I'm a level three, and then how do I, how do I approach this sparking? Cause you know he's gonna do it. I think we sparking read. You know we cooked that up special for you. How's it going everybody? Datodoy here with another round of Dragon Ball Fighters ranked matches. And today we're playing a suggested team of all the Androids. Now with this team specifically, I don't have a lot of experience with these characters other than Android 17. That's a character I've put a decent amount of time into. The others, not so much. I've seen a lot of 16 starting, you know, I've played Dragon Ball FighterZ since it was first released. And Adroid 16 used to be a completely different beast. Still good today. But not even his shadow would swarm herself, honestly. And we just got to go straight out to 18 right away. Man, people on PC are down checking. I get it. You know, I understand. Oh, snapping already. He wanted that. I'm too tall for my own good. Let me out of here. Okay, understandable. I didn't hit the assist. My hopes and dreams were bigger than what I could actually do.  Let me get you in this corner real quick. Up we go. Now I understand this is customary. Gotta throw that out. Nope. You need better setups of that. We're getting snapped again. Hey, I'm the one playing 16. Okay, I should be doing the snapping. Oh, we almost had that blocked. If we made it to that vegeta part, it was easy reflect from there. Can we get spirit bombs? No, okay. You just want this guy is a snap. God. That's it. That's that's probably the death. I am actually never mind. Whoa, I got a little bit ahead of myself. No, never mind. No, come on. No, you can't run. Don't run. Oh, I let him go because I dropped it. I got too excited. Now I'm getting snapped again. You know what? I am. I am done getting snapped. The only thing snapping now is my mental state. It's all over. I don't I wanted this check. I'm a bag that year. Let me see Goku GT. He might level three. But I was born 16 years ago. Never mind. I'm like I'm literally six. By the way, I'm not 16. Last time I made a joke like that. I said I was born in 1976. I'm like that was 85. He doesn't sound a day over 97. All right, knock him up here. We are playing behind by the way ever since I let that drop get to me. Oh, wow. We are playing terribly and not even because of the characters. We're just like we're just playing terrible Dragon Ball fighters. I let's 17 get back in on the I'm gonna start this off with a record. No, I could have had that. Oh, come on. I was mashing. I this is why you this is why you take with the buttons. You take with Dragon Rush. Don't try taking with lights.  I will live this. But it's not really about living. It's about. Oh, beautiful, but I hate you for it. Beautiful, but I got to say if I'm being honest, I dislike you for that one. I can't believe it. I didn't think he'd have that calm sense of mine. Let me down tech. Now, let me get this man. He wouldn't do it twice. He did it twice. Why would you do it twice? I like that part. Okay, that part I don't get, bro. Why would you let me do it once to you, huh? Let me let me. I got to say this might be the this might be the most disappointed. I've ever been in a match. That was probably the most disappointing ending I've ever seen. He's going to rake up in the next match and just leave me enough. That that is unacceptable. You will not leave me here. That is unacceptable, sir. You will stay here and you will take you will take your limb. Licks limbs.  I super dash to early. Let me switch. Let me not let me not panic. Let me just take it calm. I can't let him rank up. Dude, that is unacceptable. I thought it knocked it. I thought it was like yoncha. That was me being uncomfortable with this DSS. I can't let him go, dude. Not after that match, not like this, not after he did it twice. That might have been the most unacceptable outcome of a game. Gotcha. Boy, you thought you saw the last of 17? Nah, let me hit you with the shadow clone. He actually did it. The mad man. You can tell I'm definitely playing with a chip on my shoulder this round. I definitely got a couple chips on my shoulder. Oh, that was bad. Got a little excited. 17. Why don't you come back out? Why don't you come back out? He's going to down tech. Never mind, but that works too. Ah, why am I reflecting nothing? Let me just ignore that. Two overheads. Okay. I didn't expect it. I'll be honest. I thought he was going to land and go for a low. This could be a level three here. I would level three. Yep. Yep. Make sense. Make sense. Oh, I let my go. Android 18 level three. You got to respect this. This is a good one. And knowing him, he's going to wake up something like DP your level three. So I'm a run up and block. This guy is unreadable. He wrote his book in invisible ink and I think that's cheating. I think that's cheating. One to one. He didn't rank up yet yet. There's a whole set where he could write up. I'm panicking, dude. Simulations were accurate. Your friendship means a lot to me. That is touching 16. Let's get this win first. Let's save the waterworks for later. Let me play some neutral. I've never seen a bad like that was that was the opposite of neutral. Oh, that never hits. Oh, you mind if I see Vegeta blue back in the doctor's office? Let me see him back here. What's that? You don't got three bars. You don't got three bars. You got to take this mix. You got to take it. Let me plant you. No, no, no bad. Okay. It still works, but I did the wrong one. Knock him over to the islands. This is where the androids come to play canonically. Okay. That was pretty dumb of me, but it's not me.  