>> Welcome back everybody. Jeff Frick here with theCUBE. We're in Mountain View, CA at Knightscope, a really interesting company that's making autonomous vehicles. They're not cars, they're robots, and they're for security. And they're deployed and they're in use, I think it's 15 states or 14 states all over the country, just closed a huge round of funding. A lot of great momentum, and we're really excited to be rejoined by Cube alumni Mercedes Soria, she is the VP Software Engineer. Mercedes, great to see you again. >> Thank you for having me. >> Absolutely, well thanks. We had you at the studio last time so thanks for having us over here where all the action's happening. >> Yeah, you're welcome to come anytime. >> So for the people that missed the first interview, just give them a quick overview of what Knightscope's all about. >> We build autonomous security robots. Those are machines, they are running around autonomously, collecting video, collecting signals like thermal signals, the signal from your phone, and collecting a bunch of information that then is transformed into a webpage that a customer can see. So they get alerts to anything that is out of the ordinary. >> So, the application is often in a mall, or in a parking lot or some of these types of places where it's really an ongoing patrol that the robot does. >> Yeah typically what you want a robot to do is the monotonous work. So, at a mall, the security guard walks around all day long, and most of the time nothing happens. So when something happens, only then you want to be notified. Otherwise, it's just a guard that walks around. So that's the job that the autonomous robots do. >> And is there somebody monitoring all of the sensors and stuff all the time, or is it more of an alert system, or is it kind of all over the map? >> It depends on our client. For example, we always monitor all the robots. We get alert systems set up so if anything happens to a robot, we will be notified. But on top of that, some of our customers like to see their video 24 hours a day to see what's going on at their facilities. Some other clients only want the security presence, so they don't look at video. It really just depends on what the client wants. >> What's the big difference by having a Knightscope robot versus just security cameras that are just pointing and on all the time. >> If you have a steady camera, by default it just doesn't move, so you can't cover that much space. You're only going to see that one box that the security camera is covering. With an autonomous machine, you can take it wherever the crime is happening; you see something is wrong, you move the machine over and you take a closer look. So it does a lot more than just this one square that you can look at all the time, you can see everything around the machine, it's 360 degree video that is running 24-7. >> And how does it impact the way that the security people do their job? Let's stick with the mall example. When you've introduced a Knightscope robot, what's the right word, is robot the right word, is it robot? >> It's a robot, yes. >> When you've introduces a Knightscope robot into a mall situation, how does that change the way that people do their jobs? >> The robot will do about 70 or so percent of what a security guard does. But now the guard, instead of having to go and walk around the mall all day long, they get to do a more interesting job So now they're more interested in robots, they know technology, they get to know how to deal with a machine, how to interact with people. Those are things of a higher level. If the machine does all of the monotonous and boring work that the guard does. So at the end of the day, that guard does something that is a lot higher level than what they were doing before. >> Do customers typically have fewer guards, the guards just doing more higher-value, how does it impact their whole security system once they bring in a Knightscope robot? >> It could be one of two things. There are some places that our customers have zero, zero, zero patrolling, so they have nothing. So in that case, if the robot comes in, now you have security that you didn't have before. Some of our other clients, they decide that one of the robots is going to do the job of maybe three people, but those three people now are doing administrator work. So their work is to become of a higher level, so it depends on the client a lot. >> We've got a bunch of the robots behind us here in the shop, I'm sure we'll have them in the intro packet, the different ones. You've got four different ones. First off let's do some of the basics: What are some of the sensors, what are some of the inputs that they are collecting, and why do you have four different ones? >> A lot of why we have four ones is because we wanted to give the customer security regardless of their environment. The first one is the K3, that's an indoor machine; it's a smaller size, it weighs about 340 pounds. >> Small one and you say it weighs 340 pounds? >> Yes, that's the small one. >> No little kids are running up and tipping that one over I don't think. >> No, they're not. People have tried, but not yet. That is for indoors. It has all of the sensors that our other machines have, they all have a thermal detection. They have their regular cameras. They have inertia measuring units. They have lighters, which is what allows you to tell that there is something in your way, that you have to get out of the way. We have ASD, which is Automated Signal Detections. Your phone emits a signal when it's trying to connect to