1 00:00:00,120 --> 00:00:03,279 Speaker 1: In smart Talks, we chat with people who are making 2 00:00:03,400 --> 00:00:06,880 Speaker 1: innovative use of advanced technologies designed by IBM and an 3 00:00:06,880 --> 00:00:11,479 Speaker 1: effort to make real world change. There's really no way 4 00:00:11,760 --> 00:00:15,880 Speaker 1: to introduce this episode without stating the obvious. The coronavirus 5 00:00:16,000 --> 00:00:20,439 Speaker 1: and COVID nineteen have caused massive disruptions and pretty much 6 00:00:20,560 --> 00:00:24,840 Speaker 1: every aspect of our lives. We're faced with tough decisions, 7 00:00:24,920 --> 00:00:28,479 Speaker 1: which become even more difficult when we find ourselves lacking 8 00:00:28,560 --> 00:00:32,440 Speaker 1: critical information. And that's going to bring us into today's topic. 9 00:00:32,600 --> 00:00:35,320 Speaker 1: But that's just one way that IBM is making its 10 00:00:35,360 --> 00:00:39,280 Speaker 1: technology available in the fight against COVID nineteen. The company 11 00:00:39,320 --> 00:00:43,800 Speaker 1: is working closely with scientists, doctors, leaders, and experts to 12 00:00:43,840 --> 00:00:47,239 Speaker 1: fight COVID nineteen in many ways, all while leveraging some 13 00:00:47,680 --> 00:00:52,400 Speaker 1: really impressive technology. Whether it's using supercomputers to help researchers 14 00:00:52,440 --> 00:00:55,080 Speaker 1: find a vaccine and that's a little bit of a 15 00:00:55,080 --> 00:00:59,160 Speaker 1: teaser for an upcoming episode, or aggregating enormous amounts of 16 00:00:59,200 --> 00:01:02,200 Speaker 1: informations so that the average American can get a local 17 00:01:02,200 --> 00:01:06,120 Speaker 1: ice snapshot of what's happening in their communities. IBM technology 18 00:01:06,200 --> 00:01:09,080 Speaker 1: is playing a big part. So what are we talking 19 00:01:09,120 --> 00:01:12,959 Speaker 1: about today? Well, the Internet is a phenomenal way to 20 00:01:12,959 --> 00:01:16,840 Speaker 1: share information, but that's a double edged sword. Over the 21 00:01:16,880 --> 00:01:21,880 Speaker 1: past several years, we've seen misinformation and even disinformation spread 22 00:01:21,920 --> 00:01:25,920 Speaker 1: across online communities, clouding our understanding of matters from the 23 00:01:25,920 --> 00:01:29,800 Speaker 1: trivial to the critical. If we're lucky, it's the trivial, 24 00:01:30,160 --> 00:01:32,959 Speaker 1: like the fact that people kept photoshopping the date on 25 00:01:33,000 --> 00:01:35,160 Speaker 1: the time circuit in the Back to the Future films 26 00:01:35,160 --> 00:01:38,800 Speaker 1: to make it today's date when Marty McFly goes to 27 00:01:38,880 --> 00:01:44,319 Speaker 1: the future. But we're increasingly seeing more bad information about 28 00:01:44,440 --> 00:01:49,000 Speaker 1: important things, from politics, to climate change to yeah, the 29 00:01:49,080 --> 00:01:52,600 Speaker 1: spread of the coronavirus, and in that kind of environment, 30 00:01:53,120 --> 00:01:57,640 Speaker 1: making the right decisions becomes increasingly more difficult to do, 31 00:01:58,120 --> 00:02:02,400 Speaker 1: just as it's becoming more urgent. That brings us to 32 00:02:02,400 --> 00:02:05,840 Speaker 1: today's topic. I sat down with Cameron Clayton, the general 33 00:02:05,880 --> 00:02:09,200 Speaker 1: manager of IBM Cloud Ecosystem and of the Weather Company 34 00:02:09,240 --> 00:02:13,600 Speaker 1: and IBM Business Well we were both sitting down, but 35 00:02:13,760 --> 00:02:16,800 Speaker 1: we also happened to be in our respective homes speaking 36 00:02:16,840 --> 00:02:19,520 Speaker 1: over the Internet in an effort to keep ourselves and 37 00:02:19,639 --> 00:02:23,560 Speaker 1: others safe and really, in many ways, that's what this 38 00:02:23,639 --> 00:02:27,000 Speaker 1: boils down to. But I'm getting ahead of myself. One 39 00:02:27,040 --> 00:02:29,520 Speaker 1: thing I do want to mention is that the unusual 40 00:02:29,600 --> 00:02:33,359 Speaker 1: circumstances mean this episode sounds a bit different from other 41 00:02:33,440 --> 00:02:37,040 Speaker 1: tech stuff episodes, because real life goes on while we podcast. 42 00:02:37,760 --> 00:02:42,120 Speaker 1: Cameron's team at the Weather Company have done something extraordinary. 43 00:02:42,240 --> 00:02:44,800 Speaker 1: If you've ever visited weather dot com or use the 44 00:02:44,840 --> 00:02:47,959 Speaker 1: Weather Channel app, you know you can get an incredibly 45 00:02:48,040 --> 00:02:51,480 Speaker 1: localized report down to the zip code in the United States. 46 00:02:52,000 --> 00:02:55,400 Speaker 1: And just so you know, there are forty one thousands 47 00:02:55,400 --> 00:03:00,120 Speaker 1: seven to zip codes in the US. I counted them, 48 00:03:00,160 --> 00:03:02,680 Speaker 1: which was tough because somewhere in the fourteen thousand range 49 00:03:02,680 --> 00:03:06,120 Speaker 1: of lost count had to start all over again. Aggregating 50 00:03:06,160 --> 00:03:09,440 Speaker 1: that much information and presenting it in a meaningful way 51 00:03:09,840 --> 00:03:13,079 Speaker 1: is no small feat. But what Cameron's team did next 52 00:03:13,480 --> 00:03:16,320 Speaker 1: was in some ways even more astounding. They took that 53 00:03:16,440 --> 00:03:19,400 Speaker 1: general approach and they applied it to the spread of 54 00:03:19,520 --> 00:03:24,080 Speaker 1: COVID nineteen. Here's my conversation with Cameron Clayton, with only 55 00:03:24,120 --> 00:03:28,000 Speaker 1: an interruption here and there to clarify some things. Cameron, 56 00:03:28,040 --> 00:03:31,160 Speaker 1: thank you so much for taking time to join us 57 00:03:31,160 --> 00:03:34,640 Speaker 1: on the show today. I really appreciate it, absolutely my pleasure. 58 00:03:34,639 --> 00:03:37,840 Speaker 1: It's great to be here now. I think it's pretty 59 00:03:37,840 --> 00:03:41,440 Speaker 1: safe to say this is not an exaggeration that everyone 60 00:03:41,920 --> 00:03:46,320 Speaker 1: has been affected by the COVID nineteen crisis to some extent. 61 00:03:46,440 --> 00:03:48,840 Speaker 1: But I think it can be a little tricky for 62 00:03:48,880 --> 00:03:53,000 Speaker 1: people to get a big picture grasp on the global 63 00:03:53,120 --> 00:03:56,200 Speaker 1: impact of this crisis. Can you kind of speak a 64 00:03:56,240 --> 00:03:59,480 Speaker 1: little bit as to your perspective on the world impact 65 00:03:59,480 --> 00:04:04,920 Speaker 1: of COVID eighteen. Certainly so. As the Weather Channel, our 66 00:04:05,000 --> 00:04:09,880 Speaker 1: mission is to map the atmosphere. So as a company 67 00:04:11,200 --> 00:04:14,680 Speaker 1: and a collective of people, our whole job is to 68 00:04:14,720 --> 00:04:18,400 Speaker 1: try and predict what's going to happen in the atmosphere tomorrow. 69 00:04:19,320 --> 00:04:26,120 Speaker 1: It's almost this impossible science math mother nature problem that 70 00:04:26,120 --> 00:04:28,159 Speaker 1: that we're that we've been working on for a long 71 00:04:28,240 --> 00:04:32,760 Speaker 1: time and make tremendous progress on. Said differently, though, and 72 00:04:32,800 --> 00:04:37,480 Speaker 1: I think this is really where it comes to COVID nineteen. 73 00:04:38,320 --> 00:04:41,680 Speaker 1: At the end of the day, we're making the invisible 74 00:04:41,880 --> 00:04:46,760 Speaker 1: visible and and so doing that, it's easier to make 75 00:04:46,800 --> 00:04:54,360 Speaker 1: decisions When you take something that's intangible, you can't see tangible, 76 00:04:54,640 --> 00:04:56,960 Speaker 1: and then therefore you can make a bit of decision 77 00:04:57,000 --> 00:04:59,280 Speaker 1: as a result of that. And so that's what we 78 00:04:59,400 --> 00:05:02,159 Speaker 1: tried to do with with COVID nineteen. And we've got 79 00:05:02,200 --> 00:05:05,640 Speaker 1: so much inbound interests from our our fans all around 80 00:05:05,680 --> 00:05:09,679 Speaker 1: the world saying, hey, you you make something we can't 81 00:05:09,760 --> 00:05:14,839 Speaker 1: see easier to digest, easier to understand, and easier to 82 00:05:14,880 --> 00:05:17,360 Speaker 1: make decisions on which you please do the same thing 83 00:05:17,440 --> 00:05:20,440 Speaker 1: for COVID nineteen. And so that's what that's what we've 84 00:05:20,480 --> 00:05:24,760 Speaker 1: been doing, is is trying to make the invisible visible. 