Oh, he did it. He managed to do it. Get the full screen. Go into sliding. Yep. Let's just go through GT for you, baby. Oh, we're too small. I don't know. I every time I play 16 people like, no, you have to do it like this. And then I learned it. I'm like, okay, I got that. I apologize, guys. Overhead. He's on. He's so small and I'm so tall.  Oh, we're getting spirit bombed. Drop it. Oh, big drops coming in hot Android 17. That that was way too slow. I left a lot up to your hands right there. You're very incapable hands. Okay. Okay. Why not? Why not? Come on. Please kill. I know it's a lot. Oh, I did it too late. I was kind of begging for that one. Can't get much. You can get a swap. I'm just gonna. Y'all mind if I just kind of just do whatever? Because I'm gonna just do it up. Now we have the big advantage. It was so obvious. I thought he would like jump out through a key blast. It would have been so good. My impulses are too much. I can't fight it, dude. That was what I was trying to do. But every time I just couldn't fight it. Double three. Knock him around just a little. I think we go for an overhead boys. He has shown that he does not want to wake up. Oh, there it is. That's the wake up punish. It didn't punish. That's it quit. That's okay. That's okay. Game three goes to us. We're out to one. Oh, man. I guess I did. I didn't know what I was getting myself into. Of course that wouldn't punish key blast. But I'll take it. I'll take it. We're we're we're safe at this point. I don't think he can rank up. Don't run up on 17. 17 doesn't want anybody getting his space. Oh, yes. We're just slapping them. It's just we're just doing whatever we have to do. Ah, we missed the down heavy on that one. Don't snap me out. Come on. Tired of the games. Help me. No, no. 16 players. I need you to like just think it out loud right now. What do I do? I need I need like the thoughts to reach me. Come on. Reach me. No, that's definitely not it. Oh, man. I can hear the insult. So ironically, wait a minute. I just got a thought from a 16 player. You wild for that one, bro, but I'll try it. Oh, no, we're dead. Hey, I'm not going to be able to try it. Let's see drops this drop it coward. Please. How many covers have I dropped in the good name? Wait? Oh, what? Oh, come on, man. That didn't look like it was going to happen. Let me just keep doing that. Oh, no three bars either. So you're going to have to just vanish because I can't hit a. That's nice. That's a that's a kill. Maybe even a one bar. Oh, yeah. That's a one bar. That's a one bar. Let me step. Let me get these squeaky shoes out of here. Onto the next one. 18 barrier. Oh, you know, we don't care about that. Ah, I always try to like cheese it there, but I never get it to go off. That's like that's good. Finally, we were able to get something off of those vanishes. This is killed by itself. Yes, it does. 18 making waves. Okay. Okay. Okay. Okay. Okay. A questionable decision on my part. I'm still kind of undecided on what I want to do. I'm a little three. I'm a level three. And then how do I how do I approach this sparking? Cause you know, he's going to do it. How do I approach it? I think we sparking read. You know, we cook it up special for you.  It gets the player. They freeze up panic and get hit. They're like, but that's going to be the end of the end of this video. Sorry. 16 didn't get to do much, but. You know, I just, I don't play that character. He was so top tier that it just, why would I ever play him? You know, that was, that was me in season one. I'm more open to trying them out now. I've changed, but I still need to learn them obviously other than that. If you guys have a favorite moment, make sure to go down below and through the comment section and let me know down there while you're there. If you enjoyed the video overall and like the channel and want to see more stuff like this, make sure you support me by hitting that like button and subscribing to the channel so that you never miss out on an upload. If you missed out on some of my older videos and want to watch them, there should be some up on your screen. So just give any of those a watch if they catch your eye, but thanks again for watching this one. I have been Dr. Doia and I will see you in the next video.


