Speaker 1: In a bit we'll hear my conversation with Dave Turik,
Speaker 1: who oversees ibm S High Performance Computing Division, to learn
Speaker 1: more about all of that. But before we get to that,
Speaker 1: I thought it would be useful to give a quick
Speaker 1: definition and overview of supercomputers. So what is a supercomputer?
Speaker 1: Generally speaking, it's a computer that can perform at a
Speaker 1: level far beyond the average computer. You know, leap tall
Speaker 1: processes at a single bound. It can be a bit
Speaker 1: of a sliding classification. It's something we apply to an
Speaker 1: elite group of computers that operate at a level above
Speaker 1: and beyond what other machines are capable of at that time.
Speaker 1: And I thought it might be a good idea to
Speaker 1: explain what those are, since otherwise the only impression you'll
Speaker 1: get is that more flops equals more good somehow. So
Speaker 1: let's start with floating point numbers and computing, you might
Speaker 1: deal with integers, and these are whole numbers with no fractions.
Speaker 1: Like the number three. Three is a good number, it's
Speaker 1: an integer. But what about point three? Now we have
Speaker 1: a number that has a decimal in it. This is
Speaker 1: not an integer, but it can be a floating point number. Now,
Speaker 1: computers are really good at working with integers. They can
Speaker 1: calculate processes on integers whippity quick, but floating point numbers
Speaker 1: those can take a bit longer, and speed is a
Speaker 1: big deal in computing. You always want answers quickly. But
Speaker 1: the reason we call them floating point numbers is that
Speaker 1: you can move that decimal around so that point three, well,
Speaker 1: we could represent that as three times ten to the
Speaker 1: power of minus one. This is an example of scientific notation,
Speaker 1: something used in lots of disciplines to help represent very
Speaker 1: large or very small numbers without having to write in
Speaker 1: all those darned zeros. For example, if I wanted to
Speaker 1: write out the number two trillion, I would write the
Speaker 1: numeral two followed by twelve zeros. That's a lot of zeros,
Speaker 1: and honestly, if I wanted to do anything use full
Speaker 1: with that number, it would end up being a real hassle.
Speaker 1: But I could represent the same number as two times
Speaker 1: ten to the twelve power. I wanted to give you
Speaker 1: guys a basic understanding of floating point operations because that's
Speaker 1: going to come into play in my discussion in this episode.
Speaker 1: So now that we've got that out of the way,
Speaker 1: we can move on. Dave Turik, vice president of High
Speaker 1: Performance Computing and Cognitive Systems at IBM, spoke with me
Speaker 1: on Thursday April two, twenty twenty about high performance computing
Speaker 1: in general and how researchers are using it in an
Speaker 1: effort to research the coronavirus and COVID nineteen. And I
Speaker 1: should also add we recorded this call over the Internet,
Speaker 1: and so the quality is not the same as what
Speaker 1: we would usually have in a studio. You're going to
Speaker 1: hear some effects because of the Internet connection. You'll probably
Speaker 1: hear some extraneous noise, and I apologize for that, but
Speaker 1: in these extraordinary circumstances, this was the best we could
Speaker 1: manage in order to have this important conversation. And I
Speaker 1: want to thank Dave for his time and patience in
Speaker 1: setting this up, and I really appreciate it. So let's
Speaker 1: jump into it. Dave, before we go into this incredible
Speaker 1: effort that we're seeing from research institutions using supercomputers to
Speaker 1: research the coronavirus and look at treatments for COVID nineteen,
Speaker 1: can you define in broad terms what is actually meant
Speaker 1: by high performance computing? Well, I think, uh, the way
Speaker 1: to think about high performance computing is in terms of
Speaker 1: the nature of the problem first of all, and then
Speaker 1: the kind of computing the supplied against it. So by
Speaker 1: nature of the problem, I mean that it's fundamentally infused
Speaker 1: with mathematical representations of systems or problem types. And then
Speaker 1: from a computing perspective, the kind of technology that puts
Speaker 1: an emphasis on floating point and very quick communications as
Speaker 1: a coal by which those problems are tackled. That just
Speaker 1: helps one distinguish between somebody saying, well, I can solve
Speaker 1: this problem on a phone, right, that's not what we're
Speaker 1: talking about here. The nature of the mathematics are complex
Speaker 1: and sometimes quite extreme, and the computing we required to
Speaker 1: tackle those have similar kinds of capabilities to overcome that complexity.
Speaker 1: So now that we've kind of got to grasp on that,
Speaker 1: we're looking at a sort of a a massive scale
Speaker 1: form of computing that does very complicated processes very very quickly.
Speaker 1: Can you talk a bit about the Higher Performance Computing Consortium?
Speaker 1: What is what is that organization? How did that come about?
