1
00:00:00,080 --> 00:00:02,880
Speaker 1: Now in this series, we're sitting down with people who

2
00:00:02,880 --> 00:00:08,080
Speaker 1: are leveraging powerful technologies developed by IBM, and they're doing

3
00:00:08,119 --> 00:00:11,440
Speaker 1: so in ways that are making a real difference out

4
00:00:11,440 --> 00:00:14,200
Speaker 1: in the world. So these are people who are implementing

5
00:00:14,280 --> 00:00:19,520
Speaker 1: technological solutions to real world problems. Today's episode is about

6
00:00:19,600 --> 00:00:23,439
Speaker 1: Project OWL, an idea that won the very first Call

7
00:00:23,600 --> 00:00:28,200
Speaker 1: for Code competition in two thousand eighteen. Project OWL aims

8
00:00:28,240 --> 00:00:32,720
Speaker 1: to restore communications and logistics capabilities to areas affected by

9
00:00:32,800 --> 00:00:37,879
Speaker 1: natural disasters, specifically hurricanes. It's a combination of hardware and

10
00:00:38,000 --> 00:00:42,959
Speaker 1: software that leverages MESH network technologies and IBM platforms such

11
00:00:43,000 --> 00:00:46,640
Speaker 1: as cloud computing, the Watson platform, and more. We'll hear

12
00:00:46,680 --> 00:00:50,080
Speaker 1: from Brian Now, one of the co founders of Project OWL,

13
00:00:50,320 --> 00:00:52,800
Speaker 1: as well as a Lisa Macklin of IBM to get

14
00:00:52,800 --> 00:00:56,280
Speaker 1: a deeper understanding about the project, its origins and the

15
00:00:56,360 --> 00:00:59,240
Speaker 1: long term goals, both for the Call for Code initiative

16
00:00:59,280 --> 00:01:03,400
Speaker 1: in general and Project OWL particularly. Now. I wanted to

17
00:01:03,440 --> 00:01:07,800
Speaker 1: start the sentence that I'm speaking right now with the

18
00:01:07,840 --> 00:01:12,280
Speaker 1: phrase the remarkable thing about this story is But it

19
00:01:12,319 --> 00:01:15,880
Speaker 1: turns out I can't because there are too many remarkable

20
00:01:15,920 --> 00:01:20,600
Speaker 1: things to pick just one. Here's a quick rundown. First,

21
00:01:21,040 --> 00:01:26,839
Speaker 1: IBM has built a suite of technologies that are incredibly powerful.

22
00:01:27,520 --> 00:01:31,039
Speaker 1: They range from cloud computing services, which give developers the

23
00:01:31,080 --> 00:01:34,760
Speaker 1: opportunity to take advantage of enormous processing power. You know,

24
00:01:34,840 --> 00:01:39,039
Speaker 1: cloud computing is where you've got servers that are able

25
00:01:39,080 --> 00:01:41,760
Speaker 1: to do processing on the back end, and through an

26
00:01:41,760 --> 00:01:44,760
Speaker 1: Internet connection you can access that, so you don't have

27
00:01:44,840 --> 00:01:49,160
Speaker 1: to have a supercomputer at your own disposal. You've got

28
00:01:49,320 --> 00:01:53,840
Speaker 1: a virtual supercomputer in the form of these cloud computing networks.

29
00:01:54,840 --> 00:01:58,200
Speaker 1: But they also have the famous Watson platform, which allows

30
00:01:58,200 --> 00:02:02,280
Speaker 1: for an incredible range of AI applications that can tap

31
00:02:02,280 --> 00:02:05,760
Speaker 1: into all sorts of different processes. So you can think

32
00:02:05,760 --> 00:02:12,120
Speaker 1: of this as a suite of refined extreme computing power.

33
00:02:12,280 --> 00:02:16,200
Speaker 1: It kind of makes me tingle just to think about

34
00:02:16,240 --> 00:02:20,560
Speaker 1: it now. Second, this story is about taking those tools

35
00:02:20,840 --> 00:02:24,760
Speaker 1: and actually applying them to solve real world, hard problems.

36
00:02:25,160 --> 00:02:29,919
Speaker 1: It's not just that the tech enables cool applications. It's

37
00:02:29,960 --> 00:02:32,600
Speaker 1: that like minded people who want to make a positive

38
00:02:32,600 --> 00:02:35,960
Speaker 1: impact are finding one another and they're coming up with

39
00:02:36,080 --> 00:02:40,880
Speaker 1: novel approaches to tackle these issues that affect millions of

40
00:02:40,960 --> 00:02:46,040
Speaker 1: lives every day. Focuses on really big challenges. The theme

41
00:02:46,080 --> 00:02:48,120
Speaker 1: for the first Call for Code in two thousand eighteen

42
00:02:48,200 --> 00:02:52,560
Speaker 1: was natural disasters. The theme for this year is climate change,

43
00:02:53,440 --> 00:02:58,360
Speaker 1: so yeah, they go big. The third thing I find

44
00:02:58,520 --> 00:03:03,120
Speaker 1: really remarkable about this story is that collaboration keeps popping

45
00:03:03,240 --> 00:03:07,239
Speaker 1: up as an important component in the projects. Not only

46
00:03:07,360 --> 00:03:11,359
Speaker 1: are individuals collaborating with their teammates, but also with subject

47
00:03:11,400 --> 00:03:16,400
Speaker 1: matter experts and IBM professionals with deep knowledge and experience

48
00:03:16,480 --> 00:03:20,480
Speaker 1: in the technologies and the company's products. So so while

49
00:03:20,520 --> 00:03:23,360
Speaker 1: we're looking at a competition framework, I mean it is

50
00:03:23,400 --> 00:03:28,040
Speaker 1: a competition, the spirit of working together permeates the entire process.

51
00:03:28,800 --> 00:03:31,120
Speaker 1: I sat down with Brian from Project Al and I

52
00:03:31,160 --> 00:03:33,240
Speaker 1: asked him to walk me through his own background and

53
00:03:33,240 --> 00:03:36,080
Speaker 1: how he found himself participating in the two thousand eighteen

54
00:03:36,200 --> 00:03:38,880
Speaker 1: Call for Code. A lot of times I think about

55
00:03:40,080 --> 00:03:45,600
Speaker 1: how lucky I was to grow up at this particular time, UM,

56
00:03:46,200 --> 00:03:50,320
Speaker 1: in this particular place, to have the resources I have

57
00:03:50,520 --> 00:03:53,560
Speaker 1: and and the tools at my disposal. And what I

58
00:03:53,600 --> 00:03:57,440
Speaker 1: mean by that is growing up like a few things

59
00:03:57,640 --> 00:04:01,120
Speaker 1: really inspired me to play around with digital technology. One was,

60
00:04:01,600 --> 00:04:05,080
Speaker 1: you know, the accessibility of personal computers and UM the

61
00:04:05,080 --> 00:04:08,240
Speaker 1: growing accessibility of the Internet, UM one of my proudest

62
00:04:08,240 --> 00:04:11,120
Speaker 1: moments is I think the first real sophisticated thing I

63
00:04:11,160 --> 00:04:13,040
Speaker 1: did with a computer when I was about eight years

64
00:04:13,040 --> 00:04:18,800
Speaker 1: old is illegally downloaded music with Napster. And despite the

65
00:04:18,800 --> 00:04:23,840
Speaker 1: concerns um my mother had about that, I think it

66
00:04:24,040 --> 00:04:29,600
Speaker 1: kind of opens your mind to this understanding that there's

67
00:04:29,680 --> 00:04:33,840
Speaker 1: so many things you can do with digital technology, um

68
00:04:33,880 --> 00:04:39,400
Speaker 1: that previously just weren't possible, uh in other industries or professions, careers,

69
00:04:39,839 --> 00:04:45,440
Speaker 1: particularly things that are limited by the physical manifestations of

70
00:04:45,440 --> 00:04:47,479
Speaker 1: the world around you. Of course, software is kind of

71
00:04:47,520 --> 00:04:50,040
Speaker 1: you know, in a way I'd like to say, it's

72
00:04:50,080 --> 00:04:55,680
Speaker 1: like not real, right, it's just code. Um, and some

73
00:04:55,800 --> 00:04:59,960
Speaker 1: other experiences too. I'll never forget. UM. What pushed me

74
00:05:00,040 --> 00:05:03,119
Speaker 1: need to learn how to code originally was the first

75
00:05:03,120 --> 00:05:06,159
Speaker 1: time I played, Uh, my favorite video game of all

76
00:05:06,200 --> 00:05:09,120
Speaker 1: time was the first Halo. I mean, did you ever

77
00:05:09,160 --> 00:05:11,480
Speaker 1: play that? Oh? I? Oh yeah, no, I'm I'm a

78
00:05:11,480 --> 00:05:17,200
Speaker 1: big Halo Marathon fan. So yeah, um, first of all,

79
00:05:17,279 --> 00:05:22,760
Speaker 1: amazing game, right, but I'll never forget when I played that,

80
00:05:22,839 --> 00:05:25,360
Speaker 1: I thought to myself, this is the coolest thing I've

81
00:05:25,360 --> 00:05:27,559
Speaker 1: ever seen. I need to learn how to make things

82
00:05:27,600 --> 00:05:31,200
Speaker 1: like that. Um. So I bought this book called three

83
00:05:31,279 --> 00:05:36,640
Speaker 1: D Game Programming All in One and it, uh, it

84
00:05:36,720 --> 00:05:39,520
Speaker 1: was like a thousand pages long and it's all about

85
00:05:39,560 --> 00:05:42,760
Speaker 1: C plus plus video game development. So I thought to myself, Yeah,

86
00:05:42,800 --> 00:05:46,200
Speaker 1: how hard could that be? Little did I know? Um,

87
00:05:46,320 --> 00:05:49,159
Speaker 1: but that was really an introduction and those kind of

88
00:05:49,200 --> 00:05:52,600
Speaker 1: experiences that I mentioned from Napster, you know, playing video

89
00:05:52,640 --> 00:05:54,719
Speaker 1: games thinking about how to build them, that was just

90
00:05:54,839 --> 00:05:58,479
Speaker 1: kind of what I was doing through my teenage years

91
00:05:58,480 --> 00:06:03,479
Speaker 1: and then even through college and well, I studied mechanical engineering. UM,

92
00:06:03,640 --> 00:06:08,240
Speaker 1: I struggled to find an outlet for the work I

93
00:06:08,320 --> 00:06:14,040
Speaker 1: wanted to do. UM. You know, I distinctly remember applying

94
00:06:14,320 --> 00:06:17,159
Speaker 1: in six months prior to my graduation and the six

95
00:06:17,200 --> 00:06:23,520
Speaker 1: months after applying to almost a hundred jobs. And these weren't,

96
00:06:23,520 --> 00:06:26,760
Speaker 1: you know, just random things that were within my area

97
00:06:26,839 --> 00:06:29,440
Speaker 1: that I studied. I graduated with degreen mechanical engineering in

98
00:06:29,560 --> 00:06:34,440
Speaker 1: four years, UM from the University of Rochester. And of

99
00:06:34,520 --> 00:06:36,839
Speaker 1: all those applications, you know how many jobs I got?

