1
00:00:04,440 --> 00:00:12,800
Speaker 1: Welcome to Tech Stuff, a production from iHeartRadio. Hey thereon

2
00:00:12,880 --> 00:00:15,040
Speaker 1: Welcome to Tech Stuff. I'm your host, Jonathan Strickland. I'm

3
00:00:15,040 --> 00:00:17,120
Speaker 1: an executive producer with iHeart Podcast And how the tech

4
00:00:17,160 --> 00:00:20,840
Speaker 1: are you? Yes, I am excited. That is why I

5
00:00:20,880 --> 00:00:24,759
Speaker 1: had the crazy high energy introduction for this episode. That's

6
00:00:24,800 --> 00:00:31,280
Speaker 1: because I have two phenomenal guests on today's episode. They

7
00:00:31,280 --> 00:00:34,960
Speaker 1: are the hosts of Daniel and Kelly's Extraordinary Universe. There

8
00:00:34,960 --> 00:00:40,120
Speaker 1: are two brilliant scientists here to talk science and tech

9
00:00:40,159 --> 00:00:43,760
Speaker 1: and depictions of science and popular media and how they

10
00:00:43,880 --> 00:00:46,360
Speaker 1: always get at one hundred percent right all the time.

11
00:00:46,400 --> 00:00:49,680
Speaker 1: It's Daniel and it's Kelly. Welcome to Tech Stuff.

12
00:00:50,159 --> 00:00:51,680
Speaker 2: Thanks very much, Thank you.

13
00:00:51,720 --> 00:00:53,440
Speaker 3: I wish you could call me every day to cheer

14
00:00:53,440 --> 00:00:56,000
Speaker 3: me up with with you know, a nice introduction just

15
00:00:56,040 --> 00:00:56,240
Speaker 3: like that.

16
00:00:56,600 --> 00:00:59,200
Speaker 1: Oh, I'd be happy too, I'd be happy to My

17
00:00:59,320 --> 00:01:02,040
Speaker 1: rates are very and pretty soon I'm going to be

18
00:01:02,080 --> 00:01:03,840
Speaker 1: looking for a gig. I don't know if you have

19
00:01:04,000 --> 00:01:06,880
Speaker 1: heard this, but I'm actually stepping away from hosting tech

20
00:01:06,959 --> 00:01:08,040
Speaker 1: Stuff in the new year.

21
00:01:08,920 --> 00:01:10,440
Speaker 3: Oh what are you going to do instead?

22
00:01:10,760 --> 00:01:15,000
Speaker 1: There's a giant, cloudy question mark in my near future. Actually,

23
00:01:15,120 --> 00:01:18,160
Speaker 1: I will be my listeners know that I'm stepping away.

24
00:01:18,319 --> 00:01:20,560
Speaker 1: I did an episode talking about it. But yeah, I've

25
00:01:20,600 --> 00:01:22,920
Speaker 1: been hosting Tech Stuff for sixteen and a half years.

26
00:01:22,959 --> 00:01:25,880
Speaker 1: I've got more than twenty three hundred episodes under my belt,

27
00:01:26,120 --> 00:01:28,080
Speaker 1: and I thought it was time for someone else to

28
00:01:28,080 --> 00:01:31,800
Speaker 1: talk about tech for a while, and so I'm stepping

29
00:01:31,840 --> 00:01:34,800
Speaker 1: back in early January, and I will be focusing on

30
00:01:34,880 --> 00:01:40,600
Speaker 1: executive producer duties moving forward, potentially hosting the occasional episode

31
00:01:40,600 --> 00:01:42,959
Speaker 1: and or show in the future. Nothing is set in stone,

32
00:01:43,080 --> 00:01:45,800
Speaker 1: but I'm excited to be able to kind of shift

33
00:01:45,840 --> 00:01:46,440
Speaker 1: some gears.

34
00:01:46,920 --> 00:01:48,480
Speaker 3: Happy retirement, Thank you.

35
00:01:48,880 --> 00:01:51,440
Speaker 1: I mean, I'll still be working like a crazy person,

36
00:01:51,480 --> 00:01:53,320
Speaker 1: it just won't be I just won't be creating three

37
00:01:53,400 --> 00:01:56,120
Speaker 1: episodes a week. But I'm so glad to have you here. Daniel,

38
00:01:56,200 --> 00:01:58,279
Speaker 1: Welcome back to the show. I don't know if you remember,

39
00:01:58,360 --> 00:02:01,200
Speaker 1: but years ago you did a little guest appearance on

40
00:02:01,320 --> 00:02:03,880
Speaker 1: Tech Stuff, And I'm sure you don't remember this, but

41
00:02:04,000 --> 00:02:06,240
Speaker 1: I do because it's an important moment in my life.

42
00:02:06,360 --> 00:02:09,679
Speaker 1: You name me an honorary engineer in that episode, and

43
00:02:10,560 --> 00:02:14,360
Speaker 1: I've been holding onto that title like with a death

44
00:02:14,440 --> 00:02:15,359
Speaker 1: grip ever since.

45
00:02:16,639 --> 00:02:18,720
Speaker 3: He hasn't given me that title.

46
00:02:19,200 --> 00:02:21,200
Speaker 2: Yeah, I'm not sure that title is something you should

47
00:02:21,200 --> 00:02:24,720
Speaker 2: be proud of. You know, scientists and engineers are usually

48
00:02:24,840 --> 00:02:27,200
Speaker 2: sort of, you know, tooth and nail against each other,

49
00:02:27,320 --> 00:02:28,440
Speaker 2: so I'm not sure I meant that in a.

50
00:02:28,360 --> 00:02:30,440
Speaker 1: Positive pay Oh, that's fair, that's fair. I think we

51
00:02:30,720 --> 00:02:34,040
Speaker 1: were chatting about how I think of engineers as people

52
00:02:34,040 --> 00:02:36,440
Speaker 1: who view the world as a series of problems that

53
00:02:36,520 --> 00:02:39,640
Speaker 1: need to be solved, and by the end of it

54
00:02:39,680 --> 00:02:41,880
Speaker 1: you said I was an honorary engineer, which made sense

55
00:02:41,880 --> 00:02:43,800
Speaker 1: because I do have an Android phone, and I feel

56
00:02:43,800 --> 00:02:47,200
Speaker 1: like that's a device made by engineers for engineers.

57
00:02:47,520 --> 00:02:48,720
Speaker 3: That's like sixty percent of it.

58
00:02:48,840 --> 00:02:51,760
Speaker 1: Yeah, yeah, yeah, yeah, Like the iPhone is incredibly intuitive

59
00:02:51,800 --> 00:02:53,560
Speaker 1: and the Android is it does stuff.

60
00:02:53,720 --> 00:02:57,600
Speaker 3: So yeah, well my engineer dad has an Android, so

61
00:02:57,680 --> 00:02:58,680
Speaker 3: that's totally consistent.

62
00:02:58,960 --> 00:03:01,000
Speaker 1: It tracks right, Like, well, there we go. That's not

63
00:03:01,080 --> 00:03:03,480
Speaker 1: confirmation by us or anything. We're just going to go

64
00:03:03,520 --> 00:03:06,440
Speaker 1: with that. Yes, So here on tech stuff, we have

65
00:03:06,560 --> 00:03:10,280
Speaker 1: this kind of underlying philosophy which does not directly relate

66
00:03:10,320 --> 00:03:12,560
Speaker 1: to tech. It relates to the direction of the show,

67
00:03:12,880 --> 00:03:16,440
Speaker 1: which is I say, it's a combination of creative thinking approach,

68
00:03:16,560 --> 00:03:21,119
Speaker 1: it's compassion, and it's critical thinking, and I'm curious if

69
00:03:21,160 --> 00:03:24,000
Speaker 1: you think of a similar philosophy for your show.

70
00:03:24,560 --> 00:03:28,640
Speaker 2: I think our show is all about curiosity and excitement

71
00:03:29,000 --> 00:03:32,639
Speaker 2: because we viewed the whole universe as like a big mystery.

72
00:03:32,800 --> 00:03:34,880
Speaker 2: That because we viewed the whole universe as like a

73
00:03:34,880 --> 00:03:38,160
Speaker 2: big mystery to unravel, and every new little piece of

74
00:03:38,240 --> 00:03:41,200
Speaker 2: truth that we pry away at, the truth minds. You know,

75
00:03:41,240 --> 00:03:44,280
Speaker 2: where we go to work every day is an exciting discovery.

76
00:03:44,560 --> 00:03:47,920
Speaker 2: It's always fantastic. Whatever the universe chooses to reveal to us,

77
00:03:47,960 --> 00:03:48,960
Speaker 2: it's always amazing.

78
00:03:49,320 --> 00:03:52,400
Speaker 3: Yeah, I think enthusiasm is huge. We're really excited about

79
00:03:52,520 --> 00:03:54,880
Speaker 3: the information we've managed to extract from the Earth and

80
00:03:54,960 --> 00:03:56,760
Speaker 3: all the cool stuff that's left to figure out.

81
00:03:56,880 --> 00:03:59,480
Speaker 1: Yeah. I've been listening to your show and I just

82
00:03:59,480 --> 00:04:01,480
Speaker 1: want to say I also want to congratulate you on

83
00:04:01,520 --> 00:04:04,800
Speaker 1: your incredible sense of timing because we're heading up to

84
00:04:04,840 --> 00:04:07,400
Speaker 1: Thanksgiving and then the most recent episode I listened to

85
00:04:07,520 --> 00:04:11,200
Speaker 1: was all about cannibalism, and I just felt that you

86
00:04:11,200 --> 00:04:14,960
Speaker 1: you really you planned those thematic shows really well.

87
00:04:16,040 --> 00:04:17,840
Speaker 3: Yeah, you know, we put a lot of thought into

88
00:04:17,839 --> 00:04:21,000
Speaker 3: each episode, and we do have a like an actual

89
00:04:21,040 --> 00:04:25,159
Speaker 3: Thanksgiving episode coming out on Thanksgiving where we Daniel. So

90
00:04:25,279 --> 00:04:27,760
Speaker 3: Daniel talks about cannibalism a lot, even though he's not

91
00:04:27,800 --> 00:04:30,360
Speaker 3: the biologist. Did you manage to get a reference to

92
00:04:30,400 --> 00:04:31,960
Speaker 3: cannibalism into that episode a bit?

93
00:04:32,320 --> 00:04:35,440
Speaker 2: Absolutely? Yes, Okay, cannibalism and aliens. I always got to

94
00:04:35,480 --> 00:04:37,640
Speaker 2: work those two angles into every episode somehow.

95
00:04:38,080 --> 00:04:42,279
Speaker 3: Yeah, So it's enthusiasm, creativity, cannibalism, aliens. That's what our podcasts.

96
00:04:42,279 --> 00:04:44,240
Speaker 1: Is kind of amazing that you didn't name the show

97
00:04:44,320 --> 00:04:44,960
Speaker 1: What's Eating You.

98
00:04:47,320 --> 00:04:50,920
Speaker 3: I had a paper published called What's Gotten Into You,

99
00:04:51,640 --> 00:04:53,600
Speaker 3: and it was all about how parasites changed behavior. But

100
00:04:53,640 --> 00:04:55,520
Speaker 3: I guess it's not What's Eating You, but it's it

101
00:04:55,560 --> 00:04:57,599
Speaker 3: was What's Gotten into You? Anyway, it was I felt

102
00:04:57,680 --> 00:04:58,839
Speaker 3: very good about that time well.

103
00:04:58,720 --> 00:05:00,280
Speaker 1: And that was kind of like one of the things

104
00:05:00,320 --> 00:05:02,760
Speaker 1: that sort of was the genesis of my idea for

105
00:05:02,800 --> 00:05:05,720
Speaker 1: this episode was that talk about you know, your work

106
00:05:06,000 --> 00:05:10,160
Speaker 1: in science and then how you view the media depiction

107
00:05:10,360 --> 00:05:13,560
Speaker 1: of science. You know, I'm sure there's like part of

108
00:05:13,600 --> 00:05:15,520
Speaker 1: you correct me if I'm wrong, but I'm sure there's

109
00:05:15,560 --> 00:05:18,240
Speaker 1: part of you that just appreciates. Oh well, obviously they

110
00:05:18,279 --> 00:05:20,520
Speaker 1: had to do this for the narrative to work, like

111
00:05:20,600 --> 00:05:24,760
Speaker 1: this is a storytelling convention, it's required. They're not presenting

112
00:05:24,760 --> 00:05:27,360
Speaker 1: this as if it's hard science. And so therefore, if

113
00:05:27,360 --> 00:05:29,560
Speaker 1: they do the Arthur C. Clark thing where they just

114
00:05:29,640 --> 00:05:32,479
Speaker 1: use science as a way to stand in for magic,

115
00:05:32,640 --> 00:05:35,719
Speaker 1: that's okay. And then there's probably part of you that's like, cush,

116
00:05:35,800 --> 00:05:40,440
Speaker 1: darn it, that's not what particle accelerators do. And I

117
00:05:40,520 --> 00:05:44,919
Speaker 1: wish they would stop because people are getting the wrong idea. So, Kelly,

118
00:05:44,960 --> 00:05:46,880
Speaker 1: one of the ones I mentioned to you was there's

119
00:05:46,960 --> 00:05:49,000
Speaker 1: the show The Last of Us, which actually they did

120
00:05:49,040 --> 00:05:52,840
Speaker 1: a fairly decent job in talking about how the fungal

121
00:05:52,880 --> 00:05:56,839
Speaker 1: parasites that can affect insects and control them and their

122
00:05:56,839 --> 00:06:00,640
Speaker 1: behavior to some extent, how that really wouldn't trate to

123
00:06:01,080 --> 00:06:03,800
Speaker 1: more complex life forms. They actually go so far as

124
00:06:03,839 --> 00:06:06,480
Speaker 1: to say that in the intro, so they lampshaded it

125
00:06:06,600 --> 00:06:08,920
Speaker 1: quite a bit, right, They said, this is not really possible,

126
00:06:09,000 --> 00:06:11,560
Speaker 1: and yet hear it happened in the show, which of

127
00:06:11,600 --> 00:06:15,680
Speaker 1: course dates back to the actual video game that inspired

128
00:06:15,880 --> 00:06:18,719
Speaker 1: that series. So when you see things like that, like,

129
00:06:18,800 --> 00:06:22,719
Speaker 1: what's your reaction having actually written about these kinds of

130
00:06:22,760 --> 00:06:25,320
Speaker 1: parasites that do alter behaviors?

