>>from Miami >>Beach, Florida It's the Q covering a Cronus Global Cyber >>Summit 2019. Brought to you by a Cronus. >>Welcome to the Qi. We are in Miami, Florida, for the Cronus Global Cyber Summit. 2019 John for your host of the Cube. We're here for two days of coverage around cybersecurity and the impact to the enterprise in society in a great guest here to kick off the event. Linda Babcock, professor of economics at Carnegie Mellon University, author of the book, Ask for It, and she has a new book she's working on, and we'll get into that. Thanks for joining me. Thanks for coming on. >>Really happy to be here. >>Thanks. So Carnegie Mellon. Great. Great. Uh, University. They stole a bunch of people when I was in school, in the computer science department. Very well known for that as well. Economics, math, machine learning. I was good stuff there. What's going on in Carnegie Mellon? What's new in your world? >>Well, it's just actually just a great place to be because of the focus on interdisciplinary work. You know, problems in the world don't come as disciplines. They come with multiple perspectives needed and So it's just a place where people can flourish, attack ideas from all kinds of angles. And so it's a really great >>one of the things I hear a lot about, and we cover a lot about the the skills gap. Certainly this is Maur job openings than there are jobs and interesting. A lot of the jobs that are new haven't been skilled, important in the classic university setting. So a lot of these jobs, like cybersecurity, cloud computing, Blockchain, crypto economic token economics, all kind of have a maths economic steam to him. So you know your computer science, you got economics and policy. I seem to be the key areas around from these new skills and challenges. Way faces a society which your take on all this >>Well, actually, there's a lot going on in this area at Carnegie Mellon. Actually, the economics group at Carnegie Mellon ISS is been proposing a new major that really focuses on this interface between economics, machine learning and technology. And I think it's going to train our students just for the next generation of problems that the world of tech is gonna have. So it's very exciting. >>So let's talk about your book. Ask for it. Okay. Um, it's not a new book that's been around for a while, but you give a talk here. What's what's the talking talking track here at the event? >>Yeah, so I have a couple of themes of research, and it focuses on women's Berries to advancement in organizations. And so most of the work that I did with this book and my first book, Women Don't Ask, was looking about how men and women approached negotiation differently. And kind of the bottom line is that women are what less likely to negotiate than men over all kinds of things, like pay like opportunities for advancement like the next promotion. And it really harms them in the workplace because men are always out there asking for it and organizations reward that. And so the book is was really about shedding light on this disparity and what organizations could do about it and what women can do about it themselves, how they can learn to negotiate more effectively. >>What did you learn when you were writing the book around? Some of the use cases of best practices that women were doing in the field was it. Maura aggressive style has a more collaborative. You're seeing a lot more solidarity amongst women themselves, and men are getting involved. A lot of companies are kind of talking the game summer walking, the talk. What the big findings that you've learned >>well, I'd say that the approach is that women use are a lot different than the approaches that menus. And it's because our world lets men do a lot of different things. It lets them engage in a cooperative way, lets them be very competitive. But our world has a very narrow view about what's acceptable behavior for women. I often call it a tight rope because women are kind of balancing that they need to go out and assert themselves. But they have to do it in a way that our side, a society finds acceptable, and that that tight rope constrains women and doesn't allow them to be their authentic Selves on DSO. It makes it difficult for women to navigate that. What's your >>take on the the balancing of being aggressive and the pressure companies have to, you know, keep the women population certainly pipeline in tech. We see it all the time and the whole me to thing and the pressure goes on because norms were forming, right? So is there any new data that you can share around how, with norms and for forming and what men can do? Particularly, I get this question a lot, and I always ask myself, What am I doing? Can I do something different? Because I want to be inclusive and I want to do the right thing. But sometimes I don't know what to do. >>Yeah, of course. And it's really important that men get involved in this conversation as allies and, like you said, sometimes men but don't know what to do because they feel like maybe they don't have standing to be in the conversation when it's about women and weigh all need men, his allies. If women are gonna try to reach equality, ATT's some point. But the new data really suggests negotiation may be playing a role. The work that show Sandberg lean in, But the newest work that we have shows that actually the day to day things that happen at work that's holding women back. So let me tell you about that. So what we find is if you think about your calendar and what you do all day there a task that you can classify as being promotable, that is, they're really your core job. Responsibility there noticed, rewarded. But there's glass of other things that happen in your organization that are often below the surface that are important to dio valued but actually not rewarded. And what our research finds is that men spend much more time than women at the tasks that are these promotable task that rewarded women spend much more time than men on these tasks that we call non promotable that are not rewarded. And it's really holding women back. And how men can help is that the reason that women are doing these tasks is because everyone is asking them to do these tasks. And so what men can do is start asking men to do some of these things that are important but yet not rewarded because the portfolio's now are really out of balance and women are really shouldering the burden of these tasks disproportionately. >>So get on the wave of the promotional off the promotional oriented things that Maura and the man can come and pick up the slack on some of the things that were delegated to the women because they could order the kitchen food or whatever >>or help others with their work. Someone has to hire the summer intern. Someone has to organize events. Someone has to resolve underlying conflicts. Those are all really important things. Women get tasked with them, and that really doesn't allow them to focus on their core job responsibilities. And so men can step up to the blade, stop, do it, start doing their fair share of that work, and really then allow women to reach their full >>potential. I've been thinking a lot about this lately around how collaboration software, how collaborative teams. You started to see the big successful coming like Amazon to pizza team concept. Smaller teams, Team Orient. If you're doing it, you're in a teen. These things go. You've given you get so I think it's probably a better environment. Is that happening or no? It's >>unclear how teams kind of shake out for women in this setting, because there's actually some research that shows when a team produces an output and the supervisor trying to figure out, like who really made the output? Who was the valued player on the team. They often overvalue the contributions of men and undervalued the contributions of women. So actually, team projects can be problematic if women don't get their fair share of >>bias. Is everywhere >>biases everywhere. And you know it's not that people are trying discriminate against women. It's just that it's a subconscious, implicit bias and so affects our judgments in ways that we don't even realize. >>It's actually probably amplifies it. You know, the game are gaining a lot of things on digital indigenous communities. We see a lot where people are hiding behind their avatars. Yeah, that's also pretty bad environment. So we've been doing a lot of thinking and reporting around communities and data. I want to get your thoughts is I never really probed at this. But is there any economic incentives? And after you're an economics professor, you seeing things like crypto economics and tokens and all kinds of new things is a potential path towards creating an incentive system that's cutting edge what's progressive thinking around any kind of incentive systems for organizations or individuals. >>Well, when you think about incentives and maybe an economist, I think about those a lot, and I emerged that with my work on various to women's advancement, I think incentives is one area that you can actually play a big role. And that is that Organizational leaders should be incentive fied incentivized to see that they have equal advancement for their male and female employees in their workforce. Because if they don't it means they're losing out on this potential that women have, that they aren't able to fully be productive. And so that's, I think, the place. I think that incentives can really be important, >>a great leader and he said, and I'm quoting him. But I feel the same way says. Our incentive is business. Get a better outcome with them. We include women, give data, goes Yeah, we make software and have people that use our software with women I don't wanna have. So I'm like, Oh, that makes a lot of sense. Biases should be in there. Four Women for women by women for women >>and women spend more money as consumers than men. And so having women on teams allows them to see perspectives that men may not see, and so it can really add two new innovative thinking that hadn't been there before by including women. >>Well, I'm excited that this there's a little bit of movement in tech we're starting to see, certainly in venture capital, starting to see a lot more when you come into the board room work to do. But I think there's a nice sign that there's more jobs that are computer related that aren't just coding. That's male dominant pretty much now and still still is for a while. But there's a lot more skills, all kinds of range now in computer science. It's interesting. How is that affecting some of the new pipeline ing? >>Yeah, well, I think the good news is that there are is increasing levels of women's attainment in stem fields. And so there are more and more female workers entering the labor market today. Way just have to make sure that those workers are valued and feel included when they do doing tech companies. Otherwise they will leave because what happens unfortunately, sometimes in tech is it doesn't feel inclusive for women. And