>> Hey, welcome back everybody. Jeffrey here with theCUBE. We're in Palo Alto, California at the Four Seasons Hotel. An interesting event, it's called Security in the Boardroom, and it's part of the security series put on by the Chertoff Group. They do a couple of events a year, and they've returned to the Four Seasons. It's really an interesting twist on the whole security discussion, really elevating it to what's happening in the boardroom. We're excited to be here, we've got some great guests lined up, and we've got our first guest of the day. He's Bob Griffin. He's the CEO of Ayasdi. >> Correct. >> Welcome, Bob. >> Thanks. >> I got the pronunciation right, so. >> You did, indeed. >> For people that aren't familiar with the company, what is Ayasdi all about? >> Well Ayasdi's an artificial intelligence platform manufacturer that builds technologies that allows us to effectively deploy enterprise class artificial intelligence applications. >> For security's specific application or beyond security? >> Yeah, beyond security. We're fundamentally focused in three areas. We're focused in the financial crimes area, specifically around doing things like anti-money laundering, risk and compliance, waste, fraud and abuse. We're focused a lot in the healthcare area, around doing things like, clinical variation management, population health risk, and we've got a very strong focus in the federal government and the public sector, mostly around the intelligence community, DoD and so forth. >> Okay. So, financial institutions, the government, and then who's the purchaser, what's the segment that buys your healthcare focus applications? >> It's traditionally both the payers and the providers. So folks that are looking at, how do we manage costs associated, but how do we make more use of healthcare practices? So, folks like Mercy Hospital, folks like Intermountain, United Healthcare, folks like that. >> So it's interesting 'cause there's a lot of talk of machine learning and AI right now, it's hot, hot, hot like beg-id was a couple years ago. But I think, a lot of people are still confused as to how is it actually being used. Is it actually being used? It's probably affecting them in ways they have no idea. So, how is the adoption of AI progressing from your point of view in these industries, and how is it helping transform them? >> Well, it's absolutely transformational technology. The reality is all applications eventually are going to have to become intelligent or they become obsolete. The biggest challenge with artificial intelligence is that it's moving incredibly quickly. The rate of change, milestones, are daily. So if you're not running to artificial intelligence applications, or developing and deploying those, you're behind the curve. If you're sitting at the stoplight right now, and you're competitors are entering the intersection using artificial intelligence, you're never going to catch up, so you have to move quickly. >> Right. >> The second thing, I think, is that, artificial intelligence now has got an opportunity that can really focus and help with real business problems. Traditionally, what we've done with artificial intelligence is we've parked it in innovation labs, or we've parked it in R&D. It's time to take it out of that and really put it to place, in areas around opportunities we talked earlier about. Anti-money laundering. How do you reduce the number of false positives to make your 5000 investigators more effectively? Artificial intelligence can do that kind of application. >> I was wondering if there's any stories you can share publicly about some of the big impacts or maybe little impacts that people would never have guessed where you can apply this type of technology to positive outcome. >> Sure. So, let's talk a little bit about, let's take anti-money laundering as an example. We have a client that has nearly 7000 investigators. And their challenge is, they're getting almost 98% false positives. They came to >> 98% false positives? >> 98 false positives, I mean think about that. >> Which is crazy. >> Out of every hundred, only two positives are actually effective. Alright so, they came to us and said, look, if we can reduce our false positives by say 3-5%, that's a home run for us, right? What do you think you can do to help us? We took their information, their data, put ourselves within their workflow. And we we're able to give them a 26% reduction in false positives. Well that changes the game for them. Just the economic savings alone is incredible. You're talking nearly 140 million dollars. So, those are real things. I'll give you one more example in the healthcare area. We've been studying type 2 diabetes for nearly 40 years. We took that same data set that people have been studying and working with one of our partners, we were able to very quickly, through our platform, segment up that data set and show that type 2 diabetes really falls into three subsegments. And those subsegments are really indicators of what's likely to happen to patients, but more importantly, they subsegment up into things like, these clients, er these patients that have these conditions are likely to develop cancer. These clients are likely to develop retinopathy, blindness. What that's doing is it's changing the way, not only they're going to prosecute a cure, but also the way they're going to prosecute the treatment of type 2 diabetes. It's changing the game. >> So, it's interesting. You got a technology platform. Do you also deliver the data to scientists? How does it work in terms of, or are you a tool that you hand to data scientists inside the organization, the one you just, given an example of and gives them a different tool, or you also delivering services to help refine and tune? 