1 00:00:04,160 --> 00:00:07,960 Speaker 1: Hello there. This is Smart Talks with IBM, a podcast 2 00:00:08,280 --> 00:00:12,399 Speaker 1: from Pushkin Industries. I Heart Media and IBM about what 3 00:00:12,440 --> 00:00:15,360 Speaker 1: it means to look at today's most challenging problems in 4 00:00:15,400 --> 00:00:20,200 Speaker 1: a new way. I'm Malcolm Glapo. Today I'm talking with 5 00:00:20,239 --> 00:00:24,040 Speaker 1: not one, but two bright folk from two very important companies. 6 00:00:24,440 --> 00:00:28,200 Speaker 1: We have Sweeny Calla Paula, Vice President of Global Technology 7 00:00:28,440 --> 00:00:33,279 Speaker 1: Strategy and Network Cloud at Verizon, as well as Steve Kannapa, 8 00:00:33,720 --> 00:00:38,920 Speaker 1: Global General Manager and Managing Director of IBM's Communications Sector. 9 00:00:39,800 --> 00:00:44,720 Speaker 1: Sweeney Calla Paula leads Verizon's technology strategy and works closely 10 00:00:44,720 --> 00:00:50,160 Speaker 1: with partners like IBM on emerging connectivity technologies. He's passionate 11 00:00:50,200 --> 00:00:54,279 Speaker 1: about a world where people, machines and systems will one 12 00:00:54,360 --> 00:00:59,680 Speaker 1: day interact seamlessly. I like me. Sweeney is extremely excited 13 00:00:59,800 --> 00:01:03,720 Speaker 1: about the technological progress we've made during the pandemic. The 14 00:01:03,880 --> 00:01:06,920 Speaker 1: digital transformations took off in the last one year because 15 00:01:07,000 --> 00:01:09,760 Speaker 1: of the core ridden because we were all kept apart 16 00:01:09,800 --> 00:01:12,000 Speaker 1: and we're still needed to communicate. We cill need to 17 00:01:12,080 --> 00:01:17,320 Speaker 1: get things done. Steve Kanappa heads IBMS Communications Sector, where 18 00:01:17,360 --> 00:01:20,640 Speaker 1: he works with the telecommunications and media firms all over 19 00:01:20,720 --> 00:01:24,640 Speaker 1: the globe to help them modernize their networks. Steve is 20 00:01:24,840 --> 00:01:28,759 Speaker 1: extremely excited about the future of AI and machine learning, 21 00:01:29,440 --> 00:01:34,680 Speaker 1: artificial intelligence, the ability to put machine learning capabilities where 22 00:01:34,840 --> 00:01:38,840 Speaker 1: processes get smarter continuously and more they execute, the smarter 23 00:01:39,000 --> 00:01:42,760 Speaker 1: they get. Right after the break my conversation with Sweeney 24 00:01:42,840 --> 00:01:47,760 Speaker 1: and Steve, the world of technological collaboration has never been 25 00:01:48,240 --> 00:02:01,559 Speaker 1: more fascinating. What the two of you ascribe the nature 26 00:02:01,680 --> 00:02:05,520 Speaker 1: of the challenge that the What is it you're trying 27 00:02:05,560 --> 00:02:10,000 Speaker 1: to address in the work that you do. So in 28 00:02:10,120 --> 00:02:13,760 Speaker 1: a word, um, what we're focused on is innovation. So 29 00:02:14,120 --> 00:02:20,919 Speaker 1: with at IBM, we work with clients in all industries retail, manufacturing, banking, 30 00:02:21,639 --> 00:02:28,119 Speaker 1: media and entertainment, TA, our communications, government agencies, and we're 31 00:02:28,200 --> 00:02:31,600 Speaker 1: helping them provide better services to our customers or their 32 00:02:31,639 --> 00:02:36,200 Speaker 1: constituents by helping them modernize the way that they bring 33 00:02:36,360 --> 00:02:42,080 Speaker 1: technology and applications to all of us. And we work 34 00:02:42,800 --> 00:02:47,040 Speaker 1: in collaboration with the TA, our communications companies and helping 35 00:02:47,080 --> 00:02:50,679 Speaker 1: them bring those new those new capabilities. So should he 36 00:02:50,800 --> 00:02:55,200 Speaker 1: tell me start by describing the nature of your partnership 37 00:02:55,280 --> 00:02:57,679 Speaker 1: with IBM. What is it that you when you work 38 00:02:57,720 --> 00:03:00,160 Speaker 1: with IBM, What are they bringing to the table. One 39 00:03:00,160 --> 00:03:03,079 Speaker 1: of the things we do is that drive reliable connectivity. 40 00:03:03,320 --> 00:03:05,960 Speaker 1: And now the need of reliable connectivity if you look 41 00:03:06,040 --> 00:03:08,120 Speaker 1: back to it and and twenty years ago, the needs 42 00:03:08,160 --> 00:03:10,679 Speaker 1: were different than the needs that are there today to 43 00:03:10,800 --> 00:03:13,079 Speaker 1: where we're going to be integers from now. So you know, 44 00:03:13,240 --> 00:03:15,120 Speaker 1: ten twenty years ago, you know, hey, I want to 45 00:03:15,160 --> 00:03:16,840 Speaker 1: move around, but I want to talk to people. Right 46 00:03:17,600 --> 00:03:20,120 Speaker 1: then that evolved into messaging, That evolved into you know 47 00:03:20,240 --> 00:03:22,400 Speaker 1: four G world where not only you are moving around, 48 00:03:22,400 --> 00:03:24,400 Speaker 1: but you're able to do things with your phone and others. 49 00:03:25,200 --> 00:03:28,519 Speaker 1: But as we look forward, and especially if you notice 50 00:03:28,600 --> 00:03:31,640 Speaker 1: what happened in the last one year, right the digital 51 00:03:31,800 --> 00:03:34,280 Speaker 1: transformation just took off in the last one year because 52 00:03:34,400 --> 00:03:37,120 Speaker 1: of the COVID and because we were all kept apart 53 00:03:37,200 --> 00:03:39,360 Speaker 1: and we still needed to communicate. We still need to 54 00:03:39,440 --> 00:03:42,040 Speaker 1: get things done. Now, the nature of connect really goes 55 00:03:42,160 --> 00:03:46,080 Speaker 1: beyond humans. It now goes into we need to deliver connectivity, 56 00:03:46,160 --> 00:03:49,400 Speaker 1: highly reliable connect rety where machines can communicate with each 57 00:03:49,440 --> 00:03:53,240 Speaker 1: other and machines can actually do things, you know, simple 58 00:03:53,520 --> 00:03:57,600 Speaker 1: things like remote health care. Right. So where IBM kind 59 00:03:57,600 --> 00:04:00,280 Speaker 1: of fits on how we collaborate is that we see 60 00:04:00,360 --> 00:04:04,640 Speaker 1: that the future is about machine mobility. So where our 61 00:04:04,720 --> 00:04:08,400 Speaker 1: networks now have to deliver connectivity two things that move around, 62 00:04:08,520 --> 00:04:11,440 Speaker 1: things that are connected, which require a lot more relevel 63 00:04:11,480 --> 00:04:15,320 Speaker 1: connectivity than humans. Humans we can adapt to errors and 64 00:04:15,480 --> 00:04:17,880 Speaker 1: changes at a at a you know, second level and 65 00:04:17,960 --> 00:04:20,680 Speaker 1: all that. But machines they are designed to be perfect, 66 00:04:20,720 --> 00:04:24,159 Speaker 1: and that means they require high level connectivity. We variazon. 