the quick rate for women in tech is over over twice the rate for men, and some of the reasons are is they're not feeling valued in their positions. They're not seeing their advancement. And so with this new wave of female workers, we have to make sure that those workplaces are ready to accept them and include them. >>That's great. Well, ask for it is a great book. I went through it and it's great handbook. I learned a lot. It really is a handbook around. Just standing up and taken what you can. You got some new, but you got a new book you're working on. What's that gonna look like? What if some of the themes in the new book >>Yeah. So the new book is on these promotable tasks, and the way I like to think about it is there's so much attention toe work, life balance, you know? How do you manage both of those with your career, your family? How does that work? But our work actually focuses on work, work, balance, and what remains is paying attention to the things that you do at work. Making sure that those things that you're doing are the things that are most valuable for your employer and are gonna be most valuable for your career. So it's a really different focus on the day to day ways that you spend your time at work and how that can propel women to the next level. >>That's awesome, Linda. Thanks for coming. I appreciate it. What do you think of the event here? Cronies? Global cyber security summit. >>Well, I got to say it's not my typical event, but I'm having a good time learning more about what's happening in the tech industry today. >>Cyber protection, Certainly a cutting edge issue. And certainly on the East Coast in Washington D certainly with national defense and all kinds of things happening, Ransomware is a big topic that kicked around here absolutely getting taken out like, Oh, my God. Yeah. Bitcoin in return for taking your systems out, >>all kinds of new stuff to add to my tool kit. >>Great to have you on. Thanks for your insight. Thanks for sharing. Appreciate it. I'm John for here at the Cube. We're here in Miami Beach for the Cronus Cyber Protection Conference. Thank you for watching

>> (narrator) From around the globe it's "theCUBE." With digital coverage of OutSystems-NextStep 2020. Brought to you by OutSystems. >> I'm Stu Miniman. And welcome back to thecubes coverage of OutSystems-NextStep. Of course, one of the items that we've been talking a lot in the industry is about how artificial intelligence machine learning are helping people as we go beyond what really human scale can do. And we need to be able to do things more machine scale to help us really dig into this topic. Happy to welcome to the program. First time, guest Antonio Alegria. He is the head of artificial intelligence at OutSystems. Antonio. Thanks so much for joining us. >> Thank you Stu. I'm really happy to be here and really talk a little bit about what we're doing at OutSystems to help our customers and how we're leveraging AI to get to those goals. >> ( Stu) Wonderful. So I saw ahead of the event, a short video that you did and talked about extreme agility with no limits. So, you know, before we dig into the product itself, maybe if you could just, you know, how should we be thinking about AI? You know, there's a broad spectrum, is that, you know, machine learning that there's various components in there, you listened to the big analyst firms, you know, the journey it's big steps and something that is pretty broad. So when we're talking about AI, you know, what does that mean to you? What does that mean to your customers? >> Yeah, so AI OutSystems really speaks to the vision and the core strategy we have for our product. Which is, you know, if you saw the keynote, you know, we talk about, you know, really enabling every company, even those that, you know, that had existed for decades, perhaps have a lot of legacy to become, you know, leading elite cloud software development companies. And really can develop digital solutions at scale really easily. But one thing we see, and then this is a big statistic. One of the things that limits CIO's the most, nowadays is really the lack of talent. Lack of, you know, engineering and software engineering, you know, ability and people that can do that. And there's a statistic that was reported by wall street journal. I saw it recently, perhaps last year. That said that according to federal jobs data in the U S, by the end of 2020 there would be about a million unfilled IET and software development jobs available, right. So there's this big problem. All of these companies really need to scale, really need to invest in digital systems. And so our belief at OutSystems, we've already been abstracting, and we've been focusing on automating as much as possible, the software development tools and applications that we use. We've already seen amazing stories of people coming from different backgrounds, really starting to develop really leading edge applications. And we want to take this to the next level. And we believe that artificial intelligence with machine learning, but also with other AI technologies that we're also taking advantage of can really help us get to a next stage of productivity. So from 10 X productivity to 100 X productivity. And we believe AI plays a role in three ways. We believe AI by learning from all of this data that we now collect in terms of, you know, the projects that are being developed. We are essentially trying to embed a tech lead, so to speak inside a product. And a tech lead that can help developers by guiding them, guiding the most junior ones, by automating some of the boring, repetitive tasks where by validating their work, making sure that they are using the best practices, making sure that it helps them as they scale to refactor their code to automatically design their architectures, things like that. >> (Stu) Wonderful . Antonio. Goncalo, stated it quite clearly in the interview that I had with him, it's really about enabling that next, you know, 10 million developers. We know that there is that skill gap, as you said, and you know, everybody right now, how can I do more? How can I react faster? So that's where, you know, the machine learning artificial intelligence should be able to help. So bring us inside. I know the platform itself has had, you know, guidance and the whole movement. You know, what we used to call low code. Was about simplifying things and allowing people to, you know, build faster. So bring us inside the product, you know, what are the enhancements? What are the new pieces? Some of the key items. >> Yeah. So one interesting thing, and I think one thing that I think OutSystems is really proud of being able to achieve is, if you look at how OutSystems has been using a AI within the platform. We started with introducing AI assistance within the, our software development environment, service studio, right? And so this capability we've been iterating it a lot. We've been evolving it, and now it's really able to accelerate significantly and guide novices, but also help pros dealing through the software development process and coding. By essentially.... and trying to infer and understanding their context and trying to infer their intent, and then automating the steps afterwards. And we do this by suggesting you the most likely, let's say function or code piece that you will need. But then at the next step, which we're introducing this year even better, which is we're trying to autofill most of the, let's say the variables and all of that, and the data flow that you need to collect. And so you get a very delightful, frictionless experience as you are coding. So you're closer to the business value even more than before. Now, this was just the first step. What you're seeing now and what we're announcing, and we're showing at this next step that we showed at the keynote, is that we are trying to fuse.... starting to fuse AI across the OutSystems products and across the software development life cycle. So we took this core technology that we use to guide developers and assist and automate their work. And we use the same capability to help developers, tech leads and architects to analyze the code. Learning from the bad patterns that exist learning from and receiving runtime information about crashes and performance. And inside the product that we call Architecture Dashboard. We're really able to give recommendations to these architects and tech leads, where should they evolve and improve their code. And we're using AI, refusing AI in this product in two very specific ways. Now that we are releasing today. Which is one, is to automatically collect and design and define the architecture. So we call this automated architecture discovery. So if you have a very large factory, you can imagine, you know, have lots of different modules, lots of different applications. And if you need to go and manually have to label everything. So this is a front end, this is a backend. That would take a lot of time. So we use machine learning. Learning from what architects have already done in the past and classifying their architecture. And we can map out your architecture completely, automatically, which is really powerful. Then we also use our AI engine to analyze your factory. And we can detect the best opportunities for refactoring. So refactoring is one of the top problems and the top smells and technical debt problems that large factories have, right? So we can completely identify and pinpoint what are these opportunities for refactoring and we guide you through it. We tell you, okay, all of these hundreds of functions and logic patterns that we see in your code, you could de-refactor this into a single function, and you can save a lots and lots of code. Because you know, the best code, the fastest code, the easiest to maintain is the code you don't write, you don't have. So we're trying to really eliminate crack from these factories, with these capabilities. >> (Stu) Well. >> It's fascinating. You're absolutely right. I'm, curious, you know, I think back to some of the earliest interactions I had with things, they'd give you guidance, you know, spellcheckers, grammar check. How much does the AI that you work on, does it learn what's specific for my organization and my preferences? Is there any community learning over time? Because there are industry best practices out there that are super valuable, but, you know, we saw in the SAS wave. When I can customize things myself, we learn over time. So how does that play into kind of today in the roadmap for AI that you're building? >> That's a great question. So our AI, let's say technology that we used, it actually uses two different, big kinds of AI. So we use machine learning definitely to learn from the community, what are the best practices and what are the most common patterns that people use. So we use that to guide developers, but also to validate and analyze their code. But then we also use automated reasoning. So this is more logic based, reasoning based