131
00:06:25,760 --> 00:06:28,839
Speaker 3: My philosophy in general is that you know fiction is

132
00:06:28,880 --> 00:06:30,880
Speaker 3: it just it's a totally different thing. And as long

133
00:06:30,920 --> 00:06:33,760
Speaker 3: as they create worlds that are consistent and then they

134
00:06:33,800 --> 00:06:35,920
Speaker 3: stick with the rules that they've made for their world,

135
00:06:36,120 --> 00:06:37,760
Speaker 3: I'm cool with that. The only thing that gets me

136
00:06:37,800 --> 00:06:40,680
Speaker 3: grumpy is when people like change their rules halfway through.

137
00:06:40,760 --> 00:06:42,760
Speaker 3: Like I hated Lost because it was like I thought

138
00:06:42,760 --> 00:06:44,680
Speaker 3: we were in the real world and now there's like

139
00:06:44,760 --> 00:06:47,760
Speaker 3: smoke monsters and like, yeah, anyway, so I thought the

140
00:06:47,839 --> 00:06:48,680
Speaker 3: Last of Us was great.

141
00:06:48,800 --> 00:06:50,960
Speaker 1: Oh gosh, I have a feeling that you and I

142
00:06:51,000 --> 00:06:55,480
Speaker 1: would have some of the best post viewing coffee sessions

143
00:06:55,720 --> 00:06:58,599
Speaker 1: for various shows and movies, because you have hit upon

144
00:06:58,839 --> 00:07:03,560
Speaker 1: one of my most passionate topics, which is consistency and rules.

145
00:07:03,800 --> 00:07:06,280
Speaker 1: As much as as I enjoyed the Buffy the Vampire

146
00:07:06,360 --> 00:07:10,080
Speaker 1: Slayer television show when it first aired, I got consistently

147
00:07:10,160 --> 00:07:12,440
Speaker 1: more upset with that show as it went on, as

148
00:07:12,480 --> 00:07:15,000
Speaker 1: I felt that it was violating the rules that it

149
00:07:15,000 --> 00:07:18,320
Speaker 1: itself had set up in previous seasons. And meanwhile, all

150
00:07:18,360 --> 00:07:20,840
Speaker 1: my friends were Buffy fans, were like, why are you

151
00:07:20,920 --> 00:07:23,320
Speaker 1: so upset about this? I'm like, it's the easiest thing

152
00:07:23,320 --> 00:07:25,440
Speaker 1: in the world to be consistent. You have to go

153
00:07:25,560 --> 00:07:27,600
Speaker 1: out of your way to be inconsistent.

154
00:07:28,560 --> 00:07:30,600
Speaker 3: We should get together for coffee because you know, I

155
00:07:30,600 --> 00:07:33,520
Speaker 3: feel like often the you need to be consistent about

156
00:07:33,560 --> 00:07:36,080
Speaker 3: the rules. People like aren't the first one to invite

157
00:07:36,120 --> 00:07:39,000
Speaker 3: to the parties, right, so we have to stick together.

158
00:07:39,080 --> 00:07:42,640
Speaker 1: Yes, we're the ones that somehow get overlooked when the

159
00:07:42,680 --> 00:07:45,000
Speaker 1: invitations go out. I don't understand it either.

160
00:07:45,640 --> 00:07:47,160
Speaker 3: It's not a fair world we live in.

161
00:07:47,320 --> 00:07:47,760
Speaker 1: It is not.

162
00:07:48,200 --> 00:07:50,320
Speaker 2: And you know, if you're watching like a murder mystery,

163
00:07:50,360 --> 00:07:52,760
Speaker 2: everybody expects you to follow the rules, right, Like if

164
00:07:52,800 --> 00:07:54,800
Speaker 2: there was a clue laid in act one, you expect

165
00:07:54,800 --> 00:07:57,760
Speaker 2: that to still be relevant in act three. But somehow

166
00:07:57,800 --> 00:07:59,960
Speaker 2: if it's science fiction and like, hey, we could just

167
00:08:00,400 --> 00:08:03,160
Speaker 2: those rules out the window. You know, let's be fair

168
00:08:03,200 --> 00:08:04,280
Speaker 2: about the standards we apply.

169
00:08:04,520 --> 00:08:07,440
Speaker 1: Yes, absolutely, same thing's true for horror movies. By the way,

170
00:08:07,800 --> 00:08:12,040
Speaker 1: if your horror movie is predicated upon information purposefully withheld

171
00:08:12,080 --> 00:08:15,360
Speaker 1: from the audience, and you can't expect me to be

172
00:08:15,480 --> 00:08:17,320
Speaker 1: wold when you have your big reveal, you need to

173
00:08:17,360 --> 00:08:19,240
Speaker 1: set that stuff up in subtle ways that I don't

174
00:08:19,280 --> 00:08:22,600
Speaker 1: pick up on, but then can appreciate at the end. Yes, amen,

175
00:08:22,880 --> 00:08:24,600
Speaker 1: I say that as someone who just watched the horror

176
00:08:24,600 --> 00:08:26,480
Speaker 1: movie this past weekend, and I was like, Oh, that's

177
00:08:26,760 --> 00:08:29,520
Speaker 1: that's not Chekhov's gun, but that is Chekhov's wooden plank

178
00:08:29,600 --> 00:08:32,720
Speaker 1: with nails sticking out of it. People who have seen

179
00:08:32,720 --> 00:08:35,720
Speaker 1: it now know what I'm talking about. Daniel, like, same

180
00:08:35,760 --> 00:08:37,920
Speaker 1: sort of thing for you. I mean, you've obviously worked

181
00:08:37,920 --> 00:08:41,760
Speaker 1: on some of the most famous particle physics projects in

182
00:08:41,840 --> 00:08:45,240
Speaker 1: the world, and not only have we seen interesting depictions

183
00:08:45,280 --> 00:08:49,080
Speaker 1: of that in fiction media, but obviously the news media

184
00:08:49,200 --> 00:08:52,920
Speaker 1: had all sorts of speculation as to what was going

185
00:08:52,960 --> 00:08:56,319
Speaker 1: to happen once the large Hadron collider was operating at

186
00:08:56,320 --> 00:08:59,120
Speaker 1: full power. People may not remember that, but there were

187
00:08:59,240 --> 00:09:03,720
Speaker 1: so many stories about everything from time traveling, conspiracy theories

188
00:09:03,960 --> 00:09:09,000
Speaker 1: involving birds dropping twigs down ventilation shafts, to some sort

189
00:09:09,040 --> 00:09:12,160
Speaker 1: of message from the future being sent back saying, for

190
00:09:12,360 --> 00:09:14,720
Speaker 1: the love of all that's holy, don't turn it on.

191
00:09:15,840 --> 00:09:18,439
Speaker 1: What was your reaction as all this was unfolding.

192
00:09:18,640 --> 00:09:21,200
Speaker 2: I thought it was sort of amazing and hilarious, but

193
00:09:21,480 --> 00:09:24,920
Speaker 2: also terrifying. I mean, it's always fun to see your

194
00:09:25,000 --> 00:09:28,040
Speaker 2: own work depicted in the popular media, and you know,

195
00:09:28,520 --> 00:09:30,640
Speaker 2: Stephen Colbert when he was still on The Daily Show,

196
00:09:30,720 --> 00:09:32,920
Speaker 2: did a whole bit about whether we should turn on

197
00:09:32,960 --> 00:09:36,520
Speaker 2: the LEDC and understanding and probability and you know, it's

198
00:09:36,520 --> 00:09:38,480
Speaker 2: not safe for work, but people should definitely go check

199
00:09:38,520 --> 00:09:41,920
Speaker 2: that out because it's really fun. But it's also a

200
00:09:41,960 --> 00:09:44,840
Speaker 2: little terrifying to see these concepts that you've worked on,

201
00:09:44,880 --> 00:09:48,240
Speaker 2: these projects that you're passionate about, sort of leave your

202
00:09:48,280 --> 00:09:52,080
Speaker 2: control and enter the broader cultural conversation where you don't

203
00:09:52,120 --> 00:09:55,480
Speaker 2: really necessarily have a voice to combat the misinformation. And

204
00:09:55,520 --> 00:09:58,960
Speaker 2: there's a lot of well meaning misinformation out there, people

205
00:09:59,160 --> 00:10:01,319
Speaker 2: who try to cover this stuff and just don't get

206
00:10:01,320 --> 00:10:04,319
Speaker 2: the details right. And I don't have, or it didn't

207
00:10:04,360 --> 00:10:07,800
Speaker 2: at the time, at least have a platform to be like, hey, actually,

208
00:10:08,120 --> 00:10:10,720
Speaker 2: this is how it works, and I don't want to

209
00:10:10,720 --> 00:10:13,160
Speaker 2: throw cold water on your ideas because the reality is

210
00:10:13,240 --> 00:10:16,320
Speaker 2: much more interesting and fascinating, right. That's the thing that

211
00:10:16,320 --> 00:10:18,800
Speaker 2: frustrates me is when they get the story wrong, but

212
00:10:18,840 --> 00:10:22,240
Speaker 2: the real story is even more exciting. It's even more

213
00:10:22,280 --> 00:10:25,559
Speaker 2: clickbaity than the misconceptions.

214
00:10:25,640 --> 00:10:28,640
Speaker 1: When you reach a certain level with science, obviously, it

215
00:10:28,640 --> 00:10:31,280
Speaker 1: gets to a point where there is no easy way

216
00:10:31,280 --> 00:10:35,959
Speaker 1: to communicate the topic to a general audience. You're either

217
00:10:36,040 --> 00:10:38,440
Speaker 1: going to have to spend an an ordinate amount of

218
00:10:38,440 --> 00:10:41,120
Speaker 1: time setting it up and building upon a foundation and

219
00:10:41,160 --> 00:10:44,000
Speaker 1: hoping that your audience stays with you for the entirety

220
00:10:44,200 --> 00:10:48,320
Speaker 1: of that, or you run the risk of oversimplifying, hoping

221
00:10:48,360 --> 00:10:50,960
Speaker 1: that they get the gist of it, knowing that you're

222
00:10:51,000 --> 00:10:54,640
Speaker 1: going to be leaving out some important nuance that it

223
00:10:54,679 --> 00:10:56,880
Speaker 1: really is needed to have a full understanding of or

224
00:10:56,920 --> 00:11:00,120
Speaker 1: even a working understanding of the topic. That's something that

225
00:11:00,160 --> 00:11:03,000
Speaker 1: I struggle with all the time on tech stuff, right, Like,

226
00:11:03,800 --> 00:11:09,080
Speaker 1: there are tech topics that are really complex, and how

227
00:11:09,120 --> 00:11:12,160
Speaker 1: do you approach discussing this in a way that is

228
00:11:12,520 --> 00:11:16,760
Speaker 1: I think responsible, so that your audience has an understanding.

229
00:11:17,080 --> 00:11:20,680
Speaker 1: And of course I always advocate that people look into things,

230
00:11:20,720 --> 00:11:23,560
Speaker 1: read up more about them if they're interested, to do

231
00:11:23,640 --> 00:11:26,120
Speaker 1: more exploration, because there's only so much I can say

232
00:11:26,280 --> 00:11:30,040
Speaker 1: in an hour long episode. But you know that still

233
00:11:30,080 --> 00:11:33,960
Speaker 1: weighs on me. That being said, sometimes there are layups

234
00:11:34,000 --> 00:11:38,480
Speaker 1: out there where you'll see something portrayed and you'll just say, well,

235
00:11:38,480 --> 00:11:42,559
Speaker 1: that's just wrong, that's just wrong. It's not even remotely

236
00:11:42,559 --> 00:11:45,120
Speaker 1: in the realm of possibility. I'm reminded. I wish I

237
00:11:45,120 --> 00:11:47,880
Speaker 1: could remember which procedural it was. It was one of

238
00:11:47,920 --> 00:11:51,160
Speaker 1: those cop procedural shows where there's always something about hacking.

239
00:11:51,520 --> 00:11:55,240
Speaker 1: So there was a scene where there are two characters

240
00:11:55,280 --> 00:11:59,960
Speaker 1: simultaneously typing on the same keyboard while trying to counteract

241
00:12:00,080 --> 00:12:02,280
Speaker 1: to hacker who's trying to get into a system. And

242
00:12:02,360 --> 00:12:06,240
Speaker 1: I thought, in one world, can a keyboard accept two

243
00:12:06,320 --> 00:12:10,120
Speaker 1: separate forms of input at the same time and haven't

244
00:12:10,320 --> 00:12:12,880
Speaker 1: mean anything. It would just be like me just smashing

245
00:12:12,880 --> 00:12:16,200
Speaker 1: my hands randomly on keys. This makes no sense. I'm like,

246
00:12:16,280 --> 00:12:18,079
Speaker 1: it couldn't have made sense when they shot it. Did

247
00:12:18,120 --> 00:12:21,559
Speaker 1: they do this on purpose? As like a wind up?

