WEBVTT - Smart Talks with IBM and Malcolm Gladwell: How 5G, Edge Computing and AI are Transforming Industries 0:00:04.160 --> 0:00:07.960 Hello there. This is Smart Talks with IBM, a podcast 0:00:08.280 --> 0:00:12.399 from Pushkin Industries. I Heart Media and IBM about what 0:00:12.440 --> 0:00:15.360 it means to look at today's most challenging problems in 0:00:15.400 --> 0:00:20.200 a new way. I'm Malcolm Glapo. Today I'm talking with 0:00:20.239 --> 0:00:24.040 not one, but two bright folk from two very important companies. 0:00:24.440 --> 0:00:28.200 We have Sweeny Calla Paula, Vice President of Global Technology 0:00:28.440 --> 0:00:33.279 Strategy and Network Cloud at Verizon, as well as Steve Kannapa, 0:00:33.720 --> 0:00:38.920 Global General Manager and Managing Director of IBM's Communications Sector. 0:00:39.800 --> 0:00:44.720 Sweeney Calla Paula leads Verizon's technology strategy and works closely 0:00:44.720 --> 0:00:50.160 with partners like IBM on emerging connectivity technologies. He's passionate 0:00:50.200 --> 0:00:54.279 about a world where people, machines and systems will one 0:00:54.360 --> 0:00:59.680 day interact seamlessly. I like me. Sweeney is extremely excited 0:00:59.800 --> 0:01:03.720 about the technological progress we've made during the pandemic. The 0:01:03.880 --> 0:01:06.920 digital transformations took off in the last one year because 0:01:07.000 --> 0:01:09.760 of the core ridden because we were all kept apart 0:01:09.800 --> 0:01:12.000 and we're still needed to communicate. We cill need to 0:01:12.080 --> 0:01:17.320 get things done. Steve Kanappa heads IBMS Communications Sector, where 0:01:17.360 --> 0:01:20.640 he works with the telecommunications and media firms all over 0:01:20.720 --> 0:01:24.640 the globe to help them modernize their networks. Steve is 0:01:24.840 --> 0:01:28.759 extremely excited about the future of AI and machine learning, 0:01:29.440 --> 0:01:34.680 artificial intelligence, the ability to put machine learning capabilities where 0:01:34.840 --> 0:01:38.840 processes get smarter continuously and more they execute, the smarter 0:01:39.000 --> 0:01:42.760 they get. Right after the break my conversation with Sweeney 0:01:42.840 --> 0:01:47.760 and Steve, the world of technological collaboration has never been 0:01:48.240 --> 0:02:01.559 more fascinating. What the two of you ascribe the nature 0:02:01.680 --> 0:02:05.520 of the challenge that the What is it you're trying 0:02:05.560 --> 0:02:10.000 to address in the work that you do. So in 0:02:10.120 --> 0:02:13.760 a word, um, what we're focused on is innovation. So 0:02:14.120 --> 0:02:20.919 with at IBM, we work with clients in all industries retail, manufacturing, banking, 0:02:21.639 --> 0:02:28.119 media and entertainment, TA, our communications, government agencies, and we're 0:02:28.200 --> 0:02:31.600 helping them provide better services to our customers or their 0:02:31.639 --> 0:02:36.200 constituents by helping them modernize the way that they bring 0:02:36.360 --> 0:02:42.080 technology and applications to all of us. And we work 0:02:42.800 --> 0:02:47.040 in collaboration with the TA, our communications companies and helping 0:02:47.080 --> 0:02:50.679 them bring those new those new capabilities. So should he 0:02:50.800 --> 0:02:55.200 tell me start by describing the nature of your partnership 0:02:55.280 --> 0:02:57.679 with IBM. What is it that you when you work 0:02:57.720 --> 0:03:00.160 with IBM, What are they bringing to the table. One 0:03:00.160 --> 0:03:03.079 of the things we do is that drive reliable connectivity. 0:03:03.320 --> 0:03:05.960 And now the need of reliable connectivity if you look 0:03:06.040 --> 0:03:08.120 back to it and and twenty years ago, the needs 0:03:08.160 --> 0:03:10.679 were different than the needs that are there today to 0:03:10.800 --> 0:03:13.079 where we're going to be integers from now. So you know, 0:03:13.240 --> 0:03:15.120 ten twenty years ago, you know, hey, I want to 0:03:15.160 --> 0:03:16.840 move around, but I want to talk to people. Right 0:03:17.600 --> 0:03:20.120 then that evolved into messaging, That evolved into you know 0:03:20.240 --> 0:03:22.400 four G world where not only you are moving around, 0:03:22.400 --> 0:03:24.400 but you're able to do things with your phone and others. 