Helping Create AI for Everyone, Everywhere with Bill Fox, Business Development Executive at SambaNova System: Audio automatically transcribed by Sonix
Helping Create AI for Everyone, Everywhere with Bill Fox, Business Development Executive at SambaNova System: this mp3 audio file was automatically transcribed by Sonix with the best speech-to-text algorithms. This transcript may contain errors.
Saul Marquez:
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Saul Marquez:
Heey everybody, Saul Marquez here and welcome back to the Outcomes Rocket. Today, I have the privilege of hosting the amazing Bill Fox. You guys have heard him on the podcast before. He’s here for the second time around doing some fantastic work. Bill leads health care and life sciences, business development at SambaNova Systems. He has over 20 years of experience in health care technology and is an internationally recognized thought leader on digital transformation in health care. He is the former Senior Vice President of AI at Change Health Care. Prior to that, he led the Global Health Care and Life Sciences vertical at Market Logic. In his legal career, he was a law firm partner focusing on health care related cases. Deputy Chief of Economic and Cyber Crime Unit at the Philadelphia District Attorney’s Office, one of the first cyber crime units in the country, and a special assistant US attorney. I’m privileged to have him back here on the podcast today to talk to us about what he’s doing at some Minova, some incredible work with artificial intelligence and all focused here in health care. So, such a privilege to have you here, Bill. Thanks so much for being with us again.
Bill Fox:
Thank you, Saul. Really happy to be here.
Saul Marquez:
Yeah. Looking forward to our chat. So, you know, you always find a way to stay at the forefront of what’s going on in health care, whether it be with the Internet or with cybercrime and the things that you’ve done. I mean, I’m always impressed with what you do. Peel back the onion for us. What is it that inspires you and your work in health care?
Bill Fox:
Yeah, you know, people always ask me, I don’t get from law to health care, but most of what I did in law was healthcare-related. And toward the end there, when I was prosecutor, we really started getting into using predictive analytics and social analytics to try to do these really complex health care life sciences cases. And it just seemed obvious that this was the direction that everything was going, that 10 years from now, you know, we weren’t going to have a bunch of people doing one off investigations into health care fraud, that we were going to be looking at massive data sets and trying to sort of unravel how we can make the system better. And from doing that, once I got over to the health care side, which was about 15, 16 years ago now, sort of immediately started looking at not just how could we do fraud analytics, but we have these data sets. How can we look at them and understand what’s a better episode of care, what treatment is working better, what sort of access? We had all that demographic data. Now its social determinants of health data. But we were already thinking 10, 15 years ago about we have all this data about people. How does that relate to their treatment and what we need to do for better outcomes. So it just as always seem to me that the far that we can get toward using the most advanced technology and applying it to this embarrassment of data that we have in health care, life sciences, that that’s what’s going to really turn the wheel and change the system.
Saul Marquez:
Yeah, you said it well, the data as it sits is an embarrassment. There’s a lot that we could do with how it’s structured and overall, how are we using it? And there’s a lot of questions around this. So talk to us a little bit about what you guys are doing to add value to the health care ecosystem.
Bill Fox:
So, you know, what we’re doing at SambaNova is when you look at where we’re at now, we’ve been talking about it for a long time. And actually speaking at this summit next week, I’m going to be talking about sort of some of these amazing practical applications of A.I. that are being utilized now using videos of eye movement, detect dementia and patients, and being able to detect diabetic retinopathy that way and using and helping to build knowledge for drug discovery, using even recommendation models like Netflix and drug discovery. I mean, there’s some amazing things going on, but we’re kind of facing the same thing that we did with databases 10 years ago. The A.I. technology of today that we’re using wasn’t built for AI. It’s being adapted for AI, it is being used for AI, but for the listeners to know what Moore’s Law is, which, you know, simply as computers are getting faster and faster, faster, faster. That’s why you wanted a new laptop every two years. That’s kind of slowed down over the last decade. So the technology that we’re using is not up to the demands of what we want to do with AI. We want to be able to use all this data. We want to use really high resolution images to try to do cancer, try to discover diagnostics for cancer. We want to use more variables and LP models not language processing models. So we’re doing at SambaNova is in essence, bringing that next generation of technology into the real world. So we have developed what the company has developed is basically a completely new computer chip. That’s not a CPU, it’s not a GPU. It’s made for A.I. and it’s much faster. You can look at an entire high resolution film on which you can reconfigure the chip. So it opens up a whole new set of capabilities. We can then apply all this health care, all this life science, all this genomic data to get to better solutions. That’s what we want to add to the ecosystem.
