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MIT AI Conference 2018: Staying Competitive in the World of AI in Finance
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About speakers

Selina Wang
Correspondent & Anchor at Bloomberg TV
Ananth Madhavan
Global Head of Research at BlackRock
Keith Rabois
General Partner at Founders Fund
Christina Qi
Co-Founder & Partner at Domeyard LP
Zulfikar Ramzan
Chief Technology Officer at RSA

https://www.bloombergmedia.com/talent/people/selina-wang/Twitter: @selinawangtv | Website: www.wangselina.com | Selina recently relocated to Beijing as a correspondent and anchor for Bloomberg TV, covering China's economy, markets, corporate giants, & technology sectors. She was previously based in San Francisco, reporting on the global technology industry, venture capital, cross-border investments, and social media for Bloomberg News. She's been a regular contributor on Bloomberg TV and a fill-in anchor for Bloomberg Technology, the network’s flagship technology show. From San Francisco and her previous bases in New York City and Hong Kong, Selina has covered breaking news and written investigative and feature stories across Bloomberg's print and broadcast platforms. Selina has interviewed a multitude of top executives and investors for Bloomberg News and TV, including SoftBank CEO Masayoshi Son, Twitter CEO Jack Dorsey, Alibaba's President, CEO of SAP, CEO of Symantec, Former Qualcomm CEO Paul Jacobs, GoPro’s CEO, the CEO of iQiYi, co-founder of Virgin Hyperloop One, Quicken Loans CEO, and numerous Forbes Midas Investors. She’s written cover stories for Businessweek Magazine, and her work has won the Front Page Award from the Newswomen’s Club of New York, and an honorable mention from the Society of Business Editors and Writers.

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Financial executive with experience leading global quantitative research and technology teams. Author of numerous publications in leading academic and practitioner journals. Co-inventor of several patents held by ITG Inc., including US 7,533,048 B2 (Fair value model based system, method, and computer program product for valuing foreign securities in a mutual fund), US 7,337,137 B2 (Investment portfolio optimization system, method and computer program product), and international patents related to factor risk models and transaction cost estimation. Author of "Exchange-traded funds and the new dynamics of investing," Oxford University Press, 2016.Specialties: Quantitative research, market microstructure, trading, portfolio transition management, and exchange-traded products. Registration: Series 7, 24, and 63

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I create new products, generate innovative business models, and love to solve intractable problems. I craft business strategies to navigate precarious business environments.I specialize in guiding early-stage startups into successful businesses: I joined PayPal when our monthly burn-rate was $6 million; I joined LinkedIn, Slide and Square when we had no revenue. Five companies I helped build are now publicly traded with market capitalizations >$1 Billion. Three others have been acquired for greater than $1 Billion or are publicly traded IPOs.I served on the Board of Directors of Xoom (NASDAQ: XOOM) from 2003 until 2015 and served on the Yelp Board from 2005-2014 (NYSE: Yelp). I served on the Board of Reddit from 2012-2019. I am a co-founder of OpenDoor and serve as our Executive Chairman. Prior angel investments include: YouTube, Airbnb, Palantir, Eventbrite, Lyft, Quora, Yammer, Skybox, Counsyl, Weebly, Wish, Eventbrite.Specialties: Consumer Internet, Business Development, business strategy, competitive strategy, regulatory risk, Government Affairs, payments innovation, mobile payments, Litigation, Politics, network businesses, eBay vulnerabilities, viral marketing, start-ups, online payments, eCommerce, Facebook apps, Facebook, social gaming, social, angel investing.

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Christina Qi serves as Founding Partner at Domeyard LP, a hedge fund known for its focus on high frequency trading. She started Domeyard 8 years ago with $1000 in savings. Domeyard trades up to $1 billion USD per day. Her company’s story has been featured on the front page of Forbes and Nikkei, and quoted in the Wall Street Journal, Bloomberg, CNN, NBC, and the Financial Times. Christina is a contributor to the World Economic Forum’s research on AI in finance. She is a visiting lecturer at MIT, including Nobel Laureate Robert Merton’s “Retirement Finance” class since 2014, and and alongside President Emerita Susan Hockfield and Dean David Schmittlein in 2019. Christina teaches Domeyard’s case study at Harvard Business School and other universities. Christina was elected as a Member of the MIT Corporation, MIT’s Board of Trustees. She was elected Co-Chair of the Board of Invest in Girls in 2019. Christina also sits on the Board of Directors of The Financial Executives Alliance (FEA) Hedge Fund Group, drives entrepreneurship efforts at the MIT Sloan Boston Alumni Association (MIT SBAA), and serves on U.S. Non-Profit Boards Committee of 100 Women in Finance. Her work in finance earned her a spot on the Forbes 30 Under 30 and Boston Business Journal 40 Under 40 lists. She holds an S.B. in Management Science from MIT and is a CAIA Charterholder.

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About the talk

Selina Wang - Reporter - Bloomberg

Ananth Madhavan - Global Head of Research for ETFs and Index Investing - Blackrock

Keith Rabois - Managing Director - Khosla Ventures

Christina Qi - Co-Founder / Partner - Domeyard Fund

Zulfikar Ramzan - CTO - RSA

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So we're going to start off with a non Smart Oven. He's a global head of research for ETF and index investing at BlackRock. So let's give him a round of applause. Thank you, Selena. Thanks everybody. So you're having really great discussion just right before this and you know, there's been a lot of talk about asset managers using AI to help investors get better returns. So give us an overview of how they're using this technology and what driving these algorithms

how we think about AI United set of techniques designed to learn in an Adaptive way and then ultimately to mimic human behavior people can play a lot of the techniques used in AI machine learning and textual analysis and so on with a I am I think it's important to understand the asset managers going back to the 70s and 80s have been using computers using quantitative techniques to improve their return if there's no Especially if you knew about the use of these

techniques, I think what is new with availability of data the cost of acquiring and storing that data which is dramatically improved the kinds of data that's available and the computational power which allows us to do more with that data. So it's a continued investment in terms of upgrading our techniques trying to learn as much as we can and you know with the ultimate goal of providing investors with better returns any higher returns and also more risk control. So Vikram Pandit

said developments in technology could see 30% of banking jobs disappearing in the next few years. So do you see a I eventually replace a human stock Pickers or do you just see this augmenting the labor force for years as a managers have increasingly relied on automation as a way of scaling we can work for so that doesn't necessarily mean that the humans being replaced by artificial intelligence means that humans just do things that are better suited for humans big block trades and negotiating those

things the smaller trade 10,000 share trade in an octave text dog could be traded by Niagara. That's not that's very, that's not a problem in terms of the question about stop picking. And so I will I think it's useful to there's always going to be the human element. I don't think that's ever going to go away and I have to think that the techniques and the and the ability to pick stocks is enhanced by quantitative techniques, but you always need the human judgment. There is only to program their computers. Augmentin to work first at BlackRock. Are you singing

