906
00:44:50,600 --> 00:44:50,960
Speaker 4: a book.

907
00:44:51,239 --> 00:44:52,960
Speaker 3: You had to have a story and be some sort

908
00:44:53,000 --> 00:44:55,120
Speaker 3: of freak at Oxford maybe like a you need like

909
00:44:55,160 --> 00:44:59,760
Speaker 3: a pencil, maybe a keyboard. Yeah, possible, barriers, it's not possible.

910
00:44:59,800 --> 00:45:03,040
Speaker 3: And now, luckily through AI, as long as you have

911
00:45:03,160 --> 00:45:04,520
Speaker 3: you know, sub money.

912
00:45:04,280 --> 00:45:06,959
Speaker 2: To pay a subscription service at a computer and VC

913
00:45:07,320 --> 00:45:09,759
Speaker 2: fundraising and subsidizing of vice service.

914
00:45:09,480 --> 00:45:13,160
Speaker 3: Ideally, yeah, then you too can be an author of

915
00:45:13,200 --> 00:45:13,800
Speaker 3: a trilogy.

916
00:45:14,800 --> 00:45:17,719
Speaker 2: Well that's that's my plan for the future. I guess

917
00:45:17,760 --> 00:45:20,680
Speaker 2: I want to end by talking about the second to

918
00:45:20,680 --> 00:45:24,399
Speaker 2: the last panel that I sat through, which was at

919
00:45:24,400 --> 00:45:27,719
Speaker 2: the AI house, and was I think yet again, this

920
00:45:27,840 --> 00:45:29,839
Speaker 2: was another one that was about they were largely talking

921
00:45:29,840 --> 00:45:32,399
Speaker 2: about ethics in this one, like AI and ethics and

922
00:45:32,480 --> 00:45:36,719
Speaker 2: like what that actually means. And Eric Pace, who on

923
00:45:36,760 --> 00:45:40,080
Speaker 2: the slide lajer City works at company but it's reassuring, Yeah,

924
00:45:40,239 --> 00:45:42,239
Speaker 2: reassuring who works at Cox Media, which is like a

925
00:45:42,280 --> 00:45:46,399
Speaker 2: big media company, yes, based on Georgia. And he had

926
00:45:46,520 --> 00:45:48,759
Speaker 2: a couple of statements that were interested in me. He

927
00:45:48,800 --> 00:45:50,719
Speaker 2: had one where he said that, like, it's kind of

928
00:45:50,760 --> 00:45:56,880
Speaker 2: incumbent upon people to develop an ethical rubric for how

929
00:45:56,960 --> 00:45:59,759
Speaker 2: and what sources and what AI is they trust and

930
00:45:59,800 --> 00:46:05,080
Speaker 2: want and figure that out. And I think what I

931
00:46:05,160 --> 00:46:07,799
Speaker 2: inferred was that, like, because it's not going to get

932
00:46:07,840 --> 00:46:10,480
Speaker 2: done by anyone else, And it kind of became clude

933
00:46:10,480 --> 00:46:12,080
Speaker 2: to me later. I think it's he also doesn't want

934
00:46:12,080 --> 00:46:13,840
Speaker 2: anyone else to do it. He wants this to be

935
00:46:13,880 --> 00:46:15,800
Speaker 2: an individual project where you have to kind of figure

936
00:46:15,840 --> 00:46:18,680
Speaker 2: that out for yourself. I was kind of unsure as

937
00:46:18,680 --> 00:46:20,960
Speaker 2: to whether he was the evil or just the pragmatic

938
00:46:21,040 --> 00:46:23,360
Speaker 2: version of this, because the pragmatic version is like, literally,

939
00:46:23,400 --> 00:46:25,200
Speaker 2: no one's going to restrict this stuff. You just have

940
00:46:25,280 --> 00:46:28,520
Speaker 2: to try to get by right, which is maybe accurate.

941
00:46:29,160 --> 00:46:31,200
Speaker 2: But there was a really interesting interaction on this panel.