67 00:04:24,480 --> 00:04:28,360 Speaker 1: We build highly reliable, high performance networks. And when you're 68 00:04:28,400 --> 00:04:31,359 Speaker 1: trying to develover these capabilities to let's say, automate industries 69 00:04:31,400 --> 00:04:36,200 Speaker 1: or automate you know, transportation sector and others, IBM understands 70 00:04:36,240 --> 00:04:39,760 Speaker 1: those sector as well. They have been digitizing those sectors 71 00:04:39,839 --> 00:04:42,720 Speaker 1: and and they understand that the main that particular domain 72 00:04:42,800 --> 00:04:45,240 Speaker 1: and what sort of solutions can actually uh you know, 73 00:04:45,320 --> 00:04:48,559 Speaker 1: can be incorporated. So when you bring these two expertise together, 74 00:04:48,640 --> 00:04:52,080 Speaker 1: now we're able to deliver this automation, this decidation in 75 00:04:52,160 --> 00:04:54,760 Speaker 1: more effective way. That's how, you know, that's how we 76 00:04:54,839 --> 00:05:02,080 Speaker 1: both you know, kind of collaborate and work together. When 77 00:05:02,160 --> 00:05:06,920 Speaker 1: we talk about how communication between machines has to have 78 00:05:07,080 --> 00:05:11,840 Speaker 1: a higher standard than communication between humans, what does that mean? 79 00:05:12,120 --> 00:05:15,320 Speaker 1: Are you just talking about reliability? Are you talking about 80 00:05:15,440 --> 00:05:18,160 Speaker 1: the size of the pipe, Are you talking about the 81 00:05:18,520 --> 00:05:21,720 Speaker 1: scale of things you want to do. So I'm going 82 00:05:21,760 --> 00:05:23,920 Speaker 1: to take an example of a let's say a car 83 00:05:24,080 --> 00:05:27,400 Speaker 1: that is connected right and it's a call it a 84 00:05:27,480 --> 00:05:30,479 Speaker 1: semi autonomous car. This car is traveling at hund miles 85 00:05:30,520 --> 00:05:34,440 Speaker 1: per hour and it's capturing, uh, you know, lots of 86 00:05:34,520 --> 00:05:37,600 Speaker 1: information and it may have to understand what are the 87 00:05:37,680 --> 00:05:40,400 Speaker 1: other things that are in that area. When you're traveling 88 00:05:40,400 --> 00:05:44,920 Speaker 1: at hundred miles per hour um, the information you collect 89 00:05:45,279 --> 00:05:48,000 Speaker 1: and the decisions that you need are going to be 90 00:05:48,080 --> 00:05:51,560 Speaker 1: in the order of milliseconds. Because if you have to 91 00:05:51,640 --> 00:05:54,440 Speaker 1: break at a you know, at a speed, if you 92 00:05:54,560 --> 00:05:56,880 Speaker 1: take a second, you're already you know, causing an accident. 93 00:05:57,240 --> 00:06:00,520 Speaker 1: You're now talking in the ranges of ten early seconds, 94 00:06:00,640 --> 00:06:03,839 Speaker 1: ranges of area. So that's one thing we talk about 95 00:06:03,839 --> 00:06:08,000 Speaker 1: result you know, highly latency sensitive networking, whether the humans 96 00:06:08,480 --> 00:06:10,840 Speaker 1: let's say you're you're browsing something that are trying to 97 00:06:10,920 --> 00:06:13,479 Speaker 1: pull a website, it takes a couple of seconds longer 98 00:06:13,640 --> 00:06:16,680 Speaker 1: you don't actually sense that field that maybe five stand 99 00:06:16,760 --> 00:06:19,880 Speaker 1: seconds you feel that. Whereas when you connect in any 100 00:06:19,960 --> 00:06:22,240 Speaker 1: kind of automated thing, you know, dron't trying to deliver 101 00:06:22,720 --> 00:06:26,560 Speaker 1: a robotic vehicle within a factory environment. These things are 102 00:06:26,600 --> 00:06:31,279 Speaker 1: moving at a pace where you can perceive a millisecond delay, 103 00:06:31,480 --> 00:06:33,919 Speaker 1: and and so the networks now have to operate at 104 00:06:33,960 --> 00:06:36,280 Speaker 1: that level is a much higher order of you know, 105 00:06:36,760 --> 00:06:39,680 Speaker 1: you know latencies. Now, the machines that we're trying to automate, 106 00:06:39,680 --> 00:06:43,000 Speaker 1: they're collecting all of this data, lots of data. We're 107 00:06:43,040 --> 00:06:45,840 Speaker 1: talking about hundreds of magnitudes higher than what you used 108 00:06:45,880 --> 00:06:47,960 Speaker 1: to get collecting. Now, if you try to send all 109 00:06:48,000 --> 00:06:51,280 Speaker 1: the data to a far away cloud, that means that 110 00:06:51,360 --> 00:06:53,800 Speaker 1: you require massive amount of networks all the way, which 111 00:06:53,839 --> 00:06:56,920 Speaker 1: which don't exist today. That's where edge computer comes from. 112 00:06:57,000 --> 00:06:59,560 Speaker 1: The picture where you collect all of this data, you 113 00:06:59,640 --> 00:07:01,480 Speaker 1: hand it over to an age cloud, but that's very 114 00:07:01,520 --> 00:07:03,680 Speaker 1: close to the user. Let's say you know a few 115 00:07:03,760 --> 00:07:07,320 Speaker 1: miles from the user. Then you process the data rumor 116 00:07:07,520 --> 00:07:09,720 Speaker 1: a lot of data that you collect is more of 117 00:07:10,120 --> 00:07:12,880 Speaker 1: what we call it's not information, it is data. Right, 118 00:07:13,160 --> 00:07:16,120 Speaker 1: You take the data, you process it locally, you get 119 00:07:16,160 --> 00:07:18,160 Speaker 1: information out of it, and then you use that to 120 00:07:18,280 --> 00:07:20,320 Speaker 1: both coming and get back to the object you're trying 121 00:07:20,320 --> 00:07:22,080 Speaker 1: to control as well as you know, send it to 122 00:07:22,120 --> 00:07:24,080 Speaker 1: where or you need to send it to. So in 123 00:07:24,800 --> 00:07:26,520 Speaker 1: These are the kind of you know, key ingredients that 124 00:07:26,560 --> 00:07:28,760 Speaker 1: you require as you look start looking into the future. 125 00:07:29,840 --> 00:07:33,120 Speaker 1: Last week, I was in Phoenix and I took a 126 00:07:33,240 --> 00:07:36,240 Speaker 1: ride on one of those autonomous vehicles. I ordered the 127 00:07:36,480 --> 00:07:39,840 Speaker 1: taxi on my app. It showed up. You know, no 128 00:07:40,120 --> 00:07:43,200 Speaker 1: no human being to be found. The digital backbone, the 129 00:07:43,280 --> 00:07:47,920 Speaker 1: communication systems. This is based on better be good because 130 00:07:48,000 --> 00:07:50,600 Speaker 1: if you know, if we drop coverage going at forty 131 00:07:50,640 --> 00:07:53,640 Speaker 1: miles down the road trying to navigate a you know, 132 00:07:54,400 --> 00:07:58,040 Speaker 1: then I'm in trouble, right, there's no backup here exactly, 133 00:07:58,520 --> 00:08:00,760 Speaker 1: And if you think about what's actually were happening in 134 00:08:01,200 --> 00:08:07,120 Speaker 1: those scenarios, it's it's about experience, so changing the experience, 135 00:08:07,360 --> 00:08:12,600 Speaker 1: it's about personalization, and oftentimes it's about delivering the insights 136 00:08:12,960 --> 00:08:16,800 Speaker 1: in real time about how you how how a process 137 00:08:16,960 --> 00:08:20,160 Speaker 1: is happening. I'll bring it back to the manufacturing shop 138 00:08:20,200 --> 00:08:22,600 Speaker 1: floor as an example to kind of tie together a 139 00:08:22,680 --> 00:08:25,360 Speaker 1: couple of the points that Strening was making. Now, we've 140 00:08:25,400 --> 00:08:29,080 Speaker 1: been instrumenting with IoT devices and manufacturing shop floors for 141 00:08:29,200 --> 00:08:33,520 Speaker 1: quite a while hundreds if not thousands, but now think 142 00:08:33,520 --> 00:08:37,120 Speaker 1: about taking that to tens of thousands of sensors on 143 00:08:37,400 --> 00:08:40,480 Speaker 1: everything that's happening in real time in that manufacturing floor. 