shrinking staff as your technology gets better. I'll go back to trading because it's been many years on a trading desk and you know when I was hired actually a Blackhawk Blackhawks predecessor be back in 2003. And then by the way bass in San Francisco, most of our flow was actually done on book by what we call voice mail pick up a phone or a chat and they wouldn't negotiate with a dealer and this is how the vast majority of our trading activity was done was 15 years ago or automated day of trading that doesn't mean that human Traders aren't important

just means they do less mechanical tasks. They do things that are more nuanced more more complex than Require more judgment the machines can do a lot of the the routine trading in an automated wave Morris control in a very rules given way that's that's consistent, you know across the globe and so we have both Wednesday than any group has been displaced to think in terms of portfolio management of the guts of asset management. There's a lot of room I think for again algorithms machine learning and AI automation to again streamline, the process

reduce errors reduce operating hours and again in power the portfolio managers to focus on more difficult the more complex tasks that require judgment. What about on the research side of things? How do they need to adapt you have algorithms that can analyze earnings and bank statements and seconds invested a particularly in Alpha Sigma Alpha side. I mean the side of our business where we are attempting to beat a benchmark. We've been investing for years decades really in the latest techniques and

maybe about 10 years ago. We started to really invest in in the collection of techniques. They were talking about today machine learning the sexual now, so these kinds of things indeed the capability to scan tens of thousands hundreds of thousands of documents extract information from that Reed images is it is useful in the at the same time. I think it's also worth pointing out that a lot of data is still available only monthly and quarterly GNP statistics things like that. So there are there very interesting things that people are doing with data

soap example, if you think about the the World Wide Web we have probably billion prices of stuff cameras, you know of two chairs, whatever posted on the internet every day and if you can track those those items, you know adjusting for the quality testing for the type. You might have more real-time indicator price movements then say the Consumer Price Index, which is published by the government based on survey methods of what's going on in this state. How far are we from that? I want to hear

the futurist William Gibson has his grade line says that the future is already here. It's just not evenly distributed. So it's say that particular but I just say that you know, a lot of it is already. There are people who are using very Advanced Techniques to try and enforce sentiments to learn a little bit more about what's going on in the economy. You know that those kinds of things are using satellite imagery they using a variety of sources of data that work there 10 years ago. So take us inside. What day is

being used to do inside BlackRock? I know you recently launched this evolved sweet. He talked a little bit about that. So, you know, one of the more popular sets of ETS tries to capture movements of sectors and that's important for investors like hedge funds. Christina and and inform others who are trying to make that's about things that are procyclical or you know or making bets about technology versus interview those kinds of things traditionally with the way sector funds have been built is that you

look at the the you look historically the revenue of companies in class by then. We took this idea and really try to do a forward-looking statements. We look through the 10 cases was a textual analysis of of the statements by companies about what businesses they would like to be at. So how come those statements are quite revealing because companies are fairly precise when they put something in writing about their intentions. So a company May announce that is going to go into

the foldable take business or talk about your driverless cars or whatever, but Look at the past revenues he look forward and then we do is which I thought was Innovative group companies in to these natural clusters where they they clustered together. Those groupings are found by the machine and a company may be more than one cluster. So we have a modern idea where for example Amazon has its web services business. It also is a retailer so it should be in both kinds of buckets. I think a lot of

interesting information. So for example of firms in it that are more anti-cancer and forms in a more focused on Diabetes, he knows how these things are revealed by the date of the data speaks to us. And then the part about adaptive learning is that you continue to learn you continue to read these texts and continuing to classify in a dynamic way these companies Revolve sweet is about a tool for investors, you know, you know Target particular best the more

more efficiently as of this is a pretty recent launch. How is the rollout been so far? How are you seeing people utilize it? I don't know if I could really really say something about how it's being used. But I would say that obviously they were there people that might want to time sectors they may use it to make a bet about whether we're going into a recession or not. So you might want to be in in relatively safe sectors. If you believe the opposite you might be in the gressive procyclical kind of sectors but

in the town, So what's black rocks Edge in all of this? Everybody has access to this technology are these algorithms being commoditize what makes black rock better experience. I mentioned that that we were early pioneers and quantitative investing and spending some of our funds have been around for 30 30 35 years. So we have a long history of using quantitative techniques to improve investment outcomes. The second one. I think it's a scale so, you know little less than six trillion dollars on the management. So there

is a lot of scale we can field a relatively big teams. And then the third element is is close ties to Academia two folks in Sanford Berkeley and in the MIT, of course and and not really around the country and the world and we've had a long history of Engagement. Academic researchers to better understand how these techniques can be used for free lunch. So you have to invest the time and have to get it right but those are the three things that I say are overshadowing the

traditional human run management around for a long time since the days of 2001 how how computer and you know, it's it's not happened yet. I do think that there was there was the cross the board a lot of interest in in the availability of new data at low cost and the ability to process that data. So I think that's one of the things that's transforming the industry. I do think that also to the other panels for me to speak. Do cyber-security or two of the use of AI techniques generally make things more efficient than it was a lot of interest even outside of the world of alpha in terms of just

saying can we can we make the process more efficient the bathroom and when can we deliver outcomes for our investors at lower cost? Can we do it more as with a greater risk control of more economies of scale? Those are all Asset Management. I think the industry is very much in the process of being transformed in that sense. What are some of the risks to more and more fun being managed by algorithms rather than human so when there is an event that triggers A sort of a rules-based fine, then all the sudden

you of trillions of dollars. I'll following potential and so yeah the risk you run is somebody has written some code where we don't fully understand how we work in some educator wear something unusual prices on reliable. And you know, we've seen various kinds of flash events and in those flash events, you know prices are not necessarily reliable and their Market structure is using, you know computers that are thinking that the information that they are getting that

some stocks are trading at Penney's when they were previously trading at $30 humans know that's not reliable and unplug the computer computers don't know that necessarily so I think we always have to be kept saying Two stage anywhere near where the computers can just run by themselves without you know, human beings keeping an eye on things the biggest Gap that you see right now between what you want and your role at Black Rock and what the technology is actually capable of doing that's a

good question coming in. You know, I feel weird at the early stages. I think everybody that I talk to at Black Rock and then car industry more generally senses the potential we've seen in the previous spintech away when internet banking which people forget we've already had a wave of fence at before this again, we have high expectations. I think we have to deliver on the promise and that's what's currently missing. So our keynote speaker talk to us talk about slight, you know.