Wifi. We can detect all of those phone signals, and then we can log that into the server. All of the machines have that, it's just how they are using in a different environment. Indoor for the K3, outdoor for the K5, we have the K1, which is a static unit. That's typically going to be put in the door of the places we're going to monitor. And then we have the K7, which is the largest unit that we have so far, and it's going to places that a machine that's smaller cannot typically transverse. >> So that's the one that looks like a little Jeep back here. >> Yeah, in a wind farm, in a solar farm, these machines don't do very well, and that's when these machines go in. >> So definitely for outside >> Outside-- >> On the road, in the dirt >> Large companies-- >> It doesn't have to be in a parking lot, in a paved environment. >> Gravel, any different type of environment. >> What is the experience, why do these things work? Is it just because you have more coverage because you've got a robot that's going places you don't have enough guards? Is it the intimidation that someone is watching me now, you're bringing a camera into the parking lot where maybe it was kind of hidden behind a little wall? Is there a two-way interaction, do people talk to these things and expect them to talk back? Where do you see the most effective, why are these things effective? >> The reason why they're more effective can be summarized in one point: Security guards don't like to do their job. There is a 300% turnover rate in the security industry, people don't normally know that. So you're getting a new team every four months. People don't like their job, it's a job that is very monotonous, very boring. We're putting a robot there to do the same job, so you can free people to do something of a higher level. And that's the main reason why they work. >> I wonder if you can speak a little to where you're using machine learning and some of the deeper technology beyond simply putting a camera on a mobile platform. >> Some of our customers, for example, at night at the malls or corporate campuses, there isn't supposed to be anyone there at night. So one of the big applications that we have is we have an image, and from that image we can train our algorithms to detect people in that image, to detect faces in that image. All of that is done by machine learning. Because we have five years of data of images and people and we train our algorithms to say, this is a pole, or this is a person, this is a tree, this is a person. So we get to detect people in a really high accuracy level, about 80%. We also do the same thing with license plates. We train our algorithms to detect that there's a license plate in an image, you detect that there's a car first, then you detect that there's a license plate, and from there you detect all of the character in that license plate. And all of that uses machine learning, even to differentiate that there is a number one opposed to a letter L. All of that had to be trained as the technologies that we're using. For the future, we're going to use prediction algorithms in the way that, now we have data of what happens around the location where the machine is deployed to. We're going to be able to say, "Okay, this area has a lot of crime that happens "on a daily basis or however often, "you probably should go patrol over in that area." That is what we will do in the future. >> The other interesting thing is you don't sell these, these are not for sale as like, going to buy a car, you actually provide it as a service. So a very different business model, very much in line with what we see more and more, it's a service, people basically rent the robot with the monitoring service? Is that accurate, or are there lots of different flavors that they can buy? >> What we do is called machine as a service. To eliminate our customers having to pay a big amount of money at the beginning, they don't cover that cost, we do. But they pay us a monthly bill. Included in that monthly bill is the machine itself, all the parts, the monitor in there with the one on our end, all of the software upgrades, which we do every two weeks, and all of the hardware upgrades, which we do every six months to every year. All of that is included in that package. How the customers chooses to monitor their machines, that is up to them. We have agreements with two of the largest security guard companies: Securitas and Allied Universal, so they can do the monitoring for the customer if they don't have a security operation center. >> Clearly, you're operating in places where they already have security in place, they have systems, so do you integrate with the existing alert systems and the existing infrastructure they already have in place, do you guys just tie into that? I would imagine there's some industry APIs that you can feed into those systems or is it a completely independent monitoring that they have to do now? >> We did a little bit of the reverse of that, we built our system for it to be integrable. The way we wrote our code, a customer system developer can call our APIs and get the information from the machine that way, so all of that is finished so they can integrate with us opposed to us integrating the other, there's hundreds of systems out there. So if somebody wants to look at data from Knightscope that's already there. >> But you've got the open API into your data feed so they can feed whatever system. >> It of course is secure, you have to have keys and passwords and codes, and all of the information is encrypted So there's measurements that we've taken to make sure that the information is secure at all times. >> So you're a hardware company, you're a software company, you're a services company, you're doing AI. >> Woman: We're doing design too. >> And design, and autonomous vehicles. >> Yes. >> What did I miss? >> Production! We build. >> Production too? >> We build. Yes. >> That's right, I noticed this says on the bottom, reminds me of an Apple product-- >> It is designed and built in California. 