1
00:00:04,440 --> 00:00:12,399
Speaker 1: Welcome to Tech Stuff, a production from iHeartRadio. Hey therein

2
00:00:12,560 --> 00:00:16,320
Speaker 1: Welcome to Tech Stuff. I'm your host Jonathan Strickland. I'm

3
00:00:16,360 --> 00:00:19,880
Speaker 1: an executive producer with iHeartRadio. And how the tech are you?

4
00:00:20,239 --> 00:00:23,360
Speaker 1: It's time for the tech news for Tuesday, May thirty,

5
00:00:23,920 --> 00:00:29,520
Speaker 1: twenty twenty three. Nvidia CEO Jensen Wang spoke at the

6
00:00:29,680 --> 00:00:34,440
Speaker 1: Computech's forum in Taiwan yesterday and said something that I'm

7
00:00:34,479 --> 00:00:38,239
Speaker 1: sure has a lot of programmers anxious. Huang said that

8
00:00:38,560 --> 00:00:43,040
Speaker 1: due to how quickly generative AI evolves, we're entering into

9
00:00:43,040 --> 00:00:46,720
Speaker 1: an era in which essentially anyone can be a programmer.

10
00:00:47,200 --> 00:00:50,080
Speaker 1: It won't require you to have studied computer languages or

11
00:00:50,120 --> 00:00:53,600
Speaker 1: even computer science. All it will take is a sufficiently

12
00:00:53,640 --> 00:00:57,760
Speaker 1: sophisticated AI model to take your prompts and then turn

13
00:00:57,840 --> 00:01:02,160
Speaker 1: that into a well designed program. Wang unveiled a platform

14
00:01:02,240 --> 00:01:09,040
Speaker 1: called the DGXGH two hundred, essentially a supercomputing pro platform.

15
00:01:09,440 --> 00:01:12,959
Speaker 1: It's designed to help build the next generation of generative

16
00:01:13,000 --> 00:01:18,080
Speaker 1: AI models. This actually reminds me of the fictional supercomputer

17
00:01:18,480 --> 00:01:21,840
Speaker 1: Deep Thought in The Hitchhiker's Guide to the Galaxy books,

18
00:01:22,280 --> 00:01:26,039
Speaker 1: which explained that it was not sufficiently powerful enough to

19
00:01:26,080 --> 00:01:29,760
Speaker 1: provide the question to life, the universe and everything. It

20
00:01:29,800 --> 00:01:32,880
Speaker 1: gave us the answer, but not the question. However, it

21
00:01:33,040 --> 00:01:37,480
Speaker 1: was powerful enough to build a computer that could do that.

22
00:01:37,480 --> 00:01:39,680
Speaker 1: That seems to be what this is indicating. It's a

23
00:01:39,720 --> 00:01:45,040
Speaker 1: supercomputer meant to build better AI. Anyway, Wang's keynote seemed

24
00:01:45,040 --> 00:01:47,440
Speaker 1: to indicate that not too long from now, you won't

25
00:01:47,440 --> 00:01:50,640
Speaker 1: need a background in programming in order to be a programmer,

26
00:01:50,720 --> 00:01:53,600
Speaker 1: or at least that's how the articles covering his keynote

27
00:01:53,680 --> 00:01:57,120
Speaker 1: seemed to frame it. I'd like to think that generative

28
00:01:57,120 --> 00:02:00,880
Speaker 1: AI will help programmers and help them be more productive

29
00:02:00,880 --> 00:02:04,160
Speaker 1: and more efficient, and that AI will give them the

30
00:02:04,240 --> 00:02:08,440
Speaker 1: tools to build code that contains fewer errors, or to

31
00:02:08,680 --> 00:02:12,640
Speaker 1: check for mistakes as they're coding that kind of thing. So,

32
00:02:12,680 --> 00:02:15,040
Speaker 1: in other words, my hope is that this isn't a

33
00:02:15,080 --> 00:02:20,320
Speaker 1: step toward invalidating an entire career path or and this

34
00:02:20,480 --> 00:02:23,320
Speaker 1: is really the cynical part of me. I hope it's

35
00:02:23,360 --> 00:02:28,640
Speaker 1: not an attempt to justify hiring relatively unskilled employees at

36
00:02:28,680 --> 00:02:31,280
Speaker 1: a much lower salary than what it would cost to

37
00:02:31,320 --> 00:02:35,880
Speaker 1: bring on a qualified programmer. Right. That's the fear is

38
00:02:35,919 --> 00:02:40,800
Speaker 1: that if you've dedicated your time and energy. You pursued

39
00:02:40,840 --> 00:02:45,919
Speaker 1: an education in computer science and computer programming, and you've

40
00:02:45,960 --> 00:02:49,640
Speaker 1: built the skill set that would normally guarantee you a

41
00:02:49,760 --> 00:02:53,119
Speaker 1: chance of landing a lucrative career in the field you love,

42
00:02:53,639 --> 00:02:56,040
Speaker 1: and then you find out, oh, no, you're overqualified. We

43
00:02:56,120 --> 00:02:57,920
Speaker 1: just need someone who can talk to this computer and

44
00:02:57,960 --> 00:03:02,200
Speaker 1: make the thing that we want. It's not good. So

45
00:03:03,360 --> 00:03:06,079
Speaker 1: my hope is that a lot of the news that

46
00:03:06,320 --> 00:03:11,360
Speaker 1: covered this was doing so in a way that was

47
00:03:11,520 --> 00:03:14,960
Speaker 1: perhaps not reflective of what Wang was actually saying. I

48
00:03:15,000 --> 00:03:17,400
Speaker 1: say that because I didn't get a chance to actually

49
00:03:17,440 --> 00:03:21,200
Speaker 1: watch his keynote, so I'm not certain how he worded it.

50
00:03:21,240 --> 00:03:24,040
Speaker 1: I can only react to the way it's been reported.

51
00:03:24,880 --> 00:03:28,280
Speaker 1: Reuter's reports that deep Media, which is a company that

52
00:03:28,320 --> 00:03:31,720
Speaker 1: works to identify and track the proliferation of deep fake

53
00:03:31,840 --> 00:03:36,320
Speaker 1: videos and other deep fake content online, has indicated that

54
00:03:36,360 --> 00:03:39,400
Speaker 1: there's been a pretty significant jump in the number of

55
00:03:39,440 --> 00:03:43,040
Speaker 1: instances of deep fakes this past year. So, according to

56
00:03:43,040 --> 00:03:46,360
Speaker 1: deep Media, there are three times as many deep fake

57
00:03:46,480 --> 00:03:50,640
Speaker 1: videos circulating this year as there were in the same

58
00:03:50,760 --> 00:03:53,600
Speaker 1: span of time back in twenty twenty two, and there

59
00:03:53,640 --> 00:03:58,520
Speaker 1: are eight times as many deep fake voice recordings and

60
00:03:58,600 --> 00:04:03,040
Speaker 1: deep fake voice examples. I'm not sure how many examples

61
00:04:03,080 --> 00:04:05,960
Speaker 1: we're talking about with that, because keep in mind, this

62
00:04:06,000 --> 00:04:09,480
Speaker 1: all depends on how many were circulating in twenty twenty two.