FlightAware, I don't think it was a good good line. I mean, I think we need to actually deliver the results and I think we might have a hundred different signal some of them like the old old line very standard valuation signal be more black box theme machine learn signals. It should be eating those returns to particular signals is a difficult task. You got a Verizon. What's a nine months? I mean if your higher frequency fund office so you can learn faster and you can adapt more

dynamically, but if we if your investors have prizes of months or years any questions before I ask nothing else any questions from the audience bok choy I love you. Just repeat the question just in case people didn't hear it. So they could operate a little bit. How do you know. Be fully automated or or not? And and if they are fully automated what checks do we have on them? So the signals are not directly putting on a signal flow to purple a construction tools, which we don't necessarily need somebody in between that

we typically do and that's the guardrail in in the training context which which I mentioned a couple of times obviously. The Creator may take an order and put it in an algorithm in that algorithm is responsible for breaking it up into little pieces and placing orders and automatically select the venue. They automatically select execution prices and it will trade dynamically in response to intraday volatility and intraday volume and seeing what it's it's getting and if it's it's fits feels like it's for rates are too slow and speed up

the. Since those things are more fully automated. Typically those are actually offered by Brokers to us. So we use the broker Ellsworth by and large. Our experience has been an excellent with a broker algorithms and Brokers and term have probably a couple of Decades of experience of putting guard rails on those things and I used to work for investment Technology Group before I go in Black Rock and indeed we spend a lot of time making sure that occurs worked as expected and put Putting in safeguards that said that there is an in our business the famous

fat finger. Sometimes it was an elbow of a guy resting on the keyboard which sent off a bunch of extra zeros on the order and those things do happen. Sometimes less than 2 minutes. Do you find that some strategies or timelines work better with a fully automated automated system than others and if so, is there anything that you can share I would imagine that again machine Lord insights and so on are a compliment to more traditional signals in the training context which

again, I mentioned a couple of times obvious abuse algorithms are designed to execute during the day and you know for the most part that's what that's how they're deployed. Great Wall we are just about out of time. Thank you so much free time. Are great and so next we're going to have keys were boy. Do I know if he is a managing director at Coastal Adventures? He's been involved in a lot of companies that we use everyday like Square Aaron and PayPal and Linkedin and the list goes on. So as a former executive at

a number of fintech startups in an investor in several, where do you see the most refill opportunities to combine artificial intelligence and finance Knoxville TN sort of invented a couple things and later been used by lots of people including like a man in the loop sort of feedback on fraud where we basically just played a bunch of transactions of a screen hired hourly workers and they had red and green buttons and it is processed pattern over pattern and that's actually how

we stopped losing $10 a month. That's what I got you I'm fired. And so that does our 2000-2001. Losing $10 a month. The business strategy and we got like three months of cash and thousands more money. Other than that, everything is going great. Com from PayPal the combination of those and I want to get a software next Monster like some dotnet platform every possible the Frog problem with this technique as well as everybody now knows what are known as referred to the top shows knocked off to the tormented. To be just forgot to tighten it we're too busy being literally didn't file a patent on what's

Nana's capture who invented it to help them stop. The Russian Foster's to the combination is two things basically is what they take off and then Front problem reason why what other watch people watching bastard personal trainer your taxi driver and no I'm not really worried about that. We're just going to take all the technique similar to PayPal friends going to build it from scratch the right way and you never had a foul ball stats like we're so then today the way I think about it may

be somewhat different which is I don't think there's a hole in a stock that allows for an economic transformation by using that and the more the economic transformation. The more leverage you have any needs or distribution customer acquisition or operating cost somewhere. We can either bring down the price and service or Tell me to allows you to tell if your house in like 3 minutes online and cries and some of the quiddity to wear a normal person's large. The class Barber Shop across the world and it does it using pretty Advanced, you know, math

statistics data, but then customer doesn't care. They don't even know like we give the evaluation site on seeing if every house in United States basically and you can decide to accept the offer that I'd be very difficult without a lot of mouth be very difficult for us to do that without losing a lot of money when I'm off now, but then customer has no idea. It doesn't really care. We actually even dumb down like how we create evaluation and make it seem like more like humans doing the work you're seeing medical which is just an example, but metaphorically very applicable Financial

Services. You can today get a device like this to read your EKG and have it diagnosed instead of a cardiologist actually buy this machine learning and in fact, the FDA approved. So like literally cardiologist is base of the right 93% of the time. Is bright 33.5% the time so your choices of consumer you can for free get red by the machine or you can pay to have a doctor read your sort of EKGs. All of that are just like parts of a value proposition. We're swapping out something that needs to be done poorly or something that needs to be done

very expensively with you have something at the boy with no marginal cost. You said you were actually simplifying the language to the consumer to make it seem as if humans are behind it. Is that because you think there's some sort of bias to the average consumer about this concept of the algorithms are driving a lot of the decisions about this and I don't think there's any stupid personally but So you spoke about some of your experiences at square and PayPal and the thoughts I didn't say I was being applied. So, where are you seeing the most mature applications of the technology

you're saying in English, not large enough during a come to call the firm that patient is underwriting either consumers with good credit or with people who are arguably for the Christ by FICA and so you see the lending it's pretty obvious application or some regulatory complexities to do in Consumer Finance. That way if you see it in small business or company that were in the best during call fun box on that using data to provide loans and inefficient way using fairly Advanced Techniques, and yeah pretty aggressively investing in hiring

scientists and you were the chief operating officer at Square in 2013, right? So at that time, how are you thinking about applying a I was in the business and how far A long. Have you seen a gun and Rod that forgot company? I think like you'll see in contrast. The company is investing Warren using blockchain Sora Technologies across the business units or even the finance is like running a Watching Project part of the company from customer support to finance has to build something I'm watching.

Different sample, there's companies that it's it where they actually expect all the customers for people join SQL queries to get their own data and be able to do their own analysis. And I think that's a statement about a company's culture that everybody's going to be like quantitatively literate and I can be a very strong thing or there's a lot of debate last couple days because they just publish design you a letter about companies that force people to write as a way out of here debating things discussing things to siding thing as opposed to meetings and or PowerPoint and I think you know,

that's a very fine culture II. So I think we're you have a new technology that has potential is the company have to care about it and maybe you want to force every employee to kind of understand the technology at least of the base level. And generally are you seeing a lot of? Good use cases of blockchain as an investor internet. Let me hear some clear communication use cases that dated back. Actually, you're over decade before it commercial. It wasn't like quite as much of a

poem is really decided. What's the best possible application of watching and because of that it's really hard to fix the right protocol coin because each one has trade-offs like some are better for fundraising Texas application Stub would be better for scalable concurrent transaction torture application. And so we're both the open-source sort of software and applications are clogs. It's very difficult to predict. What one should do the master side. You can salmon in Dawson everything which is a reasonable hypothesis like

that. I can't figure out how to find every possible credible protocol that has a significant ecology team and just give them all money or one could say Hey, you know, I'm actually pretty smart and I can see the future and this application. Eventually happen. It's ridiculous a sand filter all the potential protocols and companies and startups by weather contribute significantly to the application histories from Health tag Finance. Summer intern Etc. Where do you see the biggest? Space between business needs and Technical capabilities when it comes to

