Speaker 1: Hello there. This is Smart Talks with IBM, a podcast
Speaker 1: from Pushkin Industries. I Heart Media and IBM about what
Speaker 1: it means to look at today's most challenging problems in
Speaker 1: a new way. I'm Malcolm Glapo. Today I'm talking with
Speaker 1: not one, but two bright folk from two very important companies.
Speaker 1: We have Sweeny Calla Paula, Vice President of Global Technology
Speaker 1: Strategy and Network Cloud at Verizon, as well as Steve Kannapa,
Speaker 1: Global General Manager and Managing Director of IBM's Communications Sector.
Speaker 1: Sweeney Calla Paula leads Verizon's technology strategy and works closely
Speaker 1: with partners like IBM on emerging connectivity technologies. He's passionate
Speaker 1: about a world where people, machines and systems will one
Speaker 1: day interact seamlessly. I like me. Sweeney is extremely excited
Speaker 1: about the technological progress we've made during the pandemic. The
Speaker 1: digital transformations took off in the last one year because
Speaker 1: of the core ridden because we were all kept apart
Speaker 1: and we're still needed to communicate. We cill need to
Speaker 1: get things done. Steve Kanappa heads IBMS Communications Sector, where
Speaker 1: he works with the telecommunications and media firms all over
Speaker 1: the globe to help them modernize their networks. Steve is
Speaker 1: extremely excited about the future of AI and machine learning,
Speaker 1: artificial intelligence, the ability to put machine learning capabilities where
Speaker 1: processes get smarter continuously and more they execute, the smarter
Speaker 1: they get. Right after the break my conversation with Sweeney
Speaker 1: and Steve, the world of technological collaboration has never been
Speaker 1: more fascinating. What the two of you ascribe the nature
Speaker 1: of the challenge that the What is it you're trying
Speaker 1: to address in the work that you do. So in
Speaker 1: a word, um, what we're focused on is innovation. So
Speaker 1: with at IBM, we work with clients in all industries retail, manufacturing, banking,
Speaker 1: media and entertainment, TA, our communications, government agencies, and we're
Speaker 1: helping them provide better services to our customers or their
Speaker 1: constituents by helping them modernize the way that they bring
Speaker 1: technology and applications to all of us. And we work
Speaker 1: in collaboration with the TA, our communications companies and helping
Speaker 1: them bring those new those new capabilities. So should he
Speaker 1: tell me start by describing the nature of your partnership
Speaker 1: with IBM. What is it that you when you work
Speaker 1: with IBM, What are they bringing to the table. One
Speaker 1: of the things we do is that drive reliable connectivity.
Speaker 1: And now the need of reliable connectivity if you look
Speaker 1: back to it and and twenty years ago, the needs
Speaker 1: were different than the needs that are there today to
Speaker 1: where we're going to be integers from now. So you know,
Speaker 1: ten twenty years ago, you know, hey, I want to
Speaker 1: move around, but I want to talk to people. Right
Speaker 1: then that evolved into messaging, That evolved into you know
Speaker 1: four G world where not only you are moving around,
Speaker 1: but you're able to do things with your phone and others.
Speaker 1: But as we look forward, and especially if you notice
Speaker 1: what happened in the last one year, right the digital
Speaker 1: transformation just took off in the last one year because
Speaker 1: of the COVID and because we were all kept apart
Speaker 1: and we still needed to communicate. We still need to
Speaker 1: get things done. Now, the nature of connect really goes
Speaker 1: beyond humans. It now goes into we need to deliver connectivity,
Speaker 1: highly reliable connect rety where machines can communicate with each
Speaker 1: other and machines can actually do things, you know, simple
Speaker 1: things like remote health care. Right. So where IBM kind
Speaker 1: of fits on how we collaborate is that we see
Speaker 1: that the future is about machine mobility. So where our
Speaker 1: networks now have to deliver connectivity two things that move around,
Speaker 1: things that are connected, which require a lot more relevel
Speaker 1: connectivity than humans. Humans we can adapt to errors and
Speaker 1: changes at a at a you know, second level and
Speaker 1: all that. But machines they are designed to be perfect,
Speaker 1: and that means they require high level connectivity. We variazon.