100
00:06:37,320 --> 00:06:42,600
Speaker 1: How many? Zero? Yikes. So it was kind of at

101
00:06:42,640 --> 00:06:44,400
Speaker 1: that moment that I was like, well, you know what,

102
00:06:44,760 --> 00:06:49,919
Speaker 1: if this isn't gonna work doing a traditional route, I

103
00:06:50,000 --> 00:06:52,480
Speaker 1: might as well just do something I'm really passionate about.

104
00:06:52,720 --> 00:06:54,799
Speaker 1: And that's when I kind of dug back into coding

105
00:06:54,800 --> 00:06:57,960
Speaker 1: a little bit and really peeled back the curtain on

106
00:06:58,000 --> 00:07:02,279
Speaker 1: the hackathon environment. And now, I'm sure you're familiar with

107
00:07:02,320 --> 00:07:05,560
Speaker 1: a hackathon or the mechanics of it, and probably most

108
00:07:05,560 --> 00:07:07,360
Speaker 1: of your listeners, but for those who are in a

109
00:07:07,440 --> 00:07:12,760
Speaker 1: hackathons kind of just like a coding competition, right and

110
00:07:12,760 --> 00:07:16,320
Speaker 1: and companies from all over the world will will put

111
00:07:16,360 --> 00:07:21,000
Speaker 1: on these competitions, from IBM certainly too Poor sher Mercedes

112
00:07:21,040 --> 00:07:25,680
Speaker 1: Benz to UH, the US government, UM, the Red Cross,

113
00:07:25,760 --> 00:07:28,520
Speaker 1: all sorts of organizations. And typically what they'll do is

114
00:07:28,520 --> 00:07:31,520
Speaker 1: they'll say something like, Hey, if we're a car company,

115
00:07:31,560 --> 00:07:34,640
Speaker 1: we've got this new a p i UM for you

116
00:07:34,720 --> 00:07:39,400
Speaker 1: to build on our on our on our vehicles, and

117
00:07:39,440 --> 00:07:41,400
Speaker 1: what we want you developers to do is come up

118
00:07:41,400 --> 00:07:45,520
Speaker 1: with amazing stuff, be creative, build out something incredible, pitch

119
00:07:45,560 --> 00:07:47,960
Speaker 1: it to us, and the best ones you're gonna get paid.

120
00:07:49,040 --> 00:07:51,160
Speaker 1: And for me, this was like the coolest thing in

121
00:07:51,200 --> 00:07:54,880
Speaker 1: the world because it put together two things that I

122
00:07:54,960 --> 00:07:58,680
Speaker 1: absolutely loved that I really couldn't find anywhere else. And

123
00:07:58,720 --> 00:08:01,640
Speaker 1: that's you know, I'm a huge sports fan Philadelphia Eagles

124
00:08:02,560 --> 00:08:05,840
Speaker 1: for life. UM. I also played soccer my my whole

125
00:08:05,840 --> 00:08:08,160
Speaker 1: life grown up, and I'm a big fan of the sport.

126
00:08:08,520 --> 00:08:11,640
Speaker 1: And I love that like innate competitive spirit, you know,

127
00:08:11,680 --> 00:08:14,080
Speaker 1: it's just get on a field and go and compete

128
00:08:14,080 --> 00:08:18,120
Speaker 1: and try to win. But I also love going back

129
00:08:18,120 --> 00:08:21,400
Speaker 1: to the Halo and Napster thing, like just that interest

130
00:08:21,480 --> 00:08:24,440
Speaker 1: to just build stuff, you know, come up with ideas.

131
00:08:25,240 --> 00:08:28,280
Speaker 1: And a hackathon I think is so unique because it's

132
00:08:28,280 --> 00:08:34,720
Speaker 1: able to put those two uh perspectives together, the you know,

133
00:08:34,800 --> 00:08:38,319
Speaker 1: the interest to just go compete in an environment at

134
00:08:38,360 --> 00:08:42,280
Speaker 1: the same time do that while building incredible creative technology.

135
00:08:43,120 --> 00:08:45,240
Speaker 1: And so for a few years I was hopping around

136
00:08:46,040 --> 00:08:48,920
Speaker 1: um going to hackathons and and I kind of got

137
00:08:49,080 --> 00:08:51,720
Speaker 1: a groove in it, started winning a few, making a

138
00:08:51,800 --> 00:08:54,640
Speaker 1: career out of it, and one thing led to the next,

139
00:08:54,679 --> 00:08:57,720
Speaker 1: and then of course competed in the IBM Call for Code,

140
00:08:57,760 --> 00:09:00,720
Speaker 1: and here we are today. I asked a Lisa Macklin

141
00:09:00,840 --> 00:09:03,160
Speaker 1: of IBM to give an elevator pitch to kind of

142
00:09:03,200 --> 00:09:06,440
Speaker 1: explain what the Call for Code is all about a

143
00:09:06,480 --> 00:09:09,560
Speaker 1: little bit about the background for for the Call for

144
00:09:09,640 --> 00:09:12,760
Speaker 1: Code challenge. And one of the things that of course,

145
00:09:13,160 --> 00:09:18,240
Speaker 1: UH it is really important is understanding what matters to developers.

146
00:09:18,920 --> 00:09:23,839
Speaker 1: And an interesting insight on developers is that eight per

147
00:09:23,920 --> 00:09:27,880
Speaker 1: cent of them code as a hobby. They work on

148
00:09:27,960 --> 00:09:31,960
Speaker 1: coding projects at night, in the morning, in over the weekends,

149
00:09:31,960 --> 00:09:37,800
Speaker 1: in their spare time, and they are inherently problem solvers.

150
00:09:38,160 --> 00:09:43,520
Speaker 1: And for most software developers, coding isn't just a profession,

151
00:09:43,720 --> 00:09:48,079
Speaker 1: it's really a passion. And one of the things that

152
00:09:48,640 --> 00:09:54,120
Speaker 1: we really see also with developers is uh tremendous use

153
00:09:54,160 --> 00:09:58,520
Speaker 1: of open source technology, and I think an interesting insight

154
00:09:58,640 --> 00:10:05,040
Speaker 1: there is that developers enjoy working together collaborating on projects

155
00:10:05,240 --> 00:10:09,160
Speaker 1: which open source helps them to do. So in thinking

156
00:10:09,200 --> 00:10:15,520
Speaker 1: about how can we help developers with their UH, their

157
00:10:15,600 --> 00:10:19,840
Speaker 1: love of side projects, of learning and developing new skills,

158
00:10:20,960 --> 00:10:25,800
Speaker 1: combined with the desire which is a long held focus

159
00:10:25,880 --> 00:10:30,760
Speaker 1: of of IBM S is using technology for good, creating

160
00:10:30,760 --> 00:10:36,120
Speaker 1: innovation that matters, taking the technology that we have with

161
00:10:36,360 --> 00:10:40,640
Speaker 1: artificial intelligence and blockchain and others and using those to

162
00:10:41,160 --> 00:10:44,040
Speaker 1: really help make the world a better place. And we

163
00:10:44,200 --> 00:10:48,120
Speaker 1: know that the majority of developers are interested in the

164
00:10:48,200 --> 00:10:51,800
Speaker 1: same thing. Most of them, as they're working on side

165
00:10:51,800 --> 00:10:56,440
Speaker 1: projects are doing work that has societal benefit. So that's

166
00:10:56,480 --> 00:11:00,280
Speaker 1: really what created the UH. The spark behind ying the

167
00:11:00,320 --> 00:11:05,920
Speaker 1: Call for Code Challenge. We wanted to see what twenty

168
00:11:05,920 --> 00:11:09,559
Speaker 1: four million developers around the world would do if they

169
00:11:09,559 --> 00:11:14,800
Speaker 1: were given access to the technology and also an understanding

170
00:11:15,400 --> 00:11:18,719
Speaker 1: of some of these UH major world problems that they

171
00:11:18,720 --> 00:11:22,760
Speaker 1: could help tackle. So with Call for Code, where IBM

172
00:11:22,920 --> 00:11:25,800
Speaker 1: is a is the founding member of Call for Code,

173
00:11:25,840 --> 00:11:30,040
Speaker 1: we launched it UH in tighten with the David Clark

174
00:11:30,120 --> 00:11:33,960
Speaker 1: caused the Linux Foundation of working with the United Nations,

175
00:11:34,720 --> 00:11:38,520
Speaker 1: and we wanted to focus on something that was really

176
00:11:38,559 --> 00:11:42,800
Speaker 1: really important each year. The Call for Code was is

177
00:11:42,840 --> 00:11:46,120
Speaker 1: a five year program and we started with a focus

178
00:11:46,160 --> 00:11:50,199
Speaker 1: on natural disasters. We knew that this is an area

179
00:11:50,520 --> 00:11:55,760
Speaker 1: that was an increasing problem around the world and one

