Speaker 1: This is Bloomberg Business Week from Bloomberg Radio. We're delighted
Speaker 1: to have back with us. Tom Siebel. He's founder and
Speaker 1: CEO at C three dot AI. He's author of the
Speaker 1: book Digital Transformation, Survive and Thrive in an Era of
Speaker 1: Mass Extinction. Tom joining us on the phone from Woodside, California. Tom,
Speaker 1: welcome back. Um. We hope you're doing well. Your family
Speaker 1: is doing well, doing great, Carol, nice to talk with you. Well.
Speaker 1: Tell us we want to get into a lot of
Speaker 1: things with you. What's what's your world like right now
Speaker 1: in California? Well, in northern California, I would say that
Speaker 1: we've been there's really been very little impact from COVID
Speaker 1: In the county that I'm in San Mateo County, there
Speaker 1: are this would be everything went in North Apollo Alto
Speaker 1: and Silicon Valley. We have three quarters of a million
Speaker 1: people as the population. There are seventeen hundred hospital beds,
Speaker 1: and on any given day there might be fifty people
Speaker 1: hospitalized with COVID. If I look at Santa Clara County,
Speaker 1: which is the county immediately south of US, where there's
Speaker 1: roughly two million people, that would be everything from Paulo
Speaker 1: Alto to San Jose, there's about two million people four
Speaker 1: thousand hospital beds. On any given day, there'll be a
Speaker 1: hundred and fifty people hospitalized for COVID in San Mateo County.
Speaker 1: I believe there are no people on ventilators. So, um,
Speaker 1: you know, most people in the town that I live in, Woodside,
Speaker 1: there have been ten people diagnosed with COVID, So it
Speaker 1: kind of missed us. Well. I mean, there's an argument
Speaker 1: meant to be made, I think Tom, and I'm guessing
Speaker 1: some of your local lawmakers would make it, which is,
Speaker 1: you guys did the right thing. I mean you sort
Speaker 1: of shut it down pretty early in the entire Bay area, right,
Speaker 1: We did shut it down early, and it kind of
Speaker 1: you know, I think the purpose for shutting it down
Speaker 1: was to keep from overwhelming the hospital systems, and we
Speaker 1: never got close to that. I mean, out of sev
Speaker 1: hospital deads on an e given day, fifty might be
Speaker 1: occupied with in this county, fifty might be occupied with
Speaker 1: COVID patients. So it uh, you know, maybe it worked.
Speaker 1: I'm you know, there's lots of different opinions on this,
Speaker 1: but it kind of never happened here. Yeah. Interesting. Interesting, Well,
Speaker 1: let's hope it stays that way. Yeah, exactly, Um, lesson learned,
Speaker 1: you know, in terms of a playbook for for how
Speaker 1: to do it. Are you guys in the studio or
Speaker 1: you're doing this from Paul, we're doing it from home.
Speaker 1: We're getting from you guys are a professional operation? Uh,
Speaker 1: it's seamless professionalists of congratulations. Well, thank thank you. Yeah,
Speaker 1: we well we've gotten good at it. We're at the
Speaker 1: end of our ninth week doing this from home, so
Speaker 1: I mean kudos to our team who got us all
Speaker 1: set up. But it is sort of it's an amazing
Speaker 1: tribute to technology, Tom, which you know far more about
Speaker 1: than we do. So let's talk about how technology is
Speaker 1: maybe helping us get our arms around this. We were
Speaker 1: talking with you earlier in the year about cyber attacks.
Speaker 1: We've got a different sort of attack on our hands now. Uh,
Speaker 1: And I do wonder how technology and this whole concept
Speaker 1: of a data lake help us understand how that's being
Speaker 1: used here. Well, you know, you recall that one of
Speaker 1: the things we spoke of when I was with you
Speaker 1: last in New York was the area of precision medicine. Okay,
Speaker 1: and precision medicine unquestionably will be one of the largest
Speaker 1: commercial and industrial applications of artificial intelligence. So we can
Speaker 1: use this for a disease prediction, adverse drug reaction, genome
Speaker 1: specific medical protocols AI assisted diagnosis. So this is the
Speaker 1: largest and most rapidly growing segment of the U. S
Speaker 1: economy and many economies, and AI is going to impact
Speaker 1: medicine in a huge way. Now enter COVID. So this
Speaker 1: is a really unique opportunity UH to apply AI to
Speaker 1: contribute to this dialogue. And we looked at all the
Speaker 1: you know, everybody has just been guessing uh and uh.
