(techno music) >> Narrator: Live from Las Vegas, it's the Cube. Covering Service Now Knowledge 2018. Brought to you by Service Now. >> Welcome back to the Cube's coverage of Service Now Knowledge 18 here in Las Vegas, Nevada. I'm your host Rebecca Knight. I'm joined by Pat Wadors. She is the Chief Talent Officer of Service Now, and Pat Tourigney who is the Senior Vice President HR Global Shared Services at Magellan Health. Pat and Pat, thanks so much for coming on the show. >> Pat Wadors: Thank you for having us. We're excited. >> Pat Tigourney: It's so great to be here Rebecca, thank you. >> Rebecca: Well you were both on the main stage this morning talking about Magellan's, Magellan Health Service Now journey. We started talking about a personal health scare that you had Pat, that really changed the way you think about the world of work, and the employers' role in that. Can you tell our viewers a little more about it? >> Pat: I'd be happy to Rebecca. So, obviously I had been working and had taken some time off to start and raise my family. And when I went back to work I started to feel unwell. And it took about two and a half years for me to finally get an answer. I had searched for many doctors, et cetera. But literally one day I was rushed to a hospital emergency room. After a few days I was diagnosed with stage three B colon cancer, and I was told I had probably about a three percent survival chance. So at that time I faced four years of surgery, and hospitalizations, and chemo and radiation. And of course during all this time you're hearing the probably outcomes and the statistics. But what I truly focused on was my purpose. Which was my family. I had two small children and they needed me, and I needed to be there for them. And so I learned a lot of lessons during that time, and I think anyone who goes through that would say that. But the two things that have really stuck with me is knowing my purpose, and leading with empathy. And it's truly changed how I live, how I work, how I interact with other people. And I think its made a huge difference in what I do every day. >> Rebecca: What Pat was just talking about, the leading with empathy, and the finding your purpose, these are two of the things that are central to the culture at Service Now. Can you describe a little bit more for our viewers, how you view this sort of purpose driven life? >> Pat Wadors: For me and for the company, its as essential to our success as our customers. So I know that purpose driven companies outperform those that don't have a purpose. And I know from a talent brand, and how we recruit and retain talent, if their personal purpose is aligned with the company purpose, not only do you get higher engagement and higher productivity, but that impacts our customers. And they have higher engagement and higher sat. So its great business. It's something that I think creates a competitive differentiation, and its something that our employees seek as an employer. So it's just something that I totally believe in and so does our company. >> Rebecca: So talk a little bit about VERN. First of all, what does VERN stand for? >> Pat: Oh I love VERN. (laughing) >> Pat: Everyone loves VERN. VERN stands for the Virtual Employee Resource Network. And a couple things that I would probably want to say about that is number one, you don't see HR in there at all. Because it's about the employee. This is a way that we are helping our employees fundamentally change how they work and how they engage with us. The reason I think VERN works is our employees voted on that name. So we had a whole campaign to launch VERN, and we offered up four different names, and our employees voted. And when VERN won we created a VERN persona, and everything else that goes with that. And he's just become part of our team. >> Rebecca: So what does VERN do? >> Pat: Well VERN is really sort of the, it took the place of our call center. VERN is a way for our employees to learn information, and answer their basic questions, and learn to work in new ways. And it helps, it's basically a consumerized HR product. If an employee can use google or shop online, they can use VERN. Its' very simple, it's easy and fun. And truly VERN has become a part of our team. So we don't have a call center anymore. We don't use email to answer questions. Our employees know that VERN is there for them twenty four seven. >> Rebecca: They have a question and ask VERN. >> Pat: Exactly. Turn to VERN, that's our motto. >> Rebecca: (laughing) I love it. So Pat, thinking about this empathic way of leading, how would you describe what it really means when it comes to HR? You had said before it really is a competitive differentiator, and that if you're happier at work, you're going to do better at work, you're going to be more energized, you're going to then provide better service to your customers. But how can companies, how can they build a culture of empathy? >> Pat: By listening. I think that when Pat and I were talking over dinner and I talked to my peers, companies that win listen. And they listen to their customers, and they reverse engineer back to their products and services. Great cultures listen. And our employees are going to tell us what's working what's not working. And if we capture those data sets, those moments, we give them the information, we give them the tools. They are joyful, they are more productive, there's