>> Announcer: Live from Madrid, Spain. It's theCUBE! Covering HBE Discover Madrid 2017. Brought to you by Hewlett Packard Enterprise. >> Calls off just Rebecca. Hi, everybody, welcome back to Madrid. This is theCUBE, the leader in live tech coverage. My name is Dave Vellante. I'm here with my cohost, Peter Burris. Day two of HPE Discover Madrid, 2017. Beena Ammanath is here. She's the Global Vice President of Big Data AI and new tech innovation at Hewlett Packard Enterprise. Beena, welcome to theCUBE, it's great to have you on. >> Thank you, Dave. >> Dave: First time on The Cube, right? >> Yes, thank you Dave, thank you Peter. I'm very glad to be here. >> Ah, you're very welcome. So, let's talk about what Hewlett Packard Enterprise is doing in AI, and you're new to the company, they brought you in. Why did Hewlett Packard tap your expertise? >> I think a lot of it is based on my previous experience and, honestly there is so much buzz going on with AI, and the hype around it, right? There is so much that we need to do with AI. There's so much potential and we are not tapping into it as much as we should. That was one of the big reasons and especially what Hewlett Packard Enterprise is doing now. We are going through this transformation, we can help our customers start on their AI journey, help them build out end to end solutions with AI, which is going to be one of my biggest charters. >> Well when we were young and started in this business, AI was the buzz, in the early to mid eighties. >> Beena: Yes. >> And that was the fifth or sixth time around with AI. >> Oh, yeah, yeah. >> That was 40 years ago. >> Yeah. >> It just obviously died, the processing power wasn't there, and I guess the data. >> Beena: Yeah. >> Why AI, why now? >> Yeah, so you know, I'll date myself here. When I was doing my undergrad, post-grad, we had AI as one of the courses and nobody wanted to do it because it was considered this very futuristic thing, never going to happen. Self-driving cars, boom. Personalized ads, even that was considered so hypothetical because we didn't have the compute, we didn't have the processing power, we didn't have the amount of data accessible to us. >> The acquisition of data was harder, the compute power wasn't there, So it was just, it was just always a science project. >> It was always a science project, it was a research, it was more ideas and it wasn't doable, but today, with the advances we've seen with cheap storage, easy access to compute, the whole game has changed. Lot of things we could only dream about is now becoming real, we are able to experiment more. And speaking to what you were saying earlier, AI has been through this hype cycle several times. If you think back, AI, the term itself was coined in 1956, and then we see those hype cycles when there is massive investment and there is nothing delivered, then it wanes down, so the AI winters keep happening. And now, I think it's again on a rise, but this time, we are actually seeing results. We are seeing self-driving cars, we are seeing first-rise marketing taken to a whole new level. We are seeing drones making deliveries, right? But if you think about it, when you started the business, you've seen about AI too right? It's still the narrow-intelligence part, right? It's not a super-intelligence or general-intelligence that scale that we've reached out to, and I think, given what I know about the analytic techniques available today or even the compute power available today, we are still going to be dabbling around in narrow-intelligence for at least the next few years, before we expand out to the next level. >> So that raises an interesting issue because, I first heard about AI back in the '70s reading Flagibon's fifth Generation Systems book, which, by then, they were talking about multiple generations of AI that supposedly already happened, but AI has, for technical reasons, for technology, for the acquisitions, has disappointed. Now, it's not disappointing, but there's still this perception of how much change is coming, and the impact of a change and let's talk about the people's side of this, Because the success of AI is going to be very closely tied to whether or not social groups abandon it because it doesn't deliver what was expected, or the impacts are greater in ways that weren't anticipated. Yeah. >> What's the people side of this change, the innovation, the social changes side? >> Yeah, yeah. So I like to look back at history, history always gives us an indication of where technology is taking us. And if you look back at the early 19th century, actually, the early 20th century when the steam engine was invented, right? What did it do? It enabled humans to expand their physical abilities. To move things, to drive things forward, so it was increasing the human muscle-power. And that whole industrial revolution that happened around that time with steam engine and the automation of lot of work that was being done by humans manually, right? And we see a similar revolution happening now because it's fundamentally changing how we work, how economies are made, and that causes a lot of fear and insecurities and, who knows, our jobs might be replaced or changed over the next few years, we don't know because this technology is coming at us very fast. The reason is because there are so many companies investing so heavily in AI. What that makes us do is it accelerates the development of the technology, it comes at us smarter and faster. And we are not prepared for it, like if you look back at our whole lives, right? I'm talking about a time when I