(mellow music) >> Hello everyone, welcome to theCUBE's coverage of AWS Startup Showcase. This is the closing panel session on AI machine learning, the top startups generating generative AI on AWS. It's a great panel. This is going to be the experts talking about riding the wave in generative AI. We got Ankur Mehrotra, who's the director and general manager of AI and machine learning at AWS, and Clem Delangue, co-founder and CEO of Hugging Face, and Ori Goshen, who's the co-founder and CEO of AI21 Labs. Ori from Tel Aviv dialing in, and rest coming in here on theCUBE. Appreciate you coming on for this closing session for the Startup Showcase. >> Thanks for having us. >> Thank you for having us. >> Thank you. >> I'm super excited to have you all on. Hugging Face was recently in the news with the AWS relationship, so congratulations. Open source, open science, really driving the machine learning. And we got the AI21 Labs access to the LLMs, generating huge scale live applications, commercial applications, coming to the market, all powered by AWS. So everyone, congratulations on all your success, and thank you for headlining this panel. Let's get right into it. AWS is powering this wave here. We're seeing a lot of push here from applications. Ankur, set the table for us on the AI machine learning. It's not new, it's been goin' on for a while. Past three years have been significant advancements, but there's been a lot of work done in AI machine learning. Now it's released to the public. Everybody's super excited and now says, "Oh, the future's here!" It's kind of been going on for a while and baking. Now it's kind of coming out. What's your view here? Let's get it started. >> Yes, thank you. So, yeah, as you may be aware, Amazon has been in investing in machine learning research and development since quite some time now. And we've used machine learning to innovate and improve user experiences across different Amazon products, whether it's Alexa or Amazon.com. But we've also brought in our expertise to extend what we are doing in the space and add more generative AI technology to our AWS products and services, starting with CodeWhisperer, which is an AWS service that we announced a few months ago, which is, you can think of it as a coding companion as a service, which uses generative AI models underneath. And so this is a service that customers who have no machine learning expertise can just use. And we also are talking to customers, and we see a lot of excitement about generative AI, and customers who want to build these models themselves, who have the talent and the expertise and resources. For them, AWS has a number of different options and capabilities they can leverage, such as our custom silicon, such as Trainium and Inferentia, as well as distributed machine learning capabilities that we offer as part of SageMaker, which is an end-to-end machine learning development service. At the same time, many of our customers tell us that they're interested in not training and building these generative AI models from scratch, given they can be expensive and can require specialized talent and skills to build. And so for those customers, we are also making it super easy to bring in existing generative AI models into their machine learning development environment within SageMaker for them to use. So we recently announced our partnership with Hugging Face, where we are making it super easy for customers to bring in those models into their SageMaker development environment for fine tuning and deployment. And then we are also partnering with other proprietary model providers such as AI21 and others, where we making these generative AI models available within SageMaker for our customers to use. So our approach here is to really provide customers options and choices and help them accelerate their generative AI journey. >> Ankur, thank you for setting the table there. Clem and Ori, I want to get your take, because the riding the waves, the theme of this session, and to me being in California, I imagine the big surf, the big waves, the big talent out there. This is like alpha geeks, alpha coders, developers are really leaning into this. You're seeing massive uptake from the smartest people. Whether they're young or around, they're coming in with their kind of surfboards, (chuckles) if you will. These early adopters, they've been on this for a while; Now the waves are hitting. This is a big wave, everyone sees it. What are some of those early adopter devs doing? What are some of the use cases you're seeing right out of the gate? And what does this mean for the folks that are going to come in and get on this wave? Can you guys share your perspective on this? Because you're seeing the best talent now leaning into this. >> Yeah, absolutely. I mean, from Hugging Face vantage points, it's not even a a wave, it's a tidal wave, or maybe even the tide itself. Because actually what we are seeing is that AI and machine learning is not something that you add to your products. It's very much a new paradigm to do all technology. It's this idea that we had in the past 15, 20 years, one way to build software and to build technology, which was writing a million lines of code, very rule-based, and then you get your product. Now what we are seeing is that every single product, every single feature, every single company is starting to adopt AI to build the next generation of technology. And that works both to make the existing use cases better, if you think of search, if you think of social network, if you think of SaaS, but also it's creating completely new capabilities that weren't possible with the previous paradigm. Now AI can generate text, it can generate image, it can describe your image, it can do so many new things that weren't possible before. >> It's going to really make the developers really productive, right? I mean, you're seeing the developer uptake strong, right? >> Yes, we have over 15,000 companies using Hugging Face now, and it keeps accelerating. I really think that maybe in like three, five years, there's not going to be any company not using AI. It's going to be really kind of the default to build all technology. >> Ori, weigh in on this. APIs, the cloud. Now I'm a developer, I want to have live applications, I want the commercial applications on this. What's your take? Weigh in here. >> Yeah, first, I absolutely agree. I mean, we're in the midst of a technology shift here. I think not a lot of people realize how big this is going to be. Just the number of possibilities is endless, and I think hard to imagine. And I don't think it's just the use cases. I think we can think of it as two separate categories. We'll see companies and products enhancing their offerings with these new AI capabilities, but we'll also see new companies that are AI first, that kind of reimagine certain experiences. They build something that wasn't possible before. And that's why I think it's actually extremely exciting times. And maybe more philosophically, I think now these large language models and large transformer based models are helping us people to express our thoughts and kind of making the bridge from our thinking to a creative digital asset in a speed we've never imagined before. I can write something down and get a piece of text, or an image, or a code. So I'll start by saying it's hard to imagine all the possibilities right now, but it's certainly big. And if I had to bet, I would say it's probably at least as big as the mobile revolution we've seen in the last 20 years. >> Yeah, this is the biggest. I mean, it's been compared to the Enlightenment Age. I saw the Wall Street Journal had a recent story on this. We've been saying that this is probably going to be bigger than all inflection points combined in the tech industry, given what transformation is coming. I guess I want to ask you guys, on the early adopters, we've been hearing on these interviews and throughout the industry that there's already a set of big companies, a set of companies out there that have a lot of data and they're already there, they're kind of tinkering. Kind of reminds me of the old hyper scaler days where they were building their own scale, and they're eatin' glass, spittin' nails out, you know, they're hardcore. Then you got everybody else kind of saying board level, "Hey team, how do I leverage this?" How do you see those two things coming together? You got the fast followers coming in behind the early adopters. What's it like for the second wave coming in? What are those conversations for those developers like? >> I mean, I think for me, the important switch for companies is to change their mindset from being kind of like a traditional software company to being an AI or machine learning company. And that means investing, hiring machine learning engineers, machine learning scientists, infrastructure in members who are working on how to put these models in production, team members who are able to optimize models, specialized models, customized models for the company's specific use cases. So it's really changing this mindset of how you build technology and optimize your company building around that. Things are moving so fast that I think now it's kind of like too late for low hanging fruits or small, small adjustments. I think it's important to realize that if you want to be good at that, and if you really want to surf this wave, you need massive investments. If there are like some surfers listening with this analogy of the wave, right, when there are waves, it's not enough just to stand and make a little bit of adjustments. You need to position yourself aggressively, paddle like crazy, and that's how you get into the waves. So that's what companies, in my opinion, need to do right now. >> Ori, what's your take on the generative models out there? We hear a lot about foundation models. What's your experience running end-to-end applications for large foundation models? Any insights you can share with the app developers out there who are looking to get in? >> Yeah, I think first of all, it's start create an economy, where it probably doesn't make sense for every company to create their own foundation models. You can basically start by using an existing foundation model, either open source or a proprietary one, and start deploying it for your needs. And then comes the second round when you are starting the optimization process. You bootstrap, whether it's a demo, or a small feature, or introducing new capability within your product, and then start collecting data. That data, and particularly the human feedback data, helps you to constantly improve the model, so you create this data flywheel. And I think we're now entering an era where customers have a lot of different choice of how they want to start their generative AI endeavor. And it's a good thing that there's a variety of choices. And the really amazing thing here is that every industry, any company you speak with, it could be something very traditional like industrial or financial, medical, really any company. I think peoples now start to imagine what are the possibilities, and seriously think what's their strategy for adopting this generative AI technology. And I think in that sense, the foundation model actually enabled this to become scalable. So the barrier to entry became lower; Now the adoption could