0:03:25.200 --> 0:03:28.519 But as we look forward, and especially if you notice 0:03:28.600 --> 0:03:31.640 what happened in the last one year, right the digital 0:03:31.800 --> 0:03:34.280 transformation just took off in the last one year because 0:03:34.400 --> 0:03:37.120 of the COVID and because we were all kept apart 0:03:37.200 --> 0:03:39.360 and we still needed to communicate. We still need to 0:03:39.440 --> 0:03:42.040 get things done. Now, the nature of connect really goes 0:03:42.160 --> 0:03:46.080 beyond humans. It now goes into we need to deliver connectivity, 0:03:46.160 --> 0:03:49.400 highly reliable connect rety where machines can communicate with each 0:03:49.440 --> 0:03:53.240 other and machines can actually do things, you know, simple 0:03:53.520 --> 0:03:57.600 things like remote health care. Right. So where IBM kind 0:03:57.600 --> 0:04:00.280 of fits on how we collaborate is that we see 0:04:00.360 --> 0:04:04.640 that the future is about machine mobility. So where our 0:04:04.720 --> 0:04:08.400 networks now have to deliver connectivity two things that move around, 0:04:08.520 --> 0:04:11.440 things that are connected, which require a lot more relevel 0:04:11.480 --> 0:04:15.320 connectivity than humans. Humans we can adapt to errors and 0:04:15.480 --> 0:04:17.880 changes at a at a you know, second level and 0:04:17.960 --> 0:04:20.680 all that. But machines they are designed to be perfect, 0:04:20.720 --> 0:04:24.159 and that means they require high level connectivity. We variazon. 0:04:24.480 --> 0:04:28.360 We build highly reliable, high performance networks. And when you're 0:04:28.400 --> 0:04:31.359 trying to develover these capabilities to let's say, automate industries 0:04:31.400 --> 0:04:36.200 or automate you know, transportation sector and others, IBM understands 0:04:36.240 --> 0:04:39.760 those sector as well. They have been digitizing those sectors 0:04:39.839 --> 0:04:42.720 and and they understand that the main that particular domain 0:04:42.800 --> 0:04:45.240 and what sort of solutions can actually uh you know, 0:04:45.320 --> 0:04:48.559 can be incorporated. So when you bring these two expertise together, 0:04:48.640 --> 0:04:52.080 now we're able to deliver this automation, this decidation in 0:04:52.160 --> 0:04:54.760 more effective way. That's how, you know, that's how we 0:04:54.839 --> 0:05:02.080 both you know, kind of collaborate and work together. When 0:05:02.160 --> 0:05:06.920 we talk about how communication between machines has to have 0:05:07.080 --> 0:05:11.840 a higher standard than communication between humans, what does that mean? 0:05:12.120 --> 0:05:15.320 Are you just talking about reliability? Are you talking about 0:05:15.440 --> 0:05:18.160 the size of the pipe, Are you talking about the 0:05:18.520 --> 0:05:21.720 scale of things you want to do. So I'm going 0:05:21.760 --> 0:05:23.920 to take an example of a let's say a car 0:05:24.080 --> 0:05:27.400 that is connected right and it's a call it a 0:05:27.480 --> 0:05:30.479 semi autonomous car. This car is traveling at hund miles 0:05:30.520 --> 0:05:34.440 per hour and it's capturing, uh, you know, lots of 0:05:34.520 --> 0:05:37.600 information and it may have to understand what are the 0:05:37.680 --> 0:05:40.400 other things that are in that area. When you're traveling 0:05:40.400 --> 0:05:44.920 at hundred miles per hour um, the information you collect 0:05:45.279 --> 0:05:48.000 and the decisions that you need are going to be 0:05:48.080 --> 0:05:51.560 in the order of milliseconds. Because if you have to 0:05:51.640 --> 0:05:54.440 break at a you know, at a speed, if you 0:05:54.560 --> 0:05:56.880 take a second, you're already you know, causing an accident. 