248
00:12:21,840 --> 00:12:24,240
Speaker 1: So there are moments like that that stand out as.

249
00:12:24,120 --> 00:12:26,720
Speaker 3: A parasitologist, as person who studies parasites. There was an

250
00:12:26,720 --> 00:12:30,120
Speaker 3: episode of Grey's Anatomy where someone had neurosistas yorcosis. So

251
00:12:30,160 --> 00:12:32,720
Speaker 3: it's this big fluid filled sack filled with tapeworms in

252
00:12:32,760 --> 00:12:35,400
Speaker 3: your brains that causes nervous system problems. Yeah, and it's

253
00:12:35,440 --> 00:12:38,160
Speaker 3: a very delicate procedure to remove that sack because if

254
00:12:38,160 --> 00:12:41,720
Speaker 3: it breaks, you have this crazy like neuroimmune response to

255
00:12:41,760 --> 00:12:43,800
Speaker 3: the sack and it's really bad. But they pulled it

256
00:12:43,840 --> 00:12:46,760
Speaker 3: out in the reveal it was a nematode, and all right,

257
00:12:46,800 --> 00:12:48,760
Speaker 3: so I get upset. No one else would care. That's

258
00:12:48,800 --> 00:12:51,720
Speaker 3: a totally different phylum than the actual parasite that causes

259
00:12:51,720 --> 00:12:54,160
Speaker 3: the problem. But it was like a squiggly worm that

260
00:12:54,200 --> 00:12:56,120
Speaker 3: fell back in and they were like reaching around trying

261
00:12:56,160 --> 00:12:58,200
Speaker 3: to get it, and I'm like, you're screwing up the

262
00:12:58,200 --> 00:13:00,720
Speaker 3: brain and like, actually it's a sack. It's not a

263
00:13:00,880 --> 00:13:04,720
Speaker 3: like the little nematode. Anyway, I was Everything was wrong

264
00:13:04,760 --> 00:13:06,000
Speaker 3: about that. That kept me up at night.

265
00:13:06,080 --> 00:13:08,000
Speaker 1: See now that makes me think of the Adventures of

266
00:13:08,000 --> 00:13:10,920
Speaker 1: Buckaroo Bonzai where there's a moment early in that film

267
00:13:10,920 --> 00:13:15,280
Speaker 1: where Buckaroo Bonzi, who is a rocket scientist neurosurgeon rock

268
00:13:15,320 --> 00:13:19,880
Speaker 1: star wow. Peter Weller played him. Fantastic, Very slow movie

269
00:13:20,200 --> 00:13:23,720
Speaker 1: nineteen eighties science fiction movie. John Lithgal plays a crazy

270
00:13:23,760 --> 00:13:26,280
Speaker 1: alien in it. If you haven't seen it, it's worth watching.

271
00:13:26,360 --> 00:13:29,240
Speaker 1: But it is slow paced and convoluted.

272
00:13:29,320 --> 00:13:31,240
Speaker 3: You're not selling it well.

273
00:13:31,520 --> 00:13:33,720
Speaker 1: I have to warn because I introduced a friend of mine,

274
00:13:33,960 --> 00:13:37,319
Speaker 1: a millennial, to this film, and she was like, that

275
00:13:37,480 --> 00:13:41,120
Speaker 1: was the slowest, most confusing science fiction film I've ever seen.

276
00:13:41,160 --> 00:13:46,520
Speaker 1: I'm like, there's a rock star neuroscientist, rocket scientist, race

277
00:13:46,600 --> 00:13:49,080
Speaker 1: car driver as the main character. How could you not

278
00:13:49,120 --> 00:13:51,320
Speaker 1: love it? But there's a moment where they're doing brain

279
00:13:51,360 --> 00:13:55,360
Speaker 1: surgery where that character says to his surgical assistant, no, no, no,

280
00:13:55,400 --> 00:13:58,120
Speaker 1: don't pull on that. You don't know what's connected to Like, okay, Well,

281
00:13:58,120 --> 00:14:00,880
Speaker 1: that seems realistic to me. That seems like realistic depiction

282
00:14:00,960 --> 00:14:03,320
Speaker 1: of brain surgery. I think I'm okay with that, Daniel.

283
00:14:03,360 --> 00:14:06,160
Speaker 1: Are there any like depictions that you have seen that

284
00:14:06,320 --> 00:14:08,920
Speaker 1: kind of stand out in your your mind? As this

285
00:14:09,000 --> 00:14:11,160
Speaker 1: might have been a very creative way to tell a story,

286
00:14:11,200 --> 00:14:15,160
Speaker 1: but it was a terrible way to leverage actual science

287
00:14:15,200 --> 00:14:16,360
Speaker 1: that you are familiar with.

288
00:14:17,080 --> 00:14:19,280
Speaker 2: Well, I have a lot of respect for science fiction authors,

289
00:14:19,320 --> 00:14:21,960
Speaker 2: and I get what they're going for. Often they read

290
00:14:22,080 --> 00:14:25,520
Speaker 2: some new idea or they hear an explanation, and then

291
00:14:25,600 --> 00:14:28,120
Speaker 2: inspire some story they imagine that they could tell only

292
00:14:28,160 --> 00:14:31,400
Speaker 2: in some alternative universe. And with Kelly, I agree, like,

293
00:14:31,480 --> 00:14:33,320
Speaker 2: it doesn't have to follow the rules of our universe

294
00:14:33,360 --> 00:14:35,600
Speaker 2: as long as they are consistent about it. But the

295
00:14:35,640 --> 00:14:38,520
Speaker 2: thing that does frustrate me is that I think it

296
00:14:38,600 --> 00:14:40,480
Speaker 2: is possible for a lot of folks out there to

297
00:14:40,720 --> 00:14:43,840
Speaker 2: understand the subtle nuances of the science as long as

298
00:14:43,840 --> 00:14:46,280
Speaker 2: it's explained well and on our show at least, we

299
00:14:46,320 --> 00:14:50,160
Speaker 2: really believe that the science can be communicated clearly and effectively,

300
00:14:50,240 --> 00:14:53,240
Speaker 2: that it's possible for people to really get these things.

301
00:14:53,280 --> 00:14:54,880
Speaker 2: I think there's a lot of folks out there who

302
00:14:55,400 --> 00:14:58,560
Speaker 2: really love science and maybe wanted to do it, but

303
00:14:58,600 --> 00:15:00,360
Speaker 2: something happened in their life they didn't end up being

304
00:15:00,360 --> 00:15:02,280
Speaker 2: a scientist, and so they want to stay up on

305
00:15:02,320 --> 00:15:04,440
Speaker 2: the details and they want to hear more than just like,

306
00:15:04,720 --> 00:15:06,760
Speaker 2: here's the pop size story. You've heard a lot about

307
00:15:06,840 --> 00:15:10,000
Speaker 2: quantum mechanics and entanglement and to really have it connect

308
00:15:10,040 --> 00:15:12,160
Speaker 2: for them, and that's what we try to do on

309
00:15:12,200 --> 00:15:15,040
Speaker 2: the show. But to directly answer your question, one of

310
00:15:15,040 --> 00:15:17,040
Speaker 2: the things that sort of grinds my gears a lot

311
00:15:17,400 --> 00:15:21,440
Speaker 2: is when people use like two entangled particles to communicate

312
00:15:21,520 --> 00:15:23,880
Speaker 2: faster than light. And you know, you hear this in

313
00:15:23,960 --> 00:15:27,120
Speaker 2: pop science all the time, and there are subtle nuances there,

314
00:15:27,160 --> 00:15:30,240
Speaker 2: like there is something happening faster than light when two

315
00:15:30,440 --> 00:15:33,720
Speaker 2: entangled particles collapse. But you can't use it to send

316
00:15:33,800 --> 00:15:36,920
Speaker 2: messages across the stars, as much as I wish you could.

317
00:15:37,160 --> 00:15:41,080
Speaker 1: Right right that whole concept of the entangled particles. Usually

318
00:15:41,120 --> 00:15:44,600
Speaker 1: we're talking about something like spin particle spin. So if

319
00:15:44,600 --> 00:15:47,200
Speaker 1: one's spinning up, the other one's spinning down, and they're

320
00:15:47,360 --> 00:15:51,280
Speaker 1: entangled together, and no matter how far apart you separate

321
00:15:51,360 --> 00:15:56,000
Speaker 1: these two particles until the system collapses, until that entanglement breaks,

322
00:15:56,240 --> 00:15:58,080
Speaker 1: then you're still going to have one spinning up, one

323
00:15:58,080 --> 00:16:00,720
Speaker 1: spinning down. There'll always be in that connect. But then

324
00:16:00,760 --> 00:16:04,880
Speaker 1: once you observe it all breaks down and that connection

325
00:16:05,040 --> 00:16:07,920
Speaker 1: is severed and you can't do anything useful with it. Yeah,

326
00:16:07,960 --> 00:16:11,200
Speaker 1: we've used that discussion when talking about things like quantum computers,

327
00:16:11,680 --> 00:16:15,760
Speaker 1: when talking about entanglement and superposition and all these really

328
00:16:15,800 --> 00:16:19,720
Speaker 1: interesting ideas. I'm fascinated with the methods people have created

329
00:16:19,960 --> 00:16:25,120
Speaker 1: to try and take advantage of these quantum effects. And

330
00:16:25,560 --> 00:16:28,200
Speaker 1: to me, like, that's one of those areas where I

331
00:16:28,560 --> 00:16:32,320
Speaker 1: definitely run up against the barriers of my ignorance. Right,

332
00:16:32,400 --> 00:16:36,800
Speaker 1: I understand up to a point, and then after that

333
00:16:36,880 --> 00:16:40,320
Speaker 1: I'm like, WHOA, Okay, there was a leap made here

334
00:16:40,720 --> 00:16:46,280
Speaker 1: that I can't follow, and I from a kind of

335
00:16:46,320 --> 00:16:49,320
Speaker 1: like a high level concept I can understand, but it's

336
00:16:49,400 --> 00:16:52,800
Speaker 1: so counterintuitive based upon just my day to day experience

337
00:16:52,880 --> 00:16:56,000
Speaker 1: with physics on the macro level, that you might as

338
00:16:56,080 --> 00:16:58,360
Speaker 1: well be describing a magic spell to me, because it's

339
00:16:58,400 --> 00:17:01,280
Speaker 1: so it's so unusual. I mean, I can see why

340
00:17:01,280 --> 00:17:03,920
Speaker 1: Einstein would call it spooky action at a distance, right

341
00:17:03,960 --> 00:17:07,240
Speaker 1: like it's it seems like it doesn't make any any

342
00:17:07,280 --> 00:17:07,879
Speaker 1: real sense.

343
00:17:08,200 --> 00:17:10,399
Speaker 2: And that's the incredible thing about the universe, right it

344
00:17:10,480 --> 00:17:13,120
Speaker 2: is this bizarre mystery. It seems like it doesn't make sense.

345
00:17:13,359 --> 00:17:16,120
Speaker 2: And yet over the years and the decades and the centuries,

346
00:17:16,160 --> 00:17:18,600
Speaker 2: we have slowly chipped away at the truth and always

347
00:17:18,640 --> 00:17:21,879
Speaker 2: found some explanation. And that explanation is often not what

348
00:17:21,920 --> 00:17:24,480
Speaker 2: we expected, not what we thought it was going to be,

349
00:17:24,760 --> 00:17:27,959
Speaker 2: and mind blowing when we understand it. But amazingly it

350
00:17:28,040 --> 00:17:30,600
Speaker 2: is possible to understand this universe like we have no

351
00:17:30,640 --> 00:17:33,480
Speaker 2: guarantee of that. It could be that eventually the universe

352
00:17:33,880 --> 00:17:37,720
Speaker 2: runs on some systems, some mathematics that are beyond our comprehension,

353
00:17:38,000 --> 00:17:40,639
Speaker 2: but so far we've always been able with our puny

354
00:17:40,680 --> 00:17:42,280
Speaker 2: little ape brains to figure it out.

355
00:17:42,320 --> 00:17:43,679
Speaker 3: But for some of this stuff, we're still in the

356
00:17:43,680 --> 00:17:45,800
Speaker 3: process of trying to understand it. And so, you know, you

357
00:17:45,840 --> 00:17:47,359
Speaker 3: were saying that you get to a point where you're like, oh,

358
00:17:47,400 --> 00:17:50,199
Speaker 3: my brain just can't make that leap. While talking to Daniel,

359
00:17:50,280 --> 00:17:52,080
Speaker 3: I've learned that a lot of the places where I

360
00:17:52,119 --> 00:17:54,359
Speaker 3: was like, oh, my brain can't make that leap. When

361
00:17:54,520 --> 00:17:56,040
Speaker 3: I talked to Daniel, He's like, oh, we just we

362
00:17:56,040 --> 00:17:57,600
Speaker 3: don't know why that happens yet, and I'm like, no,

363
00:17:58,240 --> 00:18:01,240
Speaker 3: we just hit against everyone's under standing there. And so,

364
00:18:01,320 --> 00:18:03,160
Speaker 3: you know, I think that's a great thing about science

365
00:18:03,160 --> 00:18:05,159
Speaker 3: if it's explained well, you can let people know that, like, no,

366
00:18:05,200 --> 00:18:06,440
Speaker 3: we're all we all get a wall.