144 00:08:41,400 --> 00:08:44,080 Speaker 1: Think about how videos changed all of our lives over 145 00:08:44,160 --> 00:08:47,040 Speaker 1: the last few years, as it's become ubiquitous in the 146 00:08:47,080 --> 00:08:51,520 Speaker 1: way we work and the way we you know, get entertainment, news, information, 147 00:08:52,160 --> 00:08:55,199 Speaker 1: the way we communicate with each other. Or video is 148 00:08:55,360 --> 00:08:58,880 Speaker 1: essentially just rich data and it and and so. Now 149 00:08:58,920 --> 00:09:01,240 Speaker 1: instead of just having swers on that shop floor, we 150 00:09:01,280 --> 00:09:05,120 Speaker 1: could have video cameras watching everything is happening, watching workers 151 00:09:05,200 --> 00:09:09,240 Speaker 1: to make sure they're in safe zones, watching whatever is 152 00:09:09,240 --> 00:09:13,600 Speaker 1: being produced coming down the factory line, understanding if it's 153 00:09:13,720 --> 00:09:16,520 Speaker 1: being done exactly the way it's supposed to be done, 154 00:09:16,800 --> 00:09:19,559 Speaker 1: Watching the machinery itself and the way it's performing to 155 00:09:19,760 --> 00:09:23,600 Speaker 1: notice the slightest changes. And the second thing that we're 156 00:09:23,640 --> 00:09:28,760 Speaker 1: applying in is artificial intelligence, the ability to put machine 157 00:09:28,920 --> 00:09:34,000 Speaker 1: learning capabilities where processes get smarter continuously and more they execute, 158 00:09:34,080 --> 00:09:37,400 Speaker 1: the smarter they get. So now when you combine those 159 00:09:37,480 --> 00:09:40,720 Speaker 1: two forces together, the ability to use high fidelity data 160 00:09:40,880 --> 00:09:44,679 Speaker 1: like video, and the ability to interrogate and analyze that 161 00:09:44,840 --> 00:09:48,079 Speaker 1: data in real time and do it right where things 162 00:09:48,160 --> 00:09:51,360 Speaker 1: are occurring, like on the shop floor, and then have 163 00:09:51,559 --> 00:09:54,720 Speaker 1: that backed by the kind of connectivity and the power 164 00:09:54,920 --> 00:09:58,880 Speaker 1: that the Verizon can bring with their their edge computing platforms. 165 00:09:59,559 --> 00:10:03,480 Speaker 1: We have the opportunity now to add tremendous value to 166 00:10:03,600 --> 00:10:07,480 Speaker 1: those processes, whether that's making sure people are safe, making 167 00:10:07,520 --> 00:10:10,560 Speaker 1: sure that those lines are as productive and as efficient 168 00:10:10,600 --> 00:10:13,439 Speaker 1: as possible, making sure the product quality is as high 169 00:10:13,480 --> 00:10:16,959 Speaker 1: as it should be. A tremendous uh, you know, amount 170 00:10:17,080 --> 00:10:19,240 Speaker 1: of value can be created by being able to apply 171 00:10:20,040 --> 00:10:24,760 Speaker 1: that that intelligence, um that that high bandwidth capability, and 172 00:10:24,840 --> 00:10:27,480 Speaker 1: to make sure that that network is there and ready, 173 00:10:27,920 --> 00:10:32,680 Speaker 1: as Shrine was describing, to constantly serve up those insights. Yeah, 174 00:10:33,000 --> 00:10:37,000 Speaker 1: imagine that. I'm a uh, I have a you know, 175 00:10:37,120 --> 00:10:41,479 Speaker 1: mid sized manufacturing company, you know, founded by my grandfather. 176 00:10:42,280 --> 00:10:45,960 Speaker 1: You know, I've been keeping reasonably keeping pace with innovation. 177 00:10:46,040 --> 00:10:48,440 Speaker 1: I think I'm pretty competitive. I have not done any 178 00:10:48,480 --> 00:10:51,080 Speaker 1: of the things you've described. The two of you show 179 00:10:51,160 --> 00:10:53,920 Speaker 1: up at my front door, and what kind of promise 180 00:10:54,000 --> 00:10:57,600 Speaker 1: can you make me? What like, for example, what when 181 00:10:57,640 --> 00:11:01,240 Speaker 1: you talk about productivity improvements are we talking about? What 182 00:11:01,320 --> 00:11:03,600 Speaker 1: are we talking about your two five percent, ten percent? 183 00:11:03,960 --> 00:11:07,679 Speaker 1: Give me kind of more tangible examples of what you 184 00:11:07,760 --> 00:11:11,000 Speaker 1: can deliver to a customer like that. So the way 185 00:11:11,200 --> 00:11:14,200 Speaker 1: industries have been evolving is that they have a lot 186 00:11:14,280 --> 00:11:19,440 Speaker 1: of these sensors, and the sensors have been more analog 187 00:11:19,640 --> 00:11:21,880 Speaker 1: and and and what I mean is that more each 188 00:11:21,920 --> 00:11:24,280 Speaker 1: one is designed to do a particular thing. I can 189 00:11:24,360 --> 00:11:26,960 Speaker 1: tell you that like temperature sense of that basically says 190 00:11:27,120 --> 00:11:29,360 Speaker 1: if you cross certain temperature, you slve the machine. You 191 00:11:29,480 --> 00:11:33,040 Speaker 1: have let's say some windspeed sens or something else, something else. 192 00:11:34,240 --> 00:11:37,360 Speaker 1: What industries are realizing is that these sensors have been 193 00:11:37,360 --> 00:11:40,720 Speaker 1: doing a certain function by now I'm going to use 194 00:11:40,720 --> 00:11:44,040 Speaker 1: the term called virtualizing, by basically making them more lean 195 00:11:44,160 --> 00:11:47,000 Speaker 1: and smarter and connect to the edge. You can now 196 00:11:47,080 --> 00:11:50,199 Speaker 1: get all this information and you can actually make decisions 197 00:11:50,240 --> 00:11:53,679 Speaker 1: based on collective intelligence intelligence of all the sensors than 198 00:11:53,840 --> 00:11:58,280 Speaker 1: each sensor doing what other things. And to do that 199 00:11:58,480 --> 00:12:01,840 Speaker 1: you need a highly releveled character. By bringing the entire 200 00:12:01,920 --> 00:12:05,720 Speaker 1: collective intelligence and and and in making these sensors more virtual. 