Smart Talks with IBM: Accelerating an answer to COVID-19

of a sliding classification. It's something we apply to an elite group of computers that operate at a level above and beyond what other machines are capable of at that time. And I thought it might be a good idea to explain what those are, since otherwise the only impression you'll get is that more flops equals more good somehow. So let's start with floating point numbers and computing, you might deal with integers, and these are whole numbers with no fractions.

Like the number three. Three is a good number, it's an integer. But what about point three? Now we have a number that has a decimal in it. This is not an integer, but it can be a floating point number. Now, computers are really good at working with integers. They can calculate processes on integers whippity quick, but floating point numbers those can take a bit longer, and speed is a

big deal in computing. You always want answers quickly. But the reason we call them floating point numbers is that you can move that decimal around so that point three, well, we could represent that as three times ten to the power of minus one. This is an example of scientific notation, something used in lots of disciplines to help represent very large or very small numbers without having to write in

all those darned zeros. For example, if I wanted to write out the number two trillion, I would write the numeral two followed by twelve zeros. That's a lot of zeros, and honestly, if I wanted to do anything use full with that number, it would end up being a real hassle. But I could represent the same number as two times ten to the twelve power. I wanted to give you guys a basic understanding of floating point operations because that's going to come into play in my discussion in this episode.

So now that we've got that out of the way, we can move on. Dave Turik, vice president of High Performance Computing and Cognitive Systems at IBM, spoke with me on Thursday April two, twenty twenty about high performance computing in general and how researchers are using it in an effort to research the coronavirus and COVID nineteen. And I should also add we recorded this call over the Internet, and so the quality is not the same as what we would usually have in a studio. You're going to

hear some effects because of the Internet connection. You'll probably hear some extraneous noise, and I apologize for that, but in these extraordinary circumstances, this was the best we could manage in order to have this important conversation. And I want to thank Dave for his time and patience in setting this up, and I really appreciate it. So let's

jump into it. Dave, before we go into this incredible effort that we're seeing from research institutions using supercomputers to research the coronavirus and look at treatments for COVID nineteen, can you define in broad terms what is actually meant by high performance computing? Well, I think, uh, the way to think about high performance computing is in terms of the nature of the problem first of all, and then

the kind of computing the supplied against it. So by nature of the problem, I mean that it's fundamentally infused with mathematical representations of systems or problem types. And then from a computing perspective, the kind of technology that puts an emphasis on floating point and very quick communications as a coal by which those problems are tackled. That just helps one distinguish between somebody saying, well, I can solve this problem on a phone, right, that's not what we're

talking about here. The nature of the mathematics are complex and sometimes quite extreme, and the computing we required to tackle those have similar kinds of capabilities to overcome that complexity. So now that we've kind of got to grasp on that, we're looking at a sort of a a massive scale form of computing that does very complicated processes very very quickly. Can you talk a bit about the Higher Performance Computing Consortium? What is what is that organization? How did that come about?

The COVID nineteen HPC Consortium came about roughly ten days ago um courtesy of the conversation between our director of research Dario Gill and people at the White House and subsequently the Department Energy to see how we could apply high performance computing or super computing, two problems associated with uh COVID nineteen and quite quickly the offers were taken up and within a matter of a couple of days

we had a website up and running. They gave the broad parameters of the resources that were available on how one could make submissions to it, and then with a passage of another couple of days, we brought in a number of additional partners as well UM to complement the capability that we initially we're able to access of via IBM and a Department of Energy excellent and one are some of the actual technologies that are being used in

this process. We've mentioned supercomputers, can you talk about any specific ones and UH, what about things like artificial intelligence, machine learning or what kind of various tech are coming together to tackle this this UH this issue. The glib answer of course is everything. But let me be a little more specific from the perspective of the supercomputers that

are part of the consortium currently UH. They range from the Power nine based supercomputers that one finds at oak Ridge and Lawrence Livermore to x AD six based systems that you might find a NASA our gone in other places. UM. For the most part, most of these systems, but not all of them use accelerators UM and UH, and that really deals with some of the floating point computations that are involved, and in some cases the systems are are