0:05:57.240 --> 0:06:00.520 You're now talking in the ranges of ten early seconds, 0:06:00.640 --> 0:06:03.839 ranges of area. So that's one thing we talk about 0:06:03.839 --> 0:06:08.000 result you know, highly latency sensitive networking, whether the humans 0:06:08.480 --> 0:06:10.840 let's say you're you're browsing something that are trying to 0:06:10.920 --> 0:06:13.479 pull a website, it takes a couple of seconds longer 0:06:13.640 --> 0:06:16.680 you don't actually sense that field that maybe five stand 0:06:16.760 --> 0:06:19.880 seconds you feel that. Whereas when you connect in any 0:06:19.960 --> 0:06:22.240 kind of automated thing, you know, dron't trying to deliver 0:06:22.720 --> 0:06:26.560 a robotic vehicle within a factory environment. These things are 0:06:26.600 --> 0:06:31.279 moving at a pace where you can perceive a millisecond delay, 0:06:31.480 --> 0:06:33.919 and and so the networks now have to operate at 0:06:33.960 --> 0:06:36.280 that level is a much higher order of you know, 0:06:36.760 --> 0:06:39.680 you know latencies. Now, the machines that we're trying to automate, 0:06:39.680 --> 0:06:43.000 they're collecting all of this data, lots of data. We're 0:06:43.040 --> 0:06:45.840 talking about hundreds of magnitudes higher than what you used 0:06:45.880 --> 0:06:47.960 to get collecting. Now, if you try to send all 0:06:48.000 --> 0:06:51.280 the data to a far away cloud, that means that 0:06:51.360 --> 0:06:53.800 you require massive amount of networks all the way, which 0:06:53.839 --> 0:06:56.920 which don't exist today. That's where edge computer comes from. 0:06:57.000 --> 0:06:59.560 The picture where you collect all of this data, you 0:06:59.640 --> 0:07:01.480 hand it over to an age cloud, but that's very 0:07:01.520 --> 0:07:03.680 close to the user. Let's say you know a few 0:07:03.760 --> 0:07:07.320 miles from the user. Then you process the data rumor 0:07:07.520 --> 0:07:09.720 a lot of data that you collect is more of 0:07:10.120 --> 0:07:12.880 what we call it's not information, it is data. Right, 0:07:13.160 --> 0:07:16.120 You take the data, you process it locally, you get 0:07:16.160 --> 0:07:18.160 information out of it, and then you use that to 0:07:18.280 --> 0:07:20.320 both coming and get back to the object you're trying 0:07:20.320 --> 0:07:22.080 to control as well as you know, send it to 0:07:22.120 --> 0:07:24.080 where or you need to send it to. So in 0:07:24.800 --> 0:07:26.520 These are the kind of you know, key ingredients that 0:07:26.560 --> 0:07:28.760 you require as you look start looking into the future. 0:07:29.840 --> 0:07:33.120 Last week, I was in Phoenix and I took a 0:07:33.240 --> 0:07:36.240 ride on one of those autonomous vehicles. I ordered the 0:07:36.480 --> 0:07:39.840 taxi on my app. It showed up. You know, no 0:07:40.120 --> 0:07:43.200 no human being to be found. The digital backbone, the 0:07:43.280 --> 0:07:47.920 communication systems. This is based on better be good because 0:07:48.000 --> 0:07:50.600 if you know, if we drop coverage going at forty 0:07:50.640 --> 0:07:53.640 miles down the road trying to navigate a you know, 0:07:54.400 --> 0:07:58.040 then I'm in trouble, right, there's no backup here exactly, 0:07:58.520 --> 0:08:00.760 And if you think about what's actually were happening in 0:08:01.200 --> 0:08:07.120 those scenarios, it's it's about experience, so changing the experience, 0:08:07.360 --> 0:08:12.600 it's about personalization, and oftentimes it's about delivering the insights 0:08:12.960 --> 0:08:16.800 in real time about how you how how a process 0:08:16.960 --> 0:08:20.160 is happening. I'll bring it back to the manufacturing shop 0:08:20.200 --> 0:08:22.600 floor as an example to kind of tie together a 0:08:22.680 --> 0:08:25.360 couple of the points that Strening was making. Now, we've 0:08:25.400 --> 0:08:29.080 been instrumenting with IoT devices and manufacturing shop floors for 0:08:29.200 --> 0:08:33.520 quite a while hundreds if not thousands, but now think 0:08:33.520 --> 0:08:37.120 about taking that to tens of thousands of sensors on 0:08:37.400 --> 0:08:40.480 everything that's happening in real time in that manufacturing floor. 