63
00:04:09,640 --> 00:04:12,560
Speaker 1: They just said it's eight times as many. So if

64
00:04:12,600 --> 00:04:15,240
Speaker 1: only one deep fake voice recording had popped up in

65
00:04:15,240 --> 00:04:17,479
Speaker 1: twenty twenty two, that would just mean that there were

66
00:04:17,560 --> 00:04:20,400
Speaker 1: eight that popped up this year, So the details matter.

67
00:04:20,520 --> 00:04:23,680
Speaker 1: This is also, by the way, when you hear about

68
00:04:23,960 --> 00:04:27,799
Speaker 1: really the growth of any business, but specifically within technology,

69
00:04:27,839 --> 00:04:31,120
Speaker 1: when you hear the growth is being expressed in percentages,

70
00:04:31,640 --> 00:04:33,120
Speaker 1: you really need to say, all right, but what are

71
00:04:33,120 --> 00:04:36,320
Speaker 1: the actual numbers, because if the actual numbers are very low,

72
00:04:36,760 --> 00:04:39,800
Speaker 1: a huge percentage in growth still can mean a pretty

73
00:04:39,839 --> 00:04:44,880
Speaker 1: low number, right, That's important anyway. Deep Media estimates that

74
00:04:45,360 --> 00:04:48,080
Speaker 1: by the end of this year, there will be around

75
00:04:48,160 --> 00:04:51,320
Speaker 1: half a million deep fake videos and voice recordings that

76
00:04:51,360 --> 00:04:56,080
Speaker 1: will be shared across social media, and as you probably suspect,

77
00:04:56,120 --> 00:04:59,440
Speaker 1: a lot of those will likely center around politics and

78
00:04:59,480 --> 00:05:03,960
Speaker 1: misinform As the deep fake generators become more sophisticated, it

79
00:05:04,000 --> 00:05:06,800
Speaker 1: can be a challenge for a normal human type person

80
00:05:07,320 --> 00:05:10,480
Speaker 1: to tell the difference between real videos and deep fake videos.

81
00:05:10,880 --> 00:05:14,200
Speaker 1: There are often indicators that you might be able to

82
00:05:14,240 --> 00:05:16,040
Speaker 1: tell if you're looking on a large enough screen and

83
00:05:16,080 --> 00:05:18,600
Speaker 1: a high enough resolution, but if you're like watching little

84
00:05:18,640 --> 00:05:22,520
Speaker 1: videos on your phone, you might not notice. There are

85
00:05:22,640 --> 00:05:26,960
Speaker 1: detection tools that are more effective for spotting really good

86
00:05:27,040 --> 00:05:30,159
Speaker 1: deep fakes out there, but you can imagine the damage

87
00:05:30,200 --> 00:05:34,560
Speaker 1: that can be done with this type of technology, particularly

88
00:05:34,600 --> 00:05:40,440
Speaker 1: for people who are already predisposed to believe certain ideologies.

89
00:05:40,960 --> 00:05:44,200
Speaker 1: If there's a video that seems to reinforce that ideology,

90
00:05:44,200 --> 00:05:48,240
Speaker 1: they may not take the time to question the authenticity

91
00:05:48,680 --> 00:05:52,279
Speaker 1: of that video. Various companies in the generative AI space

92
00:05:52,360 --> 00:05:56,000
Speaker 1: have been working on different approaches to mitigate this issue

93
00:05:56,040 --> 00:05:59,880
Speaker 1: to prevent the misuse of the technology, but these are

94
00:06:00,320 --> 00:06:04,160
Speaker 1: are nowhere close to being comprehensive or fully effective. Already,

95
00:06:04,200 --> 00:06:06,200
Speaker 1: there have been a few cases in which folks in

96
00:06:06,240 --> 00:06:09,400
Speaker 1: the political sphere I shall not name names, but some

97
00:06:09,880 --> 00:06:14,360
Speaker 1: prominent people in politics have shared deep fake videos, sometimes

98
00:06:14,400 --> 00:06:17,120
Speaker 1: with like a half hearted disclaimer of something along the

99
00:06:17,120 --> 00:06:19,240
Speaker 1: lines of, I don't know if this is real or not,

100
00:06:19,880 --> 00:06:22,720
Speaker 1: so just a side note. If you don't know if

101
00:06:22,720 --> 00:06:26,720
Speaker 1: it's real, just don't share it. But you know, no,

102
00:06:26,800 --> 00:06:28,840
Speaker 1: I get it. You know. The folks who share this

103
00:06:28,920 --> 00:06:31,839
Speaker 1: kind of stuff typically they don't really care if it's

104
00:06:31,880 --> 00:06:34,680
Speaker 1: real or not. They're just looking for the effect. I mean,

105
00:06:34,720 --> 00:06:37,800
Speaker 1: if it's real, it's better, but it doesn't really matter

106
00:06:37,839 --> 00:06:41,480
Speaker 1: because they're just looking to stir up a group of people,

107
00:06:42,000 --> 00:06:44,240
Speaker 1: so it doesn't really matter if it's real. All I

108
00:06:44,279 --> 00:06:47,320
Speaker 1: can say is that the use of critical thinking is

109
00:06:47,360 --> 00:06:52,640
Speaker 1: more important than ever, that employing critical thinking also takes work.

110
00:06:52,800 --> 00:06:56,640
Speaker 1: You have to be actively working and engage in critical thinking.

111
00:06:56,680 --> 00:07:00,000
Speaker 1: You can't just, you know, lean back and rely on

112
00:07:00,040 --> 00:07:02,600
Speaker 1: upon it to kick in. Because goodness knows, I've been

113
00:07:02,600 --> 00:07:04,960
Speaker 1: guilty of being too lazy to use it in the past.

114
00:07:05,600 --> 00:07:08,160
Speaker 1: It's happened to me. I talk about critical thinking all

115
00:07:08,200 --> 00:07:11,040
Speaker 1: the time, but I'm also guilty of not employing it

116
00:07:11,080 --> 00:07:13,720
Speaker 1: on occasion. I have to think about it, I have

117
00:07:13,760 --> 00:07:16,400
Speaker 1: to actively do it, and we all need to try

118
00:07:16,440 --> 00:07:19,920
Speaker 1: harder because it's getting tricky to tell the difference between

119
00:07:19,960 --> 00:07:22,600
Speaker 1: fact and fiction. There are a lot of tools out

120
00:07:22,640 --> 00:07:25,520
Speaker 1: there and a lot of bad actors out there that

121
00:07:25,680 --> 00:07:31,160
Speaker 1: collectively can start to push false narratives and to trick us.