I would say the top five most impactful deaths. That's that's not the subject. We're gonna say Michael Jackson, Michael Jackson. No, no, no, no, no Kobe Bryant. I'm gonna just say something, man. Kobe got to be in front of Michael Jackson because we don't know how Michael Jackson went. Yeah. And there was a child involved. It was a lot, man. That was it was deep. And that was deep. So we're gonna say Kobe Bryant. I'm gonna say Nipsey Hussle. Okay. After that, only because the way Nipsey went out in the age that Nipsey went out at Michael Jackson, just because he was the biggest icon of the icon. What about Whitney? Whitney Houston. That's big. What about Muhammad Ali? Legend. Because because it was parades in Kentucky. What about Freddie Mercury? No, for me. Not seriously, Kurt Cobain, bro. That was crazy. But that's a crazy joint. What about Freddie Mercury from Queen? We are the job. What's your point? But what if Freddie Mercury died of AIDS, didn't he? He died of AIDS, man. Listen, Gil. Easy. AIDS. Yeah, Gil.  What about you personally? Who impacted you if you said it was a person? The most impactful death to me? I don't even want to count Nipsey because I should have real homies. No, no, no, no, no. It's only a couple celebrities. Let me just be honest. It's only a couple celebrities that passed and I actually shed tears. Nipsey was a friend of mine. Right. That's different. So that that that touch close to home. When Tupac Shakur. Was murdered. It was me and my homie Ricky together riding in North Philly in a powdered blue, some type of old car. When we when that shit came across the radio that Tupac Shakur was shot and lost and we just both cried and it was crazy because we really cried like we knew the nigga. That's how it was for me.  Like, and that was like, that was like, uh, that was like a shout out to Poc to how how much his music touched us as as youngins. You know what I'm saying? That we felt like we knew him. You know what I'm saying? When I was growing up, the artists and the rappers that we that I connected to was the rappers. I felt like I knew him like that's what I say all the time. I, you know, was connected to me when Poc died. You know, it was crazy. It was just no BS. I just finished robbing like a CVS. I was coming out of a CVS. I robbed it. I jumped in the car with, you know what I mean? With a with a OG of mine and we driving and it was on the radio. What was his name? Well, I'm not saying you're right. I'm not saying no names, but it was like, I literally just like, like moments before that it came across by 99 in the radio. I was in Philadelphia in the North Philly section robbing and see it was just crazy coming out of it. And I jumped in the car. I'm like, what? And they just start playing all pop music. I was like, yeah, if it that music that music hit different man, Prodigy mob deep is my number one, man. Number one in Aliyah. Aliyah was just super crazy, bro. Damn. Yeah. Shout out to Aliyah. All right. You had left eye plane. Like it was just like, yo, it's crazy, man. Right. And I think I think a lot of these times when they hit you for that moment, you feel like, man, I got to appreciate life more. Like a lot of times we we take like, like a lot of people take for granted like life.   He's immortal to them. You feel what I'm saying? You can down. We got a lot of money. Steve job died.   I'm paying attention on the road. I'm putting his phone away. I'm driving safe.  Let me let me. I was beefing with my uncle and my mom and my brothers and my cousins, man. Life too short, man. Kobe Bryant just died at 41 42.  Yeah, we're not your man because you know, it's like because people look at people with money as though they ain't dying young because they got enough money to get rid of all the diseases. They didn't get rid of anything. They could get rid of it, but in the actuality, man, when is your time to go? It's your time to go.  No money. No, no, no doctors. No one is your time to go is your time to go. So at the end of the day, man, uh, I will probably say the top five most impactful deaths of my livin had to be Kobe Bryant, Tupac Shakur, Michael Jackson, Biggie Smalls. I got to go with six. Muhammad Ali and Nipsey Hussle. And shout out to Mike. Shout out to John Singleton. Yeah, shout out to Heath Ledger, Prince Whitney Houston, everybody. Aliyah. We named a lot of people.  So a lot of the people who was most impactful to me are going to be black. Shout out to somebody that was very important to me personally, like him, Freddie Mercury. What's it? He was white Freddie Mercury Queen. He was a legend. All right. You know, him and Freddie had a lot of things in comments like also.  Also, I want to give a shout out to somebody special.  Now, Anthony Bourdain. Oh, I thought you were going to say karate. Oh, no, no, no. He was. That was no. It was coming. That's kind of hold up. Man, it's stuck on. Anthony Bourdain, right? Legendary parts unknown. No reservations. The layover had three shows. You know, he was on drugs, came back at all these shows, traveled the world, documented, you know, introduced people to different cultures. I used to watch this guy in prison religiously. And I said, when I get out, I'm going to meet him. I never got a chance to meet Anthony. You know, first of all, his I'm pretty sure his name wasn't Anthony and it was Anthony, but it's cool. Anthony, whatever. And I just, you know, he was a legend to his family. You know, if you could hear this man, he was, you know, somebody I got some game from that's traveling and introducing people to information because that's what he did. And you know, he taught me that. But, uh, the biggest impact in my life from on a celebrity standpoint of a person dying. It's karate or a world-renowned mixed martial arts grandmaster black belt neighborhood kung fu master. He was all like, he was everything that you can think of of a black karate, a black karate man in the ghetto. Protect and serve. It was a neighborhood trick. Stop.  