 They call me Dr. Goh. I am Senior Vice President and Chief Technology Officer of AI at Hewlett Packard Enterprise. And today, I'm in Munich, Germany. Home to one and a half million people, Munich is famous for everything from BMW to beer to breathtaking architecture and festive markets. The Bavarian capital is the beating heart of Germany's automobile industry. Over 50,000 of its residents work in automotive engineering and to date, Munich allocated around 30 million euros to boost electric vehicles and infrastructure for them. Well, my name is Dr. Jerome Bodri. I am a professor at the University of Alabama in Huntsville. Our mission is to use computational resources to accelerate the discovery of drugs that will be useful and efficient against the COVID-19 virus. On the one hand, there is this terrible crisis. And on the other hand, there is this absolutely unique and rare global effort to fight it, and that I think is a very positive thing. I am working with the Cray HP machine called Sentinel. This machine is so amazing that it can actually mimic the screening of hundreds of thousands, almost millions of chemicals a day. What would take weeks, if not months or years, we can do in a matter of a few days. And it's really the key to accelerating the discovery of new drugs, new pharmaceuticals. We're all in this together. Thank you. Hello, everyone. And I'm so pleased to be here to interview Dr. Jerome Bodri of the University of Alabama in Huntsville. Hello, Dr. Gu. I'm very happy to be meeting with you here today. I have a lot of questions for you as well. And I'm looking forward to this conversation with you guys. Yes, yes. And I got lots of COVID-19 and computational science questions lined up for you too, Jerome. So let's interview each other then. Absolutely. Let's do that. Let's interview each other. I've got many questions for you and we have a lot in common, and yet a lot of things we are addressing from a different point of view. So I'm very much looking forward to your ideas and insights. Yeah, especially now, you know, with COVID-19, many of us will have to pivot a lot of our research and development work to address the most current issues. I watch your video and I've seen that you're very much focused on drug discovery using supercomputing, the central notebook you did. I'm very excited about that, you know, and can tell us a bit more about how that works, yeah. Yes, I'd be happy to. In fact, I watched your video as well on manufacturing and it's actually quite surprisingly close what we do with drugs, with what other people do with planes or cars or assembly lanes, you know. We are calculating forces on molecules when on drug candidates when they hit parts of the viruses and we essentially try to identify what small molecules will hit the viruses or its components the hardest to mess with its function in a way. And that's not very different from what you're doing, what you are describing people in the industry are in the transportation industry. So that's our problem. So to speak is to deal with a lot of small molecules, calculating a lot of forces. So that's not a main problem or main problem is to make intelligent choices about what calculates what kind of data should we incorporate in our calculations and what kind of data should we give to the people who are going to do the testing. This is something I would like you to help us understand better. How do you see our future intelligence helping us putting our hands on the right that that to start with in order to produce the right that that to end. Yeah, that's that's a great question and and it is a question that we've been pondering in our strategy as a company a lot recently. It's more and more. Now we realize that the data is being generated at the far out edge by edge. I mean, you know, something that's outside of the cloud and data center, right. Like, for example, a more recent COVID-19 work, doing a lot of cryo electron microscope work right to to try and get high resolution pictures of the virus. And at different angles so creating lots of movies under electron microscope to try and create a 3D model of the virus. And we realize that's the edge right because that's where the microscope is away from the data center and massive amounts of data is generated terabytes and terabytes of data per day generator and we had to develop means like a workflow means to get that data off the microscope and provide pre processing and processing so that they can achieve results without delay. So we learned quite a few lessons there, right, especially trying to get the edge to be more intelligent to deal with the onslaught of data coming in from from these devices. That's fantastic that you're saying that and that you're using this very example of cryo EM because that's the kind of data that feeds our computations. And indeed, we have found that it is very, very difficult to get the right cryo EM data to us. We've been working with an HP supercomputer Sentinel, as you may know, for 19 work so we have a lot of computational power, but we will be even faster and better frankly, if we knew what kind of cryo EM that that focus on. That must have for discussions are based on not so much how to compute the forces of the molecules, which we do quite well in an HP supercomputer. But again, what cryo EM three dimensional stream to look at, and it's becoming almost a bottleneck have access to that. And it's been a lot of time. Do you envision the point where I would be able to help us to make this kind of cold almost live or at least as close to live as possible. Has it that that comes from the age how to take it and, and what triage it but prioritize it for the best possible Well what what a what a visionary question and desire right that's exactly the vision we have right. Of course the ultimate vision you aim for the for the best and that will be a real