367
00:18:06,480 --> 00:18:09,480
Speaker 1: Eventually, we'll be back in just a moment to talk

368
00:18:09,560 --> 00:18:13,800
Speaker 1: more about science and science, depictions of media, and who

369
00:18:13,880 --> 00:18:17,040
Speaker 1: knows what else with Daniel and Kelly after these messages.

370
00:18:26,520 --> 00:18:28,360
Speaker 1: When I used to write for How Stuff Works, there

371
00:18:28,400 --> 00:18:31,280
Speaker 1: was a point where I was given the assignment of

372
00:18:31,320 --> 00:18:35,520
Speaker 1: how string theory works. So I was I was diving

373
00:18:35,560 --> 00:18:40,080
Speaker 1: into everything string theory. And I'm an English Lit major, y'all.

374
00:18:40,359 --> 00:18:43,920
Speaker 1: My major was in Shakespeare, and I can rattle Shakespeare

375
00:18:43,960 --> 00:18:46,439
Speaker 1: off like nobody's business. But you asked me to describe

376
00:18:46,440 --> 00:18:48,560
Speaker 1: string theory, and we got ourselves a problem.

377
00:18:48,800 --> 00:18:50,760
Speaker 2: I want to hear a sonnet about string theory.

378
00:18:51,119 --> 00:18:52,639
Speaker 1: You know what? Give me a week and I'll have

379
00:18:52,680 --> 00:18:58,360
Speaker 1: it for you. I'm assuming you mean Shakespearean not Edwardians,

380
00:18:58,400 --> 00:19:02,200
Speaker 1: because you're Spencerian. Okay, good, I can do that. I'm

381
00:19:02,240 --> 00:19:06,280
Speaker 1: an expert at Shakespearean's on It's excellent. I remember distinctly

382
00:19:06,320 --> 00:19:09,600
Speaker 1: I watched a I think it was a Nova special

383
00:19:09,680 --> 00:19:13,480
Speaker 1: about string theory, and I'm taking notes and I mean,

384
00:19:13,520 --> 00:19:16,560
Speaker 1: I've got like like seven eight pages of handwritten notes

385
00:19:16,680 --> 00:19:19,959
Speaker 1: and I'm just sweating bullets watching this and everyone's really

386
00:19:20,160 --> 00:19:22,639
Speaker 1: a great communicator on that special. And it gets to

387
00:19:22,640 --> 00:19:25,040
Speaker 1: one point where one of the experts is being interviewed

388
00:19:25,040 --> 00:19:26,760
Speaker 1: and he says, yeah, I think this is the point

389
00:19:26,800 --> 00:19:29,040
Speaker 1: where I just have to admit I don't understand how

390
00:19:29,080 --> 00:19:30,840
Speaker 1: it works. I just know that this is what the

391
00:19:30,880 --> 00:19:33,199
Speaker 1: math says. And I'm like, oh my gosh, if you

392
00:19:33,320 --> 00:19:37,679
Speaker 1: don't know, what hope is there for me? But that,

393
00:19:37,800 --> 00:19:39,920
Speaker 1: to me is fascinating. It's one of the reasons why,

394
00:19:40,240 --> 00:19:43,600
Speaker 1: by the way, I love doing a tech podcast is

395
00:19:43,640 --> 00:19:50,439
Speaker 1: that technology is science manifest into devices and gadgets, and

396
00:19:50,800 --> 00:19:54,200
Speaker 1: it's proof that science works, right because if science didn't work,

397
00:19:54,240 --> 00:19:57,680
Speaker 1: the tech wouldn't work. So I've always pointed at like

398
00:19:57,720 --> 00:20:03,479
Speaker 1: we are constantly surrounded by proof that the scientific approach works,

399
00:20:03,640 --> 00:20:06,640
Speaker 1: because otherwise the things that we come in contact with

400
00:20:06,960 --> 00:20:10,920
Speaker 1: on a minute by minute basis wouldn't function absolutely.

401
00:20:11,359 --> 00:20:13,119
Speaker 3: What gets me about a lot of tech, though, is

402
00:20:13,119 --> 00:20:16,000
Speaker 3: that it works, but the science we don't always even

403
00:20:16,040 --> 00:20:18,760
Speaker 3: know why it works. I was talking to someone who

404
00:20:18,760 --> 00:20:21,320
Speaker 3: does deep brain stimulation, so like the essentially, you know,

405
00:20:21,400 --> 00:20:23,600
Speaker 3: electrocute the center of your brain to try to stop

406
00:20:23,680 --> 00:20:26,719
Speaker 3: the electrical storms that cause seizures. And I asked her,

407
00:20:26,760 --> 00:20:30,440
Speaker 3: I was like, why does like putting electrical circuits through

408
00:20:30,440 --> 00:20:32,600
Speaker 3: someone's brain stop it? And they're like, we don't know,

409
00:20:32,720 --> 00:20:34,720
Speaker 3: but it's great that it works. And I was like, wow,

410
00:20:35,640 --> 00:20:38,080
Speaker 3: So I mean sometimes just because engineering works doesn't mean

411
00:20:38,119 --> 00:20:39,840
Speaker 3: we understand it at the level that we'd like to.

412
00:20:39,960 --> 00:20:41,080
Speaker 3: But it's great that it works.

413
00:20:41,200 --> 00:20:45,080
Speaker 1: Yeah, that's true. Yeah, there are lots of examples of that. Like,

414
00:20:45,080 --> 00:20:47,600
Speaker 1: it's to me, it's interesting that we have great stories

415
00:20:48,080 --> 00:20:51,159
Speaker 1: going in both directions, right. We have the stories of

416
00:20:51,200 --> 00:20:56,120
Speaker 1: the people who were real innovators, really phenomenal. They thought

417
00:20:56,160 --> 00:20:59,480
Speaker 1: outside the box. You know, they're probably building upon work

418
00:20:59,480 --> 00:21:03,240
Speaker 1: that previous generations had done, but they've done something themselves

419
00:21:03,320 --> 00:21:07,560
Speaker 1: that has transformed thinking in some way and has really

420
00:21:07,560 --> 00:21:10,960
Speaker 1: pushed science forward. So the person I love to talk about,

421
00:21:11,119 --> 00:21:13,119
Speaker 1: and I was actually just chatting about this with my

422
00:21:13,200 --> 00:21:17,800
Speaker 1: producer the other day, Ada Lovelace. You know Lord Byron's daughter.

423
00:21:18,280 --> 00:21:22,160
Speaker 1: Ada Lovelace worked with Charles Babbage, who built the Decision Machine,

424
00:21:22,200 --> 00:21:25,920
Speaker 1: the Decision Engine, and she was one of the first people,

425
00:21:25,960 --> 00:21:28,560
Speaker 1: probably the first person that I know of, who came

426
00:21:28,640 --> 00:21:31,600
Speaker 1: up with the concept of what if we took things

427
00:21:31,840 --> 00:21:37,680
Speaker 1: like pictures or music and we converted that into mathematical statements,

428
00:21:38,080 --> 00:21:40,720
Speaker 1: and then we used a machine like this to process that.

429
00:21:40,880 --> 00:21:43,960
Speaker 1: We could create art, we could create music, we could

430
00:21:44,040 --> 00:21:47,639
Speaker 1: alter existing pieces. And so she was essentially talking about

431
00:21:47,800 --> 00:21:53,080
Speaker 1: computer programming, you know, generations before anyone else would even

432
00:21:53,119 --> 00:21:55,480
Speaker 1: be able to do this, and the fact that she

433
00:21:55,640 --> 00:21:59,880
Speaker 1: was able to ideate around that without there being any

434
00:22:00,119 --> 00:22:02,080
Speaker 1: lead up, Like, as far as I can tell, she

435
00:22:02,240 --> 00:22:04,880
Speaker 1: just sort of had a Eureka moment. And those are

436
00:22:04,920 --> 00:22:06,879
Speaker 1: so rare, Like it's easy for us to point at

437
00:22:06,920 --> 00:22:08,800
Speaker 1: things and say, this is the inventor of such and such,

438
00:22:08,840 --> 00:22:10,760
Speaker 1: but when you look into it, you're like, oh, actually

439
00:22:10,800 --> 00:22:14,159
Speaker 1: they were iterating on previous generations of stuff. But as

440
00:22:14,160 --> 00:22:16,240
Speaker 1: far as I can tell, This is like Ada Lovelace

441
00:22:16,320 --> 00:22:19,639
Speaker 1: coming up with a truly novel idea. And that's one

442
00:22:19,680 --> 00:22:22,800
Speaker 1: of the things I love most about podcasting is finding

443
00:22:22,840 --> 00:22:26,000
Speaker 1: those stories, as well as the ones where someone's like, yeah,

444
00:22:26,040 --> 00:22:27,520
Speaker 1: we didn't know if it would work, and we tried

445
00:22:27,560 --> 00:22:29,320
Speaker 1: it and we work. We don't know why it works,

446
00:22:29,359 --> 00:22:31,720
Speaker 1: but it does seem to work. So we'll figure out

447
00:22:31,760 --> 00:22:35,359
Speaker 1: the why someday maybe, but for now, the fact that

448
00:22:35,400 --> 00:22:36,800
Speaker 1: it works is important enough.

449
00:22:37,080 --> 00:22:39,760
Speaker 2: Yeah, and it really highlights the human side of science.

450
00:22:39,840 --> 00:22:42,720
Speaker 2: You know, I feel like there's a sandwich there. People

451
00:22:42,720 --> 00:22:44,879
Speaker 2: feel like, oh, science, maybe it's sort of like a

452
00:22:45,040 --> 00:22:48,919
Speaker 2: sterile or it's intellectual, or it's bigger than people, but

453
00:22:49,080 --> 00:22:52,040
Speaker 2: you know, it really exists only in a human context,

454
00:22:52,240 --> 00:22:54,920
Speaker 2: both because of the philosophical side of it, like you're

455
00:22:54,960 --> 00:22:57,239
Speaker 2: talking about, Oh, we understand the math, but what does

456
00:22:57,280 --> 00:23:00,520
Speaker 2: this really mean? Right? We always want to understand signs

457
00:23:00,560 --> 00:23:02,800
Speaker 2: in terms of a story, like what is this telling

458
00:23:02,880 --> 00:23:05,160
Speaker 2: us about how the universe works? About what is real?

459
00:23:05,359 --> 00:23:07,080
Speaker 2: But then also on the other side of it, like

460
00:23:07,119 --> 00:23:09,640
Speaker 2: what does it mean for humans to live? How does

461
00:23:09,680 --> 00:23:12,679
Speaker 2: this technology change our lives? How does it change what

462
00:23:12,720 --> 00:23:14,280
Speaker 2: it's like to be a human in the world? What

463
00:23:14,320 --> 00:23:16,399
Speaker 2: does it let us do? What kind of lives can

464
00:23:16,400 --> 00:23:18,840
Speaker 2: we lead that we couldn't lead before? I mean, there's

465
00:23:19,040 --> 00:23:21,240
Speaker 2: human stories everywhere on both sides of it, from the

466
00:23:21,240 --> 00:23:22,800
Speaker 2: philosophy to the engineering.

467
00:23:23,520 --> 00:23:25,960
Speaker 1: Yeah, we're seeing a lot of that too right now.

468
00:23:26,240 --> 00:23:29,680
Speaker 1: Just a lot of overt discussion about that with the

469
00:23:29,760 --> 00:23:32,760
Speaker 1: explosion of generative AI, which some people are now saying

470
00:23:32,800 --> 00:23:36,560
Speaker 1: like we might be hitting like peak AI for at

471
00:23:36,640 --> 00:23:39,240
Speaker 1: least the time being, not that it won't go any higher,

472
00:23:39,400 --> 00:23:42,640
Speaker 1: but that the level of evolution may be slowing down

473
00:23:42,680 --> 00:23:45,199
Speaker 1: a bit as we're kind of hitting the limits of

474
00:23:45,200 --> 00:23:46,639
Speaker 1: what large language models can do.

475
00:23:46,840 --> 00:23:48,320
Speaker 2: Oh my gosh, can I rant about that? For am

476
00:23:48,280 --> 00:23:50,920
Speaker 2: a lead? Because I see people saying like, let's spend

477
00:23:50,960 --> 00:23:55,040
Speaker 2: a trillion dollars and build a huge, large language model

478
00:23:55,080 --> 00:23:58,040
Speaker 2: because it will solve physics. And I'm like, you know what,

479
00:23:58,200 --> 00:24:00,879
Speaker 2: let's spend a trillion dollars on human physics first and

480
00:24:00,960 --> 00:24:04,320
Speaker 2: try that, because trust me, if we spend that much

481
00:24:04,359 --> 00:24:06,640
Speaker 2: money on physics, we could probably bigen the thing out.

482
00:24:07,160 --> 00:24:09,200
Speaker 2: Like why do we need to dumb do an AI?

