(upbeat music) >> Announcer: From the cube Studios in Palo Alto and Boston, it's the cube! 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. A multi-day program, tons of content, and it's our pleasure to be able to bring in experts, practitioners, customers, and partners. Sriram Raghavan is here. He's the Vice President of IBM Research in AI. Sriram, 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.(laughs) So, tell us a little bit about your role and your background. You came out of Stanford, you had the pleasure, I'm sure, of hanging out in South San Jose at the Almaden labs. Beautiful place to create. But give us a little background. >> Absolutely, yeah. So, let me start, maybe go backwards in time. What do I do now? My role's responsible for AI strategy, planning, and execution in IBM Research across our global footprint, all our labs worldwide and their working area. I also work closely with the commercial parts. The parts of IBM, our Software and Services business that take the innovation, AI innovation, from IBM Research to market. 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 Almaden Research Center up in San Jose, up in the hills. Beautiful, I had in a view. 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, NLP. 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 AI strategy. >> That's awesome. Let's talk a little bit about AI, the landscape of AI. IBM has always made it clear that you're not doing consumer AI. You're really tying to help businesses. But how do you look at the landscape? >> So, it's a great question. It's one of those things that, you know, we constantly measure ourselves and our partners tell us. I think we, you've probably heard us talk about the cloud journey . But look barely 20% of the workloads are in the cloud, 80% still waiting. AI, at that number is even less. But, of course, it varies. Depending on who you ask, you would say AI adoption is anywhere from 4% to 30% depending on who you ask in this case. But I think it's more important to look at where is this, directionally? And it's very, very clear. 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 investment, 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. And I think, you know, the second imperative that businesses 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 an AI model, I got to accelerate it, and then comes operationalizing. But this is happening, and the upward trajectory is very, very clear. >> We've been talking a lot on theCUBE 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 technology to that data, being able to scale it, across clouds, on-prem, wherever the data lives. So. >> Right. >> Having said that, you know, you've had a journey. You know, 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 how we, 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. 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 computing power. Two, 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 sort of 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 return makes sense and you can scale and operationalize. So, we took both of these imperatives. The AI narrow-to-broad journey, and the need to scale and operationalize. And what of 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 and we think of our AI agenda in three pieces: Advancing, trusting, and scaling AI. Advancing is the piece of pushing the boundary, making AI narrow to broad. 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, you know, 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 build 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 our AI. >> Yeah, so I've been doing a little work in this around this notion of DataOps. Essentially, you know, DevOps applied to the data and the data pipeline, and I had a great conversation recently with Inderpal Bhandari, IBM's Global Chief Data Officer, and he explained to me how, first of all, customers will tell you, it's very hard to operationalize AIs. 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. It 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 with speed, right? So, and I think of it really in three parts. It's AI for data automation, our DataOps. AI used in better discovery, better cleansing, better configuration, faster linking, quality assessment, all of that. Using AI to do all of those data problems that you had to do. And I called 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, hyperparameter optimization, having them all do work, why should a data scientist take weeks and months experimenting? If the AI can accelerate that from weeks to a matter of hours? That's data science automation. And then comes the important part, also, which is operations automation. Okay, I've 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 it was trained on, how do I quickly detect it? And a lot of the work from Research that was part of that Watson OpenScale 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 up. >> So, I always like to ask folks that are deep into R&D, how they are 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, what your focus is there? >> Yeah, so that'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 closed partnership. So when we, the Research part of AI and our portfolio, it's a closed partnership where we're constantly both drawing problem as well as building technology 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 OpenScale. In fact, OpenScale came out of Research working Trusted AI, and is now a centerpiece of our Watson project. Let me give a very different example. We have a very, very strong portfolio and focus in NLP, Natural Language Processing. 