(uptempo music) >> Narrator: Live from Anaheim, California it's The Cube! Covering Nutanix dot Next 2019 brought to you by Nutanix >> Welcome back everyone to The Cube's live coverage of Nutanix dot Next here in Anaheim California, I'm your host, Rebecca Knight along with my cohost John Furrier. We are joined by Dr. Ayanna Howard, she's the professor and chair of the School of Interactive Computing in the College of Computing at Georgia Institute of Technology. Welcome Dr. Howard to The Cube! Thank you, thank you. I'm excited about this conversation. >> Yeah so you, you're a fascinating person, when you were a little girl, watching Bionic Woman you said "I wanna be a scientist," you started your career at NASA. >> Ayanna: Correct. >> You are an entrepreneur, a researcher. Tell us what you're doing today. >> So what I'm doing today, and what I'm really excited about is bringing robots into the home of children with special needs. So one of the things about kids and those that may have a developmental disability is that there's not enough contact hours with human clinicians. And so, how do you augment that in the home environment? How do you bring technology into the home to do therapy with them, to do even education? And so that's what I focus on. >> So, we want to hear so much more about that, but what are you gonna be talking about at this conference? It's the future of AI, and robots. >> Yes, I'm gonna talk about the things that make my robots work. And so, the future of AI and robotics and where it leads, it's a combination of things like wearables. So if you think about all the data around us, we have wearables with our phones, and our smart watches, all that data that's being collected about us, allows our machines to do very interesting personalized things with us and for us. The other thing is that if you think about collaborative AI, collaborative machines, we're going to the place where the workforce and how you do your work, you're going to have an AI as a companion, a robot as an assistant, so you might not be sitting next to a human, you might be sitting next to a robot. And so, what does that look like? And then, of course, emotional AI, and so, yes, machines do have emotions, which is, counts kind of weird, but in order for us to work with others, we typically have a bond, so why not have a bond with our machines? >> What's the software look like? I'm rifting in my mind here, I'm just thinking about, I'm gonna write some software that might be dynamic, a neural network, these kinds of words have been kicked around in the industry. How do you make software have emotion in AI? Because it has to be random, but yet not, it has to be programmable. >> It does. But think about it. Emotions are not necessarily random. Emotions are pretty repetitive, i.e., if you're hurt, what do you do? If you're young you cry, if you're older you hide the cry, right? I mean, it's very repetitive, if you're happy there's a certain emotion, what makes you happy? There are certain things that we can all say if I suddenly woke up and I won a prize, I'd be happy. Emotions are actually very predictable, they're not that hard to model. >> And the data sources could be coming off my Fit Bit, facial recognition, you know the morning... >> Well facial recognition, you can see it in the face, in fact your pulse, and you sweat a little bit when your emotions change. Remember the mood rings back in the day? (laughter) >> Sure! >> OK those were fake, but still, their concept about them was that your body gives a response based on the emotions inside. >> Yeah, that's so cool. So what's the state of the art, you look at bleeding edge and state of the art kind of mainstream, where are people with software, machine learning, AI, what's some of the things that are notable to you that are important to highlight? >> Yes, so I think that the two areas that are the furthest ahead, one is facial recognition and emotion detection, and it's because the application are out there. As an example, airports are putting in these systems, and so imagine, I mean, the positive is, is that you don't have to book or print out your ticket, right? You just walk into the airport, you walk though security, you don't get padded down, and you walk to your gate and get on the plane. I mean, just imagine that. You're like How would you do that? Well, if I know who you look like, and I can model you, and I grab your wearable, and your data, I know who you are! So, I don't have to make sure that you are who you are, I know. I mean, so that's kind of a benefit. Of course, there's some negatives, which we won't talk about, but that's one area, this facial recognition aspect. The other I think it's in healthcare, I think it's in the fact that our data, and about us, about our health, it's so much there, and as we mine it we just get better. There's, for example, some research that shows stress can be detected and I can then have a, think about it, I can have an AI that if I know you're stressed like, I'm not going to send you that email, I'm going to halt a little bit, until I realize that your stress level is a little bit better, and then I will give you the bad news. Right? Like, because we don't want to be stressed. >> I need that, I need that app. >> Rebeca: But that's a manager with really good intentions, I mean, you can really see the perils of this going... >> No, that's, that's the negative. That's the aspect of, all these things are, really have good return on investment, good quality, but the negatives are is that if you have nefarious manager or an organization like I just wanna make money, money, money, you can sway that, and I think, though, that most organizations are thinking about this. I think there's this push now to do things like regulations, to basically protect us, but still insure that we have a positive relationship with AI and robotics. >> What's the coolest thing you've seen or built recently that could tie into the robotics? >> So, I will personally say it's one of our machines, that has, it emotionally responds to you based on what you're doing, and so what does this mean? It means I have robots