201 00:12:06,160 --> 00:12:08,240 Speaker 1: A couple of things you're doing. One, you're going to 202 00:12:08,360 --> 00:12:12,839 Speaker 1: bring in latest capabilities in UM called product quality and 203 00:12:12,920 --> 00:12:17,559 Speaker 1: manufacturing and others, meaning that you would improve the radios downtimes, 204 00:12:17,760 --> 00:12:23,480 Speaker 1: improve you know, the amount of productivity, improve quality control issues. 205 00:12:24,160 --> 00:12:27,040 Speaker 1: And you're actually going to enable the factory to continue 206 00:12:27,040 --> 00:12:30,000 Speaker 1: to adapt to the new or digital capabilities that are 207 00:12:30,040 --> 00:12:32,280 Speaker 1: going to come out. And each one of these capabilities, 208 00:12:32,280 --> 00:12:34,559 Speaker 1: where as AI and whatever else, they're all going to 209 00:12:34,679 --> 00:12:38,240 Speaker 1: add to your productivity, your quality, or you know, the 210 00:12:38,320 --> 00:12:41,480 Speaker 1: key metrics that you're you're looking at. So for a 211 00:12:41,559 --> 00:12:45,280 Speaker 1: hypothetical imagine which is saying, is I suppose I have 212 00:12:45,400 --> 00:12:48,719 Speaker 1: a machine, a very expensive piece of machinery. My my 213 00:12:49,480 --> 00:12:52,880 Speaker 1: my cousin is the president of a company in Queens 214 00:12:52,960 --> 00:12:57,040 Speaker 1: that makes Jamaican beef paddies and they get these machines 215 00:12:57,160 --> 00:13:00,439 Speaker 1: from Italy. The cost just to say, in amounts of 216 00:13:00,480 --> 00:13:03,120 Speaker 1: money that you know, take the beat patty from what 217 00:13:03,600 --> 00:13:05,240 Speaker 1: and when the when something goes wrong, it's like a 218 00:13:05,360 --> 00:13:07,840 Speaker 1: crisis because someone has to fly in from Italy. Right, 219 00:13:07,920 --> 00:13:11,679 Speaker 1: So listening to I'm thinking, well, suppose we discovered that 220 00:13:11,760 --> 00:13:15,800 Speaker 1: the machine starts to make lots of airs when humidity 221 00:13:16,120 --> 00:13:18,640 Speaker 1: is hits a certain point in the company. It's kind 222 00:13:18,720 --> 00:13:21,280 Speaker 1: in the factory floor, the temperature is such when it's 223 00:13:21,320 --> 00:13:23,760 Speaker 1: been running for X number of hours in a row. 224 00:13:24,320 --> 00:13:28,360 Speaker 1: When the operator makes this kind of mistake. I mean, 225 00:13:28,360 --> 00:13:30,640 Speaker 1: I can imagine ten different things that we could say 226 00:13:30,840 --> 00:13:34,760 Speaker 1: might affect performance, and you could have sensors on all 227 00:13:34,800 --> 00:13:37,480 Speaker 1: those ten things and do a calculation at any given 228 00:13:37,520 --> 00:13:40,679 Speaker 1: time about what my chances are that the machine might 229 00:13:40,760 --> 00:13:43,760 Speaker 1: have a malfunction and presumably warned me before it happens. 230 00:13:43,840 --> 00:13:46,960 Speaker 1: Is that exactly exactly? And what you've what you've just done, 231 00:13:47,040 --> 00:13:50,200 Speaker 1: is you've crossed over from observation into prediction, which becomes 232 00:13:50,360 --> 00:13:53,800 Speaker 1: really powerful so that you can begin to understand what's 233 00:13:53,800 --> 00:13:57,120 Speaker 1: about to happen actually before it occurs, and you can 234 00:13:57,200 --> 00:14:01,200 Speaker 1: take corrective actions to do it. As a perfect example 235 00:14:01,320 --> 00:14:03,560 Speaker 1: of the kind of tremendous value that can be gained here. 236 00:14:04,400 --> 00:14:07,679 Speaker 1: Adding to my little scenario from before, we could have 237 00:14:07,840 --> 00:14:10,439 Speaker 1: sensors that tell me when my raw materials are getting low. 238 00:14:11,240 --> 00:14:14,800 Speaker 1: We could have an AI system which reminds me when 239 00:14:15,640 --> 00:14:19,880 Speaker 1: variations in demand and says you actually, every year have 240 00:14:19,960 --> 00:14:24,080 Speaker 1: a huge uptake in at easter, you're you know orders 241 00:14:25,200 --> 00:14:26,880 Speaker 1: that means you've got to order X, Y and Z, right. 242 00:14:26,880 --> 00:14:29,640 Speaker 1: I mean, this is all the kinds of things you 243 00:14:29,720 --> 00:14:32,400 Speaker 1: might want to do. How hard if you walked in 244 00:14:32,720 --> 00:14:35,480 Speaker 1: how hard? What is it to build that kind of system? 245 00:14:36,200 --> 00:14:39,840 Speaker 1: Is this something you can do relatively quickly and efficiently? 246 00:14:40,040 --> 00:14:42,160 Speaker 1: Or is it does it take months to kind of 247 00:14:42,320 --> 00:14:46,480 Speaker 1: customize a system for a customer like that. So for 248 00:14:46,920 --> 00:14:51,840 Speaker 1: for you know, any different clients, some things can be 249 00:14:51,920 --> 00:14:57,200 Speaker 1: basically prepackaged and ready to deploy UM. And you know, 250 00:14:57,480 --> 00:15:01,040 Speaker 1: as as these solutions of all UM, you know, there 251 00:15:01,040 --> 00:15:04,520 Speaker 1: will be some offerings that are essentially load and go 252 00:15:04,880 --> 00:15:08,640 Speaker 1: as a service. Others will be more customized or tailored 253 00:15:08,800 --> 00:15:12,520 Speaker 1: based on what given client will want to do. But 254 00:15:12,680 --> 00:15:16,520 Speaker 1: one of the things that I think really important in 255 00:15:16,680 --> 00:15:19,040 Speaker 1: what we're working on and what Verizon is working on, 256 00:15:19,280 --> 00:15:22,720 Speaker 1: is there are a set of standards that the marketplace 257 00:15:22,840 --> 00:15:27,440 Speaker 1: is embracing. And those standards allow for an ecosystem of 258 00:15:27,520 --> 00:15:33,280 Speaker 1: players to come together and to work seamlessly. And and 259 00:15:33,400 --> 00:15:36,120 Speaker 1: for IBM, you may have heard, you know, the term 260 00:15:37,000 --> 00:15:42,320 Speaker 1: our open hybrid cloud approach. We're very very focused working 261 00:15:42,680 --> 00:15:47,440 Speaker 1: in collaboration with the telecommunication providers in the marketplace to 262 00:15:47,560 --> 00:15:51,320 Speaker 1: help bring these open standards because two really powerful things 263 00:15:51,360 --> 00:15:56,840 Speaker 1: That one is we create an enormous pool of innovators 264 00:15:57,400 --> 00:16:00,720 Speaker 1: that can contribute and accelerate the rate piece of innovation. 