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 unstructurally. And a great example of that is the Working Project Debater that you might have heard, which is a grand challenge in Research about building a machine that can do debate. Now, look, we weren't looking to go sell you a debating machine. But what did we build as part of doing that, is advances in NLP that are all making their way into assistant and discovery. And we actually just talked about earlier this year, announced a set of capabilities around better clustering, advanced summarization, deeper sentiment analysis. These made their way into Assistant and Discovery but are born out of research innovation and solving a grand problem like building a debating machine. That's just an example of how that journey from research to product happens. >> Yeah, 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 7, in the Think Event. And I would encourage you, actually the documentary around how Debater happened, sort of featuring back of the you know, backdoor interviews with the scientist who created it was actually featured last minute at Copenhagen International Documentary Festival. I'll invite viewers to go to Channel 7 and Data and AI Tech On-Demand to go take a look at that documentary. >> Yeah, you should take a look at it. It's actually quite astounding and amazing. Sriram, what are you working on these days? What 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, you know, just really the project you're talking about, NLP. NLP in the enterprise, look, text is a language of business, right? Text is the way business is communicated. Within each other, with their partners, with the entire world. So, helping machines understand language, but in an enterprise context, recognizing that data and the enterprises live in complex documents, unstructured documents, in e-mail, 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 what it wants, it's a beautiful way. And the third big focus continues to be on automation. So, NLP, trust, automation. Those are, like, three big focus areas for us. >> sriram, 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 arc 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, the narrow-to-broad journey's not like a one-week, one-year. We're talking about a decade 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 "neuro-symbolic AI," which is AI that brings together two sort of fundamentally different approaches to building intelligence systems. One approach of building intelligence system is what we call "knowledge driven." Understand data, understand concept, logically, reasonable. 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 to get value. We're at a point where we're going to bring both of them together. Cause you can't build trustworthy, explainable systems using only one, you can't get away from not using all of the data that you have to make them. So, neuro-symbolic AI is, I think, going to be the linchpin of how we advance AI and make it more powerful and trustworthy. >> So, are you, like, living your childhood dream here or what? >> Look, for me I'm fascinated. I've always been fascinated. And any time you can't find a technology person who hasn't dreamt of building an intelligent machine. To have a job where I can work across our worldwide set of 3,000 plus researchers and think and brainstorm on strategy with AI. And then, most importantly, not to forget, right? That you talked about being able to move it into our portfolios so it actually makes a difference for our clients. I think it's a dream job and a whole lot of fun. >> Well, Sriram, it was 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 coming on. >> Fantastic. It's been a pleasure to be here. >> And thank you for watching, everybody. You're watching theCUBE's coverage of IBM Think 2020. This is Dave Vellante. We'll be right back right after this short break. (upbeat music)

>>From the cube studios in Palo Alto in Boston covering IBM thing brought to you by IBM. >>Hi everybody. We're back and this is Dave Vellante and you're watching the cubes continuous coverage of the IBM think 2020 digital events experience Sriram these monotonous here he is the global managing director for government healthcare and life sciences three. Ron, thanks so much for coming on the cube. >>Great to be with you Dave. I wish we were Darren but it's, it's great to be here digitally indeed >>be good to be face to face and in San Francisco but this certainly will help our audience understand what's happening in these critical sectors. I mean you were at the heart of it. I mean these are three sectors and then there are sub sectors in there. Let's try to understand how you're communicating with your clients, what you've been doing in the near term and then I want to really try to understand, you know, what you see coming out of this, but please tell us what's been going on in your, in your world. >>You're right. I mean these sectors are keeping, keeping the engine running right now in terms of keeping society running, right? So if you look at the federal government, the state government, the local government, you look at providers of healthcare, you look at payers, we're making sure that their members are getting the, getting the advice and the service they need. You look at a life sciences companies or rapidly trying to find a cure for this, uh, for this virus. And then you look at education where, um, you know, the educational establishments are trying to work remotely and make sure that our children get the education they need. So kind of existential industries right front and center of this ninety-five, interestingly, they have 95% of IBM has, have continued to work from home and yet we are able to support the core operations of our clients. So if you look at some of the things that we've been doing over the last eight or nine weeks that we've been under this kind of lockdown, um, IBM, IBM is involved in the engine room. >>I would like to call it the engine room of many of these operations, right? Whether it just to keep a city running or a hospital running. Um, our systems, our software, our services teams are engaged in making sure that the core systems that allow those entities to function are actually operational, um, during these times. So we've had no blips. We've been able to support that. And that's a, that's a key part of it. Now, of course, there are extraordinary things we've done on top. For instance, you know, in the first two weeks after the crisis started, we used, um, a supercomputer with the department of energy that you must've heard about, uh, to narrow down over 8,000 compounds that could potentially be cures for the COBIT 19 virus and narrowed down to 80. That could be applicable, right? Um, so sharpening the