Speaker 1: We build highly reliable, high performance networks. And when you're
Speaker 1: trying to develover these capabilities to let's say, automate industries
Speaker 1: or automate you know, transportation sector and others, IBM understands
Speaker 1: those sector as well. They have been digitizing those sectors
Speaker 1: and and they understand that the main that particular domain
Speaker 1: and what sort of solutions can actually uh you know,
Speaker 1: can be incorporated. So when you bring these two expertise together,
Speaker 1: now we're able to deliver this automation, this decidation in
Speaker 1: more effective way. That's how, you know, that's how we
Speaker 1: both you know, kind of collaborate and work together. When
Speaker 1: we talk about how communication between machines has to have
Speaker 1: a higher standard than communication between humans, what does that mean?
Speaker 1: Are you just talking about reliability? Are you talking about
Speaker 1: the size of the pipe, Are you talking about the
Speaker 1: scale of things you want to do. So I'm going
Speaker 1: to take an example of a let's say a car
Speaker 1: that is connected right and it's a call it a
Speaker 1: semi autonomous car. This car is traveling at hund miles
Speaker 1: per hour and it's capturing, uh, you know, lots of
Speaker 1: information and it may have to understand what are the
Speaker 1: other things that are in that area. When you're traveling
Speaker 1: at hundred miles per hour um, the information you collect
Speaker 1: and the decisions that you need are going to be
Speaker 1: in the order of milliseconds. Because if you have to
Speaker 1: break at a you know, at a speed, if you
Speaker 1: take a second, you're already you know, causing an accident.
Speaker 1: You're now talking in the ranges of ten early seconds,
Speaker 1: ranges of area. So that's one thing we talk about
Speaker 1: result you know, highly latency sensitive networking, whether the humans
Speaker 1: let's say you're you're browsing something that are trying to
Speaker 1: pull a website, it takes a couple of seconds longer
Speaker 1: you don't actually sense that field that maybe five stand
Speaker 1: seconds you feel that. Whereas when you connect in any
Speaker 1: kind of automated thing, you know, dron't trying to deliver
Speaker 1: a robotic vehicle within a factory environment. These things are
Speaker 1: moving at a pace where you can perceive a millisecond delay,
Speaker 1: and and so the networks now have to operate at
Speaker 1: that level is a much higher order of you know,
Speaker 1: you know latencies. Now, the machines that we're trying to automate,
Speaker 1: they're collecting all of this data, lots of data. We're
Speaker 1: talking about hundreds of magnitudes higher than what you used
Speaker 1: to get collecting. Now, if you try to send all
Speaker 1: the data to a far away cloud, that means that
Speaker 1: you require massive amount of networks all the way, which
Speaker 1: which don't exist today. That's where edge computer comes from.
Speaker 1: The picture where you collect all of this data, you
Speaker 1: hand it over to an age cloud, but that's very
Speaker 1: close to the user. Let's say you know a few
Speaker 1: miles from the user. Then you process the data rumor
Speaker 1: a lot of data that you collect is more of
Speaker 1: what we call it's not information, it is data. Right,
Speaker 1: You take the data, you process it locally, you get
Speaker 1: information out of it, and then you use that to
Speaker 1: both coming and get back to the object you're trying
Speaker 1: to control as well as you know, send it to
Speaker 1: where or you need to send it to. So in
Speaker 1: These are the kind of you know, key ingredients that
Speaker 1: you require as you look start looking into the future.