180
00:11:56,160 --> 00:11:59,760
Speaker 1: that technology could could help, and so in launching Call

181
00:11:59,840 --> 00:12:06,800
Speaker 1: for Code in we asked developers to create solutions for

182
00:12:06,840 --> 00:12:12,880
Speaker 1: these problems, and we were really blown away by the

183
00:12:12,920 --> 00:12:15,920
Speaker 1: amount of engagement that we got. We had over a

184
00:12:15,960 --> 00:12:21,480
Speaker 1: hundred thousand participants in that first year, creating some three

185
00:12:21,520 --> 00:12:26,200
Speaker 1: thousand software applications, and over the last two years it's

186
00:12:26,240 --> 00:12:31,920
Speaker 1: it's grown tremendously. We had nearly two hundred thousand participants

187
00:12:32,000 --> 00:12:37,400
Speaker 1: in the Call for Code nineteen and UH. What I

188
00:12:37,440 --> 00:12:42,400
Speaker 1: think is really amazing too is we had participation across

189
00:12:42,480 --> 00:12:47,160
Speaker 1: a hundred and sixty five countries. So we saw a

190
00:12:47,280 --> 00:12:53,320
Speaker 1: tremendous amount of of interest and engagement in creating these

191
00:12:53,720 --> 00:12:58,720
Speaker 1: sustainable solutions, leveraging open source and working not just developers,

192
00:12:58,760 --> 00:13:02,720
Speaker 1: but typically teams that involved other experts. We had teams

193
00:13:02,760 --> 00:13:10,720
Speaker 1: that were comprised of developers and UH first responders, medical professionals, students,

194
00:13:10,920 --> 00:13:15,160
Speaker 1: groups coming together to work on an amazing range array

195
00:13:15,240 --> 00:13:18,600
Speaker 1: of problems. And as Brian would explain to me, Call

196
00:13:18,720 --> 00:13:23,000
Speaker 1: for Code stands apart from other hackathon type events. The

197
00:13:23,120 --> 00:13:28,240
Speaker 1: IBM Call for Code UH focused on originally, well still

198
00:13:28,280 --> 00:13:31,839
Speaker 1: to this day, natural disasters, although they've kind of segmented

199
00:13:31,880 --> 00:13:34,120
Speaker 1: and focused even a little more under that umbrella. I

200
00:13:34,120 --> 00:13:37,719
Speaker 1: believe this year's competition is focusing on climate change. UH

201
00:13:37,800 --> 00:13:45,800
Speaker 1: certainly has implications to natural disasters UM. And so one

202
00:13:45,840 --> 00:13:48,120
Speaker 1: of the things that I think was really interesting about

203
00:13:48,160 --> 00:13:52,040
Speaker 1: the IBM Call for Code UM apart from all the

204
00:13:52,080 --> 00:13:54,120
Speaker 1: other hackathons I've been to, and one of the things

205
00:13:54,160 --> 00:13:58,600
Speaker 1: that kind of left me frustrated with the hackathon community was,

206
00:13:59,480 --> 00:14:02,880
Speaker 1: you know, you go to these events. UM. Sometimes they're smaller,

207
00:14:02,960 --> 00:14:05,720
Speaker 1: sometimes they're larger than the Call for Code is certainly

208
00:14:05,720 --> 00:14:09,719
Speaker 1: a huge hackathon, um, but even you know, there are

209
00:14:09,720 --> 00:14:12,320
Speaker 1: other in person events Call for codes at three month long,

210
00:14:12,800 --> 00:14:15,400
Speaker 1: or at least it wasn't it might be longer shorter

211
00:14:15,559 --> 00:14:18,400
Speaker 1: now three month long virtual things, so you can compete

212
00:14:18,440 --> 00:14:21,880
Speaker 1: from anywhere. But some of the hackathons I would go to, uh,

213
00:14:21,920 --> 00:14:26,760
Speaker 1: like we would put three hundred developers in the basement

214
00:14:26,840 --> 00:14:29,720
Speaker 1: of the Venetian Hotel in Las Vegas for a weekend,

215
00:14:30,520 --> 00:14:33,920
Speaker 1: and you'd like compete on different enterprise hackathons like PayPal

216
00:14:33,920 --> 00:14:38,760
Speaker 1: would be their visa some other folks. Um. But what's

217
00:14:38,800 --> 00:14:41,640
Speaker 1: so unique about Call for Code, and what I think

218
00:14:41,680 --> 00:14:45,160
Speaker 1: really sets this apart is their commitment to actually see

219
00:14:45,320 --> 00:14:49,320
Speaker 1: the solutions through. And I think that's really important, particularly

220
00:14:49,360 --> 00:14:52,560
Speaker 1: within the context of natural disasters, because so often that

221
00:14:52,680 --> 00:14:55,480
Speaker 1: these hackathons you'll see great ideas or at least that

222
00:14:55,600 --> 00:14:59,080
Speaker 1: first like nugget of an idea. You know, if great

223
00:14:59,120 --> 00:15:03,800
Speaker 1: ideas are one percent inspiration and perspiration, and hackathon is

224
00:15:03,840 --> 00:15:06,360
Speaker 1: kind of like that one percent. The part of Call

225
00:15:06,400 --> 00:15:09,440
Speaker 1: for Code that really impresses me and the reason I

226
00:15:09,440 --> 00:15:13,280
Speaker 1: think it's the most important technology competition in the world

227
00:15:13,320 --> 00:15:17,520
Speaker 1: today is the commitment to see the work through in

228
00:15:17,560 --> 00:15:19,960
Speaker 1: the end to actually make an impact in the world,

229
00:15:20,560 --> 00:15:24,040
Speaker 1: and I think that is fostered an exceptional community of developers.

230
00:15:24,720 --> 00:15:27,080
Speaker 1: Like I said at the top of the episode, one

231
00:15:27,120 --> 00:15:30,080
Speaker 1: of the interesting things about Call for Code is that

232
00:15:30,120 --> 00:15:36,040
Speaker 1: it brings together too seemingly opposing philosophies cooperation and competition. Well,

233
00:15:36,120 --> 00:15:39,120
Speaker 1: Call for Code is structured as a competition. There's a

234
00:15:39,200 --> 00:15:42,640
Speaker 1: deep culture of collaboration throughout the program. A Lissa of

235
00:15:42,720 --> 00:15:47,080
Speaker 1: IBM explains, one of the things that really struck me

236
00:15:47,320 --> 00:15:51,040
Speaker 1: early on is that, you know, the the open source

237
00:15:51,160 --> 00:15:58,160
Speaker 1: movement is about collaboration and about achieving more faster by

238
00:15:58,200 --> 00:16:03,840
Speaker 1: working together, and that also means that projects live on

239
00:16:04,160 --> 00:16:07,120
Speaker 1: in open source. So one of the things that really

240
00:16:07,160 --> 00:16:11,360
Speaker 1: inspired me hearing from I participated in a number of

241
00:16:11,360 --> 00:16:15,320
Speaker 1: of hackathons around the world. We do about six hundred

242
00:16:15,400 --> 00:16:20,200
Speaker 1: of these every year, bringing developers and uh different parts

243
00:16:20,240 --> 00:16:23,280
Speaker 1: of the world working with them together on their solutions.

244
00:16:23,560 --> 00:16:28,640
Speaker 1: And what I found incredibly motivating hearing from the developers

245
00:16:28,960 --> 00:16:32,720
Speaker 1: is that they were excited that this wasn't a one

246
00:16:32,760 --> 00:16:35,520
Speaker 1: and done, It wasn't you know, a twenty four hour

247
00:16:35,640 --> 00:16:38,400
Speaker 1: hack where they come in, they create things, are great ideas,

248
00:16:38,440 --> 00:16:41,800
Speaker 1: and then they move on. These are projects that can

249
00:16:41,920 --> 00:16:47,120
Speaker 1: live on and be built out by developers around the world.

250
00:16:47,400 --> 00:16:50,160
Speaker 1: And that was also the thing in working with the

251
00:16:50,240 --> 00:16:54,240
Speaker 1: United Nations that they really keyed in on, because there

252
00:16:54,240 --> 00:17:00,160
Speaker 1: are so many software driven solutions that don't require lot

253
00:17:00,200 --> 00:17:03,720
Speaker 1: of infrastructure, even things like you know, early warning systems

254
00:17:03,800 --> 00:17:07,320
Speaker 1: for tsunamis as an example, if you have access to

255
00:17:07,320 --> 00:17:12,360
Speaker 1: the weather data, if you have a smartphone, you can

256
00:17:12,520 --> 00:17:15,080
Speaker 1: get those alerts which save lives. And this is the

257
00:17:15,119 --> 00:17:19,680
Speaker 1: type of thing that someone could create working on open

258
00:17:19,720 --> 00:17:23,320
Speaker 1: source in one part of the world, and then it

259
00:17:23,359 --> 00:17:26,600
Speaker 1: could be adapted for conditions in different parts of the world.

260
00:17:27,320 --> 00:17:32,199
Speaker 1: Buy software developers virtually any place anywhere somebody has a

261
00:17:32,320 --> 00:17:34,359
Speaker 1: you know, a laptop and a smartphone. And the U

262
00:17:34,480 --> 00:17:38,520
Speaker 1: n was particularly interested in this because they saw the

263
00:17:38,600 --> 00:17:44,159
Speaker 1: long term sustainability and in terms of how the developers

264
00:17:44,200 --> 00:17:46,359
Speaker 1: are working together. I think this is one of the

265
00:17:46,480 --> 00:17:50,520
Speaker 1: things that really inspired me as well over the last

266
00:17:50,560 --> 00:17:53,520
Speaker 1: two years, is seeing how these groups come together. And

267
00:17:53,920 --> 00:17:56,040
Speaker 1: I think that Brian may have told you that the

268
00:17:56,840 --> 00:18:00,320
Speaker 1: five members of the team that he's on with jacked

269
00:18:00,320 --> 00:18:02,960
Speaker 1: oal from different parts of the United States and different

270
00:18:03,000 --> 00:18:04,639
Speaker 1: parts of the world, and they met on a Slack

271
00:18:04,720 --> 00:18:08,760
Speaker 1: channel uh, and we saw a lot of that. UH.