Speaker 1: The COVID nineteen HPC Consortium came about roughly ten days
Speaker 1: ago um courtesy of the conversation between our director of
Speaker 1: research Dario Gill and people at the White House and
Speaker 1: subsequently the Department Energy to see how we could apply
Speaker 1: high performance computing or super computing, two problems associated with
Speaker 1: uh COVID nineteen and quite quickly the offers were taken
Speaker 1: up and within a matter of a couple of days
Speaker 1: we had a website up and running. They gave the
Speaker 1: broad parameters of the resources that were available on how
Speaker 1: one could make submissions to it, and then with a
Speaker 1: passage of another couple of days, we brought in a
Speaker 1: number of additional partners as well UM to complement the
Speaker 1: capability that we initially we're able to access of via
Speaker 1: IBM and a Department of Energy excellent and one are
Speaker 1: some of the actual technologies that are being used in
Speaker 1: this process. We've mentioned supercomputers, can you talk about any
Speaker 1: specific ones and UH, what about things like artificial intelligence,
Speaker 1: machine learning or what kind of various tech are coming
Speaker 1: together to tackle this this UH this issue. The glib
Speaker 1: answer of course is everything. But let me be a
Speaker 1: little more specific from the perspective of the supercomputers that
Speaker 1: are part of the consortium currently UH. They range from
Speaker 1: the Power nine based supercomputers that one finds at oak
Speaker 1: Ridge and Lawrence Livermore to x AD six based systems
Speaker 1: that you might find a NASA our gone in other places. UM.
Speaker 1: For the most part, most of these systems, but not
Speaker 1: all of them use accelerators UM and UH, and that
Speaker 1: really deals with some of the floating point computations that
Speaker 1: are involved, and in some cases the systems are are
Speaker 1: absent UM UH accelerators, So those are homogeneous systems. So
Speaker 1: that's the hardware characterization when we begin to talk about
Speaker 1: machine learning deep learning in those things, that's a combination
Speaker 1: of software running in sync with particular hardware attributes. So
Speaker 1: from a deep learning from a model training perspective, there's
Speaker 1: a premium placed on the availability of accelerators. So the
Speaker 1: Summit system at oak Ridge, for examples, and fused with
Speaker 1: about accelerators, so it's terrific for helping people train models.
Speaker 1: But then as you begin to do influencing in some
Speaker 1: of the other machine learning techniques, the emphasis UM exclusively
Speaker 1: on accelerators evolves a little bit and you get to
Speaker 1: employ different kinds of architectural approaches to UH to look
Speaker 1: at actually inferencing problems. So it's a combination of software
Speaker 1: and hardware that's meant to be reasonably flexible. Not One
Speaker 1: of the things I'll say, of course, and oak Ridge
Speaker 1: along with IBM, have been a pioneer in this is
Speaker 1: that there's not a sharp dichotomy between AI R at large,
Speaker 1: which includes machine learning, natural language processing, deep learning, and
Speaker 1: so on, and HPC. In fact, that two domains have
Speaker 1: really come together in the last couple of years where
Speaker 1: problems now get decomposed in ways where maybe certain parts
Speaker 1: of the problems are tackled with classic HPC methodologies and
Speaker 1: other parts of the problems are not tackled with more
Speaker 1: current AI approaches. So it's this amalgamation at capabilities that
Speaker 1: are brought together under software control that creates the impact.
Speaker 1: Dave Turik mentioned a few things I feel I should
Speaker 1: unpack here, and let's start with talking about one of
Speaker 1: the supercomputers he alluded to, the Summit Supercomputer at oak
Speaker 1: Ridge National Laboratory. Now, this is just one of the
Speaker 1: supercomputers that are part of this consortium, and it is
Speaker 1: currently the reigning champ of supercomputers, and researchers are using
Speaker 1: it to do everything from understanding how molecular interactions and
Speaker 1: human cells could lead to much more complex traits uh
Speaker 1: to exploring the physics of propulsion systems and an effort
Speaker 1: to make better, more efficient ones in the future. If
Speaker 1: computers were people, Summit would be that amazing overachiever you
Speaker 1: know who tackles any type of olunge with enthusiasm. Someone
Speaker 1: alone can achieve a peak performance of two hundred pedaphlops.
Speaker 1: That's two hundred thousand trillion calculations of floating point operations
Speaker 1: per second. Dave also mentioned inference problems, and that gets
Speaker 1: down to looking at data and inferring probabilities based on
Speaker 1: the data you've gathered, and building probabilistic tables is an
Speaker 1: important part of science and when done properly, can really
Speaker 1: speed things up. You look at which options appear to
Speaker 1: be the most promising, and you focus on those, and
Speaker 1: you might discard all the ones that have a very
Speaker 1: low probability of being helpful, or at least put them
Speaker 1: to the side. If you exhaust all the most promising
Speaker 1: options without a result, then you can revisit some of
Speaker 1: the other ones. But really it's a great way to
Speaker 1: eliminate options, giving you the ability to focus on the
Speaker 1: best chance for success. Let's get back to the interview.
Speaker 1: So with COVID nineteen in particular, what are the ways
Speaker 1: some of the ways that reas searchers are leveraging these
Speaker 1: technologies to specifically look at that crisis. So I think
Speaker 1: the first way to think of it is to just
Speaker 1: take a second and inform your listeners about the modern
Speaker 1: ways in which chemistry, biology and biochemistry are done, because
Speaker 1: I think many lay people have this image from their
Speaker 1: high school or college days of speakers and pipettes and
Speaker 1: things like that, sort of the what I would characterize
Speaker 1: the representation of science in the analog world, what you
Speaker 1: touch and feel and deal with every day. But what's
Speaker 1: transpired over the last several decades is this movement to
Speaker 1: progressively infuse science and the scientific method with more and
Speaker 1: more computational capability. Now, what that comes down to in
Speaker 1: the case of COVID nineteen is one begins to take
Speaker 1: first principles kinds of theories of the way adams are structure,
Speaker 1: molecules of structure, in the way adams be a and
Speaker 1: how they interact with one another, represent that mathematical form,
Speaker 1: and use the computers to explore the behavior from a
Speaker 1: first principle's perspective. Before you ever get to a physical laboratory.