Speaker 1: Last week, I was in Phoenix and I took a
Speaker 1: ride on one of those autonomous vehicles. I ordered the
Speaker 1: taxi on my app. It showed up. You know, no
Speaker 1: no human being to be found. The digital backbone, the
Speaker 1: communication systems. This is based on better be good because
Speaker 1: if you know, if we drop coverage going at forty
Speaker 1: miles down the road trying to navigate a you know,
Speaker 1: then I'm in trouble, right, there's no backup here exactly,
Speaker 1: And if you think about what's actually were happening in
Speaker 1: those scenarios, it's it's about experience, so changing the experience,
Speaker 1: it's about personalization, and oftentimes it's about delivering the insights
Speaker 1: in real time about how you how how a process
Speaker 1: is happening. I'll bring it back to the manufacturing shop
Speaker 1: floor as an example to kind of tie together a
Speaker 1: couple of the points that Strening was making. Now, we've
Speaker 1: been instrumenting with IoT devices and manufacturing shop floors for
Speaker 1: quite a while hundreds if not thousands, but now think
Speaker 1: about taking that to tens of thousands of sensors on
Speaker 1: everything that's happening in real time in that manufacturing floor.
Speaker 1: Think about how videos changed all of our lives over
Speaker 1: the last few years, as it's become ubiquitous in the
Speaker 1: way we work and the way we you know, get entertainment, news, information,
Speaker 1: the way we communicate with each other. Or video is
Speaker 1: essentially just rich data and it and and so. Now
Speaker 1: instead of just having swers on that shop floor, we
Speaker 1: could have video cameras watching everything is happening, watching workers
Speaker 1: to make sure they're in safe zones, watching whatever is
Speaker 1: being produced coming down the factory line, understanding if it's
Speaker 1: being done exactly the way it's supposed to be done,
Speaker 1: Watching the machinery itself and the way it's performing to
Speaker 1: notice the slightest changes. And the second thing that we're
Speaker 1: applying in is artificial intelligence, the ability to put machine
Speaker 1: learning capabilities where processes get smarter continuously and more they execute,
Speaker 1: the smarter they get. So now when you combine those
Speaker 1: two forces together, the ability to use high fidelity data
Speaker 1: like video, and the ability to interrogate and analyze that
Speaker 1: data in real time and do it right where things
Speaker 1: are occurring, like on the shop floor, and then have
Speaker 1: that backed by the kind of connectivity and the power
Speaker 1: that the Verizon can bring with their their edge computing platforms.
Speaker 1: We have the opportunity now to add tremendous value to
Speaker 1: those processes, whether that's making sure people are safe, making
Speaker 1: sure that those lines are as productive and as efficient
Speaker 1: as possible, making sure the product quality is as high
Speaker 1: as it should be. A tremendous uh, you know, amount
Speaker 1: of value can be created by being able to apply
Speaker 1: that that intelligence, um that that high bandwidth capability, and
Speaker 1: to make sure that that network is there and ready,
Speaker 1: as Shrine was describing, to constantly serve up those insights. Yeah,
Speaker 1: imagine that. I'm a uh, I have a you know,
Speaker 1: mid sized manufacturing company, you know, founded by my grandfather.
Speaker 1: You know, I've been keeping reasonably keeping pace with innovation.
Speaker 1: I think I'm pretty competitive. I have not done any
Speaker 1: of the things you've described. The two of you show
Speaker 1: up at my front door, and what kind of promise
Speaker 1: can you make me? What like, for example, what when
Speaker 1: you talk about productivity improvements are we talking about? What
Speaker 1: are we talking about your two five percent, ten percent?
Speaker 1: Give me kind of more tangible examples of what you
Speaker 1: can deliver to a customer like that. So the way
Speaker 1: industries have been evolving is that they have a lot
Speaker 1: of these sensors, and the sensors have been more analog
Speaker 1: and and and what I mean is that more each
Speaker 1: one is designed to do a particular thing. I can
Speaker 1: tell you that like temperature sense of that basically says
Speaker 1: if you cross certain temperature, you slve the machine. You
Speaker 1: have let's say some windspeed sens or something else, something else.
Speaker 1: What industries are realizing is that these sensors have been
Speaker 1: doing a certain function by now I'm going to use
Speaker 1: the term called virtualizing, by basically making them more lean
Speaker 1: and smarter and connect to the edge. You can now
Speaker 1: get all this information and you can actually make decisions
Speaker 1: based on collective intelligence intelligence of all the sensors than
Speaker 1: each sensor doing what other things. And to do that
Speaker 1: you need a highly releveled character. By bringing the entire
Speaker 1: collective intelligence and and and in making these sensors more virtual.
