Speaker 1: In smart Talks, we chat with people who are making
Speaker 1: innovative use of advanced technologies designed by IBM and an
Speaker 1: effort to make real world change. There's really no way
Speaker 1: to introduce this episode without stating the obvious. The coronavirus
Speaker 1: and COVID nineteen have caused massive disruptions and pretty much
Speaker 1: every aspect of our lives. We're faced with tough decisions,
Speaker 1: which become even more difficult when we find ourselves lacking
Speaker 1: critical information. And that's going to bring us into today's topic.
Speaker 1: But that's just one way that IBM is making its
Speaker 1: technology available in the fight against COVID nineteen. The company
Speaker 1: is working closely with scientists, doctors, leaders, and experts to
Speaker 1: fight COVID nineteen in many ways, all while leveraging some
Speaker 1: really impressive technology. Whether it's using supercomputers to help researchers
Speaker 1: find a vaccine and that's a little bit of a
Speaker 1: teaser for an upcoming episode, or aggregating enormous amounts of
Speaker 1: informations so that the average American can get a local
Speaker 1: ice snapshot of what's happening in their communities. IBM technology
Speaker 1: is playing a big part. So what are we talking
Speaker 1: about today? Well, the Internet is a phenomenal way to
Speaker 1: share information, but that's a double edged sword. Over the
Speaker 1: past several years, we've seen misinformation and even disinformation spread
Speaker 1: across online communities, clouding our understanding of matters from the
Speaker 1: trivial to the critical. If we're lucky, it's the trivial,
Speaker 1: like the fact that people kept photoshopping the date on
Speaker 1: the time circuit in the Back to the Future films
Speaker 1: to make it today's date when Marty McFly goes to
Speaker 1: the future. But we're increasingly seeing more bad information about
Speaker 1: important things, from politics, to climate change to yeah, the
Speaker 1: spread of the coronavirus, and in that kind of environment,
Speaker 1: making the right decisions becomes increasingly more difficult to do,
Speaker 1: just as it's becoming more urgent. That brings us to
Speaker 1: today's topic. I sat down with Cameron Clayton, the general
Speaker 1: manager of IBM Cloud Ecosystem and of the Weather Company
Speaker 1: and IBM Business Well we were both sitting down, but
Speaker 1: we also happened to be in our respective homes speaking
Speaker 1: over the Internet in an effort to keep ourselves and
Speaker 1: others safe and really, in many ways, that's what this
Speaker 1: boils down to. But I'm getting ahead of myself. One
Speaker 1: thing I do want to mention is that the unusual
Speaker 1: circumstances mean this episode sounds a bit different from other
Speaker 1: tech stuff episodes, because real life goes on while we podcast.
Speaker 1: Cameron's team at the Weather Company have done something extraordinary.
Speaker 1: If you've ever visited weather dot com or use the
Speaker 1: Weather Channel app, you know you can get an incredibly
Speaker 1: localized report down to the zip code in the United States.
Speaker 1: And just so you know, there are forty one thousands
Speaker 1: seven to zip codes in the US. I counted them,
Speaker 1: which was tough because somewhere in the fourteen thousand range
Speaker 1: of lost count had to start all over again. Aggregating
Speaker 1: that much information and presenting it in a meaningful way
Speaker 1: is no small feat. But what Cameron's team did next
Speaker 1: was in some ways even more astounding. They took that
Speaker 1: general approach and they applied it to the spread of
Speaker 1: COVID nineteen. Here's my conversation with Cameron Clayton, with only
Speaker 1: an interruption here and there to clarify some things. Cameron,
Speaker 1: thank you so much for taking time to join us
Speaker 1: on the show today. I really appreciate it, absolutely my pleasure.
Speaker 1: It's great to be here now. I think it's pretty
Speaker 1: safe to say this is not an exaggeration that everyone
Speaker 1: has been affected by the COVID nineteen crisis to some extent.
Speaker 1: But I think it can be a little tricky for
Speaker 1: people to get a big picture grasp on the global
Speaker 1: impact of this crisis. Can you kind of speak a
Speaker 1: little bit as to your perspective on the world impact
Speaker 1: of COVID eighteen. Certainly so. As the Weather Channel, our
Speaker 1: mission is to map the atmosphere. So as a company
Speaker 1: and a collective of people, our whole job is to
Speaker 1: try and predict what's going to happen in the atmosphere tomorrow.
Speaker 1: It's almost this impossible science math mother nature problem that
Speaker 1: that we're that we've been working on for a long
Speaker 1: time and make tremendous progress on. Said differently, though, and
Speaker 1: I think this is really where it comes to COVID nineteen.
Speaker 1: At the end of the day, we're making the invisible
Speaker 1: visible and and so doing that, it's easier to make
Speaker 1: decisions When you take something that's intangible, you can't see tangible,
Speaker 1: and then therefore you can make a bit of decision
Speaker 1: as a result of that. And so that's what we
Speaker 1: tried to do with with COVID nineteen. And we've got
Speaker 1: so much inbound interests from our our fans all around
Speaker 1: the world saying, hey, you you make something we can't
Speaker 1: see easier to digest, easier to understand, and easier to
Speaker 1: make decisions on which you please do the same thing
Speaker 1: for COVID nineteen. And so that's what that's what we've
Speaker 1: been doing, is is trying to make the invisible visible.