that are just looking so cute, right? You look at them, and anyone looks at them, and it's just , it's like, it's real, it's intelligent, it like understands me. Of course, it's programmed based on modeling but it's just as fascinating, and I watch people interact with robots, and it's like oh, my gosh, this person, this individual, is really engaged with my robotic creation. >> And you mean, in conversation or just in feeling the comradery? >> In conversation, in interaction, and the robots, they have a limited script, but people will adapt to that, right? And they will, it's just like when you talk to your phone, have you noticed that when your phone doesn't understand you, what do you do? You speak a little slower. You might choose different words, right? I see that with the robots, you change your behavior based on the limitations. >> Speaking with someone who doesn't speak lour language natively. >> Correct. Same thing with robots. >> So describe what you see... Returning to the beginning of our conversation talking in particularly with kids with special needs. >> Ayanna: Yes. >> Describe what you see, the changes in the child, who is developing a relationship, a bond with a robot. >> Yes, so what we've actually shown, not just seen and observed, is that when we have a child interacting with a robot their, and what we call, whatever milestone we're doing, so maybe it's movement therapy, which means I want them to say, move a little faster than their normal space of moving, what I see is with the robot there is a partner encouraging, guiding, providing them input on how well they're doing, or in terms of correcting, the child improves their behavior, and so between day zero and day n, the child has gotten better. We see that. We have the data that shows that. >> Incredible. I wanna also ask about women in technology, and this is, this is really a theme at every single tech conference you go to because it's such a problem, it's such an issue that is finally getting the attention it deserves. We know about the dearth of women leaders, the dearth of underrepresented minorities, particularly in management leadership positions, what do you see as you role in tackling this problem, as the head of an important department in technology and also as a woman of color? >> Yeah, so I think there's always been kind of two dilemmas, one is what they call the pipeline, which is now the pathway, like how do you get women to come into stem? And the data has shown that is not that girls are not interested in stem, it's that they lose interest because of their society, right? So that's one thing. It's like make sure that where they are in the society is encouraging. The other is that when you get older, you look up, you're like, okay there's no one there. Obviously, I'm not supposed to be here, or when things get tough, it's like, okay, I need to move out. And so the other is, how do you do mentorship and sponsorship, so that women are pushed forward as managers and supervisors. So those are kind of the two things. And so, as a, and I consider myself a leader in this space, I actually feel it's my duty to be up front, and be a mentor, and be a lead, and actually be vocal, and make others realize like, if I'm in a room, and we're deciding on, you know, a student or a candidate, and there's no representation, you know, I'm comfortable enough to say, hey, I should not be the one that says this, right? And eventually what you see is that people start looking and thinking about this, at every instance of time. >> Do you feel like it's getting better? >> I do. It's getting better. And it's not perfect but it's getting better. Like, if I look in the classrooms, I look in the computer science curriculums, I see more female students coming in, and lasting, and then going into corporate America and continue on to grad school. I see it being better, of course it's not on parity, but is is better. >> That's awesome. And the technology has shifted the definition. It's not programming, or electrical engineering, the surface area for tech is gaming to analytics, data science, it's huge. >> Human-centered interaction. >> There's new artistry around us, so I think it's a great surface area. >> It is, and I think one of the reasons why it's so important is that the world is diverse, I mean, in terms of all the different aspects. An so, if you're gonna create products for a diverse world, you should have individuals that are also diverse, creating them for everyone so that there's some equality in the process. >> As the analog world connects with the digital world, fascinating we talked before we came on camera around the technology in digital. So the human experience for me, whether having robots, detecting emotions, and having some sort of new notifications, like hey, you know, cheer up, or do something clever... >> Right >> Is that you can now immerse, so augmented reality has been the first killer app before virtual reality, but gaming is an indicator of what's happening onscreen, so, the onscreen digital realm is intersecting with our lives. >> Ayanna: It is. >> What's your view on this? Because this is an area that's new, it's cutting edge, it's a first generation problem, an opportunity. >> Opportunity. I think this, this blending of the, I would say, even, I would say the blending of the digital and physical and the gamefication aspects, is really gonna enhance two areas. One is education, and the retraining, and so what does that mean? It means that, instead of me having to, not to say go to college for four years, but instead of me trying to study everything in this one-semester course, it's like, I just need some basic knowledge and I can then work in the field, and I have my augment reality and so I see things and there's some scaffolding, there's some indication of here's step one, here's step two, ahh, you did that step two a little bit wrong, let's revise it. So you learn with real-time training and that's with doctors, well except for live patients, but you know, with doctors or residents, factory workers, or even teachers, teachers who are teaching say, calculus, that may have an