0:08:41.400 --> 0:08:44.080 Think about how videos changed all of our lives over 0:08:44.160 --> 0:08:47.040 the last few years, as it's become ubiquitous in the 0:08:47.080 --> 0:08:51.520 way we work and the way we you know, get entertainment, news, information, 0:08:52.160 --> 0:08:55.199 the way we communicate with each other. Or video is 0:08:55.360 --> 0:08:58.880 essentially just rich data and it and and so. Now 0:08:58.920 --> 0:09:01.240 instead of just having swers on that shop floor, we 0:09:01.280 --> 0:09:05.120 could have video cameras watching everything is happening, watching workers 0:09:05.200 --> 0:09:09.240 to make sure they're in safe zones, watching whatever is 0:09:09.240 --> 0:09:13.600 being produced coming down the factory line, understanding if it's 0:09:13.720 --> 0:09:16.520 being done exactly the way it's supposed to be done, 0:09:16.800 --> 0:09:19.559 Watching the machinery itself and the way it's performing to 0:09:19.760 --> 0:09:23.600 notice the slightest changes. And the second thing that we're 0:09:23.640 --> 0:09:28.760 applying in is artificial intelligence, the ability to put machine 0:09:28.920 --> 0:09:34.000 learning capabilities where processes get smarter continuously and more they execute, 0:09:34.080 --> 0:09:37.400 the smarter they get. So now when you combine those 0:09:37.480 --> 0:09:40.720 two forces together, the ability to use high fidelity data 0:09:40.880 --> 0:09:44.679 like video, and the ability to interrogate and analyze that 0:09:44.840 --> 0:09:48.079 data in real time and do it right where things 0:09:48.160 --> 0:09:51.360 are occurring, like on the shop floor, and then have 0:09:51.559 --> 0:09:54.720 that backed by the kind of connectivity and the power 0:09:54.920 --> 0:09:58.880 that the Verizon can bring with their their edge computing platforms. 0:09:59.559 --> 0:10:03.480 We have the opportunity now to add tremendous value to 0:10:03.600 --> 0:10:07.480 those processes, whether that's making sure people are safe, making 0:10:07.520 --> 0:10:10.560 sure that those lines are as productive and as efficient 0:10:10.600 --> 0:10:13.439 as possible, making sure the product quality is as high 0:10:13.480 --> 0:10:16.959 as it should be. A tremendous uh, you know, amount 0:10:17.080 --> 0:10:19.240 of value can be created by being able to apply 0:10:20.040 --> 0:10:24.760 that that intelligence, um that that high bandwidth capability, and 0:10:24.840 --> 0:10:27.480 to make sure that that network is there and ready, 0:10:27.920 --> 0:10:32.680 as Shrine was describing, to constantly serve up those insights. Yeah, 0:10:33.000 --> 0:10:37.000 imagine that. I'm a uh, I have a you know, 0:10:37.120 --> 0:10:41.479 mid sized manufacturing company, you know, founded by my grandfather. 0:10:42.280 --> 0:10:45.960 You know, I've been keeping reasonably keeping pace with innovation. 0:10:46.040 --> 0:10:48.440 I think I'm pretty competitive. I have not done any 0:10:48.480 --> 0:10:51.080 of the things you've described. The two of you show 0:10:51.160 --> 0:10:53.920 up at my front door, and what kind of promise 0:10:54.000 --> 0:10:57.600 can you make me? What like, for example, what when 0:10:57.640 --> 0:11:01.240 you talk about productivity improvements are we talking about? What 0:11:01.320 --> 0:11:03.600 are we talking about your two five percent, ten percent? 0:11:03.960 --> 0:11:07.679 Give me kind of more tangible examples of what you 0:11:07.760 --> 0:11:11.000 can deliver to a customer like that. So the way 0:11:11.200 --> 0:11:14.200 industries have been evolving is that they have a lot 0:11:14.280 --> 0:11:19.440 of these sensors, and the sensors have been more analog 0:11:19.640 --> 0:11:21.880 and and and what I mean is that more each 0:11:21.920 --> 0:11:24.280 one is designed to do a particular thing. I can 0:11:24.360 --> 0:11:26.960 tell you that like temperature sense of that basically says 0:11:27.120 --> 0:11:29.360 if you cross certain temperature, you slve the machine. You 0:11:29.480 --> 0:11:33.040 have let's say some windspeed sens or something else, something else. 0:11:34.240 --> 0:11:37.360 What industries are realizing is that these sensors have been 0:11:37.360 --> 0:11:40.720 doing a certain function by now I'm going to use 0:11:40.720 --> 0:11:44.040 the term called virtualizing, by basically making them more lean 0:11:44.160 --> 0:11:47.000 and smarter and connect to the edge. You can now 0:11:47.080 --> 0:11:50.199 get all this information and you can actually make decisions 0:11:50.240 --> 0:11:53.679 based on collective intelligence intelligence of all the sensors than 0:11:53.840 --> 0:11:58.280 each sensor doing what other things. And to do that 0:11:58.480 --> 0:12:01.840 you need a highly releveled character. By bringing the entire 0:12:01.920 --> 0:12:05.720 collective intelligence and and and in making these sensors more virtual. 