Don't forget. If you go, just, you go, shout him out. That was Earl was in Earl, Earl jumped out, Earl jumped out of me. You see, Earl jumped out of me. No, don't say Earl jumped out of you. Oh my God. I felt her. No, no, no, no, no, no, don't make him jump out of me. I'm just saying that was the energy of Earl. It'll always go back to Earl touchie at the park that they don't because it jumped out of you. You got to jump in. Yeah.  Jump with your mom. Jump with your mom. So go ahead, bro. That was the energy of Earl. It was like, uh, I just want to say that Earl, man. He was everything, man.  It was a whole friends, family, kids, man, and then the Nipsey things just seeing it happen.   I travel in a world where there was a lot of entertainers evolved and, uh, not many times you could walk inside a room, you know, because I do my thing. You know, people might know me from social media, but I'm never expecting people to know me. Um, and with, and with Nipsey, it was like, it was just super love and it was crazy.  So I was already tuned in and it's like, he like, damn, wow, Lord, what's up?    No, be scared. I whispered to me. So I put them out there. No, be scared when you come around. You be like, damn, I ain't even doing it. Bitch, I'm not even going to tell it to me. I'm going to put them out there.  Gilly. You, you wanted to be scared. I'm around. Not as you get scared.  Not as they get a time cast money.  That's all I'm saying.


This is stock number 8C58A. We are here at Summit Automotive in Fond du Lac, Wisconsin. You're new and used SUV headquarters today. We are checking out this super clean 2017 Chevy Traverse This vehicle has the 3.6 liter v6 motor From this HD video you will be able to tell that this vehicle is extremely clean all the way around Inside and out headlights are nice and clear Siren red Metallic is the color We shoot all of our videos in 1080p So if you have HD capabilities on your computer tablet or smartphone device turn them on now because it's like you're right here looking at the vehicle with me Very very clean down this side. It has the factory 20 inch painted alloy rims No scuffs or scrapes on them bridge stone dualer 255 55 hour 20 tires with probably about I'd say 70 to 80 percent of the tread left Hood is nice and clean. No dents or dings on that Has a projector lamp fog lamps front valance is a nice shape. I didn't see any cracks or scuffs on that Passengers side rim is in excellent condition as well No scuffs or scrapes on that and as we go down this side of the vehicle You can see just how clean the body is how reflective and Mirror like that paint is We take these HD videos So if you are far away or even if you're close by and just cannot make the trip down You can still see the vehicle hear the vehicle and of confidence in what you're looking at before you even get here Very very clean down this side. I didn't see any dents or dings down this side The back rim is in excellent condition as well and the back tires have just as much tread as the front tires As we come around to the back You can see that it has the backup sensors also has a receiver hitch and seven pin wiring The rear gate is in excellent condition. I didn't see any dents or dings on that It does have the cover for that receiver hitch back here and the back storage area is very clean There is some extra wiring back here for that hitch and Just another storage area these seats do fold down Made when those seats are up they go down completely flat But very clean back here and this is just a manual closed gate Does have a backup camera as You go down this side of the vehicle just as clean as that passenger side This rim is absolutely perfect as well No dents or dings on these doors. It does have the built-in directional signals on the mirrors Inside the LT1 package gives you the gray cloth interior There are no rips or tears on these seats Has the aqua blue stitching factory floor mats throughout Power windows power locks and power mirrors Has auto headlamps inside here you can see that this vehicle has 8,000 and 38 miles Instrument cluster is nice and clear has the leather wrapped steering wheel Bluetooth audio controls on that side cruise controls on that side comes with the touchscreen radio Backup camera Yeah, this is the Chevy my link system You have your these seats are heated and you also have your traction control and a tow-haul mode as well rear air controls to Passenger seat is very clean as well. I don't think this vehicle has ever been smoked in Headliner is nice and clear as well the home link buttons On-star and passenger side floor mat Take a quick look at the back seats and then we will check out under the hood The back seats are just as clean as those front seats. No rips. No tears back here Very very clean Those seats do fold down You see