85% of what you see in this machine is United States. >> Pretty amazing. So what's next? What's the next big challenge? I know the Seven is not released yet, is it just more form factors, is it different sensors? As you kind look forward from an engineering challenge, what is some of the next big hills that you guys want to take to move this thing along? >> The three next big hills that we have: Number one is getting the K7 out there and patrolling. Number two is concealed weapon detection, that has been requested by a lot of our customers. >> Concealed weapons detection? >> A lot of our customers are requesting that. And third, on the software side of things, the actual prediction of crime that could potentially happen. Those are the next three big goals for Knightscope. >> I would imagine that with the concealed weapons it's just more types of sensors that can see X-rays or whatever to get more visibility. >> Yes. >> The big V. Not necessarily visible light, but visibility from the machine. >> Some of those things have already been requested by our customers, because what we've build is actually a platform. We can add other sensors to the machine depending on the needs of a customer. For example, we have a customer who wanted the machine to be able to identify people, so they wouldn't have to swipe a card. Put the sensor inside, it's accessible, it's already there, you get your sensor and your information. What's the biggest surprise that you hear from customers after they've had one of these deployed for, I don't know what's a reasonable time, six months, so they're kind of used to it in their workflow, how does it really their world, what do they tell you? >> There are two things, number one is how quickly people like the machine, how quickly they go, "Oh, yeah, it's here and it's working." And then also how much crime has actually been eliminated. They thought, "Okay, maybe I have one break-in into "a car every week, well maybe it will just go to less." It goes down to zero. There's people who had lots of crime, and just by the machine being there, they get nothing, they get zero. So that was our surprise for them, and that was a surprise for us as well. That's how the effective the machine actually is. >> It's weird when I just drove up today, there was one right in the middle of the driveway as I was coming in. I was like, "Is it going to move, am I going to move?" It's very, much more intimidating than you might think. It's a presence, for sure. >> Yeah, and we have things like car backup detection, because the machine could be going down the street, and a car could be coming out of there, so we have to detect stuff like that so we don't get run over. All of those little things that we can think of, we have to do that a lot. >> Alright Mercedes, well thanks for inviting us over, it's fun to actually see the machines for real. >> Thank you so much for coming. >> She's Mercedes, I'm Jeff Frick, you're watching theCUBE, we're at Knightscope in Mountain View, California. (upbeat music)

(upbeat techno music) >> And welcome back everybody, Jeff Frick here with theCUBE. We're having a CUBE Conversation in our Palo Alto Studios. We're excited to have our next guest, who is an ABIE award winner from the Grace Hopper Celebration. Would've been competing in early October, we tried to get her on then, schedules didn't mesh so it took us a few months, but we're really excited to have our next guest. She's Mercedes Soria, she is a VP of Software Engineering for Knightscope. Mercedes, welcome. >> Thank you, thank you, I am so happy to be here. >> Absolutely, so, congratulations again on your award of leadership and part of the winnings of that is you got to keynote in front of 18,000 people. So A, What was your impression of Grace Hopper and B, how did you like keynoting in front of 18,000 folks? >> Yes, how was Grace Hopper, it was a huge community of women in technology. I was so excited to be there, everybody was just looking up to women, everybody was trying to help each other. How do you go forward in your career, and I was really focused on STEM careers, which is one of my passions. So I was so glad to be there. And how it was to keynote in front of 18,000 people, so I hadn't done that before, so I can check it off my bucket list, that was one thing. And it was amazing, there were so many women who just clapped and they just kept supporting it and I had to stop several times while I was giving the speech, so it was once in a lifetime opportunity that I'm very grateful for. >> It's an amazing accomplishment, again, congratulations, and it's amazing show, if you haven't been to Grace Hopper, you have to sign up, how fast you say it sold out? >> Mercedes: 25 minutes. >> 25 minutes, oh. Go to anitaborg. or anitab.org now, that's right, they changed the URL, yeah, I'll have to check it out. So let's jump in about Knightscope. So for the people who aren't familiar, go the website, knightscope.com, a bunch of really cool fun stuff, tell us about what Knightscope's all about. >> So Knightscope is a company that is trying to cut the crime