272
00:18:09,000 --> 00:18:13,399
Speaker 1: The The winner of of Call for Code nineteen is

273
00:18:13,440 --> 00:18:18,080
Speaker 1: a team that at Barcelona based and there's a firefighter,

274
00:18:18,920 --> 00:18:24,400
Speaker 1: a nurse and three developers. So it's different different groups

275
00:18:24,520 --> 00:18:28,000
Speaker 1: collaborating together, meeting each other either in person or in

276
00:18:28,119 --> 00:18:34,600
Speaker 1: hackathons or meeting virtually working on these open source projects,

277
00:18:34,760 --> 00:18:39,600
Speaker 1: and they continue to collaborate over time. Brian confirmed what

278
00:18:39,680 --> 00:18:43,119
Speaker 1: Alisa was saying describing how his team came together during

279
00:18:43,160 --> 00:18:46,560
Speaker 1: the first Call for Code. I had known a few

280
00:18:46,600 --> 00:18:49,520
Speaker 1: of the co founders previously, we competed against each other

281
00:18:49,600 --> 00:18:53,200
Speaker 1: at hackathons UM, but one of the founders of Project Owl,

282
00:18:53,760 --> 00:18:56,879
Speaker 1: Magus Pereira, him and I had just met in the slack.

283
00:18:57,080 --> 00:19:00,040
Speaker 1: I think digital technology and like meeting someone on a

284
00:19:00,040 --> 00:19:03,200
Speaker 1: message board and Slack might seem like a little weird

285
00:19:03,240 --> 00:19:05,800
Speaker 1: and different, right, you didn't run into this person in

286
00:19:05,840 --> 00:19:10,880
Speaker 1: a room, you didn't meet at like a conference, UM,

287
00:19:10,920 --> 00:19:15,000
Speaker 1: but it's really effective at putting people together who have

288
00:19:15,240 --> 00:19:19,280
Speaker 1: like minds, like skill sets, and like ambitions. And I

289
00:19:19,359 --> 00:19:22,359
Speaker 1: remember Maggis had just posted some message about what he

290
00:19:22,400 --> 00:19:24,800
Speaker 1: was interested in. We connected and had a call and

291
00:19:25,440 --> 00:19:30,200
Speaker 1: I just still to this day distinctly remember his creativity,

292
00:19:30,240 --> 00:19:34,640
Speaker 1: passion and interest in building unique solutions, and so even

293
00:19:34,680 --> 00:19:37,400
Speaker 1: after one call, we we just kind of agreed, like, man,

294
00:19:37,440 --> 00:19:41,080
Speaker 1: we gotta work together, Like how can we facilitate this

295
00:19:41,880 --> 00:19:45,000
Speaker 1: because I think there's a lot you have to offer

296
00:19:45,080 --> 00:19:46,920
Speaker 1: that you're interested in. And I feel the same way

297
00:19:46,960 --> 00:19:50,160
Speaker 1: about myself and the team we've already put together. So

298
00:19:51,280 --> 00:19:54,080
Speaker 1: we I don't know if they still use Slack or

299
00:19:54,160 --> 00:19:57,560
Speaker 1: how they do the collaborative piece, but the environment of

300
00:19:57,640 --> 00:20:00,320
Speaker 1: all these developers showing up to just want to build

301
00:20:00,440 --> 00:20:04,320
Speaker 1: something great while still inspiring people to have that competitive

302
00:20:04,400 --> 00:20:08,600
Speaker 1: nature um I think is a really fascinating experience. And

303
00:20:08,720 --> 00:20:11,480
Speaker 1: IBM puts on call for code satellite events, so like

304
00:20:11,520 --> 00:20:15,840
Speaker 1: many hackathons all over the world during the main overarching event,

305
00:20:16,840 --> 00:20:19,280
Speaker 1: and this is another way to like plug in, try

306
00:20:19,359 --> 00:20:21,760
Speaker 1: something quickly, see if you've got an idea for the

307
00:20:21,760 --> 00:20:26,200
Speaker 1: bigger competition, and meet other technologists in the environment to

308
00:20:26,200 --> 00:20:29,800
Speaker 1: to really make this happen. This brings us up with

309
00:20:29,880 --> 00:20:33,480
Speaker 1: what Project al would specifically focus on, which all revolved

310
00:20:33,520 --> 00:20:38,640
Speaker 1: around hurricanes specifically with regards to what we did as

311
00:20:38,720 --> 00:20:42,639
Speaker 1: we were going into call for code. Uh, we uniquely are.

312
00:20:42,680 --> 00:20:44,879
Speaker 1: Our original team of five was spread out across the

313
00:20:45,000 --> 00:20:48,560
Speaker 1: United States still is today, but we were at the time, Um,

314
00:20:48,760 --> 00:20:52,880
Speaker 1: Charlie was in Houston, Magus in North Carolina, myself, Terra,

315
00:20:52,880 --> 00:20:58,920
Speaker 1: Core and Nick in New York City, and very recently

316
00:20:59,640 --> 00:21:04,240
Speaker 1: at the time, Charlie and Houston had gone through Hurricane Harvey,

317
00:21:04,720 --> 00:21:07,920
Speaker 1: a massive hurricane caused a lot of economic damage in Houston,

318
00:21:08,640 --> 00:21:13,120
Speaker 1: and Magus during the competition went through Hurricane Florence, so,

319
00:21:14,240 --> 00:21:17,280
Speaker 1: you know, and kind of the outset, we felt, well,

320
00:21:17,840 --> 00:21:20,160
Speaker 1: it seems like hurricanes are probably a pretty good thing

321
00:21:20,240 --> 00:21:25,000
Speaker 1: to try to approach here. These have been quite a problem, um,

322
00:21:25,080 --> 00:21:28,240
Speaker 1: And not only are they a problem that hits close

323
00:21:28,280 --> 00:21:30,600
Speaker 1: to home for us, because we've all been through them,

324
00:21:30,640 --> 00:21:32,119
Speaker 1: you know, even still to this day. I'm here in

325
00:21:32,160 --> 00:21:35,520
Speaker 1: New York City. I live on the L Train. Hurricane

326
00:21:35,560 --> 00:21:38,960
Speaker 1: Sandy ripped through New York City in two thousand twelve,

327
00:21:39,680 --> 00:21:42,800
Speaker 1: and uh, they're still shutting down the L Train to

328
00:21:42,880 --> 00:21:45,800
Speaker 1: repair it for like eight years later. So the team

329
00:21:45,880 --> 00:21:48,760
Speaker 1: knew what type of natural disaster they wanted to focus

330
00:21:48,800 --> 00:21:52,119
Speaker 1: on next. They thought about how technology could help people

331
00:21:52,200 --> 00:21:55,800
Speaker 1: affected by a hurricane. So in the absence of being

332
00:21:55,800 --> 00:21:59,359
Speaker 1: able to stop these natural disasters, we really couldn't do

333
00:21:59,440 --> 00:22:02,679
Speaker 1: that yet um, and we can't do that now, and

334
00:22:02,720 --> 00:22:05,720
Speaker 1: maybe in the long run there might be something there,

335
00:22:05,760 --> 00:22:08,960
Speaker 1: but in the absence of being able to stop them,

336
00:22:09,040 --> 00:22:13,719
Speaker 1: we felt okay, Well, what we can do is enable

337
00:22:13,880 --> 00:22:18,359
Speaker 1: people to prepare for and deal with them as effectively

338
00:22:18,400 --> 00:22:24,360
Speaker 1: as possible. And the first obvious problem is that when

339
00:22:24,359 --> 00:22:28,119
Speaker 1: a hurricane rips through it destroys everything, most notably the

340
00:22:28,160 --> 00:22:36,159
Speaker 1: infrastructure to provide organization, whereabouts and logistics in a community,

341
00:22:36,720 --> 00:22:40,240
Speaker 1: and that, of course is partly where the name Owl

342
00:22:40,320 --> 00:22:46,000
Speaker 1: came from. UM. So our focus was really that if

343
00:22:46,080 --> 00:22:50,840
Speaker 1: we could find a way to quickly, easily and cheaply

344
00:22:51,520 --> 00:22:54,879
Speaker 1: bring back communications in a place that either didn't have

345
00:22:55,000 --> 00:22:57,960
Speaker 1: it or lost it, that could be a really advantageous

346
00:22:58,200 --> 00:23:02,600
Speaker 1: solution to these communities. And while the immediate devastation left

347
00:23:02,600 --> 00:23:05,640
Speaker 1: behind after a hurricane is playing to see, Brian's team

348
00:23:05,720 --> 00:23:09,280
Speaker 1: knew that they challenge lasts longer than a day or

349
00:23:09,320 --> 00:23:12,959
Speaker 1: a week following a hurricane, and that communications plays a

350
00:23:13,080 --> 00:23:16,520
Speaker 1: vital role in that timeframe. I think it's important to

351
00:23:16,600 --> 00:23:21,600
Speaker 1: note that particularly for hurricanes, though I suspect this is

352
00:23:21,640 --> 00:23:25,919
Speaker 1: true for most other types of disasters. To UM, the

353
00:23:25,920 --> 00:23:30,120
Speaker 1: the majority of the death toll and the economic devastation

354
00:23:30,280 --> 00:23:34,680
Speaker 1: occurs not during the immediacy of you know, the wind

355
00:23:34,720 --> 00:23:37,879
Speaker 1: and the rain and the thunder. It's it's the long

356
00:23:37,920 --> 00:23:42,440
Speaker 1: tail after. When you don't have roads and physical infrastructure,

357
00:23:42,440 --> 00:23:44,200
Speaker 1: you can't get to places. You can't get to love

358
00:23:44,280 --> 00:23:46,920
Speaker 1: the ones, You can't get to medical facilities. When you

359
00:23:46,960 --> 00:23:50,199
Speaker 1: don't have communications, you can't effectively coordinate to put the

360
00:23:50,320 --> 00:23:53,520
Speaker 1: right resources in the places they need to be UM,

361
00:23:53,640 --> 00:23:55,840
Speaker 1: nor can you contact loved ones to see if they

362
00:23:55,880 --> 00:23:59,960
Speaker 1: need help. UM. Elderly can't get medications they need food.

