Smart Talks with IBM: Tracking COVID-19

critical information. And that's going to bring us into today's topic. But that's just one way that IBM is making its technology available in the fight against COVID nineteen. The company is working closely with scientists, doctors, leaders, and experts to fight COVID nineteen in many ways, all while leveraging some

really impressive technology. Whether it's using supercomputers to help researchers find a vaccine and that's a little bit of a teaser for an upcoming episode, or aggregating enormous amounts of informations so that the average American can get a local ice snapshot of what's happening in their communities. IBM technology is playing a big part. So what are we talking about today? Well, the Internet is a phenomenal way to

share information, but that's a double edged sword. Over the past several years, we've seen misinformation and even disinformation spread across online communities, clouding our understanding of matters from the trivial to the critical. If we're lucky, it's the trivial, like the fact that people kept photoshopping the date on the time circuit in the Back to the Future films to make it today's date when Marty McFly goes to

the future. But we're increasingly seeing more bad information about important things, from politics, to climate change to yeah, the spread of the coronavirus, and in that kind of environment, making the right decisions becomes increasingly more difficult to do, just as it's becoming more urgent. That brings us to

today's topic. I sat down with Cameron Clayton, the general manager of IBM Cloud Ecosystem and of the Weather Company and IBM Business Well we were both sitting down, but we also happened to be in our respective homes speaking over the Internet in an effort to keep ourselves and others safe and really, in many ways, that's what this

boils down to. But I'm getting ahead of myself. One thing I do want to mention is that the unusual circumstances mean this episode sounds a bit different from other tech stuff episodes, because real life goes on while we podcast. Cameron's team at the Weather Company have done something extraordinary. If you've ever visited weather dot com or use the Weather Channel app, you know you can get an incredibly localized report down to the zip code in the United States.

And just so you know, there are forty one thousands seven to zip codes in the US. I counted them, which was tough because somewhere in the fourteen thousand range of lost count had to start all over again. Aggregating that much information and presenting it in a meaningful way is no small feat. But what Cameron's team did next was in some ways even more astounding. They took that general approach and they applied it to the spread of

COVID nineteen. Here's my conversation with Cameron Clayton, with only an interruption here and there to clarify some things. Cameron, thank you so much for taking time to join us on the show today. I really appreciate it, absolutely my pleasure. It's great to be here now. I think it's pretty safe to say this is not an exaggeration that everyone has been affected by the COVID nineteen crisis to some extent. But I think it can be a little tricky for people to get a big picture grasp on the global

impact of this crisis. Can you kind of speak a little bit as to your perspective on the world impact of COVID eighteen. Certainly so. As the Weather Channel, our mission is to map the atmosphere. So as a company and a collective of people, our whole job is to try and predict what's going to happen in the atmosphere tomorrow. It's almost this impossible science math mother nature problem that that we're that we've been working on for a long

time and make tremendous progress on. Said differently, though, and I think this is really where it comes to COVID nineteen. At the end of the day, we're making the invisible visible and and so doing that, it's easier to make decisions When you take something that's intangible, you can't see tangible, and then therefore you can make a bit of decision as a result of that. And so that's what we

tried to do with with COVID nineteen. And we've got so much inbound interests from our our fans all around the world saying, hey, you you make something we can't see easier to digest, easier to understand, and easier to make decisions on which you please do the same thing for COVID nineteen. And so that's what that's what we've been doing, is is trying to make the invisible visible. And like like you said, I think every single person on the planet is impacted by COVID nineteen, just like

they're impacted by the weather, right. And so as a result of our reach in our scale with literally hundreds of millions of users around the world, uh, we're able to also communicate with them in a way that they're familiar with as part of their daily habit already. H And so you know, we'll be able to try and

provide trusted data to our fans around the world. You know, as you point out, Cameron, contextualizing data is really critically important for people to be able to make use of it, right, to be able to take something that is conceptually this huge thing, but it's very hard for us to boil that down into actionable things that we can do as people. I think one thing that kind of helps again, you know, as humans were not really good at dealing with big

numbers just on our own. One thing that really helps is is kind of anchoring things to personal experience as well. So before we jump into all the background on IBM and the weather companies work with UH, the technologies to track COVID nineteen, I was wondering, can you talk a little bit about how this crisis has affected you personally

so far? Oh? Yeah, sure, I think. Uh So, I have four children, I'm married with with all kids, UH and a dog, and about two and a half weeks ago, all our kids, you know, came home and have been on sort of Zoom and Google classroom, you know, with x is all all day every day, along with with me doing the same thing on UH working here from the house. And you know, I think we're thankfully safe and healthy and doing well. But Kevin fever is a