actually accelerate. >> There's a lot of integration aspects here in this new wave that's a little bit different. Before it was like very monolithic, hardcore, very brittle. A lot more integration, you see a lot more data coming together. I have to ask you guys, as developers come in and grow, I mean, when I went to college and you were a software engineer, I mean, I got a degree in computer science, and software engineering, that's all you did was code, (chuckles) you coded. Now, isn't it like everyone's a machine learning engineer at this point? Because that will be ultimately the science. So, (chuckles) you got open source, you got open software, you got the communities. Swami called you guys the GitHub of machine learning, Hugging Face is the GitHub of machine learning, mainly because that's where people are going to code. So this is essentially, machine learning is computer science. What's your reaction to that? >> Yes, my co-founder Julien at Hugging Face have been having this thing for quite a while now, for over three years, which was saying that actually software engineering as we know it today is a subset of machine learning, instead of the other way around. People would call us crazy a few years ago when we're seeing that. But now we are realizing that you can actually code with machine learning. So machine learning is generating code. And we are starting to see that every software engineer can leverage machine learning through open models, through APIs, through different technology stack. So yeah, it's not crazy anymore to think that maybe in a few years, there's going to be more people doing AI and machine learning. However you call it, right? Maybe you'll still call them software engineers, maybe you'll call them machine learning engineers. But there might be more of these people in a couple of years than there is software engineers today. >> I bring this up as more tongue in cheek as well, because Ankur, infrastructure's co is what made Cloud great, right? That's kind of the DevOps movement. But here the shift is so massive, there will be a game-changing philosophy around coding. Machine learning as code, you're starting to see CodeWhisperer, you guys have had coding companions for a while on AWS. So this is a paradigm shift. How is the cloud playing into this for you guys? Because to me, I've been riffing on some interviews where it's like, okay, you got the cloud going next level. This is an example of that, where there is a DevOps-like moment happening with machine learning, whether you call it coding or whatever. It's writing code on its own. Can you guys comment on what this means on top of the cloud? What comes out of the scale? What comes out of the benefit here? >> Absolutely, so- >> Well first- >> Oh, go ahead. >> Yeah, so I think as far as scale is concerned, I think customers are really relying on cloud to make sure that the applications that they build can scale along with the needs of their business. But there's another aspect to it, which is that until a few years ago, John, what we saw was that machine learning was a data scientist heavy activity. They were data scientists who were taking the data and training models. And then as machine learning found its way more and more into production and actual usage, we saw the MLOps become a thing, and MLOps engineers become more involved into the process. And then we now are seeing, as machine learning is being used to solve more business critical problems, we're seeing even legal and compliance teams get involved. We are seeing business stakeholders more engaged. So, more and more machine learning is becoming an activity that's not just performed by data scientists, but is performed by a team and a group of people with different skills. And for them, we as AWS are focused on providing the best tools and services for these different personas to be able to do their job and really complete that end-to-end machine learning story. So that's where, whether it's tools related to MLOps or even for folks who cannot code or don't know any machine learning. For example, we launched SageMaker Canvas as a tool last year, which is a UI-based tool which data analysts and business analysts can use to build machine learning models. So overall, the spectrum in terms of persona and who can get involved in the machine learning process is expanding, and the cloud is playing a big role in that process. >> Ori, Clem, can you guys weigh in too? 'Cause this is just another abstraction layer of scale. What's it mean for you guys as you look forward to your customers and the use cases that you're enabling? >> Yes, I think what's important is that the AI companies and providers and the cloud kind of work together. That's how you make a seamless experience and you actually reduce the barrier to entry for this technology. So that's what we've been super happy to do with AWS for the past few years. We actually announced not too long ago that we are doubling down on our partnership with AWS. We're excited to have many, many customers on our shared product, the Hugging Face deep learning container on SageMaker. And we are working really closely with the Inferentia team and the Trainium team to release some more exciting stuff in the coming weeks and coming months. So I think when you have an ecosystem and a system where the AWS and the AI providers, AI startups can work hand in hand, it's to the benefit of the customers and the companies, because it makes it orders of magnitude easier for them to adopt this new paradigm to build technology AI. >> Ori, this is a scale on reasoning too. The data's out there and making sense out of it, making it reason, getting comprehension, having it make decisions is next, isn't it? And you need scale for that. >> Yes. Just a comment about the infrastructure side. So I think really the purpose is to streamline and make these technologies much more accessible. And I think we'll see, I predict that we'll see in the next few years more and more tooling that make this technology much more simple to consume. And I think it plays a very important role. There's so many aspects, like the monitoring the models and their kind of outputs they produce, and kind of containing and running them in a production environment. There's so much there to build on, the infrastructure side will play a very significant role. >> All right, that's awesome stuff. I'd love to change gears a little bit and get a little philosophy here around AI and how it's going to transform, if you guys don't mind. There's been a lot of conversations around, on theCUBE here as well as in some industry areas, where it's like, okay, all the heavy lifting is automated away with machine learning and AI, the complexity, there's some efficiencies, it's horizontal and scalable across all industries. Ankur, good point there. Everyone's going to use it for something. And a lot of stuff gets brought to the table with large language models and other things. But the key ingredient will be proprietary data or human input, or some sort of AI whisperer kind of role, or prompt engineering, people are saying. So with that being said, some are saying it's automating intelligence. And that creativity will be unleashed from this. If the heavy lifting goes away and AI can fill the void, that shifts the value to the intellect or the input. And so that means data's got to come together, interact, fuse, and understand each other. This is kind of new. I mean, old school AI was, okay, got a big model, I provisioned it long time, very expensive. Now it's all free flowing. Can you guys comment on where you see this going with this freeform, data flowing everywhere, heavy lifting, and then specialization? >> Yeah, I think- >> Go ahead. >> Yeah, I think, so what we are seeing with these large language models or generative models is that they're really good at creating stuff. But I think it's also important to recognize their limitations. They're not as good at reasoning and logic. And I think now we're seeing great enthusiasm, I think, which is justified. And the next phase would be how to make these systems more reliable. How to inject more reasoning capabilities into these models, or augment with other mechanisms that actually perform more reasoning so we can achieve more reliable results. And we can count on these models to perform for critical tasks, whether it's medical tasks, legal tasks. We really want to kind of offload a lot of the intelligence to these systems. And then we'll have to get back, we'll have to make sure these are reliable, we'll have to make sure we get some sort of explainability that we can understand the process behind the generated results that we received. So I think this is kind of the next phase of systems that are based on these generated models. >> Clem, what's your view on this? Obviously you're at open community, open source has been around, it's been a great track record, proven model. I'm assuming creativity's going to come out of the woodwork, and if we can automate open source contribution, and relationships, and onboarding more developers, there's going to be unleashing of creativity. >> Yes, it's been so exciting on the open source front. We all know Bert, Bloom, GPT-J, T5, Stable Diffusion, that work up. The previous or the current generation of open source models that are on Hugging Face. It has been accelerating in the past few months. So I'm super excited about ControlNet right now that is really having a lot of impact, which is kind of like a way to control the generation of images. Super excited about Flan UL2, which is like a new model that has been recently released and is open source. So yeah, it's really fun to see the ecosystem coming together. Open source has been the basis for traditional software, with like open source programming languages, of course, but also all the great open source that we've gotten over the years. So we're happy to see that the same thing is happening for machine learning and AI, and hopefully can help a lot of companies reduce a little bit the barrier to entry. So yeah, it's going to be exciting to see how it evolves in the next few years in that respect. >> I think the developer productivity angle that's been talked about a lot in the industry will be accelerated significantly. I think security will be enhanced by this. I think in general, applications are going to transform at a radical rate, accelerated, incredible rate. So I think it's not a big wave, it's the water, right? I mean, (chuckles) it's the new thing. My final question for you guys, if you don't mind, I'd love to get each of you to answer the question I'm going to ask you, which is, a lot of conversations around data. Data infrastructure's obviously involved in this. And the common thread that I'm hearing is that every company that looks at this is asking themselves, if we don't rebuild our company, start thinking about rebuilding our business model around AI, we might be dinosaurs, we might be extinct. And it reminds me that scene in Moneyball when, at the end, it's like, if we're not building the model around your model, every company will be out of business. What's your advice to companies out there that are having those kind of moments where it's like, okay, this is real, this is next gen, this is happening. I better start thinking and putting into motion