85 00:05:25,680 --> 00:05:29,120 Speaker 1: And like like you said, I think every single person 86 00:05:29,160 --> 00:05:33,560 Speaker 1: on the planet is impacted by COVID nineteen, just like 87 00:05:33,640 --> 00:05:38,479 Speaker 1: they're impacted by the weather, right. And so as a 88 00:05:38,520 --> 00:05:43,679 Speaker 1: result of our reach in our scale with literally hundreds 89 00:05:43,680 --> 00:05:47,240 Speaker 1: of millions of users around the world, uh, we're able 90 00:05:47,279 --> 00:05:50,599 Speaker 1: to also communicate with them in a way that they're 91 00:05:50,640 --> 00:05:54,240 Speaker 1: familiar with as part of their daily habit already. H 92 00:05:54,480 --> 00:05:56,719 Speaker 1: And so you know, we'll be able to try and 93 00:05:56,720 --> 00:06:01,640 Speaker 1: provide trusted data to our fans around the world. You know, 94 00:06:01,680 --> 00:06:07,520 Speaker 1: as you point out, Cameron, contextualizing data is really critically 95 00:06:07,520 --> 00:06:10,240 Speaker 1: important for people to be able to make use of it, right, 96 00:06:10,320 --> 00:06:15,359 Speaker 1: to be able to take something that is conceptually this 97 00:06:15,560 --> 00:06:18,200 Speaker 1: huge thing, but it's very hard for us to boil 98 00:06:18,279 --> 00:06:22,279 Speaker 1: that down into actionable things that we can do as people. 99 00:06:23,120 --> 00:06:26,120 Speaker 1: I think one thing that kind of helps again, you know, 100 00:06:26,279 --> 00:06:28,400 Speaker 1: as humans were not really good at dealing with big 101 00:06:28,480 --> 00:06:32,680 Speaker 1: numbers just on our own. One thing that really helps 102 00:06:32,880 --> 00:06:36,440 Speaker 1: is is kind of anchoring things to personal experience as well. 103 00:06:36,520 --> 00:06:42,240 Speaker 1: So before we jump into all the background on IBM 104 00:06:42,279 --> 00:06:47,080 Speaker 1: and the weather companies work with UH, the technologies to 105 00:06:47,200 --> 00:06:50,240 Speaker 1: track COVID nineteen, I was wondering, can you talk a 106 00:06:50,279 --> 00:06:53,840 Speaker 1: little bit about how this crisis has affected you personally 107 00:06:53,960 --> 00:06:57,479 Speaker 1: so far? Oh? Yeah, sure, I think. Uh So, I 108 00:06:57,480 --> 00:07:01,400 Speaker 1: have four children, I'm married with with all kids, UH 109 00:07:01,440 --> 00:07:05,840 Speaker 1: and a dog, and about two and a half weeks ago, 110 00:07:06,360 --> 00:07:10,640 Speaker 1: all our kids, you know, came home and have been 111 00:07:10,880 --> 00:07:16,560 Speaker 1: on sort of Zoom and Google classroom, you know, with 112 00:07:16,960 --> 00:07:20,520 Speaker 1: x is all all day every day, along with with 113 00:07:20,560 --> 00:07:25,080 Speaker 1: me doing the same thing on UH working here from 114 00:07:25,080 --> 00:07:31,040 Speaker 1: the house. And you know, I think we're thankfully safe 115 00:07:31,080 --> 00:07:35,040 Speaker 1: and healthy and doing well. But Kevin fever is a 116 00:07:35,080 --> 00:07:39,680 Speaker 1: real issue, especially with two boys. It's been a big 117 00:07:39,760 --> 00:07:43,080 Speaker 1: change in the sense of we often ask, you know, 118 00:07:43,440 --> 00:07:45,120 Speaker 1: how's your day going. Are you having a good day? 119 00:07:46,560 --> 00:07:49,200 Speaker 1: And I think many of us are still asking that question, 120 00:07:50,560 --> 00:07:53,560 Speaker 1: but we're not used to the answer being actually no, 121 00:07:53,720 --> 00:07:58,480 Speaker 1: I'm not. I'm really not okay. Uh, And and we're 122 00:07:58,520 --> 00:08:00,480 Speaker 1: getting a few of those answers now. I had a 123 00:08:00,480 --> 00:08:04,760 Speaker 1: couple of those onswers this morning. And so how we 124 00:08:04,840 --> 00:08:08,760 Speaker 1: rallied together as a community, how we relied together ash 125 00:08:09,240 --> 00:08:15,000 Speaker 1: as companies, how we relied together as humans really really matters. 126 00:08:15,280 --> 00:08:18,560 Speaker 1: And I'm certainly super proud of the way our teams 127 00:08:18,640 --> 00:08:22,800 Speaker 1: rallied together and and IBMS relied together. Were also super 128 00:08:22,840 --> 00:08:27,120 Speaker 1: proud about our clients and partners and neighbors and uh 129 00:08:27,160 --> 00:08:34,040 Speaker 1: and others like Uh. We had a block street party 130 00:08:34,040 --> 00:08:38,160 Speaker 1: where everybody was in their cause. They decorated their cause 131 00:08:38,240 --> 00:08:41,000 Speaker 1: and drove by the five old people that live in 132 00:08:41,000 --> 00:08:43,800 Speaker 1: the street and who are in the window to try 133 00:08:43,840 --> 00:08:46,960 Speaker 1: and share them up. So this just amazing touches of 134 00:08:47,040 --> 00:08:49,960 Speaker 1: humanity happening. That's that's really cool to see. And that's 135 00:08:49,960 --> 00:08:54,480 Speaker 1: a great way to segue into talking about what the 136 00:08:54,520 --> 00:08:57,800 Speaker 1: Weather Company, what IBM are doing and in a way 137 00:08:57,840 --> 00:09:02,360 Speaker 1: to give people more tool so that they can make 138 00:09:02,480 --> 00:09:06,520 Speaker 1: decisions that are critically important for themselves and for the 139 00:09:06,559 --> 00:09:10,760 Speaker 1: people around them, whether it's relatives, coworkers, loved ones, just 140 00:09:10,840 --> 00:09:14,760 Speaker 1: strangers on the street. We all have this responsibility So 141 00:09:14,960 --> 00:09:18,840 Speaker 1: let's talk kind of in general terms what exactly you 142 00:09:18,880 --> 00:09:23,319 Speaker 1: guys are doing in an effort to give people these tools. 143 00:09:23,320 --> 00:09:26,120 Speaker 1: So kind of from a very high level, as I 144 00:09:26,200 --> 00:09:32,199 Speaker 1: understand it, you're pulling data that's localized to specific regions, 145 00:09:32,920 --> 00:09:36,240 Speaker 1: contextualizing that and presenting it in a way that's easily 146 00:09:36,280 --> 00:09:40,679 Speaker 1: digestible so that people have and up to date understanding 147 00:09:40,800 --> 00:09:43,720 Speaker 1: what's going on in their communities. Is that more or 148 00:09:43,760 --> 00:09:49,520 Speaker 1: less correct? Yeah, that's that's right. We're we're trying to 149 00:09:49,559 --> 00:09:58,880 Speaker 1: make this invisible virus contextual and localized. The important thing 150 00:09:58,880 --> 00:10:00,959 Speaker 1: when you do that is it has to be from 151 00:10:00,960 --> 00:10:04,200 Speaker 1: a trusted source. So the sources of the data have 152 00:10:04,440 --> 00:10:12,280 Speaker 1: to be really high integrity, and integrity beats all other 153 00:10:14,520 --> 00:10:18,360 Speaker 1: aspects of data. Right. It's more important than timeliness. It's 154 00:10:18,400 --> 00:10:22,200 Speaker 1: more important than you know, how large the field is. 155 00:10:22,679 --> 00:10:25,559 Speaker 1: The most important thing is it's a trusted data source. 156 00:10:25,600 --> 00:10:30,200 Speaker 1: And so all the data that we're collecting and displaying 157 00:10:30,360 --> 00:10:36,520 Speaker 1: in our solution is from local government, state government, or 158 00:10:36,520 --> 00:10:41,320 Speaker 1: federal government sources. So we're not we're not doing crowdsourcing. 159 00:10:41,400 --> 00:10:45,840 Speaker 1: We're not pulling in social media opinions or things like that, 160 00:10:45,880 --> 00:10:48,200 Speaker 1: although those things have their place and are helpful in 161 00:10:48,240 --> 00:10:52,360 Speaker 1: their own way. This is really about about aggregating and 162 00:10:52,360 --> 00:10:56,520 Speaker 1: collecting all the local sources so that you can see 163 00:10:56,520 --> 00:10:59,880 Speaker 1: what's going on in your community, right And I think 164 00:10:59,880 --> 00:11:03,800 Speaker 1: that is really important. You know, I have a have 165 00:11:03,960 --> 00:11:09,120 Speaker 1: a uncle that was in Louisiana. He's a great guy, 166 00:11:09,520 --> 00:11:11,800 Speaker 1: but he's a free spurt. He does his own thing. 