942
00:46:31,239 --> 00:46:33,280
Speaker 2: One of the other people there was doctor Martin Clancy,

943
00:46:33,280 --> 00:46:37,319
Speaker 2: who was an Irish academic and a musician who was

944
00:46:37,440 --> 00:46:39,880
Speaker 2: on the panel again to talk about like creativity and ethics,

945
00:46:40,040 --> 00:46:42,560
Speaker 2: and made a comment that I found was really interesting

946
00:46:42,600 --> 00:46:44,239
Speaker 2: and I don't know, I wouldn't say I agree or

947
00:46:44,280 --> 00:46:46,000
Speaker 2: disagree with it, but I found it really interesting where

948
00:46:46,000 --> 00:46:49,600
Speaker 2: he was like, actually, I'm not at all concerned comparatively

949
00:46:49,800 --> 00:46:53,120
Speaker 2: about having an AI give me medical advice. I'm deeply

950
00:46:53,160 --> 00:46:56,360
Speaker 2: concerned about letting an AI recommend music or movies to me,

951
00:46:56,800 --> 00:46:58,640
Speaker 2: which I found a really interesting attitude and kind of

952
00:46:58,640 --> 00:47:00,600
Speaker 2: a thought provoking yell that it is interesting, which was

953
00:47:00,640 --> 00:47:03,520
Speaker 2: immediately spoiled by Eric Pace going like, well, I don't

954
00:47:03,520 --> 00:47:05,760
Speaker 2: see why anyone would have an issue with an AI doctor.

955
00:47:05,800 --> 00:47:07,480
Speaker 2: Doctors get things wrong all the time.

956
00:47:07,600 --> 00:47:10,680
Speaker 3: And then he just like let that statement set.

957
00:47:09,320 --> 00:47:14,439
Speaker 2: I have heard this before, and ais have a lot

958
00:47:14,480 --> 00:47:17,040
Speaker 2: more data. And they ended it by saying because everyone

959
00:47:17,040 --> 00:47:18,600
Speaker 2: asked like, what were their big wins of the year,

960
00:47:18,680 --> 00:47:21,919
Speaker 2: and his big win was that his wife hated chat

961
00:47:21,960 --> 00:47:23,920
Speaker 2: ept and didn't want to use it, and he convinced

962
00:47:23,960 --> 00:47:26,319
Speaker 2: her to use it to plan their vacation. It kind

963
00:47:26,320 --> 00:47:28,239
Speaker 2: of sounded like he bullied her into it, but that

964
00:47:28,360 --> 00:47:30,600
Speaker 2: was his big win for the year. I didn't like him.

965
00:47:31,080 --> 00:47:31,960
Speaker 1: My win is I.

966
00:47:33,719 --> 00:47:37,160
Speaker 2: My wife into using a chat bot to plan special

967
00:47:37,239 --> 00:47:40,600
Speaker 2: time vacationing together because we're not creative enough to figure

968
00:47:40,600 --> 00:47:42,719
Speaker 2: out how to go on a fucking trip.

969
00:47:42,480 --> 00:47:44,279
Speaker 4: Hashtag AI win Jesus.

970
00:47:44,480 --> 00:47:47,440
Speaker 2: I don't know anyway. I think that's good for episode

971
00:47:47,440 --> 00:47:49,799
Speaker 2: one from Cees. Come back next week. We'll we'll all

972
00:47:49,840 --> 00:47:52,359
Speaker 2: have more or listen to better offline where ed will

973
00:47:52,400 --> 00:47:55,359
Speaker 2: have just a shocking amount of content from a lot

974
00:47:55,440 --> 00:47:59,960
Speaker 2: of the relatively few and constantly shrinking stable of saying

975
00:48:00,120 --> 00:48:01,759
Speaker 2: people reporting on technology.

976
00:48:01,960 --> 00:48:02,680
Speaker 5: See you next week.

977
00:48:06,080 --> 00:48:08,600
Speaker 1: It could Happen Here is a production of cool Zone Media.

978
00:48:08,760 --> 00:48:11,840
Speaker 1: For more podcasts from cool Zone Media, visit our website

979
00:48:11,920 --> 00:48:15,480
Speaker 1: coolzonmedia dot com, or check us out on the iHeartRadio app,

980
00:48:15,560 --> 00:48:19,120
Speaker 1: Apple Podcasts, or wherever you listen to podcasts. You can

981
00:48:19,160 --> 00:48:21,480
Speaker 1: now find sources for it could Happen here, listed directly

982
00:48:21,520 --> 00:48:23,800
Speaker 1: in episode descriptions. Thanks for listening.