Speaker 1: And like like you said, I think every single person
Speaker 1: on the planet is impacted by COVID nineteen, just like
Speaker 1: they're impacted by the weather, right. And so as a
Speaker 1: result of our reach in our scale with literally hundreds
Speaker 1: of millions of users around the world, uh, we're able
Speaker 1: to also communicate with them in a way that they're
Speaker 1: familiar with as part of their daily habit already. H
Speaker 1: And so you know, we'll be able to try and
Speaker 1: provide trusted data to our fans around the world. You know,
Speaker 1: as you point out, Cameron, contextualizing data is really critically
Speaker 1: important for people to be able to make use of it, right,
Speaker 1: to be able to take something that is conceptually this
Speaker 1: huge thing, but it's very hard for us to boil
Speaker 1: that down into actionable things that we can do as people.
Speaker 1: I think one thing that kind of helps again, you know,
Speaker 1: as humans were not really good at dealing with big
Speaker 1: numbers just on our own. One thing that really helps
Speaker 1: is is kind of anchoring things to personal experience as well.
Speaker 1: So before we jump into all the background on IBM
Speaker 1: and the weather companies work with UH, the technologies to
Speaker 1: track COVID nineteen, I was wondering, can you talk a
Speaker 1: little bit about how this crisis has affected you personally
Speaker 1: so far? Oh? Yeah, sure, I think. Uh So, I
Speaker 1: have four children, I'm married with with all kids, UH
Speaker 1: and a dog, and about two and a half weeks ago,
Speaker 1: all our kids, you know, came home and have been
Speaker 1: on sort of Zoom and Google classroom, you know, with
Speaker 1: x is all all day every day, along with with
Speaker 1: me doing the same thing on UH working here from
Speaker 1: the house. And you know, I think we're thankfully safe
Speaker 1: and healthy and doing well. But Kevin fever is a
Speaker 1: real issue, especially with two boys. It's been a big
Speaker 1: change in the sense of we often ask, you know,
Speaker 1: how's your day going. Are you having a good day?
Speaker 1: And I think many of us are still asking that question,
Speaker 1: but we're not used to the answer being actually no,
Speaker 1: I'm not. I'm really not okay. Uh, And and we're
Speaker 1: getting a few of those answers now. I had a
Speaker 1: couple of those onswers this morning. And so how we
Speaker 1: rallied together as a community, how we relied together ash
Speaker 1: as companies, how we relied together as humans really really matters.
Speaker 1: And I'm certainly super proud of the way our teams
Speaker 1: rallied together and and IBMS relied together. Were also super
Speaker 1: proud about our clients and partners and neighbors and uh
Speaker 1: and others like Uh. We had a block street party
Speaker 1: where everybody was in their cause. They decorated their cause
Speaker 1: and drove by the five old people that live in
Speaker 1: the street and who are in the window to try
Speaker 1: and share them up. So this just amazing touches of
Speaker 1: humanity happening. That's that's really cool to see. And that's
Speaker 1: a great way to segue into talking about what the
Speaker 1: Weather Company, what IBM are doing and in a way
Speaker 1: to give people more tool so that they can make
Speaker 1: decisions that are critically important for themselves and for the
Speaker 1: people around them, whether it's relatives, coworkers, loved ones, just
Speaker 1: strangers on the street. We all have this responsibility So
Speaker 1: let's talk kind of in general terms what exactly you
Speaker 1: guys are doing in an effort to give people these tools.
Speaker 1: So kind of from a very high level, as I
Speaker 1: understand it, you're pulling data that's localized to specific regions,
Speaker 1: contextualizing that and presenting it in a way that's easily
Speaker 1: digestible so that people have and up to date understanding
Speaker 1: what's going on in their communities. Is that more or
Speaker 1: less correct? Yeah, that's that's right. We're we're trying to
Speaker 1: make this invisible virus contextual and localized. The important thing
Speaker 1: when you do that is it has to be from
Speaker 1: a trusted source. So the sources of the data have
Speaker 1: to be really high integrity, and integrity beats all other
Speaker 1: aspects of data. Right. It's more important than timeliness. It's
Speaker 1: more important than you know, how large the field is.
Speaker 1: The most important thing is it's a trusted data source.
Speaker 1: And so all the data that we're collecting and displaying
Speaker 1: in our solution is from local government, state government, or
Speaker 1: federal government sources. So we're not we're not doing crowdsourcing.
Speaker 1: We're not pulling in social media opinions or things like that,
Speaker 1: although those things have their place and are helpful in
Speaker 1: their own way. This is really about about aggregating and
Speaker 1: collecting all the local sources so that you can see
Speaker 1: what's going on in your community, right And I think
Speaker 1: that is really important. You know, I have a have
Speaker 1: a uncle that was in Louisiana. He's a great guy,
Speaker 1: but he's a free spurt. He does his own thing.