AI. And we pair these two technologies to really create a system that is able to learn from data, but also be able to reason it at a higher order, about what are good practices and kind of reach conclusions from there and learn new things from there. Now we started by applying these technologies to more of the community data and kind of standard best practices, but our vision is to more and more start learning specifically, and allowing tech leads and architects even, in the future to tailor these engines of AI, perhaps to suggest these are the best practices for my factory. These patterns perhaps are good best practices in general. But in my factory, I do not want to use them because I have some specificities for compliance or something like that. And our vision is that, architects and tech leads can just provide just a few examples of what they like and what they don't like. And the engine just automatically learns and gets tailored to their own environment. >> (Stu) Oh, important that you're able to, you know, have the customers move things forward to the direction that makes sense on their end. I'm also curious, you talk about, you know, what partnerships OutSystems has out there, you know, being able to tie into things like what the public cloud is doing, lots of industry collaboration. So, you know, how does health systems fit into the kind of the broader AI ecosystem? >> Yeah. So one thing I did not mention, and to your point is, so we have kind of two complimentary visions and strategies for AI. So one of them is we really want to improve our own product, improve the automation in the product in the abstraction by using AI, together with great user experience and the best programming language for software automation. Right? So that's one, that's what we generally call AI assistant development and infusing AI across the software development life cycle. The other one is we also believe that, you know, true elite cloud software companies that create frictionless experiences, one of the things that they use to really be super competitive and create these frictionless experiences is that they can themselves use AI and machine learning. To automate processes, create really, really delightful experiences. So we're also investing and we've shown. And we're launching and announcing at NextStep. We've just shown this at the keynote. One tool that we call the machine learning builder, ML builder. So this essentially speaks to the fact that, you know, a lot of companies do not have access to data science talent. They really struggle to adopt machine learning. Like just one out of 10 companies are able to go and put AI in production. So we're essentially abstracting also that. We're also increasing the productivity for customers to implement AI and machine learning. We use partners behind the scenes and cloud providers for the core technology, with automated machine learning and all of that. But we abstract all of the experience. So developers can essentially just pick the data they have already inside the AI systems platform. And they want to just select. I want to train this machine learning model to predict this field, just click, click, click. And it runs dozens of experiments, selects the best algorithms transforms the data for you without you needing to have a lot of data science experience. And then you can just drag and drop into platform. Integrate in your application. And you're good to go. >> (Stu) Well. Sounds, you know, phenomenal. You mentioned data scientists. We talked about that the skill gap. Do you have any statistics, you know, is this helping people, you know, hire faster, lower the bar to entry for people to get on board, you know, increase productivity, what kind of hero numbers do your customers typically, you know, how do they measure success? >> Yeah. So we know that in, for machine learning adoption at companies, we know that, sorry, this is one of the top challenges that they have, right? So companies do not, it's not only that they do not have the expertise to implement machine learning in their products and their applications. They don't even have a good understanding of what are the use cases in order the technology opportunities for them to apply. Right? So this has been listed by lots of different surveys that this is the top problem. These are the two of the top problems that companies have to adopt AI's. Access to skill, data science skill, and understanding of the use case. And that's exactly what we're trying to kind of package up in a very easy to use product, where you can see the use cases you have available. You just select your data. You just click train, you do not need to know the nitty gritty details. And for us, a measure of success is that we've seen customers that are starting to experiment with ML builder. Is that in just a day or a few days, they can iterate over several machine learning models and put them in production. We have customers that have, you know, no machine learning models in production ever. And they just now have two. And they're starting to automate processes. They're starting to innovate with business. And that for us is we've seen as kind of the measure of success for businesses. Initially, what they want to do is they want to do POC's, and they want to experiment and they want to get to production, start getting to field for it and iterate. >> (Stu) From a product standpoint, is the AI just infused in, or there are additional licensing, you know, how do customers, you know, take advantage of it? What's the impact on that from the relationships without systems? >> Yeah. So for AI and machine learning that is fused into our product. And for automation, validation and guidance, there's, you know, no extra charges, just part of the product is what we believe is kind of a core building block in a core service for everything we do in our product. For machine learning services and components that customers can use to.... in their own applications. We allow you to integrate with cloud providers and the billing is done separately. And that's something that we're working towards and building great technical partnerships and exploring other avenues for deeper integration, so that developers and customers do not really have to worry about those things as well. >> (Stu) Yeah. Well, it's such a great way to really democratize the use of this technology platform that they're used to. They start doing it. What's general feedback from your customers? Do they just like, "Oh, it's there." "I started playing with it." "It's super easy, it makes it better." Are there any concerns or pushback? Have we gotten beyond that? What do you hear? Any good customer examples you can share as to general adoption? >> Yeah. So as I said, as we reduce the friction for adopting these technologies. We've seen one thing that's very interesting. So we have a few customers that are, for example, more in the logistics side of industry and vertical. And so they have a more conservative management, like they take time to adopt. They're more of a laggard in adopting these kinds of technologies. The business is more skeptical. Doesn't want to spend a lot of time playing around, right. And once they saw what they could do with a platform, they quickly did a proof of concept. They showed to the business and the business had lots of ideas. So they just started interacting a lot more with IT. Which is something we see with OutSystems platform, not just for AI machine learning, but generally in the digital transformation. Is when the IT can start really being very agile and iterating and innovating. And they start collaborating a lot with the business. And so what we see is, customers asking us for even more. So customers want more use cases to be supported like this. Customers also the ones that are more mature, that already have their centers of excellence, and they have their data scientists for example. They want to understand how they can also bring in perhaps, their use of very specialized tools. How can they integrate that into the platform so that, you know, for certain use cases, developers can very quickly train their own models, but so specialized data science teams can also bring in and developers can integrate their models easily and put them into production. Which is one of the big barriers. We see in a lot of companies, people working on year long projects, they develop the models, but they struggle to get them to production. And so we really want to focus on the whole end to end journey. Either you're building everything within the AI platform, or you're bringing it from a specialized pro tool. We want to make that whole journey frictionless and smooth. >> ( STU) Antonio, final question I have for you. Of course, this space we're seeing maturing, you know, rapid new technologies out there. Give us a little look forward. What should we be expecting to see from OutSystems or things even a little broader as you look at your partner ecosystem over kind of the next six, 12, 18 months? >> Yeah. So what... We're going to continue to see a trend, I think from the cloud service providers of democratization of the AI services. So this is during, just starting to advanced and accelerate. As these providers started packaging. It's like, what our system is also doing, starting to packaging some specific well-defined use cases. And then making the journey for training these models and deploying super simple. That's one thing that's continued to ramp up. And we're going to move from a AI services, more focused on cognitive pre-trained models, right. That, which is kind of the status quo. To custom AI models based on your data. That's kind of the trend we're going to start seeing in that, OutSystems also pushing forward. Generally from the AI and machine learning application and technology side of thing. I think one thing that we are leading on is that you know, machine learning and deep learning is definitely one of the big drivers for the innovation that we're seeing in AI. But you're start seeing more and more what is called hybrid AI. Which is taking machine learning and database artificial intelligence with more logic based automated reasoning techniques in pairing these two to really create systems that are able to operate at a really higher level. A higher cognitive level, which is what OutSystems is investing internally in terms of research and development. And with partnerships with institutions like Carnegie Mellon University, and such as that. >> (Stu) Wonderful. Antonio, who doesn't want, you know, a tech expert sitting next to them, helping get rid of some of the repetitive, boring things or challenges. Thank you so much for sharing the updates. >> (Antonio) Thank you Stu Congratulations on your progress And definitely look forward to hearing more in the future. >> (Antonio) Thank you Stu. Have a good day. >> (Stu) All right. >> Stay tuned for more from OutSystems NextStep. I'm Stu Miniman. And thank you for watching the "theCUBE."