a stickiness that I can not only survive there I'll thrive. And so by being empathetic, by seeing where the pain points are, by seeing what gets you joyful, and measuring those things and turning my dials accordingly, that to me is a winning situation. >> Rebecca: We're at a point in time where we have five generations in the workforce all at once. Can you describe what that's like, from your company perspective, from talent management and HR, and how catering to these very different segments of people who their comfort with technology is one thing, but also their phase of life. How do you do that? >> Pat: Well I think, honestly, there's this joyfulness, you used that word and I love that word, of how all these different generations really do work together and help one another. In a way we're all learning from each other. And we're not afraid to learn in front of each other. And that really makes a difference I think. And I think there's just this mutual respect of, we're all there to help each other and do the right thing for the company. And I think the empathy piece of it really comes across because, when you truly understand one another in a way that you care and you're showing that, it's not about age anymore or anything else, it's that we're all people working together trying to do our best work and we're there for each other. To me that's what it means. >> Pat: The only thing I would add to that is, when you look at consumerization of the enterprise, when you look at seamless, what they call frictionless solutions, it demystifies the technology. So if you have the older generation going "I've not used a bot" or "I don't know what machine learning is" I'm like can you type in your question? I can do that. And if I serve you knowledge bites that I can digest that answers my question and move on with my life, that's a gift. And so I think that if you make it more human, if you make it more approachable, then every generation appreciates that. And I also know that from my studies and from working in the valley for a long time in tech, is that every generation wants the same thing. They want to be heard, they want to be appreciated, treated respectfully, and know that they can do their best work. That they matter. >> Rebecca: So Pat you are relatively new to Service Now. You're from LinkedIn. You are so committed to the company you dyed your hair to match the brand identity. What drew you to Service Now? >> Pat: I was a customer of Service Now while at LinkedIn. And my goldilocks is a growth company. I'm a builder. I love creating culture and leading through change. And I also love geeking out with my peeps in HR. And so Service Now has a talent place, they are helping HR solve problems, and I get to geek out with them. I get to meet people like Pat, and have a wonderful dinner and a great conversation. That feeds my soul. I don't think I am unique in the problems I'm facing, and I copy shamelessly. I'm trying to steal VERN from her. (Pat laughing) I think that's awesome, I want a VERN button. >> Pat: I'm going to get you one. >> Pat: And then the added sauce for me where I fell in love, is when John Donahoe became the CEO and wanted my partnership to build an enduring high performing healthy company. And I'm like, sign me up. >> Rebecca: Talking about the culture of Service Now and Magellan Health, culture is so hard. It's just one of those things that, or maybe its not, maybe I'm making it out to be, but when you have large companies dispersed employees, i'ts sort of hard to always stay on message and to have everyone pulling in the same direction. How do you do it? What would you say you do at Magellan? I'm interested in how you do it at Service Now too. >> Pat: Want to go first? >> Pat: I'll take a stab. So, you got to think about where you're going. So what's your purpose? I'm going back to purpose. How do you serve the customer? What are those four key milestones that matter? And repeat, and I say rinse, and then repeat. So everyone hears it. You know the top five goals in the company. And we talk about it all hands, we refer to them in our internal portal, we talk about them, we measure them. We tell the employees this is what we wanted to do, this is what we did or didn't do. This is what we do next. And we're as transparent as we possibly can be. And the magic comes when every employee can look up and say I made that goal happen. And when they start seeing those dots connect, they can't wait to connect more dots. And that's when the journey starts accelerating. That's when you get more flywheel going in the organization where what I do is actually impacting profit, impacting customer success, impacting joy. >> Rebecca: And taking some ownership of it. >> Pat: I agree. I think that when everyone sort of shares in that purpose, and they understand what they do, how it affects that, it makes a huge difference. But I also think as an organization from a leadership perspective, if you model the behavior that you're seeking, and you set your expectations really high for that, and that in a very sort of respectful way when you see things that aren't right you say something about it, the culture does start to shift. And you start to build this feeling of we're there, we're together, we have each other's backs, we treat each other with dignity and respect, and honesty and openness, and you can really start to just shift it almost organically. >> Rebecca: Pat Tourigney, Pat Wadors, thanks so much for coming on the Cube. It was a great conversation. >> Pat: Oh thank you Rebecca. It's been great. >> Pat: Thank you for having us. >> Rebecca: We'll have more with the Cube's live coverage of Service Now just after this. (techno music)