machine learning and artificial intelligence filter things. So I'm sort of a Founder driven and duster. Should I start with like is this team unreasonable unreasonably strong in different ways and they're trying to tackle. Do they have an unfair Advantage basically and so I don't actually care about like the market on Market opportunity as much as I care about like is fix it. For example, if you'd invested in a lot of alternative energy companies are sustainable companies and that really doesn't do in Boston Mass

amount of money. My partner's you know, before me lost a lot of money a lot of money and I can clean Talk. However, if you'd said there's this crazy guy named Elon who talked about before we got fired he wants to build like a you know, I do automotive company. You did a lot of money regardless of what's around you can test it in that what price you paid so I thought that's why I think about it. Like is this founder going to accomplish something croat? Not like is there a missing opportunities David ramshaw? Give me an example of when I did do more of a top-down like now since which

is in cyber cyber security in insurance auto insurance cyber insurance and a top-down perspective. Useless to directly wrong and all these people are underwriting based upon either historical data center literally clipboard paste questionnaires. And that makes no sense in the modern world becoming increasingly more important for everybody just like this every day and so is like while the combination of this seems like there's a billion dollar company like to come by

your starter investing but that's very very rare and it's not clear to the public house exactly. They're being gathered and created so from an ethical standpoint, how do you think started sneezing make sure that they're minimizing any potential bias? A couple points. They're the most important thing I'm interested in is can you get access to data than better answer Starbucks don't have data and data are going to give it to you for Friday reason some good and some bad

for chickens and somewhat regular reason. So getting your hands on massive scale data, as a founder of the start of words in a duster is like by far the biggest like first step now, I like the way you know, we think about it is Is it better or worse than what humans do so for example, we funded a company several years ago pymetrics in New York that basically notches people applying for jobs with companies that are hiring using purely games. Like you played like he's eight games about 20 minutes and it tells you what jobs what companies are perfect for you. So large people it

works really well for things like investment Banks. I call metoxen Earhardt a class every year Mackenzie Harden crossover. You're being the federal and it turned out that The Oz people now works several large entities companies use this for like your on-campus recruiting either for mbas are under garage and it turned out it significantly reduced increased just by changing about increase the diversity and the acceptance rate at the same time for all of our customers. And so maybe the algorithms are biased but they're less by

his stuff they were doing the day before. So as long as you're making that progress, I think it's a good thing like the idea that you're going to have a completely neutral detached algorithm and data it probably wrong but that statement is it is it better or worse than what humans are doing and I've been doing it for a hundred years in a particular field and all that. You can get a benchmarks. The cardiologist. There's a pretty good way to figure out how good cardiologist are and what they do and so the FDA just said prove us prove to us that you're better as long as you do will prove it in the

same thing in Radiology. There's a pretty simple way to hang a grade the reading of an x-ray to getting certain FDA approval to have just all rooms read X-rays and actually pretty easy as long as you can see how will humans do a task. It's pretty easy to understand the ethical implications are some things. It's just hard to get a Baseline and that that's a little bit more complex how to get data. And now there's this Global today debate right now about a lot of large tech companies that have too much data on it's particularly Facebook in the Cambridge

analytica issue and the ability for advertisers to Target people very granularly. So, you know, what's your take on all of that? And is this whole era of move fast and break things from the start of perspective sort of over someone contrary to one may be conventional Look everybody knew that the state of gathering what's going on like a Google search on venturebeat or inside Facebook or TechCrunch like the idea or his dick Costello said, you know that I'm shocked that gambling is going on. There is nobody who was involved in technology. That didn't know what day did

Facebook was collecting for at least 7 to 10 years by Phil the one felt company I did that you crossed over and slide and we build applications on Facebook. And at one point we are massively popular Billy applications on MySpace and Facebook and we had all of this data times every Facebook user and you're actually ironically enough send a Tweet about this. We started missing you today to one day and say cicadas and is my apps like the one I actually ran my team and isn't everything all of misuse of the day. They actually targeted pro-consumer but violated Facebook

policies at the margin into they suspended our eyes. It's been biopsied call Top friends for like two months. So that's why they're bothering policing me other people, but the fact that obviously we had all these Facebook people and you're the journalist covered or I've got suspended, you know, why do you know that I've got suspended for slightly using things at the edge basically would show you who your top friends to friends were so you can see which is very similar to Cambridge was doing all of this is kind of You do a hell

of a lot better targeting a drink now that you can on Facebook like this is a dirty secret that anybody does customer acquisition. So, for example, if you want to figure out an income band, Jack Nails much better than Facebook can't do that on Facebook very well. You want it. Do you want Target by type of business can't do that face before you want to get I want to mail to Madison Square for squares. It's very very easy to Target in ways to Facebook can't do that politicians didn't know about this way is ridiculous.

The part that is a massive change that I think I started to need to calculate a little bit differently is a metaphor. I should have started using a couple years ago with his shirt. Pirates of the Navy silver most of my life looking Valatie was like the Pirates of famous Steve Jobs quotes about this and you know, we have a pirate flags at Apple and it was kind of to try to overthrow or improve Society in Silicon Valley was like the Creative Energy and your people root for the Pirates like watching movies were the pirates of the heroes actually of those kind of ironic if you think that

would actually do but once you once software sorate the world and like Andreessen Horowitz Framing and technology companies became more more important and more more powerful. It's very difficult act like the Pirates anymore when your actions of the Navy like it so you have the brute force in the Navy you'll have the resources of the Navy have to start acting like the Navy and I think there was some transformation sometime between 2010 and 15. We're more software became like the Navy than the Pirates but without necessarily all the companies realizing that are

the founders realizing that Why the recruiter so I just wanted to start out and ask why start a high frequency trading hedge fund. It's a very competitive environment eloquent, since they didn't know that we knew each other and then they just put us on the same. He knows the same event. And so this is amazing when you make it to MIT, they send a high school, you know seniors to MIT for weekends who preview and I was assigned by total random coincidence to Selena other hosts. When I was a student

there any way back to Salinas question just really good questions. Like why would you start a high frequency trading had fun when it's it's such a controversial industry obviously and it's very competitive. Like everyone says I look really good question and I'll actually go off of them actually keeps answer from which is really nice. I said how you know how in health care for it since it's actually not a lot of data available. It's hard to come across actually in finance with interesting was there was a . Of time back, you know, maybe six seven years ago. We were starting our business

where there was actually a lot more financial data available. Then the number of like want or high-frequency trading hedge funds out there. So if you ever want to feel like a celebrity by the way, what I eat when I did was actually we would go to these big Financial expos in Chicago and you would literally walk around people would line up to see you like it would literally lineups like talk to me. I'm About You by Christine and I'm selling, you know, this latest text your newsfeed better than the other five years ago being like we