real issue, especially with two boys. It's been a big change in the sense of we often ask, you know, how's your day going. Are you having a good day? And I think many of us are still asking that question, but we're not used to the answer being actually no, I'm not. I'm really not okay. Uh, And and we're getting a few of those answers now. I had a

couple of those onswers this morning. And so how we rallied together as a community, how we relied together ash as companies, how we relied together as humans really really matters. And I'm certainly super proud of the way our teams rallied together and and IBMS relied together. Were also super proud about our clients and partners and neighbors and uh and others like Uh. We had a block street party

where everybody was in their cause. They decorated their cause and drove by the five old people that live in the street and who are in the window to try and share them up. So this just amazing touches of

humanity happening. That's that's really cool to see. And that's a great way to segue into talking about what the Weather Company, what IBM are doing and in a way to give people more tool so that they can make decisions that are critically important for themselves and for the people around them, whether it's relatives, coworkers, loved ones, just

strangers on the street. We all have this responsibility So let's talk kind of in general terms what exactly you guys are doing in an effort to give people these tools. So kind of from a very high level, as I understand it, you're pulling data that's localized to specific regions, contextualizing that and presenting it in a way that's easily digestible so that people have and up to date understanding what's going on in their communities. Is that more or

less correct? Yeah, that's that's right. We're we're trying to make this invisible virus contextual and localized. The important thing when you do that is it has to be from a trusted source. So the sources of the data have to be really high integrity, and integrity beats all other aspects of data. Right. It's more important than timeliness. It's more important than you know, how large the field is. The most important thing is it's a trusted data source.

And so all the data that we're collecting and displaying in our solution is from local government, state government, or federal government sources. So we're not we're not doing crowdsourcing. We're not pulling in social media opinions or things like that, although those things have their place and are helpful in their own way. This is really about about aggregating and collecting all the local sources so that you can see what's going on in your community, right And I think

that is really important. You know, I have a have a uncle that was in Louisiana. He's a great guy, but he's a free spurt. He does his own thing. He lives his own way, and he doesn't really listen that well too, friends or family. He's going to do his own thing. And so, you know, as we started building building this tool and I started seeing the data that was going on there, I was able to use it to show him that, you know what what and

this was you know, last weekend and Monday. That would show him, Look, there's really some really serious outbreaks going on in Louisiana right now. This is really accelerating. Uh. You might not take this seriously, but your people in your community really need you to to listen to the local authorities and stay put right, stay inside and and

behave um. And so I think, you know, you take my example and extrapolate that out across Every American has a story somewhat similar to had I think, Uh, and so we're all looking and seeing what's happening to friends and family around the world and across the United States, and uh and you know, staying home, stopping the spread ultimately saves lives, right it is that that's simple right now, and be able to show people what's happening in their community.

We're doing in three ways. So we're showing them the data, how many people have been tested and shown as positive in their county, how many deaths have been recorded. We're showing the trend line for that county day over day. So is it getting steeper, is the curve getting steeper or is it plateau ng or hopefully at some point here we'll see it declining in places, but right now we're not. It's still on the upswing. Then we we let them choose between their state view or their county

view of that trained analysis. Then we're making it tangible on a map. Right What we found with with weather and we're doing now with the COVID virus is plotting it on a map so you can actually see it in a geographical context. So your county versus the county next to you, and across the entire United States, every

county that that's producing data we're ingesting. There are some places that aren't producing data, particularly in some rural counties, but for the most part, the major population centers are all producing this data, and you can see what's going on, right, and I think we've seeing people make better decisions as a result of it, and that's the whole purpose. That's why we did this. One of the things you mentioned

was about the trusted sources. I'm glad you went into that and explained where you're pulling information from, because obviously we're right now in an era where there's missing for nation and even disinformation running rampant online. So it's good to be able to point to a tool and actually know where where's this tool pulling information from. And it's also really interesting to me that it's taking a very

similar approach to what the Weather Company has done. I think everyone has had the experience of using either the app or the website and looking at, you know, weather forecasts for specific zip codes and so kind of taking that same thinking and applying that to the COVID nineteen outbreak is really interesting to me too. I'm wondering, um, with that in mind, uh, are the sources you're pulling from? You know there are local, state and federal government. Are

they in different formats? Because to me, that would present itself an enormous challenge because machines. Humans are really good at pulling information from whatever format we encounter, machines typically