that the third row is very clean as well Formats and carpeting very clean. You got two USB jacks back there and then the rear air controls as well that second row does slide forward and back to and Under the hood we have the 3.6 liter V6 motor As soon as I find the latch here Not much to see just a big plastic cover, but Very clean underneath here runs very smooth This vehicle has been fully safety and inspected by our service shop has a fresh oil and filter change All the fluids have been checked and topped off And this Chevy is 100% ready to go Very nice very clean ride and to see more pictures of this one or one of our other 400 new and used Cars trucks SUVs minivans wranglers. You name it. We got it go to our website www.SummitAuto.com Full pictures and descriptions of every single vehicle on our lot videos of every single used vehicle that we have all at SummitAuto.com Thank you so much for checking out the video if you want to make this one yours give us a call right now<PHONE_NUMBER> Ask for one of our sales associates to make this Chevy Traverse ears today. Once again that number is<PHONE_NUMBER>. Thanks again for checking out the video if you want to check out more You can go to youtube.com slash summit auto remember to like subscribe and share on this video and all the videos that you see there In fact in a second you will see a link to subscribe to our YouTube channel on your left a Link to more used inventory videos like this one on your right And if you have not been to our website on the bottom a link to this vehicle on our website click those check us out And we really look forward to helping with this super clean 2017 Chevy Traverse LT1. Thanks again


from theCUBE Studios in Palo Alto in Boston. It's theCUBE, covering IBM Think. Brought to you by IBM. Hi everybody, this is Dave Vellante of theCUBE and you're watching our coverage of the IBM digital event experience, the multi-day program, tons of content and it's our pleasure to be able to bring in experts, practitioners, customers and partners. Triram Raghavan is here, he's the Vice President of IBM Research in AI. Triram, thanks so much for coming on theCUBE. Thank you, pleasure to be here. I love this title, I love the role, it's great work if you're qualified for it, so tell us a little bit about your role and in your background, you came out of Stanford, you had the pleasure of sure of hanging out in South San Jose at the Alma Den Labs, a beautiful place to create, but give us a little background. Absolutely, yeah, so let me start, maybe go back to the present time, but what do I do now? My role is responsible for AI strategy, setting and execution in IBM Research across our global footprint, all our labs worldwide and they're working in AI. I also closely with the commercial, the parts of IBM are software and services business that take the innovation, AI innovation from IBM Research to market to our product, right? That's the second part of what I do. And where did I begin life in IBM? As you said, I began life at our Albertan Research Center up in San Jose, up in the hills, beautiful, I still think it's the best view I had. I spent many years there doing work at the intersection of AI and large scale data management and LP, went back to India. I was running the India Lab there for a few years and now I'm back here in New York, running the AI strategy for IBM. That's awesome. Let's talk a little bit about sort of AI, the landscape of IBM has always made it clear that you're not doing consumer AI, you're really trying to help businesses, but how do you look at the landscape? So it's a great question and it's one of those things that we constantly measure ourselves and we are partners sellers. I think you probably heard us talk about the cloud journey, that look, we're like very 20% of the workloads are in the cloud, 80% still waiting. AI, that number is even less. And but of course it varies, but depending on who you ask, you would say AI adoption is anywhere from 4% to 30% depending on who you ask in this pre-use case. But I think it's more important to look at where is this directionally? And it's very, very clear that adoption is rising. The value is more, it's getting better appreciated. But I think more important I think is there is broader recognition awareness and interest in knowing that to get value out of AI, you start with where AI begins, which is data. So the story around having a solid enterprise information architecture as the base on which to drive AI is starting to happen. So as the investments in data platform becoming, making your data ready for AI starts to come through, we're definitely seeing that adoption increase. And I think the second imperative that business look for obviously is the skills, the tools and the skills to scale AI. It can't take me months and months and hours to go build AI model, I got it accelerated and then comes operationalizing. But this is happening and the upward trajectory is very, very clear. We've been talking a lot on the cube over the last couple of years. It's not the innovation engine of our industry is no longer Moore's law, it's a combination of data. You just talked about data, applying machine to that data, being able to scale it across clouds, on-prem, wherever the data lives. So having said that, you've had a journey. You started out kind of playing Jeopardy, if you will. It was a very narrow use case and