0:12:06.160 --> 0:12:08.240 A couple of things you're doing. One, you're going to 0:12:08.360 --> 0:12:12.839 bring in latest capabilities in UM called product quality and 0:12:12.920 --> 0:12:17.559 manufacturing and others, meaning that you would improve the radios downtimes, 0:12:17.760 --> 0:12:23.480 improve you know, the amount of productivity, improve quality control issues. 0:12:24.160 --> 0:12:27.040 And you're actually going to enable the factory to continue 0:12:27.040 --> 0:12:30.000 to adapt to the new or digital capabilities that are 0:12:30.040 --> 0:12:32.280 going to come out. And each one of these capabilities, 0:12:32.280 --> 0:12:34.559 where as AI and whatever else, they're all going to 0:12:34.679 --> 0:12:38.240 add to your productivity, your quality, or you know, the 0:12:38.320 --> 0:12:41.480 key metrics that you're you're looking at. So for a 0:12:41.559 --> 0:12:45.280 hypothetical imagine which is saying, is I suppose I have 0:12:45.400 --> 0:12:48.719 a machine, a very expensive piece of machinery. My my 0:12:49.480 --> 0:12:52.880 my cousin is the president of a company in Queens 0:12:52.960 --> 0:12:57.040 that makes Jamaican beef paddies and they get these machines 0:12:57.160 --> 0:13:00.439 from Italy. The cost just to say, in amounts of 0:13:00.480 --> 0:13:03.120 money that you know, take the beat patty from what 0:13:03.600 --> 0:13:05.240 and when the when something goes wrong, it's like a 0:13:05.360 --> 0:13:07.840 crisis because someone has to fly in from Italy. Right, 0:13:07.920 --> 0:13:11.679 So listening to I'm thinking, well, suppose we discovered that 0:13:11.760 --> 0:13:15.800 the machine starts to make lots of airs when humidity 0:13:16.120 --> 0:13:18.640 is hits a certain point in the company. It's kind 0:13:18.720 --> 0:13:21.280 in the factory floor, the temperature is such when it's 0:13:21.320 --> 0:13:23.760 been running for X number of hours in a row. 0:13:24.320 --> 0:13:28.360 When the operator makes this kind of mistake. I mean, 0:13:28.360 --> 0:13:30.640 I can imagine ten different things that we could say 0:13:30.840 --> 0:13:34.760 might affect performance, and you could have sensors on all 0:13:34.800 --> 0:13:37.480 those ten things and do a calculation at any given 0:13:37.520 --> 0:13:40.679 time about what my chances are that the machine might 0:13:40.760 --> 0:13:43.760 have a malfunction and presumably warned me before it happens. 0:13:43.840 --> 0:13:46.960 Is that exactly exactly? And what you've what you've just done, 0:13:47.040 --> 0:13:50.200 is you've crossed over from observation into prediction, which becomes 0:13:50.360 --> 0:13:53.800 really powerful so that you can begin to understand what's 0:13:53.800 --> 0:13:57.120 about to happen actually before it occurs, and you can 0:13:57.200 --> 0:14:01.200 take corrective actions to do it. As a perfect example 0:14:01.320 --> 0:14:03.560 of the kind of tremendous value that can be gained here. 0:14:04.400 --> 0:14:07.679 Adding to my little scenario from before, we could have 0:14:07.840 --> 0:14:10.439 sensors that tell me when my raw materials are getting low. 0:14:11.240 --> 0:14:14.800 We could have an AI system which reminds me when 0:14:15.640 --> 0:14:19.880 variations in demand and says you actually, every year have 0:14:19.960 --> 0:14:24.080 a huge uptake in at easter, you're you know orders 0:14:25.200 --> 0:14:26.880 that means you've got to order X, Y and Z, right. 0:14:26.880 --> 0:14:29.640 I mean, this is all the kinds of things you 0:14:29.720 --> 0:14:32.400 might want to do. How hard if you walked in 0:14:32.720 --> 0:14:35.480 how hard? What is it to build that kind of system? 0:14:36.200 --> 0:14:39.840 Is this something you can do relatively quickly and efficiently? 