are not. So this is actually the heart of the technology challenge we've had and and we've basically used IBM S Watson AI tools to be able to ingest this data from literally thousands and thousands of data sources, all in different formats, right, everything from a PDF that's actually an image like a photograph essentially two HTML files to or everything else in between. And every single counties website

is built differently. There's no technology standard that's been applied. UH. And so you know, I think that one of the one of the amazing things from a technology only perspective in this is that the Watson's AI was able to INGI and learn all of these different formats based really on its own and ing all this data from all these different technology types, uh and put it into a standardized format that we could then produce and and and show in our websites and our acts and and that

whole training from when we started to when we had the first standardized file was thirty six hours, so we're not talking about two weeks or or anything. This is like, you know, a data hoff to learn and train and collect the data and put it into a standardized format. Cameron mentioned training the system on data, and some of you might wonder what that actually means. It's a term used in machine learning in which engineers feed information into

computer models in an effort to produce particular results. And typically you start out doing this by knowing what results you want ahead of time when you're first training the system. That way, you can see what comes out of the model, you compare it to what you expected to see, and if the two don't match up, you can go back and tweak the model. So, for example, let's say you're training an image recognition computer model to recognize pictures of fire hydrants. You might feed a ton of images of

fire hydrants to the model. Then you might introduce a mixture of images that include fire hydrants and other stuff, seeing if the model can tell the difference sorting the images properly. So you analyze those results and if they're good, you keep going. You might use millions of points of data to train your model. Over time, and often this is a painstakingly slow process, particularly when engineers need to step in and change something about the model that isn't

quite working. Once you are able to get reliable results from the model, you can put it to more practical use, though it may require future tweaks when the model encounters something well outside the norm. Okay, let's get back to my conversation with Cameron Clayton, the general manager of IBM Cloud Ecosystem and of the Weather Company and IBM business.

You're talking about things like natural language processing, being able to access all these different styles of data, whether it's an image file or it's something that can be searched with an algorithm, and then taking the meaning of that not just the fact that the data is there. You have to the MA gene has to understand what the meaning is in order to put it into its model

and then present it. These are really tough engineering challenges in computer AI in general, and so to see that application being put so quickly in place, exactly can you give us an idea of how long it took too, from the point of ideation to the point of implementation that you guys went through in order to produce this. So the timeline from when we started UH this getting it getting it live was probably about ten days. We

got inbound interest from our fans around the world. We then mocked up how we wanted to present the data from a visual point of view. We then, in parallel, we're rapidly trying to source all these local data sources. We wanted it from the very beginning of as local as possible UH, and we rapidly realized this was not something that you could do manually. You had to do this UH in an automated way at scale, and so that's when we brought in Watson and AI to help.

Thirty six hours later we had data. We had, you know, a format, but then we really spent a lot of time testing right to make sure that the data was correct, to make sure that you know, when when you know, Governor Cumos speaks in New York at eleven thirty every morning, within a few minutes, we were able to up dat the data based on the numbers he's sharing with the media,

but on the New York State website, for example. And so there's all these different factors both technology and format that we had to take into account, but also just testing this. The second is we always, due to our scale and the number of users we have, we have to load test and make sure that technologically we can deliver to the millions of people that use our properties and platforms. And so we've got this uh live a few days ago and beginning to end is probably seven days.

There's a remarkable achievement. I mean, you're you're talking about everything from coming up with the idea to the design saying what do we actually do to make this possible? To even even something that seems to someone like me simple, like how do you present that information to the consumers so in a way that makes sense, Like we we only see it at the end, right, we see it after you've made all those decisions, and we look at it and we say, oh, yeah, of course that makes sense.

But you have to get there first on the design side. And I think a lot of people don't understand or appreciate how challenging that part of technology is to not just the making it work, but making it work in a way that is ultimately useful to the end consumer too,

so that yeah, it doesn't just work, it works for you. Um, and we were you know, one of the other things I wanted to talk about was that this design process not only was it rapid, but obviously it was unusual in that you weren't all working in the same workspace at the same time because of the concerns, the health concerns. So what was that Like, how did your team respond to working in a decentralized approach. So I gotta say the team really has leaned in here hod like countless

almost before. We had sixty people touched this project. Over the course of the week. I would say fifty five of the sixty worked multiple twenty four hour days in that time period. Uh And so sleep deprivation was a real issue as well, right, But but we have we have great tools, you know, whether it's video conferencing and collaboration tools where we can actually iterate and design products remotely, but all on the same screen at the same time.