plans to refactor my business, 'cause it's happening, business transformation is happening on the cloud. This kind of puts an exclamation point on, with the AI, as a next step function. Big increase in value. So it's an opportunity for leaders. Ankur, we'll start with you. What's your advice for folks out there thinking about this? Do they put their toe in the water? Do they jump right into the deep end? What's your advice? >> Yeah, John, so we talk to a lot of customers, and customers are excited about what's happening in the space, but they often ask us like, "Hey, where do we start?" So we always advise our customers to do a lot of proof of concepts, understand where they can drive the biggest ROI. And then also leverage existing tools and services to move fast and scale, and try and not reinvent the wheel where it doesn't need to be. That's basically our advice to customers. >> Get it. Ori, what's your advice to folks who are scratching their head going, "I better jump in here. "How do I get started?" What's your advice? >> So I actually think that need to think about it really economically. Both on the opportunity side and the challenges. So there's a lot of opportunities for many companies to actually gain revenue upside by building these new generative features and capabilities. On the other hand, of course, this would probably affect the cogs, and incorporating these capabilities could probably affect the cogs. So I think we really need to think carefully about both of these sides, and also understand clearly if this is a project or an F word towards cost reduction, then the ROI is pretty clear, or revenue amplifier, where there's, again, a lot of different opportunities. So I think once you think about this in a structured way, I think, and map the different initiatives, then it's probably a good way to start and a good way to start thinking about these endeavors. >> Awesome. Clem, what's your take on this? What's your advice, folks out there? >> Yes, all of these are very good advice already. Something that you said before, John, that I disagreed a little bit, a lot of people are talking about the data mode and proprietary data. Actually, when you look at some of the organizations that have been building the best models, they don't have specialized or unique access to data. So I'm not sure that's so important today. I think what's important for companies, and it's been the same for the previous generation of technology, is their ability to build better technology faster than others. And in this new paradigm, that means being able to build machine learning faster than others, and better. So that's how, in my opinion, you should approach this. And kind of like how can you evolve your company, your teams, your products, so that you are able in the long run to build machine learning better and faster than your competitors. And if you manage to put yourself in that situation, then that's when you'll be able to differentiate yourself to really kind of be impactful and get results. That's really hard to do. It's something really different, because machine learning and AI is a different paradigm than traditional software. So this is going to be challenging, but I think if you manage to nail that, then the future is going to be very interesting for your company. >> That's a great point. Thanks for calling that out. I think this all reminds me of the cloud days early on. If you went to the cloud early, you took advantage of it when the pandemic hit. If you weren't native in the cloud, you got hamstrung by that, you were flatfooted. So just get in there. (laughs) Get in the cloud, get into AI, you're going to be good. Thanks for for calling that. Final parting comments, what's your most exciting thing going on right now for you guys? Ori, Clem, what's the most exciting thing on your plate right now that you'd like to share with folks? >> I mean, for me it's just the diversity of use cases and really creative ways of companies leveraging this technology. Every day I speak with about two, three customers, and I'm continuously being surprised by the creative ideas. And the future is really exciting of what can be achieved here. And also I'm amazed by the pace that things move in this industry. It's just, there's not at dull moment. So, definitely exciting times. >> Clem, what are you most excited about right now? >> For me, it's all the new open source models that have been released in the past few weeks, and that they'll keep being released in the next few weeks. I'm also super excited about more and more companies getting into this capability of chaining different models and different APIs. I think that's a very, very interesting development, because it creates new capabilities, new possibilities, new functionalities that weren't possible before. You can plug an API with an open source embedding model, with like a no-geo transcription model. So that's also very exciting. This capability of having more interoperable machine learning will also, I think, open a lot of interesting things in the future. >> Clem, congratulations on your success at Hugging Face. Please pass that on to your team. Ori, congratulations on your success, and continue to, just day one. I mean, it's just the beginning. It's not even scratching the service. Ankur, I'll give you the last word. What are you excited for at AWS? More cloud goodness coming here with AI. Give you the final word. >> Yeah, so as both Clem and Ori said, I think the research in the space is moving really, really fast, so we are excited about that. But we are also excited to see the speed at which enterprises and other AWS customers are applying machine learning to solve real business problems, and the kind of results they're seeing. So when they come back to us and tell us the kind of improvement in their business metrics and overall customer experience that they're driving and they're seeing real business results, that's what keeps us going and inspires us to continue inventing on their behalf. >> Gentlemen, thank you so much for this awesome high impact panel. Ankur, Clem, Ori, congratulations on all your success. We'll see you around. Thanks for coming on. Generative AI, riding the wave, it's a tidal wave, it's the water, it's all happening. All great stuff. This is season three, episode one of AWS Startup Showcase closing panel. This is the AI ML episode, the top startups building generative AI on AWS. I'm John Furrier, your host. Thanks for watching. (mellow music)

>>Hey, everyone, Thanks for taking them to join the story. Hope you and your loved ones are safe during these tough times. Let me start by introducing myself. My name is Michelle. When I walk for GlaxoSmithKline, GSK as an engineering manager in my current role, A little protocol platform A P s, which is part of the already data platform here in G S, K R and D Tech. I live in Dallas, Texas. I have a Masters degree in computer science on a bachelor's in electronics and communication engineering. I started my career as a software developer on over these years again a lot of experience in leading and building, not scale and predicts products and solutions. I also have a complete accountability for container platforms here at GSK or any tick. I've been working very closely with Dr Enterprise, which is no Miranda's for more than three years to enable container platforms that yes, came on mainly in our own Itek. So that's me. Let >>me give you a quick overview on agenda for today's talk. I'll start with what we do here at GSK on what is RND data platform. Then I'll give you an overview on What are the business drivers that >>motivated US toe? Take this container Germany on some insight into learnings on accomplishments over these years. Working with Dr Enterprise on the container platforms Lately, you must have seen a lot of articles off there which talk about how ts case liberating technologies like artificial intelligence, mission learning, UN data and analytics for the Douglas Corey process. I'm very excited to see the progress we have made in technology, but what makes us truly unique is our commitment to the patient. >>We're G escape, help millions of people, do more, feel better and live longer. Wear a global company that is focused on three were tickles pharmaceuticals vaccines on consumer healthcare. Our main intent is to lower the >>burden on the impact of diseases on the patients. Here at GSK, we allow science to drive the technology. This helps us toe build innovative products. That's helps our scientists to make better and faster additions throughout the drug discovery by plane. >>With that, let me give you some >>context on what currently data platform is how it is enabled. A T escape started in mid 2016. What used to be called us are any information platform whose main focus was to centralize curate on rationalized all the data produced within the others are in the business systems in orderto drive, a strategic business value, standardization of clinical trials, Genome Wide Association Study Analysis, also known as Jesus Storage and Crossing Off Rheal. World Evidence data some of the examples off how the only platform was used to deliver the business value four years later. No, a new set off business rivals of changing our landscape. The irony Information Platform is evolving to be a hybrid, multi cloud solution and is known as already did a platform refering to 20 >>19 GSK's annual report. These are the four teams that there are any platform will be mainly focused on. We're expanding our data capabilities to support the use. Escape by a former company on evolving into a hybrid medical platform is one of the many steps that we're taking to be future ready. Our key focus will still be making >>greater recommendations better and faster by using that wants us. We're making the areas like artificial intelligence and machine learning. No doc brings us toe. What is Germany is important. Why are we taking this German with that? Let me take you to the next topic off. Like the process of discovery, Francisco is not an easy process. Talking about the recent events occurred over the last few months on the way. How all our lives are impacted. It is a lot of talk on information going about. Why did drug discovery process is so tough working for a global health care company? I get asked this question very frequently. From many people I interact with. Question is like, Why is that? This car is so tough on why it takes so much time. Drug discovery is a complex process that involves multiple different stages on at each and every stage. There is huge amounts of data that the scientists have took process to make some decisions. Studies have shown that only 3% off small molecules entering the human studies actually become medicines. If you're new to drug discovery, you may ask, like what is the targets? Targets so low? We humans are very complex species, >>not going into the details of the process. We're G escape >>have made a lot of investments into technology that enabled us to make data river conditions. Throw the drug Discovery pipeline >>as we implement. As we started implementing these tools and technologies to enable already did a platform, we started to get a better appreciation off how these tools in track on integrate >>with each other. Our goal wants to make this platform a jail, the platform