167 00:11:12,000 --> 00:11:15,960 Speaker 1: He lives his own way, and he doesn't really listen 168 00:11:16,480 --> 00:11:19,640 Speaker 1: that well too, friends or family. He's going to do 169 00:11:19,760 --> 00:11:23,439 Speaker 1: his own thing. And so, you know, as we started 170 00:11:23,440 --> 00:11:26,000 Speaker 1: building building this tool and I started seeing the data 171 00:11:26,080 --> 00:11:27,679 Speaker 1: that was going on there, I was able to use 172 00:11:27,720 --> 00:11:30,880 Speaker 1: it to show him that, you know what what and 173 00:11:30,880 --> 00:11:34,880 Speaker 1: this was you know, last weekend and Monday. That would 174 00:11:34,920 --> 00:11:39,240 Speaker 1: show him, Look, there's really some really serious outbreaks going 175 00:11:39,280 --> 00:11:43,199 Speaker 1: on in Louisiana right now. This is really accelerating. Uh. 176 00:11:43,280 --> 00:11:46,040 Speaker 1: You might not take this seriously, but your people in 177 00:11:46,080 --> 00:11:50,560 Speaker 1: your community really need you to to listen to the 178 00:11:50,600 --> 00:11:55,600 Speaker 1: local authorities and stay put right, stay inside and and 179 00:11:55,679 --> 00:11:59,360 Speaker 1: behave um. And so I think, you know, you take 180 00:11:59,480 --> 00:12:04,760 Speaker 1: my example and extrapolate that out across Every American has 181 00:12:04,800 --> 00:12:09,200 Speaker 1: a story somewhat similar to had I think, Uh, and 182 00:12:09,240 --> 00:12:12,240 Speaker 1: so we're all looking and seeing what's happening to friends 183 00:12:12,240 --> 00:12:16,680 Speaker 1: and family around the world and across the United States, 184 00:12:16,720 --> 00:12:21,600 Speaker 1: and uh and you know, staying home, stopping the spread 185 00:12:22,200 --> 00:12:25,840 Speaker 1: ultimately saves lives, right it is that that's simple right now, 186 00:12:26,920 --> 00:12:31,000 Speaker 1: and be able to show people what's happening in their community. 187 00:12:31,040 --> 00:12:34,560 Speaker 1: We're doing in three ways. So we're showing them the data, 188 00:12:34,679 --> 00:12:39,760 Speaker 1: how many people have been tested and shown as positive 189 00:12:39,920 --> 00:12:43,520 Speaker 1: in their county, how many deaths have been recorded. We're 190 00:12:43,559 --> 00:12:47,200 Speaker 1: showing the trend line for that county day over day. 191 00:12:47,280 --> 00:12:50,319 Speaker 1: So is it getting steeper, is the curve getting steeper 192 00:12:50,400 --> 00:12:53,000 Speaker 1: or is it plateau ng or hopefully at some point 193 00:12:53,000 --> 00:12:56,000 Speaker 1: here we'll see it declining in places, but right now 194 00:12:56,040 --> 00:13:00,160 Speaker 1: we're not. It's still on the upswing. Then we we 195 00:13:00,240 --> 00:13:02,560 Speaker 1: let them choose between their state view or their county 196 00:13:02,640 --> 00:13:06,920 Speaker 1: view of that trained analysis. Then we're making it tangible 197 00:13:06,960 --> 00:13:09,160 Speaker 1: on a map. Right What we found with with weather 198 00:13:09,240 --> 00:13:12,360 Speaker 1: and we're doing now with the COVID virus is plotting 199 00:13:12,360 --> 00:13:14,800 Speaker 1: it on a map so you can actually see it 200 00:13:14,840 --> 00:13:19,200 Speaker 1: in a geographical context. So your county versus the county 201 00:13:19,240 --> 00:13:22,600 Speaker 1: next to you, and across the entire United States, every 202 00:13:22,640 --> 00:13:27,520 Speaker 1: county that that's producing data we're ingesting. There are some 203 00:13:27,640 --> 00:13:32,160 Speaker 1: places that aren't producing data, particularly in some rural counties, 204 00:13:32,160 --> 00:13:36,079 Speaker 1: but for the most part, the major population centers are 205 00:13:36,080 --> 00:13:39,640 Speaker 1: all producing this data, and you can see what's going on, right, 206 00:13:39,720 --> 00:13:44,319 Speaker 1: and I think we've seeing people make better decisions as 207 00:13:44,320 --> 00:13:47,920 Speaker 1: a result of it, and that's the whole purpose. That's 208 00:13:47,920 --> 00:13:50,959 Speaker 1: why we did this. One of the things you mentioned 209 00:13:51,120 --> 00:13:53,840 Speaker 1: was about the trusted sources. I'm glad you went into 210 00:13:53,880 --> 00:13:57,240 Speaker 1: that and explained where you're pulling information from, because obviously 211 00:13:57,320 --> 00:13:59,880 Speaker 1: we're right now in an era where there's missing for 212 00:14:00,080 --> 00:14:05,439 Speaker 1: nation and even disinformation running rampant online. So it's good 213 00:14:05,440 --> 00:14:08,920 Speaker 1: to be able to point to a tool and actually 214 00:14:08,960 --> 00:14:14,040 Speaker 1: know where where's this tool pulling information from. And it's 215 00:14:14,040 --> 00:14:16,200 Speaker 1: also really interesting to me that it's taking a very 216 00:14:16,240 --> 00:14:18,959 Speaker 1: similar approach to what the Weather Company has done. I 217 00:14:19,040 --> 00:14:23,240 Speaker 1: think everyone has had the experience of using either the 218 00:14:23,320 --> 00:14:27,200 Speaker 1: app or the website and looking at, you know, weather 219 00:14:27,280 --> 00:14:31,320 Speaker 1: forecasts for specific zip codes and so kind of taking 220 00:14:31,360 --> 00:14:35,320 Speaker 1: that same thinking and applying that to the COVID nineteen 221 00:14:36,040 --> 00:14:42,040 Speaker 1: outbreak is really interesting to me too. I'm wondering, um, 222 00:14:42,080 --> 00:14:47,320 Speaker 1: with that in mind, uh, are the sources you're pulling from? 223 00:14:47,360 --> 00:14:50,000 Speaker 1: You know there are local, state and federal government. Are 224 00:14:50,000 --> 00:14:53,240 Speaker 1: they in different formats? Because to me, that would present 225 00:14:53,240 --> 00:14:57,800 Speaker 1: itself an enormous challenge because machines. Humans are really good 226 00:14:57,840 --> 00:15:01,640 Speaker 1: at pulling information from whatever format we encounter, machines typically 227 00:15:01,840 --> 00:15:06,600 Speaker 1: are not. So this is actually the heart of the 228 00:15:06,640 --> 00:15:13,080 Speaker 1: technology challenge we've had and and we've basically used IBM 229 00:15:13,160 --> 00:15:18,000 Speaker 1: S Watson AI tools to be able to ingest this 230 00:15:18,280 --> 00:15:23,200 Speaker 1: data from literally thousands and thousands of data sources, all 231 00:15:23,280 --> 00:15:27,000 Speaker 1: in different formats, right, everything from a PDF that's actually 232 00:15:27,040 --> 00:15:33,560 Speaker 1: an image like a photograph essentially two HTML files to 233 00:15:34,440 --> 00:15:38,600 Speaker 1: or everything else in between. And every single counties website 234 00:15:38,640 --> 00:15:43,720 Speaker 1: is built differently. There's no technology standard that's been applied. UH. 235 00:15:43,760 --> 00:15:46,440 Speaker 1: And so you know, I think that one of the 236 00:15:46,600 --> 00:15:50,160 Speaker 1: one of the amazing things from a technology only perspective 237 00:15:51,120 --> 00:15:54,960 Speaker 1: in this is that the Watson's AI was able to 238 00:15:55,800 --> 00:16:00,280 Speaker 1: INGI and learn all of these different formats based really 239 00:16:00,320 --> 00:16:04,720 Speaker 1: on its own and ing all this data from all 240 00:16:04,760 --> 00:16:08,440 Speaker 1: these different technology types, uh and put it into a 241 00:16:08,480 --> 00:16:11,760 Speaker 1: standardized format that we could then produce and and and 242 00:16:11,880 --> 00:16:15,640 Speaker 1: show in our websites and our acts and and that 243 00:16:16,000 --> 00:16:20,880 Speaker 1: whole training from when we started to when we had 244 00:16:20,920 --> 00:16:24,680 Speaker 1: the first standardized file was thirty six hours, so we're 245 00:16:24,720 --> 00:16:28,840 Speaker 1: not talking about two weeks or or anything. This is like, 246 00:16:29,120 --> 00:16:33,720 Speaker 1: you know, a data hoff to learn and train and 247 00:16:33,800 --> 00:16:36,960 Speaker 1: collect the data and put it into a standardized format. 248 00:16:37,600 --> 00:16:41,080 Speaker 1: Cameron mentioned training the system on data, and some of 249 00:16:41,120 --> 00:16:44,760 Speaker 1: you might wonder what that actually means. It's a term 250 00:16:45,040 --> 00:16:48,640 Speaker 1: used in machine learning in which engineers feed information into 251 00:16:48,680 --> 00:16:52,720 Speaker 1: computer models in an effort to produce particular results. And 252 00:16:52,760 --> 00:16:55,920 Speaker 1: typically you start out doing this by knowing what results 253 00:16:56,000 --> 00:16:58,960 Speaker 1: you want ahead of time when you're first training the system. 