>> Narrator: From theCUBE Studios in Palo Alto and Boston, connecting with thought leaders all around the world, this is a CUBE conversation. >> Hello everyone, welcome to this special CUBE interview. We are here at theCUBE Virtual covering AWS Summit Virtual Online. This is Amazon's Summits that they normally do all around the world. They're doing them now virtually. We are here in the Palo Alto COVID-19 quarantine crew getting all the interviews here with a special guest, Vice President of Machine Learning, we have Swami, CUBE Alumni, who's been involved in not only the machine learning, but all of the major activity around AWS around how machine learning's evolved, and all the services around machine learning workflows from transcribe, recognition, you name it. Swami, you've been at the helm for many years, and we've also chatted about that before. Welcome to the virtual CUBE covering AWS Summit. >> Hey, pleasure to be here, John. >> Great to see you. I know times are tough. Everything okay at Amazon? You guys are certainly cloud scaled, not too unfamiliar of working remotely. You do a lot of travel, but what's it like now for you guys right now? >> We're actually doing well. We have been I mean, this many of, we are working hard to make sure we continue to serve our customers. Even from their site, we have done, yeah, we had taken measures to prepare, and we are confident that we will be able to meet customer demands per capacity during this time. So we're also helping customers to react quickly and nimbly, current challenges, yeah. Various examples from amazing startups working in this area to reorganize themselves to serve customer. We can talk about that common layer. >> Large scale, you guys have done a great job and fun watching and chronicling the journey of AWS, as it now goes to a whole 'nother level with the post pandemic were expecting even more surge in everything from VPNs, workspaces, you name it, and all these workloads are going to be under a lot of pressure to do more and more value. You've been at the heart of one of the key areas, which is the tooling, and the scale around machine learning workflows. And this is where customers are really trying to figure out what are the adequate tools? How do my teams effectively deploy machine learning? Because now, more than ever, the data is going to start flowing in as virtualization, if you will, of life, is happening. We're going to be in a hybrid world with life. We're going to be online most of the time. And I think COVID-19 has proven that this new trajectory of virtualization, virtual work, applications are going to have to flex, and adjust, and scale, and be reinvented. This is a key thing. What's going on with machine learning, what's new? Tell us what are you guys doing right now. >> Yeah, I see now, in AWS, we offer broadest-- (poor audio capture obscures speech) All the way from like expert practitioners, we offer our frameworks and infrastructure layer support for all popular frameworks from like TensorFlow, Apache MXNet, and PyTorch, PowerShell, (poor audio capture obscures speech) custom chips like inference share. And then, for aspiring ML developers, who want to build their own custom machine learning models, we're actually building, we offer SageMaker, which is our end-to-end machine learning service that makes it easy for customers to be able to build, train, tune, and debug machine learning models, and it is one of our fastest growing machine learning services, and many startups and enterprises are starting to standardize their machine learning building on it. And then, the final tier is geared towards actually application developers, who did not want to go into model-building, just want an easy API to build capabilities to transcribe, run voice recognition, and so forth. And I wanted to talk about one of the new capabilities we are about to launch, enterprise search called Kendra, and-- >> So actually, so just from a news standpoint, that's GA now, that's being announced at the Summit. >> Yeah. >> That was a big hit at re:Invent, Kendra. >> Yeah. >> A lot of buzz! It's available. >> Yep, so I'm excited to say that Kendra is our new machine learning powered, highly accurate enterprise search service that has been made generally available. And if you look at what Kendra is, we have actually reimagined the traditional enterprise search service, which has historically been an underserved market segment, so to speak. If you look at it, on the public search, on the web search front, it is a relatively well-served area, whereas the enterprise search has been an area where data in enterprise, there are a huge amount of data silos, that is spread in file systems, SharePoint, or Salesforce, or various other areas. And deploying a traditional search index has always that even simple persons like when there's an ID desk open or when what is the security policy, or so forth. These kind of things have been historically, people have to find within an enterprise, let alone if I'm actually in a material science company or so forth like what 