483
00:24:09,840 --> 00:24:12,920
Speaker 1: I've got very strong my listeners know I don't need

484
00:24:12,960 --> 00:24:15,040
Speaker 1: to go on another rant for my listeners but I

485
00:24:15,080 --> 00:24:18,280
Speaker 1: have very strong feelings about generative AI. I don't think

486
00:24:18,280 --> 00:24:23,640
Speaker 1: it's a inherently bad technology. I mean, it's again going

487
00:24:23,680 --> 00:24:25,919
Speaker 1: back to Shakespeare, there's nothing either good nor bad, but

488
00:24:26,000 --> 00:24:28,560
Speaker 1: thinking makes it. So this is what AI. You've got

489
00:24:28,640 --> 00:24:31,680
Speaker 1: artificial thinking. But I have a lot of strong opinions

490
00:24:31,720 --> 00:24:33,680
Speaker 1: on it. I even did an episode of tech Stuff

491
00:24:33,680 --> 00:24:37,800
Speaker 1: where I titled it. This episode was written by AI

492
00:24:38,560 --> 00:24:42,280
Speaker 1: open bracket, sort of in bracket. And what I did was,

493
00:24:42,320 --> 00:24:46,639
Speaker 1: I had I had that do an episode like, I

494
00:24:46,640 --> 00:24:48,960
Speaker 1: gave it a prompt chat GPT is what I used.

495
00:24:49,280 --> 00:24:51,199
Speaker 1: I gave it a prompt, I told it to make

496
00:24:51,240 --> 00:24:55,040
Speaker 1: an episode of tech stuff, and so it had me

497
00:24:55,440 --> 00:24:56,400
Speaker 1: inserted in there.

498
00:24:56,960 --> 00:24:57,240
Speaker 2: Wow.

499
00:24:57,280 --> 00:25:00,560
Speaker 1: And the thing that I found the most distressed First,

500
00:25:00,560 --> 00:25:04,240
Speaker 1: it got stuff wrong, which is bad, right, But I'm like, well,

501
00:25:04,400 --> 00:25:07,359
Speaker 1: I get stuff wrong too, sometimes I'm human. It does happen.

502
00:25:07,600 --> 00:25:11,160
Speaker 1: But the thing that upset me was it invented experts

503
00:25:11,600 --> 00:25:14,679
Speaker 1: to deliver points of data. So it was as if

504
00:25:14,720 --> 00:25:17,720
Speaker 1: I had interviewed I think it was three or four

505
00:25:17,800 --> 00:25:21,000
Speaker 1: different people in the transcript that it printed, but none

506
00:25:21,040 --> 00:25:23,760
Speaker 1: of those people even exist, let alone. I never spoke

507
00:25:23,800 --> 00:25:26,320
Speaker 1: to anyone obviously, but they don't even exist. There were

508
00:25:26,400 --> 00:25:31,159
Speaker 1: these names and they were given titles and given a

509
00:25:31,200 --> 00:25:33,840
Speaker 1: position at places. So I tried to verify it. Not

510
00:25:33,920 --> 00:25:37,560
Speaker 1: a single person existed, And I thought, you're inventing quotes,

511
00:25:38,280 --> 00:25:41,439
Speaker 1: and then you're attributing those invented quotes to invented people,

512
00:25:42,200 --> 00:25:46,639
Speaker 1: and you're presenting it without any indication that these are inventions.

513
00:25:46,640 --> 00:25:48,159
Speaker 1: I mean, this is where we get into you know,

514
00:25:48,200 --> 00:25:52,720
Speaker 1: confabulations or hallucinations with AI, and to me, that immediately

515
00:25:52,720 --> 00:25:55,600
Speaker 1: sets off enormous alarms, which is why I would not

516
00:25:55,880 --> 00:26:01,639
Speaker 1: want to entrust using large language model based generative AI

517
00:26:01,800 --> 00:26:06,840
Speaker 1: to tackle the greatest problems in physics, because ultimately you

518
00:26:06,920 --> 00:26:11,919
Speaker 1: don't know at what point it's just creating the statistically

519
00:26:13,040 --> 00:26:17,080
Speaker 1: likely sentence, right Like, it's just statistically choosing words to

520
00:26:17,119 --> 00:26:20,320
Speaker 1: create grammatically correct sentences, and there's no regard as to

521
00:26:20,359 --> 00:26:22,240
Speaker 1: whether or not there's truth in there.

522
00:26:22,480 --> 00:26:23,760
Speaker 2: Yeah.

523
00:26:23,840 --> 00:26:26,040
Speaker 3: Yeah. We wrote a book about living in space and

524
00:26:26,080 --> 00:26:27,840
Speaker 3: we had a chapter on food and space, and we

525
00:26:27,840 --> 00:26:29,919
Speaker 3: thought we had found all the books on food and space.

526
00:26:30,359 --> 00:26:32,440
Speaker 3: And after the book came out, we were on chat

527
00:26:32,480 --> 00:26:35,119
Speaker 3: GPT and we were like, tell us the ten books

528
00:26:35,119 --> 00:26:37,840
Speaker 3: about food and space, and it popped out ten because

529
00:26:37,840 --> 00:26:40,199
Speaker 3: we had asked for ten and some of them were

530
00:26:40,240 --> 00:26:41,880
Speaker 3: books we had, and the rest we were like, oh,

531
00:26:41,920 --> 00:26:43,760
Speaker 3: how did we miss these books? And we looked them

532
00:26:43,840 --> 00:26:46,080
Speaker 3: up and they didn't exist. And just like you said,

533
00:26:46,440 --> 00:26:48,760
Speaker 3: we asked, like, well, tell us more about the person

534
00:26:48,800 --> 00:26:50,879
Speaker 3: who wrote this book that doesn't seem to exist, and

535
00:26:50,920 --> 00:26:54,199
Speaker 3: they gave them a bio, they gave them affiliations. And

536
00:26:54,480 --> 00:26:57,040
Speaker 3: these hallucinations are really scary if you're going to rely

537
00:26:57,160 --> 00:26:58,000
Speaker 3: on it for research.

538
00:26:58,080 --> 00:27:00,920
Speaker 2: Oh yeah, yeah, nobody I ever should rely on it

539
00:27:01,000 --> 00:27:03,520
Speaker 2: for research. It's not a research tool, right, Yeah. It

540
00:27:03,640 --> 00:27:06,840
Speaker 2: generates the facsimile of research. It doesn't do any research,

541
00:27:06,840 --> 00:27:10,040
Speaker 2: doesn't do any thinking. There's no reasoning there, and sometimes

542
00:27:10,040 --> 00:27:12,639
Speaker 2: that's very useful for research. I use Genai in my

543
00:27:12,720 --> 00:27:16,320
Speaker 2: research all the time because it's very good at generating examples,

544
00:27:16,320 --> 00:27:19,760
Speaker 2: and sometimes you need something which can rapidly generate examples.

545
00:27:20,040 --> 00:27:22,719
Speaker 2: But you have to know exactly what you're asking to do, yeah,

546
00:27:22,760 --> 00:27:23,680
Speaker 2: because it can't think.

547
00:27:23,880 --> 00:27:27,080
Speaker 1: Yeah. I've used it to create like a bullet point

548
00:27:27,240 --> 00:27:30,240
Speaker 1: of a bullet list of summary points from like really

549
00:27:30,320 --> 00:27:33,159
Speaker 1: long articles, and then I went back and checked to

550
00:27:33,200 --> 00:27:35,600
Speaker 1: make sure that what it was creating was reflected in

551
00:27:35,600 --> 00:27:38,359
Speaker 1: the articles, and in that case, at least that specific case,

552
00:27:38,640 --> 00:27:41,840
Speaker 1: it was whether or not that's ninety nine times out

553
00:27:41,840 --> 00:27:43,560
Speaker 1: of one hundred or one hundred times out of one hundred.

554
00:27:43,600 --> 00:27:46,800
Speaker 1: I can't say. In my one use case, which is

555
00:27:46,840 --> 00:27:50,600
Speaker 1: a terribly small sample size, it did work out. But

556
00:27:50,760 --> 00:27:53,080
Speaker 1: I am not naive enough to suggest that that means

557
00:27:53,080 --> 00:27:55,159
Speaker 1: it's going to work every single time you use it.

558
00:27:55,440 --> 00:27:57,159
Speaker 1: But yeah, same sort of thing. I've used it a

559
00:27:57,160 --> 00:28:00,480
Speaker 1: couple of times for the purposes of like organizing thoughts,

560
00:28:00,600 --> 00:28:02,960
Speaker 1: that kind of thing, because otherwise I get, as you

561
00:28:02,960 --> 00:28:06,320
Speaker 1: can probably tell from this episode, real Lucy Goosey with

562
00:28:06,520 --> 00:28:09,719
Speaker 1: my approach, my approach to podcasting.

563
00:28:09,280 --> 00:28:11,600
Speaker 2: Well, fundamentally, I think that humans have to play a

564
00:28:11,680 --> 00:28:14,640
Speaker 2: role in science because science is a human thing. It's

565
00:28:14,720 --> 00:28:17,800
Speaker 2: like by people and for people. Right, we have questions

566
00:28:17,840 --> 00:28:21,000
Speaker 2: we want answers to and then we want those answers

567
00:28:21,040 --> 00:28:23,600
Speaker 2: to make sense to us. So it doesn't make any

568
00:28:23,600 --> 00:28:25,840
Speaker 2: sense to me to take humans out of that equation,

569
00:28:26,400 --> 00:28:27,960
Speaker 2: you know, to say it to an AI like go

570
00:28:28,000 --> 00:28:31,640
Speaker 2: figure out the universe, Like I want to understand the universe,

571
00:28:32,080 --> 00:28:33,919
Speaker 2: and it has to make sense to me and it

572
00:28:33,960 --> 00:28:37,840
Speaker 2: has to answer my questions. So humanity is an important

573
00:28:37,840 --> 00:28:38,520
Speaker 2: part of science.

574
00:28:38,840 --> 00:28:40,880
Speaker 3: What if it answered the question and explained it to

575
00:28:40,920 --> 00:28:42,960
Speaker 3: you clearly, why would it not be okay that AI

576
00:28:43,040 --> 00:28:43,680
Speaker 3: got the answer.

577
00:28:44,560 --> 00:28:46,720
Speaker 2: It would be fine if AI found the answer, but

578
00:28:46,960 --> 00:28:49,080
Speaker 2: it's I got to ask the questions and I have

579
00:28:49,160 --> 00:28:50,520
Speaker 2: to be satisfied with the answer.

580
00:28:50,680 --> 00:28:53,800
Speaker 1: I would like to refer both of you to the

581
00:28:54,240 --> 00:28:57,320
Speaker 1: great nonfiction work of The Hitchhiker's Guide to the Galaxy,

582
00:28:58,120 --> 00:29:01,160
Speaker 1: in which we create an incredibly colligent computer that tells

583
00:29:01,200 --> 00:29:03,120
Speaker 1: us the answer to life, the universe, and everything is

584
00:29:03,160 --> 00:29:06,400
Speaker 1: forty two, and then spends the next eternity trying to

585
00:29:06,400 --> 00:29:07,880
Speaker 1: figure out what the question is.

586
00:29:09,400 --> 00:29:09,920
Speaker 2: Exactly.

587
00:29:09,920 --> 00:29:12,280
Speaker 1: But that seems exactly like what you were talking about there,

588
00:29:12,360 --> 00:29:14,479
Speaker 1: Daniel is like, we don't want to create a world

589
00:29:15,280 --> 00:29:18,280
Speaker 1: that is ideal for AI. We want to make sure

590
00:29:18,360 --> 00:29:20,640
Speaker 1: that we continue to work on a world that's ideal

591
00:29:20,680 --> 00:29:23,760
Speaker 1: for humans, which involves making some tough choices like maybe

592
00:29:23,760 --> 00:29:26,960
Speaker 1: not using so much AI that's incredibly hungry for all

593
00:29:27,000 --> 00:29:31,560
Speaker 1: the electricity, and maybe maybe going down that pathway is

594
00:29:31,600 --> 00:29:35,520
Speaker 1: not the most productive. So, Kelly, I'm curious. I'm sure

595
00:29:35,680 --> 00:29:39,360
Speaker 1: you must have read and or seen The Martian. Oh yeah,

596
00:29:39,400 --> 00:29:39,960
Speaker 1: what's your take?

597
00:29:40,000 --> 00:29:40,240
Speaker 3: On that.

598
00:29:40,360 --> 00:29:42,520
Speaker 1: I'm curious because I remember when I read it, I thought,

599
00:29:42,720 --> 00:29:48,280
Speaker 1: this feels like it's fairly realistic, maybe with a couple

600
00:29:48,320 --> 00:29:49,520
Speaker 1: of caveats.

601
00:29:50,080 --> 00:29:52,959
Speaker 3: Yeah, I think it's fairly realistic with a couple of caveats.

602
00:29:53,160 --> 00:29:55,360
Speaker 3: And again, like I think, the rules are what matters,

603
00:29:55,400 --> 00:29:57,760
Speaker 3: and he was very andy. Weir was very consistent with

604
00:29:57,800 --> 00:29:59,920
Speaker 3: the rules of the world we created. So mars Is

605
00:30:00,000 --> 00:30:02,920
Speaker 3: atmosphere is one percent of Earth's. That's enough to support

606
00:30:03,000 --> 00:30:05,920
Speaker 3: dust storms that engulf the entire planets, but it's probably

607
00:30:05,920 --> 00:30:08,800
Speaker 3: not enough to knock over their giant rocket because one

608
00:30:08,840 --> 00:30:11,160
Speaker 3: percent atmosphere is just not enough. And that's the premise

609
00:30:11,200 --> 00:30:13,960
Speaker 3: that sets up the whole movie. Yep. And additionally, Mark

610
00:30:14,000 --> 00:30:19,560
Speaker 3: Wattney is growing potatoes in soil and his own feces. Yes, Martian,

611
00:30:19,680 --> 00:30:20,600
Speaker 3: it's called regolith.