cost to the US in half. So most people don't know that the US spends about one trillion dollars a year just to deal with crime in the US, so our goal at Knightscope is to cut that in half with the use of new technologies like artificial intelligence, machine learning, and robotics. A group is software plus hardware plus humans, so we take the good things that humans do, which is make strategic decisions, the good things that machines do, which is do the monotonous work and store data for a very long time, and we combine those to try to help with crime. >> Right, so that's a nice explanation. The short answer is, if you go to the website, it's all rolled up into these cool robots that look like C-3PO, and I'm wondering if there's a little man inside there, but we'll get into that later. But I think it's a really interesting concept because you are bringing together many of the hot topics in technology right now, so one of'em just with robotics. You got these robots of various shapes and sizes, but as you said, really, it's the synergy of the robots with the people that give kind of a one plus one makes three effect. How is it, where are those points of intersection, and how does the robot help the human do a better job, and how does the human help the robot do a better job? >> So the robot helps the human because, in this case, security guards have to walk around the same places all day long, right, they have their route, they do that all day long and they get very, very bored, and they get to the point where they don't care anymore and they just scan a badge and then that is the job, right? So that's what the robots do, which is, they don't mind going around the same area all day long, recording data, recording video. That's where the synergy is. Now what the robots, at this point, can do is make a decision in terms of, okay, I have this five things, should I make an alarm to my supervisor and say a guard needs to come. The robot only provides information, so all of that information that we provide is so the human can make a decision on what to do next. >> And does it feed into, I mean obviously these are big security systems that already exist inside these big buildings and these big facilities. Does your robot tie back into those facilities, is it a different layer on top of it, how does it work with the existing security infrastructure that's already in place? >> So the existing security infrastructure is a bit separate at this time. There is a project that we're working on in terms to integrate because there's so many security systems out there, for a start up like us, we need to be very smart in terms of where we spend our resources. So we got to do studies and figure out which were the better senders, the better companies that we need to partner with to do that. But at this point, it's a separate tool, so you open it and all the gear you need is a current browser, you can open it from anywhere in the world, and your security people can look at all the data the machine has collected. >> Right, so the other interesting piece that you're tying together via these machines is really this combination of AI and ML, artificial intelligence, machine learning, but also your background is in user interface, so it can't just be happening in the background because these machines need to do their job, executing through and with people, on the UI side and the security guards and the security infrastructure behind them. So as you've introduced more AI and machine learning into the software components that you can drive the UI, how is that changing the world, how is the UI world changing because now you've got so much more data and so much more kind of compute behind that before it even gets to the actual user that's interfacing with it? >> Yeah so the UI's a little more rich these days, it used to be a webpage and HTML and JavaScript page, and that's all it did, right, but now we have a lot more information that we can provide. For example, we have machine learning algorithms that detect if there's people in an image, so I don't only tell you this is my video, but I also give you a picture of the person that I just saw, and then I tell you, hey, this is what I saw. It makes your experience a lot more incursive. >> Right, and another potential integration point, right obviously with photos in the security system for IDs and passes and all those things. >> Yeah, even face detection at some point as well is very important for us. >> Now you have four different models, why do you have so many models, what's the use cases that would drive you to have four different models? Hard to support four models instead of one as a startup. >> Yeah so our customers have very different needs. Crime doesn't happen just in a shopping mall, crime happens at PG&E offices, it happens at the mall, it happens at different locations, it could be outside, it could be inside, it could be in a hospital, it can be in a parking lot, so what we tried to do was to cover all of those potential places where crime will be. So with that we have four products; we have the K5, which is our first product. It goes into ADA compliant environments like hospitals and data centers, it's a big robot and mainly used for things like a parking lot to detect license plates, to make sure that it's monitoring all the outside. Our second product is the K3 which is a smaller machine, and what it does is mainly goes inside, it can go through a door and it can do things like monitoring who's at the office at night, raising an alert if there was a fire, stuff