122
00:07:31,600 --> 00:07:34,480
Speaker 1: And it's across the spectrum. It's not like it's just

123
00:07:34,600 --> 00:07:37,840
Speaker 1: one group doing this. There are lots of different people

124
00:07:37,920 --> 00:07:42,120
Speaker 1: with different motivations doing the same sort of stuff, and

125
00:07:42,160 --> 00:07:44,560
Speaker 1: we have to be on the lookout for it. And

126
00:07:44,600 --> 00:07:48,680
Speaker 1: now for a piece about the consequences of relying on

127
00:07:48,840 --> 00:07:54,200
Speaker 1: generative AI. So, a lawyer named Stephen Schwartz apparently used

128
00:07:54,280 --> 00:07:57,640
Speaker 1: chat GPT while doing some legal research on a case

129
00:07:57,680 --> 00:08:02,240
Speaker 1: that he was working. Chat GPT provided some background research

130
00:08:02,360 --> 00:08:06,920
Speaker 1: that Schwartz apparently then used in filing this case. But

131
00:08:07,000 --> 00:08:11,720
Speaker 1: there was a truly huge problem. Chat GPT cited other

132
00:08:12,000 --> 00:08:16,440
Speaker 1: legal cases in order to provide support for Schwartz's legal argument,

133
00:08:17,040 --> 00:08:22,760
Speaker 1: except those cases weren't real. They had never happened. Chat

134
00:08:22,800 --> 00:08:25,880
Speaker 1: GPT created, or, if you want to use the parlance

135
00:08:25,960 --> 00:08:30,920
Speaker 1: of the AI times, it hallucinated these cases. And I've

136
00:08:30,920 --> 00:08:34,400
Speaker 1: talked about AI hallucinations not too long ago on this

137
00:08:34,640 --> 00:08:38,560
Speaker 1: very show. You how generative AI models are essentially using

138
00:08:38,600 --> 00:08:43,679
Speaker 1: a complicated statistical model to generate responses. And this model

139
00:08:43,760 --> 00:08:47,280
Speaker 1: draws on a lot of archived information, but sometimes it

140
00:08:47,480 --> 00:08:52,160
Speaker 1: just invents stuff and essentially is saying what word would

141
00:08:52,200 --> 00:08:56,240
Speaker 1: most likely follow this word. Then you get a response

142
00:08:56,280 --> 00:09:00,240
Speaker 1: that sounds perfectly cromulent, as the Simpsons would say, but

143
00:09:00,559 --> 00:09:05,680
Speaker 1: is in fact hogwash or gibber jabber, or balderdash or

144
00:09:05,760 --> 00:09:09,040
Speaker 1: just plain fake. And as you might imagine, presenting fake

145
00:09:09,200 --> 00:09:12,200
Speaker 1: court cases as if they are a legitimate precedent to

146
00:09:12,240 --> 00:09:15,959
Speaker 1: your own case is not looked upon kindly by the court.

147
00:09:16,600 --> 00:09:19,920
Speaker 1: If you claim a precedent, then you should expect the

148
00:09:19,960 --> 00:09:22,560
Speaker 1: court to look into the precedent to make sure that

149
00:09:22,600 --> 00:09:25,600
Speaker 1: what you're saying is accurate. And when the court did

150
00:09:25,920 --> 00:09:29,480
Speaker 1: do that, when they double checked Schwartz's filings, they found

151
00:09:29,480 --> 00:09:34,000
Speaker 1: that the numerous cases Schwartz presented as suggested by chat

152
00:09:34,040 --> 00:09:39,040
Speaker 1: GPT didn't actually exist. And when pressed, Schwartz said he

153
00:09:39,080 --> 00:09:42,720
Speaker 1: was not aware that chat GPT could just invent stuff.

154
00:09:42,920 --> 00:09:47,320
Speaker 1: He assumed that everything that chat GPT presented came from actual,

155
00:09:47,480 --> 00:09:52,000
Speaker 1: real information that had been stored somewhere. Now, the judge

156
00:09:52,040 --> 00:09:54,840
Speaker 1: has ordered a hearing a few weeks from now to

157
00:09:55,000 --> 00:10:02,440
Speaker 1: quote unquote discuss potential sanctions. Woof So. On the one hand, yeah, absolutely,

158
00:10:03,000 --> 00:10:06,400
Speaker 1: it would be unthinkable to let people submit fake evidence

159
00:10:06,440 --> 00:10:10,440
Speaker 1: to support their arguments and then receive no repercussions afterward.

160
00:10:10,960 --> 00:10:13,679
Speaker 1: On the other hand, I mean, the hype around chat

161
00:10:13,720 --> 00:10:17,640
Speaker 1: GPT and other generative AI models is absolutely painting an

162
00:10:17,679 --> 00:10:21,680
Speaker 1: inaccurate picture of what they can do. Though, honestly, it

163
00:10:21,720 --> 00:10:24,120
Speaker 1: really doesn't take that much work to find out that

164
00:10:24,160 --> 00:10:27,480
Speaker 1: these AI systems are flawed. It's just that I could

165
00:10:27,600 --> 00:10:32,920
Speaker 1: understand why someone would put too much stock in chat

166
00:10:32,960 --> 00:10:39,120
Speaker 1: GPT's performance because of the way it has been hyped. Again,

167
00:10:39,600 --> 00:10:42,600
Speaker 1: it really doesn't take that much work to find where

168
00:10:42,640 --> 00:10:46,280
Speaker 1: the problems are. So I can't give Schwartz a pass here.

169
00:10:46,320 --> 00:10:49,200
Speaker 1: I could just say I could understand why he would think, oh,

170
00:10:49,240 --> 00:10:50,839
Speaker 1: this is a valid tool for me to use, and

171
00:10:50,840 --> 00:10:53,240
Speaker 1: there shouldn't be any problems. I can kind of understand that.

172
00:10:53,800 --> 00:10:56,160
Speaker 1: But if he had taken even just a little bit

173
00:10:56,200 --> 00:11:00,320
Speaker 1: of effort, he would have seen that perhaps rely so

174
00:11:00,360 --> 00:11:03,640
Speaker 1: heavily on chat GPT, without you know, fact checking, it

175
00:11:03,679 --> 00:11:08,280
Speaker 1: would have been foolish. Okay, we've got a lot more

176
00:11:08,400 --> 00:11:10,720
Speaker 1: news to cover before we get to that. Let's take

177
00:11:10,880 --> 00:11:22,319
Speaker 1: a quick break. So we're back, and we've got another

178
00:11:22,360 --> 00:11:26,360
Speaker 1: open letter, actually just a short warning from various AI

179
00:11:26,480 --> 00:11:30,679
Speaker 1: experts about the potential dangers of AI. It is very short.

180
00:11:30,720 --> 00:11:32,959
Speaker 1: It is to the point I'll actually read the whole

181
00:11:33,000 --> 00:11:37,680
Speaker 1: thing because it isn't long. Quote. Mitigating the risk of

182
00:11:37,720 --> 00:11:41,760
Speaker 1: extinction from AI should be a global priority alongside other

183
00:11:41,840 --> 00:11:46,520
Speaker 1: societal scale risks such as pandemics and nuclear war end

184
00:11:46,600 --> 00:11:51,160
Speaker 1: quote that is it, that's the warning putting AI on

185
00:11:51,240 --> 00:11:54,440
Speaker 1: the same level as things like pandemics and nuclear war

186
00:11:54,559 --> 00:11:58,160
Speaker 1: as a potential threat to the human race. A lot

187
00:11:58,160 --> 00:12:02,440
Speaker 1: of high profile AI experts have already signed their names

188
00:12:02,480 --> 00:12:05,760
Speaker 1: to the statement. That includes Sam Altman, the CEO of

189
00:12:05,800 --> 00:12:09,120
Speaker 1: Open Ai, who's been sending some kind of mixed messages

190
00:12:09,120 --> 00:12:13,160
Speaker 1: about AI regulation in the United States versus in the EU,

191
00:12:13,400 --> 00:12:17,719
Speaker 1: and then the CEO of Google's Deep Mind added their

192
00:12:17,760 --> 00:12:21,079
Speaker 1: signature to it, among other experts. So, in other words,

193
00:12:21,080 --> 00:12:24,480
Speaker 1: this warning has the backing of some very influential, educated

194
00:12:24,559 --> 00:12:27,120
Speaker 1: and knowledgeable people who are in the field of AI.