Saul Marquez:
That’s phenomenal. I mean, this is the necessary structure, the new railroad, the new bridge Right. that we need to cross to the opportunity on the other side of this. So help us to understand, Bill, what is the difference? Right., CPU, GPU, but the difference of your chip, like what is it called and why is it that much better?
Bill Fox:
Yeah, so I don’t want to get too focused on chip because we don’t really sell chips and nobody wants to buy a chip. People want to buy something that works.
Saul Marquez:
That’s fair.
Bill Fox:
So yeah, that works. They don’t want to start building a computer. So basically we sell a stack that includes our new chip, but also the compiler and even the more software farther up the stack that allows you to simply plug it in and use it. So some of our customers and some of this stuff is on YouTube and whatnot said we open the box. Forty five minutes later, we were running algorithms on it. So what we wanted to do was enable you. The ecosystem around it is very open source. So if you wrote these algorithms on PI torch and — which are the most sort of the two popular things that’s going to plug right in, whatever you’re using in your data center or whatever technology sockets, all that, we wanted to make it so that you can take advantage of this technology without and in fact democratize this technology so that not only the most advanced enterprises that have one hundred people doing A.I. can use it, but that if you don’t have all those data scientists don’t have all this machine learning experts, you can still take advantage of this next gen technology by building everything that needs to go around it so that you can just use it and get to solving the problems that you want to solve.
Saul Marquez:
Love it. Well said. And thanks for reframing that. My mind was going to the chip, but it’s bigger than that. It’s an environment. It’s a stack. It’s an opportunity to really use this technology to harness it and solve the problems that we need to solve. So you’ve clearly expressed what makes it different. Talk to us about what you’re doing today and how you’re improving outcomes or really just where you’re at in the journey.
Bill Fox:
I mean, that’s why this is so exciting. So the company came out of stealth like three months ago. So we are right at the beginning now. We are at the National Labs, at Lawrence Livermore, at Los Alamos. We’re being used for COVID things, for cancer research. So we are already making an impact. But the reason that I came on just a few months ago when we came in stealth is to bring this into commercial health care. Spent the last 15 years working with the largest payers in the United States and global pharmas, most of the top 15 global farmers around the world, and was doing a lot of database work. I saw what the data looks like, kind of the problems that they’re trying to solve. Since then, we’ve had this kind of rise of genomics. And when you start talking about genomics, the cell, I was listening to something today and they were saying the cell is twenty thousand dimensional unit that you’re getting to the trillions really quickly. So the opportunity is presented here is to work with these organizations that have been working with for the last 15 years and say, you can really do this. The technology now exists for you to look at that whole high resolution image or for you to put to do an LP model with two hundred billion variables to do those kinds of things, to get more precise targeting.
Bill Fox:
That’s a big area here is drug discovery. So I heard a good metaphor that it’s like an industrial revolution. So before the industrial revolution, like an artisan sat at a bench and he made this thing and it was very high quality. But it’s a very slow and expensive way to do it. And that’s why we have this 10-year multibillion-dollar drug discovery pipeline. What if we can build a knowledge graph using an LP and then put a recommendation model similar to Netflix recommendation model on top of that, and that can find these drug targets? And this is something that they’re doing at AstraZeneca. So we’re at the beginning of thinking about how we can use these multiple capabilities and help computer vision recommendation together to sort of turn this into an industrial process so that we can get better targets, faster to the scientists to then go into this sort of in vitro process. So combining that in silico and in vitro process to speed all this stuff up.
Saul Marquez:
Fascinating. And you gave a great analogy of that small shop making the quilts to now being mass-produced. Same thing with an LP. AI. These things don’t need to be krafts. It could be industrialized. And it’s exciting to think about a health care world and a world in general, really, where we can do these types of things. And it’s exciting. As you think about the setbacks. I know your journey that you’re on now. You just started a couple of months ago, but like, think about the setbacks the industry has experienced around this. What are some of the key learnings that you guys are leveraging at SambaNova to make sure there’s there’s wide spread success with the use of these technologies?