you're expanding that use case. I wonder if you could talk about that journey specifically in the context of your vision. Yeah, so let me step back and say for IBM Research AI, when I think about what's our strategy and vision, we think of it as in two parts. One part is the evolution of the science and techniques behind AI itself. And you said it, right? From narrow bespoke AI that all it can do is this one thing that it's really trained for, it takes a large amount of data, a lot of compute power. To how do you have the techniques and the innovation for AI to learn from one use case to the other? Be less data hungry, less resource hungry. Be more trustworthy and explainable. So we call that the journey from narrow to broad AI. And one part of our strategy as scientists and technologists is the innovation to make that happen. So that's one part. But as you said, as people involved in making AI work in the enterprise and IBM Research AI vision would be incomplete without the second part, which is what are the challenges in scaling and operationalizing AI? It isn't sufficient that I can tell you AI can do this. How do I make AI do this so that you get the right ROI, the investment relative to the term makes sense and you can scale and operationalize it. So we took both of these imperatives, the AI to go to the company and the need to scale and operationalize it. And what are the things that are making it hard? The things that make scaling and operationalizing harder, data challenges, we talked about that, skills challenges. And the fact that in enterprises you have to govern and manage AI. And we took that together. We think of an AI agenda and really keep advancing, trusting and scaling AI. Advancing is the piece of pushing the boundary, making AI narrow to product. Trusting is building AI which is trustworthy, is explainable. You can control and understand its behavior, make sense of it and all of the technology that goes with it. And scaling AI is when we address the problem of how do I reduce the time and cost for data prep? How do I reduce the time for model tweaking and engineering? How do I make sure that a model that you built today when something changes in the data, I can quickly allow for you to close the loop and improve the model? All of the things, think of day two operations of AI. All of that is part of our scaling AI strategy. So advancing, trusting, scaling is sort of the three big mantras around which the way we think about AI. Yeah, so I've been doing a little work in this, around this notion of data ops. Essentially, you know, dev ops applied to data and data pipeline. And I had a great conversation recently in Bandari, I'm a Global Chief Data Officer. And he explained to me how, first of all, customers will tell you, it's very hard to operationalize AI. He and his team took that challenge on themselves and have had some great success. And you know, we all know the problem. It's that, you know, AI has to wait for the data, has to wait for the data to be cleansed and wrangled. Can AI actually help with that part of the problem, compressing that? 100%. In fact, the way we think of the automation and scaling story is what we call the AI for AI story. So AI in service of helping you build the AI that helps you make this thing, right? So we, and I think of it really in three parts. It's AI for data automation or data ops. AI used in better discovery, better cleansing, better curation, faster linking or quality assessment. All of that, using AI to do all of those data problems that you have to do. And I call it AI for data automation. The second part is using AI to automatically figure out the best model. And that's AI for data science automation, which is feature engineering, hyper parameter optimization. Having done all that work, why should a data scientist make weeks and months experimenting if the AI can accelerate that from week to a matter of hours? That's data science automation. And then comes the important part also, which is operations automation. Okay, I put a data model into an application. How do I monitor its behavior? If the data that it's seeing is different from the data that was trained on, how do I quickly detect? And a lot of the work from research that was part of the Watson open scale offering is really addressing the operational side. So AI for data, AI for data science automation and AI to help automate production of AI is the way we break that problem out. So I always like to ask folks that are deep into R&D, how they ultimately are translating into commercial products and offerings? Because ultimately you got to make money to fund more R&D. So can you talk a little bit about how you do that and what your focus is there? Yeah, so it's a great question and I'm going to use a few examples as well. But let me say at the outset, this is a very, very close partnership. So between the research part of AI and our portfolio, close partnership where we're constantly both drawing properly as well as building the quality that goes into the offering. So a lot of our work, much of our work in AI automation that we were talking about is part of our Watson studio, Watson machine learning, Watson open scale. In fact, open scale came out of research working in trusted AI and is now a centerpiece of that, our Watson machine. Let me give a very different example. We have a very, very strong portfolio and focus in