Speaker 1: He lives his own way, and he doesn't really listen
Speaker 1: that well too, friends or family. He's going to do
Speaker 1: his own thing. And so, you know, as we started
Speaker 1: building building this tool and I started seeing the data
Speaker 1: that was going on there, I was able to use
Speaker 1: it to show him that, you know what what and
Speaker 1: this was you know, last weekend and Monday. That would
Speaker 1: show him, Look, there's really some really serious outbreaks going
Speaker 1: on in Louisiana right now. This is really accelerating. Uh.
Speaker 1: You might not take this seriously, but your people in
Speaker 1: your community really need you to to listen to the
Speaker 1: local authorities and stay put right, stay inside and and
Speaker 1: behave um. And so I think, you know, you take
Speaker 1: my example and extrapolate that out across Every American has
Speaker 1: a story somewhat similar to had I think, Uh, and
Speaker 1: so we're all looking and seeing what's happening to friends
Speaker 1: and family around the world and across the United States,
Speaker 1: and uh and you know, staying home, stopping the spread
Speaker 1: ultimately saves lives, right it is that that's simple right now,
Speaker 1: and be able to show people what's happening in their community.
Speaker 1: We're doing in three ways. So we're showing them the data,
Speaker 1: how many people have been tested and shown as positive
Speaker 1: in their county, how many deaths have been recorded. We're
Speaker 1: showing the trend line for that county day over day.
Speaker 1: So is it getting steeper, is the curve getting steeper
Speaker 1: or is it plateau ng or hopefully at some point
Speaker 1: here we'll see it declining in places, but right now
Speaker 1: we're not. It's still on the upswing. Then we we
Speaker 1: let them choose between their state view or their county
Speaker 1: view of that trained analysis. Then we're making it tangible
Speaker 1: on a map. Right What we found with with weather
Speaker 1: and we're doing now with the COVID virus is plotting
Speaker 1: it on a map so you can actually see it
Speaker 1: in a geographical context. So your county versus the county
Speaker 1: next to you, and across the entire United States, every
Speaker 1: county that that's producing data we're ingesting. There are some
Speaker 1: places that aren't producing data, particularly in some rural counties,
Speaker 1: but for the most part, the major population centers are
Speaker 1: all producing this data, and you can see what's going on, right,
Speaker 1: and I think we've seeing people make better decisions as
Speaker 1: a result of it, and that's the whole purpose. That's
Speaker 1: why we did this. One of the things you mentioned
Speaker 1: was about the trusted sources. I'm glad you went into
Speaker 1: that and explained where you're pulling information from, because obviously
Speaker 1: we're right now in an era where there's missing for
Speaker 1: nation and even disinformation running rampant online. So it's good
Speaker 1: to be able to point to a tool and actually
Speaker 1: know where where's this tool pulling information from. And it's
Speaker 1: also really interesting to me that it's taking a very
Speaker 1: similar approach to what the Weather Company has done. I
Speaker 1: think everyone has had the experience of using either the
Speaker 1: app or the website and looking at, you know, weather
Speaker 1: forecasts for specific zip codes and so kind of taking
Speaker 1: that same thinking and applying that to the COVID nineteen
Speaker 1: outbreak is really interesting to me too. I'm wondering, um,
Speaker 1: with that in mind, uh, are the sources you're pulling from?
Speaker 1: You know there are local, state and federal government. Are
Speaker 1: they in different formats? Because to me, that would present
Speaker 1: itself an enormous challenge because machines. Humans are really good
Speaker 1: at pulling information from whatever format we encounter, machines typically
Speaker 1: are not. So this is actually the heart of the
Speaker 1: technology challenge we've had and and we've basically used IBM
Speaker 1: S Watson AI tools to be able to ingest this
Speaker 1: data from literally thousands and thousands of data sources, all
Speaker 1: in different formats, right, everything from a PDF that's actually
Speaker 1: an image like a photograph essentially two HTML files to
Speaker 1: or everything else in between. And every single counties website
Speaker 1: is built differently. There's no technology standard that's been applied. UH.
Speaker 1: And so you know, I think that one of the
Speaker 1: one of the amazing things from a technology only perspective
Speaker 1: in this is that the Watson's AI was able to
Speaker 1: INGI and learn all of these different formats based really
Speaker 1: on its own and ing all this data from all
Speaker 1: these different technology types, uh and put it into a
Speaker 1: standardized format that we could then produce and and and
Speaker 1: show in our websites and our acts and and that
Speaker 1: whole training from when we started to when we had
Speaker 1: the first standardized file was thirty six hours, so we're
Speaker 1: not talking about two weeks or or anything. This is like,
Speaker 1: you know, a data hoff to learn and train and
Speaker 1: collect the data and put it into a standardized format.
Speaker 1: Cameron mentioned training the system on data, and some of
Speaker 1: you might wonder what that actually means. It's a term
Speaker 1: used in machine learning in which engineers feed information into
Speaker 1: computer models in an effort to produce particular results. And
Speaker 1: typically you start out doing this by knowing what results
Speaker 1: you want ahead of time when you're first training the system.