265 00:16:01,120 --> 00:16:03,560 Speaker 1: So we're working very closely with Verizon on on some 266 00:16:03,720 --> 00:16:07,480 Speaker 1: of those open standard architectures. And then Secondly, I'm really 267 00:16:07,960 --> 00:16:11,920 Speaker 1: creating an ecosystem of partners. So when we announced just 268 00:16:12,080 --> 00:16:15,040 Speaker 1: at towards the end of last year, are IBM Cloud 269 00:16:15,120 --> 00:16:20,760 Speaker 1: for Telco, we announced with UM over forty different partners 270 00:16:21,280 --> 00:16:24,560 Speaker 1: that are coming together to be able to bring innovation 271 00:16:24,920 --> 00:16:27,560 Speaker 1: UM to these kinds of solutions. And I think that's 272 00:16:28,080 --> 00:16:31,400 Speaker 1: that's going to help us UM really accelerate the opportunity 273 00:16:31,440 --> 00:16:36,640 Speaker 1: to bring these kinds of solutions. Were describing treaty. Is 274 00:16:36,680 --> 00:16:41,000 Speaker 1: there somebody out there, an industry, a sector that you 275 00:16:41,120 --> 00:16:44,800 Speaker 1: think could benefit the most from the kind of services 276 00:16:44,880 --> 00:16:48,680 Speaker 1: that I'd be having Verizon provide. I mean, who where 277 00:16:48,760 --> 00:16:52,520 Speaker 1: could you make an extraordinary difference? Retail? Retail is a 278 00:16:52,680 --> 00:16:57,920 Speaker 1: very interesting area because both with COVID you started getting 279 00:16:57,920 --> 00:17:00,400 Speaker 1: into this ideas of touch free detail and you do 280 00:17:00,640 --> 00:17:04,639 Speaker 1: things more customized and you users and others. But retail 281 00:17:04,640 --> 00:17:08,000 Speaker 1: itself is actually changing. UM. You know, you hear about 282 00:17:08,119 --> 00:17:11,600 Speaker 1: these UH stores without any assistance that you're walking, you 283 00:17:11,680 --> 00:17:14,280 Speaker 1: buy and walk out. In all of these cases, what 284 00:17:14,400 --> 00:17:18,320 Speaker 1: you're seeing is automation really taking a bigger place. For example, 285 00:17:19,040 --> 00:17:21,680 Speaker 1: you know one of the retailers in a decent sized 286 00:17:21,720 --> 00:17:26,080 Speaker 1: store is looking to put about high definition cameras. Now 287 00:17:26,280 --> 00:17:30,359 Speaker 1: the cameras work as call it computer vision sensors, and 288 00:17:30,480 --> 00:17:33,720 Speaker 1: they capture the information and using that you can actually 289 00:17:33,840 --> 00:17:36,760 Speaker 1: understand what you know, individuals are. You can do lots 290 00:17:36,800 --> 00:17:38,920 Speaker 1: of things with the computers and of what people are doing, 291 00:17:39,200 --> 00:17:41,120 Speaker 1: how much stock you have in the store, and lots 292 00:17:41,119 --> 00:17:44,280 Speaker 1: of other things. Now you take all that, you process 293 00:17:44,400 --> 00:17:46,879 Speaker 1: that and uh and you could you know, if you 294 00:17:47,119 --> 00:17:50,960 Speaker 1: introduce automation at the point where people can come and 295 00:17:51,000 --> 00:17:53,000 Speaker 1: pick up what they want, they can walk out without 296 00:17:53,119 --> 00:17:55,600 Speaker 1: having to even interact with anybody out there. Right, that's 297 00:17:55,640 --> 00:17:58,720 Speaker 1: one extreme of it. I just imagine that I'm I'm 298 00:17:58,760 --> 00:18:01,240 Speaker 1: running a store, did a bunch of cameras. I got 299 00:18:01,520 --> 00:18:05,240 Speaker 1: fifty stores across America. I've just put in my spring line, 300 00:18:06,160 --> 00:18:08,639 Speaker 1: and I got a bunch of new dresses. So you 301 00:18:08,680 --> 00:18:10,840 Speaker 1: could tell me that, Malcolm, you have this new orange 302 00:18:10,920 --> 00:18:13,280 Speaker 1: dress which you put in all fifty stores. Not a 303 00:18:13,359 --> 00:18:15,679 Speaker 1: single person has looked at that dress in the retail 304 00:18:15,720 --> 00:18:17,760 Speaker 1: store in the last week. You can also tell me 305 00:18:19,440 --> 00:18:22,840 Speaker 1: seventy of the people stopped in the thing lingered in 306 00:18:22,920 --> 00:18:27,200 Speaker 1: front of the teal sweatshirt, and you're out of teal 307 00:18:27,200 --> 00:18:31,639 Speaker 1: sweatshirts as a result. Order more teal sweatshirts now and 308 00:18:31,800 --> 00:18:34,680 Speaker 1: forget about the dress. It's not going anywhere. And not 309 00:18:34,800 --> 00:18:37,639 Speaker 1: only not only that, I'm not a retail expert. By there, 310 00:18:37,760 --> 00:18:39,320 Speaker 1: I can tell you that. Not only that, we can 311 00:18:39,359 --> 00:18:42,520 Speaker 1: also tell you that people who looked at something for 312 00:18:42,720 --> 00:18:45,879 Speaker 1: how longer time, two seconds, two minutes, whatever, tend to 313 00:18:45,960 --> 00:18:48,120 Speaker 1: buy things versus if they don't look at it, that means, 314 00:18:48,200 --> 00:18:50,040 Speaker 1: you know, if they're just walking off, that means you 315 00:18:50,119 --> 00:18:52,760 Speaker 1: know that you're not gonna So you can get such 316 00:18:52,800 --> 00:18:56,240 Speaker 1: a deeper set of insights that both can help you 317 00:18:56,320 --> 00:18:59,560 Speaker 1: as a business to figure out how do you, you know, 318 00:18:59,640 --> 00:19:02,200 Speaker 1: how do you interact with your customers. But at the 319 00:19:02,280 --> 00:19:06,800 Speaker 1: same time, you can also use this this data to 320 00:19:06,960 --> 00:19:09,840 Speaker 1: actually uh you know call it push your prior to 321 00:19:09,920 --> 00:19:12,680 Speaker 1: the customer and you know, based on their personal preferences, 322 00:19:12,800 --> 00:19:16,240 Speaker 1: choices and color circumstances. Right. So that's the kind of 323 00:19:16,960 --> 00:19:19,359 Speaker 1: the data gives you so much and so much what 324 00:19:19,480 --> 00:19:21,959 Speaker 1: you call ability to kind of you know, customize yourself 325 00:19:22,560 --> 00:19:25,280 Speaker 1: to to meet the customer demands. You know, in a way, 326 00:19:25,359 --> 00:19:27,480 Speaker 1: what we're talking about here is you know, a using 327 00:19:27,600 --> 00:19:30,440 Speaker 1: video as we talked about as a rich fidelity set 328 00:19:30,480 --> 00:19:33,280 Speaker 1: of data but also applying AI or intelligence to that 329 00:19:33,440 --> 00:19:36,320 Speaker 1: so that you can take action on it. But part 330 00:19:36,400 --> 00:19:40,720 Speaker 1: of this is about humans and machines interacting, which makes 331 00:19:41,160 --> 00:19:45,480 Speaker 1: the human in doing that role more effective. Another area 332 00:19:45,600 --> 00:19:49,480 Speaker 1: where we both are excited about is industrial automation. So 333 00:19:49,600 --> 00:19:52,399 Speaker 1: if you look at you know, malcome, what happened over 334 00:19:52,440 --> 00:19:55,639 Speaker 1: the last few years is the world has digitized. You know, 335 00:19:55,760 --> 00:19:58,760 Speaker 1: everything can be digital. The back office is the manufacturing 336 00:19:58,840 --> 00:20:02,040 Speaker 1: process and others they have not being disguised. So you know, 337 00:20:02,119 --> 00:20:05,359 Speaker 1: in a number of situations, you put this nice digital 338 00:20:05,520 --> 00:20:08,480 Speaker 1: store front and digital experience in the front, but your 339 00:20:08,520 --> 00:20:11,920 Speaker 1: backup is still lagging with you know, older factories and 340 00:20:12,000 --> 00:20:14,600 Speaker 1: older sensors and whatnot. And the problem is that at 341 00:20:14,680 --> 00:20:16,159 Speaker 1: some point the problem is going to catch up, and 342 00:20:16,320 --> 00:20:18,120 Speaker 1: and that is you will not be able to keep 343 00:20:18,240 --> 00:20:21,840 Speaker 1: your front digital experiences without really you know, knowing what 344 00:20:22,080 --> 00:20:24,160 Speaker 1: is going on your factories or do you have the product? 