254 00:16:59,320 --> 00:17:01,920 Speaker 1: That way, you can see what comes out of the model, 255 00:17:02,160 --> 00:17:04,560 Speaker 1: you compare it to what you expected to see, and 256 00:17:04,600 --> 00:17:06,720 Speaker 1: if the two don't match up, you can go back 257 00:17:06,760 --> 00:17:10,200 Speaker 1: and tweak the model. So, for example, let's say you're 258 00:17:10,240 --> 00:17:14,159 Speaker 1: training an image recognition computer model to recognize pictures of 259 00:17:14,400 --> 00:17:17,960 Speaker 1: fire hydrants. You might feed a ton of images of 260 00:17:18,000 --> 00:17:20,720 Speaker 1: fire hydrants to the model. Then you might introduce a 261 00:17:20,760 --> 00:17:24,360 Speaker 1: mixture of images that include fire hydrants and other stuff, 262 00:17:24,680 --> 00:17:27,520 Speaker 1: seeing if the model can tell the difference sorting the 263 00:17:27,560 --> 00:17:31,119 Speaker 1: images properly. So you analyze those results and if they're good, 264 00:17:31,160 --> 00:17:34,399 Speaker 1: you keep going. You might use millions of points of 265 00:17:34,520 --> 00:17:37,800 Speaker 1: data to train your model. Over time, and often this 266 00:17:37,880 --> 00:17:42,760 Speaker 1: is a painstakingly slow process, particularly when engineers need to 267 00:17:42,760 --> 00:17:45,720 Speaker 1: step in and change something about the model that isn't 268 00:17:45,800 --> 00:17:49,560 Speaker 1: quite working. Once you are able to get reliable results 269 00:17:49,600 --> 00:17:52,600 Speaker 1: from the model, you can put it to more practical use, 270 00:17:52,880 --> 00:17:56,440 Speaker 1: though it may require future tweaks when the model encounters 271 00:17:56,600 --> 00:18:00,399 Speaker 1: something well outside the norm. Okay, let's get back to 272 00:18:00,440 --> 00:18:03,880 Speaker 1: my conversation with Cameron Clayton, the general manager of IBM 273 00:18:03,920 --> 00:18:07,560 Speaker 1: Cloud Ecosystem and of the Weather Company and IBM business. 274 00:18:08,200 --> 00:18:12,240 Speaker 1: You're talking about things like natural language processing, being able 275 00:18:12,280 --> 00:18:15,600 Speaker 1: to access all these different styles of data, whether it's 276 00:18:15,640 --> 00:18:18,400 Speaker 1: an image file or it's something that can be searched 277 00:18:19,080 --> 00:18:23,160 Speaker 1: with an algorithm, and then taking the meaning of that 278 00:18:23,280 --> 00:18:25,320 Speaker 1: not just the fact that the data is there. You 279 00:18:25,359 --> 00:18:28,040 Speaker 1: have to the MA gene has to understand what the 280 00:18:28,080 --> 00:18:31,479 Speaker 1: meaning is in order to put it into its model 281 00:18:31,560 --> 00:18:36,240 Speaker 1: and then present it. These are really tough engineering challenges 282 00:18:36,440 --> 00:18:40,719 Speaker 1: in computer AI in general, and so to see that 283 00:18:40,800 --> 00:18:46,639 Speaker 1: application being put so quickly in place, exactly can you 284 00:18:46,680 --> 00:18:48,880 Speaker 1: give us an idea of how long it took too, 285 00:18:49,760 --> 00:18:52,800 Speaker 1: from the point of ideation to the point of implementation 286 00:18:53,480 --> 00:18:56,480 Speaker 1: that you guys went through in order to produce this. 287 00:18:57,040 --> 00:19:01,680 Speaker 1: So the timeline from when we started UH this getting 288 00:19:01,720 --> 00:19:06,520 Speaker 1: it getting it live was probably about ten days. We 289 00:19:06,640 --> 00:19:09,960 Speaker 1: got inbound interest from our fans around the world. We 290 00:19:10,080 --> 00:19:13,760 Speaker 1: then mocked up how we wanted to present the data 291 00:19:13,880 --> 00:19:17,480 Speaker 1: from a visual point of view. We then, in parallel, 292 00:19:17,520 --> 00:19:20,800 Speaker 1: we're rapidly trying to source all these local data sources. 293 00:19:20,800 --> 00:19:22,760 Speaker 1: We wanted it from the very beginning of as local 294 00:19:22,800 --> 00:19:26,960 Speaker 1: as possible UH, and we rapidly realized this was not 295 00:19:27,040 --> 00:19:30,280 Speaker 1: something that you could do manually. You had to do 296 00:19:30,400 --> 00:19:34,080 Speaker 1: this UH in an automated way at scale, and so 297 00:19:34,080 --> 00:19:37,760 Speaker 1: that's when we brought in Watson and AI to help. 298 00:19:39,000 --> 00:19:42,760 Speaker 1: Thirty six hours later we had data. We had, you know, 299 00:19:43,320 --> 00:19:45,800 Speaker 1: a format, but then we really spent a lot of 300 00:19:45,840 --> 00:19:50,080 Speaker 1: time testing right to make sure that the data was correct, 301 00:19:50,160 --> 00:19:53,560 Speaker 1: to make sure that you know, when when you know, 302 00:19:53,760 --> 00:19:58,080 Speaker 1: Governor Cumos speaks in New York at eleven thirty every morning, 303 00:19:58,359 --> 00:20:00,240 Speaker 1: within a few minutes, we were able to up dat 304 00:20:00,320 --> 00:20:03,359 Speaker 1: the data based on the numbers he's sharing with the media, 305 00:20:03,440 --> 00:20:07,919 Speaker 1: but on the New York State website, for example. And 306 00:20:07,960 --> 00:20:12,440 Speaker 1: so there's all these different factors both technology and format 307 00:20:12,800 --> 00:20:14,840 Speaker 1: that we had to take into account, but also just 308 00:20:15,480 --> 00:20:20,000 Speaker 1: testing this. The second is we always, due to our 309 00:20:20,040 --> 00:20:22,520 Speaker 1: scale and the number of users we have, we have 310 00:20:22,600 --> 00:20:26,040 Speaker 1: to load test and make sure that technologically we can 311 00:20:26,080 --> 00:20:29,720 Speaker 1: deliver to the millions of people that use our properties 312 00:20:29,720 --> 00:20:34,480 Speaker 1: and platforms. And so we've got this uh live a 313 00:20:34,520 --> 00:20:39,720 Speaker 1: few days ago and beginning to end is probably seven days. 314 00:20:40,600 --> 00:20:43,800 Speaker 1: There's a remarkable achievement. I mean, you're you're talking about 315 00:20:43,920 --> 00:20:47,360 Speaker 1: everything from coming up with the idea to the design 316 00:20:47,440 --> 00:20:50,240 Speaker 1: saying what do we actually do to make this possible? 317 00:20:50,320 --> 00:20:56,080 Speaker 1: To even even something that seems to someone like me simple, 318 00:20:56,200 --> 00:21:00,560 Speaker 1: like how do you present that information to the consumers 319 00:21:00,640 --> 00:21:03,639 Speaker 1: so in a way that makes sense, Like we we 320 00:21:03,720 --> 00:21:05,600 Speaker 1: only see it at the end, right, we see it 321 00:21:05,640 --> 00:21:07,959 Speaker 1: after you've made all those decisions, and we look at 322 00:21:08,000 --> 00:21:10,040 Speaker 1: it and we say, oh, yeah, of course that makes sense. 323 00:21:11,080 --> 00:21:14,359 Speaker 1: But you have to get there first on the design side. 324 00:21:14,480 --> 00:21:17,000 Speaker 1: And I think a lot of people don't understand or 325 00:21:17,040 --> 00:21:19,760 Speaker 1: appreciate how challenging that part of technology is to not 326 00:21:19,920 --> 00:21:21,760 Speaker 1: just the making it work, but making it work in 327 00:21:21,800 --> 00:21:26,680 Speaker 1: a way that is ultimately useful to the end consumer too, 328 00:21:26,720 --> 00:21:31,520 Speaker 1: so that yeah, it doesn't just work, it works for you. Um, 329 00:21:31,640 --> 00:21:33,720 Speaker 1: and we were you know, one of the other things 330 00:21:33,720 --> 00:21:36,160 Speaker 1: I wanted to talk about was that this design process 331 00:21:36,240 --> 00:21:39,639 Speaker 1: not only was it rapid, but obviously it was unusual 332 00:21:39,800 --> 00:21:43,719 Speaker 1: in that you weren't all working in the same workspace 333 00:21:43,760 --> 00:21:47,560 Speaker 1: at the same time because of the concerns, the health concerns. 