that can work at scale so that we can provide a great user experience and contribute back to the bread discovery pipeline so that the scientists can make faster editions. We want our ardently users to consume the data, and the service is available on the platform seamlessly in a self service fashion. And we also have to accomplish this by establishing trust. And then we have to end also enable the academic partnerships, acquisitions, collaborations that DSK has, which actually brings a lot of data on value to our scientists. So when we talk about so many collaborations and a lot of these systems, what this brings in is wide range off systems and platforms that are fundamentally built on different infrastructure. This is where Doctor comes into fiction on our containers significance. >>We have realized the power of containers on how we can simplify this complex ecosystem by using containers and provide a faster access off data to war scientists who didn't go >>back and contribute back to the drug discovery by play. >>With that, let me take talk to you about >>the containers journey and she escaped. So we started our container journey in late 2017. We started working with Dr Enterprise to enable the container platform. This is on our on prem infrastructure Back then, or first year or so we walked through multiple Pelosis did a lot of testing to make sure our platform is stable before we onboard either the data or the user applications. I was part of this complete journey on Dr Stream has worked with us very closely towards you. The first milestone off establishing a stable container platform. A tsk. Now, getting into 2019 we started deploying our applications in production environment. I cannot go into the details of what this Absar, but they do include both data pipelines as well as Web services. You know, initial days we have worked a lot on swamp, but in 2019 is when we started looking into communities in the same year, we enable kubernetes orchestration on the doctor and replace platform here at GSK and also made it as a de facto orchestra coming into 2020. All our micro service applications are undead. A pipelines are migrated to the container platforms on all of these are orchestrated by Cuban additional on these air applications that are running in production. As of today, we have made the container forced approach as an architectural standard across already taking GSK. We also started deploying our AML training models onto containers on All this work is happening on our Doctor Enterprise platform. Also as part off are currently platforms hybrid multicolored journey. We started enabling container and kubernetes based platforms on public clubs. Now going into 2021 on future. Enabling our RND users to easily access data and applications in a platform agnostic way is very crucial for our success because previously we had only onto him. Now we have public clothes that are getting involved on One of >>the many steps we're taking through this journey is to >>watch allies the data on ship data and containers or kubernetes volumes on demand to our our end users of scientists. And this allows us to deliver data to our scientists wherever they want in a very security on. We're leveraging doctor to do it. So that's >>our future. Learning on with that, let's take a deep dive into fuel for >>our accomplishments over these years. I want to start with a general demand and innovative one very interesting use case that we developed on Dr. This is a rapid prototyping capability that enabled our scientists seamlessly to Monday cluster communication. This was one off the biggest challenges which way his face for a long time and with the help of containers, were able to solve this on provide this as a capability to our scientists. We actually have shockers this capability in one of the doctor conferences before next. As I've said before, by migrating all over web services into containers, we not only achieved horizontal scalability for those specific services, but also saved more than 50% in support costs for the applications which we have migrated by making Docker image as an immutable artifact In our bill process, we are now able to deploy our APS or models in any container or Cuban, its base platform, either in on Prem or in a public club. We also made significant improvements towards the process. A not a mission By leveraging docker containers, containers have played a significant role in keeping US platform agnostic and thus enabling our hybrid multi cloud Germany valuable for out already did scientists. As I mentioned before, data virtualization is another viewpoint we have in terms off our next steps off where we want to take kubernetes on where we wanna leverage open it. Us. What you see here are just a few off many accomplishments which we have our, um, achieved by using containers for the past three years or so. So with that before I close all the time and acknowledge all our internal partners who has contributed a lot to this journey mainly are in the business are on the deck on the broader take. Organizations that escape also want to time document present Miranda's for being such a great partner throughout this journey and also giving us an opportunity to share this success story today. Lastly, thanks for everyone to listening to the stop and please feel free to reach out. If you have any questions or suggestions, let's be fit safe. Thank you

>> From The Cube Studios in Palo Alto and Boston, bringing you data driven insights from The Cube and ETR, this is Breaking Analysis with Dave Vellante. >> Despite it's faster growth and infrastructure as a service, relative to AWS and Azure, Google Cloud platform remains a third wheel in the race for cloud dominance. Google begins its Cloud Next online event starting July fourteenth in a series of nine rolling sessions that go through early September. Ahead of that, we want to update you on our most current data on Google's cloud business. Hello everyone, this is Dave Vellante, and welcome to this week's Wikibon Cube insights, powered by ETR. In this session, we'll review the current state of cloud, and Google's position in the market. We'll drill into the ETR data and share fresh insights from our partner and the Cube community. So let's get right into it. You know, Google, if you think about it, was actually very early into the cloud game. Google's 2004 IPO was a milestone event for the tech industry, and in you know many ways, it really marked the end of the post-dotcom malaise. It signaled the beginning of a new era of innovation. During this time, Google was busy building out its massive, global cloud infrastructure, probably the largest in the world, with undersea cables, global data centers, and tools like the Google file system, and of course Bigtable. But it took many years for Google to pull its head out of its ad serving butt and realize the opportunity to sell its cloud services to global enterprises. Bigtable, Google's no-sequel database, for example, was released in 2005, but it wasn't until 2015 that Google made this service available to its customers. That was the same year Google brought in VMware founder, Diane Greene to begin its enterprise journey in earnest. Now Google, they have a dizzying array of services in compute, storage, database, networking, IT ops, dev tools, machine learning, AI, analytics, big data, security, on and on and on. Name a category and it's likely that Google has something in it as a cloud service. But Google, to this day, still hasn't figured out how to sell to the enterprise. It really struggles to find the right formula. So, as you know, Google brought in Thomas Kurian from Oracle, to figure this out. Of course Kurian is, he's going to go with Google's strengths like analytics and database, but it has to have differentiation, so it comes up with unique pricing models like sustained discounts, which automatically apply discount for heavy usage, as opposed to forcing users to buy reserved instances such as what AWS does. You know Google is more aggressive partnering around multi-cloud, for instance, with Anthos, and it's smartly open-sourced Kubernetes really to minimize the importance of, physically, where workloads run. The bottom-line, however, is that these moves are necessary for Google to compete because it lags behind the leaders. And it has a long way to go before it's going to be satisfied with its cloud business. Let's look at the IaaS market in context. Now, I don't want to say it's all gloom and doom for Google. Far from it. Earnings for Q2, they're going to start rolling out later this month, but this chart shows our latest estimates of IaaS and PaaS for the big three cloud players. Now, I got to caution you, as I did before, other than AWS, which reports very clean numbers each quarter on IaaS and PaaS, we have to estimate Azure and GCP revenue because they bundle in other things. I'll give an example. Google reports its overall cloud numbers which include G Suite. Microsoft reports a category they call intelligent cloud. Now that includes public, private clouds, hybrid, sequel server, Windows server, system center, GitHub, enterprise support and consulting services. And Azure, the IaaS and PaaS numbers are also in there too. So what we have to do is to squint through the earnings reports and the 10 Ks and try to get a clean IaaS and PaaS figure for these players, and that's what we show here. Now there's really two points that we want to stress with this data. First, on a trailing 12 month basis, the big three cloud players now account for nearly 60 billion dollars in IaaS and PaaS revenue. And this 60 billion dollars, on a weighted average basis, is growing in the mid 40% range. So well on its way to being a 100 billion dollar business. Just for these three firms. And as we've reported, that's eating directly into the on-premises infrastructure install base, which is a flat to declining market. And that trend is going to play out in a big way this decade. We've predicted that public cloud is going to out pace on-prem infrastructure by more that 1800 basis points over the next 10 years, from a spending standpoint. Now the second point that I want to make relates to Google IaaS and PaaS growth. We peg it at greater than 70%, based on public statements, reading the 10 Ks and ETR data, which we'll discuss in a moment. So, very healthy growth, but from a much smaller install base than, or base than AWS and Azure. But in our view it's not enough, because AWS and Azure are so large and strong still, growth wise, that we feel Google is going to remain a distant third, really indefinitely. Nonetheless, a lot of companies would be thrilled to have a four billion dollar cloud business and there's certainly good news in the data for Google. So let's look at some of that survey data. Now, as we've reported in the past, Google pushes G Suite very hard, as part of its cloud story, and it leads often times with G Suite in its messaging. You know, but to us that's never really been that compelling. So let me start with some anecdotal data from ETR. ETR runs a regular program, they call it VENN, and in the VENN they invite clients into a private session to listen to named CIOs talk about their experience with vendors and overall spending intentions. It's a facilitated session. And we've had ETR's Eric Bradley on as a guest who directs the VENN program, and does much of the facilitation, and here's a statement from a recent VENN session quoting a CIO at a midsize Telco, that I think sums it up nicely. He says Google's G Suite is fine and dandy, but I don't see that truly as an enterprise solution. And frankly, it's still not of the quality of an Office application, talking about Microsoft. All in all I really like the infrastructure-as-a-service and the platform-as-a-service components that GCP had. And I thought they were coming along very very well in that space. Now, the reason that I share this is because the IT buyers that we speak with, you know they're very serious about exploring Google. They want options other than Azure and AWS and they see Google as having great tech and as a viable alternative. So let's talk about GCP and the enterprise. We looking, when we look into the ETR data for the most recent survey, which ran in June and early July, GCP is showing strength in one really important bellwether category, the giant public and private companies. These are the largest firms in the ETR dataset and often point to secular trends. Now, before we get into that, let's look at the picture for GCP using ETR's net score up methodology. This is fundamental to the ETR approach, and remember, each quarter ETR goes out and asks its respondents, are you planning to spend more or less? In its July survey, ETR focuses on second half spending. The next chart captures results across Google's entire portfolio. So here's the breakdown for, for Google across all sectors. 