363
00:24:00,000 --> 00:24:03,840
Speaker 1: Food is not adequately distributed to the places that need

364
00:24:03,880 --> 00:24:07,960
Speaker 1: it most based on who needs what UM. So when

365
00:24:07,960 --> 00:24:13,399
Speaker 1: these infrastructures go down, particularly the communications piece, you lose

366
00:24:13,520 --> 00:24:19,320
Speaker 1: the ability for society to function on the plane that

367
00:24:19,400 --> 00:24:23,200
Speaker 1: we are currently accustomed to. So their solution was twofold,

368
00:24:23,440 --> 00:24:28,000
Speaker 1: create small, durable and inexpensive network hardware that responders could

369
00:24:28,080 --> 00:24:31,520
Speaker 1: rapidly deploy in a region, and a software platform that

370
00:24:31,640 --> 00:24:36,800
Speaker 1: enabled communications and other operations across that network. The idea

371
00:24:36,920 --> 00:24:41,280
Speaker 1: seems simple and elegant, but as Brian and his team discovered,

372
00:24:41,720 --> 00:24:44,200
Speaker 1: achieving that goal in the real world is a bit

373
00:24:44,280 --> 00:24:48,639
Speaker 1: more complicated. To be clear, there were a whole host

374
00:24:48,720 --> 00:24:51,879
Speaker 1: of challenges at the outset, and there are many many

375
00:24:51,920 --> 00:24:55,400
Speaker 1: more today that we still need to solve. Um. A

376
00:24:55,440 --> 00:24:58,560
Speaker 1: phrase I like to use that never seems to connect

377
00:24:58,600 --> 00:25:02,600
Speaker 1: with people, but I feel as appropriate is something maybe

378
00:25:02,600 --> 00:25:05,359
Speaker 1: you've ever heard, the saying, uh, it's turtles all the

379
00:25:05,400 --> 00:25:07,680
Speaker 1: way down. I think it came from like a joke

380
00:25:07,720 --> 00:25:12,879
Speaker 1: in physics that, uh, some person said to a physics professor,

381
00:25:13,560 --> 00:25:15,440
Speaker 1: you know, the Earth isn't in space, it's just sitting

382
00:25:15,480 --> 00:25:19,240
Speaker 1: on the back of a turtle. And the professor says, oh, okay, okay, sure,

383
00:25:19,720 --> 00:25:22,400
Speaker 1: So what's that turtle sitting on? And the person responds

384
00:25:22,440 --> 00:25:27,160
Speaker 1: like another turtle And there the professor goes, okay, and

385
00:25:27,200 --> 00:25:29,560
Speaker 1: what's that turtle sitting on And the person goes, oh,

386
00:25:29,640 --> 00:25:34,119
Speaker 1: it's turtles all the way down. My my point and

387
00:25:34,160 --> 00:25:36,879
Speaker 1: the reason I think this is appropriate. Maybe I'm crazy,

388
00:25:36,880 --> 00:25:40,720
Speaker 1: maybe this has no relationship, but the point is, as

389
00:25:40,760 --> 00:25:43,360
Speaker 1: we are kind of peeling back the covers. I'm, by

390
00:25:43,400 --> 00:25:46,560
Speaker 1: the way, I'm not an IoT guy. Sure, I'm a technologist,

391
00:25:46,680 --> 00:25:51,400
Speaker 1: like I can code. I know how to use Google right, um,

392
00:25:51,440 --> 00:25:55,080
Speaker 1: which interestingly is like one of the core skill sets

393
00:25:55,080 --> 00:25:58,000
Speaker 1: of programmer needs to be able to have how to

394
00:25:58,320 --> 00:26:03,520
Speaker 1: learn new technologies and figure things out. Um. Nobody on

395
00:26:03,560 --> 00:26:07,000
Speaker 1: our team was like a professional IoT developer. I mean

396
00:26:07,160 --> 00:26:11,359
Speaker 1: even when we started developing, I would be writing code

397
00:26:11,400 --> 00:26:14,400
Speaker 1: for the firmware, but I didn't actually know what language

398
00:26:14,400 --> 00:26:16,960
Speaker 1: I was writing in. I just knew that it worked. Yeah,

399
00:26:17,280 --> 00:26:20,399
Speaker 1: none of us had like a professional background to do

400
00:26:20,440 --> 00:26:22,560
Speaker 1: any of this. So if anybody's thinking like you need

401
00:26:22,640 --> 00:26:26,200
Speaker 1: some you know, college degree or a career, No, none

402
00:26:26,200 --> 00:26:28,359
Speaker 1: of that. And how this relates to my point of

403
00:26:28,359 --> 00:26:30,240
Speaker 1: its turtles all the way down. As we were starting,

404
00:26:30,880 --> 00:26:33,360
Speaker 1: we started like peeling back the covers on. Okay, well,

405
00:26:33,359 --> 00:26:35,359
Speaker 1: what if we could get this little IoT device to

406
00:26:35,400 --> 00:26:38,600
Speaker 1: connect to another device? That would be pretty cool. And uh,

407
00:26:38,760 --> 00:26:41,199
Speaker 1: you know, we peel back the cover, play around with

408
00:26:41,240 --> 00:26:44,800
Speaker 1: the radio technologies we used Laura nine fifteen mega hurts

409
00:26:44,960 --> 00:26:49,400
Speaker 1: here in the United States. Um, And when we solve

410
00:26:49,480 --> 00:26:51,760
Speaker 1: one problem, we make little progress. We'd say, oh that's

411
00:26:51,800 --> 00:26:53,320
Speaker 1: really cool. All right, what if we could do the

412
00:26:53,359 --> 00:26:55,760
Speaker 1: next thing? And that's when we realize, wait, we need

413
00:26:55,800 --> 00:27:01,879
Speaker 1: to like write a codec for this radio. Geez okay, Um,

414
00:27:01,920 --> 00:27:05,159
Speaker 1: all right, well once we figured that out, all right,

415
00:27:05,320 --> 00:27:09,160
Speaker 1: these Laura radios say they can communicate over like two kilometers. Great,

416
00:27:09,880 --> 00:27:11,880
Speaker 1: Well wait a minute. When we went down to Puerto Rico,

417
00:27:12,000 --> 00:27:14,480
Speaker 1: they're only working at two ds. What the hell is

418
00:27:14,480 --> 00:27:17,680
Speaker 1: going on? Oh? Heat and humidity are a big problem,

419
00:27:17,760 --> 00:27:21,760
Speaker 1: and foliage and metals just like another huge problem for radio.

420
00:27:21,880 --> 00:27:27,639
Speaker 1: So every problem we would find and then address, there

421
00:27:27,640 --> 00:27:31,119
Speaker 1: would just be like five others underneath it that we

422
00:27:31,160 --> 00:27:34,000
Speaker 1: had to to solve. And I feel another way to

423
00:27:34,040 --> 00:27:39,080
Speaker 1: think about this technology challenges like it's a game of

424
00:27:39,119 --> 00:27:44,480
Speaker 1: whack a mole of like infinite size. Right, So you

425
00:27:44,600 --> 00:27:46,920
Speaker 1: keep knocking down a couple of moles and a few

426
00:27:46,920 --> 00:27:49,840
Speaker 1: more keep popping up and and what's impressive and I

427
00:27:49,880 --> 00:27:53,040
Speaker 1: think shows your ability to execute, is how quickly you

428
00:27:53,080 --> 00:27:58,600
Speaker 1: can move through the field. Um. And on the IoT side,

429
00:27:58,640 --> 00:28:04,360
Speaker 1: you know it's hard. People say hardware is hard. It's true. Um.

430
00:28:04,440 --> 00:28:08,560
Speaker 1: And also you have to consider in the long run, uh,

431
00:28:08,600 --> 00:28:13,720
Speaker 1: the business case for hardware you're making and for project out.

432
00:28:13,880 --> 00:28:17,200
Speaker 1: This was a discussion and a conversation we thought about

433
00:28:17,240 --> 00:28:21,640
Speaker 1: for a long time. UM, because we understood and still

434
00:28:21,680 --> 00:28:24,640
Speaker 1: do to this day that ultimately, if we were ever

435
00:28:24,680 --> 00:28:27,159
Speaker 1: to make a dent in a market. You know, I

436
00:28:27,200 --> 00:28:29,960
Speaker 1: think we've got a great brand. I think we have

437
00:28:30,240 --> 00:28:34,680
Speaker 1: incredible nomenclature and design. But at the end of the day,

438
00:28:34,720 --> 00:28:38,720
Speaker 1: if Intel looked at any of our stuff, they could say,

439
00:28:38,840 --> 00:28:41,480
Speaker 1: all right, let's put ten engineers on this and give

440
00:28:41,520 --> 00:28:43,920
Speaker 1: them five million dollars and they'll have a better, faster,

441
00:28:44,040 --> 00:28:45,800
Speaker 1: cheaper product than you, and we will put you out

442
00:28:45,800 --> 00:28:48,280
Speaker 1: of business tomorrow. Oh and we also have economies of

443
00:28:48,320 --> 00:28:54,680
Speaker 1: scale for manufacturing. So you need to consider how can

444
00:28:54,760 --> 00:28:57,280
Speaker 1: this integrate into a business model, because that's what it

445
00:28:57,360 --> 00:28:59,120
Speaker 1: enables you to work on this for a very long

446
00:28:59,160 --> 00:29:03,520
Speaker 1: period of time. Project OWL faces many challenges, both from

447
00:29:03,680 --> 00:29:07,600
Speaker 1: technical and market standpoints. Not only must the team build

448
00:29:07,600 --> 00:29:12,320
Speaker 1: a working system in which custom built hardware and software

449
00:29:12,440 --> 00:29:15,320
Speaker 1: work together, they must also find a way to make

450
00:29:15,360 --> 00:29:18,680
Speaker 1: that a sustainable business. The hardware side of the project

451
00:29:18,720 --> 00:29:21,480
Speaker 1: presented many challenges as the team worked to create a

452
00:29:21,480 --> 00:29:26,920
Speaker 1: working mesh network infrastructure that was durable, deployable, and cost effective.