334 00:21:47,600 --> 00:21:50,160 Speaker 1: So what was that Like, how did your team respond 335 00:21:50,320 --> 00:21:56,600 Speaker 1: to working in a decentralized approach. So I gotta say 336 00:21:56,640 --> 00:22:02,200 Speaker 1: the team really has leaned in here hod like countless 337 00:22:02,680 --> 00:22:07,400 Speaker 1: almost before. We had sixty people touched this project. Over 338 00:22:07,440 --> 00:22:12,600 Speaker 1: the course of the week. I would say fifty five 339 00:22:12,680 --> 00:22:16,679 Speaker 1: of the sixty worked multiple twenty four hour days in 340 00:22:16,760 --> 00:22:21,399 Speaker 1: that time period. Uh And so sleep deprivation was a 341 00:22:21,440 --> 00:22:25,800 Speaker 1: real issue as well, right, But but we have we 342 00:22:25,840 --> 00:22:29,679 Speaker 1: have great tools, you know, whether it's video conferencing and 343 00:22:29,720 --> 00:22:36,160 Speaker 1: collaboration tools where we can actually iterate and design products remotely, 344 00:22:36,480 --> 00:22:38,560 Speaker 1: but all on the same screen at the same time. 345 00:22:39,359 --> 00:22:42,160 Speaker 1: So our design is ractually able to you know, one 346 00:22:42,160 --> 00:22:44,400 Speaker 1: of them draws a line and the other one can 347 00:22:44,520 --> 00:22:48,800 Speaker 1: raise it as as they're drawing it. Uh. And so 348 00:22:49,640 --> 00:22:53,359 Speaker 1: that was both fun and challenging at the same time. 349 00:22:53,680 --> 00:22:56,760 Speaker 1: I would say the goal of the product was to 350 00:22:56,840 --> 00:23:02,960 Speaker 1: make it clean and simple, so it is digestible, and 351 00:23:04,160 --> 00:23:06,560 Speaker 1: I think you know, we can always add more data 352 00:23:06,600 --> 00:23:10,919 Speaker 1: fields over time and and add more information, but the 353 00:23:11,000 --> 00:23:15,360 Speaker 1: real purpose of this was to help people understand what's 354 00:23:15,359 --> 00:23:17,560 Speaker 1: happening in the community so that they would stay home, 355 00:23:17,840 --> 00:23:21,000 Speaker 1: stop the spread, and ultimately save lives. Right. Making that 356 00:23:21,600 --> 00:23:24,879 Speaker 1: invisible visible was was the goal of this UM and 357 00:23:24,920 --> 00:23:28,640 Speaker 1: so I think we've we've achieved that, but it's only 358 00:23:28,680 --> 00:23:32,600 Speaker 1: through hard work of a small group of folks that 359 00:23:33,640 --> 00:23:41,080 Speaker 1: that you know, really worked hard for seven days without sleep. Wow, 360 00:23:41,160 --> 00:23:44,440 Speaker 1: I mean, that's that's incredible. So, so they're working hard 361 00:23:44,640 --> 00:23:47,960 Speaker 1: putting this all together. Meanwhile, you've got the AI and 362 00:23:48,040 --> 00:23:51,400 Speaker 1: tools working hard in the background to synthesize all that data. 363 00:23:51,440 --> 00:23:53,639 Speaker 1: Can you talk a little bit more about the specific 364 00:23:54,080 --> 00:23:57,520 Speaker 1: technologies running in the background. We've mentioned Watson, but is 365 00:23:57,560 --> 00:24:00,800 Speaker 1: there anything else along with that. This is a whole 366 00:24:01,040 --> 00:24:05,199 Speaker 1: whole variety of things that that live in the background 367 00:24:05,240 --> 00:24:08,920 Speaker 1: that make this possible and so and almost all of 368 00:24:08,960 --> 00:24:10,960 Speaker 1: those things are things that we don't think about as 369 00:24:11,040 --> 00:24:15,680 Speaker 1: consumers of whether dot Com or our mobile apps. So 370 00:24:15,920 --> 00:24:20,800 Speaker 1: one is just the cloud infrastructure itself. The fact that 371 00:24:22,040 --> 00:24:27,639 Speaker 1: you know on Monday night when we launch, you know, 372 00:24:27,920 --> 00:24:30,520 Speaker 1: we were alive for like three hours. We had about 373 00:24:30,520 --> 00:24:35,679 Speaker 1: a million users in three hours used the property. On Tuesday, 374 00:24:35,840 --> 00:24:39,600 Speaker 1: we had three and a half million users start using 375 00:24:39,600 --> 00:24:44,560 Speaker 1: the property. On Wednesday it was up to like five 376 00:24:44,640 --> 00:24:47,959 Speaker 1: and a half or six million users. You can't scale 377 00:24:48,080 --> 00:24:51,879 Speaker 1: like that, and those are each individual, unique visitors, some 378 00:24:51,920 --> 00:24:55,840 Speaker 1: of them visited multiple times and checks, you know, tens 379 00:24:55,840 --> 00:24:59,440 Speaker 1: of locations around the country for friends and family. Uh. 380 00:24:59,480 --> 00:25:01,800 Speaker 1: And you can't have that kind of scale without having 381 00:25:01,800 --> 00:25:05,760 Speaker 1: a really robust cloud infrastructure behind it. And so uh, 382 00:25:06,000 --> 00:25:10,640 Speaker 1: IBM Cloud was has been and continues to be instrumental 383 00:25:10,960 --> 00:25:15,640 Speaker 1: and sort of invisibly in the background, keeping the infrastructure alive. 384 00:25:16,240 --> 00:25:17,520 Speaker 1: And one of the one of the things about that, 385 00:25:17,560 --> 00:25:19,639 Speaker 1: I'll take a quick story on it, that was super 386 00:25:19,680 --> 00:25:23,000 Speaker 1: impressive to me. And I see this these kinds of 387 00:25:23,280 --> 00:25:27,720 Speaker 1: launches fairly often with our products. But as we launched 388 00:25:27,720 --> 00:25:30,879 Speaker 1: on Monday night, what I was not used to seeing 389 00:25:31,040 --> 00:25:37,439 Speaker 1: was security h automatically being implemented. And so what I 390 00:25:37,440 --> 00:25:39,640 Speaker 1: mean by that is we were actually having a denial 391 00:25:39,680 --> 00:25:43,280 Speaker 1: of service attack, so hackers, we're trying to hack into 392 00:25:43,359 --> 00:25:47,639 Speaker 1: weather dot com as we were launching the side, and 393 00:25:47,680 --> 00:25:52,480 Speaker 1: because of the security elements of IBM Cloud, it didn't 394 00:25:52,520 --> 00:25:55,160 Speaker 1: stop out. I say to our team, should we stop, 395 00:25:55,359 --> 00:25:58,520 Speaker 1: like this is seems like a really big deal, and 396 00:25:58,560 --> 00:26:01,800 Speaker 1: they were like, no, this is totally fine. We deal 397 00:26:01,840 --> 00:26:04,919 Speaker 1: with this all the time, all the securities you know 398 00:26:05,240 --> 00:26:07,880 Speaker 1: in place. I don't say that to try and bring 399 00:26:07,920 --> 00:26:13,640 Speaker 1: on anymore uh challenges. We don't definitely don't want that. 400 00:26:14,359 --> 00:26:16,320 Speaker 1: But it was really impressive to me to see how 401 00:26:16,400 --> 00:26:20,360 Speaker 1: the cloud has like got these capabilities built into it. Natively, 402 00:26:21,160 --> 00:26:23,120 Speaker 1: the man out team didn't have to worry. We don't 403 00:26:23,119 --> 00:26:27,119 Speaker 1: have to stop or delay our launch because we were having, uh, 404 00:26:27,200 --> 00:26:30,560 Speaker 1: you know, a security incident. We were able to deploy 405 00:26:30,760 --> 00:26:34,480 Speaker 1: seamlessly without interruptions. That's what that's one example is sort 406 00:26:34,520 --> 00:26:37,040 Speaker 1: of something that you don't think about when you use 407 00:26:37,040 --> 00:26:39,800 Speaker 1: a website or you lose use a mobile app. But 408 00:26:39,840 --> 00:26:42,199 Speaker 1: it's how important the cloud infrastructure behind it is and 409 00:26:42,200 --> 00:26:46,359 Speaker 1: how secure it is that really matters. Cameron mentioned a 410 00:26:46,400 --> 00:26:50,480 Speaker 1: denial of service attack or DNS. This is a common 411 00:26:50,520 --> 00:26:53,800 Speaker 1: attack that the Internet makes possible. There are multiple ways 412 00:26:53,840 --> 00:26:57,600 Speaker 1: hackers carry out such attacks, but here's a quick example. 413 00:26:58,200 --> 00:27:01,480 Speaker 1: When you get down to basic commun nucations across the Internet, 414 00:27:01,560 --> 00:27:05,199 Speaker 1: it's all about machines making contact with one another to 415 00:27:05,320 --> 00:27:09,040 Speaker 1: initiate communication. One machine will send a quick message a 416 00:27:09,200 --> 00:27:12,119 Speaker 1: pay to another one, which will then respond to the 417 00:27:12,160 --> 00:27:15,560 Speaker 1: first computer. It's kind of like someone ringing your doorbell. 418 00:27:16,000 --> 00:27:19,960 Speaker 1: Imagine that every time someone rang your doorbell, you absolutely 419 00:27:20,200 --> 00:27:22,720 Speaker 1: had to go to your door to answer it. You 420 00:27:22,800 --> 00:27:26,000 Speaker 1: have no other options. And I know I find such 421 00:27:26,000 --> 00:27:29,120 Speaker 1: a hypothetical world horrifying too, but that's kind of how 422 00:27:29,160 --> 00:27:32,760 Speaker 1: the internet works. Now, imagine your doorbell rings, You go 423 00:27:32,800 --> 00:27:37,280 Speaker 1: to the door, you open it, No one's there, darn kids. 424 00:27:37,680 --> 00:27:39,800 Speaker 1: So you close the door and you turn around to 425 00:27:39,800 --> 00:27:41,800 Speaker 1: go back to doing whatever it was you were doing before. 