14% of the respondents are adopting new, that's the lime green. 39% plan to increase spending in the second half versus the first half, that's the forest green. Then there's a big fat middle, that's flat, and you see that in the gray area. And the 7% are spending less, with 2% replacing, that's the pinkish and dark red, respectively. So, I would say this result is mixed, in my opinion. Yeah, it's not bad, don't get me wrong, and we've, we'll see once ETR comes out of its quite period, how this compares to Azure and AWR, so remember, I can only share limited data until ETR clients get the data and have time to act on it. But this calculates out to a net score of 44%, which is respectable, but frankly not overly inspiring. So let's look across the GCP portfolio using the ETR taxonomy and see what it looks like. This chart shows the net score comparisons across three different surveys, October 19, April 20, and July 20. So reading the bars left to right, you can see Google's strong suit really is machine learning and AI. Container platforms are also very strong, as are functions, or server-less, and databases, very solid, we'll talk more about that in a minute. You know, video conferencing was just added by ETR and sure it pops up with the work from home. Cloud is actually holding firm when compared to October of last year. But surprisingly, analytics is looking a bit softer. And ETR for the first time added G Suite with, it shows a 26% net score, first time out, which is pretty tepid. I mean not very impressive at all. But overall, the picture looks pretty good for Google. So let's dig further into the giant public and private sector, that bellwether I talked about. And let's peal the onion a bit and look closer at the results from the largest companies in the dataset. So this chart shows the giant public, plus private organizations. So it would include like monster public companies but also large companies like a Cargill or a Coke Industries, if in fact they responded in this survey. And you can see, in that all important sector, it's a story of a lot of green with hardly any red, so quite a positive sign for Google within those bellwethers. Here's what I think is happening here. Is these large, and often far flung organizations, have realized that they have multiple cloud vendors, and they're asking their senior IT leadership to bring some consistency and sanity to their cloud strategies. So they look at the big three and say, okay, what's the best strategic fit for each workload? So they might say for instance let's use AWS for core IaaS, let's use Azure for productivity workloads, and we'll sprinkle some Google in for machine learning and related projects. So we do see some real strength in some of the larger strongholds for Google, although interestingly ETR sort of tells me that there's softness in the midsize and smaller companies that have powered AWS for so many years. And of course this, with Google's base, but compare that to AWS and AWS is much stronger in those smaller companies, start-ups and the like, and of course COVID's the wild car in all this. You know, we have to take that into account, and we will with Sagar Kadakia, who's ETR's director of research in the coming weeks. But I want to look at Google in the all important database category. So before we wrap, let's look at database. You remember, Google's playing catch up in the cloud and its marketing takes a more open posture around partners and things like multi-cloud and you know you can contrast that with AWS for example, but look, make no mistake, Google wants you data in their cloud, and that's why database is so strategic and so important. Look, it's the mother of all lock specs. All you got to do is look at Oracle and their success. Now, as we've reported many times, there's a new workload emerging in the cloud around this idea of the modern data warehouse. I mean I don't even like that term anymore, data warehouse, because it sounds just so static. But anyway, any rate, I'm talking about workloads that bring database, machine learning, AI, data science, compute and storage along with visualization tools to deliver real-time insights and operational analytics. Database is at the heart of everything here. Win the database and everything else falls into place. Now, Google has six or seven database products and one of the most impressive, in my opinion, is BigQuery. I mean, for those who have followed me over the years you know I love the technology behind Google's banner, but BigQuery is where much of the action is around this new workload that I'm talking about. So, let's look at, deeper at Google's position in database. This chart shows one of my favorite views. On the Y axis is the net score, or spending momentum, and on the X axis is market share or pervasiveness in the ETR dataset. The chart plots various database companies and their position within the all important giant public plus private sector. So these are the companies in the ETR survey that are the largest, and oftentimes, again, are a bellwether. And you can see Microsoft and Oracle and AWS have very strong presence on the horizontal axis. Mongo, MongoDB looms large, MemSQL, they just raised 50 million dollars this past May, MariaDB just raised another 25 million this month. You can see Couchbase and Redis, they show up, and they're on my radar. I'm learning more about those companies. Folks, database is hot. VC's are pouring money in and it's something that's very important to the Cube community to look at. And of course you see Google in the chart, with a strong net score, you know, but not the type of market presence that you see from the other big cloud players. In fact, they've pulled back a little somewhat in this last ETR survey. So despite some bright spots in the enterprise in terms of spending momentum, just not quite enough presence yet. Oh, by the way, look who's right there with Google. I know I sound like a broken record, but Snowflake is everywhere. You'll find them in AWS, you'll find them in Azure and on GCP. Now remember, Snowflake is only about one tenth the size of Google's IaaS and PaaS business. But it has stronger spending momentum than all the big guys, and it continues to creep its way to the right in terms of market share or presence. You know, but Google has great database tech and BigQuery is at the heart of its strategy to support analytics at scale, and automate the data pipeline. BigQuery's very well designed, it started as a cloud native database, it's based on server-less, it's highly scalable, and it's very cost-effective. In fact, ESG, enterprise strategy group, wrote a report comparing the TCO of the cloud databases. Let me pull that up and show you. Now the report was commissioned by Google, so I got to caution you there. But it was very well done in my opinion by a guy named Aviv Kaufmann, and you can see here it compares BigQuery with the other cloud databases, and of course, you know, BigQuery wins, got the lowest TCO, but again I thought the report was really detailed and well researched. I have no doubt that Snowflake has an answer for the big brown bar, which is on-demand cloud cost. I think ESG was making certain assumptions, maybe worst case assumptions, about the need to over-provision resources for Snowflake, which I'm sure ESG can defend, but I'll bet dollars to donuts that Snowflake, you know, has an answer to that or a comeback. I'm going to ask them. But the point I want to make here is that BigQuery was designed from day one, again, as a cloud-native database. We've been talking about that a lot. It's very efficient and is going to be competitive. So you can see, there are some bright spots in the enterprise, for Google. Okay, let's wrap up. Now, having called out some of the positives, and there are many, Google is still not getting it done in the enterprise, in my opinion. I certainly would not say too little too late, but I would say they spotted the competition a huge lead, and the only reason is Google just didn't act on the opportunity staring them in the face, within the enterprise, fast enough, and they finally woke up. But enterprise sales are, they're really hard. Thomas Kurian, for all his experience, is coming from way, way behind with regard to the enterprise go to market, systems and processes, pricing, partnerships, special deals for the enterprise. Google's still learning how to sell the business outcomes and is relying far too much on its technology chops, which, while impressive, are not going to win the day without better enterprise sales, marketing, and ecosystem integration. Now I feel like for years, Google has said to the enterprise market, give me heat and I'll add the wood. Meaning we have the best tech, go ahead and use it. That strategy just doesn't work in the enterprise. Kurian knows it and I suspect that's why Google's showing some strength within these large, giant public and private companies. They're probably applying focused sales resources to nail customer success with some of its top accounts where they have a presence, and then once they nail that they'll broaden to the market. But they got to move fast. We'll learn more about Google's intentions and its progress over the next few, next few months as they try their online event experiment, and of course we'll be there providing our wall to wall coverage. Remember, these Breaking Analysis episodes, they're all available as podcasts. ETR is shortly exiting its quiet period, this week, and will be rolling out the data, so check out etr.plus. I publish weekly on wikibon.com and siloconeangle.com and as always please comment on my LinkedIn posts, I really appreciate the feedback. This is Dave Vellante for the Cube Insights, powered by ETR. Thanks for watching everyone. We'll see you next time.