453
00:29:27,200 --> 00:29:30,240
Speaker 1: But I wanted to know more about the software side

454
00:29:30,320 --> 00:29:33,320
Speaker 1: and how Brian's team tapped into IBM, S Watson platform

455
00:29:33,480 --> 00:29:37,840
Speaker 1: and cloud computing capabilities. Yeah, so that there were kind

456
00:29:37,840 --> 00:29:42,520
Speaker 1: of like two halves to our solution, uh and and

457
00:29:42,560 --> 00:29:45,040
Speaker 1: they nicely fit under the hardware and the software, and

458
00:29:45,080 --> 00:29:48,760
Speaker 1: both of them enabled unique capability that I think together

459
00:29:48,920 --> 00:29:51,840
Speaker 1: made one complete solution. UM. So the way we kind

460
00:29:51,880 --> 00:29:55,040
Speaker 1: of looked at it was the software provided an incident

461
00:29:55,080 --> 00:30:01,000
Speaker 1: management of sorts away to UM leverage data form analytics,

462
00:30:01,400 --> 00:30:03,840
Speaker 1: do some intelligent things, and then the ducks, of course

463
00:30:04,360 --> 00:30:06,960
Speaker 1: provided the way to acquire that data on the ground,

464
00:30:07,120 --> 00:30:11,440
Speaker 1: particularly in locations that don't have any infrastructure. Electricity has gone,

465
00:30:11,480 --> 00:30:17,520
Speaker 1: communications are gone. The software is UM. It's it's changed

466
00:30:17,520 --> 00:30:20,080
Speaker 1: a bit from the competition. You know, we pitched a

467
00:30:20,120 --> 00:30:25,080
Speaker 1: lot of stuff that we found people just didn't care about.

468
00:30:25,400 --> 00:30:28,600
Speaker 1: Our customers, clients, partners didn't care about but we thought

469
00:30:28,640 --> 00:30:32,640
Speaker 1: it was really cool. UM. But when we originally pitched

470
00:30:32,640 --> 00:30:35,040
Speaker 1: the solution, I think at the time they were like

471
00:30:35,120 --> 00:30:39,240
Speaker 1: twelve IBM, Watson, a p I, s UM, and we

472
00:30:39,280 --> 00:30:42,160
Speaker 1: had incorporated every single one of them in the solution.

473
00:30:42,240 --> 00:30:45,680
Speaker 1: So these things, these things were like text to speech,

474
00:30:45,760 --> 00:30:50,520
Speaker 1: speech to text UM. There are some others like knowledge catalog,

475
00:30:50,600 --> 00:30:53,800
Speaker 1: pattern recognitions, and I don't remember all of them by

476
00:30:53,880 --> 00:30:56,240
Speaker 1: name at this point in time, but they could enable

477
00:30:56,280 --> 00:30:58,880
Speaker 1: you to do certain things, whether it was speech to

478
00:30:58,920 --> 00:31:03,120
Speaker 1: text being kind of giving it an intelligent feel um,

479
00:31:03,160 --> 00:31:05,840
Speaker 1: and then some other things like Watson Discovery giving it

480
00:31:06,000 --> 00:31:10,480
Speaker 1: more of an intelligent brain. Uh. And this these API

481
00:31:10,560 --> 00:31:14,120
Speaker 1: s enabled you to act on data to do certain

482
00:31:14,160 --> 00:31:20,880
Speaker 1: things that would be very hard to write code for yourself. UM.

483
00:31:20,880 --> 00:31:24,840
Speaker 1: You know, natural language understanding. So we had a conversational

484
00:31:24,880 --> 00:31:27,960
Speaker 1: assistant you could just talk to for the OWL. UH.

485
00:31:28,400 --> 00:31:29,880
Speaker 1: At the time, it was the I M S it's

486
00:31:29,880 --> 00:31:33,800
Speaker 1: now the Data Management System the d M s UM.

487
00:31:33,840 --> 00:31:36,160
Speaker 1: And when someone would write in a message like hey,

488
00:31:36,240 --> 00:31:41,400
Speaker 1: can I create an incident Hurricane Florence and it's in uh,

489
00:31:41,440 --> 00:31:45,080
Speaker 1: North Carolina, we could run Natural Language Understanding and that

490
00:31:45,080 --> 00:31:48,400
Speaker 1: would pull out things like the name, locations and tell

491
00:31:48,480 --> 00:31:51,080
Speaker 1: us other interesting things this person said, versus of course,

492
00:31:51,680 --> 00:31:53,680
Speaker 1: if you're just writing raw code to do that, you're

493
00:31:53,720 --> 00:31:57,240
Speaker 1: never gonna be able to match the sophistication of a

494
00:31:57,280 --> 00:32:01,520
Speaker 1: cloud software tool like this. So IBM Watson was great

495
00:32:01,880 --> 00:32:05,720
Speaker 1: in that it UM it provided all these cool A

496
00:32:05,840 --> 00:32:07,480
Speaker 1: p I s you could play with. And I think

497
00:32:08,040 --> 00:32:11,440
Speaker 1: one element that IBM did exceptionally well was they have

498
00:32:11,560 --> 00:32:14,680
Speaker 1: these code patterns that you can just go online, pull down,

499
00:32:14,760 --> 00:32:18,920
Speaker 1: play with um. You know, with if anyone listening has

500
00:32:18,920 --> 00:32:22,120
Speaker 1: ever written code before, you know that one of the

501
00:32:22,160 --> 00:32:24,840
Speaker 1: best ways to learn is to just find someone else's

502
00:32:24,880 --> 00:32:27,360
Speaker 1: code and screw around with it or adapted to another

503
00:32:27,480 --> 00:32:31,800
Speaker 1: use case. And that's what IBM is. A great ecosystem

504
00:32:31,840 --> 00:32:35,640
Speaker 1: of not only the services to build with, but kind

505
00:32:35,640 --> 00:32:38,400
Speaker 1: of the tutorials and the information to like say, okay,

506
00:32:38,440 --> 00:32:40,800
Speaker 1: this sounds cool, like can you just show me how

507
00:32:40,840 --> 00:32:43,960
Speaker 1: to make something with it. As Brian mentioned, Project Al

508
00:32:44,080 --> 00:32:48,920
Speaker 1: tapped into ibms Watson platform in several significant ways. Watson

509
00:32:49,000 --> 00:32:52,440
Speaker 1: is a suite of services from IBM, and it leverages

510
00:32:52,520 --> 00:32:56,480
Speaker 1: artificial intelligence to an incredible extent. It's designed in such

511
00:32:56,480 --> 00:32:59,920
Speaker 1: a way that developers can tap into these powerful processes

512
00:33:00,440 --> 00:33:03,960
Speaker 1: without having to build everything themselves. So if a developer

513
00:33:04,160 --> 00:33:07,200
Speaker 1: has an idea for a cool application that would lean

514
00:33:07,280 --> 00:33:10,920
Speaker 1: heavily on something that's traditionally really hard to do, like

515
00:33:11,640 --> 00:33:15,600
Speaker 1: natural language processing, you know, having a computer understand what

516
00:33:15,640 --> 00:33:19,800
Speaker 1: we mean when we communicate the way we typically communicate,

517
00:33:20,320 --> 00:33:22,600
Speaker 1: not as a machine would, but as a human would.

518
00:33:23,240 --> 00:33:25,760
Speaker 1: Machines are not naturally good at that. You have to

519
00:33:25,840 --> 00:33:29,600
Speaker 1: really work hard to make them understand well. Most developers

520
00:33:29,640 --> 00:33:32,120
Speaker 1: can't do that on their own, but they could lean

521
00:33:32,200 --> 00:33:35,600
Speaker 1: on a Watson a p I that's the application programming

522
00:33:35,640 --> 00:33:40,440
Speaker 1: interface to handle that part of their service, so they

523
00:33:40,440 --> 00:33:42,640
Speaker 1: can focus on whatever it is the app is supposed

524
00:33:42,640 --> 00:33:45,680
Speaker 1: to do, and the natural language part can be handled

525
00:33:45,760 --> 00:33:50,120
Speaker 1: by the Watson platform. Now, I think most people who

526
00:33:50,200 --> 00:33:53,720
Speaker 1: have heard about IBM Watson think back to the system's

527
00:33:53,840 --> 00:33:57,840
Speaker 1: famous appearance on the television game show Jeopardy, and Watson

528
00:33:57,880 --> 00:34:01,520
Speaker 1: acquitted itself pretty well on that show it won the competition.

529
00:34:01,640 --> 00:34:04,560
Speaker 1: But it turns out it's much more than a trivia

530
00:34:04,640 --> 00:34:07,600
Speaker 1: answering machine. In a way. You can think of Watson

531
00:34:07,720 --> 00:34:11,640
Speaker 1: as access to an array of AI capabilities, and the

532
00:34:11,680 --> 00:34:15,560
Speaker 1: Project Al team attempted to take advantage of every single

533
00:34:15,719 --> 00:34:20,359
Speaker 1: one of those. Interestingly, as Brian mentioned, they found out

534
00:34:20,400 --> 00:34:24,440
Speaker 1: that in the real world people didn't necessarily use all

535
00:34:24,440 --> 00:34:28,200
Speaker 1: the features Project Owl had included in their service, and

536
00:34:28,280 --> 00:34:33,279
Speaker 1: this helps illustrate another big challenge facing any developer or engineer.