time stream of process data coming off the microscope straight providing you need right, we are not there. But that's the aim, the ability to push more and more intelligence forward, so that by the time the data reaches you, it is what you need, right, without any further processing, and a lot of AI is applied there, particularly in prior EM where they do particle picking, right they do a lot of pictures and movies of the virus and then what they do is they they rotate the virus a little bit right and then to try and figure out in all the different images in the movies to try and pick the particles in there, and this is very much image image processing that AI is very good at So many different stages application is made the key thing is to deal with the data that's flowing at this at this speed and to get the data to you in the right form that in time. So yes, that's the desire. It will be a game changer. We'll be able to get things in a matter of weeks. Instead of a matter of years to the colleagues will be doing the best if the AI can help me learn from calculation that didn't exactly turn out the way we wanted to be very, very helpful. I can see that I can I can envision AI being able to live AI to be able to really revolutionize all the process not only from great discovery but all the way to the clinical to the patient to the hospital. But that that's that's a great point. Now, in fact, I caught on to your term live AI that that's actually what we are trying to achieve although I have not used that term before perhaps I'll borrow it for next time. You see, yes, we have done I've been doing also recent work on gene expression data. So a vaccine clinical trial, they have to get the blood from the volunteers after the first day, and then to to run very very fast AI analytics on the gene expression data that the the one day the transcription data before translation to amino acid, the transcription data is enormous we're talking 30,000 60,000 different items, transcripts, and how to use that high high dimensional data to predict on day one whether this volunteer will get an adverse event or will have a good antibody outcome right for efficacy. So yes how to do it so quickly. To get the blood, go through an essay, right, get the transcript and then run the analytics and AI to produce an outcome. So that's exactly what we're trying to achieve. Yeah, yes, I always emphasize that you know, ultimately the doctor makes that decision. Yeah, I only only suggest based on the data. This is the likely outcome based on all the previous data that the machine has learned from. Oh, I agree, we wouldn't want the machine to decide the fate for patient, but to assist the doctor or nurse making the decision that would be an employee. And are you aware of any kind of industries that already is using this kind of live AI or is there anything in I don't know in sport or crowd control or is there any kind of industry I will be curious to see who is ahead of us in terms of making this this kind of a mini based decisions using AI. Yes, yes. In fact, this this is very pertinent question we as in fact, you know, COVID-19 lots of effort working on it. Right. But now industries and different countries are starting to work on returning to work. Right. Returning to the offices, returning to the factories, returning to the manufacturing plants, but yet, you know, the employers need to reassure the employees that things as appropriate measures are taken for safety, but yet maintain privacy. Right. So, our robot organization actually developed a solution called contact location trade tracing inside buildings inside factories, right. Why they built this and needed a lot of machine learning methods in there to do very, very well, as you say, live AI, right, to offer a solution. Well, let me describe the problem. The problem is in certain countries and certain states, certain cities where regulations require that if someone is ill. Right. You actually have to go in and disinfect the area person has been to is a requirement. But if you don't know precisely where the ill person has been to you actually disinfect the whole factory. And if you have that if you do that, you know, it becomes impractical and cost prohibitive for the company to keep operating profitably. So what what they are doing today with Aruba is that they carry this Bluetooth low energy tag, which is a quarter size. Right. The reason they do that is so that they abstract the tag from the person. And then, and then this the system tracks, you know, everybody, all the employees we have one company that's 10,000 employees right tracks everybody with the tag. And if there is a person ill immediately a floor plan is brought up with hotspots. And then you just targeted the cleaning services there. The same thing contact tracing is also produced automatically you could say anybody that is coming contact with this person within two meters and more than 15 minutes. It comes out the list. And we privacy is our focus tier. There's a separation between the tech and the person on only restricted people are allowed to see the association. And then things like washrooms and all that are not tracked. So yes, a live AI trying to make very, very quick decisions right because this affects people. And for you if you haven't made it that clearly and has to be with the same thing. No, it is more a question about hardware about the computer up here, if I may, we're having, we're spending a lot of time computing on number crunching giant machines, like Sentinel, for instance, which is a dream to use but it's very good at something that we have also spent a lot of time moving back and forth so that from clouds from storage from AI processing to the computing cycles back and forth back and forth vision and architecture that we kind of combine the hardware needed for a massively and also very large