426 00:27:42,440 --> 00:27:45,160 Speaker 1: But then the doorbell rings again, so you turn around, 427 00:27:45,640 --> 00:27:49,560 Speaker 1: you open the door, and again no one's there. Now 428 00:27:49,680 --> 00:27:51,760 Speaker 1: you close the door again, and as soon as the 429 00:27:51,800 --> 00:27:54,920 Speaker 1: door closes, the doorbell rings, so you have to answer 430 00:27:54,960 --> 00:27:57,640 Speaker 1: the door again. Remember you always have to answer the door, 431 00:27:58,240 --> 00:28:01,720 Speaker 1: so you're stuck answer the door over and over. You 432 00:28:01,760 --> 00:28:06,000 Speaker 1: can't get anything else done. That's kind of like a 433 00:28:06,040 --> 00:28:09,199 Speaker 1: basic denial of service attack. Hackers will set up a 434 00:28:09,200 --> 00:28:13,240 Speaker 1: computer or a network of computers, sometimes an entire bot 435 00:28:13,280 --> 00:28:16,160 Speaker 1: net of computers that was created through the spread of malware, 436 00:28:16,400 --> 00:28:19,400 Speaker 1: but that's a matter for another episode. And they'll send 437 00:28:19,400 --> 00:28:22,560 Speaker 1: out a series of pings to a particular web server, 438 00:28:22,960 --> 00:28:25,679 Speaker 1: and the goal is to overload that server so that 439 00:28:25,760 --> 00:28:28,919 Speaker 1: it can't get anything else done, perhaps even causing the 440 00:28:28,960 --> 00:28:32,440 Speaker 1: server to crash. Now, as I mentioned, there are a 441 00:28:32,520 --> 00:28:36,119 Speaker 1: lot of variations on this basic idea, and companies have 442 00:28:36,200 --> 00:28:40,040 Speaker 1: had to find innovative ways to counteract those tactics. Big 443 00:28:40,040 --> 00:28:44,560 Speaker 1: companies like IBM spend countless hours developing techniques to detect 444 00:28:44,680 --> 00:28:47,280 Speaker 1: and nullify d n S attacks in an effort to 445 00:28:47,320 --> 00:28:51,640 Speaker 1: make their services stable and dependable. You're talking about the 446 00:28:51,920 --> 00:28:55,160 Speaker 1: two big ones, security and scale. Like if you if 447 00:28:55,200 --> 00:28:58,040 Speaker 1: you need something that's actually going to reach everybody in 448 00:28:58,080 --> 00:29:01,120 Speaker 1: the United States, then it's not something that you can 449 00:29:01,160 --> 00:29:03,840 Speaker 1: look at to gradually scale up the way we see, 450 00:29:03,920 --> 00:29:07,000 Speaker 1: you know, your typical startup where they'll launch in a 451 00:29:07,120 --> 00:29:10,480 Speaker 1: very localized area and then gradually build out from there. 452 00:29:10,560 --> 00:29:13,840 Speaker 1: You had to go from zero to one hundred in 453 00:29:13,960 --> 00:29:17,719 Speaker 1: a single step once you you know, metaphorically flip the switch, 454 00:29:18,800 --> 00:29:21,200 Speaker 1: and without that sort of stability, you can't do that. 455 00:29:21,720 --> 00:29:24,440 Speaker 1: So I'm glad that you brought that up too, because 456 00:29:24,480 --> 00:29:27,040 Speaker 1: it's again one of those things that just sort of 457 00:29:28,120 --> 00:29:30,360 Speaker 1: we've I think we're in an era where we just 458 00:29:30,440 --> 00:29:34,720 Speaker 1: expect things to just work and we we lose perspective 459 00:29:34,760 --> 00:29:37,840 Speaker 1: on what it takes to make that happen, you know, 460 00:29:37,920 --> 00:29:41,640 Speaker 1: to keep that to keep things working. Yeah, I think 461 00:29:41,680 --> 00:29:45,880 Speaker 1: it's it's it's been amazing to see how our friends 462 00:29:45,880 --> 00:29:50,440 Speaker 1: and colleagues across IBM of help support us and and 463 00:29:50,840 --> 00:29:53,440 Speaker 1: the amazing tools that they've provided to make this possible. 464 00:29:53,480 --> 00:29:57,600 Speaker 1: It's it's super inspiring and it feels great to be 465 00:29:58,280 --> 00:30:00,920 Speaker 1: out of that, you know, in the all the challenges 466 00:30:00,920 --> 00:30:04,600 Speaker 1: we're going through to be part of a purpose trivenal organization, 467 00:30:05,000 --> 00:30:09,280 Speaker 1: you know, just personally feels really great. And you know, 468 00:30:09,360 --> 00:30:12,600 Speaker 1: we've we've talked a little bit about the uh, you know, 469 00:30:12,720 --> 00:30:16,080 Speaker 1: the fact that we have this very localized approach to 470 00:30:16,480 --> 00:30:19,920 Speaker 1: tracking COVID nineteen, which I think already sets it apart 471 00:30:20,040 --> 00:30:23,240 Speaker 1: from other There are great tools out there, right World 472 00:30:23,280 --> 00:30:27,480 Speaker 1: Health Organization or Johns Hopkins have COVID nineteen tracking tools, 473 00:30:27,480 --> 00:30:29,960 Speaker 1: but this is one where it's you know that's looking 474 00:30:30,000 --> 00:30:32,160 Speaker 1: at grand scale. This looks at grand scale, but also 475 00:30:32,200 --> 00:30:34,680 Speaker 1: you drilled down to that local level where you can 476 00:30:34,760 --> 00:30:39,800 Speaker 1: really have the view of our things shifting. Is there 477 00:30:39,840 --> 00:30:44,200 Speaker 1: a greater emphasis on UM stay at home. I know 478 00:30:44,280 --> 00:30:46,600 Speaker 1: you're not far from the city of Atlanta. I live 479 00:30:46,720 --> 00:30:48,680 Speaker 1: in the city of Atlanta. We are in a stay 480 00:30:48,680 --> 00:30:53,920 Speaker 1: at home order right now, So seeing that reflected in 481 00:30:53,960 --> 00:30:58,840 Speaker 1: a tracker really does bring home how important obeying that 482 00:30:58,960 --> 00:31:01,920 Speaker 1: kind of order is in order to you know, protect 483 00:31:02,000 --> 00:31:06,640 Speaker 1: people and and to mitigate the spreading of this virus. 484 00:31:07,440 --> 00:31:10,560 Speaker 1: You mentioned earlier that maybe in the future there might 485 00:31:10,600 --> 00:31:14,600 Speaker 1: be other types of data incorporated into this sort of 486 00:31:14,640 --> 00:31:20,160 Speaker 1: tracking system. Do you anticipate perhaps working with either leaders 487 00:31:20,320 --> 00:31:23,840 Speaker 1: or medical personnel in order to be able to use 488 00:31:23,880 --> 00:31:27,760 Speaker 1: this kind of information in a way where perhaps on 489 00:31:27,800 --> 00:31:34,080 Speaker 1: a logistics side, we could see resources UH moved perhaps 490 00:31:34,160 --> 00:31:37,720 Speaker 1: proactively to where they are going to be needed. Yeah, 491 00:31:37,720 --> 00:31:42,280 Speaker 1: we've actually seen that already with the amount of inbound 492 00:31:42,400 --> 00:31:48,400 Speaker 1: interest from government officials, from UH, supply chain logistics companies, 493 00:31:48,920 --> 00:31:55,280 Speaker 1: from hospitals. UH. It's they're using this tool to to 494 00:31:55,520 --> 00:31:59,120 Speaker 1: see what the train curves that are occurring out in 495 00:31:59,160 --> 00:32:03,920 Speaker 1: the various local communities, and then they're redeploying resources. I 496 00:32:03,960 --> 00:32:08,400 Speaker 1: got an email on Wednesday from one hospital group that 497 00:32:08,480 --> 00:32:14,960 Speaker 1: was moving ventilators from Arkansas to Louisiana, for example, because 498 00:32:14,960 --> 00:32:18,120 Speaker 1: of the outbreak that they saw happening in Louisiana and 499 00:32:18,120 --> 00:32:20,160 Speaker 1: they said that the place they saw it was on 500 00:32:20,200 --> 00:32:24,840 Speaker 1: our website, right, And so also it wasn't necessarily designed 501 00:32:24,880 --> 00:32:31,680 Speaker 1: for that purpose. When you put local data out in 502 00:32:31,720 --> 00:32:35,280 Speaker 1: a transparent, easy to consume way, all walks of life, 503 00:32:35,520 --> 00:32:37,600 Speaker 1: I think make better decisions as a result of it. 504 00:32:37,680 --> 00:32:40,280 Speaker 1: And so we're seeing decision makers at all levels and 505 00:32:40,360 --> 00:32:44,800 Speaker 1: all industries used the tool. And I do think, you know, 506 00:32:45,000 --> 00:32:47,760 Speaker 1: we're starting to now to think about what do we 507 00:32:47,840 --> 00:32:52,440 Speaker 1: do and add to it, what's next? And you know, 508 00:32:52,480 --> 00:32:54,880 Speaker 1: we have lots of ideas around that. Sure. I mean, 509 00:32:54,920 --> 00:32:58,720 Speaker 1: I'm just speaking with you. I'm my brain starts to 510 00:32:58,800 --> 00:33:02,160 Speaker 1: free associate with ways, like not necessarily ways that would 511 00:33:02,560 --> 00:33:05,600 Speaker 1: be presented to people like me, right, Not necessarily it 512 00:33:05,600 --> 00:33:08,560 Speaker 1: would be all packaged in with the tracker, because obviously 513 00:33:08,560 --> 00:33:12,480 Speaker 1: you want to keep that tool simple and easy to understand. 