612
00:30:20,760 --> 00:30:21,160
Speaker 1: Yeah.

613
00:30:21,280 --> 00:30:24,040
Speaker 3: That dirt on the surface is laced with prochlorates, which

614
00:30:24,040 --> 00:30:27,520
Speaker 3: are endocrine disrupting hormones that mess up your metabolism. So

615
00:30:27,960 --> 00:30:31,320
Speaker 3: if he had been growing his potatoes directly in there,

616
00:30:31,360 --> 00:30:34,160
Speaker 3: he would have been massively poisoning himself. In addition, all

617
00:30:34,200 --> 00:30:36,560
Speaker 3: the like fecal material he had in there that he'd

618
00:30:36,560 --> 00:30:37,800
Speaker 3: have to be careful to clean up.

619
00:30:37,880 --> 00:30:40,240
Speaker 1: Wow, Yeah, I didn't know that part. I know that

620
00:30:40,680 --> 00:30:44,880
Speaker 1: NASA has been using simulacrums of regolith in order to

621
00:30:45,600 --> 00:30:48,560
Speaker 1: do experimentation about what could or could not be grown

622
00:30:48,640 --> 00:30:50,720
Speaker 1: on the surface of Mars. That part I knew, but

623
00:30:50,760 --> 00:30:55,120
Speaker 1: I didn't know about the potential for really messing up

624
00:30:55,120 --> 00:30:58,480
Speaker 1: your system. Yeah. I agree that, Like the thin atmosphere

625
00:30:58,520 --> 00:31:00,239
Speaker 1: was one of those things that kind of tripped me up.

626
00:31:00,240 --> 00:31:02,760
Speaker 1: Another was, and I'm sure it was explained in the book,

627
00:31:02,840 --> 00:31:04,680
Speaker 1: and it's been a long time since I've read the book.

628
00:31:04,840 --> 00:31:07,640
Speaker 1: I've seen the movie more recently, but the concept of

629
00:31:07,680 --> 00:31:11,520
Speaker 1: a short term Martian expedition made no sense to me,

630
00:31:11,600 --> 00:31:15,200
Speaker 1: simply because to have the planets line up properly so

631
00:31:15,240 --> 00:31:17,440
Speaker 1: that you can make the journey with the least amount

632
00:31:17,560 --> 00:31:21,760
Speaker 1: of fuel needed to do it, you would, in my understanding,

633
00:31:21,800 --> 00:31:24,320
Speaker 1: need to kind of be ready for like a two

634
00:31:24,480 --> 00:31:28,040
Speaker 1: year stay on the planet before you could try and

635
00:31:28,040 --> 00:31:30,760
Speaker 1: make your trip back home. At least in the film version,

636
00:31:30,840 --> 00:31:32,280
Speaker 1: it felt like it was supposed to be like, oh,

637
00:31:32,320 --> 00:31:34,160
Speaker 1: this is a mission that lasts maybe a month and

638
00:31:34,240 --> 00:31:37,400
Speaker 1: a half and then we head back and I'm like, well,

639
00:31:37,440 --> 00:31:40,240
Speaker 1: how does that work? How much fuel are you bringing

640
00:31:40,280 --> 00:31:42,640
Speaker 1: with you or is this a nuclear powered rocket? What's

641
00:31:42,720 --> 00:31:44,280
Speaker 1: going on? So that was one of those things that

642
00:31:44,320 --> 00:31:46,960
Speaker 1: tripped me up to But yeah, the windstorm, that was

643
00:31:47,000 --> 00:31:50,040
Speaker 1: another one where you would need to be able to

644
00:31:50,080 --> 00:31:52,760
Speaker 1: go out and clean the solar panels, clear them of

645
00:31:52,840 --> 00:31:55,040
Speaker 1: dust and all that. But it wouldn't be enough to

646
00:31:55,360 --> 00:32:00,520
Speaker 1: maybe drive a big metal shard through someone's suit into

647
00:32:00,560 --> 00:32:03,520
Speaker 1: their body the way it does near the beginning of

648
00:32:03,560 --> 00:32:04,120
Speaker 1: the story.

649
00:32:04,760 --> 00:32:07,400
Speaker 3: Yeah, or knock over that rocket, which, yeah, which was

650
00:32:07,960 --> 00:32:10,840
Speaker 3: part of the rocket. Yeah, and Daniel would be very

651
00:32:10,840 --> 00:32:14,600
Speaker 3: happy that they included cannibalism in there. That was really important.

652
00:32:16,120 --> 00:32:19,840
Speaker 1: Yeah, I'm glad that we can stick on topic. I

653
00:32:19,880 --> 00:32:23,160
Speaker 1: didn't think of any Italian cannibal horror movies that I

654
00:32:23,160 --> 00:32:26,680
Speaker 1: could add into the discussion here. Also, it would be

655
00:32:26,760 --> 00:32:28,880
Speaker 1: terrifying to find out that you had watched those. But

656
00:32:32,360 --> 00:32:34,560
Speaker 1: Daniel and Kelly have a lot more to say as

657
00:32:34,560 --> 00:32:39,360
Speaker 1: we talk all things science and geekiness. And I'm absolutely

658
00:32:39,360 --> 00:32:41,440
Speaker 1: loving every second of this. But first let's take a

659
00:32:41,480 --> 00:32:53,160
Speaker 1: quick break to thank our sponsors. Let me ask you this,

660
00:32:53,320 --> 00:32:55,440
Speaker 1: out of all the topics you've covered so far on

661
00:32:55,480 --> 00:32:58,160
Speaker 1: your show, are there any standouts of things that were

662
00:32:58,320 --> 00:33:02,760
Speaker 1: just either delight or surprising to you are just you

663
00:33:02,800 --> 00:33:05,800
Speaker 1: were really eager to sink your teeth into when you

664
00:33:05,840 --> 00:33:07,960
Speaker 1: were going to communicate those to your audience.

665
00:33:08,320 --> 00:33:10,160
Speaker 2: From my point of view, I like to drill down

666
00:33:10,200 --> 00:33:12,680
Speaker 2: into the basics, and because I think that people are

667
00:33:12,680 --> 00:33:15,920
Speaker 2: curious about the universe and have pretty simple questions that

668
00:33:15,960 --> 00:33:20,320
Speaker 2: they want answers to, questions like what is space? Or

669
00:33:20,360 --> 00:33:23,680
Speaker 2: how does time work? Or like what is everything made

670
00:33:23,720 --> 00:33:26,160
Speaker 2: out of? And all of the answer in the end,

671
00:33:26,480 --> 00:33:29,120
Speaker 2: the disappoint Kelly is almost always like, well, we know

672
00:33:29,240 --> 00:33:31,840
Speaker 2: up to hear, and then we have questions. I think

673
00:33:31,880 --> 00:33:33,920
Speaker 2: it's worthwhile to bring people up to that point of

674
00:33:34,040 --> 00:33:37,320
Speaker 2: understanding of these really basic questions that remain, because I

675
00:33:37,360 --> 00:33:39,520
Speaker 2: think a lot of times people get the impression that

676
00:33:39,560 --> 00:33:42,160
Speaker 2: science has progressed so far and we've basically figured it out.

677
00:33:42,240 --> 00:33:44,640
Speaker 2: It's just like a couple of details remaining. But there's

678
00:33:44,720 --> 00:33:48,560
Speaker 2: like very basic, vast questions about the universe we don't

679
00:33:48,640 --> 00:33:51,960
Speaker 2: have simple answers to, and I think it's totally fun

680
00:33:52,040 --> 00:33:54,480
Speaker 2: and very worthwhile to explain those to people in a

681
00:33:54,480 --> 00:33:55,720
Speaker 2: way they can really get.

682
00:33:55,960 --> 00:33:57,520
Speaker 3: I think it's hard to pick a favorite, you know,

683
00:33:57,560 --> 00:34:01,240
Speaker 3: like it's hard to pick a favorite child. We've interviewed

684
00:34:01,320 --> 00:34:04,479
Speaker 3: these episodes haven't come out yet, but we interviewed James

685
00:34:04,520 --> 00:34:06,880
Speaker 3: sa Corey, so the guys who wrote the expanse and

686
00:34:07,200 --> 00:34:09,839
Speaker 3: Mary Roach and I love all of those people, and

687
00:34:09,960 --> 00:34:13,120
Speaker 3: podcasting is a great opportunity to reach out to amazing

688
00:34:13,160 --> 00:34:15,279
Speaker 3: people and ask them to talk to you for an hour.

689
00:34:15,520 --> 00:34:17,960
Speaker 3: So that was great, but I really all also have

690
00:34:18,120 --> 00:34:21,120
Speaker 3: enjoyed the episodes on space and time because Daniel is

691
00:34:21,160 --> 00:34:23,840
Speaker 3: really good at explaining things and I really love learning

692
00:34:23,880 --> 00:34:26,640
Speaker 3: about stuff that I don't already know. And physics. I

693
00:34:26,680 --> 00:34:29,680
Speaker 3: took physics in college. I did fine, but I definitely

694
00:34:29,719 --> 00:34:31,120
Speaker 3: studied to get an A on the test and then

695
00:34:31,160 --> 00:34:34,920
Speaker 3: forget it immediately afterwards. Yeah, and I know, sorry, there's

696
00:34:34,920 --> 00:34:38,000
Speaker 3: a lot of stuff I didn't understand, and it feels

697
00:34:38,000 --> 00:34:40,759
Speaker 3: good to have like professionals say okay, we've reached the

698
00:34:40,880 --> 00:34:44,040
Speaker 3: end of what we understand and be like, oh okay,

699
00:34:44,080 --> 00:34:46,640
Speaker 3: I can follow it up until the edge, and like, anyway,

700
00:34:46,640 --> 00:34:47,959
Speaker 3: I've learned a lot and it's been fun.

701
00:34:48,040 --> 00:34:51,800
Speaker 1: That's cool. Yeah. So both my parents are science fiction authors,

702
00:34:52,120 --> 00:34:55,239
Speaker 1: so yeah, so I grew up going to science fiction

703
00:34:55,320 --> 00:34:59,839
Speaker 1: conventions being around other authors as well as science can

704
00:34:59,840 --> 00:35:02,080
Speaker 1: meet indicators and stuff. I had a really fortunate childhood

705
00:35:02,080 --> 00:35:04,000
Speaker 1: in that way, and I keep thinking one of my

706
00:35:04,040 --> 00:35:07,240
Speaker 1: missed opportunities is I never had my dad on the podcast.

707
00:35:07,239 --> 00:35:08,960
Speaker 1: I could still ask him if he'd want to do it,

708
00:35:09,800 --> 00:35:13,280
Speaker 1: because I would love to have a conversation about things

709
00:35:13,280 --> 00:35:17,720
Speaker 1: that science fiction authors predicted that ultimately came to pass.

710
00:35:17,960 --> 00:35:20,560
Speaker 1: So Arthur C. Clark is obviously a big example with

711
00:35:20,640 --> 00:35:24,360
Speaker 1: geostationary satellites, and you know, you have Isaac Asimov and

712
00:35:24,400 --> 00:35:28,359
Speaker 1: the laws of robotics and things of that nature. My dad, famously,

713
00:35:28,480 --> 00:35:32,200
Speaker 1: in my mind anyway, made an incredible prediction. He predicted

714
00:35:32,200 --> 00:35:33,600
Speaker 1: the existence of Teddy Ruxman.

715
00:35:34,280 --> 00:35:37,320
Speaker 3: Oh nice, Yeah, I loved Teddy Ruxman.

716
00:35:37,480 --> 00:35:42,800
Speaker 1: And he also predicted commercials that do not, upon first glance,

717
00:35:42,880 --> 00:35:46,480
Speaker 1: tell you what the heck they're advertising. He called it

718
00:35:46,560 --> 00:35:52,080
Speaker 1: the oblique cell, but that was in his fantasy novel anyway.

719
00:35:52,400 --> 00:35:55,040
Speaker 1: It just makes your discussion there just made me think

720
00:35:55,080 --> 00:35:58,399
Speaker 1: about that as well. I love how people can take

721
00:35:58,440 --> 00:36:02,359
Speaker 1: inspiration from not just the observations they make, but from

722
00:36:02,360 --> 00:36:05,160
Speaker 1: fiction as well and say like, well, well, there's no

723
00:36:05,239 --> 00:36:07,799
Speaker 1: reason why that shouldn't work if we just figure some

724
00:36:07,880 --> 00:36:10,920
Speaker 1: things out and then work toward that. Now, obviously it

725
00:36:10,920 --> 00:36:13,719
Speaker 1: doesn't work for everything. Like my own personal opinion with

726
00:36:13,760 --> 00:36:15,800
Speaker 1: Star Trek is that if you were to use a transporter,

727
00:36:15,920 --> 00:36:18,400
Speaker 1: you're committing suicide and you're just creating a copy of

728
00:36:18,400 --> 00:36:21,359
Speaker 1: yourself somewhere else. And that's not really you. It's someone

729
00:36:21,400 --> 00:36:23,960
Speaker 1: else who has all your thoughts and beliefs and everything,

730
00:36:24,000 --> 00:36:28,239
Speaker 1: but you are. You're dead, and that's a copy. And

731
00:36:28,600 --> 00:36:30,480
Speaker 1: so I'm a bones McCoy kind of guy when it

732
00:36:30,520 --> 00:36:32,239
Speaker 1: comes to the transporter.