storage fast IO to be more AI friendly, so to speak, you see on the high horizon some kind of a, I would say you need or some machine maybe it's to be the turn to ambitious a term but something that plans the AI ahead in terms of passing the vector to the massively problem inside you that makes sense. Makes a lot of sense and and you ask it I know because it is a tough problem to solve. You know, as we always say, computation, right, it's growing capability enormously, but you know, bandwidth you have to pay for latency you sweat for moving data is ultimately going to be the problem. It is. Yeah, and we move the data a lot of times right you back and forth back and forth back and forth from the edge you that's what we try to pre process it, you know, before you put it in storage. Yeah, but then once you arrive in storage you move it to memory to do some work and bring it back and move it to memory again. Right and then that's for HPC and then you put it back into storage and then the AI comes in you, you do the learning, the other way around also so lots of back and forth right. So tough problem to solve, but more and more, we are looking at a new architecture. Right. Currently, this architecture was built for the AI site first, but we're now looking at see how we can expand that. And this is that that's reason why we announce HP as morale data fabric. Yeah. What it does is that it takes care of the data, all the way from the edge point of view the minute it is ingested at the edge. It is incorporated into global namespace. So that eventually where the data arrived lands that geographically one or lands that we got the temperature, hot data, warm data or cold data. Regardless of eventually where it lands at this data fabric tracks everything from in a global namespace in a unified way. So that's the first step. So that data is not seen as in different places, different pieces. It is a unified view of all the data the minute the instant that's that from the edge. I think it's important that we communicate that AI is a possible good, you know, a lot of sci-fi movies unfortunately showcase some sci-fi computers or teams of evil scientists who want to take over the world that how can we communicate better that it's a tool for a change, a tool for good. So key differences. I always point out this that at least we have still judgment relative to the machine. And part of the reason we still have judgment is because our brain, you know, logical center is automatically connected to our emotional center. So, whatever our logic say is tempered by emotion and whatever our emotion wants to act, wants to do right is tempered by our logic. But then AI machine is many call them artificial specific intelligence. They are just focused on that decision making and are not connected to other in well more culturally sensitive or emotionally sensitive type of networks. They are focused networks. Although there are people trying to build them. That's the reason why with judgment. I always use the phrase, right? What's correct is not always the right thing to do. There is a difference, right? We need to be there to be the last judge of what's right here. So that's just one of the big thing. The other one, the other one I bring up is that humans are different from machines, generally in a sense that we are highly subtractive. We filter, right? Well, machine is highly accumulative today. So an AI machine, they accumulate, you know, bring in a loss of data and tune the network. But our brains, a few people realize we've been working with brain researchers in our work. Between three and 30 years old, our brain actually goes through a pruning process of our connections. So, you know, for those of us like me after 30, it's done, right? Keep the brain active because it prunes away connections you don't use. Try and conserve energy, right? I always say, you know, to remind our engineers about this point, about prunings because of energy efficiency, right? A slice of pizza drives our brain for three hours. That's why, you know, sometimes when I need to get my engineers to work longer, I just offer them pizza three more hours. That's a very universal solution to all our problems. Absolutely. This is wonderful indeed, indeed. There is always a need for a human consciousness. It's not just a logic. It's not like Mr. Spock in Star Trek who always speaks about logic but forgets the humanity aspect of the advising. Yes, yes. The connection between the, you know, the logic centers and emotional centers. Yeah, yeah. And the thing is, sleep research is saying that when you don't get enough REM sleep, this connection is weakened. Therefore, your decision making gets affected if you don't get enough sleep. So I was thinking, you know, people do alcohol test, breathalyzer test before they are allowed to operate sensitive or make sensitive decisions. Perhaps in the future you have to check whether you have enough REM sleep before you. This COVID-19 crisis is obviously a dramatic and I wish it never happened, but there is something that I never experienced before is how people are talking to each other. People like you and me, we have a lot in common, but I hear more about the industry outside of my field. And I talk a lot to people like cryoEM people or gene expression people. I would have gotten to that before and process it. Now we have a dialogue across the board in all aspects of industry, science and society. And I think that could be something wonderful that we should keep after we finally fix this COVID-19 crisis. Yes, yes. That's a great point. In fact, it's something that I've been thinking about for employees. Things have changed because of COVID-19, but very likely the change will continue. Yes, because there are a few positive outcomes. COVID-19 is a tough outcome, but there are a few positive side of things like communicating in this