0:14:40.040 --> 0:14:42.160 Or is it does it take months to kind of 0:14:42.320 --> 0:14:46.480 customize a system for a customer like that. So for 0:14:46.920 --> 0:14:51.840 for you know, any different clients, some things can be 0:14:51.920 --> 0:14:57.200 basically prepackaged and ready to deploy UM. And you know, 0:14:57.480 --> 0:15:01.040 as as these solutions of all UM, you know, there 0:15:01.040 --> 0:15:04.520 will be some offerings that are essentially load and go 0:15:04.880 --> 0:15:08.640 as a service. Others will be more customized or tailored 0:15:08.800 --> 0:15:12.520 based on what given client will want to do. But 0:15:12.680 --> 0:15:16.520 one of the things that I think really important in 0:15:16.680 --> 0:15:19.040 what we're working on and what Verizon is working on, 0:15:19.280 --> 0:15:22.720 is there are a set of standards that the marketplace 0:15:22.840 --> 0:15:27.440 is embracing. And those standards allow for an ecosystem of 0:15:27.520 --> 0:15:33.280 players to come together and to work seamlessly. And and 0:15:33.400 --> 0:15:36.120 for IBM, you may have heard, you know, the term 0:15:37.000 --> 0:15:42.320 our open hybrid cloud approach. We're very very focused working 0:15:42.680 --> 0:15:47.440 in collaboration with the telecommunication providers in the marketplace to 0:15:47.560 --> 0:15:51.320 help bring these open standards because two really powerful things 0:15:51.360 --> 0:15:56.840 That one is we create an enormous pool of innovators 0:15:57.400 --> 0:16:00.720 that can contribute and accelerate the rate piece of innovation. 0:16:01.120 --> 0:16:03.560 So we're working very closely with Verizon on on some 0:16:03.720 --> 0:16:07.480 of those open standard architectures. And then Secondly, I'm really 0:16:07.960 --> 0:16:11.920 creating an ecosystem of partners. So when we announced just 0:16:12.080 --> 0:16:15.040 at towards the end of last year, are IBM Cloud 0:16:15.120 --> 0:16:20.760 for Telco, we announced with UM over forty different partners 0:16:21.280 --> 0:16:24.560 that are coming together to be able to bring innovation 0:16:24.920 --> 0:16:27.560 UM to these kinds of solutions. And I think that's 0:16:28.080 --> 0:16:31.400 that's going to help us UM really accelerate the opportunity 0:16:31.440 --> 0:16:36.640 to bring these kinds of solutions. Were describing treaty. Is 0:16:36.680 --> 0:16:41.000 there somebody out there, an industry, a sector that you 0:16:41.120 --> 0:16:44.800 think could benefit the most from the kind of services 0:16:44.880 --> 0:16:48.680 that I'd be having Verizon provide. I mean, who where 0:16:48.760 --> 0:16:52.520 could you make an extraordinary difference? Retail? Retail is a 0:16:52.680 --> 0:16:57.920 very interesting area because both with COVID you started getting 0:16:57.920 --> 0:17:00.400 into this ideas of touch free detail and you do 0:17:00.640 --> 0:17:04.639 things more customized and you users and others. But retail 0:17:04.640 --> 0:17:08.000 itself is actually changing. UM. You know, you hear about 0:17:08.119 --> 0:17:11.600 these UH stores without any assistance that you're walking, you 0:17:11.680 --> 0:17:14.280 buy and walk out. In all of these cases, what 0:17:14.400 --> 0:17:18.320 you're seeing is automation really taking a bigger place. For example, 0:17:19.040 --> 0:17:21.680 you know one of the retailers in a decent sized 0:17:21.720 --> 0:17:26.080 store is looking to put about high definition cameras. Now 0:17:26.280 --> 0:17:30.359 the cameras work as call it computer vision sensors, and 0:17:30.480 --> 0:17:33.720 they capture the information and using that you can actually 0:17:33.840 --> 0:17:36.760 understand what you know, individuals are. You can do lots 0:17:36.800 --> 0:17:38.920 of things with the computers and of what people are doing, 0:17:39.200 --> 0:17:41.120 how much stock you have in the store, and lots 0:17:41.119 --> 0:17:44.280 of other things. Now you take all that, you process 0:17:44.400 --> 0:17:46.879 that and uh and you could you know, if you 0:17:47.119 --> 0:17:50.960 introduce automation at the point where people can come and 0:17:51.000 --> 0:17:53.000 pick up what they want, they can walk out without 0:17:53.119 --> 0:17:55.600 having to even interact with anybody out there. Right, that's 0:17:55.640 --> 0:17:58.720 one extreme of it. I just imagine that I'm I'm 0:17:58.760 --> 0:18:01.240 running