3M was trying to do. Enable collaboration of researchers spread across the world, to search their experiment archives and so forth. It has been super hard for them to be able to things, and this is one of those areas where Kendra has enabled the new, of course, where Kendra is a deep learning powered search service for enterprises, which breaks down data silos, and collects actually data across various things all the way from S3, or file system, or SharePoint, and various other data sources, and uses state-of-art NLP techniques to be able to actually index them, and then, you can query using natural language queries such as like when there's my ID desk-scoping, and the answer, it won't just give you a bunch of random, right? It'll tell you it opens at 8:30 a.m. in the morning. >> Yeah. >> Or what is the credit card cashback returns for my corporate credit card? It won't give you like a long list of links related to it. Instead it'll give you answer to be 2%. So it's that much highly accurate. (poor audio capture obscures speech) >> People who have been in the enterprise search or data business know how hard this is. And it is super, it's been a super hard problem, the old in the old guard models because databases were limiting to schemas and whatnot. Now, you have a data-driven world, and this becomes interesting. I think the big takeaway I took away from Kendra was not only the new kind of discovery navigation that's possible, in terms of low latency, getting relevant content, but it's really the under-the-covers impact, and I think I'd like to get your perspective on this because this has been an active conversation inside the community, in cloud scale, which is data silos have been a problem. People have had built these data silos, and they really talk about breaking them down but it's really again hard, there's legacy problems, and well, applications that are tied to them. How do I break my silos down? Or how do I leverage either silos? So I think you guys really solve a problem here around data silos and scale. >> Yeah. >> So talk about the data silos. And then, I'm going to follow up and get your take on the kind of size of of data, megabytes, petabytes, I mean, talk about data silos, and the scale behind it. >> Perfect, so if you look at actually how to set up something like a Kendra search cluster, even as simple as from your Management Console in the AWS, you'll be able to point Kendra to various data sources, such as Amazon S3, or SharePoint, and Salesforce, and various others. And say, these are kind of data I want to index. And Kendra automatically pulls in this data, index these using its deep learning and NLP models, and then, automatically builds a corpus. Then, I, as in user of the search index, can actually start querying it using natural language, and don't have to worry where it comes from, and Kendra takes care of things like access control, and it uses finely-tuned machine learning algorithms under the hood to understand the context of natural language query and return the most relevant. I'll give a real-world example of some of the field customers who are using Kendra. For instance, if you take a look at 3M, 3M is using Kendra to support search, support its material science R&D by enabling natural language search of their expansive repositories of past research documents that may be relevant to a new product. Imagine what this does to a company like 3M. Instead of researchers who are spread around the world, repeating the same experiments on material research over and over again, now, their engineers and researchers will allow everybody to quickly search through documents. And they can innovate faster instead of trying to literally reinvent the wheel all the time. So it is better acceleration to the market. Even we are in this situation, one of the interesting work that you might be interested in is the Semantic Scholar team at Allen Institute for AI, recently opened up what is a repository of scientific research called COVID-19 Open Research Dataset. These are expert research articles. (poor audio capture obscures speech) And now, the index is using Kendra, and it helps scientists, academics, and technologists to quickly find information in a sea of scientific literature. So you can even ask questions like, "Hey, how different is convalescent plasma "treatment compared to a vaccine?" And various in that question and Kendra automatically understand the context, and gets the summary answer to these questions for the customers, so. And this is one of the things where when we talk about breaking the data silos, it takes care of getting back the data, and putting it in a central location. Understanding the context behind each of these documents, and then, being able to also then, quickly answer the queries of customers using simple query natural language as well. >> So what's the scale? Talk about the scale behind this. What's the scale numbers? What are you guys seeing? I see you guys always do a good job, I've run a great announcement, and then following up with general availability, which means I know you've got some customers using it. What are we talking about in terms of scales? Petabytes, can you give some insight into the kind of data scale you're talking about here? >> So the nice thing about Kendra is it is easily linearly scalable. So I, as a developer, I can keep adding more and more data, and