Speaker 1: That way, you can see what comes out of the model,
Speaker 1: you compare it to what you expected to see, and
Speaker 1: if the two don't match up, you can go back
Speaker 1: and tweak the model. So, for example, let's say you're
Speaker 1: training an image recognition computer model to recognize pictures of
Speaker 1: fire hydrants. You might feed a ton of images of
Speaker 1: fire hydrants to the model. Then you might introduce a
Speaker 1: mixture of images that include fire hydrants and other stuff,
Speaker 1: seeing if the model can tell the difference sorting the
Speaker 1: images properly. So you analyze those results and if they're good,
Speaker 1: you keep going. You might use millions of points of
Speaker 1: data to train your model. Over time, and often this
Speaker 1: is a painstakingly slow process, particularly when engineers need to
Speaker 1: step in and change something about the model that isn't
Speaker 1: quite working. Once you are able to get reliable results
Speaker 1: from the model, you can put it to more practical use,
Speaker 1: though it may require future tweaks when the model encounters
Speaker 1: something well outside the norm. Okay, let's get back to
Speaker 1: my conversation with Cameron Clayton, the general manager of IBM
Speaker 1: Cloud Ecosystem and of the Weather Company and IBM business.
Speaker 1: You're talking about things like natural language processing, being able
Speaker 1: to access all these different styles of data, whether it's
Speaker 1: an image file or it's something that can be searched
Speaker 1: with an algorithm, and then taking the meaning of that
Speaker 1: not just the fact that the data is there. You
Speaker 1: have to the MA gene has to understand what the
Speaker 1: meaning is in order to put it into its model
Speaker 1: and then present it. These are really tough engineering challenges
Speaker 1: in computer AI in general, and so to see that
Speaker 1: application being put so quickly in place, exactly can you
Speaker 1: give us an idea of how long it took too,
Speaker 1: from the point of ideation to the point of implementation
Speaker 1: that you guys went through in order to produce this.
Speaker 1: So the timeline from when we started UH this getting
Speaker 1: it getting it live was probably about ten days. We
Speaker 1: got inbound interest from our fans around the world. We
Speaker 1: then mocked up how we wanted to present the data
Speaker 1: from a visual point of view. We then, in parallel,
Speaker 1: we're rapidly trying to source all these local data sources.
Speaker 1: We wanted it from the very beginning of as local
Speaker 1: as possible UH, and we rapidly realized this was not
Speaker 1: something that you could do manually. You had to do
Speaker 1: this UH in an automated way at scale, and so
Speaker 1: that's when we brought in Watson and AI to help.
Speaker 1: Thirty six hours later we had data. We had, you know,
Speaker 1: a format, but then we really spent a lot of
Speaker 1: time testing right to make sure that the data was correct,
Speaker 1: to make sure that you know, when when you know,
Speaker 1: Governor Cumos speaks in New York at eleven thirty every morning,
Speaker 1: within a few minutes, we were able to up dat
Speaker 1: the data based on the numbers he's sharing with the media,
Speaker 1: but on the New York State website, for example. And
Speaker 1: so there's all these different factors both technology and format
Speaker 1: that we had to take into account, but also just
Speaker 1: testing this. The second is we always, due to our
Speaker 1: scale and the number of users we have, we have
Speaker 1: to load test and make sure that technologically we can
Speaker 1: deliver to the millions of people that use our properties
Speaker 1: and platforms. And so we've got this uh live a
Speaker 1: few days ago and beginning to end is probably seven days.
Speaker 1: There's a remarkable achievement. I mean, you're you're talking about
Speaker 1: everything from coming up with the idea to the design
Speaker 1: saying what do we actually do to make this possible?
Speaker 1: To even even something that seems to someone like me simple,
Speaker 1: like how do you present that information to the consumers
Speaker 1: so in a way that makes sense, Like we we
Speaker 1: only see it at the end, right, we see it
Speaker 1: after you've made all those decisions, and we look at
Speaker 1: it and we say, oh, yeah, of course that makes sense.
Speaker 1: But you have to get there first on the design side.
Speaker 1: And I think a lot of people don't understand or
Speaker 1: appreciate how challenging that part of technology is to not
Speaker 1: just the making it work, but making it work in
Speaker 1: a way that is ultimately useful to the end consumer too,
Speaker 1: so that yeah, it doesn't just work, it works for you. Um,
Speaker 1: and we were you know, one of the other things
Speaker 1: I wanted to talk about was that this design process
Speaker 1: not only was it rapid, but obviously it was unusual
Speaker 1: in that you weren't all working in the same workspace
Speaker 1: at the same time because of the concerns, the health concerns.
Speaker 1: So what was that Like, how did your team respond
Speaker 1: to working in a decentralized approach. So I gotta say
Speaker 1: the team really has leaned in here hod like countless
Speaker 1: almost before. We had sixty people touched this project. Over
Speaker 1: the course of the week. I would say fifty five
Speaker 1: of the sixty worked multiple twenty four hour days in
Speaker 1: that time period. Uh And so sleep deprivation was a
Speaker 1: real issue as well, right, But but we have we
Speaker 1: have great tools, you know, whether it's video conferencing and
Speaker 1: collaboration tools where we can actually iterate and design products remotely,
Speaker 1: but all on the same screen at the same time.