time and allowing researchers not to focus on 80 compounds and stuff, 8,000 so that we can get a vaccine to market faster. >>And that's tremendous, right? I mean we've, we formed a, um, uh, you know, collaboration, uh, with, with 27 other, uh, partners, uh, that, who are all co innovating, uh, using modeling techniques, uh, to try and find a cure faster. The other end, um, you look at things like what we're doing with the state of New York, where we work for the government, uh, the duet to get 350,000 tablets with the right security software, with the right educational software so that students can continue to learn while, uh, you know, what they are, uh, when they're remote, but the right connectivity. So, um, extremes. And then of course as a backbone, you know, be using, we are starting to see real use of our AI tools, chat bots to stop it, that we have. Uh, we have allowed, uh, uh, customers to use for free. So they began answer that we can, we can consume the latest CDC advice, the latest advice from the governors and the state, and then, um, allow the technology to answer a lot of queries that are coming through, uh, with, with, uh, with citizens being worried about what, where they stand every single day. >>Yeah. So let's kind of break down some of the sectors that you follow. Um, let's start with, with government. I mean, certainly in the United States it's been all about the fiscal policy, the monetary policy, injecting cash into the system, liquidity, you know, supporting the credit markets. Certainly central banks around the world are facing, you know, similar, but somewhat different depending on their financial situations. Um, and so that's been the near term tactical focus and it actually seems to be working pretty well. Uh, you know, the stock market's any indicator, but going forward, I'm interested in your thoughts. You wrote a blog and you basically, it was a call to action to the government to really kind of reinvent its workforce, bringing in, uh, millennials. Um, and, and so my, my, my question is, how do you think the millennial workforce, you know, when we exit this thing, will embrace the government. What does the government have to do to attract millennials who want the latest and greatest technology? I mean, give us your thoughts on that. >>Well, it's an, it's a really interesting question. A couple of years ago I was talking about, uh, this is the time where governments have to have to really transform. They have to change. If you, if you go back in time compared governments to other industries, uh, governments have embraced technology, but it's been still kind of slow, incremental, right? Lots of systems of record, big massive systems that take 10 years, five years to implement. So we've implemented systems record. We've, we've started using data and analytics to kind of inform policymaking, but they tend to be sequential. And I think, uh, you know, coming back to the, the, the changing workforce, uh, what is it? By 2025, 75% of the workforce are going to be millennials, right? Um, and as they come into the workforce, I think they're going to demand that, uh, that we work in new ways in new, um, more integrated, more digitally savvy pace and uh, strange enough, I think this crisis is going to be a, is a proof point, right? >>Um, many governments are working remotely and yet they're functioning okay. Um, the, the, the world of, um, you know, providing policy seems to be working even if you are, if you are remote. So a lot of the naysayers who said we could not operate digit, operate digitally, um, now are starting to starting to get past that, uh, that bias if you like. And so I think as, as digital natives come into the for what we are going to see is this is a Stressless innovation of why do we do things the same way as we've done them for the last 20, 30 years. Um, granted we need to still have the, um, the, the division of policies, make sure that we are enforcing the policies of government. But at the same time, if you look at workflow, uh, this is the time where you can use automation, intelligent workflows, right? >>This is the time where we can use insights about what our citizens need so that services are tuned, a hyper-local are relevant to what the citizen is going through at that particular time. Uh, contextual and, um, are relevant to what, what that individual needs at that particular time. Uh, rather than us having to go to a portal and, uh, submit an application and submit relevant documents and then be told a few hours or a few minutes later then that you've got, you've got approval for something, right? So I think there's this period of restless innovation coming through that is from a citizen engagement perspective, but behind the scenes in terms of how budgeting works, how approvals work, how uh, uh, you know, the divisions between federal, state, local, how the handoffs between agencies work. All of that is going to be restlessly innovative. And, uh, this is the moment I think this is going to be a trigger point. We believe it's going to be a trigger point for that kind of a transformation? >>No, sure. I'm, I've talked to a number of, of CEOs in, in sort of hard hit industries, um, hospitality, you know, certainly, you know, the restaurant business, airlines and, and you know, they just basically have a dial down spending, um, and really just shift to only mission critical activities. Uh, and in your segments it's, it's mixed, right? I mean, obviously government, you use the engine room, uh, analogy before some of use the war room metaphor, but you think about healthcare, the frontline workers. So it's, it's, it's mixed what our CIO is telling you in, in the industries in which you're focused. >>Well, the CIO is right now. I mean, you're going to go through different phases, right? Phase one is just reactive. It's just coping with the, uh, with the situation today where you suddenly have 95%, a hundred percent of your workforce working remote, providing the ability to, it's providing the leadership, the ability to, to work remotely where possible. Um, and it take IBM for instance, you know, we've got 300,000 people around the world, but 95% of whom are working remotely. Um, but we've