So our design is ractually able to you know, one of them draws a line and the other one can raise it as as they're drawing it. Uh. And so that was both fun and challenging at the same time. I would say the goal of the product was to make it clean and simple, so it is digestible, and I think you know, we can always add more data fields over time and and add more information, but the real purpose of this was to help people understand what's happening in the community so that they would stay home,

stop the spread, and ultimately save lives. Right. Making that invisible visible was was the goal of this UM and so I think we've we've achieved that, but it's only through hard work of a small group of folks that that you know, really worked hard for seven days without sleep. Wow, I mean, that's that's incredible. So, so they're working hard putting this all together. Meanwhile, you've got the AI and tools working hard in the background to synthesize all that data.

Can you talk a little bit more about the specific technologies running in the background. We've mentioned Watson, but is there anything else along with that. This is a whole whole variety of things that that live in the background that make this possible and so and almost all of those things are things that we don't think about as consumers of whether dot Com or our mobile apps. So one is just the cloud infrastructure itself. The fact that you know on Monday night when we launch, you know,

we were alive for like three hours. We had about a million users in three hours used the property. On Tuesday, we had three and a half million users start using the property. On Wednesday it was up to like five and a half or six million users. You can't scale like that, and those are each individual, unique visitors, some of them visited multiple times and checks, you know, tens of locations around the country for friends and family. Uh. And you can't have that kind of scale without having

a really robust cloud infrastructure behind it. And so uh, IBM Cloud was has been and continues to be instrumental and sort of invisibly in the background, keeping the infrastructure alive. And one of the one of the things about that, I'll take a quick story on it, that was super impressive to me. And I see this these kinds of launches fairly often with our products. But as we launched on Monday night, what I was not used to seeing

was security h automatically being implemented. And so what I mean by that is we were actually having a denial of service attack, so hackers, we're trying to hack into weather dot com as we were launching the side, and because of the security elements of IBM Cloud, it didn't stop out. I say to our team, should we stop, like this is seems like a really big deal, and they were like, no, this is totally fine. We deal with this all the time, all the securities you know

in place. I don't say that to try and bring on anymore uh challenges. We don't definitely don't want that. But it was really impressive to me to see how the cloud has like got these capabilities built into it. Natively, the man out team didn't have to worry. We don't have to stop or delay our launch because we were having, uh, you know, a security incident. We were able to deploy

seamlessly without interruptions. That's what that's one example is sort of something that you don't think about when you use a website or you lose use a mobile app. But it's how important the cloud infrastructure behind it is and how secure it is that really matters. Cameron mentioned a denial of service attack or DNS. This is a common attack that the Internet makes possible. There are multiple ways hackers carry out such attacks, but here's a quick example.

When you get down to basic commun nucations across the Internet, it's all about machines making contact with one another to initiate communication. One machine will send a quick message a pay to another one, which will then respond to the first computer. It's kind of like someone ringing your doorbell. Imagine that every time someone rang your doorbell, you absolutely had to go to your door to answer it. You

have no other options. And I know I find such a hypothetical world horrifying too, but that's kind of how the internet works. Now, imagine your doorbell rings, You go to the door, you open it, No one's there, darn kids. So you close the door and you turn around to go back to doing whatever it was you were doing before. But then the doorbell rings again, so you turn around,

you open the door, and again no one's there. Now you close the door again, and as soon as the door closes, the doorbell rings, so you have to answer the door again. Remember you always have to answer the door, so you're stuck answer the door over and over. You can't get anything else done. That's kind of like a basic denial of service attack. Hackers will set up a computer or a network of computers, sometimes an entire bot net of computers that was created through the spread of malware,

but that's a matter for another episode. And they'll send out a series of pings to a particular web server, and the goal is to overload that server so that it can't get anything else done, perhaps even causing the server to crash. Now, as I mentioned, there are a lot of variations on this basic idea, and companies have had to find innovative ways to counteract those tactics. Big companies like IBM spend countless hours developing techniques to detect and nullify d n S attacks in an effort to

make their services stable and dependable. You're talking about the two big ones, security and scale. Like if you if you need something that's actually going to reach everybody in the United States, then it's not something that you can look at to gradually scale up the way we see, you know, your typical startup where they'll launch in a very localized area and then gradually build out from there.