Speaker 1: So our design is ractually able to you know, one
Speaker 1: of them draws a line and the other one can
Speaker 1: raise it as as they're drawing it. Uh. And so
Speaker 1: that was both fun and challenging at the same time.
Speaker 1: I would say the goal of the product was to
Speaker 1: make it clean and simple, so it is digestible, and
Speaker 1: I think you know, we can always add more data
Speaker 1: fields over time and and add more information, but the
Speaker 1: real purpose of this was to help people understand what's
Speaker 1: happening in the community so that they would stay home,
Speaker 1: stop the spread, and ultimately save lives. Right. Making that
Speaker 1: invisible visible was was the goal of this UM and
Speaker 1: so I think we've we've achieved that, but it's only
Speaker 1: through hard work of a small group of folks that
Speaker 1: that you know, really worked hard for seven days without sleep. Wow,
Speaker 1: I mean, that's that's incredible. So, so they're working hard
Speaker 1: putting this all together. Meanwhile, you've got the AI and
Speaker 1: tools working hard in the background to synthesize all that data.
Speaker 1: Can you talk a little bit more about the specific
Speaker 1: technologies running in the background. We've mentioned Watson, but is
Speaker 1: there anything else along with that. This is a whole
Speaker 1: whole variety of things that that live in the background
Speaker 1: that make this possible and so and almost all of
Speaker 1: those things are things that we don't think about as
Speaker 1: consumers of whether dot Com or our mobile apps. So
Speaker 1: one is just the cloud infrastructure itself. The fact that
Speaker 1: you know on Monday night when we launch, you know,
Speaker 1: we were alive for like three hours. We had about
Speaker 1: a million users in three hours used the property. On Tuesday,
Speaker 1: we had three and a half million users start using
Speaker 1: the property. On Wednesday it was up to like five
Speaker 1: and a half or six million users. You can't scale
Speaker 1: like that, and those are each individual, unique visitors, some
Speaker 1: of them visited multiple times and checks, you know, tens
Speaker 1: of locations around the country for friends and family. Uh.
Speaker 1: And you can't have that kind of scale without having
Speaker 1: a really robust cloud infrastructure behind it. And so uh,
Speaker 1: IBM Cloud was has been and continues to be instrumental
Speaker 1: and sort of invisibly in the background, keeping the infrastructure alive.
Speaker 1: And one of the one of the things about that,
Speaker 1: I'll take a quick story on it, that was super
Speaker 1: impressive to me. And I see this these kinds of
Speaker 1: launches fairly often with our products. But as we launched
Speaker 1: on Monday night, what I was not used to seeing
Speaker 1: was security h automatically being implemented. And so what I
Speaker 1: mean by that is we were actually having a denial
Speaker 1: of service attack, so hackers, we're trying to hack into
Speaker 1: weather dot com as we were launching the side, and
Speaker 1: because of the security elements of IBM Cloud, it didn't
Speaker 1: stop out. I say to our team, should we stop,
Speaker 1: like this is seems like a really big deal, and
Speaker 1: they were like, no, this is totally fine. We deal
Speaker 1: with this all the time, all the securities you know
Speaker 1: in place. I don't say that to try and bring
Speaker 1: on anymore uh challenges. We don't definitely don't want that.
Speaker 1: But it was really impressive to me to see how
Speaker 1: the cloud has like got these capabilities built into it. Natively,
Speaker 1: the man out team didn't have to worry. We don't
Speaker 1: have to stop or delay our launch because we were having, uh,
Speaker 1: you know, a security incident. We were able to deploy
Speaker 1: seamlessly without interruptions. That's what that's one example is sort
Speaker 1: of something that you don't think about when you use
Speaker 1: a website or you lose use a mobile app. But
Speaker 1: it's how important the cloud infrastructure behind it is and
Speaker 1: how secure it is that really matters. Cameron mentioned a
Speaker 1: denial of service attack or DNS. This is a common
Speaker 1: attack that the Internet makes possible. There are multiple ways
Speaker 1: hackers carry out such attacks, but here's a quick example.
Speaker 1: When you get down to basic commun nucations across the Internet,
Speaker 1: it's all about machines making contact with one another to
Speaker 1: initiate communication. One machine will send a quick message a
Speaker 1: pay to another one, which will then respond to the
Speaker 1: first computer. It's kind of like someone ringing your doorbell.
Speaker 1: Imagine that every time someone rang your doorbell, you absolutely
Speaker 1: had to go to your door to answer it. You
Speaker 1: have no other options. And I know I find such
Speaker 1: a hypothetical world horrifying too, but that's kind of how
Speaker 1: the internet works. Now, imagine your doorbell rings, You go
Speaker 1: to the door, you open it, No one's there, darn kids.
Speaker 1: So you close the door and you turn around to
Speaker 1: go back to doing whatever it was you were doing before.