been, we've been preparing for moments like this where, uh, you know, we've got the tools, we've got the network bandwidth, we've got the security parameters. Uh, we have been modernizing our applications. Um, so you've been going to a hybrid cloud kind of architecture, but you're able to scale up and scale down, stand up additional capacity when you need it. So I think a lot of the CEOs that we talk to are, uh, you know, phase one was all about how do I keep everything running? >>Phase two is how do I prepare for the new norm where I think more collaborative tools are going to come into, into the work environment. Um, CEO's are going to be much more involved in how do I get design in the center of everything that we do no matter what kind of industry. Alright. So, um, it's, it's going gonna be an interesting change as to the role of the CIO going forward. Dave and I think, uh, again, it's a catalyst to saying why do we have to do things the same way we've been doing? Why do we need so many people in an office building doing things in traditional ways? And why can't we use these digital techniques as the new norm? >>Yeah, there are a lot of learnings going on and I think huge opportunities to, to, to, to save money going forward because we've had to do that in the near term. But, but more importantly, it's like how are we going to invest in the future? And that's, that's something that I think a lot of people are beginning now to think about. They haven't had much time to do anything other than think tactically. But now we're at the point where, okay, we're maybe starting to come out of this a little bit, trying to envision how we come back. And organizations I think are beginning to think about, okay, what is our mid to longer term strategy? It's, we're not just going to go back to 2019. So what do we do going forward? So we're starting to spend more cycles and more energy, you know, on that topic. What do you see? >>Yeah, I mean, take every segment of my, uh, my sector, right? Take the education industry, will you, uh, will you spend 60, $70,000 a year to send a child to university, um, when a lot of the learning is available digitally and when, when we've seen that they can learn as much and probably more, uh, you know, more agile manner and follow their interests. So I think the whole education industry is going to leverage digital in a big way. And I think you're going to see partnerships form, you can see more, uh, you're going to see more choice, uh, for the student and for the parents, uh, in the education industry. And so that industry, which has been kind of falling the same type of pattern, uh, you know, for a hundred years, it's suddenly going to reinvent itself. Take the healthcare industry. Um, you know, it's interesting, a lot of providers are following, uh, following staff because elective, uh, elective treatment as really, you know, uh, fallen tremendously. >>Right? On the one hand you have huge demand for covert 19 related, uh, treatment on the other hand, electives have come down. So cost is a big issue. So I, I believe we're going to see M and a activity, uh, in that sector. And as you see that what's going to happen is people are gonna, uh, restlessly reinvent. So w you know, I think telemedicine is going to, is not going to become a reality. I think, um, if you look at the payer space and if you look at the insurance providers, they're all going to be in the market saying, Harbor, how do I capture more members and retain them and how do I give them more choice? Um, and how do I keep them safe? It's interesting, I was speaking to a colleague in Japan, uh, yesterday and he was saying to me in the automotive industry that, um, I was arguing that, you know, you will see a huge downfall. >>Uh, but his argument back was people are actually so afraid of taking public transport that, uh, they're expecting to see a spike in personal transportation. Right? So I think from a government perspective, the kind of policy implications, um, you know, whether there would be economic stimulus related in the short term, governments are going to introduce inefficiencies to get the economy back to where it needs to be. But over a long term I think we're back to these efficiencies. We are going to look at supply chain, there's going to be a postmortem on how do we get where we got to now. And um, so I think in terms of citizen engagement, in terms of supply chain, in terms of back office operations, in terms of how agencies coordinate, um, do stockpiling command and control, all of that is going to change, right? And it's an exciting time in a way to be at the forefront of these industries shaping, shaping the future. >>I want to ask your thoughts on, on education and excuse me, drill into that a little bit. I've actually got pretty personal visibility in sort of let's, let's break it down. Um, you know, secondary universities, uh, nine through 12 and K through six and then you're seeing some definite differences. Uh, I think actually the universities are pretty well set up. They've been doing online courses for quite some time. They've, they've started, you know, revenue streams in that regard and, and so their technology is pretty good and their processes are pretty good at the other end of the spectrum, sort of the K through six, you know, there's a lot of homeschooling going on and, and parents are at home, they're adjusting pretty well. Whether it's young kids with manipulatives or basic math and vocabulary skills, they're able to support that and you know, adjust their work lives accordingly. >>I find in the, in the high school it's, it's really different. I mean it's new to these folks. I had an interesting conversation with my son last night and he was explaining to me, he spends literally hours a day just trying to figure out what he's got to do because every process is different from every teacher. And so that's that sort of fat middle, if you will, which is a critical time, especially for juniors in high school and so forth where that is so new. And I wonder what you're seeing and maybe those three sectors, is that sort of consistent with what you see and, and what do you see coming out of this? >>I think it's, it's broadly