Speaker 1: And you know, as you change from you one TV
Speaker 1: channel to another, and you listen to Neil Ferguson at
Speaker 1: King's College or the person at Stanford, and one person
Speaker 1: says the morbidity rate is going to be between two
Speaker 1: percent and five percent. And another expert with the same
Speaker 1: level of expertise says, the morbidity rate is gonna be
Speaker 1: gonna be like, you know, one one one percent. What
Speaker 1: is a policymaker to do. Well, what we did is
Speaker 1: we formed a coalition that we call the C three
Speaker 1: AI Digital Transformation Institute, and we founded this with Microsoft.
Speaker 1: We funded this to a two and of about four
Speaker 1: hundred million dollars, and we aggregated the human capital at
Speaker 1: m I T. Carnegie, Mellon, Princeton, the University of Chicago,
Speaker 1: the University of Illinois, and UM and you see Berkeley
Speaker 1: to engage in large scale research on applying AI to
Speaker 1: mitigate COVID pandemic. And so this is AI and machine
Speaker 1: learning models to mitigate disease, bioinformatic modeling and simulation of propagation.
Speaker 1: So that's a that's a major initiative. It's underway, it's
Speaker 1: really exciting, and that is one of the efforts that
Speaker 1: we've been engaged in. All Right, our guest at this
Speaker 1: hour is Tom Seebel, founder and CEO at C three
Speaker 1: dot AI, author of Digital Transformation, Survive and Thrive in
Speaker 1: an Era of Mass Extinction. He joins us on the
Speaker 1: phone from Woodside, California. So, Tom, you really laid out
Speaker 1: what data Lake is all about. What's your goal? So
Speaker 1: this is you know, COVID nineteen data collection. What are
Speaker 1: you hoping that it does or what do you expected
Speaker 1: to do? And and what's a time timeline on it? Well,
Speaker 1: in order to perform data science in order to get
Speaker 1: accurate prediction whether it be course of disease or the
Speaker 1: effocacy of social mitigations. These sitis need data. So what
Speaker 1: we have done in the past month is we have
Speaker 1: taken the twenty two largest data sources that are available
Speaker 1: in the world about COVID from JOHNS. Hopkins and coord
Speaker 1: nineteen and the New York Times, in the Milligan Institute
Speaker 1: and what have you. These are ct scans, These are
Speaker 1: mortality data, core morbidity coursive disease, and we have aggregated
Speaker 1: those data into a unified, federated image that we've made available.
Speaker 1: This is called the C three AI COVID nineteen Data Lake,
Speaker 1: and we've made this resource available to the world at
Speaker 1: no cost to be able to do research, and we've
Speaker 1: we've had so this is by far the world's largest
Speaker 1: copus uh uh corpus of COVID data available researchers. This
Speaker 1: is being powered by our friends at AWS who provided
Speaker 1: the the cloud platform to do it. And I think
Speaker 1: this will be an enormously important resource for people to research,
Speaker 1: to do research, understand the course of the disease and
Speaker 1: control that this epidemic and other epidemics like it. I mean, Tom,
Speaker 1: it's interesting, you know, and we wanted to talk to
Speaker 1: you a lot about Silicon Valley sort of what's going
Speaker 1: on there, But and maybe as a bridge to that,
Speaker 1: it does feel like there is this dare I say,
Speaker 1: And maybe I'm just optimistic here on a Friday afternoon,
Speaker 1: but you know, this sort of spirit of collaboration and
Speaker 1: maybe urgent collaboration that's happening around this particular pandemic. And
Speaker 1: I don't know why that is, if it's just sort
Speaker 1: of the scope and scale of it, if it's because
Speaker 1: it is so dynamic and fast moving, and the effect
Speaker 1: economically and on our individual lives has has been so traumatic.
Speaker 1: Am I overstating that? You think? No, I think you've daled.
Speaker 1: And I think we're dealing with an ex essential event
Speaker 1: for people, for families, for communities, and for companies. And
Speaker 1: company and individuals are pulling together. Research institutions are pulling together,
Speaker 1: Countries are pulling together in concerted, extraordinarily large scale efforts
Speaker 1: to understand the pandemic and control it. And it's I
Speaker 1: think it's very you know, it's very inspiring to watch
Speaker 1: it happen, and it's uh and it's it's really exciting
Speaker 1: to be able to be part of it. Well, and
Speaker 1: I do wonder. I mean, you know, speaking of silicon value,
Speaker 1: I mean, this is kind of old school silicon value.