>>From around the globe. It's the cube with digital coverage of AWS reinvent 2020, sponsored by Intel and AWS. >>Welcome back to the queue. It's virtual coverage of Avis reinvent 2020 I'm John furry, your host. Um, this is a cute virtual we're here. Not in, in remote. We're not in person this year, so we're doing the remote interviews. And then this segment is going to build on the quantum conversation we had last year, Richard moles, general manager of Amazon bracket and aid was quantum computing and Fernando Brandao head of quantum algorithms at AWS and Brent professor of theoretical physics at Caltech. Fernando, thanks for coming on, Richard. Thanks for joining us. >>You're welcome to be here. >>So, Fernando, first of all, love your title, quantum algorithms. That's the coolest title I've heard so far and you're pretty smart because you're a theoretical professor of physics at Caltech. So, um, which I'd never be able to get into, but I wish I could get into there someday, but, uh, thanks for coming on. Um, quantum has been quite the rage and you know, there's a lot of people talking about it. Um, it's not ready for prime time. Some say it's moving faster than others, but where are we on quantum right now? What are, what are you, what are you seeing Fernanda where the quantum, where are peg us in the evolution of, of, uh, where we are? >>Um, yeah, what quantum, uh, it's an emerging and rapidly developing fields. Uh, but we are see where are you on, uh, both in terms of, uh, hardware development and in terms of identifying the most impactful use cases of one company. Uh, so, so it's, it's, it's early days for everyone and, and we have like, uh, different players and different technologies that are being sport. And I think it's, it's, it's early, but it's exciting time to be doing quantum computing. And, uh, and it's very interesting to see the interest in industry growing and, and customers. Uh, for example, Casa from AWS, uh, being, uh, being willing to take part in this journey with us in developmental technology. >>Awesome. Richard, last year we talked to bill Vass about this and he was, you know, he set expectations really well, I thought, but it was pretty much in classic Amazonian way. You know, it makes the announcement a lot of progress then makes me give us the update on your end. You guys now are shipping brackets available. What's the update on your end and Verner mentioned in his keynote this week >> as well. Yeah, it was a, it was great until I was really looking at your interview with bill. It was, uh, that was when we launched the launch the service a year ago, almost exactly a year ago this week. And we've come a long way. So as you mentioned, we've, uh, we've, uh, we've gone to general availability with the service now that that happened in August. So now a customer can kind of look into the, uh, to the bracket console and, uh, installed programming concept computers. You know, there's, uh, there's tremendous excitement obviously, as, as you mentioned, and Fernando mentioned, you know, quantum computers, uh, we think >>Have the potential to solve problems that are currently, uh, uh, unsolvable. Um, the goal of bracket is to fundamentally give customers the ability to, uh, to go test, uh, some of those notions to explore the technology and to just start planning for the future. You know, our goal was always to try and solve some of the problems that customers have had for, you know, gee, a decade or so now, you know, they tell us from a variety of different industries, whether it's drug discovery or financial services, whether it's energy or there's chemical engineering, machine learning, you know, th the potential for quantum computer impacts may industries could potentially be disruptive to those industries. And, uh, it's, it's essential that customers can can plan for the future, you know, build their own internal resources, become experts, hire the right staff, figure out where it might impact their business and, uh, and potentially disrupt. >>So, uh, you know, in the past they're finding it hard to, to get involved. You know, these machines are very different, different technologies building in different ways of different characteristics. Uh, the tooling is very disparate, very fragmented. Historically, it's hard for companies to get access to the machines. These tend to be, you know, owned by startups or in, you know, physics labs or universities, very difficult to get access to these things, very different commercial models. Um, and, uh, as you, as you suggested, a lot of interests, a lot of hype, a lot of claims in the industry, customers want to cut through all that. They want to understand what's real, uh, what they can do today, uh, how they can experiment and, uh, and get started. So, you know, we see bracket as a catalyst for innovation. We want to bring together end-users, um, consultants, uh, software developers, um, providers that want to host services on top of bracket, try and get the industry, you know, rubbing along them. You spoke to lots of Amazonians. I'm sure you've heard the phrase innovation flywheel, plenty of times. Um, we see the same approach that we've used successfully in IOT and robotics and machine learning and apply that same approach to content, machine learning software, to quantum computing, and to learn, to bring it together. And, uh, if we get the tooling right, and we make it easy, um, then we don't see any reason why we can't, uh, you know, rapidly try and move this industry forward. And >>It was fun areas where there's a lot of, you know, intellectual computer science, um, technology science involved in super exciting. And Amazon's supposed to some of that undifferentiated heavy. >>That's what I am, you know, it's like, >>There's a Maslow hierarchy of needs in the tech industry. You know, people say, Oh, why five people freak out when there's no wifi? You know, you can't get enough compute. Right. So, you know, um, compute is one of those things with machine learning is seeing the benefits and quantum there's so much benefits there. Um, and you guys made some announcements at, at re-invent, uh, around BRACA. Can you share just quickly share some of those updates, Richard? >>Sure. I mean, it's the way we innovate at AWS. You know, we, we start simple and we, and we build up features. We listen to customers and we learn as we go along, we try and move as quickly as possible. So since going public in, uh, in, in August, we've actually had a string of releases, uh, pretty consistent, um, delivering new features. So we try to tie not the integration with the platform. Customers have told us really very early on that they, they don't just want to play with the technology. They want to figure out how to, how to envisage a production quantum computing service, how it might look, you know, in the context of a broad cloud platform with AWS. So we've, uh, we launched some integration with, uh, other AWS capabilities around security, managing limits, quotas, tagging resources, that type of thing, things that are familiar to, uh, to, to, to current AWS users. >>Uh, we launched some new hardware. Uh, all of our partners D-Wave launched some, uh, uh, you know, a 5,000 cubit machine, uh, just in September. Uh, so we made that available on bracket the same day that they launched that hardware, which was very cool. Um, you know, we've made it, uh, we've, we've made it easier for researchers. We've been, you know, impressed how many academics and researchers have used the service, not just large corporations. Um, they want to have really deep access to these machines. They want to program these things at a low level. So we launched some features, uh, to enable them to do their research, but reinvent, we were really focused on two things, um, simulators and making it much easier to use, uh, hybrid systems systems that, uh, incorporate classical compute, traditional digital computing with quantum machinery, um, in the vein that follow some of the liens that we've seen, uh, in machine learning. >>So, uh, simulators are important. They're a very important part of, uh, learning how to use concepts, computers. They're always available 24, seven they're super convenient to use. And of course they're critical in verifying the accuracy of the results that we get from