Speaker 1: So what that nets out to is you can now
Speaker 1: use the power of computing to assess thousands and hundreds
Speaker 1: of thousands of molecules in terms of their potential impact
Speaker 1: on the virus and explore the behavior and the and
Speaker 1: the constraints and the amplifications of combinations of molecules digitally
Speaker 1: before you ever have to go to the laboratory to
Speaker 1: try to recreate the results you've seen digitally in the
Speaker 1: analog world. And that's been a tremendous speed up in
Speaker 1: terms of time. You know, pharmaceutical companies today they may
Speaker 1: have at their disposal billions of molecules that they might
Speaker 1: want to look at for particular pharmaceutical impact, and sorting
Speaker 1: through that is just gigantic task. And the ability to
Speaker 1: have co uters to come in and say, look, I
Speaker 1: know you're looking at eight thousand molecules here, which is
Speaker 1: what researchers at oak Ridge did, but I can cut
Speaker 1: that down to seventy seven just by using digital approaches
Speaker 1: and simulation and computation, so that you don't have to
Speaker 1: worry about trying to analyze all eight thousands and the
Speaker 1: laboratory you can focus on seventy seven so that's the
Speaker 1: first big step of what's happening here. No, that's amazing
Speaker 1: because just the idea of cutting out that step in
Speaker 1: the wet lab where you're having to physically uh analyze
Speaker 1: reactions or maybe not even analyze, you're just detecting to
Speaker 1: see if one is happening. Cutting that downs that you
Speaker 1: can really focus on the best uh potential solutions is phenomenal.
Speaker 1: Can we talk a little bit about what is it
Speaker 1: about these simulations that make them so challenging that high
Speaker 1: performance computing is suitable for tackling that kind of thing? Well,
Speaker 1: I think that um. One of the principal methodologies that
Speaker 1: people used in these investigations is molecular dynamics, and what
Speaker 1: that entails is, first of all, the characterization of a
Speaker 1: molecule in atoms, and and then the application of forces
Speaker 1: at the atomic level in terms of how they interact
Speaker 1: with one another. And so those forces are complicated, the
Speaker 1: time steps are extraordinarily small, and yet you want to
Speaker 1: observe how these things interact. Not only yet, let's say
Speaker 1: tend to the minus fifteen seconds, which actually like to
Speaker 1: see how they behave in in in real seconds, in
Speaker 1: minutes and hours and days, and those time scales just
Speaker 1: create a tremendous number of computational steps that one has
Speaker 1: to pursue in the concept of looking at these atomic
Speaker 1: forces that are operating on the target molecules and atoms
Speaker 1: and how they interact with one another. So the mathematics
Speaker 1: is stunningly complex. The time frames are just so extreme
Speaker 1: that it requires tremendous amount of compute power just to
Speaker 1: simulate a handful of seconds. And by virtue of having
Speaker 1: gigantic supercomputers operate on this, we can actually do this
Speaker 1: in a reasonable way and a reasonable amount of wall
Speaker 1: clock time. So let's consider what researchers are doing in
Speaker 1: this case. A virus consists of at least two parts.
Speaker 1: You've got a nucleic acid genome, which contains the material
Speaker 1: the virus needs to make copies of itself once it
Speaker 1: is a proper host cell. And then you've got a
Speaker 1: protein capsid or shell that contains the nucleic acid until
Speaker 1: the virus can attach itself and inject that material into
Speaker 1: the aforementioned host cell. Together, this is called the nucleocapsid.
Speaker 1: Many animal viruses also have a lipid envelope, and that
Speaker 1: is a membrane that has lots of stuff in it,
Speaker 1: including viral y programmed proteins in it. One of the
Speaker 1: purposes of those proteins is to bind with compatible receptors
Speaker 1: on host cells. So can kind of think of it
Speaker 1: as a virus has a special kind of plug and
Speaker 1: it's looking for cells that have a compatible outlet, and
Speaker 1: when it finds such a cell, it can plug in
Speaker 1: connect to that cell, injecting the nucleic acid of the
Speaker 1: virus into the host cell, and then the code in
Speaker 1: that nucleic acid hijacks the host cell turns it into
Speaker 1: a virus replication engine. Scientists need to know how specific
Speaker 1: molecules will interact with each other, the virus, host cells,
Speaker 1: and more. These interactions happen at such a small scale,
Speaker 1: and it's such minute slices or steps of time that
Speaker 1: it is difficult to describe. And this is where the
Speaker 1: speed of high performance computing really comes into play. First,
Speaker 1: breaking down elements of time gets mind boggling. We tend
Speaker 1: to think of it in terms of, as Dave says,
Speaker 1: wall clock time, you know, seconds, minutes, and hours. We
Speaker 1: can get our minds wrapped around shorter slices of time
Speaker 1: because counting on the second might sound like one mrs cippy,
Speaker 1: so we can definitely think of just one right, that's shorter.
Speaker 1: But eventually we hit a point where it's hard for
Speaker 1: us to really understand time at very tiny slices. We
Speaker 1: can always find a way to slice time into smaller increments.