537
00:34:33,719 --> 00:34:36,320
Speaker 1: What seems like a brilliant idea in the conference room

538
00:34:36,440 --> 00:34:38,920
Speaker 1: or as was the case with Project Owl on the

539
00:34:38,960 --> 00:34:43,279
Speaker 1: Slack channel, may not translate in the real world. It's

540
00:34:43,320 --> 00:34:47,399
Speaker 1: not that the idea itself is bad necessarily, but rather

541
00:34:47,520 --> 00:34:51,560
Speaker 1: that it's less applicable than the designer's first thought, so

542
00:34:51,600 --> 00:34:53,680
Speaker 1: some ideas might turn out to be best suited for

543
00:34:53,800 --> 00:34:57,400
Speaker 1: other applications in the future. The development process at Project

544
00:34:57,400 --> 00:35:01,759
Speaker 1: Owl continues. The original design led heavily on individual Mesh

545
00:35:01,880 --> 00:35:06,200
Speaker 1: network devices the team called ducks because like a rubber duck,

546
00:35:06,239 --> 00:35:08,840
Speaker 1: they were meant to be small and capable of floating.

547
00:35:09,200 --> 00:35:12,440
Speaker 1: The individual ducks linked together through a hub unit called

548
00:35:12,560 --> 00:35:15,960
Speaker 1: a Mama duck, which can then send information over to

549
00:35:16,360 --> 00:35:21,000
Speaker 1: a infrastructure component called a Papa duck that links the

550
00:35:21,200 --> 00:35:25,480
Speaker 1: Mesh network to the Internet at large. But even without PAPA,

551
00:35:25,560 --> 00:35:28,759
Speaker 1: the Mesh network itself can provide on site communications and

552
00:35:28,840 --> 00:35:33,080
Speaker 1: logistics support within the region. The team's pitch one the

553
00:35:33,080 --> 00:35:35,600
Speaker 1: two thousand eight teen Call for Code, and I asked

554
00:35:35,640 --> 00:35:39,920
Speaker 1: Brian what that actually meant on a practical level. Of course,

555
00:35:39,960 --> 00:35:42,200
Speaker 1: there was a monetary prize and that kind of enabled

556
00:35:42,280 --> 00:35:46,319
Speaker 1: us to, you know, not focus on other things. And

557
00:35:46,400 --> 00:35:49,200
Speaker 1: there was also a lot of support from IBM, so

558
00:35:49,239 --> 00:35:52,399
Speaker 1: a commitment from IBM r S to help us see

559
00:35:52,400 --> 00:35:56,400
Speaker 1: the work through, and one of the biggest manifestations of

560
00:35:56,440 --> 00:35:59,840
Speaker 1: that was the Corporate Service Corps deployment. So in March

561
00:36:00,160 --> 00:36:07,080
Speaker 1: last year, five handpicked ibm r s from around the world. UM,

562
00:36:07,120 --> 00:36:12,200
Speaker 1: if I'm recalling correctly, they were from the United States, Canada, Israel,

563
00:36:12,480 --> 00:36:17,200
Speaker 1: the United Kingdom. Yeah, that that's total. Two of them

564
00:36:17,200 --> 00:36:20,840
Speaker 1: were from the United States. UM. And this group was

565
00:36:20,880 --> 00:36:24,280
Speaker 1: exceptional with a diverse skill set of talents and being

566
00:36:24,320 --> 00:36:27,920
Speaker 1: able to like go to Puerto Rico with our very

567
00:36:27,960 --> 00:36:32,200
Speaker 1: you know rough hackathon project and have five expert ibm

568
00:36:32,280 --> 00:36:35,840
Speaker 1: r s there with skills in you know, from branding

569
00:36:35,840 --> 00:36:40,960
Speaker 1: and communications to design, to engineering to back end to

570
00:36:41,000 --> 00:36:46,360
Speaker 1: front end software development. UM. This really kicked our solution

571
00:36:46,360 --> 00:36:50,960
Speaker 1: into high gear, not just with the work they produced,

572
00:36:51,560 --> 00:36:56,680
Speaker 1: but also I have vivid memories leading up to our

573
00:36:57,280 --> 00:37:00,320
Speaker 1: our Puerto Rico one as we called it to pointment

574
00:37:00,520 --> 00:37:06,600
Speaker 1: last March, our first official deployment to Puerto Rico. UM,

575
00:37:06,640 --> 00:37:09,280
Speaker 1: I have memories of you know, hopping on our screen

576
00:37:09,320 --> 00:37:13,879
Speaker 1: shares in the mornings and uh hearing the Corporate Serve

577
00:37:14,440 --> 00:37:17,719
Speaker 1: Corporate Service Corps members saying to us like, hey, project OWL,

578
00:37:18,640 --> 00:37:25,120
Speaker 1: none of your stuff works right now. We were like, uh, yeah,

579
00:37:25,280 --> 00:37:30,160
Speaker 1: you're right. Um, you know, because again like a hackathon

580
00:37:30,360 --> 00:37:34,239
Speaker 1: is is about pitching an idea, and anyone who's been

581
00:37:34,280 --> 00:37:37,759
Speaker 1: through hackathon understands that you don't have a fleshed out

582
00:37:37,880 --> 00:37:42,560
Speaker 1: enterprise product. It's just not possible to do that given

583
00:37:42,600 --> 00:37:46,279
Speaker 1: your time constraints and resource constraints. So if you can

584
00:37:46,480 --> 00:37:48,719
Speaker 1: cobble together the idea, you can kind of work out

585
00:37:48,760 --> 00:37:51,879
Speaker 1: the kinks if people take an interest in it. And

586
00:37:51,920 --> 00:37:55,080
Speaker 1: I think the commitment of IBM to provide that support,

587
00:37:55,200 --> 00:38:01,120
Speaker 1: to provide experts, to provide UH the help in the

588
00:38:01,160 --> 00:38:06,920
Speaker 1: field as you're deploying it is a huge benefit. Two,

589
00:38:07,400 --> 00:38:12,600
Speaker 1: you're fledgling organization's ability to scale and grow, and so

590
00:38:12,680 --> 00:38:16,680
Speaker 1: that for me was the in a way, was the

591
00:38:16,719 --> 00:38:21,920
Speaker 1: most valuable part that we didn't really even consider at

592
00:38:21,960 --> 00:38:24,319
Speaker 1: the time when we had won, but in hindsight it

593
00:38:24,440 --> 00:38:29,600
Speaker 1: was really exceptional. Project Owl continues to refine their technology

594
00:38:29,680 --> 00:38:33,359
Speaker 1: and approach, taking the experiences they've encountered in the real

595
00:38:33,400 --> 00:38:36,320
Speaker 1: world and using them to create a more focused approach

596
00:38:36,360 --> 00:38:40,200
Speaker 1: to achieving their goal creating a nimble, robust, and effective

597
00:38:40,200 --> 00:38:44,840
Speaker 1: communications platform using custom built hardware and software. Winning the

598
00:38:44,840 --> 00:38:48,120
Speaker 1: competition wasn't the end of the line, but just the beginning.

599
00:38:48,480 --> 00:38:50,880
Speaker 1: Brian told me about the next steps for the project

600
00:38:50,960 --> 00:38:53,880
Speaker 1: as it strives to achieve the goals of the co founders.

601
00:38:54,280 --> 00:38:57,400
Speaker 1: So what's the next for Project Owl? We really after

602
00:38:57,440 --> 00:38:59,920
Speaker 1: the competition where we had spent a lot of time

603
00:39:00,000 --> 00:39:03,479
Speaker 1: focusing on engineering and design, you know, product development, coming

604
00:39:03,520 --> 00:39:09,160
Speaker 1: up with the idea, executing on that. UM. To enable

605
00:39:09,440 --> 00:39:13,520
Speaker 1: your organization to work on a challenge over a long

606
00:39:13,560 --> 00:39:18,000
Speaker 1: period of time, you have to build a sustainable business model.

607
00:39:18,840 --> 00:39:22,480
Speaker 1: So we've spent a lot of time and energy. Uh.

608
00:39:22,520 --> 00:39:25,960
Speaker 1: You know, if I was engineering software in the beginning,

609
00:39:26,000 --> 00:39:30,240
Speaker 1: I'm spending a lot of time engineering the company now.

610
00:39:30,840 --> 00:39:34,840
Speaker 1: So that's who how does project outfit in the market,

611
00:39:34,880 --> 00:39:37,080
Speaker 1: and who do we service? You know, I had mentioned

612
00:39:37,080 --> 00:39:41,919
Speaker 1: earlier our incident management system. We had all these capabilities

613
00:39:41,960 --> 00:39:44,400
Speaker 1: in it, a whole bunch of different things that could do.

614
00:39:44,600 --> 00:39:48,319
Speaker 1: And we realized that the people we wanted to go

615
00:39:48,440 --> 00:39:51,359
Speaker 1: and support, we're only really asking for one or two

616
00:39:51,440 --> 00:39:55,439
Speaker 1: of those things. So like them, we can just get

617
00:39:55,520 --> 00:39:59,279
Speaker 1: rid of them, um. And And that's a realization that

618
00:39:59,320 --> 00:40:01,600
Speaker 1: took a long time to come to, through a lot

619
00:40:01,640 --> 00:40:04,839
Speaker 1: of conversations and a lot of trial and error. And

620
00:40:04,920 --> 00:40:09,000
Speaker 1: so I spend a lot of time thinking about working

621
00:40:09,000 --> 00:40:11,520
Speaker 1: with the team is certainly still on technology. I mean,

622
00:40:11,560 --> 00:40:14,200
Speaker 1: we're all nerds here and this is what we love

623
00:40:14,239 --> 00:40:17,600
Speaker 1: to do, but a lot of times thinking about how

624
00:40:18,120 --> 00:40:21,960
Speaker 1: we can put Project Out in a position to succeed

625
00:40:22,040 --> 00:40:25,680
Speaker 1: over the long term, because if we do that, then

626
00:40:25,760 --> 00:40:28,680
Speaker 1: that enables us to think about some of this other

627
00:40:28,760 --> 00:40:32,960
Speaker 1: crazy technology. When Brian and I first talked way back

628
00:40:33,040 --> 00:40:36,480
Speaker 1: at IBM think two thousand nineteen, the Project OUT team

629
00:40:36,560 --> 00:40:40,040
Speaker 1: was mainly working with the basic duck units and they

630
00:40:40,080 --> 00:40:42,680
Speaker 1: were meant to be spread over a region on the

631
00:40:42,719 --> 00:40:46,399
Speaker 1: ground mostly. But things have evolved a bit since then

632
00:40:46,800 --> 00:40:49,440
Speaker 1: and the team has come up with some more variations

633
00:40:49,480 --> 00:40:53,360
Speaker 1: on this basic technology design that they hope to develop further.