>>from around the globe. It's the cue with digital coverage of out systems. Next Step 2020 Brought to you by out systems. I'm stupid, man. And welcome back to the cubes Coverage of out systems Next step course. One of the items that we've been talking a lot in the industry is about how artificial intelligence, machine learning or helping people is. We go beyond what really human scale can do and we need to be ableto do things more machine scale. Help us really dig into this topic. Happy to welcome to the program First time guest Antonio Alegria. He is the head of artificial intelligence at out systems. Tonio, thanks so much for joining us. >>Thank you. So I'm really happy to be here and and really talk a little bit about what? We're doing it out systems to help our customers and our leverage eai to get to those goals. >>Wonderful. So I I saw ahead of the event a short video that you did and talked about extreme agility with no limits. So, you know, before we drink, dig into the product itself. Maybe if you could just how should we be thinking about a I you know, there's broad spectrum. Is that machine learning that there's various components in there? Listen to the big analyst firms. You know, the journey. It's big steps and something that that is pretty broad. So when we're talking about A I, you know, what does that mean to you? What does that mean to your customers? >>Eso So AI out systems really speaks to division and the core strategy we have for our product, which is, you know, if you saw the keynote, no, we talk about no, really enabling every company, even those that you know, that existed for decades, perhaps have a lot of legacy to become. You know, leading elite cloud software development companies and really can develop digital solutions at scale really easily. But one thing we see and then this is a big statistic. One of the things that limits limits CEOs the most nowadays is really the lack of town lack of engineering, a softer engineering, you know, ability and people that that that could do that. And there's a statistic that was reported by The Wall Street Journal. I saw it recently, perhaps last year, that said that according to federal jobs dating the U. S. By the end of 2. 2020 there would be about a million unfilled I E. T s after development jobs available. Right? So there's this big problem All of these companies really need to scale, really need to invest in digital systems and so horribly fed out systems. We've already been abstracting and we've been focusing automating as much as possible the softer development tools and applications that use. We've already seen amazing stories of people coming from different backgrounds really starting to develop, really leading edge applications. And we want to take this to the next level. And we believe that artificial intelligence with machine learning but also with other AI technologies that were also taking advantage of can really help us get to a next stage of productivity. So from 10 x productivity to 100 x productivity and we believe AI plays a rolling three ways. We believe II by learning from all of this data that we not collect in terms of, you know, projects are being developed. We're essentially trying to embed a tech lead, so to speak, inside a product and attack Lee that can help developers by guiding them got in the most junior ones by automating some of the boring, repetitive tasks were by validating their work. Making sure that they're using the best practice is making sure that it helps them as they scale to re factor on their code to automatically designed architectures. Things like that >>Wonderful. Antonio Gonzalo stated it quite clearly in the interview that I had with him. It's really about enabling that next you know, 10 million developers. We know that there is that skill gap, as you said, and you know everybody right now how can I do more? How can I react faster? Eso that's where you know, the machine learning artificial intelligence should be able to help. So bring us inside. I know the platform itself has had, you know, guidance and and the whole movement. You know, what we used to call low code was about simplifying things and allowing people to, you know, build faster. So bring us inside the product. You know what? The enhancements? One of the new pieces. Some of the key key items, >>Yes, So 11 interesting thing. And I think one thing that I think out system is really proud of being able to achieve is if you look at how out system has been using a AI within the platform. We started with introducing AI assistance within the Our Software Development Environment Service studio. Right? And so this capability, we've been generating it a lot. We've been evolving it, and now it's really able to accelerate significantly and guide novices, but also help pros dealing through software development process and coding by essentially trying to infer understanding their context and trying to infer their intent and then automating the steps afterwards. And we do this by suggesting you the most likely let's say function or or code p sexual one you need. But then, at the next step, which we're introducing this year, even better, which is we're trying to auto fill most of them. Let's see the variables and all of that in the data flow that you need to collect. And so you get a very delightful frictionless experience as you are coating, so you're closer to the business value even more than before. Now this is the This was just the first step, what you're seeing now and what we're announcing, and we're showing up at this next step that we show that the keynote is that we're trying to fuse starting to fuse AI across the out systems products and across this after development life cycle. So he took this core technology that we used to guide developers and assistant automate their work. Um, and we use the same capability to help developers. Tech leads an architect's to analyze the code, learning from the bad patterns that exist, learning from and receiving runtime information about crashes and performance and inside the product recall architecture, dashboard were really able to give recommendations to these architects and tech leads. Where should they evolve and improve their code? And we're using AI refusing AI in this product into very specific ways. Now that we're releasing today, which is one is to automatically collect and design and defined the architecture. So we call this automated architecture discovery. So if you have a very large factory, you can imagine, you know have lots of different modules, lots of different applications, and if you need to go and manually have to label everything so this is ah, front, and this is the back end. That would take a lot of time. So we use machine learning, learning from what architects have already done in the past, classifying their architecture. And we can map out your architecture completely automatically, which is really powerful. Then we also use our AI engine to analyze your factory and weaken detect the best opportunities for re factoring. Sorry. Factoring is one of the top problems in the top smells and technical depth problems that large factories have. Right, So we can completely identify and pinpoint. What are these opportunities for re factory and we guide you through it, which held you okay, all of these hundreds of functions and logic patterns that we see in your code Could you re factor this into a single function and you can save a lots and lots of code because, as you know, the best code the fastest coast easiest to maintain is the Cody. Don't ride. You don't have. So we're trying to really eliminate Kurt from these factories with these kids ability. >>Well, it's fascinating. You're absolutely right. I'm curious. You know, I think back to some of the earliest interactions I had with things that give you guys spell checkers. Grammar check. How much does the AI that you work on. Does it learn what specific for my organization in my preferences? Is there any community learning over time? Because there are industry breast pack that best practices out there that are super valuable. But, you know, we saw in the SAS wave when I can customize things myself were learned over time. So how does that play into kind of today in the road map for a I that you're building >>that? That's a good question. So our AI let's say technology that we use it actually uses to two different big kinds of AI. So we use machine learning definitely to learn from the community. What are the best practices and what are the most common pattern that people use? So we use that to guide developers, but also to validate and analyze their code. But then we also use automated reasoning. So this is more logic based reasoning based AI and repair these two technologies to really create a system that is able to learn from data but also be able to reason at a higher order about what are good practices and kind of reach conclusions from there and learn new things from there now. We started by applying these technologies to more of the community data and kind of standard best practices. But our vision is to more and more start learning specifically and allowing tech leads an architect even in the future. To Taylor. These engines of AI, perhaps to suggest these are the best practices for my factory. These patterns perhaps, are good best practices in general. But in my factory, I do not want to use them because I have some specificities for compliance or something like that. And our vision is that architects and techniques can just provide just a few examples of what they like and what they don't like in the engine just automatically learns and gets tailor to their own environment. >>So important that you're able to, uh, you know, have the customers move things forward in the direction that makes sense on their