Speaker 1: But then the doorbell rings again, so you turn around,
Speaker 1: you open the door, and again no one's there. Now
Speaker 1: you close the door again, and as soon as the
Speaker 1: door closes, the doorbell rings, so you have to answer
Speaker 1: the door again. Remember you always have to answer the door,
Speaker 1: so you're stuck answer the door over and over. You
Speaker 1: can't get anything else done. That's kind of like a
Speaker 1: basic denial of service attack. Hackers will set up a
Speaker 1: computer or a network of computers, sometimes an entire bot
Speaker 1: net of computers that was created through the spread of malware,
Speaker 1: but that's a matter for another episode. And they'll send
Speaker 1: out a series of pings to a particular web server,
Speaker 1: and the goal is to overload that server so that
Speaker 1: it can't get anything else done, perhaps even causing the
Speaker 1: server to crash. Now, as I mentioned, there are a
Speaker 1: lot of variations on this basic idea, and companies have
Speaker 1: had to find innovative ways to counteract those tactics. Big
Speaker 1: companies like IBM spend countless hours developing techniques to detect
Speaker 1: and nullify d n S attacks in an effort to
Speaker 1: make their services stable and dependable. You're talking about the
Speaker 1: two big ones, security and scale. Like if you if
Speaker 1: you need something that's actually going to reach everybody in
Speaker 1: the United States, then it's not something that you can
Speaker 1: look at to gradually scale up the way we see,
Speaker 1: you know, your typical startup where they'll launch in a
Speaker 1: very localized area and then gradually build out from there.
Speaker 1: You had to go from zero to one hundred in
Speaker 1: a single step once you you know, metaphorically flip the switch,
Speaker 1: and without that sort of stability, you can't do that.
Speaker 1: So I'm glad that you brought that up too, because
Speaker 1: it's again one of those things that just sort of
Speaker 1: we've I think we're in an era where we just
Speaker 1: expect things to just work and we we lose perspective
Speaker 1: on what it takes to make that happen, you know,
Speaker 1: to keep that to keep things working. Yeah, I think
Speaker 1: it's it's it's been amazing to see how our friends
Speaker 1: and colleagues across IBM of help support us and and
Speaker 1: the amazing tools that they've provided to make this possible.
Speaker 1: It's it's super inspiring and it feels great to be
Speaker 1: out of that, you know, in the all the challenges
Speaker 1: we're going through to be part of a purpose trivenal organization,
Speaker 1: you know, just personally feels really great. And you know,
Speaker 1: we've we've talked a little bit about the uh, you know,
Speaker 1: the fact that we have this very localized approach to
Speaker 1: tracking COVID nineteen, which I think already sets it apart
Speaker 1: from other There are great tools out there, right World
Speaker 1: Health Organization or Johns Hopkins have COVID nineteen tracking tools,
Speaker 1: but this is one where it's you know that's looking
Speaker 1: at grand scale. This looks at grand scale, but also
Speaker 1: you drilled down to that local level where you can
Speaker 1: really have the view of our things shifting. Is there
Speaker 1: a greater emphasis on UM stay at home. I know
Speaker 1: you're not far from the city of Atlanta. I live
Speaker 1: in the city of Atlanta. We are in a stay
Speaker 1: at home order right now, So seeing that reflected in
Speaker 1: a tracker really does bring home how important obeying that
Speaker 1: kind of order is in order to you know, protect
Speaker 1: people and and to mitigate the spreading of this virus.
Speaker 1: You mentioned earlier that maybe in the future there might
Speaker 1: be other types of data incorporated into this sort of
Speaker 1: tracking system. Do you anticipate perhaps working with either leaders
Speaker 1: or medical personnel in order to be able to use
Speaker 1: this kind of information in a way where perhaps on
Speaker 1: a logistics side, we could see resources UH moved perhaps
Speaker 1: proactively to where they are going to be needed. Yeah,
Speaker 1: we've actually seen that already with the amount of inbound
Speaker 1: interest from government officials, from UH, supply chain logistics companies,
Speaker 1: from hospitals. UH. It's they're using this tool to to
Speaker 1: see what the train curves that are occurring out in
Speaker 1: the various local communities, and then they're redeploying resources. I
Speaker 1: got an email on Wednesday from one hospital group that
Speaker 1: was moving ventilators from Arkansas to Louisiana, for example, because
Speaker 1: of the outbreak that they saw happening in Louisiana and
Speaker 1: they said that the place they saw it was on
Speaker 1: our website, right, And so also it wasn't necessarily designed
Speaker 1: for that purpose. When you put local data out in
Speaker 1: a transparent, easy to consume way, all walks of life,
Speaker 1: I think make better decisions as a result of it.
Speaker 1: And so we're seeing decision makers at all levels and
Speaker 1: all industries used the tool. And I do think, you know,
Speaker 1: we're starting to now to think about what do we
Speaker 1: do and add to it, what's next? And you know,
Speaker 1: we have lots of ideas around that. Sure. I mean,
Speaker 1: I'm just speaking with you. I'm my brain starts to
Speaker 1: free associate with ways, like not necessarily ways that would
Speaker 1: be presented to people like me, right, Not necessarily it
Speaker 1: would be all packaged in with the tracker, because obviously
Speaker 1: you want to keep that tool simple and easy to understand.