English background. That's where it is. >> The progressions are not linear like they used to be. >> Ayanna: No! >> They are different, and now you have dated instrumentation with on-demand digital robots... >> Robots, agents... >> John: Agents, assistants... >> Adaptation, taking things from other places, so if I, for example, learn the best way to provide information to this human and this factory, well guess what, I can take that information, connect to the cloud, connect to the data centers, and apply that information to another worker in a different factory, but very similar characteristics, and so you have this transfer of knowledge as well. >> So education was one. What's the other one? Healthcare? >> Of course it's healthcare! (laughter) Of course. >> As someone who is immersed in it and a believer in technology, what do you do to disconnect? Well, first of all, do you disconnect? Do you worry about our over reliance on these little devices in our pockets, and what do you do to sort of leave the digital world behind for a while? >> Yeah, so I do worry about our over reliance because we've shown, and other researchers have shown, that there's actually an over trust factor. We will use devices, and of course these devices they have errors, right, even if it's you know 1% of the time, and that 1% of the time when they have errors we find that a lot of individuals will trust those errors, because they're over relying, they kind of go in zone mode, they're like, it worked all this time, so that 1%, they just don't question it. >> It must be real news! (laughter) >> But it's scary! >> Yeah, it is. >> It's scary. I do worry about that. And we're thinking about ways to try to mitigate that, 'cause that does worry me. How do I disconnect? I think that with anything mind, body and soul, so I love listening to music, although that's not disconnecting from technology 'cause I'm using technology to listen, but it's this zone period. Exercise, I think most of us think about exercise I'm fairly religious, even when I'm traveling, like okay, I'm going to find the gym and at least walk on the treadmill because we do have to have that combination, in order to be healthy ourselves. >> Finally, for that little girl, the little girl you, who's watching Bionic Woman I think that's the thing, we need more shows like that, to get, to get >> Click us interested >> Well exactly, what would be your advice to the smaller you, who says I want to be a scientist someday? >> So I would, and this is like some advice that people told me as I was growing up, and I didn't realize I had really good mentors, is one is, don't listen to the naysayers, i.e., believe in yourself, right? And I think that's the one thing we sometimes forget to do, like believe in that dream, even if others say that it's not possible, and it's like, no, everything is possible if you believe in yourself. >> Words to live by. Thank you so much for coming on the show. >> Thank you. >> Rebeca: This was great conversation. >> Awesome! >> I'm Rebeca Knight for John Furrier. We will be back here tomorrow with more from Nutanix dot Next. We hope to see you then. (electronic music)

Narrator: From theCUBE studios in Palo Alto and Boston, it's theCUBE, covering IBM Think, brought to you by IBM. >> We're back. You're watching theCUBE and our coverage of IBM Think 2020, the digital IBM thinking. We're here with Jamie Thomas, who's the general manager of strategy and development for IBM Systems. Jamie, great to see you. >> It's great to see you as always. >> You have been knee deep in qubits, the last couple years. And we're going to talk quantum. We've talked quantum a lot in the past, but it's a really interesting field. We spoke to you last year at IBM Think about this topic. And a year in this industry is a long time, but so give us the update what's new in quantum land? >> Well, Dave first of all, I'd like to say that in this environment we find ourselves in, I think we can all appreciate why innovation of this nature is perhaps more important going forward, right? If we look at some of the opportunities to solve some of the unsolvable problems, or solve problems much more quickly, in the case of pharmaceutical research. But for us in IBM, it's been a really busy year. First of all, we worked to advance the technology, which is first and foremost in terms of this journey to quantum. We just brought online our 53 qubit computer, which also has a quantum volume of 32, which we can talk about. And we've continued to advance the software stack that's attached to the technology because you have to have both the software and the hardware thing, right rate and pace. We've advanced our new network, which you and I have spoken about, which are those individuals across the commercial enterprises, academic and startups, who are working with us to co-create around quantum to help us understand the use cases that really can be solved in the future with quantum. And we've also continued to advance our community, which is serving as well in this new digital world that we're finding ourselves in, in terms of reaching out to developers. Now, we have over 300,000 unique downloads of the programming model that represents the developers that we're touching out there every day with quantum. These developers have, in the last year, have run over 140 billion quantum circuits. So, our machines in the cloud are quite active, and the cloud model, of course, is serving us well. The data's, in addition, to all the other things that I mentioned. >> So Jamie, what metrics are you trying to optimize on? You mentioned 53 qubits I saw that actually came online, I think, last fall. So you're nearly six months in now, which is awesome. But what are you measuring? Are you measuring stability or coherence or error rates? Number of qubits? What are the things that you're trying to optimize on to measure progress? >> Well, that's a good question. So we have this metric that we've defined over the last year or two called quantum volume. And quantum volume 32, which is the capacity of our current machine really is a representation of many of the things that you mentioned. It