Bill Fox:
Yeah, I read something a little bit about this week with the announcements from Ivan Watson. We had everybody thought of Berkshire Hathaway, Amazon and JPMC. I mean, they are going to do this health care thing. They’re going to bring financial expertise, technology expertise. I mean, it’s going to kill it. It did. IBM Watson, phenomenal, phenomenal effort. Now they’re going to sell off. So what we see in health care is that it can’t just be about technology. You really need to understand the system. You need to be resilient and you need to be really smart and sort of strategic, sort of Tangerang talks about where you’re going to put this first so that you can make sure that it works and you get that sort of groundswell, that momentum within an organization to start doing this throughout. So I think that those are going to be the challenges and kind of lived through this cycle. Another big start up, bringing a new technology into a system that given the outcomes, the importance of the outcomes. One of the friend of mine uses the analogy like, if the GAP sends you the wrong color sweater, not that big of a deal. If you get the wrong treatment in the hospital, big deal.
Bill Fox:
So you’ve got to understand that health care is not the same as other industries and you have to really work closely with the customer, whether that’s a payer or provider system or a pharma and kind of understand where they’re at. Meet them there and say, how can your technology help them do what they’re trying to do? And I mean, that’s one of the things that really attracted me about SambaNov, was that they had thought of all that. It’s a lot of really experienced people besides all the really smart people, but there’s also a lot of really smart, experienced people. And they understood that this need to be put together, needed to be put together in a way that enables you to roll it into the data center or into the cloud and start using it without a lot of technological barriers to that and to start seeing output and results quickly. So I think that that’s going to be the barrier is going to be, hey, this is super cool, but we’re here and sort of getting over that objection, saying we know you’re there and you can still use it and start moving forward.
Saul Marquez:
Yeah, that’s fantastic. And the examples you’ve provided around Maven and Watson, and it’s not about muscle, it’s about muscle and also understanding the system. And so it’s that critical juncture between those two things that that are going to make the difference. Sounds like you guys have both. So as you think about the future, do some some horizon viewing here with you, Bill, what are you most excited about?
Bill Fox:
Yeah, I think the thing that most excited about it is I’ve seen over the last 15 years the things that people want to do with data and health care and life science. And the data is there. And I’ve been involved in lots of projects, lots of successful projects on sort of the data integration front, sort of moving away from the data silos and getting the data together. And I think we all understand the promise of A&M that it really should be pervasive throughout the system. And we’ve talked a bunch about of it in health care, especially with what happened with the pandemic and the move to. Massive move forward in telehealth, every aspect of that experience from picking the doctor to the symptom checker to the virtual assistant to scheduling to every one of those is an AI algorithm that’s helping you choose the doctor, that’s deciding whether or not you should go get a test, that’s figuring out your scheduling, that’s answering your questions. So whatever part to be able to go into these organizations and say you’re trying to build a model to do this, you’re trying to build a recommendation model to accelerate your telehealth that you’re trying to use do a better job of diagnosing these kinds of tumors, your computer vision. We can accelerate that generation right now. That, to me, is the cool thing. That’s why when I started my discussions with SambaNova, I was like, this is what I want to be out there talking about, because this is this is how we can jump forward a generation. And you’re asking all the right questions in recognition of the barriers that exist and challenges that exist so that it can happen and to be able to have those conversations and push that forward and then start actually seeing it come about.I mean, that’s that’s why I’m doing it.
Saul Marquez:
I love it. And if this isn’t enough to pique your curiosity, folks around what SambaNova Bill, his team, the organization is doing, I don’t know it is. But I think this is a good opportunity for you to explore more about what they’re up to. I highly, highly, highly urge you don’t get left behind. Its SambaNova.AI. Before we conclude, Bill. I love to hear a closing thought. What should we be thinking about? And aside from sambanova.ai, what’s the best place that the listeners could get in touch with you or the team to partner?
Bill Fox:
Yeah, so, I mean, my email is bill.fox@sambanova.ai. and @foxbigdata on Twitter. I post almost every day something in the wheelhouse of A.I. Solutions across health care, Lifesciences. On LinkedIn, the same thing I’m posting almost every day. So I’m happy to I’d love to talk to any of your listeners, anything they want to know about what we’re doing in health care, life sciences, and have those conversations and sort of walk them through what we’re doing because, you know, I couldn’t be more excited about it. So that’s what I’m doing. And I would love to speak to anyone about it.
Saul Marquez:
Outstanding, Bill. Well, we appreciate the invite and listeners, make sure you take Bill up on it. There’s an opportunity here for you to scale improve. I mean, just generationally what you’re doing today. So take them up on it. Bill, always a pleasure to have you on here. Certainly look forward to doing a part two on this. I know you’re at the start of this, so maybe in six months to 12 months we get you back on to hear about the progress.
Bill Fox:
Absolutely. Of course, Saul. Thanks a lot for having me.
Saul Marquez:
Great to have you again, Bill. Take care.
Saul Marquez:
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