Speaker 1: You don't want to overly complicate it and then lose
Speaker 1: the message in the process. So, but there are lots
Speaker 1: of different potential applications. I can I can sort of
Speaker 1: imagine where you know, you you say, this isn't meant
Speaker 1: for public consumption, but maybe we start looking at predictive
Speaker 1: models to try and help people just even just getting
Speaker 1: the word out, even if it's not let's get resources there.
Speaker 1: But we might say, well, based on this predictive model,
Speaker 1: we want to tell the people of St. Louis, Missouri
Speaker 1: that seriously, guys, stay at home for the next few days.
Speaker 1: It's it's that's going to be tough, but trust us,
Speaker 1: based upon everything we're seeing, it will help prevent a
Speaker 1: much bigger problem down the line. Like that's just one
Speaker 1: potential possibility I could imagine. Obviously, you've got to be
Speaker 1: very careful when you're talking about predict of models. But
Speaker 1: that's one of those things that that sort of accursed
Speaker 1: me and probably I know I'm preaching to the choir
Speaker 1: if I'm talking to someone from the Weather Company. Predictive
Speaker 1: models are kind of your thing. They are, but you
Speaker 1: do have to be careful with them, right they And
Speaker 1: so you know, we're looking at that. I think the
Speaker 1: next steps for us to put UH this product and
Speaker 1: to translated into Spanish, so for the Hispanic community to
Speaker 1: get it UH in in Spanish. Then we're looking to
Speaker 1: add other countries around the world to the to the
Speaker 1: maps and things, so that is UH similar. You know,
Speaker 1: I don't know if we can get quite to the
Speaker 1: liver granularity and other countries. We're doing that a country
Speaker 1: by country basis, so I don't want to see false expectations.
Speaker 1: But but we're trying the best we can uh in
Speaker 1: various markets around the world to localize as much as
Speaker 1: as possible with trusted sources, and so they'll take us
Speaker 1: a little bit of time to get get that done.
Speaker 1: And then the other part of that is also translations,
Speaker 1: right um, and so whether dot coms and eight five
Speaker 1: languages today around the world, and so it's not a
Speaker 1: small effort to translate this kind of complex data and
Speaker 1: make sure it's done correctly, contextually and medically accurate is
Speaker 1: also obviously obviously super important around the world. And so
Speaker 1: those are the next steps for us we're excited about.
Speaker 1: But we also are seeing sentiment analysis come up as
Speaker 1: something from the mental health community saying, you know what,
Speaker 1: fear is spreading almost as rapidly, if not faster, than
Speaker 1: the virus itself. And how people feel is also really
Speaker 1: really important, and having an outlet for them to share
Speaker 1: how they feel all is important, and so we're looking
Speaker 1: at that too. I'm not sure that we play a
Speaker 1: role there, but we're looking at at maybe it's as
Speaker 1: simple as the frowny face the smiley face a moticon,
Speaker 1: but we're trying to figure that out one And I
Speaker 1: think the important thing for us to remember is that
Speaker 1: getting this information, getting this localized information gives us. It
Speaker 1: empowers us to make decisions. It makes us more confident
Speaker 1: when we're making those choices of let's stay at home,
Speaker 1: even though it might be, you know, difficult for us
Speaker 1: if we're able to say, yeah, but I'm looking here
Speaker 1: at this chart and I don't want to be part
Speaker 1: of that red bar that is of the COVID nineteen cases.
Speaker 1: I don't want to potentially put my family at risk
Speaker 1: or someone else that I might encounter. And to me,
Speaker 1: that is an incredible, incredible tool and an incredible story
Speaker 1: to tell, is that this is a way for us
Speaker 1: to kind of really think, how is this affecting the
Speaker 1: people around me? Not just these big numbers that I
Speaker 1: hear on the news where I can easily get lost
Speaker 1: because once you get past a certain number, I can't
Speaker 1: really even conceptualize it. This puts it in that context
Speaker 1: of no, these are the people I know, And this
Speaker 1: is why it's important for me to keep that in mind,
Speaker 1: to stay safe and to protect not just myself but
Speaker 1: those in my community. You know, I think this is
Speaker 1: uh a tool to help people make better decisions, to
Speaker 1: put their local neighbors almost a hit of themselves as
Speaker 1: much as possible, and for us all to really together.
Speaker 1: And when we say really together, is really together and
Speaker 1: stay separated. Uh And and that's not intuitive, but it's
Speaker 1: the most important thing we can do right now is
Speaker 1: stay home and start to spread. That's gonna say, lives.