was in my twenties and just thinking about AI, it was mythical and futuristic, and now, today, there are self-driving cars. It's happening in our lifetime where things have changed so rapidly and we don't know what it's going to look like 20 years from now. The piece that I am optimistic about is, unlike a number of luminaries who are spelling doom of mankind and elimination of human race and jobs and so much more, for me, it seems like, look, at the end of the day, we are building AI. We have the power to shape it the way we want. The fear exists because there is so much unknown. And it is also because it's a select few group of people who are shaping AI. So, how do we actually get more people involved? How do we truly democratize AI so that we get different view points? Like, should a computer scientist be building an AI product in isolation, without full partnership from a lawyer or for similar domain products? The domain experts have to be involved. And today that's not happening. So we don't, and if you're building... And I stick to legal just because something I can relate to is if a lawyer is actively involved in building an AI Legal product, he or she knows all the checks and balances we need to put in place so that AI doesn't go rogue. When a pure computer science person is driving that product and building the product, he or she may not be aware of all the checks and balances. And we may not put the right guard rails in place to prevent that program from going rogue. At the end of the day, AI is something that we own, and we should be able to build it in a way with the right guard rails in place. And if you look at, we are all so dependent on our phones, and what is that? That is AI today. But we are not afraid of it, we use it, we leverage it. And that's how I think AI will be 20, 30 years from now. Is really helping us extend our brain power, right? Remove the monotonous tasks we have to do and help us be more creative and really elevate the human aspects of all of us. >> So, let's carry that through. >> Beena: Yeah. >> So you mentioned the industrial revolution? >> Beena: Yeah. >> Machines have always replaced humans at certain tasks. >> Peter: There's always been substitution. >> Always. >> M-hm. >> But, for the first time, it's happening with cognitive tasks. >> M-hm. >> So, people get scared. And then you quote the statistics, median income in the United States has dropped since the late '90s from $55,000 down to $50,000. >> Yeah. >> Part of that is you can see it, and you know there aren't paper hangers on billboards anymore, or barely there are. Or you go the airports and kiosks have replaced tickets issuers. Hopefully, they can replace-- (laughing) And so people are concerned, as you rightly pointed out. But you also said that we have the opportunity to shape this so the answer, many of us feel, is education around creativity, how to combine different inputs to create value, but many people are afraid, they say, "Let's stop progress." That's not gonna happen. >> Right, yes. >> We know that, so what has to happen from a socio-economic, a public policy standpoint in order to create those borders that you talked about? >> Right, right. I think education itself has to fundamentally change where we infuse more creativity into the education system, where we start to allow it to be more focused on the science or math aspect, which is where you go for computer scientists, but you need that human aspect like built out in all of us, right? And so, but it's also an opportunity for us to leverage AI to make our education better. So, more personalized education. But, from a social aspect, I think one of the things that's missing is really the policy aspect. We don't know, this technology is coming at us so fast, we don't have all the policies figured out. We are building out the policies as the technology evolves. And, that is kind of causing that fear of friction, so to speak. So, I think there needs to be this group, or the governments actually need to take more ownership and start putting in those guard rails into place from a policy perspective and that needs to come from the industry themselves, right? >> Yeah, yeah, yeah. >> There needs to be these thought leaders. I think everybody who is scared of AI should be starting to take an active role to understand it and drive this policy forward. >> Well, it has to be bipartisan too. >> Beena: Yes. >> Which, right now, doesn't look too-- >> Well, whatever the partisan is 'cause in other areas it's not just bipartisan like it is in the US but, coming back to this question, I've got a couple quick questions for you. One is that you mentioned earlier that the computer scientists probably should not be the one that's necessarily making a decision about a legal issue. It suggests that there is going to be a renaissance of cross-disciplinary skills required within a, certainly within computing, so, for example, the people that are best at describing how human interactions evolve and maintain, might be philosophers, which gets turned into law. Talk a little bit about the renaissance of the whole promise of cross-discipline thinking in computing because we're attacking new kinds of problems that just aren't algorithmic. >> Exactly and you need to have deep domain experts deeply involved in building out these AI products, which is kind of a gap today, so I think you're absolutely right. >> So second thing is, related to that, is we've done some research and we're in the midst right now of a pretty sizeable project on envisioning what we call, or the needs and how it