195
00:12:27,240 --> 00:12:31,040
Speaker 1: Of course, that's assuming that the signatures are legit, because

196
00:12:31,080 --> 00:12:34,200
Speaker 1: we did have a case not that long ago where

197
00:12:34,240 --> 00:12:38,640
Speaker 1: there was an open letter warning about AI that contained

198
00:12:38,679 --> 00:12:42,640
Speaker 1: signatures from people who subsequently said they never signed it

199
00:12:42,760 --> 00:12:46,520
Speaker 1: or even heard about it. So assuming that this is

200
00:12:46,559 --> 00:12:50,200
Speaker 1: all legit, it sounds like we should really pay attention, right,

201
00:12:50,240 --> 00:12:53,600
Speaker 1: I mean, these are people who are working in that field.

202
00:12:54,559 --> 00:12:57,280
Speaker 1: That being said, I actually worry that some folks are

203
00:12:57,280 --> 00:12:59,719
Speaker 1: going to interpret this as meaning AI is on the

204
00:12:59,840 --> 00:13:04,800
Speaker 1: verse of becoming super intelligent and self aware or something.

205
00:13:05,120 --> 00:13:08,360
Speaker 1: As I mentioned earlier, generative AI leans on statistics and

206
00:13:08,440 --> 00:13:12,560
Speaker 1: training to create stuff. It's not thinking in the same

207
00:13:12,559 --> 00:13:15,440
Speaker 1: way that humans do, and I worry that if people

208
00:13:15,840 --> 00:13:20,000
Speaker 1: interpreted otherwise, then folks will be focusing on trying to

209
00:13:20,040 --> 00:13:23,720
Speaker 1: solve the wrong problem. AI definitely has the potential to

210
00:13:23,760 --> 00:13:27,840
Speaker 1: cause harm. Don't get me wrong. AI is potentially very harmful,

211
00:13:28,040 --> 00:13:30,840
Speaker 1: but it doesn't need to be, you know, brainiac in

212
00:13:30,920 --> 00:13:33,080
Speaker 1: order to be harmful. We can just look at the

213
00:13:33,120 --> 00:13:37,439
Speaker 1: instances of vehicles that have been operating in autonomous modes

214
00:13:37,679 --> 00:13:41,319
Speaker 1: that subsequently got involved in fatal car accidents. That proves

215
00:13:41,440 --> 00:13:44,240
Speaker 1: AI can be harmful and it doesn't need to be

216
00:13:44,320 --> 00:13:47,319
Speaker 1: super intelligent to do so. So I guess what I'm

217
00:13:47,360 --> 00:13:51,240
Speaker 1: saying is I believe that this warning is warranted. I

218
00:13:51,280 --> 00:13:54,080
Speaker 1: do think AI poses a threat and that we need

219
00:13:54,120 --> 00:13:58,640
Speaker 1: to have rules and regulations and approaches to AI that

220
00:13:58,760 --> 00:14:02,840
Speaker 1: are responsible and are least likely to cause harm. But

221
00:14:03,120 --> 00:14:06,400
Speaker 1: you know, it's it's trying to figure out how to

222
00:14:06,480 --> 00:14:10,400
Speaker 1: do that by framing the problem correctly. That's going to

223
00:14:10,400 --> 00:14:12,360
Speaker 1: be a big challenge. Right. You need to make sure

224
00:14:12,400 --> 00:14:16,080
Speaker 1: that you understand what the actual problem is and not

225
00:14:16,240 --> 00:14:20,800
Speaker 1: conflate it with something like super intelligence, in order to

226
00:14:20,880 --> 00:14:26,360
Speaker 1: create the proper framework to actually rectify the issue. And

227
00:14:26,400 --> 00:14:29,320
Speaker 1: in fact, like the people behind the statement said that

228
00:14:29,760 --> 00:14:34,120
Speaker 1: the whole goal of making something short and blunt was

229
00:14:34,440 --> 00:14:39,960
Speaker 1: to avoid issues of perhaps suggesting an approach that then

230
00:14:40,000 --> 00:14:43,960
Speaker 1: would just devolve into an argument over the best way forward.

231
00:14:44,720 --> 00:14:47,000
Speaker 1: But on the flip side, I would argue, well, yeah,

232
00:14:47,000 --> 00:14:50,560
Speaker 1: but if we're not suggesting approaches, then what good is

233
00:14:50,600 --> 00:14:54,280
Speaker 1: the warning? Like it's obviously we're not going to stop

234
00:14:54,320 --> 00:14:57,800
Speaker 1: developing AI. I mean, even the people who signed this

235
00:14:58,000 --> 00:15:02,480
Speaker 1: warning are actively pro promoting and developing AI right now.

236
00:15:02,840 --> 00:15:05,520
Speaker 1: There's like an AI arms race going on in the

237
00:15:05,520 --> 00:15:10,800
Speaker 1: world of computer science. So if is it just so

238
00:15:10,880 --> 00:15:12,920
Speaker 1: that you can fall back and say, well, I know,

239
00:15:13,000 --> 00:15:15,880
Speaker 1: we blew up the world, but we warned you back

240
00:15:15,920 --> 00:15:19,640
Speaker 1: in twenty twenty three. I don't know. Maybe I just

241
00:15:19,680 --> 00:15:25,280
Speaker 1: am a little too cynical about how these experts are

242
00:15:25,360 --> 00:15:29,840
Speaker 1: viewing the dangers of AI without any actual solutions to

243
00:15:30,520 --> 00:15:35,240
Speaker 1: address the problem. Moving on from AI, Japan's Space Administration

244
00:15:35,720 --> 00:15:39,080
Speaker 1: JACKSA is working with private companies in Japan to do

245
00:15:39,120 --> 00:15:43,520
Speaker 1: something pretty ambitious, So The plan is to launch collecting

246
00:15:43,600 --> 00:15:47,320
Speaker 1: satellites into space as early as twenty twenty five, and

247
00:15:47,360 --> 00:15:51,440
Speaker 1: these satellites will collect solar energy, convert that energy into

248
00:15:51,720 --> 00:15:56,080
Speaker 1: microwave beams, and then beam that energy down to receiving

249
00:15:56,120 --> 00:16:01,480
Speaker 1: stations here on Earth, which is entirely feasible. Questions remain

250
00:16:01,600 --> 00:16:04,160
Speaker 1: regarding how much energy these satellites will be able to

251
00:16:04,240 --> 00:16:07,280
Speaker 1: collect and transmit, but the point of this project is

252
00:16:07,320 --> 00:16:10,040
Speaker 1: to explore whether or not we can make solar collection

253
00:16:10,160 --> 00:16:13,440
Speaker 1: in space and microwave transmission a part of a larger

254
00:16:13,800 --> 00:16:17,760
Speaker 1: renewable energy strategy. For a country like Japan that might

255
00:16:17,800 --> 00:16:22,680
Speaker 1: have limited space for things like terrestrial solar arrays, this

256
00:16:22,720 --> 00:16:24,320
Speaker 1: could have a lot of appeal. I mean, it all

257
00:16:24,360 --> 00:16:27,880
Speaker 1: depends on how large the receiving station has to be.