just started our company. How do you know who we are, you know, because we were so knew back then but that just gives you a sign of just how how competitive it was in the dataspace already back then but they just weren't in a lot of people who were out there really applying. Into the you know, when two different usable signal in the financial space since we started it back then and then have kind of grown The Firm And we're still alive today. So really happy about that high-level at speaking, you know, this industry has really been thrust into the public light over the past few years

with publications of mainstream bucks, like like Flash Boys. So from your perspective, you started your junk yard before that. That book came out an interesting time and you know, there was a couple I think the book unfortunately like it does it did cause like to really big misconceptions in our industry which you know today is a little bit better with our understanding is conception is over the first one being that front-running even though all HIV from a few front-running which is not true actually a

lot of the people who buy order flowing through front-running, you know are the larger, you know, the larger that are here today. So so, you know, you can be hydrated and we don't do what Michael Lewis called front running. FrontRunner itself is illegal to do that today and it's about the other thing about, you know, you can buy order flow that's different in so just definition why the kind of confused the entire industry and definitely set us up for a lot of challenges moving forward. So like one instances when I go to conferences like

these, you know, people always ask me like, well, what are you doing in your industry to improve it? Like, you know what you're doing at the goal and someone hi Christina. I hate everything you're doing and you should be in jail. He looks like all kinds of crazy stuff like that. So we have to be able to represent the entire industry, even though we just so you know where I can throw it up in our space and yet here I am getting kcgn Citadel and and I have to defend myself just as much as they do. Have you seen the industry aside from the public.

Deductions are misconceptions. Have you seen the industry changed over the past five or so years? It's changed a lot of all let me tell you this. So for instance, like when we first started up when an investor what else goes like what's your advantage when we see different from all the other high frequency Traders out there and you know back then I would say one word answer I be like Steve and believe me and you know the same investor if they came up to us and said, what was your advantage and if I said speed he was actually laughing like that he or she has today speed is

the requirements of any qualms van really to kind of stay on top of things. And so, you know, you have to be good. If not better Risk Management Systems. You have to have a better training pipeline. You have a better idea decoration better technology. Everything has to be really on top of things in order to kind of stay afloat today. I was a couple of other changes actually just in from the pipeline. So today I say, you know, if you want to enter Finance Finance in school you want to study Computer science or mathematics right? Where is like 10 years ago usually study

traditional finance and you learn about Lido Key ratios and Fulton Market ratios and things like that. And so it's definitely the headphones basis just like change this well to like when people approach me and they just listen to I usually assume it's either crypto funds or like some kind of Quant today. That's just kind of the Assumption there is not a lot of you know terms of your lung only traditional fund on the chance of succeeding are very slim unless you have like four years of experience that leads me to my next question. I'm

going to play the role of investors. So in this world were Quant hedge funds are popping up all the time. How do you stay competitive and better now? That's a really good question. And this is something. You know what he wants me. Like, how do you stabilize and what do you recommend for start-up hedge funds out there? I was there a couple of things like one thing. Differently was Green Bay's Venture Capital back then and you know, five six years ago. Like I was almost almost unheard of early-stage VC funding and that bought us a lot of time and a lot of Runway to be able to do things.

Right? So we had to build up in his heart rate and ecosystem in our office from scratch and then definitely like any quarters feel like when people tell me like I'm starting my only have lysine $100,000 in our bank account. That's not enough. Right. So like are the legal cost of setting up our fun structure, which is sick different entity. That was like $300,000 just to Legal backbone of like a company. And then from there you have your gate data licenses or colocation. Your computer is your server is

everything combined and hiring all the time and getting an office all those operational expenses. That's like what 5 10 million dollars right there. So today in order to kind of get up and running definitely recommend in a raising some form of funding if you're going to be technical spawn in a company on the inside. And then definitely don't cut any Corners, like don't just think that you can self lawyer and start a fun that way because either you run into a lawn from will the SEC and within a say and it's all right, though. These were designed and created in house. How long did that

take? And did you need a limb and assume you have a very lean Workforce as well? So I'll give you a sense it took us two and a half years to launch the fun and those two and a half years were not easy at all. I mean we were and I was busy writing Venture Capital at the time and money in the fund. So LP GP Capital our company again, 15 was busy way to build order management system the date of food handlers like a lot of time and a lot of a lot of trial-and-error

because there weren't a lot of service writer. So, yeah, I mean It's our first penny, please do actually there was a company that came over and bacon PC phone and they came over to MIT after I graduated and they said hey, you know, what a bunch of start up here in five different flies for 3 minutes and I went up there and I said hi and Christina, you know, I heard of this type of ratings on and immediately the CEO of the VC firm there like wait time with us and I went I went home being like that was the worst talk I have ever given and I does I apologize my co-founders on slack as like, I'm sorry

guys, so then it was 11:30 p.m. And other Co called me and he says hi Christina can you can meet at my hotel and bring her to go counters and was like, oh God, it's like hotel and he was like, okay I'm going to invest and and that was that was it that was our first investment and and it's Avicii for a basic lease with Renren who have their own by SoftBank and we got a really great is the biggest fence a conductor in the world. We got a really great investor there just fine. 1. 3 minute pitch that went terribly and just to give

you guys some perspective as well. Like, you know, we have talked and it was pitched and failed hundred times over and that was the first time that it had succeeded, you know, and so it wasn't like an easy thing where I just went there one day and it's only worked it was a two-and-a-half-year process for me. So serendipitous would also needs hard work. So, you know more recently a lot of high-frequency trading funds have have had tough times middle of all time. Lee and expensive cost of keeping that technology in the network running so what sort of opportunity have you

found that a lot of these may be more mainstream investors have not and what do you make of the consolidation a good question as well? I'm so the opportunity that we actually look at when we started off as well as well today is like we may trade in foreign markets in products that other larger firms May Overlook just because like if you're creating team at like a Goldman Sachs, they're managing what like billions of dollars in assets under management and so they can't afford to look at some of the smaller Market or the more Niche markets out there because you know, they're not there

might not be in the following reminder being a player is out there and it's just not worth it worth the effort for them whereas for us to know because we can look into those other markets in the TV for us to enter and here we can make an additional even if it's an additional $20,000 a day like for us that's where it's at a larger from Life Coleman stock store. Like a black rock, you know, it may not be worth the effort that they have to take to to do that kind of stuff. So that's definitely one area that you know, we try to do a little bit differently and then the other, and I had to

make his like, you know, you probably have run a lot of headlines in the news these days about the training dead, you know, like all the firms are dying and I think the only common to have to say about that is actually I want dinner is dying. I mean actually Finance is moving towards context to like today if you can start a finance company, I just assumed I was dying and the underlying technology is still going to be it's going to go even more and more into Finance. Basically. The industry is maturing is what it is. So

can you give us a little dive into how you're applying artificial intelligence at domeyard and what the limitations of it are right now for professionals. So are really good at in finance today is areas of Finance where there's like a large datasets the like text Will news feed where you know, there's tons of news articles coming out day today and it's pretty easy to kind of aggravate that analyze it and figure out like what the keywords are and what the signals are from there. So that's really easy to do a lot of not just like us but even like

want firms will look at these and also there's even like this Russian your friends like ICP on a discretionary trading desk, even the most discretionary guys out there actually use some Automation in their strategies to all kinds of things out there. Do you say I in particular, you know, we'll look at like for a single generation we do use some, you know to look for singles will look at new neural nets for instance and then look at some texts as well, which is really exciting for us to but those are the main areas that we kind of use a little bit

of a high today. Limitations of these Machinery techniques, by the way, it is. Machine learning optimizes for a 3-foot rather than for leniency meaning. Song for us, you know, because every morning like this morning in the New York Stock Exchange we get what 350 million signal is the water data, and if you are not fast enough to process all my data within the same hour and to actually turn that into strategy within that same hour then then like, you know, you can't really see my visit in the space station right now still the speed issue. It will get better over time. I'm sure.