that is it linearly scales to whatever scale our customers want. So and that is one of the underpinnings of Kendra search engine. So this is where even if you see like customers like PricewaterhouseCoopers is using Kendra to power its regulatory application to help customers search through regulatory information quickly and easily. So instead of sifting through hundreds of pages of documents manually to answer certain questions, now, Kendra allows them to answer natural language question. I'll give another example, which is speaks to the scale. One is Baker Tilly, a leading advisory, tax, and assurance firm, is using Kendra to index documents. Compared to a traditional SharePoint-based full-text search, now, they are using Kendra to quickly search product manuals and so forth. And they're able to get answers up to 10x faster. Look at that kind of impact what Kendra has, being able to index vast amount of data, with in a linearly scalable fashion, keep adding in the order of terabytes, and keep going, and being able to search 10x faster than traditional, I mean traditional keyword search based algorithm is actually a big deal for these customers. They're very excited. >> So what is the main problem that you're solving with Kendra? What's the use case? If I'm the customer, what's my problem that you're solving? Is it just response to data, whether it's a call center, or support, or is it an app? I mean, what's the main focus that you guys came out? What was the vector of problem that you're solving here? >> So when we talked to customers before we started building Kendra, one of the things that constantly came back for us was that they wanted the same ease of use and the ability to search the world wide web, and customers like us to search within an enterprise. So it can be in the form of like an internal search to search within like the HR documents or internal wiki pages and so forth, or it can be to search like internal technical documentation or the public documentation to help the contact centers or is it the external search in terms of customer support and so forth, or to enable collaboration by sharing knowledge base and so forth. So each of these is really dissected. Why is this a problem? Why is it not being solved by traditional search techniques? One of the things that became obvious was that unlike the external world where the web pages are linked that easily with very well-defined structure, internal world is very messy within an enterprise. The documents are put in a SharePoint, or in a file system, or in a storage service like S3, or on naturally, tell-stores or Box, or various other things. And what really customers wanted was a system which knows how to actually pull the data from various these data silos, still understand the access control behind this, and enforce them in the search. And then, understand the real data behind it, and not just do simple keyword search, so that we can build remarkable search service that really answers queries in a natural language. And this has been the theme, premise of Kendra, and this is what had started to resonate with our customers. I talked with some of the other examples even in areas like contact centers. For instance, Magellan Health is using Kendra for its contact centers. So they are able to seamlessly tie like member, provider, or client specific information with other inside information about health care to its agents so that they can quickly resolve the call. Or it can be on internally to do things like external search as well. So very satisfied client. >> So you guys took the basic concept of discovery navigation, which is the consumer web, find what you're looking for as fast as possible, but also took advantage of building intelligence around understanding all the nuances and configuration, schemas, access, under the covers and allowing things to be discovered in a new way. So you basically makes data be discoverable, and then, provide an interface. >> Yeah. >> For discovery and navigation. So it's a broad use cat, then. >> Right, yeah that's sounds somewhat right except we did one thing more. We actually understood not just, we didn't just do discovery and also made it easy for people to find the information but they are sifting through like terabytes or hundreds of terabytes of internal documentation. Sometimes, one other things that happens is throwing a bunch of hundreds of links to these documents is not good enough. For instance, if I'm actually trying to find out for instance, what is the ALS marker in an health care setting, and for a particular research project, then, I don't want to actually sift through like thousands of links. Instead, I want to be able to correctly pinpoint which document contains answer to it. So that is the final element, which is to really understand the context behind each and every document using natural language processing techniques so that you not only find discover the information that is relevant but you also get like highly accurate possible precise answers to some of your questions. >> Well, that's great stuff, big fan. I was really liking the announcement of Kendra. Congratulations on the GA of that. We'll make some room on our CUBE Virtual site for your team to put more Kendra information up. I think it's fascinating. I think that's going to be the beginning of how the world changes, where this, this certainly