258
00:16:27,920 --> 00:16:30,120
Speaker 1: I would imagine has to be quite big, so that

259
00:16:30,160 --> 00:16:33,160
Speaker 1: you're not having a super concentrated beam of microwave energy.

260
00:16:33,600 --> 00:16:36,920
Speaker 1: But it's neat like. If you can get this to

261
00:16:37,000 --> 00:16:41,120
Speaker 1: work and become a supplemental part of your energy strategy,

262
00:16:41,160 --> 00:16:44,520
Speaker 1: then it could be really useful, especially since with the

263
00:16:44,960 --> 00:16:48,240
Speaker 1: positioning of satellites, if you have a whole network out there,

264
00:16:48,840 --> 00:16:52,160
Speaker 1: you can be collecting solar energy twenty four to seven,

265
00:16:52,760 --> 00:16:57,200
Speaker 1: So and weather will never be an issue. I mean,

266
00:16:57,240 --> 00:16:59,760
Speaker 1: solar weather could potentially be an issue if you have

267
00:16:59,840 --> 00:17:02,600
Speaker 1: like a chronal mass ejection or something like that, but

268
00:17:03,120 --> 00:17:05,200
Speaker 1: you know, terrestrial weather wouldn't be an issue. So a

269
00:17:05,240 --> 00:17:07,560
Speaker 1: lot of the complaints around solar the fact that you

270
00:17:07,600 --> 00:17:10,400
Speaker 1: know you're only collecting when it's daytime and only when

271
00:17:10,440 --> 00:17:13,600
Speaker 1: you have a clear view of the sun, that ends

272
00:17:13,680 --> 00:17:17,199
Speaker 1: up being negated. But you still have questions of all, right, well,

273
00:17:17,200 --> 00:17:19,240
Speaker 1: how efficient is this going to be. How much energy

274
00:17:19,280 --> 00:17:22,000
Speaker 1: are you going to lose converting from solar to microwave,

275
00:17:22,400 --> 00:17:25,280
Speaker 1: you know, how much is lost in the transmission. These

276
00:17:25,320 --> 00:17:27,480
Speaker 1: are all questions that need to be answered as well.

277
00:17:27,520 --> 00:17:31,240
Speaker 1: But this is a cool step toward answering those questions.

278
00:17:31,560 --> 00:17:34,680
Speaker 1: Will it pan out, I don't know. I hope it does,

279
00:17:36,359 --> 00:17:39,040
Speaker 1: though obviously it also opens up other things that you

280
00:17:39,080 --> 00:17:44,160
Speaker 1: have to consider, like the potential for more space junk, right,

281
00:17:44,240 --> 00:17:47,080
Speaker 1: So there's always other things you have to bring into

282
00:17:47,119 --> 00:17:50,840
Speaker 1: the equation. But I think it's a cool project. Now.

283
00:17:50,880 --> 00:17:54,119
Speaker 1: Over this past weekend, I actually went to the amusement

284
00:17:54,160 --> 00:17:58,040
Speaker 1: park six Flags Over Georgia's for the first time in many,

285
00:17:58,760 --> 00:18:00,960
Speaker 1: many years, and I have to say. The park has

286
00:18:01,080 --> 00:18:04,679
Speaker 1: digitized a lot of operations since the last time I

287
00:18:04,840 --> 00:18:08,119
Speaker 1: went there, but at a couple of other Six Flags parks,

288
00:18:08,119 --> 00:18:09,919
Speaker 1: not at Georgia, but a couple of other ones, one

289
00:18:09,960 --> 00:18:13,919
Speaker 1: in California and one in New Jersey, that digitization is

290
00:18:14,080 --> 00:18:17,240
Speaker 1: ramping up a bit. And that's because those two parks

291
00:18:17,400 --> 00:18:21,760
Speaker 1: have partnered with Amazon and Coca Cola to incorporate one

292
00:18:21,800 --> 00:18:27,000
Speaker 1: of Amazon's grab and go cashierless shop concepts into the parks.

293
00:18:27,760 --> 00:18:31,480
Speaker 1: So just as you would in one of Amazon's operated stores,

294
00:18:31,840 --> 00:18:34,200
Speaker 1: you would be able to walk into one of these places,

295
00:18:34,640 --> 00:18:37,720
Speaker 1: pick up a product, and then stroll right out, and

296
00:18:37,760 --> 00:18:40,639
Speaker 1: you would be builled automatically through the Amazon system on

297
00:18:40,680 --> 00:18:44,399
Speaker 1: the back end. So products will include stuff like Coca

298
00:18:44,400 --> 00:18:47,679
Speaker 1: Cola products just as you would imagine, plus like snacks

299
00:18:48,280 --> 00:18:50,919
Speaker 1: and other stuff like the New Jersey location says it

300
00:18:50,960 --> 00:18:54,360
Speaker 1: will include necessities like sunblock and rain ponchos, that kind

301
00:18:54,400 --> 00:18:57,439
Speaker 1: of thing. And it's interesting to hear that the parks

302
00:18:57,440 --> 00:19:01,720
Speaker 1: are actually doing this because Amazon has actually shut down

303
00:19:01,760 --> 00:19:04,600
Speaker 1: several of its own locations, I think like eight total

304
00:19:04,840 --> 00:19:08,280
Speaker 1: in cities like New York and Seattle. However, it wouldn't

305
00:19:08,280 --> 00:19:10,640
Speaker 1: surprise me if the plan all along was to attract

306
00:19:10,640 --> 00:19:14,520
Speaker 1: partners like six Flags, where Amazon can serve as the

307
00:19:14,560 --> 00:19:17,159
Speaker 1: back end operations, but someone else is in charge of,

308
00:19:17,200 --> 00:19:20,240
Speaker 1: you know, restocking and cleaning the place and that kind

309
00:19:20,280 --> 00:19:24,600
Speaker 1: of thing. Over the weekend, Toyota entered a hydrogen powered

310
00:19:24,720 --> 00:19:28,320
Speaker 1: racing car into an endurance race in Japan. The company

311
00:19:28,320 --> 00:19:30,399
Speaker 1: said it looked at the race as a sort of

312
00:19:30,480 --> 00:19:34,560
Speaker 1: testing ground for the technology and an opportunity to uncover

313
00:19:34,680 --> 00:19:39,320
Speaker 1: areas of improvement that wouldn't necessarily pop up in a

314
00:19:39,359 --> 00:19:43,320
Speaker 1: laboratory setting, which is understandable, right Like in a lab

315
00:19:43,400 --> 00:19:46,920
Speaker 1: you can test technology quite a bit and see where

316
00:19:46,960 --> 00:19:49,520
Speaker 1: there may be areas that you need to focus on

317
00:19:49,560 --> 00:19:52,440
Speaker 1: to fix things, but it's not until you really get

318
00:19:52,480 --> 00:19:55,119
Speaker 1: something out in the real world and really put to

319
00:19:55,160 --> 00:19:58,760
Speaker 1: the test that some problems will become evident. And let

320
00:19:58,840 --> 00:20:01,160
Speaker 1: me tell you, an endurance race, a twenty four hour

321
00:20:01,280 --> 00:20:04,960
Speaker 1: endurance race. That's a heck of a test for a technology.