514 00:33:12,560 --> 00:33:15,040 Speaker 1: You don't want to overly complicate it and then lose 515 00:33:15,040 --> 00:33:18,360 Speaker 1: the message in the process. So, but there are lots 516 00:33:18,360 --> 00:33:21,080 Speaker 1: of different potential applications. I can I can sort of 517 00:33:21,080 --> 00:33:25,040 Speaker 1: imagine where you know, you you say, this isn't meant 518 00:33:25,080 --> 00:33:28,040 Speaker 1: for public consumption, but maybe we start looking at predictive 519 00:33:28,080 --> 00:33:31,600 Speaker 1: models to try and help people just even just getting 520 00:33:31,640 --> 00:33:35,360 Speaker 1: the word out, even if it's not let's get resources there. 521 00:33:35,440 --> 00:33:37,520 Speaker 1: But we might say, well, based on this predictive model, 522 00:33:37,560 --> 00:33:40,480 Speaker 1: we want to tell the people of St. Louis, Missouri 523 00:33:40,840 --> 00:33:45,800 Speaker 1: that seriously, guys, stay at home for the next few days. 524 00:33:46,040 --> 00:33:49,000 Speaker 1: It's it's that's going to be tough, but trust us, 525 00:33:49,040 --> 00:33:51,760 Speaker 1: based upon everything we're seeing, it will help prevent a 526 00:33:51,880 --> 00:33:55,080 Speaker 1: much bigger problem down the line. Like that's just one 527 00:33:55,200 --> 00:33:58,320 Speaker 1: potential possibility I could imagine. Obviously, you've got to be 528 00:33:58,400 --> 00:34:01,120 Speaker 1: very careful when you're talking about predict of models. But 529 00:34:01,920 --> 00:34:04,200 Speaker 1: that's one of those things that that sort of accursed 530 00:34:04,280 --> 00:34:07,280 Speaker 1: me and probably I know I'm preaching to the choir 531 00:34:07,320 --> 00:34:09,640 Speaker 1: if I'm talking to someone from the Weather Company. Predictive 532 00:34:09,640 --> 00:34:13,520 Speaker 1: models are kind of your thing. They are, but you 533 00:34:13,560 --> 00:34:16,440 Speaker 1: do have to be careful with them, right they And 534 00:34:16,560 --> 00:34:20,080 Speaker 1: so you know, we're looking at that. I think the 535 00:34:20,160 --> 00:34:24,520 Speaker 1: next steps for us to put UH this product and 536 00:34:24,600 --> 00:34:28,799 Speaker 1: to translated into Spanish, so for the Hispanic community to 537 00:34:28,840 --> 00:34:33,280 Speaker 1: get it UH in in Spanish. Then we're looking to 538 00:34:33,440 --> 00:34:38,000 Speaker 1: add other countries around the world to the to the 539 00:34:38,040 --> 00:34:42,239 Speaker 1: maps and things, so that is UH similar. You know, 540 00:34:42,280 --> 00:34:43,480 Speaker 1: I don't know if we can get quite to the 541 00:34:43,520 --> 00:34:47,840 Speaker 1: liver granularity and other countries. We're doing that a country 542 00:34:47,840 --> 00:34:51,600 Speaker 1: by country basis, so I don't want to see false expectations. 543 00:34:51,640 --> 00:34:54,320 Speaker 1: But but we're trying the best we can uh in 544 00:34:54,440 --> 00:34:58,160 Speaker 1: various markets around the world to localize as much as 545 00:34:58,160 --> 00:35:04,120 Speaker 1: as possible with trusted sources, and so they'll take us 546 00:35:04,120 --> 00:35:06,040 Speaker 1: a little bit of time to get get that done. 547 00:35:06,040 --> 00:35:08,520 Speaker 1: And then the other part of that is also translations, 548 00:35:08,680 --> 00:35:11,960 Speaker 1: right um, and so whether dot coms and eight five 549 00:35:12,040 --> 00:35:16,319 Speaker 1: languages today around the world, and so it's not a 550 00:35:16,360 --> 00:35:21,839 Speaker 1: small effort to translate this kind of complex data and 551 00:35:21,880 --> 00:35:27,360 Speaker 1: make sure it's done correctly, contextually and medically accurate is 552 00:35:27,360 --> 00:35:31,680 Speaker 1: also obviously obviously super important around the world. And so 553 00:35:33,320 --> 00:35:36,400 Speaker 1: those are the next steps for us we're excited about. 554 00:35:37,400 --> 00:35:41,200 Speaker 1: But we also are seeing sentiment analysis come up as 555 00:35:41,239 --> 00:35:45,120 Speaker 1: something from the mental health community saying, you know what, 556 00:35:46,040 --> 00:35:49,040 Speaker 1: fear is spreading almost as rapidly, if not faster, than 557 00:35:49,080 --> 00:35:54,960 Speaker 1: the virus itself. And how people feel is also really 558 00:35:55,040 --> 00:35:59,240 Speaker 1: really important, and having an outlet for them to share 559 00:35:59,280 --> 00:36:01,759 Speaker 1: how they feel all is important, and so we're looking 560 00:36:01,800 --> 00:36:04,400 Speaker 1: at that too. I'm not sure that we play a 561 00:36:04,480 --> 00:36:07,120 Speaker 1: role there, but we're looking at at maybe it's as 562 00:36:07,120 --> 00:36:11,160 Speaker 1: simple as the frowny face the smiley face a moticon, 563 00:36:11,239 --> 00:36:14,600 Speaker 1: but we're trying to figure that out one And I 564 00:36:14,640 --> 00:36:17,839 Speaker 1: think the important thing for us to remember is that 565 00:36:18,280 --> 00:36:21,799 Speaker 1: getting this information, getting this localized information gives us. It 566 00:36:21,840 --> 00:36:26,440 Speaker 1: empowers us to make decisions. It makes us more confident 567 00:36:26,560 --> 00:36:29,600 Speaker 1: when we're making those choices of let's stay at home, 568 00:36:29,680 --> 00:36:32,279 Speaker 1: even though it might be, you know, difficult for us 569 00:36:32,880 --> 00:36:35,680 Speaker 1: if we're able to say, yeah, but I'm looking here 570 00:36:35,719 --> 00:36:37,960 Speaker 1: at this chart and I don't want to be part 571 00:36:38,040 --> 00:36:41,880 Speaker 1: of that red bar that is of the COVID nineteen cases. 572 00:36:41,920 --> 00:36:45,479 Speaker 1: I don't want to potentially put my family at risk 573 00:36:45,800 --> 00:36:49,480 Speaker 1: or someone else that I might encounter. And to me, 574 00:36:49,680 --> 00:36:54,560 Speaker 1: that is an incredible, incredible tool and an incredible story 575 00:36:54,600 --> 00:36:57,759 Speaker 1: to tell, is that this is a way for us 576 00:36:57,800 --> 00:37:01,200 Speaker 1: to kind of really think, how is this affecting the 577 00:37:01,239 --> 00:37:04,719 Speaker 1: people around me? Not just these big numbers that I 578 00:37:04,760 --> 00:37:07,400 Speaker 1: hear on the news where I can easily get lost 579 00:37:07,440 --> 00:37:09,800 Speaker 1: because once you get past a certain number, I can't 580 00:37:09,800 --> 00:37:13,160 Speaker 1: really even conceptualize it. This puts it in that context 581 00:37:13,200 --> 00:37:15,600 Speaker 1: of no, these are the people I know, And this 582 00:37:15,680 --> 00:37:18,600 Speaker 1: is why it's important for me to keep that in mind, 583 00:37:18,640 --> 00:37:22,359 Speaker 1: to stay safe and to protect not just myself but 584 00:37:22,880 --> 00:37:26,600 Speaker 1: those in my community. You know, I think this is 585 00:37:27,200 --> 00:37:32,040 Speaker 1: uh a tool to help people make better decisions, to 586 00:37:32,160 --> 00:37:36,719 Speaker 1: put their local neighbors almost a hit of themselves as 587 00:37:36,800 --> 00:37:40,040 Speaker 1: much as possible, and for us all to really together. 588 00:37:41,440 --> 00:37:44,879 Speaker 1: And when we say really together, is really together and 589 00:37:44,960 --> 00:37:51,880 Speaker 1: stay separated. Uh And and that's not intuitive, but it's 590 00:37:51,920 --> 00:37:54,160 Speaker 1: the most important thing we can do right now is 591 00:37:54,880 --> 00:37:58,840 Speaker 1: stay home and start to spread. That's gonna say, lives. 