Speaker 1: We can continue to make smaller and smaller slices of time.
Speaker 1: For example, there's a femto second. A femto second is
Speaker 1: just one quadrillionth of a second. That's tend to the
Speaker 1: power of minus fifteen. So imagine simulating the interactions between
Speaker 1: molecules in a series of these unimaginably short slices of
Speaker 1: time up to the point that collectively they amount to
Speaker 1: enough that it would reach our perceptible world. So we're
Speaker 1: talking about a quadrillion slices of time to make up
Speaker 1: just one second here, and there could be numerous important
Speaker 1: interactions on the molecular level within that short time frame.
Speaker 1: And this is why supercomputers are necessary for this sort
Speaker 1: of work. It allows for a precision and that we
Speaker 1: otherwise would find impossible. And again it tells us if
Speaker 1: a potential molecule shows promise in our efforts, or if
Speaker 1: it's likely to be a bust. Back to my conversation
Speaker 1: with Dave Turik, vice president of High Performance Computing and
Speaker 1: Cognitive Systems at IBM. With supercomputers being able to tackle
Speaker 1: this kind of thing through their various methodologies, this I
Speaker 1: would imagine would be something that if we were to
Speaker 1: use a classic computer, it could take thousands of years.
Speaker 1: Is that accurate? Yes? Um, And and in some sense
Speaker 1: it wouldn't even be possible because modern supercomputers, which I'll
Speaker 1: declare is roughly the error from UM, really are systems
Speaker 1: that are built on this concept of parallel processing parallel computing,
Speaker 1: which in turn revolves around this idea that you can
Speaker 1: decompose a problem into its component elements, and if you
Speaker 1: have enough elemental computing entities in your supercomputer, you can
Speaker 1: assign each one of those little problem parts to a
Speaker 1: different computing yell element and orchestrate the execution of the
Speaker 1: computation against that and and just radically reduce the amount
Speaker 1: of time required from for computation. So let me put
Speaker 1: it this way, UM, on a standard laptop computer, for example,
Speaker 1: you're gonna be running, principally to some order of magnitude UM,
Speaker 1: a serial kind of process. You know, you're gonna execute
Speaker 1: and solve problem A, which is followed by B, C,
Speaker 1: D and so on. In the parallel world, you'll take A, B, C,
Speaker 1: and D and you'll all run them at the same time,
Speaker 1: but in different parts of the supercomputer, and then through
Speaker 1: software orchestration, you'll sort of coalesce all those outputs and
Speaker 1: render a conclusion based on the set of calculations you've run. Now,
Speaker 1: I gave an example of maybe a decomposition to four pieces,
Speaker 1: but what we really may be talking about maybe a
Speaker 1: hundred thousand or or a million or ten million pieces
Speaker 1: and uh, and it's very complicated to try to orchestrate
Speaker 1: all that activity. So a laptop computer doesn't have the
Speaker 1: ability to do that. And that's why when people think
Speaker 1: about some supercomputers, they sort of render it in terms of, well,
Speaker 1: this is the equivalent of what ten million laptops could
Speaker 1: do or a hundred million laptops could do. But I
Speaker 1: remember laptops or standalone entities. In the supercomputer world, all
Speaker 1: of those computing entities have to be managed, and it
Speaker 1: has to be brain power to orchestrate the way they
Speaker 1: tackle the problem. And the supercomputers are really architected to
Speaker 1: handle that, right, So this is this is a specific,
Speaker 1: purpose built approach to that problem, whereas we've seen things
Speaker 1: like grid computing as sort of an ad hoc approach
Speaker 1: that problem, where it tries to do a similar thing,
Speaker 1: but obviously at exponentially lower levels of processing capability. And
Speaker 1: when we're talking about things like floating point operations. Just
Speaker 1: for you guys out there, you listeners out there, you
Speaker 1: know you might have seen a graphics processing unit that
Speaker 1: talks about things in the Tarraf flop range, which you're
Speaker 1: talking about, you know, a million million floating point operations
Speaker 1: per second. We're looking at Pata flop ranges here, a
Speaker 1: thousand million million floating point operations per second. As I
Speaker 1: understand it, which that's incredible. Uh, it's again for someone
Speaker 1: like me, maybe it's just my limited imagination. I have
Speaker 1: real trouble putting this into a context that I can
Speaker 1: get my hands around. But it's it's an incredibly fascinating thing.
Speaker 1: And this is not this isn't like it's unprecedented. We've
Speaker 1: seen researchers, doctors, scientists use supercomputers to research stuff like
Speaker 1: vaccines for for the flu before as well. Right, well, absolutely,
Speaker 1: in fact, when h one N one came out. I
Speaker 1: guess it was around two thousand and nine. IBM S
Speaker 1: Computational Biology group actually began to model the evolutionary tripe
Speaker 1: victory of the virus because if you think about viruses,
Speaker 1: and I don't want people to be confused that I'm
Speaker 1: equating flu to corona, But if you think about flu
Speaker 1: for a second, the virus is not a static thing.
Speaker 1: It will evolve over the course of time, and that's
Speaker 1: why you have a different kind of flu shot every year.