that happens inside. We have the K7 which goes to outside places where you have things like speed bumps, you have different kind of terrain, gravel or other type. And then the K1 which is our static model that what we're working on that for the future is to have concealed weapon detection at that point, which is something that is very useful for places that have, like for hospitals, when somebody comes in, they want to be able to know if these people are armed. >> Right, I'm just curious if you can share where customers have seen the most impact, the most benefit by using one of your robots. What specific behaviors have just been a game changer when they put in the Knightscope robot? >> Yeah, so I can't tell you the actual customer, that is something >> No, no, that's okay. >> That we can't say, but I would tell you one example. We have, for example, a hospital and this place is open 24/7, obviously the emergency room, and when they will have, it's down in LA, so they will have at least one break-in every week at the parking lot. So we put our machines there and the past seven months that they have been there, they got zero, they got no break-ins. And the nurses now feel safer going to their cars, people feel safer going there at night, so that is one example. We also had an example of a shopping mall where there was a guy who was basically exposing himself and nobody could catch him because he would drive, as soon as he saw a security guard, he would drive out. So we were able to catch that person as well. There are some people to steal merchandise, so they came, they stole something, they left, and the very next day, they come back and they try to sell this back to the mall people, so by seeing who these people are then determining that they came back to the mall, we were able to apprehend them as criminals. >> Right, on the first example, on the parking lot example, does the robot have active deterrents that it can do, can it sound alarms, light lights, to make people feel safer in a parking lot, that's very different than just monitoring things? >> Yeah so what the robot does is, it has a sound that it's all day it's playing that sound, there's a lot of lights, the lights change color based on what's happening around the robot. Another thing that we have that helps a lot of people feel safe, we have a push-to-talk functionality, so if you were feeling something was wrong at night, you can push that button and you can directly talk to the people at the security operation center. They can walk you through what to do, they can follow you while you go to your car, there's different functionality that we have that helps people feel that they're safe outside. >> Right, and on the shoplifting one, it's interesting 'cause lots of stores have cameras, right, that's not a new thing. So what did your system do differently that the regular camera that they had in there before probably would've filmed the person but didn't necessarily wasn't firing off the alert, recognizing they were back again, did somebody go in and manually type in this particular person's a shoplifter. How did you guys take it to a much different level than just kind of a static security cam? >> So the main thing that you should keep in mind for static cameras is there's always black spots, blind spots, there's no way that they can see everything, and mainly you have cameras inside of the shops, you don't have them outside, so what we did is, we not only saw that we not only got the video of the person inside of the shop, but we saw them when they came outside, we saw them when they were moving, all of this is recorded in video and that we can then match them and see the people who were. Another thing that we do that cameras don't do is we can detect your mobile devices, anything that has that's looking for a network, we can identify that device, and that is always for you and that is always for that device, so we can match those devices when they come in. >> You shouldn't have waited this long but one of the most interesting things about the company and what you guys do, and it's highlighted by what you just said, is the way you go to market. People are not buying these robots, right, you offer the robots as a service, so really interesting model and really brings up interesting things like you said where you can do all kinds of software upgrades, you can do hardware upgrades, you can do all types of changes to the actual unit that the customer just benefits, it's a classic SAS model. So how did you get to that stage and how do people like having, now, kind of a simple monthly payment with all the upgrades and constant, I would imagine, a lot of upgrades coming pretty consistently? Pretty interesting way to go to market, how's that received in the market? >> It's very well, people really accepted, especially when it's new technology. We decided from the beginning that we wanted to be, to own the whole technology stack, and even the robot itself because we knew there would be a lot of upgrades, we knew there would be changes and we wanted to serve our customers in the very best way that was possible. So to help people adopt new technology, we help them with how do they perceive it on a daily basis. If you come to somebody and says they want you to buy a hundred thousand dollar robot, uh, you don't know what that's going to be, but if you said, I charge you ten dollars an hour and give you a robot, that not only changes software every other week, it changes hardware every six months, and you have whatever robot will fit your needs