What's your ultimate vision for for domeyard one of your co-founders as set in in a news article that he sees your machine learning algorithms being applied in other areas even outside. If Inez some wondering how you're thinking about it because this was the area that we knew we had like an edge in but I'm now that we've kind of grown bigger and we've reached capacity and we talked about 11.5 billion dollars day now, so we are looking at things like let's do some low frequency Claus and we know we're selling our trading platform that we know. We're

lucky. We had created it back then we selling or trading platform. She's in the larger banks in in the Boston area right now and they're they're utilizing that to do some of the some of their trading strategies as well. So yeah, we can come up and text me and then later use, you know, if the date ever becomes available like in the healthcare space for instance, we can always go and do research in in that area as well. So I think The key for us if we keeping an open mind, you never want to be close-minded to just you know, just high frequency trading because it may not be around

forever. So right I mean so in the next 5 years, what do you think the high-frequency trading industry is going to look like as these Technologies continue to accelerate? I think I think it will continue to kind of stay around for quite some time. And so yeah, that's only one area that you know, I think you should because we are starting to see who liked the winners are in our space, you know, so like a bird to acquiring kcg with a huge acquisition North Face HRT aquariums. I was like that's exciting and also but there's definitely space for startups as well as he's

really good environment right now. It depends on how you view it. Very expensive, but then it's also good because there's so many sources of data and there's so many different vendors out there that cater to your industry now, so so kind of good and bad at the same time that you mentioned earlier that you assume people starting new funds that is going to be related to crypto and more than a hundred of them have been created since the whole Bitcoin surge. So, how are you looking at that spaced you ever consider investing in it for good

question. So for some reason like everyday someone reaches out to me on LinkedIn thing like I Christine I'm starting a crypto hedge fund and here's what we're doing and and you know, it's hard to not roll my eyes and feel like a because they're they're everywhere and all I would you know stays just the same thing like you can't cut any Corners when you're doing a special Lee it is a very very new feels like I've been busy creating maybe 10 years ago right now is crypto still like it the same thing as a very Newfield there's there's a lot of people who are entering it and we are today and

that could be good about to let me have one of our Do you season and he tells me the same things are going to win her out there and basically maybe one of them will succeed and that's about it. And so that's all I need is like okay. Yeah, we do my mind like a little bit of crypto trading here and there and just because as a young, you know Millennial manager, I think it would be a shame not to understand the underlying concept underlying technology behind it behind

it going behind a dream. It's going to be around for quite some time. And so that's important for anybody. I think in this room to understand no matter what you think of crypto whether he can get the freaking bubble doesn't matter like you should probably do some diligence in terms of understanding how it works because there are going to be more and more start up his nose in a double or triple that you're the hedge funds basically. Have any questions we have 5 minutes left right there.

We since you've developed lots of new technology. Can you put out some of the stuff in open source in the ideas will come to you and all kinds of shapes and forms some things and open source, and we actually work with some schools as well on open source projects. And those are those are areas that have benefited us significantly and also just in the community in general. It's not easy to do open source. I'll let you know that I mean cuz we we have budget constraints. We're not a giant Goldman Sachs, right? But we still try to do that because I think it's

in our stage now. I think it's important to be able to not only give back to the community and show them a little bit of the glimpse of what the black box kind of looks like but also in terms of helping our students in the future people are interested community and understand a little bit more. So yeah, that's the only thing that were we were trying to do more and I guess I got time for one more question is coming to you. You mentioned earlier that it took about two years to get funding a what did you do in that time to prepare and to make

sure that your Tech was advancing in that you are advancing to the company is a team like a question so we didn't have any money so and so and we don't come from Rich families feel like I was spying with Ramen from like the Shaws nearby and like that's the flu how he lived for two years. It was not easy and sit during that time going from prepared from the ground up on the technology side first. So we look out of the service writers out there we decided there was nothing out there that cater to our training and exactly what we wanted. And so we've built it ourselves

orders and takes him something to be built the date of food handlers. We've all the servers me configured everything and basically we had by the two and a half years by that time. We had everything going where we could kind of run a strategy. Exactly, like hook up with like to see me or another stained and basically run get something to get data from exchange get a signal and then going to make us think basically place an order to the seems like all that whole entire pipeline. We had within those like 2 years or so and so. That was the good thing

about it was a cradle out of my pee and I think that I pee was what excited like a bunch of stuff. Thank you so much Christina for the dynamic conversation. Who introduced Bill fikar ramzan? Who is the CTO at RSA? He's had sort of a marathon weekend with a big RSA conference going on. So very excited to have him here today. I go to the recipe I said about a whole week. So I was a kosher job. I can make it down after my shower. I should be able to squeeze Us in before

going back to the, or say works with companies in practically every industry. So just heard of broadly speaking. How are you seeing AI being applied across these broad swath of company is to protect security markets and he's certainly no government and send text to be two of the biggest areas where we see Lanza man. I'll be Securities a premium in both of those areas. If it was a result. There's an active interest that we are seeing is that you're generally speaking. We live in a world where people are collecting increasing

amounts of data and RSA is part or part of the MCU because it Storage company now part of del Alba to the major technology company in so many ways and we're noticing is that when you'll be able to do anything useful with it to avoid having a d landfill and really quit mordidita Lake it's critical to be able to find meaningful insights from that data and AI machine learning and the most meaningful in second. In fact, it's kind of interesting to see this Resurgence and talk about AI even though many of the underlying Concepts have

been studied carefully. I place like I might be in Stanford and others for a fight a long time to find an actual production environments to clean meaningful insights. They have proven its value of the challenges of deploying AI systems at large-scale working with some of the biggest companies in the world. Hundred twenty million people use assistance on a regular basis to clean Mini Blind Side. I would have noticed is that machine learning is pretty easily got caught up in the latest type in the latest. You're my neural network is bigger than your neural networking and so on and so forth is really

what's amazing is there's a disproportionate amount of time spent on the classifier side to side as supposed to be underlined side of gathering the right data. So, you know him very much garbage. Can you add grapes? And so we look at the right data set? Can you collect the right high-fidelity the next question becomes what are the meaningful questions to ask if you did what kind of features do you want to extract and that's if you want to start up for trying to develop AI Bay solutions. They tell me about all their grade