Speaker 1: It's it's an effort to try to create a vaccine
Speaker 1: to intercept the next generation of where this where this
Speaker 1: particular virus has evolved too. And the way you do that,
Speaker 1: the way the industry does that is they use computational
Speaker 1: techniques to kind of predict the evolutionary pathway and they
Speaker 1: build their vaccine to target where the where the virus
Speaker 1: will be in three months as opposed to where it
Speaker 1: is today, because the lead time to design and build
Speaker 1: a virus is, you know, takes a little bit of time.
Speaker 1: You can't wait for the virus to hit. The same
Speaker 1: kind of logic will be applied to the investigation of
Speaker 1: COVID nineteen UM. You know, depending on us on a
Speaker 1: discovery of science in terms of the extent and how
Speaker 1: it will evolve over the course of time. But the
Speaker 1: expectation is it will evolve, and so you'll use computational
Speaker 1: techniques to begin to fathom that infinite possible ways in
Speaker 1: which it could evolve and choose those that are most
Speaker 1: likely to represent where the virus will be in a
Speaker 1: handful of months, and you'll use that to inform the
Speaker 1: way you design your vaccine to intercept it. So we're
Speaker 1: talking about forming probabilistic models to really determine where are
Speaker 1: the most likely pathways that this virus might take evolutionary
Speaker 1: a lee speaking, it's very similar to how I from
Speaker 1: a concept level, It's very similar to how I would
Speaker 1: look at something like IBM Watson when you know everyone
Speaker 1: knows about it competing on Jeopardy. It had probabilistic approaches
Speaker 1: to which answers would be the most accurate, and only
Speaker 1: if it reached a certain threshold of certainty would it
Speaker 1: would buzz in. But of course, obviously now we're talking
Speaker 1: about a much more complex thing and much higher stakes,
Speaker 1: but it's that same sort of approach of where can
Speaker 1: we predict where this is going, how can we get
Speaker 1: ahead of it? Then how can we create you know,
Speaker 1: a dead version or an inert version I should say,
Speaker 1: of the virus to make a vaccine. And then you
Speaker 1: have the other challenges that come in vaccinations, which is
Speaker 1: just you know, the manufacturing process, distribution, that sort of thing.
Speaker 1: But this shortening the pathway to this part to me
Speaker 1: seems like it is Uh, it is absolutely crucial, and
Speaker 1: it's also one of the areas where I would think
Speaker 1: that you would see the longest delay. So seeing the
Speaker 1: the application of supercomputers is really inspiring to me. Are
Speaker 1: there other ways that IBM is contributing to various efforts
Speaker 1: to either track or fight COVID nineteen. Yes, And in fact,
Speaker 1: just last Friday, a IBM released for free on our
Speaker 1: website a UM, an artificial intelligence package that speculatively designs
Speaker 1: new molecules for the treatment of COVID nineteen. So let
Speaker 1: me back up for a second. If you think about
Speaker 1: what's been going on at oak Ridge with Jeremy Smith's
Speaker 1: effort to look at eight thousand compounds and whittle that
Speaker 1: down to seventy seven for further investigation as potential therapies
Speaker 1: to treat COVID nineteen. Well, those eight thousand existed, somebody
Speaker 1: had already built them. Question is, are there new kinds
Speaker 1: of molecules that could be designed that don't exist today
Speaker 1: that could be used to treat COVID nineteen. So the
Speaker 1: artificial intelligence package that IBM put out on Friday lets
Speaker 1: you do that, and it's free and it's open to anyone,
Speaker 1: So anybody can get on the website and begin playing
Speaker 1: with it, and maybe you kick up a new molecule
Speaker 1: which ends up his input to the next generation of
Speaker 1: the work that goes on within the COVID high performance
Speaker 1: computing consortion. So there's innovation at both ends of the process,
Speaker 1: the design and designation new molecules and then of course
Speaker 1: the assessment of existing molecules, including the newly designed or
Speaker 1: invented ones, to assess efficacy against against the COVID nineteen
Speaker 1: virus um. And these ideas need to work in concert,
Speaker 1: and they will. Science is all about us discovering the
Speaker 1: rules of the universe. That sounds grandiose, but it is true.
Speaker 1: The rules exist with or without us. Science is our
Speaker 1: process for figuring out what those rules are and sometimes
Speaker 1: leads to us learning how to take advantage of those rules,
Speaker 1: or to avoid things that might cause us harm or
Speaker 1: pushing back the boundaries of what we see as our limitations.
Speaker 1: Understanding those rules, we can build complicated virtual environments that
Speaker 1: let us play with creating new molecules. The rules are
Speaker 1: the foundation of these virtual environments. The rules include which
Speaker 1: atoms can bond with which other atoms, and under what circumstances.
Speaker 1: So we start off with what is physically possible based
Speaker 1: on how we understand chemistry. Molecules that could exist can
Speaker 1: be fair game. Molecules that cannot exist are a no
Speaker 1: go because it doesn't really help the end cause if
Speaker 1: the solution you propose is physically impossible. After all, as
Speaker 1: Dave mentioned, IBM opened up this artificial intelligence tool to
Speaker 1: anyone who wants to work with it, so chemists, doctors, researchers,
Speaker 1: and others can contribute to the efforts to do so.
Speaker 1: You can visit the website. Here's the address www dot
Speaker 1: research dot IBM dot com, slash COVID nineteen slash deep
Speaker 1: dash search. I'm also curious about other applications of the supercomputers. Obviously,
Speaker 1: right now we're very much focused on the COVID nineteen crisis,
Speaker 1: as we should be. But once we're through this crisis,
Speaker 1: it's not like the work stops for high performance computing.