Speaker 1: A couple of things you're doing. One, you're going to
Speaker 1: bring in latest capabilities in UM called product quality and
Speaker 1: manufacturing and others, meaning that you would improve the radios downtimes,
Speaker 1: improve you know, the amount of productivity, improve quality control issues.
Speaker 1: And you're actually going to enable the factory to continue
Speaker 1: to adapt to the new or digital capabilities that are
Speaker 1: going to come out. And each one of these capabilities,
Speaker 1: where as AI and whatever else, they're all going to
Speaker 1: add to your productivity, your quality, or you know, the
Speaker 1: key metrics that you're you're looking at. So for a
Speaker 1: hypothetical imagine which is saying, is I suppose I have
Speaker 1: a machine, a very expensive piece of machinery. My my
Speaker 1: my cousin is the president of a company in Queens
Speaker 1: that makes Jamaican beef paddies and they get these machines
Speaker 1: from Italy. The cost just to say, in amounts of
Speaker 1: money that you know, take the beat patty from what
Speaker 1: and when the when something goes wrong, it's like a
Speaker 1: crisis because someone has to fly in from Italy. Right,
Speaker 1: So listening to I'm thinking, well, suppose we discovered that
Speaker 1: the machine starts to make lots of airs when humidity
Speaker 1: is hits a certain point in the company. It's kind
Speaker 1: in the factory floor, the temperature is such when it's
Speaker 1: been running for X number of hours in a row.
Speaker 1: When the operator makes this kind of mistake. I mean,
Speaker 1: I can imagine ten different things that we could say
Speaker 1: might affect performance, and you could have sensors on all
Speaker 1: those ten things and do a calculation at any given
Speaker 1: time about what my chances are that the machine might
Speaker 1: have a malfunction and presumably warned me before it happens.
Speaker 1: Is that exactly exactly? And what you've what you've just done,
Speaker 1: is you've crossed over from observation into prediction, which becomes
Speaker 1: really powerful so that you can begin to understand what's
Speaker 1: about to happen actually before it occurs, and you can
Speaker 1: take corrective actions to do it. As a perfect example
Speaker 1: of the kind of tremendous value that can be gained here.
Speaker 1: Adding to my little scenario from before, we could have
Speaker 1: sensors that tell me when my raw materials are getting low.
Speaker 1: We could have an AI system which reminds me when
Speaker 1: variations in demand and says you actually, every year have
Speaker 1: a huge uptake in at easter, you're you know orders
Speaker 1: that means you've got to order X, Y and Z, right.
Speaker 1: I mean, this is all the kinds of things you
Speaker 1: might want to do. How hard if you walked in
Speaker 1: how hard? What is it to build that kind of system?
Speaker 1: Is this something you can do relatively quickly and efficiently?
Speaker 1: Or is it does it take months to kind of
Speaker 1: customize a system for a customer like that. So for
Speaker 1: for you know, any different clients, some things can be
Speaker 1: basically prepackaged and ready to deploy UM. And you know,
Speaker 1: as as these solutions of all UM, you know, there
Speaker 1: will be some offerings that are essentially load and go
Speaker 1: as a service. Others will be more customized or tailored
Speaker 1: based on what given client will want to do. But
Speaker 1: one of the things that I think really important in
Speaker 1: what we're working on and what Verizon is working on,
Speaker 1: is there are a set of standards that the marketplace
Speaker 1: is embracing. And those standards allow for an ecosystem of
Speaker 1: players to come together and to work seamlessly. And and
Speaker 1: for IBM, you may have heard, you know, the term
Speaker 1: our open hybrid cloud approach. We're very very focused working
Speaker 1: in collaboration with the telecommunication providers in the marketplace to
Speaker 1: help bring these open standards because two really powerful things
Speaker 1: That one is we create an enormous pool of innovators
Speaker 1: that can contribute and accelerate the rate piece of innovation.