end. I'm also curious. You talk about, um, you know what what partnerships out systems has out there, you know, being able to tie into things like what the public cloud is doing. Lots of industry collaboration. So how does health system fit into the kind of the broader ai ecosystem. >>Yes. So one thing I did not mention and to your point is eso were have kind of to, um Teoh Complementary visions and strategies for a I. So one of them is we really want to improve our own product, improve the automation in the product in the abstraction by using AI together with great user experience and the best programming language for software on automation. Right, So that's one. That's what we generally call AI assisted development. And if using AI across this software development life cycle, the other one is We also believe that you know, true elite cloud software companies that create frictionless experiences. One of the things that they used to really be super competitive and create this frictionless experiences is that they can themselves use AI and machine learning to to automate processes created really, really delightful experiences. So we're also investing and we've shown and we're launching, announcing that next step we just showed this at at the keynote one tool that we call the machine learning builder ml builder. So this essentially speaks to the fact that you know, a lot of companies do not have access to data science talent. They really struggle to adopt machine learning. Like just one out of 10 companies are able to go and put a I in production. So we're essentially abstracting also that were also increasing the productivity for you for customers to implement an AI and machine learning we use. We use partners behind the scenes and cloud providers for the core technology with automated machine learning and all of that. But we abstract all of the experience so developers can essentially just pick of the data they have already in the inside the all systems platform, and they want to just select. I want to trade this machine learning model to predict this field, just quickly click and it runs dozens of experiments, selects the best algorithms, transforms that the data for you without you needing to have a lot of data science experience. And then you can just drag and drop in the platform integrating your application. And you're good to go. >>Well, it sounds comes Ah, you know, phenomenal. You mentioned data scientists. We talked about that. The skill gap. Do you have any statistics? You know? Is this helping people you know? Higher, Faster. Lower the bar the entry for people to get on board, you know, increased productivity. What kind of hero numbers do your customers typically, you know, how do they measure success? >>Yes, So we know that in for machine learning adoption at cos we know that. Sorry, This is one of the top challenges that they have, right? So companies do not. It's not only that they do not have the expertise to implement machine learning at in their products in their applications. They don't even have a good understanding of what are the use cases in or out of the technology opportunities for them to apply. Right? So this has been listed by lots of different surveys that this is the top problem. These other 22 of the top problems that companies have to adopt a ice has access to skilled. They decided skill, understanding of the use case. And that's exactly what we're trying to kind of package up in a very easy to use product where you can see the use cases you have available, we just select your data, you just click train. You do not need to know that many greedy details and for us, a measure of success is that we've seen customers that are starting to experiment with ML Builder is that in just a day or a few days that can iterating over several machine learning models and put them in production. We have customers that have, you know, no machine learning models and production ever, and they just now have to, and they're starting to automate processes. They're starting to innovate with business. And that, for us, is we've seen it's kind of the measure of success for businesses initially, what they want to do is they want to do. POC is and they want to experiment and they want to get to production stopped. Getting to field for it and generate from >>a product standpoint, is the A. I just infused in or there's there additional licensing, how to customers, you know to take advantage of it. What's the impact on that from the relationship without systems? >>Yes. So for for for a I in machine learning that is fused into our product and for automation, validation and guidance, there's no extra charge is just part of the product. It's what we believe is kind of a core building block in a course service for everything we do in our product for machine learning services and components that customers can use to in their own applications. We allow you to integrate with cloud providers, and the building is is done separately on. That's something that that we're working towards and building great technical partnerships and exploring other avenues for deeper integration so that developers and customers do not really have to worry about those things. Well, >>it's it's It's such a great way to really democratize the use of this technology platform that they're used to. They start doing it. What's general feedback from your customers? Did they just like, Oh, it's there. I start playing with it. It's super easy. It makes it better there any concerns or push back. Have we gotten beyond that? What? What? What do you hear any any good customer examples you can share us toe general adoption? >>Yes. So, as I said, as we re reduce the friction for adopting these technologies, we've seen one thing that's very interesting. So we have a few customers that are present more in the logistics site of industry and vertical, and so they they have a more conservative management, like take time to adopt and more of a laggard in adopting these kinds of technologies, the businesses more skeptical. But I want to spend a lot of time playing around right and whence they saw. Once they saw what they could do with a platform, they quickly did a proof of concept. They show to the business and the business had lots of ideas. So they just started interacting a lot more with I t, which is something we see without systems platform not just for a I machine learning, but generally in the jib. Digital transformation is when the I teak and can start really being very agile in iterating and innovating, and they start collaborating a lot with the business. And so what we see is customers asking us for even more so customers want more use cases to be supported like this. Customers also the ones that are more mature than already, have their centers of excellence and they have their data scientists, for example. They want to understand how they can also bring in perhaps their use of very specialized tool talking in it. Integrate that into the platform so that you know, for certain use cases. Developer scan very quickly trained their own models. But so specialized data science teams can also bring in. And developers can integrate their models easily and put them into production, which is one of the big barriers we see in a lot of companies people working on yearlong projects. They develop the models that they struggle to get them to production. And so we really want to focus on the whole into in journey. Either you're building everything within the octopus platform or you're bringing it from a specialized pro tool. We want to make that whole journey frictionless in school. >>And Tony a final question I have for you. Of course, this space we're seeing maturing, you know, rapid Ah, new technologies out there gives a little look forward. What should we be expecting to see from out systems or things even a little broader? If you look at your your partner ecosystem over kind of the next 6, 12 18 months, >>Yes. So, um, what you're going to continues to see a trend, I think, from from the closer providers of democratization of the AI services. So this is during that just starting to advanced and accelerate as these providers started packaging. It's like what out systems also doing, starting to packaging Cem some specific, well defined use cases and then making the journey for training these models and deploying Super super simple. That's one thing that's continued to ramp up, and we're going to move from A I services more focused on cognitive, pre trained models, right, that which is kind of the status quo to custom ai models based on your data. That's kind of the train we're going to start seeing in that out systems also pushing forward generally from the AI and machine learning application and technology side of thing. I think one thing that we're leading leading on is that you know, machine learning and deep learning is definitely one of the big drivers for the innovation that we're seeing in a I. But you're start seeing more and more what is called hybrid I, which is taking machine learning and data based artificial intelligence with more logic based automated reasoning techniques, impairing these two to really create systems that are able to operate at a really higher level, higher cognitive level of which is what out systems investing internally in terms of research and development and with partnerships with institutions like Carnegie Mellon University and >>rely Antonio, who doesn't want, you know, a tech experts sitting next to them helping get rid of some of the repetitive, boring things or challenges. Thank you so much for sharing the update. Congratulations. Definitely Look forward to hearing war in the future. >>Thank you. Do have a good day >>Stay tuned for more from out systems. Next step is to minimum and thank you for watching.