quantum hardware. When we launched the service behind free simulator for customers to help debug their circuits and experiments quickly, um, but simulating large experiments and large systems is a real challenge on classical computers. You know, it, wasn't hard on classical. Uh, then you wouldn't need a quantum computer. That's the whole point. So running large simulations, you know, is expensive in terms of resources. It's complicated. Uh, we launched a pretty powerful simulator, uh, back in August, which we thought at the time was always powerful managed. Quantum stimulates circuit handled 34 cubits, and it reinvented last week, we launched a new simulator, which actually the first managed simulator to use tensor network technology. >>And it can run up to 50 cubits. So we think is, we think is probably the most powerful, uh, managed quantum simulator on the market today. And customers can flip easily between either using real quantum hardware or either of our, uh, stimulators just by changing a line of code. Um, the other thing we launched was the ability to run these hybrid systems. You know, quantum computers will get more, no don't get onto in a moment is, uh, today's computers are very imperfect, you know, lots of errors. Um, we working, obviously the industry towards fault-tolerant machines and Fernando can talk about some research papers that were published in that area, but right now the machines are far from perfect. And, uh, and the way that we can try to squeeze as much value out of these devices today is to run them in tandem with classical systems. >>We think of the notion of a self-learning quantum algorithm, where you use a classical optimization techniques, such as we see machine learning to tweak and tune the parameters of a quantum algorithm to try and iterate and converge on the best answer and try and overcome some of these issues surrounding errors. That's a lot of moving parts to orchestrate for customers, a lot of different systems, a lot of different programming techniques. And we wanted to make that much easier. We've been impressed with a, a, an open projects, been around for a couple of years, uh, called penny lane after the Beatles song. And, um, so we wanted to double down on that. We were getting a lot of positive feedback from customers about the penny lane talk it, so we decided to, uh, uh, make it a first class citizen on bracket, make it available as a native feature, uh, in our, uh, in our Jupiter notebooks and our tutorials learning examples, um, that open source project has very similar, um, guiding principles that we do, you know, it's open, it's cross platform, it's technology agnostic, and we thought he was a great fit to the service. >>So we, uh, we announced that and made it available to customers and, uh, and, and, uh, already getting great feedback. So, uh, you know, finishing the finishing the year strongly, I think, um, looking forward to 2021, you know, looking forward to some really cool technology it's on the horizon, uh, from a hardware point of view, making it easy to use, um, you know, and always, obviously trying to work back from customer problems. And so congratulations on the success. I'm sure it's not hard to hire people interested, at least finding qualified people it'd be different, but, you know, sign me up. I love quantum great people, Fernando real quick, understanding the relationship with Caltech unique to Amazon. Um, tell us how that fits into the, into this, >>Uh, right. John S no, as I was saying, it's it's early days, uh, for, for quantum computing, uh, and to make progress, uh, in abreast, uh, put together a team of experts, right. To work both on, on find new use cases of quantum computing and also, uh, building more powerful, uh, quantum hardware. Uh, so the AWS center for quantum computing is based at Caltech. Uh, and, and this comes from the belief of AWS that, uh, in quantum computing is key to, uh, to keep close, to stay close of like fresh ideas and to the latest scientific developments. Right. And Caltech is if you're near one computing. So what's the ideal place for doing that? Uh, so in the center, we, we put together researchers and engineers, uh, from computer science, physics, and other subjects, uh, from Amazon, but also from all the academic institutions, uh, of course some context, but we also have Stanford and university of Chicago, uh, among others. So we broke wrongs, uh, in the beauty for AWS and for quantum computer in the summer, uh, and under construction right now. Uh, but, uh, as we speak, John, the team is busy, uh, uh, you know, getting stuff in, in temporary lab space that we have at cottage. >>Awesome. Great. And real quick, I know we've got some time pressure here, but you published some new research, give a quick a plug for the new research. Tell us about that. >>Um, right. So, so, you know, as part of the effort or the integration for one company, uh, we are developing a new cubix, uh, which we choose a combination of acoustic and electric components. So this kind of hybrid Aquacel execute, it has the promise for a much smaller footprint, think about like a few microliters and much longer storage times, like up to settlements, uh, which, which is a big improvement over the scale of the arts sort of writing all export based cubits, but that's not the whole story, right? On six, if you have a good security should make good use of it. Uh, so what we did in this paper, they were just put out, uh, is, is a proposal for an architecture of how to build a scalable quantum computer using these cubits. So we found from our analysis that we can get more than a 10 X overheads in the resources required from URI, a universal thought around quantum computer. >>Uh, so what are these resources? This is like a smaller number of physical cubits. Uh, this is a smaller footprint is, uh, fewer control lines in like a smaller approach and a consistent, right. And, and these are all like, uh, I think this is a solid contribution. Uh, no, it's a theoretical analysis, right? So, so the, uh, the experimental development has to come, but I think this is a solid contribution in the big challenge of scaling up this quantum systems. Uh, so, so, so John, as we speak like, uh, data blessed in the, for quantum computing is, uh, working on the experimental development of this, uh, a highly adequacy architecture, but we also keep exploring other promising ways of doing scalable quantum computers and eventually, uh, to bring a more powerful computer resources to AWS customers. >>It's kind of like machine learning and data science, the smartest people work on it. Then you democratize that. I can see where this is going. Um, Richard real quick, um, for people who want to get involved and participate or consume, what do they do? Give us the playbook real quick. Uh, so simple, just go to the AWS console and kind of log onto the, to the bracket, uh, bracket console, jump in, you know, uh, create, um, create a Jupiter notebook, pull down some of our sample, uh, applications run through the notebook and program a quantum computer. It's literally that simple. There's plenty of tutorials. It's easy to get started, you know, classic cloud style right now from commitment. Jump in, start simple, get going. We want you to go quantum. You can't go back, go quantum. You can't go back to regular computing. I think people will be running concert classical systems in parallel for quite some time. So yeah, this is the, this is definitely not a one way door. You know, you go explore quantum computing and see how it fits into, uh, >>You know, into the, into solving some of the problems that you wanted to solve in the future. But definitely this is not a replacement technology. This is a complimentary technology. >>It's great. It's a great innovation. It's kind of intoxicating technically to get, think about the benefits Fernando, Richard, thanks for coming on. It's really exciting. I'm looking forward to keeping up keeping track of the progress. Thanks for coming on the cube coverage of reinvent, quantum computing going the next level coexisting building on top of the shoulders of other giant technologies. This is where the computing wave is going. It's different. It's impacting people's lives. This is the cube coverage of re-invent. Thanks for watching.