way effectively. So we were part of the consortium that developed a natural language processing system in the AI system that would allow scientists to do... I can send you the link to that website to allow you to do a query. So tell me the latest on the binding energy between the SARS-CoV-2 virus spike protein and the ACE2 receptor. And then you will give you a list of 10 answers and give you a link to the papers that say those answers. If you key that in today to the NLP, you see 315-15.7 kcal per mole, which is the general consensus answer. And you see a few that are highly out of range. And then when you go further, you realize those are the earlier papers. So I think this NLP system will be useful. I'm sorry I didn't mean to interrupt, but I'm enthusiastic about it because I have used that. And it's a game changer indeed. It is amazing indeed. Many times by using this kind of intelligent conceptual analysis of the literatures, I think indeed you guys have developed things. I have found connections between facts or between clinical or pharmaceutical aspects of COVID-19 that I wasn't really aware of. So it's a tool for creativity as well. I find it builds something. It just doesn't analyze what has been done, but it creates connections. It creates a network of knowledge and intelligence. That's why it's not 3 to 30 years old when it stops pruning. I know, I know. But children are amazing at that respect. They see things that we don't see anymore. They make connections that we don't necessarily think of because we are used to think a certain way. And the eyes of a child are bringing always something new, which I think is what AI could potentially bring here. So look, this is fascinating really. Yes, yes. The difference between filtering, subtractive and the machine being accumulative. That's why I believe the two working together can have a stronger outcome if used properly. Absolutely. And I think that's how AI will be a force for good indeed. Help us see things that we would have missed that would end up being very important. Well, we are very interested in a quest for drug discovery against COVID-19. We have been quite successful so far. We have accelerated the process by another magnitude. So we're having molecules that have been tested against the virus. Otherwise, it would have taken maybe three or four years to get to that point. So first thing, we have been very fast, but we are very interested in natural products, that chemicals that come from plants essentially. We found a way to mine, I don't want to say exploit, but leverage the knowledge of hundreds of years of people documenting in a very historical way of what plants do against what diseases in different parts of the world. So that really has been not only very useful in our work, but a fantastic bridge to our common human history basically. And second, yes, plants have chemicals and of course we have chemicals. Every living cell has chemicals. The chemicals that are in plants have been fine tuned by evolution to actually have some biological function. They are not there just to look good. They have a role in the cell. And if we're trying to come with a new drug from scratch, which is also something we want to do, of course, then we have to engineer a function that evolution has already found a solution for in plants. So in a way, it's also artificial intelligence. We have natural solutions to our problems. Why don't we try to find them and see if they work in ourselves? And we don't necessarily have to reinvent the wheel each time. Hundreds of millions of years of evolution. Hundreds of millions of years. Any operations. Yes, and in millions of different plants with all kind of chemical diversity. So we have a lot of that disposal here. If only we find the right way to analyze them and bring them to our supercomputers, then we will really leverage this longest amount of knowledge. Instead of having to reinvent the wheel each time we want to take our car, we'll find that there are cars with wheels already that we should be borrowing instead of building one each time. Most of the keys are out there. If we can find them, there are no disposal. Yeah, nature has done the work after hundreds of millions of years. Yes. It's to figure out which is it. Exactly. Exactly. Hence the importance of biodiversity. Yeah, I think this is related to the knowledge graph where you have two objects and the linking parameter. And then you have hundreds of millions of these. Absolutely. A chemical to the end outcome and the link to it. Yes, that's exactly what is absolutely the kind of things we're pursuing very much so absolutely. Not only only building the graph, but building the dynamics of the graph in the future. If you eat too much crème brûlée or if you don't run enough or if you sleep well, then yourselves will have different connections on this graph of the legend. We interact with that molecule in a different way than if you had more sleep or didn't eat that much crème brûlée or or exercised a bit more. So, so insightful. Dr. Bowdry. Yeah, your span of knowledge right impress me and such a fascinating talking to you. Right. Probably next time when we get together, we'll have a bit of crème brûlée together. Yes, let's find out scientifically what it does. We have to do double blind and try three times to make sure you got the right statistics. Three phases, three of the clinical trial phases, right. It's been a pleasure talking to you. I like, I like we agree, you know, this of all the COVID-19 problems, the way that people talk to each other is I think the things that I want to keep in this, you know, past COVID-19 war. I appreciate very much your insight and it's very encouraging the way you see things. So let's make it happen. We'll work together. Hope to see you soon in person. Indeed in person. Yes. Yes. Thank you. Thank you. Good talking to you.