>> Announcer: Live from Las Vegas, it's theCUBE covering HPE Discover 2017, brought to you by Hewlett Packard Enterprise. >> Okay, welcome back everyone. We're live here in Las Vegas for SiliconANGLE's CUBE coverage of HPE Discover 2017. This is our seventh year of covering HPE Discover Now. HPE Discover in its second year. I'm John Furrier, my co-host Dave Vellante. We've got two great guests, two doctors, PhD's in the house here. So Eng Lim Goh, VP and SGI CTO, PhD, and Tuomas Sandholm, Professor at Carnegie Mellon University of Computer Science and also runs the marketplace lab over there, welcome to theCube guys, doctors. >> Thank you. >> Thank you. >> So the patient is on the table, it's called machine learning, AI, cloud computing. We're living in a really amazing place. I call it open bar and open source. There's so many new things being contributed to open source, so much new hardware coming on with HPE that there's a lot of innovation happening. So want to get your thoughts first on how you guys are looking at this big trend where all this new software is coming in and these new capabilities, what's the vibe, how do you look at this. You must be, Carnegie Mellon, oh this is an amazing time, thoughts. >> Yeah, it is an amazing time and I'm seeing it both on the academic side and the startup side that you know, you don't have to invest into your own custom hardware. We are using HPE with the Pittsburgh Supercomputing Center in academia, using cloud in the startups. So it really makes entry both for academic research and startups easier, and also the high end on the academic research, you don't have to worry about maintaining and staying up to speed with all of the latest hardware and networking and all that. You know it kind of. >> Focus on your research. >> Focus on the research, focus on the algorithms, focus on the AI, and the rest is taken care of. >> John: Eng talk about the supercomputer world that's now there, if you look at the abundant computer intelligent edge we're seeing genome sequencing done in minutes, the prices are dropping. I mean high performance computing used to be this magical, special thing, that you had to get a lot of money to pay for or access to. Democratization is pretty amazing can I just hear your thoughts on what you see happening. >> Yes, Yes democratization in the traditional HPC approach the goal is to prediction and forecasts. Whether the engine will stay productive, or financial forecasts, whether you should buy or sell or hold, let's use the weather as an example. In traditional HPC for the last 30 years what we do to predict tomorrows weather, what we do first is to write all the equations that models the weather. Measure today's weather and feed that in and then we apply supercomputing power in the hopes that it will predict tomorrows weather faster than tomorrow is coming. So that has been the traditional approach, but things have changed. Two big things changed in the last few years. We got these scientists that think perhaps there is a new way of doing it. Instead of calculating your prediction can you not use data intensive method to do an educated guess at your prediction and this is what you do. Instead of feeding today's weather information into the machine learning system they feed 30 years everyday, 10 thousand days. Everyday they feed the data in, the machine learning system guess at whether it will rain tomorrow. If it gets it wrong, it's okay, it just goes back to the weights that control the inputs and adjust them. Then you take the next day and feed it in again after 10 thousand tries, what started out as a wild guess becomes an educated guess, and this is how the new way of doing data intensive computing is starting to emerge using machine learning. >> Democratization is a theme I threw that out because I think it truly is happening. But let's get specific now, I mean a lot of science has been, well is climate change real, I mean this is something that is in the news. We see that in today's news cycle around climate change things of that as you mentioned weather. So there's other things, there's other financial models there's other in healthcare, in disease and there's new ways to get at things that were kind of hocus pocus maybe some science, some modeling, forecasting. What are you seeing that's right low hanging fruit right now that's going to impact lives? What key things will HPC impact besides weather? Is healthcare there, where is everyone getting excited? >> I think health and safety immediately right. Health and safety, you mentioned gene sequencing, drug designs, and you also mentioned in gene sequencing and drug design there is also safety in designing of automobiles and aircrafts. These methods have been traditionally using simulation, but more and more now they are thinking while these engines for example, are flying can you collect more data so you can predict when this engine will fail. And also predict say, when will the aircraft lands what sort of maintenance you should be applying on the engine without having to spend some time on the ground, which is unproductive time, that time on the ground diagnosing the problems. You start to see application of data intensive methods increased in order to improve safety and health. >> I think that's good and I agree with that. You could also kind of look at some of the technology perspective as to what kind of AI is going to be next and if you look back over the last five to seven years, deep learning has become a very hot part of machine learning and machine learning is part of AI. So that's really lifted that up. But what's next there is not just classification or prediction, but decision making on top of that. So we'll see AI move up the chain to actual decision making on top of just the basic machine learning. So optimization, things like that. Another category is what we call strategic reasoning. Traditionally in games like chess, or checkers and now Go, people have fallen to AI and now we did this in January in poker as well, after 14 years of research. So now we can actually take real strategic reasoning under imperfect information settings and apply it to various settings like business strategy optimization, automated negotiation, certain areas of finance, cyber security, and so forth. >> Go ahead. >> I'd like to interject, so we are very on it and impressed right. If we look back years ago IBM beat the worlds top chess player right. And that was an expert system and more recently Google Alpha Go beat even a more complex game, Go, and beat humans in that. But what the Professor has done recently is develop an even more complex game in a sense that it is incomplete information, it is poker. You don't know the other party's cards, unlike in the board game you would know right. This is very much real life in business negotiation in auctions, you don't quite know what the other party' thinking. So I believe now you are looking at ways I hope right, that poker playing AI software that can handle incomplete information, not knowing the other parties but still able to play expertly and apply that in business. >> I want to double down on that, I know Dave's got a question but I want to just follow this thread through. So the AI, in this case augmented intelligence, not so much artificial, because you're augmenting without the perfect information. It's interesting because one of the debates in the big data world has been, well the streaming of all this data is so high-velocity and so high-volume that we don't know what we're missing. Everyone's been trying to get at the perfect information in the streaming of the data. And this is where the machine learning if I get your point here, can do this meta reasoning or this reasoning on top of it to try to use that and say, hey let's not try to solve the worlds problems and boil the ocean over and understand it all, let's use that as a variable for AI. Did I get that right? >> Kind of, kind of I would say, in that it's not just a technical barrier to getting the big data, it's also kind of a strategic barrier. Companies, even if I could tell you all of my strategic information, I wouldn't want to. So you have to worry not just about not having all the information but are there other guys explicitly hiding information, misrepresenting and vice versa, you doing strategic action as well. Unlike in games like Go or chess, where it's perfect information, you need totally different kinds of algorithms to deal with these imperfect information games, like negotiation or strategic pricing where you have to think about the opponents responses. >> It's your hairy window. >> In advance. >> John: Knowing what you don't know. >> To your point about huge amounts of data we are talking about looking for a needle in a haystack. But when the data gets so big and the needles get so many you end up with a haystack of needles. So you need some augmentation to help you to deal with it. Because the humans would be inundated with the needles themselves. >> So is HPE sort of enabling AI or is AI driving HPC. >> I think it's both. >> Both, yeah. >> Eng: Yeah, that's right, both together. In fact AI is driving HPC because it is a new way of using that supercomputing power. Not just doing computer intensive calculation, but also doing it data intensive AI, machine learning. Then we are also driving AI because our customers are now asking the same questions, how do I transition from a computer intensive approach to a data intensive one also. This is where we come in. >> What are your thoughts on how this affects society, individuals, particularly students coming in. You mentioned Gary Kasparov losing to the IBM supercomputer. But he didn't stop there, he said I'm going to beat the supercomputer, and he got supercomputers and humans together and now holds a contest every year. So everybody talks about the impact of machines replacing humans and that's always happened. But what do you guys see, where's the future of work, of creativity for young people and the future of the economy. What does this all mean? >> You want to go first or second? >> You go ahead first. (Eng and Tuomas laughing) >> They love the fighting. >> This is a fun topic, yeah. There's a lot of worry about AI of course. But I think of AI as a tool, much like a hammer or a saw So It's going to make human lives better and it's already making human lives better. A lot of people don't even understand all the things that already have AI that are helping them out. There's this worry that there's going to be a super species that's AI that's going to take over humans. I don't think so, I don't think there's any demand for a super species of AI. Like a hammer and a saw, a hammer and a saw is better than a hammersaw, so I actually think of AI as better being separate tools for separate applications and that is very important for mankind and also nations and the world in the future. One example is our work on kidney exchange. We run the nationwide kidney exchange for the United Network for Organ Sharing, which