PhD in machine learning and AI what's often missing is the person that domain expertise to say this is what you need to look at and a broad level on this date. Set up with the good data good features and then classifier in that order. Where is most places they do it in reverse while he will hit all the time limit the classifier and then the features and then they Place less importance on the date of the right date on the right features. It almost doesn't matter. What are you pick? I think most of the Commercial off-the-shelf classifieds you can find are going to perform with in a fraction or

percentage each other. And in fact, if those kisses were better off getting a suit or pacifier one that you can understand where you can understand how the results would arrive and are able to fix issues because ultimately data is never pervert in the real world. And because they did have her perfect in the real world, you may have to make adjustments and other aspects of backflow in that pipeline to account for that unless you understand about the end of that pipeline the more difficult it is to make those adjustments to get successful results. That would be applied in real-world

environments. Now a I can bring a lot of efficiencies to companies but it can also open up vulnerabilities. So what are the potential and possibilities for companies at to have their AI algorithms to be hot? This is a huge issue one, which I believe is not being careful to look at these days. So they ultimately when you collect a lot of data if I drive inside from it what day I can still give you some type of model that tries to explain that data and allows you to make important decisions based on that Data Systems will not designed to function and adversarial environments

location point I'll just pay per view recently where they show that by putting some duct tape and very colorful places on street signs. You could. Driving cars without too much effort because ultimately these models are are assuming that the date is good until we get word about it. Some people collect all the data and they try to run an album. They don't fully understand if I'm a doctor and I want to really talk to business if I can add a bit of noise to your data in the right places. There's a good chance you'll not take the noise. More importantly when the models come out the other

end, you won't be able to detect what actually went wrong along the way and we start talking about using AI scale especially in areas like fintech where there's a lot of money involved. I'll certainly I could see situations where a one more than a doctor with a little bit of effort can recall on a packet and probably find ways to benefit from it financially right now. Keep jumping idle problem whenever somebody out to ruin your future. Is it security I think data manipulation in in poisoning of data will be one of the biggest issues will face now we're seeing very early and we see a text

on ransomware where somebody will go into environmental encrypt all the computers in that environment and then demand a ransom payment in the form of Bitcoin typically to decrypt a car find a reminder pick up that uses technology to develop in Agra fee to make that secure for the attacker. So it would be an example of cyber security technique being used for What is an ultimately what we're finding is that for doctors realized value in data and people are willing to pay money to regain access to the data and in many cases we talked about fintech but a lot of our customers are hospitals in for

them. If a critical hospital system is taken down because of ransomware that can be it a true life reading impact. We are seeing is blurring of boundaries between the digital and physical in a way that can be quite concerning. So, how are you seeing these threat actors actually use artificial intelligence. And is that an increasing concern as they can come up with adversarial algorithms? I can sort of learn things on the fly from there often someone lazy. I said, we're not lady to buy we're doing good things in their time. It's so what they typically fighter do is extend the minimal

effort. They need to succeed in whatever Endeavor to find a juice in other words to try to make money. Now, what is there so many basic vulnerabilities and systems that you don't need to do anything fancy cases. For example, we saw a small scale start after developed technology to self captures. We talked about earlier. Those are those in a really annoying things are going to type in like letters and numbers when you try to log in online in for a long time. The premise behind cop shows was that it was supposed to be easy for human the salt and hard for a computer. So over time

did he come harder and harder for humans to solve like they've been I can never tell is that a 6 or 9 or you know, what's funny is it now computers have gotten much better at solving in human offer you a sack service if you can you certain amount of money per month. I will actually solve all those couches for you in this is great for people like scammers in front of harvest accounts with scale and the pricing is pretty compelling. So, you know, right after I want to get them a Isaac service, we already seen that in practice and of course, he's a toy examples, but

between campus at least two examples end up involving Get worse over time the only thing better. Annual cost of cyberattacks is projected to reach 6 trillion dollars or 10% of the world's GDP in just a few years and Equifax alone expose half the data of all US citizens could better AI have to have stopped stop that I believe I would often times would make me one simple thing that company forgot to do in the right way to ultimately led to the bridge but what mistake people make when they think about creatures that it's not about just the week itself doesn't intrusion

that precedes the breach and it's the intrusion they can ultimately lead to Newbridge another word think of this way and I can you are trying to build a secure bank right? You can build for the bank. But the bank robbers goal is not to get in the front or the bank. In fact, no matter how hard you try their flight going to find a way in because your Bank is open for business at some point, but the actress goal is to get to the money that's in the fault and you can take quite some time between when somebody first get in to when they get out with the most critical assets which means there was a

window of opportunity to even if you make a mistake early on and you don't have the right front door in place. There's a lot of opportunity to attack activity throughout the course of the attack life cycle and we are seeing a lot of use of AI technician that Ramen Upon returning the point in Arlington TX. NR say we've been able to detect in one case. I know at least one situation where we work with the foreign government who is being attacked by other foreign government and we were able to detect that using him some pretty sophisticated conceptual ideas that

complicated riddles and texting pretty profound things. Just Windows take nice alone. So that's why I think they could be a lot of value is in doing a better job of Predictive Analytics. Today is a problem where people get so focused on the threat part of it, but beneath the threat you got to start off with understanding your broader risk portfolio understand. What is it right after going after why would they go after it and what impact it could make your business analytics lend itself to take me from AI machine learning when you talk about can I predict where my risk going to lie my

environment and that enables our customers and turn to be able to do the right kind of thing. So they don't let infant budget of time, but they can find a way to prioritize what matters most of their organization and there's a huge opportunity. I believe they are for a guy to play a major role. Why does it take organization so long to actually deal with the problem once when there's an initial signal and we were just talkin earlier and you mentioned the average time is is like 9 months a year. Yeah. I know. It's amazing between the time that a company is first intrude upon to when they

first recognized the intrusion occurred. The average time is 270 days not depends on who you ask and how you measure but I wouldn't put it this way if somebody was living in your house for 9 months and you didn't know it would be a pretty shocking thing to consider the physical world. Right? And so when we look in the digital world, it seems to happen all the time now. I think the reason for that is that historical people thought about security issues. They've been so focused on prevention. Can I stop the bad guys from getting in and they haven't we focus more on detection and response. Can I

really understand? What's happening across the entire sweat life cycle we need to have visibility is foundation. Is necessary but not sufficient give you the day that you need but you have to be able to bring meaningful insights from the visibility so you can actually make decisions that improve your security posture. And that's where a machine-learning really really powerful role. The big part of your role is also working with a lot of your partners and clients and she going out the market needs. So what are you hearing? What's the biggest concern right

now? Turn I think people in a generally speaking mean when you buzz words come out in a one of the biggest concerns at least security-wise understand the implications that buzz word in their environment machine learning passwords in last 2 years older technology deployment you a lot of our customers are concerned about how many automated decision-making that comes with that and whether or not you can truly gain efficiencies you want without creating Middleton catastrophic Ripple effects when you don't understand the full cycle, and of course we're seeing a

lot of customers asking about things like blockchain, for example, I am contacting cryptocurrencies & Beyond and again, The thinking about is not just how do I look at the technology and isolation is how do I bring this technology into my environment? But in the safe and sane fashion, why don't reduce the risks that I'm not fully aware of and it having fully create a competent controls around watching since you mentioned it has sort of been touted as a Panacea to a lot of problems and protecting digital identity. I can say as a reporter with given all those Cambridge analytica issues.