the best. People are really accepting of that model, to the point that all the companies are jumping into the same thing. >> It's really interesting because then it begs where you guys will develop as a company, you know, are you are robotics company, are you a software company, are you a software monitoring company, do you become really a security AI company that pulls from lots of different data and lots of different sources? It really opens up a broad range of opportunities for you guys in which you want to go or where you find your most expertise or where the market takes you. Pretty exciting way to go to market. >> Yeah so what we decided to was we wanted to be the Apple of security guards, so what Apple does is they have their software, their hardware, they own all of it, and therefore they have a very loyal following. We want to be that for security guards, so we own the whole environment, we make changes when we wanted to, and then we go to market that way. >> Okay, that's a great story and again it's knightscope.com, they're fun pictures for one, but it's a great story. But before I let you go, Telly would not be happy if I didn't take a few minutes to talk about your journey. How did you get here, VP of Software Engineering? You know, software's eating the world, it's a great place to be, you've got a solutions based system, but really it's a bunch of metal wrapped up with software inside. So how did you get here, and I wonder if you can share a little bit of your journey to become VP of Software Engineering? >> Yeah so I'm an immigrant, I'm not from the US. I was born South America, and when you're in South America and somebody tells you, hey there's an opportunity for you to go study in the US, you take that opportunity. So I came to the US to study for college, I had a Bachelors in Computer Science and then a Masters in Computer Science. >> Where did you go to school? >> I went to Middle Tennessee State University, and like I said, when somebody tells you, you're going to the US, you don't ask questions, you just go. >> So who made you that offer, how did that come about? >> My university in Ecuador, where I was from, they had an agreement with the university in Tennesee. So they would send students back and forth in an exchange program. >> So you're a good student, they identified you as having great potential and you got picked for that program? >> So 5,000 people apply for 20 spots when I applied. >> Wow. >> So 20 of us came, and out of the 20, the only two people who are staying in the US, my sister and I, we're twins, I have a twin sister. >> 'Cause you ask your sister for support, maybe? Twin sister. >> If I really, it probably had a lot to do with it. And then with technology, I found my way into Knightscope, and Knightscope is a really good company for women in technology specifically, and that is some of the work that I pushed myself to do. Our women in technology numbers are about 25% to 28% of the company which is a huge number for Silicon Valley. So we hire women, we try to mentor them, I myself take time to spend time with them, and then help them get a career that they're excited about. >> And when did you discover your affinity for computer science? It's always a great debate as to when is the best time, or when is the optimal time, or the most popular time for young girls and eventually young women to get involved in STEM? What was your experience? >> So I live with my uncle in Ecuador and my mother, so I always knew I wanted to do something structured, and at the beginning, he was an architect, so I thought I would be an architect, but then I started reading some science fiction books and the closest thing for me to science fiction, making that a reality, was a career in computer science and technology. So that's how I started, and that has led me to, now, Knightscope, and we're doing the most advanced technology that is out there, we're out there with artificial intelligence, we have machine learning, all of the technologies that are out there, robotics, we are using them to put them to use for the greater good. Our job is to keep America safe, and we all are working towards that goal. >> But I think you just want to make something fun that looked like C-3PO. >> It's more like R2-D2 actually, and if you want to see more, go to knightscope.com. >> Okay, and final question. So you're advice, more general advice, to older girls or young women, in terms of what they should do if they want to get into this or why they should consider a career in STEM if they haven't already. >> A career in STEM is very, very rewarding. You're going to be doing sometimes things that nobody else has done ever before. You're out there in front of everything that's happening with technology, and it's actually exciting. When you find other women that do what you want to do, look at people's backgrounds, look at what they've done, look what they're trying to accomplish, and then, make sure that you get into their lives and they'll help you through it. There's a lot of women who would be happy to help out and one of those is me, I'd be glad to help people out. >> Well, Mercedes, thank you so much, again, for spending some time. Congratulations on the award and comin' in and tellin' us your story and educating us more on Knightscope. >> Thank you, and if anybody wants to know, knightscope.com, they can find all about our technology. >> Alright, she's Mercedes, I'm Jeff Frick, we've been having a CUBE conversation in Palo Alto, thanks for watching, we'll catch you next time. (light techno music)