You had to go from zero to one hundred in a single step once you you know, metaphorically flip the switch, and without that sort of stability, you can't do that. So I'm glad that you brought that up too, because it's again one of those things that just sort of we've I think we're in an era where we just expect things to just work and we we lose perspective on what it takes to make that happen, you know,

to keep that to keep things working. Yeah, I think it's it's it's been amazing to see how our friends and colleagues across IBM of help support us and and the amazing tools that they've provided to make this possible. It's it's super inspiring and it feels great to be out of that, you know, in the all the challenges we're going through to be part of a purpose trivenal organization,

you know, just personally feels really great. And you know, we've we've talked a little bit about the uh, you know, the fact that we have this very localized approach to tracking COVID nineteen, which I think already sets it apart from other There are great tools out there, right World Health Organization or Johns Hopkins have COVID nineteen tracking tools, but this is one where it's you know that's looking

at grand scale. This looks at grand scale, but also you drilled down to that local level where you can really have the view of our things shifting. Is there a greater emphasis on UM stay at home. I know you're not far from the city of Atlanta. I live

in the city of Atlanta. We are in a stay at home order right now, So seeing that reflected in a tracker really does bring home how important obeying that kind of order is in order to you know, protect people and and to mitigate the spreading of this virus. You mentioned earlier that maybe in the future there might be other types of data incorporated into this sort of

tracking system. Do you anticipate perhaps working with either leaders or medical personnel in order to be able to use this kind of information in a way where perhaps on a logistics side, we could see resources UH moved perhaps proactively to where they are going to be needed. Yeah, we've actually seen that already with the amount of inbound interest from government officials, from UH, supply chain logistics companies,

from hospitals. UH. It's they're using this tool to to see what the train curves that are occurring out in the various local communities, and then they're redeploying resources. I got an email on Wednesday from one hospital group that was moving ventilators from Arkansas to Louisiana, for example, because of the outbreak that they saw happening in Louisiana and they said that the place they saw it was on our website, right, And so also it wasn't necessarily designed

for that purpose. When you put local data out in a transparent, easy to consume way, all walks of life, I think make better decisions as a result of it. And so we're seeing decision makers at all levels and all industries used the tool. And I do think, you know, we're starting to now to think about what do we do and add to it, what's next? And you know, we have lots of ideas around that. Sure. I mean,

I'm just speaking with you. I'm my brain starts to free associate with ways, like not necessarily ways that would be presented to people like me, right, Not necessarily it would be all packaged in with the tracker, because obviously you want to keep that tool simple and easy to understand. You don't want to overly complicate it and then lose the message in the process. So, but there are lots

of different potential applications. I can I can sort of imagine where you know, you you say, this isn't meant for public consumption, but maybe we start looking at predictive models to try and help people just even just getting the word out, even if it's not let's get resources there. But we might say, well, based on this predictive model, we want to tell the people of St. Louis, Missouri that seriously, guys, stay at home for the next few days.

It's it's that's going to be tough, but trust us, based upon everything we're seeing, it will help prevent a much bigger problem down the line. Like that's just one potential possibility I could imagine. Obviously, you've got to be very careful when you're talking about predict of models. But that's one of those things that that sort of accursed me and probably I know I'm preaching to the choir if I'm talking to someone from the Weather Company. Predictive

models are kind of your thing. They are, but you do have to be careful with them, right they And so you know, we're looking at that. I think the next steps for us to put UH this product and to translated into Spanish, so for the Hispanic community to get it UH in in Spanish. Then we're looking to add other countries around the world to the to the maps and things, so that is UH similar. You know, I don't know if we can get quite to the

liver granularity and other countries. We're doing that a country by country basis, so I don't want to see false expectations. But but we're trying the best we can uh in various markets around the world to localize as much as as possible with trusted sources, and so they'll take us a little bit of time to get get that done.

And then the other part of that is also translations, right um, and so whether dot coms and eight five languages today around the world, and so it's not a small effort to translate this kind of complex data and make sure it's done correctly, contextually and medically accurate is also obviously obviously super important around the world. And so those are the next steps for us we're excited about.