represents the power of the quantum machine, if you will. It includes a definition of our ability to provide error correction, to maintain states, to really accomplish workloads with the computer. So there's a number of factors that go into quantum volume, which we think are important. Now, qubits and the number of qubits is just one such metric. It really depends on the coherence and the effect of error correction, to really get the value out of the machine, and that's a very important metric. >> Yeah, we love to boil things down to a single metric. It's more complicated than that >> Yeah, yeah. >> specifically with quantum. So, talk a little bit more about what clients are doing and I'm particularly interested in the ecosystem that you're forming around quantum. >> Well, as I said, the ecosystem is both the network, which are those that are really intently working with us to co-create because we found, through our long history in IBM, that co-creation is really important. And also these researchers and developers realize that some of our developers today are really researchers, but as you as you go forward you get many different types of developers that are part of this mix. But in terms of our ecosystem, we're really fundamentally focused on key problems around chemistry, material science, financial services. And over the last year, there's over 200 papers that have been written out there from our network that really embody their work with us on this journey. So we're looking at things like quadratic speed up of things like Monte Carlo simulation, which is used in the financial services arena today to quantify risk. There's papers out there around topics like trade settlements, which in the world today trade settlements is a very complex domain with very interconnected complex rules and trillions of dollars in the purview of trade settlement. So, it's just an example. Options pricing, so you see examples around options pricing from corporations like JPMC in the area of financial services. And likewise in chemistry, there's a lot of research out there focused on batteries. As you can imagine, getting everything to electric powered batteries is an important topic. But today, the way we manufacture batteries can in fact create air pollution, in terms of the process, as well as we want batteries to have more retention in life to be more effective in energy conservation. So, how do we create batteries and still protect our environment, as we all would like to do? And so we've had a lot of research around things like the next generation of electric batteries, which is a key topic. But if you can think, you know Dave, there's so many topics here around chemistry, also pharmaceuticals that could be advanced with a quantum computer. Obviously, if you look at the COVID-19 news, our supercomputer that we installed at Oak Ridge National Laboratory for instance, is being used to analyze 8000 different compounds for specifically around COVID-19 and the possibilities of using those compounds to solve COVID-19, or influence it in a positive manner. You can think of the quantum computer when it comes online as an accelerator to a supercomputer like that, helping speed up this kind of research even faster than what we're able to do with something like the Summit supercomputer. Oak Ridge is one of our prominent clients with the quantum technology, and they certainly see it that way, right, as an accelerator to the capacity they already have. So a great example that I think is very germane in the time that we find ourselves in. >> How 'about startups in this ecosystem? Are you able to-- I mean there must be startups popping up all over the place for this opportunity. Are you working with any startups or incubating any startups? Can you talk about that? >> Oh yep. Absolutely. There's about a third of our network are in VC startups and there's a long list of them out there. They're focused on many different aspects of quantum computing. Many of 'em are focused on what I would call loosely, the programming model, looking at improving algorithms across different industries, making it easier for those that are, perhaps more skilled in domains, whether that is chemistry or financial services or mathematics, to use the power of the quantum computer. Many of those startups are leveraging our Qiskit, our quantum information science open programming model that we put out there so it's open. Many of the startups are using that programming model and then adding their own secret sauce, if you will, to understand how they can help bring on users in different ways. So it depends on their domain. You see some startups that are focused on the hardware as well, of course, looking at different hardware technologies that can be used to solve quantum. I would say I feel like more of them are focused on the software programming model. >> Well Jamie, it was interesting hear you talk about what some of the clients are doing. I mean obviously in pharmaceuticals, and battery manufacturers do a lot of advanced R and D, but you mentioned financial services, you know JPMC. It's almost like they're now doing advanced R and D trying to figure out how they can apply quantum to their business down the road. >> Absolutely, and we have a number of financial institutions that we've announced as part of the network. JPMC is just one of our premiere references who have written papers about it. But I would tell you that in the world of Monte Carlo simulation, options pricing, risk management, a small change can make a big difference in dollars. So we're talking about operations that in many cases they could achieve, but not achieve in the right amount of time. The ability to use quantum as an accelerator for these kind of operations is very important. And I can tell you, even in the last few weeks, we've had a number of briefings with financial companies for five hours on this topic. Looking at what could they do and learning from the work that's already done out there. I think this kind of advanced research is going to be very important. We also had new members that we announced at the beginning of the year at the