saves hundreds of lives. This is an example not only that saves lives and makes better decisions than humans can. >> In terms of kidney candidates, timing, is all of that. >> That's a long story, but basically, when you have willing but incompatible live donors, incompatible with the patient they can swap their donors. Pair A gives to pair B gives to pair C gives to pair A for example. And we also co-invented this idea of chains where an altruist donor creates a while chain through our network and then the question of which combination of cycles and chains is the best solution. >> John: And no manual involvement, your machines take over the heavy lifting? >> It's hard because when the number of possible solutions is bigger than the number of atoms in the universe. So you have to have optimization AI actually make the decisions. So now our AI makes twice a week, these decisions for the country or 66% of the transplant centers in the country, twice a week. >> Dr. Goh would you would you add anything to the societal impact of AI? >> Yes, absolutely on the cross point on the saw and hammer. That's why these AI systems today are very specific. That's why some call them artificial specific intelligence, not general intelligence. Now whether a hundred years from now you take a hundred of these specific intelligence and combine them, whether you get an emergent property of general intelligence, that's something else. But for now, what they do is to help the analyst, the human, the decision maker and more and more you will see that as you train these models it's hard to make a lot of correct decisions. But ultimately there's a difference between a correct decision and, I believe, a right decision. Therefore, there always needs to be a human supervisor there to ultimately make the right decision. Of course, he will listen to the machine learning algorithm suggesting the correct answer, but ultimately the human values have to be applied to decide whether society accepts this decision. >> All models are wrong, some are useful. >> So on this thing there's a two benefits of AI. One is a this saves time, saves effort, which is a labor savings, automation. The other is better decision making. We're seeing the better decision making now become more of an important part instead of just labor savings or what have you. We're seeing that in the kidney exchange and now with strategic reasoning, now for the first time we can do better strategic reasoning than the best humans in imperfect information settings. Now it becomes almost a competitive need. You have to have, what I call, strategic augmentation as a business to be competitive. >> I want to get your final thoughts before we end the segment, this is more of a sharing component. A lot of young folks are coming in to computer science and or related sciences and they don't need to be a computer science major per se, but they have all the benefits of this goodness we're talking about here. Your advice, if both of you could share you opinion and thoughts in reaction to the trend where, the question we get all the time is what should young people be thinking about if they're going to be modeling and simulating a lot of new data scientists are coming in some are more practitioner oriented, some are more hard core. As this evolution of simulations and modeling that we're talking about have scale here changes, what should they know, what should be the best practice be for learning, applying, thoughts. >> For me you know the key thing is be comfortable about using tools. And for that I think the young chaps of the world as they come out of school they are very comfortable with that. So I think I'm actually less worried. It will be a new set of tools these intelligent tools, leverage them. If you look at the entire world as a single system what we need to do is to move our leveraging of tools up to a level where we become an even more productive society rather than worrying, of course we must be worried and then adapt to it, about jobs going to AI. Rather we should move ourselves up to leverage AI to be an even more productive world and then hopefully they will distribute that wealth to the entire human race, becomes more comfortable given the AI. >> Tuomas your thoughts? >> I think that people should be ready to actually for the unknown so you've got to be flexible in your education get the basics right because those basics don't change. You know, math, science, get that stuff solid and then be ready to, instead of thinking about I'm going to be this in my career, you should think about I'm going to be this first and then maybe something else I don't know even. >> John: Don't memorize the test you don't know you're going to take yet, be more adaptive. >> Yes, creativity is very important and adaptability and people should start thinking about that at a young age. >> Doctor thank you so much for sharing your input. What a great world we live in right now. A lot of opportunities a lot of challenges that are opportunities to solve with high performance computing, AI and whatnot. Thanks so much for sharing. This is theCUBE bringing you all the best coverage from HPE Discover. I'm John Furrier with Dave Vellante, we'll be back with more live coverage after this short break. Three days of wall to wall live coverage. We'll be right back. >> Thanks for having us.

>>The cube at IBM. Impact 2014 is brought to you by headline sponsor. IBM. Here are your hosts, John furrier and Paul Gillin. Okay, welcome back. Everyone live in Las Vegas at IBM impact. This is the cube, our flagship program. We go out to the events, instruct us to live in the noise. I'm John Ferrari, the founder of SiliconANGLE Joe, my close Paul Gillen. And our next special guest is great bushes as a legend in the software development community. And then she went to st this school in Santa Barbara. My son goes there, he's a freshman, but there's a whole nother conversation. Um, welcome to the cube. Thank you. Uh, one of the things we really exciting about when we get all the IBM guys get the messaging out, you know, the IBM talk, but the groundbreaking work around, um, computer software where hardware is now exploding and capability, big data's instrumentation of data. >>Um, take us to a conversation around cognitive computing, the future of humanity, society, the societal changes that are happening. There's a huge, uh, intersection between computer science and social science. Something that's our tagline for Silicon angle. And so we are passionate about. So I want to, I just want to get your take on that and, and tell about some of the work you're doing at IBM. Um, what does all this, where's all this leading to? Where is this unlimited compute capacity, the mainframe in the cloud, big data instrumentation, indexing, human thought, um, fit, Fitbit's wearable computers, um, the sensors, internet of things. This all taking us in the direction. What's your vision? There are three things that I think are inevitable and they're irreversible, that have unintended consequences, consequences that, you know, we can't, we have to attend to and they will be in our face eventually. >>The first of these is the growth of computational power in ways we've only begun to see. The second is the development of systems that never forget with storage beyond even our expectations now. And the third is a pervasive connectivity such that we see the foundations for not just millions of devices, but billions upon billions of devices. Those three trends appear to be where technology is heading. And yet if you follow those trends out, one has to ask. The question is you begin to, what are the implications for us as humans? Um, I think that the net of those is an interesting question indeed to put in a personal blog. My wife and I are developing a documentary or the computer history with the computer history museum for public television on that very topic, looking at how computing intersects with the human experience. So we're seeing those changes in every aspect of it too, that I'll dwell upon here, which I think are germane to this particular conference are some of the ethical and moral implications. >>And second, what the implications are for cognitive systems. On the latter case we saw on the news, I guess it was today or yesterday, there's a foundation led by the Gates foundation. It's been looking at collecting data for kids in various schools. A number of States set up for it. But as they begin to realize what the implications of aggregating that information were for the privacy of that child, the parents became, became cognizant of the fact that, wow, we're disclosing things for which there can be identification of the kid in ways that maybe we wouldn't want to do that. So I think the explosion of big data and explosion of computational power has a lot of us as a society to begin asking those questions, what are the limits of ownership and the rights of that kind of information. And that's a dialogue that will continue on in the cognitive space. >>It kind of follows on because one of the problems of big data, and it's not just you know, big, big data, but like you see in at CERN and the like, but also these problems of aggregation of data, there are, there are such an accumulation information at such a speed in ways that an individual human cannot begin to reason about it in reasonable ways. Thus was born. What we did with Watson a few years ago, Watson jeopardy. I think the most important thing that the Watson jeopardy experience led us to realize is that theory is an architectural framework upon which we can do many interesting reasoning things. And now that Watson has moved from research into the Watson group, we're seeing that expand out in so many domains. So the journey is really just beginning as we take what we can know to do in reason with automated systems and apply it to these large data systems. >>It's going to be a conversation we're going to have for a few generations. You were beginning to see, I mean computing has moved beyond the, the, the role of automate or of automating rote manual tasks. We're seeing, uh, it's been, uh, I've seen forecast of these. Most of the jobs that will be automated out of existence in the next 20 years will be, will be, uh, knowledge jobs and uh, even one journalism professor of forecasting, the 80% of journalism jobs will go away and be replaced by computer, uh, over the next couple of decades. Is this something for people to fear? I'm not certain fear will do us any good, especially if the change like that is inevitable. Fear doesn't help. But I think that what will help is an understanding as to where those kinds of software systems will impact various jobs and how we as individuals should relate to them. >>We as a society, we as individuals in many ways are slowly surrendering ourselves to computing technology. And what describe is one particular domain for that. There's been tremendous debate in the economic and business community as to whether or not computing has impacted the jobs