'Cause obviously it's always implied that these things, not only do you pump the data in, that there's a continuing ongoing process of learning as they, continue to get smarter. >> Absolutely. The answer actually is yes. We provide a platform, and that platform really comes with capabilities to enable our clients to develop artificial intelligence applications in real time or near real time. So, it has things like an SDK, it has REST APIs, but more importantly, it has a tool we build called Envision. And that Envision really allows our clients to very rapidly prototype new artificial intelligence applications and get them into production incredibly quickly. Now to your point, there are, some of our clients that don't have the technological skills or prowess, but yet, need to take advantage of the technology. So we have a professional services capability that will come in. We'll bring in data scientists as required. We'll bring in subject matter experts as needed. We'll bring in program managers and so forth, and we'll take them from kind of, cradle to grave, in helping them build out those applications. As part of that we'll train them, educate them and let them to become self-sufficient. Because, one of the things that I think is incredibly important about artificial intelligence that nobody's talking about, is any machine-intelligent application has to be able to do five things. It has to be able to discover. You know, find out and do observational discovery. What does it not know about itself, What can it learn? And that's important, because if you can do unsupervised discovery, then you can do the next thing, prediction, much more effectively. So it has to be able to discover, it has to be able to do prediction, from the past we can predict the future. It has to be able to do justification, and that's probably one of the most important areas that we talk about. Justification is not necessarily what is it the algorithm did, but why did it do that, why did it take that action? Why did it segment the population to these sizes? What is it that it proved? Why did that sensor go off? And so forth. >> This is really, to kind of, unveil the black box a little bit. 'Cause nobody wants the white box anymore. >> Absolutely. And then lastly, it's got to be able to do two additional things. It's got to be able to act on what it has discovered, what it's predicted, what it's justified. And then lastly, it's got to be episodic, it's got to learn. So what did I learn from the last episode, and how do I apply that back to a new form of discovery, a new form of prediction, the next level of justification and action. >> That's a great summary, Bob. And it's interesting. 'Cause you guys talk a lot about, I was doing some homework before I came in on the justification piece. You got to open up that black box, it's no longer good enough just to kick out an answer. >> Absolutely. And if you can't on it, what's the point, you know? It's kind of more of a science experiment. Before I let you go, we're running out of time, but, the roots of the company, is around this thing called topological data analysis. And you're not a data scientist, nor am I, but conceptually, what was different about that approach, that people weren't doing previously? >> Well so, topological data science, data analysis, is the study of the shape of data. All data comes in shape. The challenge historically is most people apply traditional algorithms to data assuming that it's going to be in a linear fashion, for example. So they'll linear regression analysis. Or if it's clustered data, they'll apply clustering technologies and so forth. The challenge is, what happens if your data is in a flare shape? Or what if it's in a circular shape? Or what if it's time series based and so forth? What we do is, with TDA, the first thing it does, is we understand the shape of the data 'cause the data will tell you a lot about itself and its shape. And from that shape you can start to ask more intelligent questions about the data so you can unlock all of the insight. >> So it's really almost like, a higher order organization if you will. 'Cause we always look for patterns, right? That's what we always do as people. Alright, well Bob, really interesting conversation. >> Thanks. >> I really look forward to the next time we get a chance to sit down. >> I appreciate it. >> We'll have to leave it there for now. >> Alright, appreciate your time. >> Alright, Bob Griffin, he's the CEO at Ayasdi. I'm Jeff Frick, you're watching theCUBE. We're at the Chernoff event, it's called Security in the Boardroom, we'll be right back.