absent UM UH accelerators, So those are homogeneous systems. So that's the hardware characterization when we begin to talk about machine learning deep learning in those things, that's a combination of software running in sync with particular hardware attributes. So from a deep learning from a model training perspective, there's

a premium placed on the availability of accelerators. So the Summit system at oak Ridge, for examples, and fused with about accelerators, so it's terrific for helping people train models. But then as you begin to do influencing in some of the other machine learning techniques, the emphasis UM exclusively on accelerators evolves a little bit and you get to employ different kinds of architectural approaches to UH to look at actually inferencing problems. So it's a combination of software

and hardware that's meant to be reasonably flexible. Not One of the things I'll say, of course, and oak Ridge along with IBM, have been a pioneer in this is that there's not a sharp dichotomy between AI R at large, which includes machine learning, natural language processing, deep learning, and

so on, and HPC. In fact, that two domains have really come together in the last couple of years where problems now get decomposed in ways where maybe certain parts of the problems are tackled with classic HPC methodologies and other parts of the problems are not tackled with more current AI approaches. So it's this amalgamation at capabilities that are brought together under software control that creates the impact.

Dave Turik mentioned a few things I feel I should unpack here, and let's start with talking about one of the supercomputers he alluded to, the Summit Supercomputer at oak

Ridge National Laboratory. Now, this is just one of the supercomputers that are part of this consortium, and it is currently the reigning champ of supercomputers, and researchers are using it to do everything from understanding how molecular interactions and human cells could lead to much more complex traits uh to exploring the physics of propulsion systems and an effort to make better, more efficient ones in the future. If computers were people, Summit would be that amazing overachiever you

know who tackles any type of olunge with enthusiasm. Someone alone can achieve a peak performance of two hundred pedaphlops. That's two hundred thousand trillion calculations of floating point operations per second. Dave also mentioned inference problems, and that gets down to looking at data and inferring probabilities based on the data you've gathered, and building probabilistic tables is an important part of science and when done properly, can really

speed things up. You look at which options appear to be the most promising, and you focus on those, and you might discard all the ones that have a very low probability of being helpful, or at least put them to the side. If you exhaust all the most promising options without a result, then you can revisit some of the other ones. But really it's a great way to eliminate options, giving you the ability to focus on the best chance for success. Let's get back to the interview.

So with COVID nineteen in particular, what are the ways some of the ways that reas searchers are leveraging these

technologies to specifically look at that crisis. So I think the first way to think of it is to just take a second and inform your listeners about the modern ways in which chemistry, biology and biochemistry are done, because I think many lay people have this image from their high school or college days of speakers and pipettes and things like that, sort of the what I would characterize the representation of science in the analog world, what you

touch and feel and deal with every day. But what's transpired over the last several decades is this movement to progressively infuse science and the scientific method with more and

more computational capability. Now, what that comes down to in the case of COVID nineteen is one begins to take first principles kinds of theories of the way adams are structure, molecules of structure, in the way adams be a and how they interact with one another, represent that mathematical form, and use the computers to explore the behavior from a first principle's perspective. Before you ever get to a physical laboratory.

So what that nets out to is you can now use the power of computing to assess thousands and hundreds of thousands of molecules in terms of their potential impact on the virus and explore the behavior and the and the constraints and the amplifications of combinations of molecules digitally before you ever have to go to the laboratory to try to recreate the results you've seen digitally in the analog world. And that's been a tremendous speed up in

terms of time. You know, pharmaceutical companies today they may have at their disposal billions of molecules that they might want to look at for particular pharmaceutical impact, and sorting

through that is just gigantic task. And the ability to have co uters to come in and say, look, I know you're looking at eight thousand molecules here, which is what researchers at oak Ridge did, but I can cut that down to seventy seven just by using digital approaches and simulation and computation, so that you don't have to worry about trying to analyze all eight thousands and the laboratory you can focus on seventy seven so that's the

first big step of what's happening here. No, that's amazing because just the idea of cutting out that step in the wet lab where you're having to physically uh analyze reactions or maybe not even analyze, you're just detecting to see if one is happening. Cutting that downs that you can really focus on the best uh potential solutions is phenomenal. Can we talk a little bit about what is it about these simulations that make them so challenging that high