634
00:40:54,080 --> 00:40:57,239
Speaker 1: We have a whole variety of ducks, um, many more

635
00:40:57,320 --> 00:41:01,200
Speaker 1: so that when I last met you. We have detector ducks,

636
00:41:01,239 --> 00:41:03,799
Speaker 1: we have disco ducks, we have cluster flocks, we have

637
00:41:03,880 --> 00:41:06,879
Speaker 1: duck ducks, we have space ducks, and there are many

638
00:41:06,920 --> 00:41:11,840
Speaker 1: others um. You can actually go to our open source

639
00:41:11,920 --> 00:41:17,080
Speaker 1: firmware uh cluster Duck Protocol dot org and there at

640
00:41:17,080 --> 00:41:19,160
Speaker 1: lists a bunch of the different duck variants. You can

641
00:41:19,520 --> 00:41:21,000
Speaker 1: check out some of the other ones we made. But

642
00:41:21,120 --> 00:41:26,120
Speaker 1: space ducks was a project I did in collaboration with

643
00:41:26,560 --> 00:41:30,399
Speaker 1: UM some engineers at cal Poly to put a duck

644
00:41:30,480 --> 00:41:33,920
Speaker 1: on a very large helium balloon and send it up

645
00:41:33,920 --> 00:41:37,640
Speaker 1: to a hundred thousand feet to acquire sensor readings and

646
00:41:37,880 --> 00:41:42,160
Speaker 1: try to transmit and see if it would break. And

647
00:41:42,239 --> 00:41:46,239
Speaker 1: so these are kind of, you know, that early seedlings

648
00:41:46,280 --> 00:41:50,399
Speaker 1: of ideas. Not only sure, we got great photos from that,

649
00:41:51,000 --> 00:41:53,600
Speaker 1: but I've been thinking to myself too, and the rest

650
00:41:53,600 --> 00:41:58,040
Speaker 1: of it's here, I should say, project out been thinking, Um,

651
00:41:58,080 --> 00:42:01,200
Speaker 1: you know, putting this community cations stuff on the ground

652
00:42:01,320 --> 00:42:03,959
Speaker 1: is great, but what if a hurricane comes through. I mean,

653
00:42:05,160 --> 00:42:08,399
Speaker 1: it kind of just destroys everything, So we're gonna lose

654
00:42:08,440 --> 00:42:12,000
Speaker 1: a lot of stuff. Well, okay, where could you put

655
00:42:12,040 --> 00:42:16,880
Speaker 1: communications things where they would stay there and even if

656
00:42:16,880 --> 00:42:20,360
Speaker 1: a hurricane rips through, they would still stay there. Well,

657
00:42:20,600 --> 00:42:23,319
Speaker 1: interesting space might work. Oh and by the way, you

658
00:42:23,360 --> 00:42:29,000
Speaker 1: know what environment radio frequencies work really well in a vacuum.

659
00:42:29,040 --> 00:42:32,000
Speaker 1: So the more we were thinking about, the more we

660
00:42:32,000 --> 00:42:34,279
Speaker 1: were considering, hey, it might be worth our time to

661
00:42:34,400 --> 00:42:37,360
Speaker 1: just start throwing some space ducks out into space and

662
00:42:37,400 --> 00:42:40,439
Speaker 1: see what catches UM, because there might be a real

663
00:42:40,480 --> 00:42:44,120
Speaker 1: long term opportunity here. Brian and his team continue to

664
00:42:44,160 --> 00:42:47,719
Speaker 1: develop Project Out to move beyond a testing phase and

665
00:42:47,760 --> 00:42:53,200
Speaker 1: into fully fledged deployments and implementations. Meanwhile, the Call for

666
00:42:53,280 --> 00:42:55,759
Speaker 1: Code is underway. I asked Brian if you had any

667
00:42:55,840 --> 00:42:58,960
Speaker 1: words of advice for competitors in this year's Call for Code,

668
00:42:59,239 --> 00:43:05,279
Speaker 1: I would encourage any developer working on solutions in the

669
00:43:05,400 --> 00:43:10,120
Speaker 1: upcoming competition to think about how you can distill a

670
00:43:10,200 --> 00:43:14,359
Speaker 1: concept down to like it's fundamental atomic parts. Because if

671
00:43:14,400 --> 00:43:17,799
Speaker 1: you can do that and you find the right atomic components,

672
00:43:17,920 --> 00:43:22,880
Speaker 1: you know, like a proton, neutron, an electron, what you

673
00:43:23,000 --> 00:43:26,240
Speaker 1: find is, oh wow, actually people can make a whole

674
00:43:26,239 --> 00:43:30,920
Speaker 1: pyra periodic table of elements with these things. Right. So

675
00:43:30,960 --> 00:43:35,359
Speaker 1: I think from Project That's perspective, we're very fortunate we

676
00:43:35,360 --> 00:43:38,200
Speaker 1: we certainly didn't know this at the time, but the

677
00:43:38,239 --> 00:43:42,120
Speaker 1: original duck Link we've now developed into a whole host

678
00:43:42,239 --> 00:43:46,080
Speaker 1: of variants for different use cases, but still leveraging that

679
00:43:46,239 --> 00:43:51,200
Speaker 1: same fundamental core. And I think this similarly goes for

680
00:43:51,280 --> 00:43:54,800
Speaker 1: the software side in the cloud, that our data management

681
00:43:54,840 --> 00:43:58,120
Speaker 1: system is really slim down and simplified to the core components,

682
00:43:58,160 --> 00:44:02,080
Speaker 1: you know, getting data in, seeing what it is, having

683
00:44:02,120 --> 00:44:05,160
Speaker 1: dots appear on a map and then a p ing

684
00:44:05,280 --> 00:44:07,480
Speaker 1: that data out if you need to put it in

685
00:44:07,600 --> 00:44:12,239
Speaker 1: external systems. So those core components are are like that

686
00:44:12,360 --> 00:44:15,799
Speaker 1: fundamental atomic nucleus that you know. We're still just in

687
00:44:15,840 --> 00:44:18,560
Speaker 1: the early days of this, but we think UM can

688
00:44:18,600 --> 00:44:22,239
Speaker 1: inspire a lot of folks to solve problems around the

689
00:44:22,239 --> 00:44:25,360
Speaker 1: world in unique ways. I want to thank Brian and

690
00:44:25,400 --> 00:44:28,640
Speaker 1: Alisa for joining me on this episode of Smart Talks.

691
00:44:28,719 --> 00:44:32,239
Speaker 1: And this is just the first in this series. You'll

692
00:44:32,280 --> 00:44:35,239
Speaker 1: hear more conversations with people using technology to make a

693
00:44:35,280 --> 00:44:38,760
Speaker 1: positive impact in the world very soon. Episodes will publish

694
00:44:38,760 --> 00:44:41,360
Speaker 1: here on tech stuff and also over on Stuff to

695
00:44:41,400 --> 00:44:44,120
Speaker 1: blow your mind. You'll learn about how some really smart

696
00:44:44,160 --> 00:44:47,720
Speaker 1: people are changing things for the better in incredible ways.

697
00:44:48,080 --> 00:44:51,640
Speaker 1: So make sure you catch every episode. You know, I

698
00:44:51,680 --> 00:44:54,360
Speaker 1: talk a lot about tech on this show, and sometimes

699
00:44:54,400 --> 00:44:57,359
Speaker 1: it's easy to get lost in how tech works and

700
00:44:57,400 --> 00:45:00,480
Speaker 1: you lose sight of why it's important. Compa Editions like

701
00:45:00,520 --> 00:45:04,120
Speaker 1: Call for Code and companies like Project Al remind us

702
00:45:04,120 --> 00:45:07,719
Speaker 1: that these powerful tools can bring about incredible change and

703
00:45:07,760 --> 00:45:10,279
Speaker 1: help those who need it most. Now I have no

704
00:45:10,360 --> 00:45:12,960
Speaker 1: doubt I'll be talking more about Project Al in the future,

705
00:45:13,440 --> 00:45:17,240
Speaker 1: describing how it helped communities see two vital functions despite

706
00:45:17,320 --> 00:45:19,799
Speaker 1: natural disasters, and I can't wait to see what the

707
00:45:19,840 --> 00:45:23,000
Speaker 1: participants in Call for Code come up with as a

708
00:45:23,080 --> 00:45:26,279
Speaker 1: tackle climate change. Make sure you check out the other

709
00:45:26,320 --> 00:45:29,239
Speaker 1: episodes in the Smart Talk series as they publish over

710
00:45:29,280 --> 00:45:32,160
Speaker 1: the next few weeks, and if you have any suggestions

711
00:45:32,160 --> 00:45:35,399
Speaker 1: for future episodes of tech Stuff, feel free to reach

712
00:45:35,440 --> 00:45:38,440
Speaker 1: out to me on Twitter or Facebook. The handle for

713
00:45:38,480 --> 00:45:41,560
Speaker 1: both of those is tech Stuff H s W and

714
00:45:41,600 --> 00:45:49,880
Speaker 1: I'll talk to you again really soon. Text Stuff is

715
00:45:49,880 --> 00:45:53,040
Speaker 1: an I Heart Radio production. For more podcasts from my

716
00:45:53,160 --> 00:45:56,759
Speaker 1: Heart Radio, visit the i Heart Radio app, Apple Podcasts,

717
00:45:56,880 --> 00:46:01,120
Speaker 1: or wherever you listen to your favorite shows. Eight