Speaker 1: There's so many different applications. Can you talk about some
Speaker 1: of the other purposes that scientists and researchers are putting
Speaker 1: these remarkable machines to, Oh? Absolutely, and and I would
Speaker 1: say the first thing is that you cannot. No person
Speaker 1: on the planet can go through a day without touching
Speaker 1: a product of service or something that's not been impacted
Speaker 1: by the application is supercomputing. Somewhere in the world. They
Speaker 1: used to design automobiles for aerodynamics and fuel efficiency. They
Speaker 1: used to design the kinds of batteries that the electric
Speaker 1: car companies are putting in their cars. They used to
Speaker 1: design air foils and airplanes. Uh. New drugs that we've
Speaker 1: talked about, they they're used for fraud detection. So when
Speaker 1: you get a call on your telephone where your credit
Speaker 1: card company says, by the way, we've signaled potential misuse
Speaker 1: of your car card, that's probably been done by a supercomputer,
Speaker 1: not smaller than the kinds that we're talking about here
Speaker 1: at a place like OK read your Lawrence Livermore or
Speaker 1: are gone, but the same sort of family, this notion
Speaker 1: of parallel computing, floating point analysis, and corporation of AI, etcetera. UM,
Speaker 1: so it's it's extraordinarily widespread. I think. One of the
Speaker 1: really tremendously promising areas for supercomputing, and by the way,
Speaker 1: people have been poking at this for for for quite
Speaker 1: some time, is the area materials science. Materials are used
Speaker 1: in everything. That's sort of um a not very profound
Speaker 1: statement to make, but but the nature materials are quite exotic.
Speaker 1: And when you look, for example, at a designing new
Speaker 1: batteries lithium ion batteries and and so on, and you say, well,
Speaker 1: how do I get more efficiency out of batteries to
Speaker 1: drive electric cars? Well, that's when materials start to come
Speaker 1: into play. Where you you start building battery elements out
Speaker 1: of new combination of alloys that no one previously explored
Speaker 1: or anticipated, especially in the context of the use to
Speaker 1: which the battery will will apply them. So the opportunity
Speaker 1: to explore worlds that don't really exist yet digitally without
Speaker 1: having to incur the expense of creating them in the
Speaker 1: analog sense. You know, you're not building laboratories and things
Speaker 1: like that. And in some cases of course, the nature
Speaker 1: of what you're doing might even be viewed as dangerous. Uh.
Speaker 1: The opportunity to use supercomputing to explore those worlds, explore
Speaker 1: those opportunities, do it safely, do it cost effectively, becomes
Speaker 1: a tremendous boon to the scientific method generally, and I
Speaker 1: would say for the last twenty five years or so,
Speaker 1: when scientists talk about the scientific method, you know, hypothesis, experimentation,
Speaker 1: data and all those things. I think computation is now
Speaker 1: factored in is a key element to the whole scientific
Speaker 1: process us its ability to see things, to explore things
Speaker 1: that you cannot get to with other kinds of scientific
Speaker 1: instruments and tools. Yeah, I I I have been covering
Speaker 1: technology for several years now, and I've talked a lot
Speaker 1: about some of the early scientists physicists who who kind
Speaker 1: of laid the groundwork for the technologies we depend upon today.
Speaker 1: You know, they learned about the science that the technology
Speaker 1: is a physical implementation of and allows us to take
Speaker 1: advantage of that science. And in many cases you're talking
Speaker 1: about people who came across something by accident, you know,
Speaker 1: it was just fortuitous that they observed something and that
Speaker 1: someone else was able to figure out how to make
Speaker 1: use of that. So having a way to virtualize that
Speaker 1: and speed up that process exponentially, to me, what that
Speaker 1: tells me is that we get a chance to enjoy
Speaker 1: the benefits of that science on a time scale that
Speaker 1: is would previously have been impossible. You might have been
Speaker 1: talking about something where, you know what, maybe that discovery
Speaker 1: could be something that impacts my great grandchild. But now
Speaker 1: we're talking about things that could potentially have a physical
Speaker 1: implementation within a decade or less in some cases, and
Speaker 1: in cases where we're actively researching a vaccine much much
Speaker 1: closer to now, which is again incredible. When we're talking
Speaker 1: about computational power, it's not just the speed at which
Speaker 1: we're solving problems, it's the speed at which we're able
Speaker 1: to take advantage of those solutions. So I'm really, well,
Speaker 1: you can tell I'm really jazzed about this conversation. I
Speaker 1: get excited about the weirdest things. This isn't weird, this
Speaker 1: is commonplace. And and you know, one of the things
Speaker 1: that's coming as a result of this is is there's
Speaker 1: a real explosion in growth of knowledge. So, uh, I'll
Speaker 1: go back to material science. If you look at material
Speaker 1: science ten or fifteen years ago, and you said, well,
Speaker 1: how many papers scientific papers are published annually in material
Speaker 1: of science? Um? And I said, guess with that number
Speaker 1: is what would you guess that number to be. Um,
Speaker 1: I'm gonna go with. No matter what number I say,
Speaker 1: it's gonna be wrong. I'm gonna say so that's actually
Speaker 1: an ambitious guess. So the ten or fifteen years ago
Speaker 1: the number was ten thousand, but last year the number
Speaker 1: was five. Now, now the point is knowledge is growing
Speaker 1: at a rate faster than humans are able to consume
Speaker 1: the knowledge. Because now these are referee papers, so they're
Speaker 1: all serious and people looked at them and you know,
Speaker 1: it's it's contributed to the to the corpus of knowledge
Speaker 1: that that are accessible to human that everybody agrees is true.