performance computing is suitable for tackling that kind of thing? Well, I think that um. One of the principal methodologies that people used in these investigations is molecular dynamics, and what that entails is, first of all, the characterization of a molecule in atoms, and and then the application of forces at the atomic level in terms of how they interact with one another. And so those forces are complicated, the time steps are extraordinarily small, and yet you want to

observe how these things interact. Not only yet, let's say tend to the minus fifteen seconds, which actually like to see how they behave in in in real seconds, in minutes and hours and days, and those time scales just create a tremendous number of computational steps that one has to pursue in the concept of looking at these atomic forces that are operating on the target molecules and atoms and how they interact with one another. So the mathematics

is stunningly complex. The time frames are just so extreme that it requires tremendous amount of compute power just to simulate a handful of seconds. And by virtue of having gigantic supercomputers operate on this, we can actually do this in a reasonable way and a reasonable amount of wall clock time. So let's consider what researchers are doing in this case. A virus consists of at least two parts.

You've got a nucleic acid genome, which contains the material the virus needs to make copies of itself once it is a proper host cell. And then you've got a protein capsid or shell that contains the nucleic acid until the virus can attach itself and inject that material into the aforementioned host cell. Together, this is called the nucleocapsid. Many animal viruses also have a lipid envelope, and that is a membrane that has lots of stuff in it,

including viral y programmed proteins in it. One of the purposes of those proteins is to bind with compatible receptors on host cells. So can kind of think of it as a virus has a special kind of plug and it's looking for cells that have a compatible outlet, and when it finds such a cell, it can plug in connect to that cell, injecting the nucleic acid of the virus into the host cell, and then the code in that nucleic acid hijacks the host cell turns it into

a virus replication engine. Scientists need to know how specific molecules will interact with each other, the virus, host cells, and more. These interactions happen at such a small scale, and it's such minute slices or steps of time that it is difficult to describe. And this is where the speed of high performance computing really comes into play. First, breaking down elements of time gets mind boggling. We tend to think of it in terms of, as Dave says,

wall clock time, you know, seconds, minutes, and hours. We can get our minds wrapped around shorter slices of time because counting on the second might sound like one mrs cippy, so we can definitely think of just one right, that's shorter. But eventually we hit a point where it's hard for us to really understand time at very tiny slices. We can always find a way to slice time into smaller increments. We can continue to make smaller and smaller slices of time.

For example, there's a femto second. A femto second is just one quadrillionth of a second. That's tend to the power of minus fifteen. So imagine simulating the interactions between molecules in a series of these unimaginably short slices of time up to the point that collectively they amount to

enough that it would reach our perceptible world. So we're talking about a quadrillion slices of time to make up just one second here, and there could be numerous important interactions on the molecular level within that short time frame. And this is why supercomputers are necessary for this sort of work. It allows for a precision and that we otherwise would find impossible. And again it tells us if a potential molecule shows promise in our efforts, or if

it's likely to be a bust. Back to my conversation with Dave Turik, vice president of High Performance Computing and Cognitive Systems at IBM. With supercomputers being able to tackle this kind of thing through their various methodologies, this I would imagine would be something that if we were to use a classic computer, it could take thousands of years.

Is that accurate? Yes? Um, And and in some sense it wouldn't even be possible because modern supercomputers, which I'll declare is roughly the error from UM, really are systems that are built on this concept of parallel processing parallel computing, which in turn revolves around this idea that you can decompose a problem into its component elements, and if you have enough elemental computing entities in your supercomputer, you can assign each one of those little problem parts to a

different computing yell element and orchestrate the execution of the computation against that and and just radically reduce the amount of time required from for computation. So let me put it this way, UM, on a standard laptop computer, for example, you're gonna be running, principally to some order of magnitude UM, a serial kind of process. You know, you're gonna execute and solve problem A, which is followed by B, C,