CES show. Delta Airlines joined. First Transportation Company, Amgen joined, a pharmaceutical, an example of pharmaceuticals, as well as a number of other research organizations. Georgia Tech, University of New Mexico, Anthem Insurance, just an example of the industries that are looking to take advantage of this kind of technology as it matures. >> Well, and it strikes me too, that as you start to bring machine intelligence into the equation, it's a game changer. I mean, I've been saying that it's not Moore's Law driving the industry anymore, it's this combination of data, AI, and cloud for scale, but now-- Of course there are alternative processors going on, we're seeing that, but now as you bring in quantum that actually adds to that innovation cocktail, doesn't it? >> Yes, and as you recall when you and I spoke last year about this, there are certain domains today where you really cannot get as much effective gain out of classical computing. And clearly, chemistry is one of those domains because today, with classical computers, we're really unable to model even something as simple as a caffeine molecule, which we're all so very familiar with. I have my caffeine here with me today. (laughs) But you know, clearly, to the degree we can actually apply molecular modeling and the advantages that quantum brings to those fields, we'll be able to understand so much more about materials that affect all of us around the world, about energy, how to explore energy, and create energy without creating the carbon footprint and the bad outcomes associated with energy creation, and how to obviously deal with pharmaceutical creation much more effectively. There's a real promise in a lot of these different areas. >> I wonder if you could talk a little bit about some of the landscape and I'm really interested in what IBM brings to the table that's sort of different. You're seeing a lot of companies enter this space, some big and many small, what's the unique aspect that IBM brings to the table? You've mentioned co-creating before. Are you co-creating, coopertating with some of the other big guys? Maybe you could address that. >> Well, obviously this is a very hot topic, both within the technology industry and across government entities. I think that some of the key values we bring to the table is we are the only vendor right now that has a fleet of systems available in the cloud, and we've been out there for several years, enabling clients to take advantage of our capacity. We have both free access and premium access, which is what the network is paying for because they get access to the highest fidelity machines. Clearly, we understand intently, classical computing and the ability to leverage classical with quantum for advantage across many of these different industries, which I think is unique. We understand the cloud experience that we're bringing to play here with quantum since day one, and most importantly, I think we have strong relationships. We have, in many cases, we're still running the world. I see it every day coming through my clients' port vantage point. We understand financial services. We understand healthcare. We understand many of these important domains, and we're used to solving tough problems. So, we'll bring that experience with our clients and those industries to the table here and help them on this journey. >> You mentioned your experience in sort of traditional computing, basically if I understand it correctly, you're still using traditional silicon microprocessors to read and write the data that's coming out of quantum. I don't know if they're sitting physically side by side, but you've got this big cryogenic unit, cables coming in. That's the sort of standard for some time. It reminds me, can it go back to ENIAC? And now, which is really excites me because you look at the potential to miniaturize this over the next several decades, but is that right, you're sort of side by side with traditional computing approaches? >> Right, effectively what we do with quantum today does not happen without classical computers. The front end, you're coming in on classical computers. You're storing your data on classical computers, so that is the model that we're in today, and that will continue to happen. In terms of the quantum processor itself, it is a silicon based processor, but it's a superconducting technology, in our case, that runs inside that cryogenics unit at a very cold temperature. It is powered by next-generation electronics that we in IBM have innovated around and created our own electronic stack that actually sends microwave pulses into the processor that resides in the cryogenics unit. So when you think about the components of the system, you have to be innovating around the processor, the cryogenics unit, the custom electronic stack, and the software all at the same time. And yes, we're doing that in terms of being surrounded by this classical backplane that allows our Q network, as well as the developers around the world to actually communicate with these systems. >> The other thing that I really like about this conversation is it's not just R and D for the sake of R and D, you've actually, you're working with partners to, like you said, co-create, customers, financial services, airlines, manufacturing, et cetera. I wonder if you could maybe kind of address some of the things that you see happening in the sort of near to midterm, specifically as it relates to where people start. If I'm interested in this, what do I do? Do I need new skills? Do I need-- It's in the cloud, right? >> Yeah. >> So I can spit it up there, but where do people get started? >> Well they can certainly come to the Quantum Experience, which is our cloud experience and start to try out the system. So, we have both easy ways to get started with visual composition of circuits, as well as using the programming model that I mentioned, the Qiskit programming model. We've provided extensive YouTube videos out there already. So, developers who are interested in starting to learn about quantum can go out there and subscribe to our YouTube channel. We've got over 40 assets already recorded out