733
00:36:32,800 --> 00:36:35,080
Speaker 3: I do ask that question every time we interview a

734
00:36:35,120 --> 00:36:36,399
Speaker 3: sci fi person on our show.

735
00:36:36,960 --> 00:36:40,480
Speaker 1: Oh, is the transporter just killing you? Yeah? Yeah, I'm

736
00:36:40,520 --> 00:36:42,560
Speaker 1: fully on board with the transporters killing you.

737
00:36:42,719 --> 00:36:45,440
Speaker 2: I'm but would you step into one knowing that it's

738
00:36:45,440 --> 00:36:46,680
Speaker 2: going to kill you and then it's going to be

739
00:36:46,680 --> 00:36:48,240
Speaker 2: another version of you on the other side.

740
00:36:48,440 --> 00:36:53,400
Speaker 1: No, I'm too egotistical. I require this version of my

741
00:36:53,560 --> 00:36:57,000
Speaker 1: ego to continue and not a perfect copy. First of all,

742
00:36:57,120 --> 00:36:58,759
Speaker 1: I don't want there to be a perfect copy of

743
00:36:58,760 --> 00:37:02,080
Speaker 1: me out there. I got enough competition out there as

744
00:37:02,120 --> 00:37:06,360
Speaker 1: it is. No, I don't think I could. And maybe

745
00:37:06,400 --> 00:37:10,160
Speaker 1: if we were talking about distances that are beyond what

746
00:37:10,160 --> 00:37:14,080
Speaker 1: we can conveniently travel today, and like, yeah you can,

747
00:37:14,239 --> 00:37:17,720
Speaker 1: you can hop on over. We've colonized a planet that's

748
00:37:18,080 --> 00:37:21,279
Speaker 1: you know, twenty light years away. There's no way you're

749
00:37:21,280 --> 00:37:25,279
Speaker 1: going to get there. Otherwise, like, well, had a good

750
00:37:25,360 --> 00:37:28,799
Speaker 1: run here, Jonathan two point zero can take it from

751
00:37:28,840 --> 00:37:33,240
Speaker 1: here and make a new life on Earth two or whatever.

752
00:37:34,360 --> 00:37:38,200
Speaker 1: Are there Are there any particular thing, you know, sources

753
00:37:38,239 --> 00:37:40,920
Speaker 1: of inspiration that got you both or either of you

754
00:37:42,120 --> 00:37:45,080
Speaker 1: interested in pursuing a career in science, Like was there

755
00:37:45,360 --> 00:37:48,560
Speaker 1: was there someone or something that you know, you thought, well,

756
00:37:48,600 --> 00:37:51,279
Speaker 1: this is this is what makes I want to do,

757
00:37:51,719 --> 00:37:54,600
Speaker 1: whether it was directly in your field or maybe even

758
00:37:54,640 --> 00:37:56,080
Speaker 1: an unrelated field of science.

759
00:37:56,400 --> 00:37:59,440
Speaker 2: Well, like many people, I was inspired by Well, like

760
00:37:59,480 --> 00:38:02,120
Speaker 2: many people, I was inspired by my high school physics teacher.

761
00:38:02,520 --> 00:38:04,359
Speaker 2: I grew up in Los Alamos, New Mexico, so there's

762
00:38:04,400 --> 00:38:07,840
Speaker 2: lots of physicists around, and one of them came to

763
00:38:07,880 --> 00:38:10,160
Speaker 2: teach our ap physics class in the morning before he

764
00:38:10,239 --> 00:38:12,520
Speaker 2: went to work at the lab, and he seemed to

765
00:38:12,560 --> 00:38:13,960
Speaker 2: be having a lot of fun and he was a

766
00:38:14,000 --> 00:38:16,600
Speaker 2: great teacher. And you know, I just want to say

767
00:38:16,640 --> 00:38:19,319
Speaker 2: thank you to all the science teachers out there in

768
00:38:19,360 --> 00:38:22,520
Speaker 2: high school working on the front lines, because They're literally

769
00:38:22,600 --> 00:38:26,480
Speaker 2: responsible for creating the next generation of scientists. Huge fraction

770
00:38:26,560 --> 00:38:29,040
Speaker 2: of the scientists I know were inspired to enter their

771
00:38:29,040 --> 00:38:32,239
Speaker 2: field chemistry or biology or whatever because of their high

772
00:38:32,280 --> 00:38:35,480
Speaker 2: school science teacher. It's such an important job.

773
00:38:35,920 --> 00:38:40,480
Speaker 3: I really liked the show er, and so I really

774
00:38:40,480 --> 00:38:42,560
Speaker 3: wanted to be an er surgeon. And then I talked

775
00:38:42,560 --> 00:38:44,600
Speaker 3: to an er surgeon who was like, actually, most of

776
00:38:44,640 --> 00:38:47,160
Speaker 3: your day is really boring. It's nowhere near as exciting.

777
00:38:47,200 --> 00:38:48,800
Speaker 3: You're like waiting for someone to come in so you

778
00:38:48,840 --> 00:38:50,880
Speaker 3: can like stitch their hand because they like cut it

779
00:38:50,920 --> 00:38:52,520
Speaker 3: on the side of their car or something. And then

780
00:38:52,560 --> 00:38:55,040
Speaker 3: I thought maybe i'd be a veterinarian, but then I'd

781
00:38:55,080 --> 00:38:59,839
Speaker 3: still have to deal with people, and I turns out

782
00:39:00,680 --> 00:39:04,440
Speaker 3: I feel okay about people. But but then I like,

783
00:39:04,480 --> 00:39:06,640
Speaker 3: I had an overwhelming course load, and so I was like,

784
00:39:06,640 --> 00:39:08,879
Speaker 3: I'm gonna take ecology as an easy a because it's

785
00:39:08,920 --> 00:39:12,160
Speaker 3: like hippies playing in the woods. And it turned out

786
00:39:12,400 --> 00:39:15,520
Speaker 3: that they were using all of these like elegant equations

787
00:39:15,560 --> 00:39:19,160
Speaker 3: to describe the behavior of the animals interacting with each other,

788
00:39:19,200 --> 00:39:21,799
Speaker 3: and I was like, this is actually rigorous, and these

789
00:39:21,840 --> 00:39:24,920
Speaker 3: people are spending like their whole lives outside collecting these

790
00:39:25,000 --> 00:39:27,440
Speaker 3: data and then they get to think about what's happening outside.

791
00:39:27,680 --> 00:39:30,040
Speaker 3: And like, I fell in love with ecology in that class,

792
00:39:30,040 --> 00:39:34,799
Speaker 3: and so that easy a totally like derailed my financial

793
00:39:35,080 --> 00:39:37,520
Speaker 3: future and I went into science instead.

794
00:39:37,880 --> 00:39:40,560
Speaker 2: But you became a rigorous hippie in the woods.

795
00:39:40,800 --> 00:39:42,600
Speaker 3: I did, Man, I love it out here and then

796
00:39:42,640 --> 00:39:44,000
Speaker 3: I but I thought that I was going to be

797
00:39:44,040 --> 00:39:47,040
Speaker 3: studying like lions in the Serengetti, and I applied to

798
00:39:47,080 --> 00:39:49,600
Speaker 3: all of those labs for grad school, and then I

799
00:39:49,680 --> 00:39:52,960
Speaker 3: ended up studying like their parent, whatever's happening in their guts.

800
00:39:53,080 --> 00:39:55,800
Speaker 3: Turns out that's what I'm most interested in. So I

801
00:39:56,320 --> 00:39:57,120
Speaker 3: like the creepy stuff.

802
00:39:57,120 --> 00:39:59,080
Speaker 2: I guess that's the edge of all knowledge, right, that's

803
00:39:59,080 --> 00:40:00,720
Speaker 2: the biological dark matter for sure.

804
00:40:00,960 --> 00:40:01,600
Speaker 3: Yeah, there you go.

805
00:40:01,760 --> 00:40:04,719
Speaker 1: Yeah, I love it. I love these stories.

806
00:40:05,160 --> 00:40:07,680
Speaker 3: What about you? How did you go from Shakespeare to

807
00:40:08,000 --> 00:40:09,000
Speaker 3: podcasting about tech?

808
00:40:09,040 --> 00:40:12,200
Speaker 1: Okay, Yeah, that's a great question. For the first several

809
00:40:12,280 --> 00:40:15,319
Speaker 1: years of my professional career, I actually worked at a

810
00:40:15,440 --> 00:40:18,799
Speaker 1: human resources management consulting firm. So I like to say

811
00:40:18,840 --> 00:40:23,720
Speaker 1: I worked for the Bobs in office space, the two Bobs. Yeah.

812
00:40:23,760 --> 00:40:26,920
Speaker 1: More often than not, I was writing up reports about

813
00:40:27,080 --> 00:40:32,120
Speaker 1: why our consultants were recommending that a company layoff ten

814
00:40:32,160 --> 00:40:35,160
Speaker 1: percent of their employees. It was very demoralizing. I was

815
00:40:35,200 --> 00:40:37,200
Speaker 1: good at what I did. I hated my job. I

816
00:40:37,200 --> 00:40:40,200
Speaker 1: love the people I worked with, but I hated my job.

817
00:40:40,400 --> 00:40:43,200
Speaker 1: But I was good at it, and so I didn't

818
00:40:43,239 --> 00:40:45,359
Speaker 1: want to leave it. One of the consultants I worked for,

819
00:40:45,560 --> 00:40:48,399
Speaker 1: his name is frank Casa Grande, or Frankie big House

820
00:40:48,440 --> 00:40:51,359
Speaker 1: as I used to call him. Frankie big House. He's

821
00:40:51,360 --> 00:40:54,000
Speaker 1: from New Jersey. Came to me, He's like, John, John,

822
00:40:54,600 --> 00:40:56,719
Speaker 1: this isn't your passion, John, you got to get out

823
00:40:56,760 --> 00:41:01,600
Speaker 1: of here. You're killing yourself meant for bigger things. And

824
00:41:01,680 --> 00:41:05,239
Speaker 1: so it really sunk in. But I was so comfortable

825
00:41:05,520 --> 00:41:08,799
Speaker 1: that I did not leave that job until they got

826
00:41:08,840 --> 00:41:11,000
Speaker 1: rid of the position I was in. So I didn't

827
00:41:11,040 --> 00:41:13,600
Speaker 1: I wasn't fired. I just didn't have a job anymore.

828
00:41:14,480 --> 00:41:16,800
Speaker 1: The distinction was lost on me. I will be honest

829
00:41:16,840 --> 00:41:20,600
Speaker 1: with you. So then for for six months, I'm out

830
00:41:20,640 --> 00:41:22,680
Speaker 1: of work, trying to get a job, and I try

831
00:41:23,040 --> 00:41:26,720
Speaker 1: applying for writing gigs at different places around Atlanta, including

832
00:41:26,760 --> 00:41:30,719
Speaker 1: places like the Cartoon Network and Turner and CNN and

833
00:41:30,800 --> 00:41:34,000
Speaker 1: which are all the same company. But whatever, and ultimately

834
00:41:34,080 --> 00:41:36,040
Speaker 1: none of those pan out. I took another job at

835
00:41:36,040 --> 00:41:39,600
Speaker 1: a different consulting firm, which was soul crushing, and six

836
00:41:39,640 --> 00:41:42,399
Speaker 1: months after that, I one of the jobs I had

837
00:41:42,520 --> 00:41:45,120
Speaker 1: i'd interviewed for but I had not heard back from,

838
00:41:45,440 --> 00:41:48,680
Speaker 1: gets back to me, and it was HowStuffWorks dot Com.

839
00:41:49,080 --> 00:41:51,440
Speaker 1: So I come in, I interview with how stuffworks dot Com.

840
00:41:52,239 --> 00:41:54,560
Speaker 1: I get the job. I become one of two staff

841
00:41:54,600 --> 00:41:57,520
Speaker 1: writers at how stuffworks dot com, the other one Tracy Wilson,

842
00:41:57,520 --> 00:41:59,920
Speaker 1: who is currently a host of Stuff you missed in

843
00:42:00,080 --> 00:42:02,880
Speaker 1: history class. That's where the stuff and tech stuff comes from.

844
00:42:03,120 --> 00:42:08,440
Speaker 1: So I start writing articles. And my background in English

845
00:42:08,480 --> 00:42:12,319
Speaker 1: literature involved lots of research and lots of writing, and

846
00:42:12,400 --> 00:42:14,480
Speaker 1: so I was just applying that same skill set. But

847
00:42:14,560 --> 00:42:18,560
Speaker 1: now I got a chance to explore topics that I

848
00:42:18,600 --> 00:42:21,080
Speaker 1: often knew very little about. So I got to learn

849
00:42:21,200 --> 00:42:23,840
Speaker 1: and it was so exciting. It was like being in college,

850
00:42:23,960 --> 00:42:26,239
Speaker 1: but without all the pressures of college and all the

851
00:42:26,320 --> 00:42:30,640
Speaker 1: distractions of college. And I learned all about different things.