D and so on. In the parallel world, you'll take A, B, C, and D and you'll all run them at the same time, but in different parts of the supercomputer, and then through software orchestration, you'll sort of coalesce all those outputs and

render a conclusion based on the set of calculations you've run. Now, I gave an example of maybe a decomposition to four pieces, but what we really may be talking about maybe a hundred thousand or or a million or ten million pieces and uh, and it's very complicated to try to orchestrate all that activity. So a laptop computer doesn't have the

ability to do that. And that's why when people think about some supercomputers, they sort of render it in terms of, well, this is the equivalent of what ten million laptops could do or a hundred million laptops could do. But I remember laptops or standalone entities. In the supercomputer world, all of those computing entities have to be managed, and it has to be brain power to orchestrate the way they

tackle the problem. And the supercomputers are really architected to handle that, right, So this is this is a specific, purpose built approach to that problem, whereas we've seen things like grid computing as sort of an ad hoc approach that problem, where it tries to do a similar thing, but obviously at exponentially lower levels of processing capability. And

when we're talking about things like floating point operations. Just for you guys out there, you listeners out there, you know you might have seen a graphics processing unit that talks about things in the Tarraf flop range, which you're talking about, you know, a million million floating point operations per second. We're looking at Pata flop ranges here, a thousand million million floating point operations per second. As I understand it, which that's incredible. Uh, it's again for someone

like me, maybe it's just my limited imagination. I have real trouble putting this into a context that I can get my hands around. But it's it's an incredibly fascinating thing. And this is not this isn't like it's unprecedented. We've seen researchers, doctors, scientists use supercomputers to research stuff like vaccines for for the flu before as well. Right, well, absolutely, in fact, when h one N one came out. I

guess it was around two thousand and nine. IBM S Computational Biology group actually began to model the evolutionary tripe victory of the virus because if you think about viruses, and I don't want people to be confused that I'm equating flu to corona, But if you think about flu for a second, the virus is not a static thing. It will evolve over the course of time, and that's why you have a different kind of flu shot every year.

It's it's an effort to try to create a vaccine to intercept the next generation of where this where this particular virus has evolved too. And the way you do that, the way the industry does that is they use computational techniques to kind of predict the evolutionary pathway and they build their vaccine to target where the where the virus will be in three months as opposed to where it is today, because the lead time to design and build a virus is, you know, takes a little bit of time.

You can't wait for the virus to hit. The same kind of logic will be applied to the investigation of COVID nineteen UM. You know, depending on us on a discovery of science in terms of the extent and how

it will evolve over the course of time. But the expectation is it will evolve, and so you'll use computational techniques to begin to fathom that infinite possible ways in which it could evolve and choose those that are most likely to represent where the virus will be in a handful of months, and you'll use that to inform the

way you design your vaccine to intercept it. So we're talking about forming probabilistic models to really determine where are the most likely pathways that this virus might take evolutionary a lee speaking, it's very similar to how I from a concept level, It's very similar to how I would look at something like IBM Watson when you know everyone

knows about it competing on Jeopardy. It had probabilistic approaches to which answers would be the most accurate, and only if it reached a certain threshold of certainty would it would buzz in. But of course, obviously now we're talking about a much more complex thing and much higher stakes, but it's that same sort of approach of where can we predict where this is going, how can we get ahead of it? Then how can we create you know, a dead version or an inert version I should say,

of the virus to make a vaccine. And then you have the other challenges that come in vaccinations, which is just you know, the manufacturing process, distribution, that sort of thing. But this shortening the pathway to this part to me seems like it is Uh, it is absolutely crucial, and it's also one of the areas where I would think that you would see the longest delay. So seeing the

the application of supercomputers is really inspiring to me. Are there other ways that IBM is contributing to various efforts to either track or fight COVID nineteen. Yes, And in fact, just last Friday, a IBM released for free on our website a UM, an artificial intelligence package that speculatively designs new molecules for the treatment of COVID nineteen. So let

me back up for a second. If you think about what's been going on at oak Ridge with Jeremy Smith's effort to look at eight thousand compounds and whittle that down to seventy seven for further investigation as potential therapies to treat COVID nineteen. Well, those eight thousand existed, somebody had already built them. Question is, are there new kinds of molecules that could be designed that don't exist today

that could be used to treat COVID nineteen. So the artificial intelligence package that IBM put out on Friday lets you do that, and it's free and it's open to anyone, So anybody can get on the website and begin playing with it, and maybe you kick up a new molecule which ends up his input to the next generation of the work that goes on within the COVID high performance

computing consortion. So there's innovation at both ends of the process, the design and designation new molecules and then of course the assessment of existing molecules, including the newly designed or invented ones, to assess efficacy against against the COVID nineteen virus um. And these ideas need to work in concert, and they will. Science is all about us discovering the rules of the universe. That sounds grandiose, but it is true.