Speaker 1: And you think about five thousand papers. That's five thousand
Speaker 1: papers a year. So how do you becount maintain currency
Speaker 1: in the field. And the answer is you can't. So
Speaker 1: now you look at the application is super computing to
Speaker 1: help you grasp, contain, and really model the knowledge that's
Speaker 1: available as as well as generate new knowledge. So these
Speaker 1: ideas embodied him things like Watson couples to supercomputing. Let
Speaker 1: you begin to explore this vast array of scientific knowledge
Speaker 1: in a very coordinated and orchestrated kind of fashion to
Speaker 1: gain insight that you have no way of getting as
Speaker 1: as the conventional way that you know people looked at
Speaker 1: acquiring knowledge fifteen or twenty years ago. You don't go
Speaker 1: to the library read five thousand papers, right, But on
Speaker 1: the other hand, you can use systems equipped with UH infrastructure,
Speaker 1: based on tools like Watson, and you can begin to
Speaker 1: fathom those five thousand papers in the blink of an
Speaker 1: eye and get an understanding of relationships that would have
Speaker 1: never occurred to you naturally, and and to begin to
Speaker 1: give you ideas of new directions to pursue. So my reference,
Speaker 1: for example, to the presentation on Friday of that UM
Speaker 1: software package from IBM using AI to speculatively help you
Speaker 1: look at new molecules for COVID nineteen are based on
Speaker 1: principles like these, harvesting human knowledge at scale that a
Speaker 1: human can't handle and coming up with novel kinds of
Speaker 1: interpretations of the knowledge that gives rise to potentially radically
Speaker 1: new and terrifically important innovations. So this is something that
Speaker 1: people really I don't think you've digested fully yet in
Speaker 1: terms of supercomputing, which they've always fewed as a means
Speaker 1: by which you do the standard scientific calculations faster. Now
Speaker 1: we're looking at this coalescence of approach that spans knowledge
Speaker 1: and data and computation and looking at it all together
Speaker 1: to give rise to insights that previously could never have
Speaker 1: been imagined. Yeah, it's it's been great to see the
Speaker 1: journey of where we were going from a point where
Speaker 1: we were gathering enormous amounts of data, you know, the
Speaker 1: early era of big data, getting a better understanding of
Speaker 1: how to manage and analyze that data to contextualize it.
Speaker 1: And now we're reaching a point or we're at a
Speaker 1: point where we have these incredible systems that are capable
Speaker 1: of of doing that on a human level. If that
Speaker 1: human level, we're you know, every human on the planet
Speaker 1: able to think about this stuff simultaneously and share that
Speaker 1: information in a hive mind. So to me, again, this
Speaker 1: is super exciting stuff and uh, I'm really I'm really
Speaker 1: optimistic about this. I think that this is uh an
Speaker 1: approach that is going to lead to some really actionable solutions,
Speaker 1: and ultimately what that tells me is that you know,
Speaker 1: we can talk about the tech and it's super cool,
Speaker 1: and how advanced it is, and how how complex it
Speaker 1: is and the sort of problems it can it can
Speaker 1: tackle from a very conceptual level. But to me, the
Speaker 1: really inspiring thing is seeing the actual impact on the
Speaker 1: world when we see these solutions enacted in ways that
Speaker 1: make a direct improvement in people's lives. To me, there's
Speaker 1: no greater story of the potential and power of technology
Speaker 1: than that, I would agree, and I think that we're
Speaker 1: a stage now where the application of this technology is
Speaker 1: becoming progressively more and more ubiquitous and accessible. And by
Speaker 1: accessible I mean with the advent of artificial intelligence over
Speaker 1: the last few years. From a commercial perspective. It's not
Speaker 1: accessible to normal humans, right. You don't have to have
Speaker 1: exotic experience in UH in computer science or exotic experience
Speaker 1: in mathematics. You can go on a system like the
Speaker 1: IBM Molecular Forecasting system, and with a little bit of
Speaker 1: knowledge of chemistry, not computers, but chemistry, you can begin
Speaker 1: to explore possibilities that would have been previously inaccessible to you.
Speaker 1: So it's a democratization of supercomputing that's happening as well
Speaker 1: as as these AI methodologies are incorporated and now dramatically
Speaker 1: expands the utility of the technology by virtue of making accessible.
Speaker 1: Thomas everyone fantastic. And this is this is a thread
Speaker 1: that when I've spoken with people at IBM, it has
Speaker 1: come up at time and time again. This not just
Speaker 1: the development of technology and not just the implementation of it,
Speaker 1: but the as you say, the democratization, the making it
Speaker 1: available for people, whether it's call for code where coders
Speaker 1: are building solutions to big problems and they're getting support
Speaker 1: through access to IBM tools, or something along these lines,
Speaker 1: or we talk about not that we should talk about
Speaker 1: this because I'll go down a rabbit hole, but IBM
Speaker 1: developing quantum computers and opening that up for people to
Speaker 1: develop for that so that they can test that out
Speaker 1: sort of the next generation of truly remarkable parallel processing.