will be structured, we call Systems of Agency, so, you observe the collection of the data, the turning the data into value through big data, and then to have a consequential action in the real world, we think there are three different ways that's gonna happen. I won't bore you right now. >> Yeah, yeah. >> But really, we're asking these systems to do something on behalf of the brand. >> Ah. >> And increasingly do something in a complex, human-centered environment. >> Yes. >> What does, and so effectively the agents for the brand. We know how to distribute authority. I'm sorry, we know how to distribute data and we know how to distribute processing; how do we think about distributing authority? >> Mmm. >> Using AI, is that something people are starting to think about in your estimation, as we think about the people problems associated with this? >> I think so. I think people are beginning to think about it. There's a lot of investments going on, not only in the technology development part, but also the human side of things. It just doesn't get as much publicity as the technology piece does, right? A robot beating somebody at a goal is much more newsworthy than-- >> Doesn't have huge-- >> Yeah. >> Moral implications for something else. So I've got one more question. >> Dave: Well, wait, in a narrow sense, would fraud detection be an example of distributing authority? >> No, because, well, I'll ask you. Is fraud detection an example of distributing authority? >> It's narrow. >> Beena: Yeah. >> It's somebody, it's a machine making a decision not to fulfill a transaction. >> Right. But the machine is not making a decision to bring an indictment against someone >> Beena: Exactly. >> And were they doing fraud? So all the machine's doing is-- >> Flagging. >> Is seeing a pattern that might indicate a problem and taking a prophylactic step to avoid it, the machine is not declaring fraud. >> No, and there are two things to it, right? The machine, before it declares fraud, it's being trained, it's being built by a human, it's being trained by human, right? Before it declares, before it goes into production and declares fraud, there has been a lot of training done by human where they're saying yes, no, this is right, this is wrong. So that training is crucial, that comes from humans, and also once this is in production, there's a human in the loop who's watching it. >> Peter: Who still has agency rights. >> Exactly. So the human is still there. >> So I've got one more question, one more question. And that other question is, at least in the US, 'cause AI is software, at least in the US, most software is covered under copyright law. Which means what software does is a speech act, which has implications for whether or not you can go after a company because their software did something wrong. >> M-hm, m-hm. >> AI as an agent can't be a speech act. There's gotta be some other remediation, we have to expect more from brands that deploy this. How is that going to evolve in your estimation? >> I think the policy part, that's where it becomes more important, right? And if you recently heard the news of a robot being given citizenship, I mean, besides the marketing and hype, what does that entail? Making us question fundamental things and the policy aspect has to cover a lot of new scenarios which we just haven't had to think about-- >> Peter: Right. >> In our whole life, right? It's just arising a lot of new scenarios that are going to make us create new policies around it. >> Dave: So, I mean, this is a very interesting discussion and when I hear it I think about what can humans do that machines can't do? And you go back, it wasn't long ago that machines couldn't climb stairs. >> Beena: Yeah, yeah, yeah, they can do-- >> Yeah, now they can, sort of. >> Gymnastics. >> Yeah, right. Okay, so. I don't know, do you think in those terms. >> Yes. >> I mean, there's empathy. There's maybe negotiation, there's things like, ya know, decisions on a jury that require a human. >> Oh yes, I'll give you the simplest one. What it cannot do, even today, it can write music, which you probably see-- >> Sure. >> But, AI still can't tell a joke. (Dave laughs) It can't write a joke because-- >> Peter: It doesn't know irony. >> It doesn't know, it doesn't understand sarcasm. And it doesn't really have that human aspect of connecting with people, and taking conversations forward, like just talking to you, I have something called an intuition or perception which helps me guide this conversation. A machine can't do that. It's just black and white, it goes by data. >> Dave: Strange, yes. >> It's strange. >> Responses. >> Yes. >> So, I always struggled with the term Artificial Intelligence. I feel like machine intelligence is more-- >> Yeah. >> More accurate. >> I don't struggle with the artificial, I struggle with the intelligence. >> Beena: Yes, it's how you define intelligence. >> Alright, we have to leave it there. Last word, on a, let's bring it back to Discover 2018. >> Beena: Yes. >> Tie it into your future vision. >> Oh, yes, I am so excited to be here and be, and I don't know if you've had a chance to walk through the floors but we're doing some amazing things with AI, with Big Data, and really looking forward to helping our customers start and execute on their AI journeys. >> Beena, thanks very much for coming in theCUBE. >> Thank you. >> It was great to meet you. Alright, keep it right there, everybody. We'll be right back with our next guest, Dave Vellante. From Peter Burris, live from HPE Discover, Madrid 2018. You're watching theCUBE. (light music)