322
00:20:05,280 --> 00:20:09,400
Speaker 1: Toyota has actually been working on hydrogen fueled cars for

323
00:20:09,480 --> 00:20:13,440
Speaker 1: a while, and in fact has even fielded hydrogen fueled

324
00:20:13,480 --> 00:20:16,760
Speaker 1: cars in various races, but the big difference in this

325
00:20:16,840 --> 00:20:20,440
Speaker 1: most recent vehicle is that the car was using liquid

326
00:20:20,680 --> 00:20:25,119
Speaker 1: hydrogen rather than gaseous hydrogen. Now, liquid hydrogen comes with

327
00:20:25,160 --> 00:20:27,720
Speaker 1: a whole bunch of challenges, like you have to keep

328
00:20:27,800 --> 00:20:30,520
Speaker 1: the hydrogen at a very low temperature to keep it liquid.

329
00:20:31,000 --> 00:20:34,239
Speaker 1: But it also means that you end up with a

330
00:20:34,320 --> 00:20:39,639
Speaker 1: higher energy density fuel, right because liquid hydrogen packs more

331
00:20:40,240 --> 00:20:45,399
Speaker 1: energy per volume than gaseous hydrogen does, which is an

332
00:20:45,440 --> 00:20:48,120
Speaker 1: important consideration for an endurance race. You know, you want

333
00:20:48,160 --> 00:20:52,240
Speaker 1: to have a fuel or an energy rich fuel. I

334
00:20:52,280 --> 00:20:56,520
Speaker 1: should say I couldn't find any information on how well

335
00:20:56,880 --> 00:20:59,800
Speaker 1: it performed. The race happened just this past weekend, but

336
00:21:00,040 --> 00:21:01,639
Speaker 1: I did see a lot of articles saying, you know,

337
00:21:02,880 --> 00:21:06,160
Speaker 1: it did it. It didn't tell me how well it did.

338
00:21:06,760 --> 00:21:09,800
Speaker 1: Critics have long argued that Toyota has been dragging its

339
00:21:09,840 --> 00:21:14,639
Speaker 1: feet on developing electric vehicles. Obviously that's where the automotive

340
00:21:14,640 --> 00:21:19,359
Speaker 1: industry is really shifting toward, and as a result, because

341
00:21:19,400 --> 00:21:24,639
Speaker 1: Toyota did not jump onto that particular approach, it is

342
00:21:24,720 --> 00:21:27,560
Speaker 1: now lagging behind competitors as it tries to make up

343
00:21:27,560 --> 00:21:33,240
Speaker 1: for lost ground. Toyota, however, has long argued that it's

344
00:21:33,280 --> 00:21:36,720
Speaker 1: going to take longer to transition to pure electric vehicles

345
00:21:37,320 --> 00:21:41,680
Speaker 1: than most people expect and as a result, in order

346
00:21:41,760 --> 00:21:46,320
Speaker 1: to bring down carbon emissions while also transitioning to electric vehicles,

347
00:21:46,720 --> 00:21:50,840
Speaker 1: Toyota has said, we need to invest in alternatives to

348
00:21:51,000 --> 00:21:54,520
Speaker 1: just pure electric vehicles. This has been Toyota's message for

349
00:21:54,680 --> 00:21:58,520
Speaker 1: years and years, with fuel cell vehicles, hydrogen powered vehicles,

350
00:21:58,560 --> 00:22:02,080
Speaker 1: various hybrids, that kind of thing. There are critics who

351
00:22:02,119 --> 00:22:06,800
Speaker 1: say that Toyota has made the wrong call, that the

352
00:22:06,960 --> 00:22:11,560
Speaker 1: company is just trying to justify its approach to a

353
00:22:11,600 --> 00:22:16,679
Speaker 1: different branch of vehicle development, and that it's kind of

354
00:22:16,720 --> 00:22:19,480
Speaker 1: like it's sort of a sunk cost fallacy. It's gone

355
00:22:19,520 --> 00:22:22,480
Speaker 1: so far that it can't come back. Although the new

356
00:22:22,520 --> 00:22:26,720
Speaker 1: leadership at Toyota has been a bit more pro ev

357
00:22:27,040 --> 00:22:34,239
Speaker 1: side than previous leadership has. It's interesting there's a lot

358
00:22:34,240 --> 00:22:39,080
Speaker 1: of challenges that are associated with hydrogen based vehicles, including

359
00:22:39,080 --> 00:22:42,840
Speaker 1: how to harvest pure hydrogen without using too much energy

360
00:22:42,920 --> 00:22:46,120
Speaker 1: in the process. You may know hydrogen is the most

361
00:22:46,119 --> 00:22:51,200
Speaker 1: plentiful element in our universe, but it binds with stuff,

362
00:22:51,240 --> 00:22:54,880
Speaker 1: which means in order to get hydrogen, we frequently need

363
00:22:54,920 --> 00:22:59,679
Speaker 1: to expel energy to break those molecular bonds to harvest

364
00:22:59,720 --> 00:23:05,680
Speaker 1: pure hydrogen. And if you are spending more energy to

365
00:23:05,720 --> 00:23:08,760
Speaker 1: separate the hydrogen from stuff. Then you're getting out of

366
00:23:08,760 --> 00:23:12,720
Speaker 1: the hydrogen itself. Then you're at working at a net loss,

367
00:23:12,760 --> 00:23:16,359
Speaker 1: and you may need to just re evaluate what you're doing.

368
00:23:16,400 --> 00:23:18,280
Speaker 1: It may turn out that there's a different thing you

369
00:23:18,320 --> 00:23:22,960
Speaker 1: can do where you just eliminate that step and you're

370
00:23:22,960 --> 00:23:26,639
Speaker 1: wasting less energy. I'll have to do another episode in

371
00:23:26,680 --> 00:23:28,440
Speaker 1: the near future to kind of go down the list

372
00:23:28,440 --> 00:23:32,440
Speaker 1: of pros and cons of things like a hydrogen based economy.