I've received tons of pictures from startup saying hey like our startup going to be doing social network on the blockchain and we would have stopped something like Cambridge analytica from happening. What's the real story of blockchain is taking over a i as the magical pixie dust of the future that's going to solve every problem every say I will address to know world hunger and solve for world peace and save the whales and cancer. I think the same thing is now being said of blockchain. My perspective is that you're blocking was really created for Enabling digital currencies will be

late to work cryptocurrencies because crypto is now the New Concept which is going around for four decades and from the security and then we're seeing the other community overtake return the reviews for a long time. So that that's got me The First Noel ransome set the record straight crypto does not mean currencies. It really means. So I said if it's a step further, it's when you look at some of the properties that matter in the context of digital currencies. You may need distribution. You want me to be some level of privacy. Give

me what will be calling me to go anywhere. You can create a transaction and have it live on forever were someone can always look at that record. And the interesting thing is that I'm not aware of any other applications it really love her and she'll need all those same exact properties. In fact, if you look at something as simple as distribution right don't want Trudy centralization today and I have a book online two things. I want I want to make sure the transaction go through quickly. So I'm not sitting there for four days before the transactions through a

number two with the book doesn't get to me. I won't be able to call somebody up or send an email from 4 to make sure something happens. We want in many cases centralization in our lives because it facilitates many many African what we do I'm so when you start to remove the need for decentralisation all of a sudden you don't need blockchain anymore. Address many of those problems with much simpler mechanism, for example, a database probably solve 90% of the applications are using blockchain or digital signature for now over 40 years

can also be used to address many of the same kind of problems. And so I get concerns, I think yes, maybe you can use lock install certain problems, but I'd be willing to bet in 99% of cases maybe even a hundred percent. You can tell this exact same problems using take me sitting around for decades with much less complexity on blockchain now implementing and in many of his company's it's probably a step back from what you been came out in the cryptocurrency room. I want to ask about the European privacy regulations gdpr that are

going into effect soon. Is that going to be game-changing for protecting user privacy? And should we apply those same regulations in our country. The perception is it's going to really be a fundamental game changers. What's Uniqua gdpr is twofold one is that gdpr is not about the territory the companies in it's about the person who was impacted to any data of a European Union citizen falls under the Aegis of GDP are not cause I'm a lawyer but that's my understanding of it. Which means that even if you are a US base company with the date of the European Union citizen have to worry

anymore sample. Some of our customers are the government government what we deal with all of our customers, are you end up living abroad after the men who have now European Union citizenship to all of a sudden Become that way the second keeping the gdpr is done is impose a substantial fine for companies that failed to meet us regulations and the fines are anywhere from 4% of their annual revenue 220 million euros, whichever is higher and that's a stencil fine for getting things wrong. There's also a

third element which is that the requirements are fairly strong from sample is reporting requirement to require that you report an incident within 72 hours when it occurs and I told you earlier that most people don't even know for 9 month baby breech 72 hours is a long way from that and so a lot of our customers are deeply concerned about their ability to comply and the fines that can result in that complication. Do you think we should apply that in the u.s. For u.s. Citizens? I think he was some form of gdpr is healthier brought I think is going to be applied for me anyway because if you're a

major reorganization, you're going to be multinational because you have any new data which means to some degree you have to comply across-the-board. I really think I'm going to try to have different compliance policies for different geodes. Have one umbrella policy for the most part because it's easier to manage One Pilots been managing 17. So I think we're going to see a situation regardless of what the regulatory landscape looks like here. People will follow through to a common denominator and more importantly a lot of the technology companies like RSA that developed Technologies for

dressings problem. We're going to be looking for the address of security issue at play here. I'm compliance to me is an artifact of security to get the security posture. Right? You'll get some degree of compliance or hopefully a full debrief compliant as a byproduct maybe with some adjustments or around that and so I think it'd make sure the Bender's are doing the right thing. Everybody will then tend to benefit from that result. We have time for one question if there any takers I'm curious about the relationship between private companies like RSA

academics and intelligence Community for the government. How what is a relationship as far as advancing technology and research and how's that played out over the years is that the federal government offer the most part we've generally had very positive interactions and it's for the most part. I'll talk about some of the newest positive change in a moment in the sense that you know, a lot of the standards of the using photography today. I were essentially approved by people like a National Institute and has an LG Ernest. I didn't really good job complete engaging the private sector in

the public sector together with Academia to develop to buy kind of Standards a lot of our customers. For example leverage something called the nist cybersecurity framework as their foundation for the security strategy. That's playing Reynaldo published finest involve about 4,000 organization. Industry groups and individuals in the process of developing the document and I think about a forty to fifty percent of Arceus customers use the Misty SF as their primary vehicle for developing. The Securities fatty acids test case should have been around some of the regulatory issues with things I

can cook shinsou. For example, if you're the apple versus the FBI issue that came up a number of months ago that if she was not Reincarnation of an issue coming up over and over again, when you start talking about security Technologies, I'll send the government want exceptional access those Technologies an exceptional access to the underlying data. We are safe and very simple. I don't believe it's possible to make a system for one person and keep it secure for everyone else and see if you were trying to weaken it for the government to access data. You're not going to succeed in making

it strong enough for everyone else. So our philosophies always been that we would never intentionally we can a system for one party philosophy. I believe most vendors in the security space in the private sector and to think that way Is where we have been a we put up a fuss and all that. We have created campaigns around providing a point of view for the private sector and just as passionately of the RSA conference with 50,000 people at the conference who shut up clock to the Dodge people from all over the world, including the public-sector and private-sector and we host a conference and

he started it actually 27 years ago and it was the entire conference exact problem, which was if you wanted to make sure that all the key actors who were worried about cyber security issues were discussing issues together and trying to move the world forward in the collaborative fashion by then trying to be antagonistic. Thank you so much for your time and fortunately we only had enough time to sort of touch on the surface level, but again really interesting conversation and thank you everyone for sticking around.

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April 21, 2020
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ai, ai responsibly, automation, data mining, deep learning, graph deep learning, machine learning

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