market. I'm not an economist, I'm a computer scientist, but I can certainly say from my input inside perspective, I see that transformational shift and I see that what we're doing is radically going to change the job market. There was, you know, if you'd go back to the Victorian age where people were, were looking for a future in which they had more leisure time because we'd have these devices to give us, you know, free us up for the mundane. We're there. And yet the reality is that we now have so many things that required our time before. It means yours in a way, not enough work to go around. >>And that's a very different shift than I think what anyone anticipated back to the beginnings of the industrial age. We're coming to grips with that. Therefore, I say this, don't fear it, but begin to understand those areas where we as humans provide unique value that the automated systems never will. And then ask ourselves the question, where can we as individuals continue to add that creativity and value because there and then we can view these machines as our companions in that journey. Great. You want to, I want to ask you about, um, the role, I mean the humans is great message. I mean that's the, they're driving the car here, but I want to talk about something around the humanization piece. You mentioned, um, there's a lot of conversations around computer science does a discipline which, um, the old generation when a hundred computer science school was, it was code architecture. >>But now computer science is literally mainstreams. There's general interest, hence why we built this cube operation to share signal from the noise around computer science. So there's also been a discussion around women in tech tolerance and different opinions and views, freedom of speech, if you will, and sensors if everything's measured, politically correctness. All of this is now kind of being fully transparent, so, so let's say the women in tech issue and also kids growing up who have an affinity towards computer science but may not know us. I want to ask you the question. With all that kind of as backdrop, computer science as a discipline, how is it going to evolve in this space? What are some of those things for the future generation? For the, my son who's in sixth grade, my son's a freshman in college and then in between. Is it just traditional sciences? >>What are some of the things that you see and it's not just so much coding and running Java or objective C? I wish you'd asked me some questions about some really deep topics. I mean, gosh, these are, these are, I'm sorry. It's about the kids. In the early days of the telephone, phone, telephones were a very special thing. Not everybody had them and it was predicted that as the telephone networks grew, we were going to need to have many, many more telephone operators. What happened is that we all became, so the very nature of telephony changed so that now I as an individual have the power to reach out and do the connection that had to be done by a human. A similar phenomenon I think is happening in computing that it is moved itself into the interstitial spaces of our world such that it's no longer a special thing out there. We used to speak of the programming priesthood in the 60s where I lost my thing here. Hang on. >>Here we go. I think we're good. We're good. I'm a software guy. I don't do hardware so my body rejects hardware. So we're moving in a place where computing very much is, is part of the interstitial spaces of our world. This has led to where I think the generation after us, cause our, our median age is, let me check. It's probably above 20, just guessing here. Uh, a seven. I think you're still seven. Uh, we're moving to a stage where the notion of computational thinking becomes an important skill that everyone must have. My wife loves to take pictures of people along the beach, beautiful sunset, whales jumping and the family's sitting there and it did it again. My body's rejecting this device. Clearly I have the wrong shape. i-Ready got it. Yeah. There we go. Uh, taking pictures of families who are seeing all these things and they're, they're very, with their heads in their iPhones and their tablets and they're so wedded to that technology. >>We often see, you know, kids going by and in strollers and they've got an iPad in front of them looking at something. So we have a generation that's growing up, uh, knowing how to swipe and knowing how to use these devices. It's part of their very world. It's, it's difficult for me to relate to that cause I didn't grow up in that kind of environment. But that's the environment after us. So the question I think you're generally asking is what does one need to know to live in that kind of world? And I think it says notions of computational thinking. It's an idea that's come out of uh, the folks at Carnegie Mellon university, which asks the question, what are some of the basic skills we need to know? Well, you need to know some things about what an algorithm is and a little bit behind, you know, behind the screen itself. >>One of the things we're trying to do with the documentary is opening the curtain behind just the windows you say and ask the question, how do these things actually work because some degree of understanding to that will be essential for anyone moving into, into, into life. Um, you talked about women in tech in particular. It is an important question and I think that, uh, I worked with many women side by side in the things that I do. And you know, frankly it saddens me to see the way our educational system in a way back to middle school produces a bias that pushes young women out of this society. So I'm not certain that it's a bias, it's built into computing, but it's a bias built in to culture. It's bias built into our educational system. And that obviously has to change because computing, you know, knows no gender or religious or sexual orientation boundaries. >>It's just part of our society. Now. I do want to, everyone needs to contribute. I'm sorry. I do want to ask you about software development since you're devoted your career to a couple of things about to defining, uh, architectures and disciplines and software development. We're seeing software development now as epitomized by Facebook, perhaps moving to much more of a fail fast mentality. Uh, try it. Put it out there. If it breaks, it's okay. No lives were lost. Uh, pull it back in and we'll try it again. Is this, is there a risk in, in this new approach to software? So many things here are first, is it a new approach? No, it's part of the agile process that we've been talking about for well over a decade, if not 15 years or so. You must remember that it's dangerous to generalize upon a particular development paradigm that's applied in one space that apply to all others. >>With Facebook in general, nobody, no one's life depends upon it. And so there are things that one can do that are simplifying assumptions. If I apply that same technique to the dialysis machine, to the avionics of a triple seven, a simple fly, you know, so one must be careful to generalize those kinds of approaches to every place. It depends upon the domain, depends upon the development culture. Ultimately depends upon the risk profile that would lead you to high ceremony or low ceremony approaches. Do you have greater confidence in the software that you see being developed for mission critical applications today than you did 10 years ago? Absolutely. In fact, I'll tell you a quick story and I to know we need to wind down. I had an elective open heart surgery or a few years ago elective because every male in my family died of an aneurysm. They are an aneurism. >>So I went in and got checked and indeed I had an aneurysm developing as well. So we had, you know, hi my heart ripped open and then dealt with before it would burst on me. I remember laying there in the, in the, uh, in the CT scan machine looking up and saying, this looks familiar. Oh my God, I know the people that wrote the software where this thing and they use the UML and I realized, Oh this is a good thing. Which is your creation. Yes. Yes. So it's a good thing because I felt confidence in the software that was there because I knew it was intentionally engineered. Great. I want to ask you some society questions around it. And computing. I see green as key and data centers take up a lot of space, right? So obviously we want to get to a smarter data center environment. >>And how do you see the role of software? I see with the cognitive all things you talked about helping businesses build a physical plant, if you will. And is it a shared plan is a Terminus, you seeing open power systems here from IBM, you hear him about the open sources source. Um, what, what does that future look like from your standpoint? May I borrow that cup of tea or coffee? I want to use it as a aid. Let's presume, Oh, it's still warm. Let's say that this is some tea and roughly the energy costs to boil water for a cup of tea is roughly equivalent to the energy costs needed to do a single Google search. Now imagine if I multiply that by a few billion times and you can begin to see the energy costs of some of the infrastructure, which for many are largely invisible. >>Some studies suggest that computing is grown to the place releasing the United States. It's consuming about 10% of our electrical energy production. So by no means is it something we can sweep under the rug. Um, you address I think a fundamental question, which is the hidden costs of computing, which believe people are becoming aware of the meaning. Ask the question also. Where can cognitive systems help us in that regard? Um, we live in, in Maui and there's an interesting phenomenon coming on where there are so many people using solar power, putting into the power grid that the electrical grid companies are losing money because we're generating so much power there. And yet you realize if you begin to instrument the way that people are actually using power down to the level of the homes themselves, then power generation companies can start making much more intelligent decisions about day to day, almost minute to minute power production. >>And that's something that black box analytics would help. But also cognitive systems, which are not really black box analytic systems, they're more learn systems, learning systems can then predict what that might mean for the energy production company. So we're seeing even in those places, the potential of using cognitive systems for, for uh, attending to energy costs in that regard. The future is a lot of possibilities. I know you've got to go, we're getting the hook here big time cause you gotta well we really appreciate it. These are important future decisions that are, we're on track to, to help solve and I really appreciate it. Looking for the documentary anytime table on that, uh, sometime before I die. Great. Thanks for coming on the, we really appreciate this. This SiliconANGLE's we'll be right back with our next guest at to nature. I break.