there, and we continue to do those. We did one last week on quantum circuits for those that are more interested in that particular domain, but I think that's a part of this journey is making sure that we have all the assets out there digitally available for those around the world that want to interact with us. We have tremendous amount of education. We're also providing education to our business partners. One of our key network members, who I'll be speaking with later, I think today, is from Accenture. Accenture's an example of an organization that's helping their clients understand this quantum journey, and of course they're providing their own assets, if you will, but once again, taking advantage of the education that we're providing to them as a business partner. >> People talk about quantum being a decade away, but I think that's the wrong way to think about it, and I'd love your thoughts on this. It feels like, almost like the return coming out of COVID-19, it's going to come in waves, and there's parts that are going to be commercialized thoroughly and it's not binary. It's not like all of a sudden one day we're going to wake, "Hey, quantum is here!" It's really going to come in layers. Your thoughts? >> Yeah, I definitely agree with that. It's very important, that thought process because if you want to be competitive in your industry, you should think about getting started now. And that's why you see so many financial services, industrial firms, and others joining to really start experimentation around some of these domain areas to understand jointly how we evolve these algorithms to solve these problems. I think that the production level characteristics will curate the rate and pace of the industry. The industry, as we know, can drive things together faster. So together, we can make this a reality faster, and certainly none of us want to say it's going to be a decade, right. I mean, we're getting advantage today, in terms of the experimentation and the understanding of these problems, and we have to expedite that, I think, in the next few years. And certainly, with this arms race that we see, that's going to continue. One of the things I didn't mention is that IBM is also working with certain countries and we have significant agreements now with the countries of Germany and Japan to put quantum computers in an IBM facility in those countries. It's in collaboration with Fraunhofer Institute or miR Scientific Organization in Germany and with the University of Tokyo in Japan. So you can see that it's not only being pushed by industry, but it's also being pushed from the vantage of countries and bringing this research and technology to their countries. >> All right, Jamie, we're going to have to leave it there. Thanks so much for coming on theCUBE and give us the update. It's always great to see you. Hopefully, next time I see you, it'll be face to face. >> That's right, I hope so too. It's great to see you guys, thank you. Bye. >> All right, you're welcome. Keep it right there everybody. This is Dave Vellante for theCUBE. Be back right after this short break. (gentle music)

>> Announcer: Live from Orlando, Florida. It's theCUBE, covering Grace Hopper's Celebration of Women in Computing, brought to you by Silicon Angle Media. (bright music) >> Welcome back to the Cube's coverage of the Grace Hopper Conference here in Orlando, Florida. I'm your host Rebecca Knight. I'm joined by Ayanna Howard. She is a professor at the Georgia Institute of Technology and also Chief Technology Officer at Zyrobotics. >> Thank you. >> Thanks so much for joining us. >> Thank you very much for having me. >> So start to tell our viewers a little bit about Zyrobotics. I know it was a spin-off of your research that you were doing at Georgia Tech. >> Yeah, so interesting enough Zyrobotics, so at Georgia Tech I focus on working in technologies, robotics for children with special needs. Primarily children with motor disabilities, cerebral palsy for example, children with autism. And so one of the things as we had developed was the ability to access computing technology because I was running robot programming camp. So I was running camps for all children, so an inclusive camp and I had typical children and children with special needs, and what happened was people kept asking me, "Oh, can we take this home?" It was like, "Yeah, no, (laughing) "that's got to stay in the lab, sorry. "But you can bring your kid back." And so the company really came out of trying to commercialize that special technology that allows inclusiveness for kids in this kind of STEM education. So that's how Zyrobotics came about. >> So talk a little bit about the technology. What does it do? How does it help kids with these different learning needs? >> So imagine you have a child who has motor limitation, and if you look now, so much is on tablets. Tablets, smartphones, even education. And if I have a motor disability, have you ever tried swiping with your fist? Right, or even if you're an older adult, and taking your finger, and if you have a tremor, like moving things around, so this is very difficult. And yet that is the way the technology is made, which isn't a service. It's just not made for everyone. And so what we've done is we've created these devices, very fun, think of it as a stuffed animal, that allows you to, if you want to stomp, if you want to do your finger, if your access point is in your foot, and you just tap your foot, it allows you to interact with the different educational apps. But what we found is that typical kids also like (laughing) playing with the toys. >> Rebecca: Right, right, right. >> So it's like, oh what is this? This is interesting. And so that's why it provided this nice blend of kids of any ability the ability to access these educational apps. So but you also are a full-time professor at Georgia Tech, and you run a traineeship in healthcare robotics. Tell our viewers a little bit more about that. >> Yeah, so I run a program called ARMS, so it's funded by the National Science Foundation. And what I've found is, a long time ago, the way that we were training our computer science