345 00:20:24,240 --> 00:20:26,239 Speaker 1: You can you shave the product on time? And if 346 00:20:26,280 --> 00:20:28,040 Speaker 1: you come in to somebody, I'm going to deliver something 347 00:20:28,080 --> 00:20:32,159 Speaker 1: away tomorrow, can you really get it there? So the 348 00:20:32,320 --> 00:20:34,440 Speaker 1: next ten years and there's gonna be a lot of 349 00:20:34,560 --> 00:20:38,920 Speaker 1: industrial automation that's going to take place now for industrial automation, UM, 350 00:20:39,040 --> 00:20:40,639 Speaker 1: a few things that are going to make a bigger, 351 00:20:40,760 --> 00:20:45,200 Speaker 1: bigger impact. One, you do need a highly reliable connectivity 352 00:20:45,240 --> 00:20:48,920 Speaker 1: within those environments. Whatever small number of sensors they have today, 353 00:20:49,119 --> 00:20:52,359 Speaker 1: they're connected using wires. But the problem the virus is 354 00:20:52,440 --> 00:20:55,040 Speaker 1: that it takes a long time to really go connectivity. 355 00:20:55,160 --> 00:20:58,840 Speaker 1: You need a highly reliable wireless communication. Number Two, we're 356 00:20:58,840 --> 00:21:02,600 Speaker 1: gonna have inordinate amount of sensors all over these environments 357 00:21:02,680 --> 00:21:07,720 Speaker 1: because to your point about your cousin's uh Jamaican beef 358 00:21:07,720 --> 00:21:12,120 Speaker 1: parties by it does sound very attractive. So they're good, 359 00:21:12,160 --> 00:21:17,439 Speaker 1: They're really good. So so now you're gonna put sensors 360 00:21:17,480 --> 00:21:19,959 Speaker 1: everywhere so that you can predict, you can understand exactly 361 00:21:20,000 --> 00:21:22,240 Speaker 1: what's going on. The third thing you're gonna do is 362 00:21:22,359 --> 00:21:25,480 Speaker 1: that use all of the data that's getting generated and 363 00:21:25,680 --> 00:21:28,680 Speaker 1: apply AI and machine learning tactics on that so that 364 00:21:29,080 --> 00:21:32,399 Speaker 1: you can not only understand the the you know, the 365 00:21:32,480 --> 00:21:34,119 Speaker 1: world is going on within the environment, but you can 366 00:21:34,119 --> 00:21:36,840 Speaker 1: actually predict. You can forecast now in a much better 367 00:21:36,920 --> 00:21:39,480 Speaker 1: way so that if your customers expecting X y Z 368 00:21:39,640 --> 00:21:42,119 Speaker 1: on certain time, you have data to tell you that 369 00:21:42,200 --> 00:21:44,680 Speaker 1: you can absolutely come into the X y Z to 370 00:21:44,800 --> 00:21:47,800 Speaker 1: be delivered to the customer on time. So it's that 371 00:21:48,080 --> 00:21:53,080 Speaker 1: combination of sensors, programming, and intelligence and the AI and others. 372 00:21:53,440 --> 00:21:56,600 Speaker 1: IBM understands a lot from that environment to the reliable 373 00:21:56,640 --> 00:21:59,080 Speaker 1: connectory that we bring and and the edge computer that 374 00:21:59,359 --> 00:22:04,080 Speaker 1: that we put to other those elements that you've just mentioned, Um, 375 00:22:05,640 --> 00:22:07,960 Speaker 1: there they can't all be at the morning. If they're 376 00:22:08,080 --> 00:22:12,040 Speaker 1: they're not all the same level of sophistication or development. 377 00:22:12,200 --> 00:22:14,560 Speaker 1: Or is there one that you worry the most about? 378 00:22:14,720 --> 00:22:16,760 Speaker 1: Like for example, when you were talking, I was thinking, 379 00:22:17,880 --> 00:22:20,040 Speaker 1: you know, the Internet in my house it's not Horizon, 380 00:22:20,560 --> 00:22:22,880 Speaker 1: but the Internet in my house it's not that good. 381 00:22:22,920 --> 00:22:27,560 Speaker 1: I mean, I have to reboot my router every two days, 382 00:22:28,240 --> 00:22:30,320 Speaker 1: and I get a little I have to have massive 383 00:22:30,400 --> 00:22:32,879 Speaker 1: backups because sometimes it's just cons I you know what 384 00:22:32,880 --> 00:22:34,680 Speaker 1: I mean. Like when I hear this, I think this 385 00:22:34,840 --> 00:22:37,400 Speaker 1: is really really wonderful, But the reality in the little 386 00:22:37,480 --> 00:22:40,400 Speaker 1: town I live in is Interne's just not that good. 387 00:22:41,480 --> 00:22:44,399 Speaker 1: So if I was a company in botttle town, how 388 00:22:44,440 --> 00:22:47,040 Speaker 1: would I do what you're doing? If my if my 389 00:22:47,160 --> 00:22:52,080 Speaker 1: fundamental services so spotty. Yeah, by the first I wish 390 00:22:52,160 --> 00:22:55,080 Speaker 1: we were your internet providers. You wouldn't be having those issues. 391 00:22:56,320 --> 00:23:03,280 Speaker 1: That's my uh So, I think the infrastructure, the broader 392 00:23:03,359 --> 00:23:08,240 Speaker 1: connectivity infrastructure UM will need to evolve. Again, I go 393 00:23:08,359 --> 00:23:11,320 Speaker 1: back to COVID because then COVID truly pointed it out 394 00:23:11,440 --> 00:23:15,159 Speaker 1: that you can operate at a normal pace if you 395 00:23:15,240 --> 00:23:17,080 Speaker 1: have a good connectivity, but if you don't have a 396 00:23:17,119 --> 00:23:20,560 Speaker 1: good connectivity, you will get left behind. We certainly continue 397 00:23:20,600 --> 00:23:24,440 Speaker 1: to deploy more and more connectivity UM. Where I see 398 00:23:25,320 --> 00:23:27,400 Speaker 1: it's not a worry, but it's more of an optimism. 399 00:23:27,560 --> 00:23:29,520 Speaker 1: Is that is five G is going to change that. 400 00:23:29,600 --> 00:23:32,320 Speaker 1: Now five G brings in higher amount of throughputs in 401 00:23:32,359 --> 00:23:34,680 Speaker 1: a wireless manner, so that you know, wired is a 402 00:23:35,280 --> 00:23:38,200 Speaker 1: difficult proposition. Digging streets is not an easy thing, and 403 00:23:38,280 --> 00:23:40,920 Speaker 1: then connectivity cost a lot if you go through that way. 404 00:23:41,280 --> 00:23:44,440 Speaker 1: But then on the other hand, a wireless high band 405 00:23:44,520 --> 00:23:48,120 Speaker 1: with connectivity, reliable connectivity makes it easier because it's something 406 00:23:48,200 --> 00:23:51,639 Speaker 1: we can deploy in a matter of hours within the factory. 