The rules exist with or without us. Science is our process for figuring out what those rules are and sometimes leads to us learning how to take advantage of those rules, or to avoid things that might cause us harm or pushing back the boundaries of what we see as our limitations. Understanding those rules, we can build complicated virtual environments that let us play with creating new molecules. The rules are

the foundation of these virtual environments. The rules include which atoms can bond with which other atoms, and under what circumstances. So we start off with what is physically possible based on how we understand chemistry. Molecules that could exist can be fair game. Molecules that cannot exist are a no go because it doesn't really help the end cause if

the solution you propose is physically impossible. After all, as Dave mentioned, IBM opened up this artificial intelligence tool to anyone who wants to work with it, so chemists, doctors, researchers, and others can contribute to the efforts to do so. You can visit the website. Here's the address www dot research dot IBM dot com, slash COVID nineteen slash deep

dash search. I'm also curious about other applications of the supercomputers. Obviously, right now we're very much focused on the COVID nineteen crisis, as we should be. But once we're through this crisis, it's not like the work stops for high performance computing. There's so many different applications. Can you talk about some of the other purposes that scientists and researchers are putting these remarkable machines to, Oh? Absolutely, and and I would

say the first thing is that you cannot. No person on the planet can go through a day without touching a product of service or something that's not been impacted by the application is supercomputing. Somewhere in the world. They used to design automobiles for aerodynamics and fuel efficiency. They used to design the kinds of batteries that the electric car companies are putting in their cars. They used to design air foils and airplanes. Uh. New drugs that we've

talked about, they they're used for fraud detection. So when you get a call on your telephone where your credit card company says, by the way, we've signaled potential misuse of your car card, that's probably been done by a supercomputer, not smaller than the kinds that we're talking about here at a place like OK read your Lawrence Livermore or are gone, but the same sort of family, this notion of parallel computing, floating point analysis, and corporation of AI, etcetera. UM,

so it's it's extraordinarily widespread. I think. One of the really tremendously promising areas for supercomputing, and by the way, people have been poking at this for for for quite some time, is the area materials science. Materials are used in everything. That's sort of um a not very profound statement to make, but but the nature materials are quite exotic.

And when you look, for example, at a designing new batteries lithium ion batteries and and so on, and you say, well, how do I get more efficiency out of batteries to drive electric cars? Well, that's when materials start to come into play. Where you you start building battery elements out of new combination of alloys that no one previously explored or anticipated, especially in the context of the use to

which the battery will will apply them. So the opportunity to explore worlds that don't really exist yet digitally without having to incur the expense of creating them in the analog sense. You know, you're not building laboratories and things like that. And in some cases of course, the nature

of what you're doing might even be viewed as dangerous. Uh. The opportunity to use supercomputing to explore those worlds, explore those opportunities, do it safely, do it cost effectively, becomes a tremendous boon to the scientific method generally, and I would say for the last twenty five years or so, when scientists talk about the scientific method, you know, hypothesis, experimentation,

data and all those things. I think computation is now factored in is a key element to the whole scientific process us its ability to see things, to explore things that you cannot get to with other kinds of scientific instruments and tools. Yeah, I I I have been covering technology for several years now, and I've talked a lot about some of the early scientists physicists who who kind of laid the groundwork for the technologies we depend upon today.