students, our engineering students in robotics was typically I would say ad hoc. So I'd have a student, and they were like, "I'm interested in healthcare robotics." And I would call up my clinician friend and say, "hey, can we do an observation?" And my student would go there and basically shadow a therapist or a doctor for the day. And then they go back. And so this is what I was doing. And I found out that most professors who had students in healthcare-related activities were doing the same thing. And I was like, wait, hold it. This sounds like it's more than just me. Maybe we can formalize this a little bit more. And so the trainee-ship program actually takes roboticist students and immerses them in the medical side. And so for example this past summer, they spent the entire summer over in the clinic and the hospital watching surgeries, I mean actually scrubbing up, following patients, understanding what is Parkinson's and how do you do assessments. And so they were fully immersed as if they were medical resident students, or resident person in the clinic. And what happens is, then, and this is all in their first year, they come back into their studies, and now they understand, "okay, if I'm designing "this technology, what does it mean "if I'm designing for someone who's recovering from stroke? "What does that really mean?" And they have a vision of the patients, not just their own, I mean, they have a real vision of Mister Joe, that they've worked with and how he might have struggled with some concept and what they're doing can actually enable. And so it gives engineers, scientists, roboticists that power. >> And the empathy to really understand how it will be used. >> Yes, and understand that and not build or design in a box, which is really unfortunate that sometimes we do that. We design based on our own beliefs, not taking into account that there are other users and you are not the user, necessarily, of your own technology. >> So I want talk a little bit about this conference. This is your third Grace Hopper Conference. What does it mean to you to be here, and what do you get out of it? Are you here for Zyrobotics? Are you here for Georgia Tech? >> I am here for women in computing. And so it's actually not linked to a specific company or an organization. It's the fact that I feel a responsibility, they call me a role model, but- >> Rebecca: We're going to go with it, we're going to go with it. >> We're going to go with it. (laughing) I mean, I had a lot of mentors growing up. Not many were women. It's only at my later age that I've actually met some great, great women mentors. And so I feel a responsibility to come to Grace Hopper and just talk, share my experiences, sometimes be vulnerable and open to the trials and tribulations, but then the pure joy you get from staying in the field and the pure joy you get from actually impacting the world with your mind, with your technology, with your stuff. And I think it's amazing how, to be here and see all these young ladies, both students and older, well-established women leaders, and say, "yeah, we got this. "We can change the world with our power." >> So we're really at this inflection point in technology where problems, the biases, the barriers that have kept women from progressing, from first of all getting into the field and also progressing, are really front-page news. And sort of the problems that women have faced in the industry, the sexism, is really being talked about. But is that a good thing in the sense, I mean, yes, it's one thing to get these problems out there, but are we also discouraging women because it's showing women how tough it is to be in this industry and this bro-grammer culture? >> I think it's a two-edged sword. So in one instance, these things were happening anyway. And if you actually look at retention, which is surprising, retention of women who've been in the computing field for a longer period of time, a lot of them were dropping out. It's like, wait, hold it. You got through the pipeline, what happened? And so we all knew a lot of this stuff was going on. We have first-hand experience with it. And so the conversation now is letting everyone know about it. And I think that's how anything happens. It's that others are like, "I didn't realize." others start empathizing. "I didn't realize that this is what you were "going through. "What can I do to help?" Even if they are not necessarily a woman or a minority. And so I think what happens is by having that conversation, it makes everyone aware of it so that things can start changing. It's a negative, the fact that maybe young women are like, "oh, I don't want to go through that." I think by having role models that are like, "hey, yeah, that's what it's like, "but guess what, I'm running the company. "I'm the CEO, and so imagine what it'd be like "if you come in now that the conversation is open "versus what I was going through "when nobody was talking about it." We didn't have anyone to say, "hey, can you help me? "I just need some assistance, just to talk about something." Now you can, you can be open about it. >> So what is your advice? I mean, we know that the numbers are bleak. Tech is comprised of 25% women, 15% in leadership positions. For black and Latina, it's abysmal. What do you tell your students about this industry? >> So I tell my students, one is, if you want to change the world, and usually students that take my course and work with me are ones that want to have an impact with their minds and their technology, and so my thing is if you want to change the world, computer science, engineering is the only way that you can because the world is based on you and your technology. And in fact, if you don't, I put in the guilt, if you don't get involved in this, then the world is not going to change. And your kids' kids will have to live in this world that you have. So it's really your responsibility (laughing) to get into this space. >> The guilt is good, that's good, yeah. >> It is, for women, guilt is really good. >> I know, it's powerful, so powerful. >> Yeah, yeah. >> I want to talk a little bit about funding because I know