 Hey, welcome back everybody. Jeff Frick here with the Cube. We're in Palo Alto, California at the Four Seasons Hotel an interesting event It's called security in the boardroom It's part of the security series put on by the chair top group to do a couple events a year and a return to the Four Seasons and And 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 got our first guest of the day is Bob Griffin He's the CEO of Iosdy. Correct. Welcome Bob. Thanks. I got the pronunciation right You did indeed for people that aren't familiar with the company. What is Iosdy all about? Well, Iosdy is an artificial intelligence platform manufacturer that builds technologies that allows us to effectively deploy Enterprise-class artificial intelligence applications for security specific security applications security beyond security But security is 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. Okay, we're focused a lot in the health care area around doing things like, you know 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 so Financial institutions the government and then who's the purchaser? What's this kind of the segment that buys kind of your health care focus? It's traditionally the both the payers and the providers Okay, so folks that are looking at that, you know, how do we manage? Costs associated, but how do we make more effective the use of health care practices, right? So, you know folks like Mercy Hospital folks like like Intermountain United health care folks like that that so it's interesting There's a lot of talk right of machine learning in AI right now It's hot hot hot like big day to us 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? You know, it's probably affecting them in ways that they have no idea So, you know, how is the adoption of AI kind of progressing from your point of view in these industries? And how is it helping transform them? Well, it's actually 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 with artificial intelligence is that it's 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 your 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 that artificial intelligence now is it's got an opportunity that can really focus and help with real business problems You know, 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, right? How do you reduce the number of false positives to make your 5,000 investigators more effectively artificial intelligence can do that kind of Application sorry, I wonder if there's any stories you can share publicly about, you know Some of the big impacts or maybe little impacts that people would never have guessed where you can apply this type of Technology to a positive outcome. Sure. So let's let's talk a little bit about Let's take anti-money laundering as an example We have a client that has nearly 7,000 investigators and their challenge is they are getting almost 98% false positives They came to 98% false positives. I mean think about that, which is crazy. Yeah out of every hundred, you know, only two You know positives are actually effective, right? So so they came to us and said look if we can reduce our false positives by say three to five percent 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 were able to give them a 26% reduction in false positives. Well, that changes the game for them That just the economic savings alone is incredible. You're talking nearly a hundred and forty million dollars. So You know those are real things. I'll give you one more example in the health care area We've been studying type 2 diabetes for nearly 40 years, right? 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 sub segments and those sub segments are really Indicators of what's likely to happen to patients, but more importantly, you know, they sub segment up into things like These clients are these patients that have this conditions are likely develop cancer These clients are likely develop an or 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. So you got a technology platform. Do you also deliver the data scientists? I mean, how does it work in terms of, you know Are you a tool that you hand a data scientist inside the organization the one you just you just Give an example of and gives them a different tool Are you also delivering services to help refine and and tune because obviously it's always implied that these things Not only do you pump the data in but there's a continuing ongoing process of learning as they Get smarter. Yeah, the answer actually is yes, you know We 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, you know, it has things like, you know, an SDK it has REST APIs But more importantly it has a tool we've built 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 are prowess that but yet or need to take advantage of the technology So we have a 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 what can it learn? And that's important because if you can do for example unsupervised discovery Then you can do the next thing prediction much more effectively, so it has to be able to do discovery It has to be able to do prediction from the past we can predict the future right 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 it but why did it do that? Why why did it take that action? Why did it segmented 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 absolutely It's a complete white box solution absolutely, okay, and then lastly it's got to be able to do two additional things It's got to be able to act right on what it's what it is discovered what it's predicted what it's justified And then lastly it's got to be episodic It's got to learn right 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 Because you guys talk a lot about doing some homework before I came in on the justification piece You know the 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 act on it, what's the point exactly? Exactly, you know, they just more of a science experiment I want to before I let you go run out of time, but Kind of the roots of the company This is around this thing called topological data analysis and you're not a data scientist nor am I but kind of conceptually What what was different about that approach? That people weren't doing previously well, so topological data silence science is a 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 apply 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 is the first thing it does is we Understand the shape of the data because 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 kind of organization if you will Dad because of course we always look for patterns, right? That's that's that's what we thought exactly people all right Well Bob really interesting conversation. I look forward to the next time we get a chance to sit down I think we have to leave it there for now. All right. Appreciate your time. All right Bob Griffin's he's a CEO I asked the I'm Jeff Rick. You're watching the Cube. We're at the church off event. It's called security in the boardroom We'll be right back into this next break. Thanks for watching