Speaker 1: In some ways. It's obviously a very competitive place. But
Speaker 1: I mean you have witnessed the other you know, sort
Speaker 1: of the best of Silicon Valley, I would imagine in
Speaker 1: some ways. I'm sure you've seen some other stuff too,
Speaker 1: but uh, you know, you understand the ethos of the place.
Speaker 1: I think that everything what is going on with COVID globally,
Speaker 1: this is a test, Okay, this is a test of
Speaker 1: us as people. This is a test of our families.
Speaker 1: It's the test of our social structure, is the test
Speaker 1: of our government's Okay, and when our government structures and
Speaker 1: you know, hopefully when history is written, we will all
Speaker 1: have passed this test. But I think this is an
Speaker 1: opportunity for all of us to be our best, to
Speaker 1: be the best we can be, Okay, and and and
Speaker 1: and and and solve this problem because it is solvable. Yeah,
Speaker 1: but it's right, but it's you can do it better
Speaker 1: and quicker, right if we all work together, and that
Speaker 1: we're staying amazing collaboration through what we're doing with the
Speaker 1: Digital Transformation Institut in the COVID data lake. We are
Speaker 1: in active cooperation with organizations all around the planet, World
Speaker 1: Health Organization, you, NEST, GOO, c d C, NIH, Stanford University,
Speaker 1: you name it. Everybody is leaning forward and all they
Speaker 1: wanted Microsoft, AWS, IBM all when you when you call him,
Speaker 1: you ask him to help, all the answer is always
Speaker 1: how to Tom, how can we help? And so it's uh,
Speaker 1: it's really really been um inspiring to see this develop
Speaker 1: and I think we're we can expect to see UM
Speaker 1: you know, I think highly efficacious solutions forthcoming in a
Speaker 1: reasonably short period time. Well that's what I wanted to
Speaker 1: ask you, because the time frames certainly has been one
Speaker 1: that we've heard everything even Bill gatesway in you know,
Speaker 1: everything from as soon as nine months to you know,
Speaker 1: maybe eighteen months President talking about a vaccine by the
Speaker 1: beginning of the year. So I do wonder what you're hearing,
Speaker 1: UM from the community, this global cooperative community, about a
Speaker 1: real time frame, because everybody we seem to talk to you,
Speaker 1: Tom says, you know, we just talked about UM with
Speaker 1: the head of the Broadway you know, theater, you know,
Speaker 1: industry that it's not until we get a vaccine. We've
Speaker 1: talked with Bob Crandell used to head up American airlines.
Speaker 1: You don't open up airlines really until you get a vaccine.
Speaker 1: So what do you hear about a real time frame
Speaker 1: about that specific sickly? Oh, I think there are lots
Speaker 1: of ways to deal with this disease other than disease,
Speaker 1: other than vaccine, and we are dealing with it today
Speaker 1: and there's but there's lots of questions about which of
Speaker 1: these which of these techniques are efficacious, and which are not.
Speaker 1: And if we have a large enough data sets, we
Speaker 1: can tell which are effications and which are not, and
Speaker 1: we can we can mitigate the spread of disease, we
Speaker 1: can save lives. And this is this is before the
Speaker 1: advent of a vaccine, which is obviously a year or
Speaker 1: two off because that's how long it takes. But this
Speaker 1: is I mean, this is a natural application of artificial
Speaker 1: intelligence and data science and now we are aggregating the
Speaker 1: data so people can make better informed, more accurate decisions,
Speaker 1: and more more accurate policy decisions. That was Tom Siebel,
Speaker 1: founder and CEO at C three dot AI and of
Speaker 1: course founder of Siebel Systems. I mean, this is someone
Speaker 1: again who has seen so much in the world of
Speaker 1: innovation and technology, and now he's trying to apply that
Speaker 1: to the virus, right, and it's all about data and Jason,
Speaker 1: I think it's safe to say that that's how we
Speaker 1: ultimately get ahead of this. Absolutely really enjoyed that conversation.
Speaker 1: You've been listening to Bloomberg Business Week Extra. Be sure
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Speaker 1: I'm Carol Masser and I'm Jason Kelly. This is Bloomberg