Speaker 1: So we're working very closely with Verizon on on some
Speaker 1: of those open standard architectures. And then Secondly, I'm really
Speaker 1: creating an ecosystem of partners. So when we announced just
Speaker 1: at towards the end of last year, are IBM Cloud
Speaker 1: for Telco, we announced with UM over forty different partners
Speaker 1: that are coming together to be able to bring innovation
Speaker 1: UM to these kinds of solutions. And I think that's
Speaker 1: that's going to help us UM really accelerate the opportunity
Speaker 1: to bring these kinds of solutions. Were describing treaty. Is
Speaker 1: there somebody out there, an industry, a sector that you
Speaker 1: think could benefit the most from the kind of services
Speaker 1: that I'd be having Verizon provide. I mean, who where
Speaker 1: could you make an extraordinary difference? Retail? Retail is a
Speaker 1: very interesting area because both with COVID you started getting
Speaker 1: into this ideas of touch free detail and you do
Speaker 1: things more customized and you users and others. But retail
Speaker 1: itself is actually changing. UM. You know, you hear about
Speaker 1: these UH stores without any assistance that you're walking, you
Speaker 1: buy and walk out. In all of these cases, what
Speaker 1: you're seeing is automation really taking a bigger place. For example,
Speaker 1: you know one of the retailers in a decent sized
Speaker 1: store is looking to put about high definition cameras. Now
Speaker 1: the cameras work as call it computer vision sensors, and
Speaker 1: they capture the information and using that you can actually
Speaker 1: understand what you know, individuals are. You can do lots
Speaker 1: of things with the computers and of what people are doing,
Speaker 1: how much stock you have in the store, and lots
Speaker 1: of other things. Now you take all that, you process
Speaker 1: that and uh and you could you know, if you
Speaker 1: introduce automation at the point where people can come and
Speaker 1: pick up what they want, they can walk out without
Speaker 1: having to even interact with anybody out there. Right, that's
Speaker 1: one extreme of it. I just imagine that I'm I'm
Speaker 1: running a store, did a bunch of cameras. I got
Speaker 1: fifty stores across America. I've just put in my spring line,
Speaker 1: and I got a bunch of new dresses. So you
Speaker 1: could tell me that, Malcolm, you have this new orange
Speaker 1: dress which you put in all fifty stores. Not a
Speaker 1: single person has looked at that dress in the retail
Speaker 1: store in the last week. You can also tell me
Speaker 1: seventy of the people stopped in the thing lingered in
Speaker 1: front of the teal sweatshirt, and you're out of teal
Speaker 1: sweatshirts as a result. Order more teal sweatshirts now and
Speaker 1: forget about the dress. It's not going anywhere. And not
Speaker 1: only not only that, I'm not a retail expert. By there,
Speaker 1: I can tell you that. Not only that, we can
Speaker 1: also tell you that people who looked at something for
Speaker 1: how longer time, two seconds, two minutes, whatever, tend to
Speaker 1: buy things versus if they don't look at it, that means,
Speaker 1: you know, if they're just walking off, that means you
Speaker 1: know that you're not gonna So you can get such
Speaker 1: a deeper set of insights that both can help you
Speaker 1: as a business to figure out how do you, you know,
Speaker 1: how do you interact with your customers. But at the
Speaker 1: same time, you can also use this this data to
Speaker 1: actually uh you know call it push your prior to
Speaker 1: the customer and you know, based on their personal preferences,
Speaker 1: choices and color circumstances. Right. So that's the kind of
Speaker 1: the data gives you so much and so much what
Speaker 1: you call ability to kind of you know, customize yourself
Speaker 1: to to meet the customer demands. You know, in a way,
Speaker 1: what we're talking about here is you know, a using
Speaker 1: video as we talked about as a rich fidelity set
Speaker 1: of data but also applying AI or intelligence to that
Speaker 1: so that you can take action on it. But part
Speaker 1: of this is about humans and machines interacting, which makes
Speaker 1: the human in doing that role more effective. Another area
Speaker 1: where we both are excited about is industrial automation. So
Speaker 1: if you look at you know, malcome, what happened over
Speaker 1: the last few years is the world has digitized. You know,
Speaker 1: everything can be digital. The back office is the manufacturing
Speaker 1: process and others they have not being disguised. So you know,
Speaker 1: in a number of situations, you put this nice digital
Speaker 1: store front and digital experience in the front, but your
Speaker 1: backup is still lagging with you know, older factories and
Speaker 1: older sensors and whatnot. And the problem is that at
Speaker 1: some point the problem is going to catch up, and
Speaker 1: and that is you will not be able to keep
Speaker 1: your front digital experiences without really you know, knowing what
Speaker 1: is going on your factories or do you have the product?