a store, did a bunch of cameras. I got 0:18:01.520 --> 0:18:05.240 fifty stores across America. I've just put in my spring line, 0:18:06.160 --> 0:18:08.639 and I got a bunch of new dresses. So you 0:18:08.680 --> 0:18:10.840 could tell me that, Malcolm, you have this new orange 0:18:10.920 --> 0:18:13.280 dress which you put in all fifty stores. Not a 0:18:13.359 --> 0:18:15.679 single person has looked at that dress in the retail 0:18:15.720 --> 0:18:17.760 store in the last week. You can also tell me 0:18:19.440 --> 0:18:22.840 seventy of the people stopped in the thing lingered in 0:18:22.920 --> 0:18:27.200 front of the teal sweatshirt, and you're out of teal 0:18:27.200 --> 0:18:31.639 sweatshirts as a result. Order more teal sweatshirts now and 0:18:31.800 --> 0:18:34.680 forget about the dress. It's not going anywhere. And not 0:18:34.800 --> 0:18:37.639 only not only that, I'm not a retail expert. By there, 0:18:37.760 --> 0:18:39.320 I can tell you that. Not only that, we can 0:18:39.359 --> 0:18:42.520 also tell you that people who looked at something for 0:18:42.720 --> 0:18:45.879 how longer time, two seconds, two minutes, whatever, tend to 0:18:45.960 --> 0:18:48.120 buy things versus if they don't look at it, that means, 0:18:48.200 --> 0:18:50.040 you know, if they're just walking off, that means you 0:18:50.119 --> 0:18:52.760 know that you're not gonna So you can get such 0:18:52.800 --> 0:18:56.240 a deeper set of insights that both can help you 0:18:56.320 --> 0:18:59.560 as a business to figure out how do you, you know, 0:18:59.640 --> 0:19:02.200 how do you interact with your customers. But at the 0:19:02.280 --> 0:19:06.800 same time, you can also use this this data to 0:19:06.960 --> 0:19:09.840 actually uh you know call it push your prior to 0:19:09.920 --> 0:19:12.680 the customer and you know, based on their personal preferences, 0:19:12.800 --> 0:19:16.240 choices and color circumstances. Right. So that's the kind of 0:19:16.960 --> 0:19:19.359 the data gives you so much and so much what 0:19:19.480 --> 0:19:21.959 you call ability to kind of you know, customize yourself 0:19:22.560 --> 0:19:25.280 to to meet the customer demands. You know, in a way, 0:19:25.359 --> 0:19:27.480 what we're talking about here is you know, a using 0:19:27.600 --> 0:19:30.440 video as we talked about as a rich fidelity set 0:19:30.480 --> 0:19:33.280 of data but also applying AI or intelligence to that 0:19:33.440 --> 0:19:36.320 so that you can take action on it. But part 0:19:36.400 --> 0:19:40.720 of this is about humans and machines interacting, which makes 0:19:41.160 --> 0:19:45.480 the human in doing that role more effective. Another area 0:19:45.600 --> 0:19:49.480 where we both are excited about is industrial automation. So 0:19:49.600 --> 0:19:52.399 if you look at you know, malcome, what happened over 0:19:52.440 --> 0:19:55.639 the last few years is the world has digitized. You know, 0:19:55.760 --> 0:19:58.760 everything can be digital. The back office is the manufacturing 0:19:58.840 --> 0:20:02.040 process and others they have not being disguised. So you know, 0:20:02.119 --> 0:20:05.359 in a number of situations, you put this nice digital 0:20:05.520 --> 0:20:08.480 store front and digital experience in the front, but your 0:20:08.520 --> 0:20:11.920 backup is still lagging with you know, older factories and 0:20:12.000 --> 0:20:14.600 older sensors and whatnot. And the problem is that at 0:20:14.680 --> 0:20:16.159 some point the problem is going to catch up, and 0:20:16.320 --> 0:20:18.120 and that is you will not be able to keep 0:20:18.240 --> 0:20:21.840 your front digital experiences without really you know, knowing what 0:20:22.080 --> 0:20:24.160 is going on your factories or do you have the product? 0:20:24.240 --> 0:20:26.239 You can you shave the product on time? And if 0:20:26.280 --> 0:20:28.040 you come in to somebody, I'm going to deliver something 0:20:28.080 --> 0:20:32.159 away tomorrow, can you really get it there? So the 0:20:32.320 --> 0:20:34.440 next ten years and there's gonna be a lot of 0:20:34.560 --> 0:20:38.920 industrial automation that's going to take place now for industrial automation, UM, 0:20:39.040 --> 0:20:40.639 a few things that are going to make a bigger, 0:20:40.760 --> 0:20:45.200 bigger impact. One, you do need a highly reliable connectivity 0:20:45.240 --> 0:20:48.920 within those environments. Whatever small number of sensors they have today, 0:20:49.119 --> 0:20:52.359 they're connected using wires. But the problem the virus is 0:20:52.440 --> 0:20:55.040 that it takes a long time to really go connectivity. 