consistent and I have personally experienced, I have one university grade, uh, university senior and I have a high school senior and I see pretty much the same pattern no matter which part of the world they're in. Right? I, I do believe that, um, you know, this notion of choice for students and how they learn and making curriculum customized to get the best out of students is the new reality. How fast we will get there. How do you get there? It's not a linear line. I think what is going to happen is you're going to, you're going to see partnerships between, uh, content providers. You're going to see partnerships between platform providers and you're going to see these educational institutions, uh, less restless. The reinvent to say, okay, this particular student learns in this way and this is, this is how I shape a personalized curriculum, but still achieving a minimum outcome. Right? I think that's going to come, but it's going to take a few years to get there. >>I think it was a really interesting observations. I mean, many children that I observed today are sort of autodidactic and if you give them the tooling to actually set their own learning curriculum, they'll, they'll absorb that and obviously the technology has gotta be there to support it. So it's sort of hitting the escape key. Let's sort of end on that. I mean, in terms of just IBM, how you're positioning in the industries that you're focused on to help people take this new technology journey. As they said, we're not going back to the last decade. It's a whole new world that we're going to going to come out of this post. Coven, how do you see IBM has positioned their Sri round? >>Dave, I think I'd be positioned brilliantly. Um, as you know, we've, Arvind Christianized is our new CEO and, uh, he, he recently talked about this on CNBC. So if you look at the core platforms that we've been building, right? Um, so CA occupies an industry, whether it's, whether it be government, healthcare, life sciences or education are going to look for speed. They're going to look for agility, they're going to look to change processes quickly so they can, they can react to situations like this in the future in a much more agile way, right? In order to do that, their it systems, their applications, their infrastructure needs to scale up and down needs to be, uh, you need to be able to configure things in a way where you can change parameters. You can change policies without having to read a long time, right? And so if you think about things like HyperCloud our investment in, uh, in, in red hat, uh, our, uh, our, uh, position on data and open technologies and, um, you know, our policies around making sure that, that our client's data and insights are their insights and we don't, we don't want to taste that. >>On of those things. Our investments in blockchain are deep, deep, uh, incumbency in services. But there'd our technology services, our consulting services, our deep industry knowledge, allowing all of these technologies to be used at to solve these problems. Um, I think we are really well positioned and, uh, you know, a great example is the New York example, right? So, uh, getting 350,000 students to work in a completely new way in a matter of two weeks. It's not something that every single company can do. It's not just a matter of providing the tech, the tool itself, it's the content, it's the consumption, it's the design must experience. And that's where a company like IBM can bring everything together. And then you have the massive issues of government, like social reform, like mental health, like making sure the stimulus money is going to the people who need it the most, um, in, in the most useful way. And that's where I work between industries, between government and banks and other industries really comes to, comes to fruition. So I think we have the technology but the services depth. And I think we've got the relevance of the industry to make a difference. And I'm excited about the future. >>Well, it's interesting that you mentioned, you know, the basically one of my takeaways is that you've got to be agile. You've gotta be flexible. You, you've been in the consulting business for most of your career and in the early part of your career. And even up until, you know, maybe recently we were automating processes that we knew well, but today the processes are, we so much is unknown. And so you've got to move fast. You've got to be agile, you've got to experiment, uh, and apply that sort of, you know, test, experiment, methodology and iterate and have that continuous improvement. That's a different world than what we've known. Obviously. You know, as I say, you've seen this over the decades. Uh, your final thoughts on, uh, on the future. >>Well, my final thoughts are, um, yeah, you're exactly right. I mean, if I take a simple example, right, that, that, uh, controls how quickly the commerce works. Think about simple things like bill of lading. Uh, the government has to issue a federal government has to prove that a state government has to prove it and local government has to prove it. Why? That's the way we've been doing it for a long time. Right? There are control points, but to your point, imagine if you can shorten that from a seven day cycle to a seven second cycle. The impact on commerce, the impact on GDP, and this is one simple process. This is the time for us to re to, to, to break it all apart and say why not do something differently? And the technology is right. The CA, the AI is getting more and more and more mature and you've got interesting things like quantum to look forward to. So I think the timing is right for, for reinventing, uh, the core of this industry. >>Yeah, I think they really are. I mean, it's difficult as this crisis has been a lot of opportunities going to present coming out of a tree room. Thanks so much for coming on the cube and making this happen. Really appreciate your time. It's great to be here. Thank you for having me. Dave, you're very welcome and thank you everybody for watching. This is Dave Volante for the Cuban or continuous coverage of the IBM think 2020 digital event experience. Keep it right there and we right back right after this short break.