with the voice activation and API-based applications integrating this in. I just see a ton of activity that this is going to have a lot of headroom. So appreciate that. The other thing I want to get to while I have you here is the news around the augmented artificial intelligence has been brought out as well. >> Yeah. >> So the GA of that is out. You guys are GA-ing everything, which is right on track with your cadence of AWS laws, I'd say. What is this about? Give us the headline story. What's the main thing to pay attention to of the GA? What have you learned? What's the learning curve, what's the results? >> So augmented artificial intelligence service, I called it A2I but Amazon A2I service, we made it generally available. And it is a very unique service that makes it easy for developers to augment human intelligence with machine learning predictions. And this is historically, has been a very challenging problem. We look at, so let me take a step back and explain the general idea behind it. You look at any developer building a machine learning application, there are use cases where even actually in 99% accuracy in machine learning is not going to be good enough to directly use that result as the response to back to the customer. Instead, you want to be able to augment that with human intelligence to make sure, hey, if my machine learning model is returning, saying hey, my confidence interval for this prediction is less than 70%, I would like it to be augmented with human intelligence. Then, A2I makes it super easy for customers to be, developers to use actually, a human reviewer workflow that comes in between. So then, I can actually send it either to the public pool using Mechanical Turk, where we have more than 500,000 Turkers, or I can use a private workflow as a vendor workflow. So now, A2I seamlessly integrates with our Textract, Rekognition, or SageMaker custom models. So now, for instance, NHS is integrated A2I with Textract, so that, and they are building these document processing workflows. The areas where the machine learning model confidence load is not as high, they will be able augment that with their human reviewer workflows so that they can actually build in highly accurate document processing workflow as well. So this, we think is a powerful capability. >> So this really kind of gets to what I've been feeling in some of the stuff we worked with you guys on our machine learning piece. It's hard for companies to hire machine learning people. This has been a real challenge. So I like this idea of human augmentation because humans and machines have to have that relationship, and if you build good abstraction layers, and you abstract away the complexity, which is what you guys do, and that's the vision of cloud, then, you're going to need to have that relationship solidified. So at what point do you think we're going to be ready for theCUBE team, or any customer that doesn't have the or can't find a machine learning person? Or may not want to pay the wages that's required? I mean it's hard to find a machine learning engineer, and when does the data science piece come in with visualization, the spectrum of pure computer science, math, machine learning guru to full end user productivity? Machine learning is where you guys are doing a lot of work. Can you just share your opinion on that evolution of where we are on that? Because people want to get to the point where they don't have to hire machine learning folks. >> Yeah. >> And have that kind support too. >> If you look at the history of technology, I actually always believe that many of these highly disruptive technology started as a way that it is available only to experts, and then, they quickly go through the cycles, where it becomes almost common place. I'll give an example with something totally outside the IT space. Let's take photography. I think more than probably 150 years ago, the first professional camera was invented, and built like three to four years still actually take a really good picture. And there were only very few expert photographers in the world. And then, fast forward to time where we are now, now, even my five-year-old daughter takes actually very good portraits, and actually gives it as a gift to her mom for Mother's Day. So now, if you look at Instagram, everyone is a professional photographer. I kind of think the same thing is about to, it will happen in machine learning too. Compared to 2012, where there were very few deep learning experts, who can really build these amazing applications, now, we are starting to see like tens of thousands of actually customers using machine learning in production in AWS, not just proof of concepts but in production. And this number is rapidly growing. I'll give one example. Internally, if you see Amazon, to aid our entire company to transform and make machine learning as a natural part of the business, six years ago, we started a Machine Learning University. And since then, we have been training all our engineers to take machine learning courses in this ML University, and a year ago, we actually made these coursework available through our Training and Certification platform in AWS, and within 48 hours, more than 100,000 people registered. Think about it, that's like a big all-time record. That's why I always like to believe that developers are always eager to learn, they're very hungry to pick up