You know, they learned about the science that the technology is a physical implementation of and allows us to take advantage of that science. And in many cases you're talking about people who came across something by accident, you know, it was just fortuitous that they observed something and that someone else was able to figure out how to make

use of that. So having a way to virtualize that and speed up that process exponentially, to me, what that tells me is that we get a chance to enjoy the benefits of that science on a time scale that is would previously have been impossible. You might have been talking about something where, you know what, maybe that discovery

could be something that impacts my great grandchild. But now we're talking about things that could potentially have a physical implementation within a decade or less in some cases, and in cases where we're actively researching a vaccine much much closer to now, which is again incredible. When we're talking about computational power, it's not just the speed at which we're solving problems, it's the speed at which we're able to take advantage of those solutions. So I'm really, well,

you can tell I'm really jazzed about this conversation. I get excited about the weirdest things. This isn't weird, this is commonplace. And and you know, one of the things that's coming as a result of this is is there's a real explosion in growth of knowledge. So, uh, I'll go back to material science. If you look at material science ten or fifteen years ago, and you said, well, how many papers scientific papers are published annually in material of science? Um? And I said, guess with that number

is what would you guess that number to be. Um, I'm gonna go with. No matter what number I say, it's gonna be wrong. I'm gonna say so that's actually an ambitious guess. So the ten or fifteen years ago the number was ten thousand, but last year the number was five. Now, now the point is knowledge is growing at a rate faster than humans are able to consume

the knowledge. Because now these are referee papers, so they're all serious and people looked at them and you know, it's it's contributed to the to the corpus of knowledge that that are accessible to human that everybody agrees is true. And you think about five thousand papers. That's five thousand papers a year. So how do you becount maintain currency

in the field. And the answer is you can't. So now you look at the application is super computing to help you grasp, contain, and really model the knowledge that's available as as well as generate new knowledge. So these

ideas embodied him things like Watson couples to supercomputing. Let you begin to explore this vast array of scientific knowledge in a very coordinated and orchestrated kind of fashion to gain insight that you have no way of getting as as the conventional way that you know people looked at

acquiring knowledge fifteen or twenty years ago. You don't go to the library read five thousand papers, right, But on the other hand, you can use systems equipped with UH infrastructure, based on tools like Watson, and you can begin to fathom those five thousand papers in the blink of an eye and get an understanding of relationships that would have never occurred to you naturally, and and to begin to

give you ideas of new directions to pursue. So my reference, for example, to the presentation on Friday of that UM software package from IBM using AI to speculatively help you look at new molecules for COVID nineteen are based on principles like these, harvesting human knowledge at scale that a human can't handle and coming up with novel kinds of interpretations of the knowledge that gives rise to potentially radically

new and terrifically important innovations. So this is something that people really I don't think you've digested fully yet in terms of supercomputing, which they've always fewed as a means by which you do the standard scientific calculations faster. Now we're looking at this coalescence of approach that spans knowledge and data and computation and looking at it all together to give rise to insights that previously could never have

been imagined. Yeah, it's it's been great to see the journey of where we were going from a point where we were gathering enormous amounts of data, you know, the early era of big data, getting a better understanding of how to manage and analyze that data to contextualize it. And now we're reaching a point or we're at a point where we have these incredible systems that are capable

of of doing that on a human level. If that human level, we're you know, every human on the planet able to think about this stuff simultaneously and share that information in a hive mind. So to me, again, this is super exciting stuff and uh, I'm really I'm really

optimistic about this. I think that this is uh an approach that is going to lead to some really actionable solutions, and ultimately what that tells me is that you know, we can talk about the tech and it's super cool, and how advanced it is, and how how complex it is and the sort of problems it can it can tackle from a very conceptual level. But to me, the really inspiring thing is seeing the actual impact on the world when we see these solutions enacted in ways that

make a direct improvement in people's lives. To me, there's no greater story of the potential and power of technology than that, I would agree, and I think that we're a stage now where the application of this technology is becoming progressively more and more ubiquitous and accessible. And by accessible I mean with the advent of artificial intelligence over the last few years. From a commercial perspective. It's not

accessible to normal humans, right. You don't have to have exotic experience in UH in computer science or exotic experience in mathematics. You can go on a system like the IBM Molecular Forecasting system, and with a little bit of knowledge of chemistry, not computers, but chemistry, you can begin to explore possibilities that would have been previously inaccessible to you.