NLP natural language process. And this directly goes into capabilities out of Watson Assistant, which is our system for conversational support and customer support. And Watson Discovery, which is about making enterprise understand unstructured data. And a great example of that is the working project debater, which is a grand challenge in research about building a machine that can do debate. Now, look, we weren't looking to go sell your debating machine, but what did we build as part of doing that is advances in NLP that are all making that into Assistant and Discovery. And we actually just talked about earlier this year, announced a set of capabilities around better trust, advanced summarization, deeper sentiment analysis. These make their way into Assistant and Discovery, but are born out of research innovation and solving a grand problem like building a debate. That's just an example of how that journey from research to product happen. Yeah, I mean, the debater documentary, I've seen some of that. It's actually quite astounding. I don't know what you're doing there. It sounds like you're taking natural language and turning it into complex queries with data science and AI, but it's quite amazing. Yes, and I would encourage you. You will see that documentary, by the way, on channel seven in the think-even, and I would encourage you, actually, the documentary around how debater happened, sort of featuring back with the, backdoor interviews with the scientists who created it, was actually featured last minute, the Copenhagen International Documentary Festival. So I'll invite viewers to go to channel seven and data and AI tech on demand to go take a look at it now. Yeah, you should take a look at it. It's actually quite astounding and amazing. Suryam, what are you working on these days? What's kind of exciting projects or what's your focus area today? Look, I think there are three imperatives that we're really focused on. And one is very, it's really the question we're talking about NLP. NLP in the enterprise, look, text is a language of business, right? Text is the way businesses communicate within each other with their partners with the entire world. So helping machines understand language, but in an enterprise context, recognizing that data in the enterprises live in complex documents, unstructured documents and email. They live in conversations with the customers. So really pushing the boundary on how all our customers and clients can make sense of this vast volume of unstructured data by pushing the advances of NLP. That's one focus area. Second focus area, we talked about trust and how important that is. And we've done amazing work in monitoring and explainability. And we're really focused now on this emerging area of causality. Using causality to explain, right? The model makes this because the model believes this is going to cost, this is a beautiful way to do it. And the third big focus continues to be on automation. So NLP, trust automation, those are like three big focus areas for us. So how far do you think we can take AI? I know it's a topic of conversation, but from your perspective, deep into the research, how far can it go? And maybe how far should it go? Look, I think we are... Let me answer it this way. I think the art of the possible is enormous. But I think we are at this inflection point in which I think the next wave of AI, the AI that's going to help us, this narrow to broad journey we talked about. Look, that narrow to broad journey is not like a one week one year. We're talking about a day of innovation. But I think we are at a point where we're going to see a wave of AI that we like to call Neurosymbolic AI, which is AI that brings together two sort of fundamentally different approaches to building intelligence. One approach to building intelligence system is what we call knowledge driven. Understand data, understand concept, logically reason about it. We human beings do that. That was really the way AI was born. The more recent last couple of decades of AI was data driven, machine learning. Give me vast volumes of data. I'll use neural techniques, deep learning to get back. We're at a point where we're going to bring both of them together. Because you can't build trust towards the explainable systems only using one. You can't get away from not using all of the data that you have to make sense of. So Neurosymbolic AI is I think going to be the lynchment of how we advance AI and make it more powerful and trust. So are you like living your childhood dream here? Oh, oh, oh, I've been fascinated. I've always been fascinated and anytime, you can't have find a technology person who has the strength of building intelligent machines. Because we have a job where I can work across our worldwide set of 3,000 plus researchers and think and brainstorm and strategy to AI. And then most importantly, not to forget, that you talked about being able to move it into our portfolio so it actually makes a difference for our client. I think it's a dream job and I'm having a lot of fun. Well, Sriram, it's a great having you on theCUBE. A lot of fun interviewing folks like you. I feel a little bit smarter just talking to you. So thanks so much for your opportunity to be here. All right, thank you for watching everybody, watching theCUBE's coverage of IBM Think 2020. This is Dave Vellante. We'll be right back right after this short break.