407 00:23:51,640 --> 00:23:56,119 Speaker 1: And what I actually worry about is the technology is 408 00:23:56,160 --> 00:24:00,600 Speaker 1: the ingredients are evolving at a rapid pace. It is 409 00:24:00,760 --> 00:24:04,760 Speaker 1: the people who operate those environments and it is the 410 00:24:05,160 --> 00:24:07,119 Speaker 1: you know, the pace at which they adopt is what 411 00:24:07,440 --> 00:24:10,359 Speaker 1: has got to catch up. Um, what are you going 412 00:24:10,440 --> 00:24:13,280 Speaker 1: to start seeing is that the tech is evolving so 413 00:24:14,040 --> 00:24:18,600 Speaker 1: rapidly that the expectations um from the consumers in terms 414 00:24:18,640 --> 00:24:21,120 Speaker 1: of you know, hey, when you guarantee something I wanted 415 00:24:21,160 --> 00:24:23,080 Speaker 1: to be guaranteed because you should have had that. You know, 416 00:24:23,320 --> 00:24:25,479 Speaker 1: you won't promise me something if you can't keep up, 417 00:24:26,440 --> 00:24:30,280 Speaker 1: those sort of expectations are going to continue to increase. 418 00:24:31,359 --> 00:24:34,800 Speaker 1: And if the the the people who are actually managing 419 00:24:34,840 --> 00:24:38,520 Speaker 1: and operating the factories and and working those factories cannot 420 00:24:38,600 --> 00:24:41,159 Speaker 1: adopt and cannot evolve fast enough, you know, that's what 421 00:24:41,320 --> 00:24:43,920 Speaker 1: we're worried about, is that the adoption and the evolution 422 00:24:44,000 --> 00:24:47,080 Speaker 1: of those environments. I mean that's a question for both 423 00:24:47,119 --> 00:24:50,760 Speaker 1: of you along those lines. You know, are are the 424 00:24:50,920 --> 00:24:55,520 Speaker 1: two of you, your two companies big enough to handle 425 00:24:55,600 --> 00:24:58,560 Speaker 1: this challenge? And I say that not I'm not trying 426 00:24:58,600 --> 00:25:02,760 Speaker 1: to provoke you, but you're talking about something where potentially 427 00:25:03,400 --> 00:25:09,520 Speaker 1: every business in this country could you know, benefit extraordinarily 428 00:25:09,640 --> 00:25:15,000 Speaker 1: from this revolution. We haven't even mentioned healthcare GDP. You know, 429 00:25:15,640 --> 00:25:18,879 Speaker 1: we're talking about hundreds of thousands, millions of people and 430 00:25:19,160 --> 00:25:21,760 Speaker 1: who are employed in that industry. Many of the industry 431 00:25:21,800 --> 00:25:24,439 Speaker 1: standards in healthcare are straight out of the nineteenth century. 432 00:25:25,080 --> 00:25:28,680 Speaker 1: You could throw an army at that problem and you 433 00:25:28,720 --> 00:25:31,720 Speaker 1: wouldn't put a dent in it. I mean, do both 434 00:25:31,800 --> 00:25:35,240 Speaker 1: your companies have to scale up to meet the challenge 435 00:25:35,320 --> 00:25:41,800 Speaker 1: of delivery of making this revolution reel. Well. For IBM, 436 00:25:41,920 --> 00:25:46,200 Speaker 1: we've been working with each of these industry sectors for 437 00:25:46,600 --> 00:25:50,399 Speaker 1: for many, many years, and so we have a pretty 438 00:25:50,440 --> 00:25:53,960 Speaker 1: good understanding and a longstanding relationship with with many of 439 00:25:54,000 --> 00:25:56,760 Speaker 1: the leaders in each of those different industries, and so 440 00:25:56,920 --> 00:25:59,400 Speaker 1: we're working day by day with them as just trying 441 00:25:59,440 --> 00:26:02,680 Speaker 1: to adopt these kinds of innovations into their business. So 442 00:26:03,520 --> 00:26:06,080 Speaker 1: as much as I think Trini would say and it 443 00:26:06,320 --> 00:26:08,879 Speaker 1: as what I did, that we both have ambitions to 444 00:26:09,359 --> 00:26:12,320 Speaker 1: to really bring a lot of value to all of 445 00:26:12,359 --> 00:26:14,360 Speaker 1: these inns because we know there will be many other 446 00:26:14,480 --> 00:26:17,720 Speaker 1: firms that will be doing the same. And what we 447 00:26:17,800 --> 00:26:20,439 Speaker 1: want to be able to do is to create UM 448 00:26:21,440 --> 00:26:25,959 Speaker 1: is open, efficient, and is automated a technology environment as 449 00:26:26,040 --> 00:26:31,600 Speaker 1: possible that allows for that innovation to happen. M hmmm, yeah, 450 00:26:32,000 --> 00:26:34,720 Speaker 1: and Steve's summarized very well. I mean, Malcolm, the way 451 00:26:34,800 --> 00:26:37,920 Speaker 1: to look at this is tech is evolving, so much, 452 00:26:38,240 --> 00:26:40,959 Speaker 1: and on multiple fronts. Right, we talked toward sense as 453 00:26:41,000 --> 00:26:43,840 Speaker 1: we talked about we talked about the connectivity, We talked 454 00:26:43,840 --> 00:26:48,280 Speaker 1: about cloud and software and computing. There's no no one 455 00:26:48,359 --> 00:26:50,399 Speaker 1: single company that can claim and say that they have 456 00:26:50,600 --> 00:26:54,119 Speaker 1: mastered all the domains. That's why this is a you know, 457 00:26:54,320 --> 00:26:59,560 Speaker 1: collaboration and kind of standards base interoperably across companies is critical. 458 00:27:00,040 --> 00:27:03,000 Speaker 1: The way I look at OS is we provided these 459 00:27:03,080 --> 00:27:07,520 Speaker 1: what we call the code ingredients, connectivities of critical code ingredient, 460 00:27:07,960 --> 00:27:11,720 Speaker 1: compute clouder code ingredients. We provide them these basic ingredients 461 00:27:11,800 --> 00:27:14,280 Speaker 1: so that others can come on, innovate on top, and 462 00:27:14,440 --> 00:27:17,400 Speaker 1: you know, delover these uh, these these kind of emerging 463 00:27:17,440 --> 00:27:21,760 Speaker 1: the new services to the to the enterprises and costomers. Yeah, 464 00:27:22,080 --> 00:27:24,160 Speaker 1: there's a couple of terms that I wanted to get 465 00:27:25,720 --> 00:27:28,560 Speaker 1: more complete definitions of, and I wanted to start with 466 00:27:28,760 --> 00:27:31,760 Speaker 1: edge computing. I know, Steve, you had talked, you had 467 00:27:31,800 --> 00:27:35,639 Speaker 1: given us a an initial definition, but imagine that I 468 00:27:35,720 --> 00:27:39,760 Speaker 1: am a complete computer literate. Let's let's really dig into 469 00:27:39,840 --> 00:27:42,920 Speaker 1: that term and what we mean when we use that term, 470 00:27:43,440 --> 00:27:47,879 Speaker 1: and how that kind of differs from what might be 471 00:27:47,960 --> 00:27:53,399 Speaker 1: a more conventional um computing model. Yeah, so let me 472 00:27:53,480 --> 00:27:57,080 Speaker 1: use an example that maybe it could help. UM. You 473 00:27:57,200 --> 00:27:59,800 Speaker 1: kind of draw a picture of what that might be. UM. 474 00:27:59,880 --> 00:28:03,480 Speaker 1: I Unfortunately, I'm based in Los Angeles. As you know, 475 00:28:03,560 --> 00:28:06,120 Speaker 1: most of your listeners would know, California has been battling 476 00:28:06,200 --> 00:28:10,359 Speaker 1: with fires, UM. You know, in the fall almost every year. Now, 477 00:28:10,680 --> 00:28:15,960 Speaker 1: this last year very devastating. UM. In an edge connected 478 00:28:16,040 --> 00:28:19,440 Speaker 1: world that Triny and I have been describing, a fire 479 00:28:19,520 --> 00:28:23,880 Speaker 1: begins in northern California on Wednesday. Everything is fine on Thursday. 