373
00:23:33,359 --> 00:23:37,720
Speaker 1: A few decades ago, that was a really big thing,

374
00:23:38,040 --> 00:23:41,000
Speaker 1: at least in rhetoric in the United States, and we

375
00:23:41,119 --> 00:23:46,760
Speaker 1: haven't really seen it mature and turn into a real

376
00:23:46,880 --> 00:23:49,600
Speaker 1: technology here in the US, and I thought it might

377
00:23:49,640 --> 00:23:51,440
Speaker 1: be a good idea to kind of do a follow

378
00:23:51,520 --> 00:23:55,000
Speaker 1: up and talk about what are those pros and cons

379
00:23:55,200 --> 00:23:59,920
Speaker 1: and is it really a viable approach and a viable

380
00:24:00,000 --> 00:24:04,200
Speaker 1: alternative to your traditional internal combustion engines. Keeping in mind

381
00:24:04,440 --> 00:24:08,000
Speaker 1: a hydrogen based car also uses combustion. It's just when

382
00:24:08,040 --> 00:24:12,440
Speaker 1: you are you know, when hydrogen goes through combustion, you're

383
00:24:12,440 --> 00:24:14,560
Speaker 1: not getting the same byproducts as you would if you

384
00:24:14,600 --> 00:24:18,720
Speaker 1: were burning gasoline. Okay, I've got a couple more things

385
00:24:18,720 --> 00:24:21,160
Speaker 1: I want to talk about But before we get to those,

386
00:24:21,200 --> 00:24:33,879
Speaker 1: let's take one more quick break. We're back. So the

387
00:24:34,040 --> 00:24:38,080
Speaker 1: United States is not the only country planning to put

388
00:24:38,119 --> 00:24:41,840
Speaker 1: people on the Moon again. So NASA obviously has Project

389
00:24:42,000 --> 00:24:46,520
Speaker 1: Artemis Artemis one, which was a test flight of the

390
00:24:47,840 --> 00:24:51,920
Speaker 1: spacecraft and the launch vehicle. That's already happened, so Artemis

391
00:24:51,920 --> 00:24:57,720
Speaker 1: one was a success. Artimist two will see a crew

392
00:24:58,200 --> 00:25:04,520
Speaker 1: of astronauts launch off Earth and then circle around the

393
00:25:04,560 --> 00:25:08,440
Speaker 1: backside of the Moon, not the dark side because there's

394
00:25:09,240 --> 00:25:12,080
Speaker 1: the dark side changes, but the backside, the far side

395
00:25:12,080 --> 00:25:14,719
Speaker 1: of the Moon, and then return to Earth. They are

396
00:25:14,760 --> 00:25:17,200
Speaker 1: not actually going to touch down. And then Artemis three

397
00:25:18,000 --> 00:25:22,840
Speaker 1: is the mission where astronauts would land on the Moon again.

398
00:25:23,240 --> 00:25:27,560
Speaker 1: That one currently is projected for twenty twenty five. I'll

399
00:25:27,560 --> 00:25:32,320
Speaker 1: be shocked if we make that goal. But meanwhile, China's

400
00:25:32,400 --> 00:25:36,800
Speaker 1: Manned Space Agency announced that it plans to land Chinese

401
00:25:36,840 --> 00:25:40,960
Speaker 1: astronauts on the Moon by twenty thirty. To accomplish that goal,

402
00:25:41,000 --> 00:25:44,679
Speaker 1: the agency is developing a new launch vehicle and a

403
00:25:44,720 --> 00:25:51,399
Speaker 1: new spacecraft. Again, that's a really aggressive goal. If the

404
00:25:52,040 --> 00:25:54,840
Speaker 1: launch vehicle and the spacecraft are still in development, it

405
00:25:55,760 --> 00:25:59,399
Speaker 1: often can take a very long time to get that

406
00:25:59,480 --> 00:26:02,480
Speaker 1: kind of stuff buttoned up. Also, it will be interesting

407
00:26:02,480 --> 00:26:05,159
Speaker 1: to see if lunar real estate becomes the next big

408
00:26:05,320 --> 00:26:09,399
Speaker 1: land grab. You know, there are obviously space treaties that

409
00:26:09,440 --> 00:26:13,080
Speaker 1: are meant to prevent such things, but I would be

410
00:26:13,160 --> 00:26:18,760
Speaker 1: shocked if we don't see some rather aggressive moves to

411
00:26:18,840 --> 00:26:23,919
Speaker 1: claim lunar landscape for various purposes. So we'll keep an

412
00:26:23,920 --> 00:26:27,240
Speaker 1: eye on that too. I can't wait for it to

413
00:26:27,240 --> 00:26:31,000
Speaker 1: become some sort of Heinland novel. All right, now we're

414
00:26:31,040 --> 00:26:34,080
Speaker 1: toward the end of the episode, and occasionally I like

415
00:26:34,200 --> 00:26:37,800
Speaker 1: to end episodes with some suggested reading material for y'all.

416
00:26:38,000 --> 00:26:41,159
Speaker 1: So rather than go through all of this as a

417
00:26:41,200 --> 00:26:44,120
Speaker 1: news item, I thought I would talk about a few articles,

418
00:26:44,160 --> 00:26:46,280
Speaker 1: three of them in particular, that I think are worth

419
00:26:46,320 --> 00:26:49,960
Speaker 1: your time to read. Also, these articles tend to fall

420
00:26:50,000 --> 00:26:56,840
Speaker 1: more into investigative journalism or interesting experiences and less on

421
00:26:56,960 --> 00:27:00,439
Speaker 1: the news side. First up is an art article in

422
00:27:00,480 --> 00:27:05,760
Speaker 1: Ours Technica that Dan goodin ours Technica phenomenal resource. If

423
00:27:05,800 --> 00:27:08,320
Speaker 1: you are not visiting ours Technica on the rag to

424
00:27:08,920 --> 00:27:12,399
Speaker 1: read up on their journalism and tech, you need to

425
00:27:12,480 --> 00:27:16,520
Speaker 1: change that because Ours Technica is a fantastic resource. The

426
00:27:16,680 --> 00:27:21,200
Speaker 1: article I'm referencing is called inner workings revealed for Predator,

427
00:27:21,400 --> 00:27:26,680
Speaker 1: the Android malware that exploited five zero days, so zero

428
00:27:26,760 --> 00:27:32,119
Speaker 1: day being an exploit that the company that's behind the

429
00:27:32,160 --> 00:27:35,320
Speaker 1: software is unaware of, and that is in there front

430
00:27:35,359 --> 00:27:37,640
Speaker 1: the very beginning, and you can just if you find

431
00:27:37,680 --> 00:27:40,080
Speaker 1: out about it, you can exploit it to your heart's

432
00:27:40,080 --> 00:27:44,840
Speaker 1: content until someone notices what's going on. So the article

433
00:27:44,920 --> 00:27:48,520
Speaker 1: details a dramatic story about how companies specializing in a

434
00:27:48,560 --> 00:27:53,320
Speaker 1: double whammy of discovering and exploiting vulnerabilities as well as

435
00:27:53,359 --> 00:27:57,760
Speaker 1: turning mobile phones into remote surveillance devices are making an

436
00:27:57,800 --> 00:28:02,000
Speaker 1: awful lot of money selling those tools to very dangerous

437
00:28:02,040 --> 00:28:06,760
Speaker 1: customers who then employ the technology to target perceived threats.

438
00:28:07,119 --> 00:28:10,280
Speaker 1: We've seen this story play out in other areas as well.