new technology, and I wouldn't be surprised if four or five years from now, machine learning is kind of becomes a normal feature of the app, the same with databases are, and that becomes less special. If that day happens, then, I would see it as my job is done, so. >> Well, you've got a lot more work to do because I know from the conversations I've been having around this COVID-19 pandemic is it's that there's general consensus and validation that the future got pulled forward, and what used to be an inside industry conversation that we used to have around machine learning and some of the visions that you're talking about has been accelerated on the pace of the new cloud scale, but now that people now recognize that virtual and experiencing it firsthand globally, everyone, there are now going to be an acceleration of applications. So we believe there's going to be a Cambrian explosion of new applications that got to reimagine and reinvent some of the plumbing or abstractions in cloud to deliver new experiences, because the expectations have changed. And I think one of the things we're seeing is that machine learning combined with cloud scale will create a whole new trajectory of a Cambrian explosion of applications. So this has kind of been validated. What's your reaction to that? I mean do you see something similar? What are some of the things that you're seeing as we come into this world, this virtualization of our lives, it's every vertical, it's not one vertical anymore that's maybe moving faster. I think everyone sees the impact. They see where the gaps are in this new reality here. What's your thoughts? >> Yeah, if you see the history from machine learning specifically around deep learning, while the technology is really not new, especially because the early deep learning paper was probably written like almost 30 years ago. And why didn't we see deep learning take us sooner? It is because historically, deep learning technologies have been hungry for computer resources, and hungry for like huge amount of data. And then, the abstractions were not easy enough. As you rightfully pointed out that cloud has come in made it super easy to get like access to huge amount of compute and huge amount of data, and you can literally pay by the hour or by the minute. And with new tools being made available to developers like SageMaker and all the AI services, we are talking about now, there is an explosion of options available that are easy to use for developers that we are starting to see, almost like a huge amount of like innovations starting to pop up. And unlike traditional disruptive technologies, which you usually see crashing in like one or two industry segments, and then, it crosses the chasm, and then goes mainstream, but machine learning, we are starting to see traction almost in like every industry segment, all the way from like in financial sector, where fintech companies like Intuit is using it to forecast its call center volume and then, personalization. In the health care sector, companies like Aidoc are using computer vision to assist radiologists. And then, we are seeing in areas like public sector. NASA has partnered with AWS to use machine learning to do anomaly detection, algorithms to detect solar flares in the space. And yeah, examples are plenty. It is because now, machine learning has become such common place that and almost every industry segment and every CIO is actually already looking at how can they reimagine, and reinvent, and make their customer experience better covered by machine learning. In the same way, Amazon actually asked itself, like eight or 10 years ago, so very exciting. >> Well, you guys continue to do the work, and I agree it's not just machine learning by itself, it's the integration and the perfect storm of elements that have come together at this time. Although pretty disastrous, but I think ultimately, it's going to come out, we're going to come out of this on a whole 'nother trajectory. It's going to be creativity will be emerged. You're going to start seeing really those builders thinking, "Okay hey, I got to get out there. "I can deliver, solve the gaps we are exposed. "Solve the problems, "pre-create new expectations, new experience." I think it's going to be great for software developers. I think it's going to change the computer science field, and it's really bringing the lifestyle aspect of things. Applications have to have a recognition of this convergence, this virtualization of life. >> Yeah. >> The applications are going to have to have that. So and remember virtualization helped Amazon formed the cloud. Maybe, we'll get some new kinds of virtualization, Swami. (laughs) Thanks for coming on, really appreciate it. Always great to see you. Thanks for taking the time. >> Okay, great to see you, John, also. Thank you, thanks again. >> We're with Swami, the Vice President of Machine Learning at AWS. Been on before theCUBE Alumni. Really sharing his insights around what we see around this virtualization, this online event at the Amazon Summit, we're covering with the Virtual CUBE. But as we go forward, more important than ever, the data is going to be important, searching it, finding it, and more importantly, having the humans use it building an application. So theCUBE coverage continues, for AWS Summit Virtual Online, I'm John Furrier, thanks for watching. (enlightening music)