So it's a democratization of supercomputing that's happening as well as as these AI methodologies are incorporated and now dramatically expands the utility of the technology by virtue of making accessible. Thomas everyone fantastic. And this is this is a thread that when I've spoken with people at IBM, it has

come up at time and time again. This not just the development of technology and not just the implementation of it, but the as you say, the democratization, the making it available for people, whether it's call for code where coders are building solutions to big problems and they're getting support through access to IBM tools, or something along these lines, or we talk about not that we should talk about this because I'll go down a rabbit hole, but IBM

developing quantum computers and opening that up for people to develop for that so that they can test that out sort of the next generation of truly remarkable parallel processing. If you want to talk about that, you go down that quantum road. And to me, that's one of those really defining features that makes me happy to have these kind of conversations because I know that my listeners, if they want to, they can actually go out and take

advantage of these tools themselves. They just have to take the step to learn and to go and be part of it. And it's not just a a a supercomputer that's locked away in a lab or deep underground or some sort of Douglas Adams Hitchhiker's Guide deep thought computer. It's something that's actually accessible to people. You just have

to take some pretty simple steps to do it. That's right, And our strategies to make more and more of these innovative technologies available on the web and free to people so that they can play with it. But by virtual playing with it infem us about the directions some of our innovations should take as well. UM. We've done this in chemistry, We've done some biology, we've done it in quantum.

I think it's um it's a very successful paradigm to produce things that are really useful compared to the old style way of um, you know, doing it locked away in a tower someplace, and then just revealing your innovation to the world, hoping for the best. Better to have the world along from the very beginning. Yeah, I think silos are best left on farms. I also agree with that. Dave. Thank you so much for your time and your expertise.

I wish you and your team all the best as you continue to put high performance computing two uses that I'm sure I can't even imagine right now. I can't wait to see what's next. Me too, and you'll be seeing things coming out of the consortium very quickly. For people who are following it UM, please go to the website COVID nineteen HPC conser Marsha and beginning next week we'll start to publish the science it's actually being done on the computers. It is encouraging to see IBM take

an open, inclusive approach towards technological solutions. The company has produced lots of complex technologies that have enormous power, but IBM also recognizes that innovation and solutions can come from any direction, and making these resources easily available speeds up the process of arriving at those solutions. In this particular instance, we're talking about a dangerous virus and the disease it causes,

but the underlying philosophy of inclusion extends beyond that. It was a pleasure to speak with Dave Turik about high performance computing and its role in the response to the COVID nineteen crisis. I have no doubt that the complicated simulations will allow for much more rapid development, which in turn will mean a faster path to effective treatments for

COVID nineteen. That's something I won't lose sight of. As I said to Dave, this technology is really cool, but not as cool as the results will see from that text application. That's all for today's episode. Before I sign off, I want to remind you guys of the Call for Code Global Challenge. This is the big coding slash hacking

challenge IBM sponsors every year. It always takes aim at a really big problem and it invites people to submit ideas for applications that could address these problems in some way, and those applications can tap into the incredible resources of IBM, including amazing IBM technologies. This year, there are two tracks for the Global Challenge. The first of the two tracks specifically focuses on COVID nineteen. If you have an idea for an application that could help address the crisis, then

you need to submit it by April for consideration. By May five, they will pick the top three COVID nineteen solutions, and then by mayfie teen they start initial deployment of those solutions. If you want to submit for the broader topic of climate change, then IBM is accepting those applications until July one. Now, to be clear, they will be accepting COVID nineteen solutions throughout the entirety of the Global Challenge, but as I said, the timeline for a consideration for

those three spots has to be submitted by April. In October, the winners of the Call for Code Global Challenge will be announced at an award ceremony. So if you have ideas, if you're looking for like minded people to work on real world solutions that can really change things for people, I highly recommend you look at the Call for Code Challenge. You can find out more at IBM dot b I

Z slash Call for Code. In the next Smart Talks on tech Stuff, I'll sit down with Grace Sue, VP of Education at I b M and Kristen Waznowski, ce IO of Design at IBM to talk about how the company's technologies are powering remote learning and remote work efforts. 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