480 00:28:24,119 --> 00:28:27,480 Speaker 1: On Thursday, we've got an unfolding disaster in you know, 481 00:28:27,640 --> 00:28:34,080 Speaker 1: some part of the state. An edge platform in proximity 482 00:28:34,160 --> 00:28:38,400 Speaker 1: to where that fire is comes to life as a platform, 483 00:28:39,040 --> 00:28:41,480 Speaker 1: and a set of applications come to life on that 484 00:28:41,680 --> 00:28:46,120 Speaker 1: edge platform, and immediately drones are flying above that fire 485 00:28:46,280 --> 00:28:49,360 Speaker 1: and taking video imaging and sending it down to that 486 00:28:49,520 --> 00:28:54,240 Speaker 1: edge platform, and a bunch of AI tools are analyzing 487 00:28:54,280 --> 00:28:57,640 Speaker 1: that video to analyze whereas the fire, what's the terrain 488 00:28:57,800 --> 00:28:59,840 Speaker 1: look like, what are the things that are in the 489 00:29:00,040 --> 00:29:02,640 Speaker 1: half of where that fire is likely to go, so 490 00:29:02,800 --> 00:29:05,640 Speaker 1: that we can begin to plan out how how to 491 00:29:05,760 --> 00:29:08,960 Speaker 1: fight it. And then data coming perhaps from our weather 492 00:29:09,080 --> 00:29:12,480 Speaker 1: company is going into those models on that edge platform, 493 00:29:12,520 --> 00:29:16,960 Speaker 1: and it's infusing intelligence about wind patterns and moisture in 494 00:29:17,000 --> 00:29:20,640 Speaker 1: the air and things that also will impact the way 495 00:29:20,680 --> 00:29:23,960 Speaker 1: that that fire is likely to perform. And that data 496 00:29:24,000 --> 00:29:26,680 Speaker 1: is being analyzed and then fed to the first responders. 497 00:29:27,280 --> 00:29:29,600 Speaker 1: And when the first responders show up on the scene, 498 00:29:30,440 --> 00:29:34,480 Speaker 1: their their trucks, their cars, their equipment is placed in 499 00:29:34,560 --> 00:29:38,320 Speaker 1: an optimal position based on the analysis has been done 500 00:29:38,440 --> 00:29:42,040 Speaker 1: on how that fire is likely to perform and where 501 00:29:42,120 --> 00:29:46,960 Speaker 1: in fact, you know, property or people are at risk 502 00:29:47,160 --> 00:29:50,920 Speaker 1: because of it. And sensors we were talking earlier about sensors. 503 00:29:51,080 --> 00:29:54,240 Speaker 1: Sensors are in the area that are measuring the amount 504 00:29:54,280 --> 00:29:56,800 Speaker 1: of moisture in the foliage. That also is going to 505 00:29:56,920 --> 00:30:00,080 Speaker 1: impact the way that the fight against that fire is 506 00:30:00,120 --> 00:30:02,800 Speaker 1: going to be done. The point is a lot of 507 00:30:02,960 --> 00:30:05,760 Speaker 1: data coming from a number of different sources, from sensors, 508 00:30:05,880 --> 00:30:09,680 Speaker 1: from video being analyzed in an environment it's very close 509 00:30:09,760 --> 00:30:11,840 Speaker 1: to where that fires occurring, so you can get that 510 00:30:11,960 --> 00:30:16,800 Speaker 1: real time response and then providing information. But the ultimate 511 00:30:16,880 --> 00:30:20,680 Speaker 1: objective to save lives, save property, get that fire out 512 00:30:20,720 --> 00:30:24,080 Speaker 1: as soon as possible, and when it's out, that edge 513 00:30:24,120 --> 00:30:28,720 Speaker 1: platform can essentially wind down and and um you know 514 00:30:28,800 --> 00:30:33,000 Speaker 1: those applications can then be kept and made ready for 515 00:30:33,080 --> 00:30:36,480 Speaker 1: the next fire wherever it may happen. That's the kind 516 00:30:36,560 --> 00:30:42,160 Speaker 1: of automation and intelligence and real tie insights that could 517 00:30:42,240 --> 00:30:46,360 Speaker 1: fundamentally be brought to bear in an edge environment. I 518 00:30:46,400 --> 00:30:49,400 Speaker 1: think that hopefully that paints a little bit of a 519 00:30:49,520 --> 00:30:56,440 Speaker 1: picture of how we see this edge environment creating tremendous value. Yeah, wonderful. Well, 520 00:30:56,920 --> 00:31:00,760 Speaker 1: thank you so much to both the Ustraenian Steve. UM. 521 00:31:00,920 --> 00:31:10,320 Speaker 1: There's been Um, it's been really fascinating. I'm so grateful 522 00:31:10,360 --> 00:31:13,400 Speaker 1: for the work that companies like Verizon and IBM are 523 00:31:13,480 --> 00:31:17,080 Speaker 1: doing to help fight wildfires, and to Scrini and Steve 524 00:31:17,480 --> 00:31:19,440 Speaker 1: for taking the time to chat with me about all 525 00:31:19,480 --> 00:31:23,240 Speaker 1: that and more. And the sensors. We can't forget the 526 00:31:23,320 --> 00:31:28,200 Speaker 1: importance of sensors. Sensors that infuse new intelligence. Whether they're 527 00:31:28,280 --> 00:31:31,400 Speaker 1: monitoring where a fire is going or making sure equipment 528 00:31:31,520 --> 00:31:36,120 Speaker 1: in retail factories is functioning safely. Sensors are crucial to 529 00:31:36,200 --> 00:31:41,360 Speaker 1: protecting people in so many different capacities. It's pretty amazing stuff. 530 00:31:42,320 --> 00:31:45,560 Speaker 1: Smart Talks with IBM is produced by Emily Rosteck with 531 00:31:45,760 --> 00:31:51,080 Speaker 1: Carl Migliori, edited by Karen Shakerji. Engineering by Martin Gonzalez, 532 00:31:51,560 --> 00:31:57,040 Speaker 1: mixed and mastered by Jason Gambrel. Music by Gramoscope. Special 533 00:31:57,080 --> 00:32:01,600 Speaker 1: thanks to Molly Sosha, Andy Kelly, Mia Label, Jacob Iceberg, 534 00:32:01,640 --> 00:32:05,640 Speaker 1: Heather Fane, Eric Sandler, Maggie Taylor and everyone at eight 535 00:32:05,680 --> 00:32:10,040 Speaker 1: Bar and IBM. Smart Talks with IBM is a production 536 00:32:10,120 --> 00:32:15,240 Speaker 1: of Pushkin Industries and iHeartMedia. You can find more Pushkin 537 00:32:15,360 --> 00:32:20,440 Speaker 1: podcasts on the iHeart Radio app, Apple Podcasts, or wherever 538 00:32:20,760 --> 00:32:24,920 Speaker 1: you like to listen. I'm Malcolm Gladwell. See you next time.