Speaker 1: You can you shave the product on time? And if
Speaker 1: you come in to somebody, I'm going to deliver something
Speaker 1: away tomorrow, can you really get it there? So the
Speaker 1: next ten years and there's gonna be a lot of
Speaker 1: industrial automation that's going to take place now for industrial automation, UM,
Speaker 1: a few things that are going to make a bigger,
Speaker 1: bigger impact. One, you do need a highly reliable connectivity
Speaker 1: within those environments. Whatever small number of sensors they have today,
Speaker 1: they're connected using wires. But the problem the virus is
Speaker 1: that it takes a long time to really go connectivity.
Speaker 1: You need a highly reliable wireless communication. Number Two, we're
Speaker 1: gonna have inordinate amount of sensors all over these environments
Speaker 1: because to your point about your cousin's uh Jamaican beef
Speaker 1: parties by it does sound very attractive. So they're good,
Speaker 1: They're really good. So so now you're gonna put sensors
Speaker 1: everywhere so that you can predict, you can understand exactly
Speaker 1: what's going on. The third thing you're gonna do is
Speaker 1: that use all of the data that's getting generated and
Speaker 1: apply AI and machine learning tactics on that so that
Speaker 1: you can not only understand the the you know, the
Speaker 1: world is going on within the environment, but you can
Speaker 1: actually predict. You can forecast now in a much better
Speaker 1: way so that if your customers expecting X y Z
Speaker 1: on certain time, you have data to tell you that
Speaker 1: you can absolutely come into the X y Z to
Speaker 1: be delivered to the customer on time. So it's that
Speaker 1: combination of sensors, programming, and intelligence and the AI and others.
Speaker 1: IBM understands a lot from that environment to the reliable
Speaker 1: connectory that we bring and and the edge computer that
Speaker 1: that we put to other those elements that you've just mentioned, Um,
Speaker 1: there they can't all be at the morning. If they're
Speaker 1: they're not all the same level of sophistication or development.
Speaker 1: Or is there one that you worry the most about?
Speaker 1: Like for example, when you were talking, I was thinking,
Speaker 1: you know, the Internet in my house it's not Horizon,
Speaker 1: but the Internet in my house it's not that good.
Speaker 1: I mean, I have to reboot my router every two days,
Speaker 1: and I get a little I have to have massive
Speaker 1: backups because sometimes it's just cons I you know what
Speaker 1: I mean. Like when I hear this, I think this
Speaker 1: is really really wonderful, But the reality in the little
Speaker 1: town I live in is Interne's just not that good.
Speaker 1: So if I was a company in botttle town, how
Speaker 1: would I do what you're doing? If my if my
Speaker 1: fundamental services so spotty. Yeah, by the first I wish
Speaker 1: we were your internet providers. You wouldn't be having those issues.
Speaker 1: That's my uh So, I think the infrastructure, the broader
Speaker 1: connectivity infrastructure UM will need to evolve. Again, I go
Speaker 1: back to COVID because then COVID truly pointed it out
Speaker 1: that you can operate at a normal pace if you
Speaker 1: have a good connectivity, but if you don't have a
Speaker 1: good connectivity, you will get left behind. We certainly continue
Speaker 1: to deploy more and more connectivity UM. Where I see
Speaker 1: it's not a worry, but it's more of an optimism.
Speaker 1: Is that is five G is going to change that.