that your trainee program, it's partly funded by the National Science Foundation. So funding is such a hot topic here, and whether you're a female entrepreneur who's trying to get money for your idea or you're a scientist trying to fund your research, tell us a little bit about the landscape, what you're seeing, what you're feeling. >> I would say that government funding, so the National Science Foundation, I would say NIH, there is more equality in the representation. >> Rebecca: There is more equality. >> It's not 50-50. But you have a fighting chance, right? I would argue, though, that in the startup world, you need to go for government funding and non-profits that may be angels because honey, VCs are not going to look at you. I truly believe that, and being a startup company, I talked to a lot of women entrepreneurs who have broke in the VC field, and they tell me basically how many frogs they had to kiss, you know? And so I think that landscape has not changed as much. But I think funding as a scientist for government grants, I think it's more, it's not fair, but it's more equal because in government, it's okay for you to say, as a program manager, "hey, something's wrong here." Because the government represents the population. So it's okay as a program manager to say that. I don't know that it's as safe to say that as a VC, like, "hey, our company portfolio doesn't look "like the rest of America." >> Right, right. So your advice there for female entrepreneurs or female researchers trying to get money is to go first to either angels or the government. >> I say that will help you keep your company alive. But you still have to kiss a lot of frogs. You still do. And eventually you will find a frog that turns into a princess and will fund you. But if you think about, how do you survive through this company and how do you keep it to the next levels, you go through any type of funding resource that you can. And so if the angel funding world in terms of government, it's not a guarantee, but it's easier, grab that, non-diluted, by the way, typically, until you go the VC direction. >> Now, in terms of the funding environment, though, NIH and NSF, do you feel they're giving as much money right now? We have an administration that is... >> Yeah, no, so overall the budgets themselves are, so NSF and NIH, this last cycle they kind of weathered a cut. But if you look overall over the last umpteen years, you see that the rate of acceptance has dropped because there's a lot more researchers going for funding, the budget doesn't keep up, necessarily, with the cost of living expenses kind of thing, cost for tuition, cost for grad students. And so overall the funding has declined. But that is not a gender issue. That is a issue just about the value of basic research in general. And the US, a lot of us understand but a lot of us do not. And so we feel that in terms of the funding process. >> So as a professor but then also as someone who's working in industry, how do you make sure that women can see themselves and see potentially rich and rewarding careers? >> So I do a couple of activities. For example, I'm going to talk about one, which CRWA grad cohort. And so what that focuses on is graduate students, women, either PhD, Master's wanting to be a PhD, and what we do is we provide those mechanisms for them to interact with community members. So we bring in these- >> Rebecca: So this is not just at Georgia Tech. This is nationwide. >> This is nationwide. Young women, they come in, like, "oh, what is this?" First off, they get to see other of their peers at other schools. Second is we bring in senior women that are doing exceptionally well, and they do things like one on one mentorship. They share. So we select these women who are open to sharing their experiences, both the good and the bad, and so it provides that network of, "okay, look, it might be hard in grad school, "but we have a peer network, take advantage. "And there are senior women you can take advantage, "to talk to and kind of ping them on different issues "that you have." So I think programs like that, and we're not the only one, but programs like CRWA grad cohort, CRAW URM, undergraduate cohort, are ways to ensure that you don't get discouraged at a younger age. >> So Zyrobotics, it's founded in 2013. What is the future of it? I mean, it's such an exciting technology and one that I think really has a lot of uses because as you said, it's not only for children but it could be for stroke victims, for aging people who are sort of losing some of their mobility. >> So my goal, I always say five years, right? So when I started it was like, five year goal cause that's like the holy grail, you make it for five years. So we're at year four, we just crossed. So we're in that five years. But what I see more as the vision, what I would say the secret magic of Zyrobotics is to make sure that accessibility is an integral part of the conversation. It's not an afterthought, it's not a someone designed technology, oh, let's think about accessibility and inclusiveness after the fact. And so I'm hoping that one, the product of course takes off, but also that it starts changing the conversation a little bit. So for example, I go out, I talk about how do you design technology that is really, really cool, is cutting edge, that's accessible at its core. It's accessible to the different learning ways, different access ways that people have of interacting with technology. How do you get that message across that, "hey, you can so this and you can still make money." So it's not like oh, accessibility, we can't make any money. Like, no, you can actually still make money even if it's a core value. So that's my vision is to have basically, have Zyrobotics lead that but then have other companies adopt it as, "oh, yeah, why haven't we done this? "Yeah, this makes total, total sense." >> Great, Ayanna Howard, thank you so much for joining us. It's been a pleasure having you on theCUBE. >> Thank you, this was fun. Thank you for the invite. >> I'm Rebecca Knight, here in Orlando, Florida at Grace Hopper. We will have more just after this. (bright music)