Speaker 1: If you want to talk about that, you go down
Speaker 1: that quantum road. And to me, that's one of those
Speaker 1: really defining features that makes me happy to have these
Speaker 1: kind of conversations because I know that my listeners, if
Speaker 1: they want to, they can actually go out and take
Speaker 1: advantage of these tools themselves. They just have to take
Speaker 1: the step to learn and to go and be part
Speaker 1: of it. And it's not just a a a supercomputer
Speaker 1: that's locked away in a lab or deep underground or
Speaker 1: some sort of Douglas Adams Hitchhiker's Guide deep thought computer.
Speaker 1: It's something that's actually accessible to people. You just have
Speaker 1: to take some pretty simple steps to do it. That's right,
Speaker 1: And our strategies to make more and more of these
Speaker 1: innovative technologies available on the web and free to people
Speaker 1: so that they can play with it. But by virtual
Speaker 1: playing with it infem us about the directions some of
Speaker 1: our innovations should take as well. UM. We've done this
Speaker 1: in chemistry, We've done some biology, we've done it in quantum.
Speaker 1: I think it's um it's a very successful paradigm to
Speaker 1: produce things that are really useful compared to the old
Speaker 1: style way of um, you know, doing it locked away
Speaker 1: in a tower someplace, and then just revealing your innovation
Speaker 1: to the world, hoping for the best. Better to have
Speaker 1: the world along from the very beginning. Yeah, I think
Speaker 1: silos are best left on farms. I also agree with that. Dave.
Speaker 1: Thank you so much for your time and your expertise.
Speaker 1: I wish you and your team all the best as
Speaker 1: you continue to put high performance computing two uses that
Speaker 1: I'm sure I can't even imagine right now. I can't
Speaker 1: wait to see what's next. Me too, and you'll be
Speaker 1: seeing things coming out of the consortium very quickly. For
Speaker 1: people who are following it UM, please go to the
Speaker 1: website COVID nineteen HPC conser Marsha and beginning next week
Speaker 1: we'll start to publish the science it's actually being done
Speaker 1: on the computers. It is encouraging to see IBM take
Speaker 1: an open, inclusive approach towards technological solutions. The company has
Speaker 1: produced lots of complex technologies that have enormous power, but
Speaker 1: IBM also recognizes that innovation and solutions can come from
Speaker 1: any direction, and making these resources easily available speeds up
Speaker 1: the process of arriving at those solutions. In this particular instance,
Speaker 1: we're talking about a dangerous virus and the disease it causes,
Speaker 1: but the underlying philosophy of inclusion extends beyond that. It
Speaker 1: was a pleasure to speak with Dave Turik about high
Speaker 1: performance computing and its role in the response to the
Speaker 1: COVID nineteen crisis. I have no doubt that the complicated
Speaker 1: simulations will allow for much more rapid development, which in
Speaker 1: turn will mean a faster path to effective treatments for
Speaker 1: COVID nineteen. That's something I won't lose sight of. As
Speaker 1: I said to Dave, this technology is really cool, but
Speaker 1: not as cool as the results will see from that
Speaker 1: text application. That's all for today's episode. Before I sign off,
Speaker 1: I want to remind you guys of the Call for
Speaker 1: Code Global Challenge. This is the big coding slash hacking
Speaker 1: challenge IBM sponsors every year. It always takes aim at
Speaker 1: a really big problem and it invites people to submit
Speaker 1: ideas for applications that could address these problems in some way,
Speaker 1: and those applications can tap into the incredible resources of IBM,
Speaker 1: including amazing IBM technologies. This year, there are two tracks
Speaker 1: for the Global Challenge. The first of the two tracks
Speaker 1: specifically focuses on COVID nineteen. If you have an idea
Speaker 1: for an application that could help address the crisis, then
Speaker 1: you need to submit it by April for consideration. By
Speaker 1: May five, they will pick the top three COVID nineteen solutions,
Speaker 1: and then by mayfie teen they start initial deployment of
Speaker 1: those solutions. If you want to submit for the broader
Speaker 1: topic of climate change, then IBM is accepting those applications
Speaker 1: until July one. Now, to be clear, they will be
Speaker 1: accepting COVID nineteen solutions throughout the entirety of the Global Challenge,
Speaker 1: but as I said, the timeline for a consideration for
Speaker 1: those three spots has to be submitted by April. In October,
Speaker 1: the winners of the Call for Code Global Challenge will
Speaker 1: be announced at an award ceremony. So if you have ideas,
Speaker 1: if you're looking for like minded people to work on
Speaker 1: real world solutions that can really change things for people,
Speaker 1: I highly recommend you look at the Call for Code Challenge.
Speaker 1: You can find out more at IBM dot b I
Speaker 1: Z slash Call for Code. In the next Smart Talks
Speaker 1: on tech Stuff, I'll sit down with Grace Sue, VP
Speaker 1: of Education at I b M and Kristen Waznowski, ce
Speaker 1: IO of Design at IBM to talk about how the
Speaker 1: company's technologies are powering remote learning and remote work efforts.
Speaker 1: I'll talk to you again really soon. Text Stuff is
Speaker 1: an I Heart Radio production. For more podcasts from I
Speaker 1: Heart Radio, visit the I Heart Radio app, Apple Podcasts,
Speaker 1: or wherever you listen to your favorite shows