592 00:38:00,000 --> 00:38:03,000 Speaker 1: It is as simple as that. I I thank you 593 00:38:03,120 --> 00:38:06,719 Speaker 1: so much for being part of the show and for 594 00:38:06,840 --> 00:38:10,800 Speaker 1: explaining the process and explaining the technologies that are required 595 00:38:10,840 --> 00:38:14,319 Speaker 1: in order to make it happen. It's a interesting convergence 596 00:38:14,360 --> 00:38:17,040 Speaker 1: of a lot of things I talk about on tech stuff, 597 00:38:17,040 --> 00:38:20,680 Speaker 1: but in the context of making real world impact. And 598 00:38:20,760 --> 00:38:25,160 Speaker 1: that's something that often gets left behind in tech conversations, 599 00:38:25,280 --> 00:38:28,120 Speaker 1: is that we talk about the how, maybe even a 600 00:38:28,160 --> 00:38:30,960 Speaker 1: little bit of the why. But it's it's rare when 601 00:38:30,960 --> 00:38:34,160 Speaker 1: we talk about how it actually rolls out into the 602 00:38:34,160 --> 00:38:36,799 Speaker 1: real world and starts to make real world change. So 603 00:38:36,960 --> 00:38:40,120 Speaker 1: thank you so much for your work and thank you 604 00:38:40,160 --> 00:38:44,480 Speaker 1: for joining me on the show. Pleasure I want to 605 00:38:44,480 --> 00:38:47,040 Speaker 1: thank Cameron for coming on the show and talking about 606 00:38:47,040 --> 00:38:49,840 Speaker 1: the work the Weather Company and IBM are doing to 607 00:38:49,920 --> 00:38:53,360 Speaker 1: give us more useful, reliable information about the outbreak of 608 00:38:53,400 --> 00:38:56,640 Speaker 1: COVID nineteen. A quick glance at my county shows me 609 00:38:56,719 --> 00:39:00,160 Speaker 1: that even as I record this bit several days is 610 00:39:00,200 --> 00:39:03,400 Speaker 1: after speaking with Cameron, we're not over the peak yet. 611 00:39:03,760 --> 00:39:06,680 Speaker 1: The curve has yet to flatten, and so it really 612 00:39:06,840 --> 00:39:10,439 Speaker 1: is important that anyone who can stay home stays home. 613 00:39:10,960 --> 00:39:13,080 Speaker 1: I know there are many of you listening who don't 614 00:39:13,120 --> 00:39:16,400 Speaker 1: have that luxury. Many of you work in necessary roles 615 00:39:16,440 --> 00:39:19,000 Speaker 1: that require you to be out and about whether you're 616 00:39:19,040 --> 00:39:23,919 Speaker 1: providing medical services, you're driving needed inventory two stores where 617 00:39:23,920 --> 00:39:27,160 Speaker 1: you're making sure the lights stay on, and so the 618 00:39:27,160 --> 00:39:29,359 Speaker 1: rest of us have to stay home to keep those 619 00:39:29,400 --> 00:39:32,719 Speaker 1: of you who don't have that option safe. To see 620 00:39:32,719 --> 00:39:35,600 Speaker 1: this tech in action for yourself and to get a 621 00:39:35,640 --> 00:39:38,520 Speaker 1: look at what's going on in your own community, download 622 00:39:38,560 --> 00:39:41,680 Speaker 1: the Weather Channel app or go to weather dot com 623 00:39:41,880 --> 00:39:45,560 Speaker 1: slash coronavirus. You're gonna see all the information there from 624 00:39:45,800 --> 00:39:49,400 Speaker 1: a state and county level. It's really useful, and again 625 00:39:49,640 --> 00:39:52,360 Speaker 1: I think it's important to apply critical thinking when we 626 00:39:52,480 --> 00:39:56,120 Speaker 1: encounter information about the coronavirus. There's a lot of data 627 00:39:56,120 --> 00:39:59,560 Speaker 1: out there that just isn't reliable. Some of it might 628 00:39:59,600 --> 00:40:03,319 Speaker 1: be well intentioned but incorrect, some of it might be 629 00:40:03,400 --> 00:40:07,400 Speaker 1: purposefully misleading. I've seen numerous messages that purport to be 630 00:40:07,520 --> 00:40:10,560 Speaker 1: from experts and more than a few that have no 631 00:40:10,640 --> 00:40:14,600 Speaker 1: citation at all, that contain erroneous information about the outbreak. 632 00:40:14,800 --> 00:40:19,080 Speaker 1: And when those supposed sources are contacted about these messages 633 00:40:19,160 --> 00:40:22,839 Speaker 1: that they've supposedly been saying, they say they've had nothing 634 00:40:22,880 --> 00:40:25,760 Speaker 1: to do with them. So knowing that the Weather Company's 635 00:40:25,840 --> 00:40:29,800 Speaker 1: COVID nineteen tracking tools are pulling only from official government 636 00:40:29,880 --> 00:40:33,080 Speaker 1: sources in real time, lets us know that the information 637 00:40:33,160 --> 00:40:36,480 Speaker 1: is solid. It's also important to remember that these numbers 638 00:40:36,520 --> 00:40:40,280 Speaker 1: are all on confirmed cases, and the number of actual 639 00:40:40,400 --> 00:40:43,480 Speaker 1: cases out in the wild is larger, though to what 640 00:40:43,640 --> 00:40:47,279 Speaker 1: extent is impossible to say. Bottom line, we can look 641 00:40:47,280 --> 00:40:50,520 Speaker 1: at the localized information presented by weather dot com and 642 00:40:50,560 --> 00:40:53,319 Speaker 1: the Weather Channel app as being the minimum number of 643 00:40:53,360 --> 00:40:55,879 Speaker 1: cases in our communities, and we should take that number 644 00:40:55,960 --> 00:40:58,279 Speaker 1: seriously and do our best to get those numbers to 645 00:40:58,320 --> 00:41:01,080 Speaker 1: come down. I'm and we're going to see a lot 646 00:41:01,120 --> 00:41:04,719 Speaker 1: more innovation in this space. One thing I draw inspiration 647 00:41:04,800 --> 00:41:08,480 Speaker 1: from is how we humans can rise to meet incredible challenges. 648 00:41:09,040 --> 00:41:12,399 Speaker 1: Sometimes it takes a problem of enormous magnitude to stir 649 00:41:12,520 --> 00:41:16,560 Speaker 1: us to action, but then we discover we're incredibly resourceful. 650 00:41:17,040 --> 00:41:20,520 Speaker 1: Defining the problem, understanding it, and then making a plan 651 00:41:20,640 --> 00:41:23,680 Speaker 1: to surmount it is all part of the human condition, 652 00:41:23,960 --> 00:41:26,680 Speaker 1: whether it's landing people on the moon or finding ways 653 00:41:26,680 --> 00:41:29,520 Speaker 1: to help people mitigate the spread of a virus, and 654 00:41:29,560 --> 00:41:32,040 Speaker 1: we all can play a part. If you listen to 655 00:41:32,080 --> 00:41:35,640 Speaker 1: our previous Smart Talks episode about Project Owl, you've heard 656 00:41:35,640 --> 00:41:38,200 Speaker 1: about the Call for Code. It's a five year series 657 00:41:38,239 --> 00:41:41,920 Speaker 1: of coding competitions in which groups pitch technological solutions to 658 00:41:41,960 --> 00:41:45,440 Speaker 1: tackle big challenges. The winners not only get a cash prize, 659 00:41:45,680 --> 00:41:48,720 Speaker 1: they also get support from IBM to implement their proposed 660 00:41:48,760 --> 00:41:52,440 Speaker 1: solutions in the real world. The theme for the challenge 661 00:41:52,480 --> 00:41:55,880 Speaker 1: is climate change, but since the publication of that episode, 662 00:41:56,080 --> 00:42:00,000 Speaker 1: IBM has expanded the Call for Code to also include 663 00:42:00,040 --> 00:42:04,600 Speaker 1: the COVID nineteen crisis. Programmers and technologists are welcome to 664 00:42:04,640 --> 00:42:08,440 Speaker 1: submit their proposed solutions to the COVID nineteen crisis by April. 665 00:42:10,719 --> 00:42:14,360 Speaker 1: Those interested in participating in the Parallel Track, which aims 666 00:42:14,400 --> 00:42:18,040 Speaker 1: to tackle climate change may submit their own proposed solutions 667 00:42:18,080 --> 00:42:23,640 Speaker 1: by July one. Learn more at developer dot IBM dot 668 00:42:23,680 --> 00:42:27,239 Speaker 1: com slash call for code. In the next episode of 669 00:42:27,280 --> 00:42:30,800 Speaker 1: smart Talks on tech Stuff, I'll speak with David Turik, 670 00:42:31,320 --> 00:42:35,040 Speaker 1: vice president for High Performance Computing and Cognitive Systems at 671 00:42:35,080 --> 00:42:39,000 Speaker 1: Open Power IBM Systems. He'll explain how the High Performance 672 00:42:39,000 --> 00:42:44,120 Speaker 1: Computing Consortium is dedicating incredible computational resources in the fight 673 00:42:44,200 --> 00:42:47,879 Speaker 1: against COVID nineteen, and tell us how supercomputers can help 674 00:42:47,880 --> 00:42:51,359 Speaker 1: researchers and their efforts to develop a vaccine. That's all 675 00:42:51,360 --> 00:42:59,560 Speaker 1: for now. I'll talk to you again really soon. Text 676 00:42:59,560 --> 00:43:03,040 Speaker 1: Stuff is an I Heart Radio production. For more podcasts 677 00:43:03,040 --> 00:43:05,800 Speaker 1: from I Heart Radio, visit the i heart Radio app, 678 00:43:05,960 --> 00:43:09,120 Speaker 1: Apple Podcasts, or wherever you listen to your favorite shows.