852
00:42:31,120 --> 00:42:33,880
Speaker 1: And then it was my job to explain what I

853
00:42:33,920 --> 00:42:37,000
Speaker 1: had learned to an audience and to synthesize that information

854
00:42:37,080 --> 00:42:40,480
Speaker 1: and communicate it outward. And I loved that too, and

855
00:42:40,760 --> 00:42:44,960
Speaker 1: it soon became clear that, unlike the other English majors

856
00:42:45,000 --> 00:42:48,560
Speaker 1: who joined the editorial department of HowStuffWorks dot com, I

857
00:42:48,680 --> 00:42:52,759
Speaker 1: was not afraid of technology. Everybody else we almost all

858
00:42:52,760 --> 00:42:55,319
Speaker 1: of us had attended the University of Georgia, almost all

859
00:42:55,360 --> 00:42:57,880
Speaker 1: of us had a degree in some form of English,

860
00:42:58,040 --> 00:43:01,040
Speaker 1: whether it was English Led or English whatever. But they

861
00:43:01,040 --> 00:43:03,720
Speaker 1: were all scared of tackling tech, and I wasn't because,

862
00:43:03,960 --> 00:43:06,520
Speaker 1: as I described to them, like tech either works or

863
00:43:06,560 --> 00:43:09,759
Speaker 1: it doesn't. It's it gets pretty simple when you get

864
00:43:09,800 --> 00:43:12,120
Speaker 1: to the macro level. It's when you dive into the

865
00:43:12,480 --> 00:43:14,880
Speaker 1: particulars where it can get a little intimidating. But I

866
00:43:14,960 --> 00:43:18,800
Speaker 1: found that those skills of being able to research a topic,

867
00:43:19,200 --> 00:43:23,799
Speaker 1: to find an understanding, and then to communicate that understanding

868
00:43:23,840 --> 00:43:27,480
Speaker 1: to an audience, that that tapped into a passion I

869
00:43:27,520 --> 00:43:31,480
Speaker 1: didn't even know I had. And so the podcast started

870
00:43:32,000 --> 00:43:35,279
Speaker 1: as an extension of the website and gave me even

871
00:43:35,320 --> 00:43:41,560
Speaker 1: more opportunity to not only communicate these these ideas about technology,

872
00:43:41,800 --> 00:43:44,120
Speaker 1: not just how it works, but how it impacts us

873
00:43:44,160 --> 00:43:47,760
Speaker 1: and how we change the tech. It also, most importantly

874
00:43:47,760 --> 00:43:53,239
Speaker 1: gave me an opportunity to make terrible puns. That's all

875
00:43:53,280 --> 00:43:56,680
Speaker 1: we really want to Yeah, getting paid to make terrible

876
00:43:56,760 --> 00:43:59,560
Speaker 1: puns and if you if you, if you position them

877
00:43:59,680 --> 00:44:02,840
Speaker 1: just you know, they had to hear it, and that's

878
00:44:02,880 --> 00:44:03,719
Speaker 1: the best.

879
00:44:05,120 --> 00:44:05,400
Speaker 3: Feeling.

880
00:44:05,560 --> 00:44:08,080
Speaker 1: Yeah, it is, it's great. It's like it's the only

881
00:44:08,120 --> 00:44:10,759
Speaker 1: thing I regret is that podcasting is a one way

882
00:44:10,840 --> 00:44:13,759
Speaker 1: communication medium and I can't hear the groans on the

883
00:44:13,800 --> 00:44:17,360
Speaker 1: other side, which is you know, that's what really nourishes

884
00:44:17,400 --> 00:44:20,080
Speaker 1: the soul in my opinion. But yeah, that's how it

885
00:44:20,080 --> 00:44:22,439
Speaker 1: all happened for me, and I have always had great

886
00:44:22,440 --> 00:44:26,719
Speaker 1: admiration for not just scientists, but science communicators who take

887
00:44:26,760 --> 00:44:31,560
Speaker 1: it very seriously, you know, learning and communicating and sort

888
00:44:31,560 --> 00:44:35,080
Speaker 1: of instilling that communication with the passion they have for

889
00:44:35,360 --> 00:44:37,880
Speaker 1: the process of learning. Like I think that's really what

890
00:44:37,920 --> 00:44:39,960
Speaker 1: it boils down to for me, is that I love

891
00:44:40,040 --> 00:44:42,680
Speaker 1: to learn. And I feel like a lot of scientists

892
00:44:42,719 --> 00:44:46,160
Speaker 1: that's kind of their core principle too. They just have

893
00:44:46,280 --> 00:44:48,160
Speaker 1: this love of learning. As you said at the beginning,

894
00:44:48,480 --> 00:44:52,920
Speaker 1: curiosity is one of those those guiding principles, and I

895
00:44:53,560 --> 00:44:57,040
Speaker 1: genuinely think that in order to live a fulfilling life,

896
00:44:57,120 --> 00:44:59,440
Speaker 1: you do have to maintain a sense of curiosity. Throughout.

897
00:44:59,480 --> 00:45:01,560
Speaker 3: It sure helps.

898
00:45:01,719 --> 00:45:03,520
Speaker 2: And it's what powers all of science.

899
00:45:03,560 --> 00:45:03,759
Speaker 3: You know.

900
00:45:03,840 --> 00:45:06,680
Speaker 2: The reason that we get millions or billions of dollars

901
00:45:06,719 --> 00:45:08,759
Speaker 2: to build a collider is because people want to know

902
00:45:08,800 --> 00:45:11,279
Speaker 2: the answer to these questions, you know, And so I

903
00:45:11,280 --> 00:45:13,799
Speaker 2: think we have an obligation not just to do the

904
00:45:13,840 --> 00:45:15,960
Speaker 2: science and to try to understand the universe, but to

905
00:45:16,040 --> 00:45:19,440
Speaker 2: share the answers with everybody, because everybody deserves to know

906
00:45:19,480 --> 00:45:27,240
Speaker 2: what we do and don't know about particles and cannibals.

907
00:45:25,040 --> 00:45:30,359
Speaker 1: All. Yeah, I don't think I find it. I think

908
00:45:30,400 --> 00:45:32,680
Speaker 1: one of the most distressing things I encounter is when

909
00:45:32,719 --> 00:45:35,640
Speaker 1: someone says, well, what good is that going to do me?

910
00:45:35,800 --> 00:45:38,080
Speaker 1: If you learn that, I'm like, oh my gosh, it's

911
00:45:38,120 --> 00:45:41,160
Speaker 1: like I'm talking to a different kind of creature because

912
00:45:41,239 --> 00:45:45,560
Speaker 1: I can't understand why the act of knowing isn't exciting

913
00:45:45,719 --> 00:45:48,920
Speaker 1: enough all on its own. Why you have to be like, okay,

914
00:45:48,920 --> 00:45:50,880
Speaker 1: but is that going to make my cell phone faster? Like,

915
00:45:51,200 --> 00:45:53,000
Speaker 1: first of all, we don't know what it'll do.

916
00:45:53,560 --> 00:45:54,160
Speaker 3: That's yeah.

917
00:45:54,360 --> 00:45:57,359
Speaker 1: Like, if you look back at all the technological advancements

918
00:45:57,360 --> 00:46:01,200
Speaker 1: that have been possible over the years, we didn't anticipate

919
00:46:01,640 --> 00:46:03,239
Speaker 1: most of those because we had no way of knowing.

920
00:46:03,280 --> 00:46:06,600
Speaker 1: I mean, certainly miniaturization alone was something no one had

921
00:46:06,640 --> 00:46:09,520
Speaker 1: thought about. Back in the twenties and thirties, everyone thought

922
00:46:09,760 --> 00:46:13,240
Speaker 1: computers were going to be the size of skyscrapers because

923
00:46:13,280 --> 00:46:18,000
Speaker 1: they had to be so like, don't worry about you know,

924
00:46:18,120 --> 00:46:23,040
Speaker 1: a specific benefit that you get beyond knowing knowing itself

925
00:46:23,080 --> 00:46:25,600
Speaker 1: as a benefit. And then on top of that, who

926
00:46:25,760 --> 00:46:28,360
Speaker 1: knows what else will be able to leverage from the

927
00:46:28,440 --> 00:46:33,120
Speaker 1: knowledge we uncover. So amen, yeah, yeah, so, gosh darn it.

928
00:46:33,280 --> 00:46:39,080
Speaker 1: Funded science, yes, please, yes, fund easy and obvious investment

929
00:46:39,239 --> 00:46:40,880
Speaker 1: in humanity and in our future.

930
00:46:40,960 --> 00:46:43,520
Speaker 2: I don't understand why it's not a bipartisan thing, you know.

931
00:46:43,600 --> 00:46:48,120
Speaker 1: Yeah, either do I I always I always don't. I

932
00:46:48,160 --> 00:46:51,400
Speaker 1: don't get why people will will portray science as somehow

933
00:46:51,440 --> 00:46:56,880
Speaker 1: having a political bias. That's not the way science works.

934
00:46:57,520 --> 00:46:57,640
Speaker 3: Uh.

935
00:46:58,760 --> 00:47:02,120
Speaker 1: Daniel and Kelly, thank you so much for joining tech stuff.

936
00:47:02,120 --> 00:47:03,640
Speaker 1: This has been a lot of fun. I know it's

937
00:47:03,800 --> 00:47:07,640
Speaker 1: kind of an unhinged whirlwind episode, but I mean, I'm

938
00:47:07,640 --> 00:47:09,920
Speaker 1: coming up to the end of my run kind of

939
00:47:09,960 --> 00:47:13,240
Speaker 1: like all the safety measures are off as far as I'm.

940
00:47:13,080 --> 00:47:16,600
Speaker 3: Concerned, the rails are off. That's the most fun. Thank you.

941
00:47:16,680 --> 00:47:18,239
Speaker 3: So much for having us on the show. This was

942
00:47:18,239 --> 00:47:18,760
Speaker 3: a blast.

943
00:47:18,960 --> 00:47:19,960
Speaker 2: Yeah, thank you very much.

944
00:47:20,040 --> 00:47:22,920
Speaker 1: I recommend everyone go check out the podcast. You have

945
00:47:23,000 --> 00:47:25,279
Speaker 1: to listen, especially if you know you're sitting there and

946
00:47:25,320 --> 00:47:28,000
Speaker 1: curious about which animals will eat their own kind, because

947
00:47:28,040 --> 00:47:31,719
Speaker 1: you'll learn. You will learn some things you cannot unlearn.

948
00:47:32,840 --> 00:47:36,799
Speaker 1: You're welcome, Thanks again, and I hope you have a

949
00:47:36,840 --> 00:47:37,359
Speaker 1: great day.

950
00:47:37,560 --> 00:47:40,080
Speaker 3: Thanks you too, Thanks very much. Well.

951
00:47:40,120 --> 00:47:42,719
Speaker 1: I hope all of you enjoyed that conversation I had

952
00:47:42,719 --> 00:47:45,680
Speaker 1: with Daniel and Kelly of Daniel and Kelly's Extraordinary Universe.

953
00:47:45,680 --> 00:47:47,839
Speaker 1: You should definitely check out that podcast. Like I said,

954
00:47:47,880 --> 00:47:51,480
Speaker 1: it's fascinating stuff. They tackle all things science. They've had

955
00:47:51,520 --> 00:47:53,960
Speaker 1: some great guests on as well to talk about stuff

956
00:47:54,000 --> 00:47:58,560
Speaker 1: like quantum computing. They really take these complicated topics and

957
00:47:58,600 --> 00:48:01,399
Speaker 1: break them down in ways that are really accessible and

958
00:48:01,480 --> 00:48:04,440
Speaker 1: fun and exciting. And it's the sort of stuff I

959
00:48:04,480 --> 00:48:07,680
Speaker 1: always aspire to do. I hope that on occasion I

960
00:48:07,880 --> 00:48:11,200
Speaker 1: achieve that. Based upon some of the wonderful messages I've

961
00:48:11,200 --> 00:48:14,760
Speaker 1: been getting from listeners who have reached out to express

962
00:48:14,800 --> 00:48:17,480
Speaker 1: how how much they appreciate the show, it feels like

963
00:48:17,560 --> 00:48:19,960
Speaker 1: once in a while I hit that target, which is

964
00:48:20,000 --> 00:48:22,719
Speaker 1: a great feeling. I hope all of you out there

965
00:48:22,840 --> 00:48:26,120
Speaker 1: are doing well. If you are here in the United States,

966
00:48:26,640 --> 00:48:30,320
Speaker 1: happy Thanksgiving. I hope you have a wonderful, safe time

967
00:48:30,400 --> 00:48:34,040
Speaker 1: with their loved ones nearby. For everyone else around the world,

968
00:48:34,320 --> 00:48:37,560
Speaker 1: have a great week. You know, if you want to

969
00:48:37,600 --> 00:48:41,440
Speaker 1: participate in Thanksgiving on kind of a metaphorical level, just

970
00:48:41,719 --> 00:48:44,439
Speaker 1: pour some gravy down your throat and be thankful. That's

971
00:48:44,480 --> 00:48:48,359
Speaker 1: what I do every year, Tari can attest. She says

972
00:48:48,400 --> 00:48:50,480
Speaker 1: it's disturbing and that if I do it again in

973
00:48:50,520 --> 00:48:53,960
Speaker 1: front of her, she's going to hr Well, have a

974
00:48:54,000 --> 00:48:57,319
Speaker 1: great week, and I'll talk to you again really soon.

975
00:49:03,440 --> 00:49:08,080
Speaker 1: Tech Stuff is an iHeartRadio production. For more podcasts from iHeartRadio,

976
00:49:08,400 --> 00:49:12,120
Speaker 1: visit the iHeartRadio app, Apple Podcasts, or wherever you listen

977
00:49:12,160 --> 00:49:13,240
Speaker 1: to your favorite shows.