Speaker 1: Now five G brings in higher amount of throughputs in
Speaker 1: a wireless manner, so that you know, wired is a
Speaker 1: difficult proposition. Digging streets is not an easy thing, and
Speaker 1: then connectivity cost a lot if you go through that way.
Speaker 1: But then on the other hand, a wireless high band
Speaker 1: with connectivity, reliable connectivity makes it easier because it's something
Speaker 1: we can deploy in a matter of hours within the factory.
Speaker 1: And what I actually worry about is the technology is
Speaker 1: the ingredients are evolving at a rapid pace. It is
Speaker 1: the people who operate those environments and it is the
Speaker 1: you know, the pace at which they adopt is what
Speaker 1: has got to catch up. Um, what are you going
Speaker 1: to start seeing is that the tech is evolving so
Speaker 1: rapidly that the expectations um from the consumers in terms
Speaker 1: of you know, hey, when you guarantee something I wanted
Speaker 1: to be guaranteed because you should have had that. You know,
Speaker 1: you won't promise me something if you can't keep up,
Speaker 1: those sort of expectations are going to continue to increase.
Speaker 1: And if the the the people who are actually managing
Speaker 1: and operating the factories and and working those factories cannot
Speaker 1: adopt and cannot evolve fast enough, you know, that's what
Speaker 1: we're worried about, is that the adoption and the evolution
Speaker 1: of those environments. I mean that's a question for both
Speaker 1: of you along those lines. You know, are are the
Speaker 1: two of you, your two companies big enough to handle
Speaker 1: this challenge? And I say that not I'm not trying
Speaker 1: to provoke you, but you're talking about something where potentially
Speaker 1: every business in this country could you know, benefit extraordinarily
Speaker 1: from this revolution. We haven't even mentioned healthcare GDP. You know,
Speaker 1: we're talking about hundreds of thousands, millions of people and
Speaker 1: who are employed in that industry. Many of the industry
Speaker 1: standards in healthcare are straight out of the nineteenth century.
Speaker 1: You could throw an army at that problem and you
Speaker 1: wouldn't put a dent in it. I mean, do both
Speaker 1: your companies have to scale up to meet the challenge
Speaker 1: of delivery of making this revolution reel. Well. For IBM,
Speaker 1: we've been working with each of these industry sectors for
Speaker 1: for many, many years, and so we have a pretty
Speaker 1: good understanding and a longstanding relationship with with many of
Speaker 1: the leaders in each of those different industries, and so
Speaker 1: we're working day by day with them as just trying
Speaker 1: to adopt these kinds of innovations into their business. So
Speaker 1: as much as I think Trini would say and it
Speaker 1: as what I did, that we both have ambitions to
Speaker 1: to really bring a lot of value to all of
Speaker 1: these inns because we know there will be many other
Speaker 1: firms that will be doing the same. And what we
Speaker 1: want to be able to do is to create UM
Speaker 1: is open, efficient, and is automated a technology environment as
Speaker 1: possible that allows for that innovation to happen. M hmmm, yeah,
Speaker 1: and Steve's summarized very well. I mean, Malcolm, the way
Speaker 1: to look at this is tech is evolving, so much,
Speaker 1: and on multiple fronts. Right, we talked toward sense as
Speaker 1: we talked about we talked about the connectivity, We talked
Speaker 1: about cloud and software and computing. There's no no one
Speaker 1: single company that can claim and say that they have
Speaker 1: mastered all the domains. That's why this is a you know,
Speaker 1: collaboration and kind of standards base interoperably across companies is critical.
Speaker 1: The way I look at OS is we provided these
Speaker 1: what we call the code ingredients, connectivities of critical code ingredient,
Speaker 1: compute clouder code ingredients. We provide them these basic ingredients
Speaker 1: so that others can come on, innovate on top, and
Speaker 1: you know, delover these uh, these these kind of emerging
Speaker 1: the new services to the to the enterprises and costomers. Yeah,
Speaker 1: there's a couple of terms that I wanted to get
Speaker 1: more complete definitions of, and I wanted to start with
Speaker 1: edge computing. I know, Steve, you had talked, you had
Speaker 1: given us a an initial definition, but imagine that I
Speaker 1: am a complete computer literate. Let's let's really dig into
Speaker 1: that term and what we mean when we use that term,
Speaker 1: and how that kind of differs from what might be
Speaker 1: a more conventional um computing model. Yeah, so let me
Speaker 1: use an example that maybe it could help. UM. You
Speaker 1: kind of draw a picture of what that might be. UM.