But we also are seeing sentiment analysis come up as something from the mental health community saying, you know what, fear is spreading almost as rapidly, if not faster, than the virus itself. And how people feel is also really really important, and having an outlet for them to share how they feel all is important, and so we're looking

at that too. I'm not sure that we play a role there, but we're looking at at maybe it's as simple as the frowny face the smiley face a moticon, but we're trying to figure that out one And I think the important thing for us to remember is that getting this information, getting this localized information gives us. It

empowers us to make decisions. It makes us more confident when we're making those choices of let's stay at home, even though it might be, you know, difficult for us if we're able to say, yeah, but I'm looking here at this chart and I don't want to be part of that red bar that is of the COVID nineteen cases. I don't want to potentially put my family at risk

or someone else that I might encounter. And to me, that is an incredible, incredible tool and an incredible story to tell, is that this is a way for us to kind of really think, how is this affecting the people around me? Not just these big numbers that I hear on the news where I can easily get lost because once you get past a certain number, I can't

really even conceptualize it. This puts it in that context of no, these are the people I know, And this is why it's important for me to keep that in mind, to stay safe and to protect not just myself but those in my community. You know, I think this is uh a tool to help people make better decisions, to put their local neighbors almost a hit of themselves as much as possible, and for us all to really together. And when we say really together, is really together and

stay separated. Uh And and that's not intuitive, but it's the most important thing we can do right now is stay home and start to spread. That's gonna say, lives. It is as simple as that. I I thank you so much for being part of the show and for explaining the process and explaining the technologies that are required in order to make it happen. It's a interesting convergence of a lot of things I talk about on tech stuff,

but in the context of making real world impact. And that's something that often gets left behind in tech conversations, is that we talk about the how, maybe even a little bit of the why. But it's it's rare when we talk about how it actually rolls out into the real world and starts to make real world change. So thank you so much for your work and thank you

for joining me on the show. Pleasure I want to thank Cameron for coming on the show and talking about the work the Weather Company and IBM are doing to give us more useful, reliable information about the outbreak of COVID nineteen. A quick glance at my county shows me that even as I record this bit several days is after speaking with Cameron, we're not over the peak yet. The curve has yet to flatten, and so it really is important that anyone who can stay home stays home.

I know there are many of you listening who don't have that luxury. Many of you work in necessary roles that require you to be out and about whether you're providing medical services, you're driving needed inventory two stores where you're making sure the lights stay on, and so the rest of us have to stay home to keep those

of you who don't have that option safe. To see this tech in action for yourself and to get a look at what's going on in your own community, download the Weather Channel app or go to weather dot com slash coronavirus. You're gonna see all the information there from a state and county level. It's really useful, and again I think it's important to apply critical thinking when we encounter information about the coronavirus. There's a lot of data

out there that just isn't reliable. Some of it might be well intentioned but incorrect, some of it might be purposefully misleading. I've seen numerous messages that purport to be from experts and more than a few that have no citation at all, that contain erroneous information about the outbreak. And when those supposed sources are contacted about these messages that they've supposedly been saying, they say they've had nothing

to do with them. So knowing that the Weather Company's COVID nineteen tracking tools are pulling only from official government sources in real time, lets us know that the information is solid. It's also important to remember that these numbers are all on confirmed cases, and the number of actual cases out in the wild is larger, though to what

extent is impossible to say. Bottom line, we can look at the localized information presented by weather dot com and the Weather Channel app as being the minimum number of cases in our communities, and we should take that number seriously and do our best to get those numbers to come down. I'm and we're going to see a lot more innovation in this space. One thing I draw inspiration from is how we humans can rise to meet incredible challenges.

Sometimes it takes a problem of enormous magnitude to stir us to action, but then we discover we're incredibly resourceful. Defining the problem, understanding it, and then making a plan to surmount it is all part of the human condition, whether it's landing people on the moon or finding ways to help people mitigate the spread of a virus, and we all can play a part. If you listen to our previous Smart Talks episode about Project Owl, you've heard

about the Call for Code. It's a five year series of coding competitions in which groups pitch technological solutions to tackle big challenges. The winners not only get a cash prize, they also get support from IBM to implement their proposed solutions in the real world. The theme for the challenge is climate change, but since the publication of that episode, IBM has expanded the Call for Code to also include

the COVID nineteen crisis. Programmers and technologists are welcome to submit their proposed solutions to the COVID nineteen crisis by April. Those interested in participating in the Parallel Track, which aims to tackle climate change may submit their own proposed solutions by July one. Learn more at developer dot IBM dot com slash call for code. In the next episode of smart Talks on tech Stuff, I'll speak with David Turik, vice president for High Performance Computing and Cognitive Systems at

Open Power IBM Systems. He'll explain how the High Performance Computing Consortium is dedicating incredible computational resources in the fight against COVID nineteen, and tell us how supercomputers can help researchers and their efforts to develop a vaccine. That's all for now. I'll talk to you again really soon. Text Stuff is an I Heart Radio production. For more podcasts from I Heart Radio, visit the i heart Radio app, Apple Podcasts, or wherever you listen to your favorite shows.