Speaker 1: It is as simple as that. I I thank you
Speaker 1: so much for being part of the show and for
Speaker 1: explaining the process and explaining the technologies that are required
Speaker 1: in order to make it happen. It's a interesting convergence
Speaker 1: of a lot of things I talk about on tech stuff,
Speaker 1: but in the context of making real world impact. And
Speaker 1: that's something that often gets left behind in tech conversations,
Speaker 1: is that we talk about the how, maybe even a
Speaker 1: little bit of the why. But it's it's rare when
Speaker 1: we talk about how it actually rolls out into the
Speaker 1: real world and starts to make real world change. So
Speaker 1: thank you so much for your work and thank you
Speaker 1: for joining me on the show. Pleasure I want to
Speaker 1: thank Cameron for coming on the show and talking about
Speaker 1: the work the Weather Company and IBM are doing to
Speaker 1: give us more useful, reliable information about the outbreak of
Speaker 1: COVID nineteen. A quick glance at my county shows me
Speaker 1: that even as I record this bit several days is
Speaker 1: after speaking with Cameron, we're not over the peak yet.
Speaker 1: The curve has yet to flatten, and so it really
Speaker 1: is important that anyone who can stay home stays home.
Speaker 1: I know there are many of you listening who don't
Speaker 1: have that luxury. Many of you work in necessary roles
Speaker 1: that require you to be out and about whether you're
Speaker 1: providing medical services, you're driving needed inventory two stores where
Speaker 1: you're making sure the lights stay on, and so the
Speaker 1: rest of us have to stay home to keep those
Speaker 1: of you who don't have that option safe. To see
Speaker 1: this tech in action for yourself and to get a
Speaker 1: look at what's going on in your own community, download
Speaker 1: the Weather Channel app or go to weather dot com
Speaker 1: slash coronavirus. You're gonna see all the information there from
Speaker 1: a state and county level. It's really useful, and again
Speaker 1: I think it's important to apply critical thinking when we
Speaker 1: encounter information about the coronavirus. There's a lot of data
Speaker 1: out there that just isn't reliable. Some of it might
Speaker 1: be well intentioned but incorrect, some of it might be
Speaker 1: purposefully misleading. I've seen numerous messages that purport to be
Speaker 1: from experts and more than a few that have no
Speaker 1: citation at all, that contain erroneous information about the outbreak.
Speaker 1: And when those supposed sources are contacted about these messages
Speaker 1: that they've supposedly been saying, they say they've had nothing
Speaker 1: to do with them. So knowing that the Weather Company's
Speaker 1: COVID nineteen tracking tools are pulling only from official government
Speaker 1: sources in real time, lets us know that the information
Speaker 1: is solid. It's also important to remember that these numbers
Speaker 1: are all on confirmed cases, and the number of actual
Speaker 1: cases out in the wild is larger, though to what
Speaker 1: extent is impossible to say. Bottom line, we can look
Speaker 1: at the localized information presented by weather dot com and
Speaker 1: the Weather Channel app as being the minimum number of
Speaker 1: cases in our communities, and we should take that number
Speaker 1: seriously and do our best to get those numbers to
Speaker 1: come down. I'm and we're going to see a lot
Speaker 1: more innovation in this space. One thing I draw inspiration
Speaker 1: from is how we humans can rise to meet incredible challenges.
Speaker 1: Sometimes it takes a problem of enormous magnitude to stir
Speaker 1: us to action, but then we discover we're incredibly resourceful.
Speaker 1: Defining the problem, understanding it, and then making a plan
Speaker 1: to surmount it is all part of the human condition,
Speaker 1: whether it's landing people on the moon or finding ways
Speaker 1: to help people mitigate the spread of a virus, and
Speaker 1: we all can play a part. If you listen to
Speaker 1: our previous Smart Talks episode about Project Owl, you've heard
Speaker 1: about the Call for Code. It's a five year series
Speaker 1: of coding competitions in which groups pitch technological solutions to
Speaker 1: tackle big challenges. The winners not only get a cash prize,
Speaker 1: they also get support from IBM to implement their proposed
Speaker 1: solutions in the real world. The theme for the challenge
Speaker 1: is climate change, but since the publication of that episode,
Speaker 1: IBM has expanded the Call for Code to also include
Speaker 1: the COVID nineteen crisis. Programmers and technologists are welcome to
Speaker 1: submit their proposed solutions to the COVID nineteen crisis by April.
Speaker 1: Those interested in participating in the Parallel Track, which aims
Speaker 1: to tackle climate change may submit their own proposed solutions
Speaker 1: by July one. Learn more at developer dot IBM dot
Speaker 1: com slash call for code. In the next episode of
Speaker 1: smart Talks on tech Stuff, I'll speak with David Turik,
Speaker 1: vice president for High Performance Computing and Cognitive Systems at
Speaker 1: Open Power IBM Systems. He'll explain how the High Performance
Speaker 1: Computing Consortium is dedicating incredible computational resources in the fight
Speaker 1: against COVID nineteen, and tell us how supercomputers can help
Speaker 1: researchers and their efforts to develop a vaccine. That's all
Speaker 1: for now. I'll talk to you again really soon. Text
Speaker 1: Stuff is an I Heart Radio production. For more podcasts
Speaker 1: from I Heart Radio, visit the i heart Radio app,
Speaker 1: Apple Podcasts, or wherever you listen to your favorite shows.