0:20:55.160 --> 0:20:58.840 You need a highly reliable wireless communication. Number Two, we're 0:20:58.840 --> 0:21:02.600 gonna have inordinate amount of sensors all over these environments 0:21:02.680 --> 0:21:07.720 because to your point about your cousin's uh Jamaican beef 0:21:07.720 --> 0:21:12.120 parties by it does sound very attractive. So they're good, 0:21:12.160 --> 0:21:17.439 They're really good. So so now you're gonna put sensors 0:21:17.480 --> 0:21:19.959 everywhere so that you can predict, you can understand exactly 0:21:20.000 --> 0:21:22.240 what's going on. The third thing you're gonna do is 0:21:22.359 --> 0:21:25.480 that use all of the data that's getting generated and 0:21:25.680 --> 0:21:28.680 apply AI and machine learning tactics on that so that 0:21:29.080 --> 0:21:32.399 you can not only understand the the you know, the 0:21:32.480 --> 0:21:34.119 world is going on within the environment, but you can 0:21:34.119 --> 0:21:36.840 actually predict. You can forecast now in a much better 0:21:36.920 --> 0:21:39.480 way so that if your customers expecting X y Z 0:21:39.640 --> 0:21:42.119 on certain time, you have data to tell you that 0:21:42.200 --> 0:21:44.680 you can absolutely come into the X y Z to 0:21:44.800 --> 0:21:47.800 be delivered to the customer on time. So it's that 0:21:48.080 --> 0:21:53.080 combination of sensors, programming, and intelligence and the AI and others. 0:21:53.440 --> 0:21:56.600 IBM understands a lot from that environment to the reliable 0:21:56.640 --> 0:21:59.080 connectory that we bring and and the edge computer that 0:21:59.359 --> 0:22:04.080 that we put to other those elements that you've just mentioned, Um, 0:22:05.640 --> 0:22:07.960 there they can't all be at the morning. If they're 0:22:08.080 --> 0:22:12.040 they're not all the same level of sophistication or development. 0:22:12.200 --> 0:22:14.560 Or is there one that you worry the most about? 0:22:14.720 --> 0:22:16.760 Like for example, when you were talking, I was thinking, 0:22:17.880 --> 0:22:20.040 you know, the Internet in my house it's not Horizon, 0:22:20.560 --> 0:22:22.880 but the Internet in my house it's not that good. 0:22:22.920 --> 0:22:27.560 I mean, I have to reboot my router every two days, 0:22:28.240 --> 0:22:30.320 and I get a little I have to have massive 0:22:30.400 --> 0:22:32.879 backups because sometimes it's just cons I you know what 0:22:32.880 --> 0:22:34.680 I mean. Like when I hear this, I think this 0:22:34.840 --> 0:22:37.400 is really really wonderful, But the reality in the little 0:22:37.480 --> 0:22:40.400 town I live in is Interne's just not that good. 0:22:41.480 --> 0:22:44.399 So if I was a company in botttle town, how 0:22:44.440 --> 0:22:47.040 would I do what you're doing? If my if my 0:22:47.160 --> 0:22:52.080 fundamental services so spotty. Yeah, by the first I wish 0:22:52.160 --> 0:22:55.080 we were your internet providers. You wouldn't be having those issues. 0:22:56.320 --> 0:23:03.280 That's my uh So, I think the infrastructure, the broader 0:23:03.359 --> 0:23:08.240 connectivity infrastructure UM will need to evolve. Again, I go 0:23:08.359 --> 0:23:11.320 back to COVID because then COVID truly pointed it out 0:23:11.440 --> 0:23:15.159 that you can operate at a normal pace if you 0:23:15.240 --> 0:23:17.080 have a good connectivity, but if you don't have a 0:23:17.119 --> 0:23:20.560 good connectivity, you will get left behind. We certainly continue 0:23:20.600 --> 0:23:24.440 to deploy more and more connectivity UM. Where I see 0:23:25.320 --> 0:23:27.400 it's not a worry, but it's more of an optimism. 0:23:27.560 --> 0:23:29.520 Is that is five G is going to change that. 0:23:29.600 --> 0:23:32.320 Now five G brings in higher amount of throughputs in 0:23:32.359 --> 0:23:34.680