439
00:28:11,200 --> 00:28:15,560
Speaker 1: There was obviously the case of the Israeli tech company

440
00:28:15,640 --> 00:28:20,879
Speaker 1: that was selling an exploit for iOS systems that took

441
00:28:21,080 --> 00:28:25,200
Speaker 1: advantage of a vulnerability and eye message. Very similar case,

442
00:28:25,320 --> 00:28:29,960
Speaker 1: and this article goes over something like that, but one

443
00:28:30,000 --> 00:28:36,240
Speaker 1: that affected Android devices as well. Highly recommended. CNBC has

444
00:28:36,320 --> 00:28:40,360
Speaker 1: an article titled Chinese apps remain hugely popular in the

445
00:28:40,480 --> 00:28:44,560
Speaker 1: US despite efforts to ban TikTok, and this one touches

446
00:28:44,600 --> 00:28:47,320
Speaker 1: on some stuff that I have mentioned on tech stuff

447
00:28:47,360 --> 00:28:51,440
Speaker 1: in the past, namely, TikTok represents a very high profile

448
00:28:51,560 --> 00:28:56,120
Speaker 1: example of a problem that actually goes well beyond TikTok,

449
00:28:56,480 --> 00:29:00,000
Speaker 1: and perhaps it might be better to take a step

450
00:29:00,200 --> 00:29:04,000
Speaker 1: back to consider whether or not TikTok is kind of

451
00:29:04,160 --> 00:29:08,400
Speaker 1: standing in as a scapegoat for a much bigger problem.

452
00:29:09,080 --> 00:29:13,640
Speaker 1: And it's a problem that even expands beyond the possibility

453
00:29:13,760 --> 00:29:17,920
Speaker 1: of a country like China harvesting all this information, because

454
00:29:17,920 --> 00:29:21,400
Speaker 1: obviously we've got all these other huge companies that are

455
00:29:21,400 --> 00:29:24,440
Speaker 1: in the United States that are also harvesting information, and

456
00:29:24,520 --> 00:29:29,360
Speaker 1: that maybe the problem isn't just with who is getting it,

457
00:29:29,400 --> 00:29:32,400
Speaker 1: but the fact that it's being done full stop. The

458
00:29:32,440 --> 00:29:34,760
Speaker 1: piece also points out something that a lot of others

459
00:29:34,760 --> 00:29:38,400
Speaker 1: have been saying for a while, that a lot of

460
00:29:38,400 --> 00:29:43,800
Speaker 1: the TikTok suspicion is being fueled by companies, primarily Meta,

461
00:29:43,920 --> 00:29:48,160
Speaker 1: that would stand to benefit tremendously if TikTok were to

462
00:29:48,280 --> 00:29:53,160
Speaker 1: go away. So knowing that Meta has a vested interest

463
00:29:53,160 --> 00:29:57,520
Speaker 1: in TikTok dying helps kind of put all this into

464
00:29:57,560 --> 00:30:00,840
Speaker 1: context as well. That's another reason why TikTok is so

465
00:30:01,080 --> 00:30:05,760
Speaker 1: prominent in this discussion because you've got companies that have

466
00:30:05,800 --> 00:30:09,760
Speaker 1: a lot of money that are very eager to support

467
00:30:09,800 --> 00:30:12,600
Speaker 1: the narrative that TikTok is a danger. That's not to

468
00:30:12,640 --> 00:30:14,760
Speaker 1: say that TikTok's not a danger. I'm not saying that.

469
00:30:14,800 --> 00:30:17,760
Speaker 1: I'm saying it's like, let's do a laser focus on

470
00:30:17,800 --> 00:30:22,960
Speaker 1: this one instance and ignore the larger problem that remains

471
00:30:23,040 --> 00:30:28,200
Speaker 1: unaddressed as long as we're only focusing on TikTok. Finally,

472
00:30:28,320 --> 00:30:33,080
Speaker 1: the third article I want to recommend is by Maxwell Stretchen,

473
00:30:33,400 --> 00:30:37,560
Speaker 1: and my apologies for that pronunciation of your name, Maxwell,

474
00:30:37,920 --> 00:30:41,240
Speaker 1: I'm sure I butchered it. But Maxwell has a piece

475
00:30:41,280 --> 00:30:45,600
Speaker 1: on Motherboard titled I asked Chat GPT to control My

476
00:30:45,720 --> 00:30:49,200
Speaker 1: life and it immediately fell apart. Now, this is a

477
00:30:49,240 --> 00:30:53,760
Speaker 1: pretty amusing story about Maxwell experimenting with chat GPT to

478
00:30:53,840 --> 00:30:58,640
Speaker 1: create a daily schedule. Like Maxwell just highlighted the things

479
00:30:58,640 --> 00:31:01,680
Speaker 1: that he needed to do and wanted to do and

480
00:31:01,880 --> 00:31:03,840
Speaker 1: gave it a chat gpt to tell them how to

481
00:31:03,880 --> 00:31:08,400
Speaker 1: do it. And it highlights a few interesting things, including

482
00:31:08,400 --> 00:31:11,960
Speaker 1: how open ai is trying to build in guardrails to

483
00:31:12,040 --> 00:31:14,920
Speaker 1: prevent bad stuff from happening, or at least to prevent

484
00:31:15,600 --> 00:31:19,880
Speaker 1: the optics from going bad. So like chat GPT saying hey,

485
00:31:20,680 --> 00:31:23,320
Speaker 1: autonomy is really important and you shouldn't just hand it

486
00:31:23,360 --> 00:31:27,200
Speaker 1: over to someone, which you know may or may not

487
00:31:27,320 --> 00:31:33,760
Speaker 1: have been a legitimate and earnest statement all the way

488
00:31:33,800 --> 00:31:36,800
Speaker 1: to how chat GPT has trouble meeting all of Maxwell's

489
00:31:36,880 --> 00:31:40,480
Speaker 1: daily goals reasonably. If you've played the sims, you know

490
00:31:40,520 --> 00:31:43,440
Speaker 1: how frustrating it is. There just aren't enough hours in

491
00:31:43,480 --> 00:31:45,400
Speaker 1: the day to do everything you need to do plus

492
00:31:45,440 --> 00:31:49,080
Speaker 1: everything you want to do. Turns out AI has that

493
00:31:49,160 --> 00:31:53,200
Speaker 1: same sort of problem, So yes, I recommend those three articles.

494
00:31:53,320 --> 00:31:57,520
Speaker 1: Check those out. All right, that's it for the news

495
00:31:57,600 --> 00:32:01,080
Speaker 1: for Tuesday, May thirtieth, twenty twenty three. I hope you

496
00:32:01,160 --> 00:32:05,400
Speaker 1: are all well and I'll talk to you again really soon.

497
00:32:11,640 --> 00:32:16,280
Speaker 1: Tech Stuff is an iHeartRadio production. For more podcasts from iHeartRadio,

498
00:32:16,600 --> 00:32:20,320
Speaker 1: visit the iHeartRadio app, Apple Podcasts, or wherever you listen

499
00:32:20,360 --> 00:32:21,400
Speaker 1: to your favorite shows.