Speaker 1: I Unfortunately, I'm based in Los Angeles. As you know,
Speaker 1: most of your listeners would know, California has been battling
Speaker 1: with fires, UM. You know, in the fall almost every year. Now,
Speaker 1: this last year very devastating. UM. In an edge connected
Speaker 1: world that Triny and I have been describing, a fire
Speaker 1: begins in northern California on Wednesday. Everything is fine on Thursday.
Speaker 1: On Thursday, we've got an unfolding disaster in you know,
Speaker 1: some part of the state. An edge platform in proximity
Speaker 1: to where that fire is comes to life as a platform,
Speaker 1: and a set of applications come to life on that
Speaker 1: edge platform, and immediately drones are flying above that fire
Speaker 1: and taking video imaging and sending it down to that
Speaker 1: edge platform, and a bunch of AI tools are analyzing
Speaker 1: that video to analyze whereas the fire, what's the terrain
Speaker 1: look like, what are the things that are in the
Speaker 1: half of where that fire is likely to go, so
Speaker 1: that we can begin to plan out how how to
Speaker 1: fight it. And then data coming perhaps from our weather
Speaker 1: company is going into those models on that edge platform,
Speaker 1: and it's infusing intelligence about wind patterns and moisture in
Speaker 1: the air and things that also will impact the way
Speaker 1: that that fire is likely to perform. And that data
Speaker 1: is being analyzed and then fed to the first responders.
Speaker 1: And when the first responders show up on the scene,
Speaker 1: their their trucks, their cars, their equipment is placed in
Speaker 1: an optimal position based on the analysis has been done
Speaker 1: on how that fire is likely to perform and where
Speaker 1: in fact, you know, property or people are at risk
Speaker 1: because of it. And sensors we were talking earlier about sensors.
Speaker 1: Sensors are in the area that are measuring the amount
Speaker 1: of moisture in the foliage. That also is going to
Speaker 1: impact the way that the fight against that fire is
Speaker 1: going to be done. The point is a lot of
Speaker 1: data coming from a number of different sources, from sensors,
Speaker 1: from video being analyzed in an environment it's very close
Speaker 1: to where that fires occurring, so you can get that
Speaker 1: real time response and then providing information. But the ultimate
Speaker 1: objective to save lives, save property, get that fire out
Speaker 1: as soon as possible, and when it's out, that edge
Speaker 1: platform can essentially wind down and and um you know
Speaker 1: those applications can then be kept and made ready for
Speaker 1: the next fire wherever it may happen. That's the kind
Speaker 1: of automation and intelligence and real tie insights that could
Speaker 1: fundamentally be brought to bear in an edge environment. I
Speaker 1: think that hopefully that paints a little bit of a
Speaker 1: picture of how we see this edge environment creating tremendous value. Yeah, wonderful. Well,
Speaker 1: thank you so much to both the Ustraenian Steve. UM.
Speaker 1: There's been Um, it's been really fascinating. I'm so grateful
Speaker 1: for the work that companies like Verizon and IBM are
Speaker 1: doing to help fight wildfires, and to Scrini and Steve
Speaker 1: for taking the time to chat with me about all
Speaker 1: that and more. And the sensors. We can't forget the
Speaker 1: importance of sensors. Sensors that infuse new intelligence. Whether they're
Speaker 1: monitoring where a fire is going or making sure equipment
Speaker 1: in retail factories is functioning safely. Sensors are crucial to
Speaker 1: protecting people in so many different capacities. It's pretty amazing stuff.
Speaker 1: Smart Talks with IBM is produced by Emily Rosteck with
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Speaker 1: you like to listen. I'm Malcolm Gladwell. See you next time.