>> Announcer: Live from Las Vegas, it's theCUBE! Covering AWS re:Invent 2019. Brought to you by Amazon Web Services and Intel. Along with it's ecosystem partners. >> Okay, welcome back everyone. It's theCUBE's live coverage here in Las Vegas for Amazon Web Series today, re:Invent 2019. It's theCUBE's seventh year covering re:Invent. Eight years they've been running this event. It gets bigger every year. It's been a great wave to ride on. I'm John Furrier, my cohost, Dave Vellante. We've been riding this wave, Dave, for years. It's so exciting, it gets bigger and more exciting. >> Lucky seven. >> This year more than ever. So much stuff is happening. It's been really exciting. I think there's a sea change happening, in terms of another wave coming. Quantum computing, big news here amongst other great tech. Our next guest is Bill Vass, VP of Technology, Storage Automation Management, part of the quantum announcement that went out. Bill, good to see you. >> Yeah, well, good to see you. Great to see you again. Thanks for having me on board. >> So, we love quantum, we talk about it all the time. My son loves it, everyone loves it. It's futuristic. It's going to crack everything. It's going to be the fastest thing in the world. Quantum supremacy. Andy referenced it in my one-on-one with him around quantum being important for Amazon. >> Yes, it is, it is. >> You guys launched it. Take us through the timing. Why, why now? >> Okay, so the Braket service, which is based on quantum notation made by Dirac, right? So we thought that was a good name for it. It provides for you the ability to do development in quantum algorithms using gate-based programming that's available, and then do simulation on classical computers, which is what we call our digital computers today now. (men chuckling) >> Yeah, it's a classic. >> These are classic computers all of a sudden right? And then, actually do execution of your algorithms on, today, three different quantum computers, one that's annealing and two-bit gate-based machines. And that gives you the ability to test them in parallel and separate from each other. In fact, last week, I was working with the team and we had two machines, an ion trap machine and an electromagnetic tunneling machine, solving the same problem and passing variables back and forth from each other, you could see the cloud watch metrics coming out, and the data was going to an S3 bucket on the output. And we do it all in a Jupiter notebook. So it was pretty amazing to see all that running together. I think it's probably the first time two different machines with two different technologies had worked together on a cloud computer, fully integrated with everything else, so it was pretty exciting. >> So, quantum supremacy has been a word kicked around. A lot of hand waving, IBM, Google. Depending on who you talk to, there's different versions. But at the end of the day, quantum is a leap in computing. >> Bill: Yes, it can be. >> It can be. It's still early days, it would be day zero. >> Yeah, well I think if you think of, we're about where computers were with tubes if you remember, if you go back that far, right, right? That's about where we are right now, where you got to kind of jiggle the tubes sometimes to get them running. >> A bug gets in there. Yeah, yeah, that bug can get in there, and all of those kind of things. >> Dave: You flip 'em off with a punch card. Yeah, yeah, so for example, a number of the machines, they run for four hours and then they come down for a half hour for calibration. And then they run for another four hours. So we're still sort of at that early stage, but you can do useful work on them. And more mature systems, like for example D-Wave, which is annealer, a little different than gate-based machines, is really quite mature, right? And so, I think as you go back and forth between these machines, the gate-based machines and annealers, you can really get a sense for what's capable today with Braket and that's what we want to do is get people to actually be able to try them out. Now, quantum supremacy is a fancy word for we did something you can't do on a classical computer, right? That's on a quantum computer for the first time. And quantum computers have the potential to exceed the processing power, especially on things like factoring and other things like that, or on Hamiltonian simulations for molecules, and those kids of things, because a quantum computer operates the way a molecule operates, right, in a lot of ways using quantum mechanics and things like that. And so, it's a fancy term for that. We don't really focus on that at Amazon. We focus on solving customer's problems. And the problem we're solving with Braket is to get them to learn it as it's evolving, and be ready for it, and continue to develop the environment. And then also offer a lot of choice. Amazon's always been big on choice. And if you look at our processing portfolio, we have AMD, Intel x86, great partners, great products from them. We have Nvidia, great partner, great products from them. But we also have our Graviton 1 and Graviton 2, and our new GPU-type chip. And those are great products, too, I've been doing a lot on those, as well. And the customer should have that choice, and with quantum computers, we're trying to do the same thing. We will have annealers, we will have ion trap machines, we will have electromagnetic machines, and others available on Braket. >> Can I ask a question on quantum if we can go back a bit? So you mentioned vacuum tubes, which was kind of funny. But the challenge there was with that, it was cooling and reliability, system downtime. What are the technical challenges with regard to quantum in terms of making it stable? >> Yeah, so some of it is on classical computers, as we call them, they have error-correction code built in. So you have, whether you know it or not, there's alpha particles that are flipping bits on your memory at all times, right? And if you don't have ECC, you'd get crashes constantly on your machine. And so, we've built in ECC, so we're trying to build the quantum computers with the proper error correction, right, to handle these things, 'cause nothing runs perfectly, you just think it's perfect because we're doing all the error correction under the covers, right? And so that needs to evolve on quantum computing. The ability to reproduce them in volume from an engineering perspective. Again, standard lithography has a yield rate, right? I mean, sometimes the yield is 40%, sometimes it's 20%, sometimes it's a really good fab and it's 80%, right? And so, you have a yield rate, as well. So, being able to do that. These machines also generally operate in a cryogenic world, that's a little bit more complicated, right? And they're also heavily affected by electromagnetic radiation, other things like that, so you have to sort of faraday cage them in some cases, and other things like that. So there's a lot that goes on there. So it's managing a physical environment like cryogenics is challenging to do well, having the fabrication to reproduce it in a new way is hard. The physics is actually, I shudder to say well understood. I would say the way the physics works is well understood, how it works is not, right? No one really knows how entanglement works, they just knows what it does, and that's understood really well, right? And so, so a lot of it is now, why we're excited about it, it's an engineering problem to solve, and we're pretty good at engineering. >> Talk about the practicality. Andy Jassy was on the record with me, quoted, said, "Quantum is very important to Amazon." >> Yes it is. >> You agree with that. He also said, "It's years out." You said that. He said, "But we want to make it practical "for customers." >> We do, we do. >> John: What is the practical thing? Is it just kicking the tires? Is it some of the things you mentioned? What's the core goal? >> So, in my opinion, we're at a point in the evolution of these quantum machines, and certainly with the work we're doing with Cal Tech and others, that the number of available cubits are starting to increase at an astronomic rate, a Moore's Law kind of of rate, right? Whether it's, no matter which machine you're looking at out there, and there's about 200 different companies building quantum computers now, and so, and they're all good technology. They've all got challenges, as well, as reproducibility, and those kind of things. And so now's a good time to start learning how to do this gate-based programming knowing that it's coming, because quantum computers, they won't replace a classical computer, so don't think that. Because there is no quantum ram, you can't run 200 petabytes of data through a quantum computer today, and those kind of things. What it can do is factoring very well, or it can do probability equations very well. It'll have affects on Monte Carlo simulations. It'll have affects specifically in material sciences where you can simulate molecules for the first time that you just can't do on classical computers. And when I say you can't do on classical computers, my quantum team always corrects me. They're like, "Well, no one has proven "that there's an algorithm you can run "on a classical computer that will do that yet," right? (men chuckle) So there may be times when you say, "Okay, I did this on a quantum computer," and you can only do it on a quantum computer. But then someone's very smart mathematician says, "Oh, I figured out how to do it on a regular computer. "You don't need a quantum computer for that." And that's constantly evolving, as well, in parallel, right? And so, and that's what's that argument between IBM and Google on quantum supremacy is that. And that's an unfortunate distraction in my opinion. What Google did was quite impressive, and if you're in the quantum world, you should be very happy with what they did. They had a very low error rate with a large number of cubits, and that's a big deal. >> Well, I just want to ask you, this industry is an arms race. But, with something like quantum where you've got 200 companies actually investing in it so early days, is collaboration maybe a model here? I mean, what do think? You mentioned Cal Tech. >> It certainly is for us because, like I said, we're going to have multiple quantum computers available, just like we collaborate with Intel, and AMD, and the other partners in that space, as well. That's sort of the nice thing about being a cloud service provider is we can give customers choice, and we can have our own innovation, plus their innovations available to customers, right? Innovation doesn't just happen in one place, right? We got a lot of smart people at Amazon, we don't invent everything, right? (Dave chuckles) >> So I got to ask you, obviously, we can take cube quantum and call it cubits, not to be confused with theCUBE video highlights. Joking aside, classical computers, will there be a classical cloud? Because this is kind of a futuristic-- >> Or you mean a quantum cloud? >> Quantum cloud, well then you get the classic cloud, you got the quantum cloud. >> Well no, they'll be together. So I think a quantum computer will be used like we used to use a math coprocessor if you like, or FPGAs are used today, right? So, you'll go along and you'll have your problem. And I'll give you a real, practical example. So let's say you had a machine with 125 cubits, okay? You could just start doing some really nice optimization algorithms on that. So imagine there's this company that ships stuff around a lot, I wonder who that could be? And they need to optimize continuously their delivery for a truck, right? And that changes all the time. Well that algorithm, if you're doing hundreds of deliveries in a truck, it's very complicated. That traveling salesman algorithm is a NP-hard problem when you do it, right? And so, what would be the fastest best path? But you got to take into account weather and traffic, so that's changing. So you might have a classical computer do those algorithms overnight for all the delivery trucks and then send them out to the trucks. The next morning they're driving around. But it takes a lot of computing power to do that, right? Well, a quantum computer can do that kind of problemistic or deterministic equation like that, not deterministic, a best-fit algorithm like that, much faster. And so, you could have it every second providing that. So your classical computer is sending out the manifests, interacting with the person, it's got the website on it. And then, it gets to the part where here's the problem to calculate, we call it a shot when you're on a quantum computer, it runs it in a few seconds that would take an hour or more. >> It's a fast job, yeah. >> And it comes right back with the result. And then it continues with it's thing, passes it to the driver. Another update occurs, (buzzing) and it's just going on all the time. So those kind of things are very practical and coming. >> I've got to ask for the younger generations, my sons super interested as I mentioned before you came on, quantum attracts the younger, smart kids coming into the workforce, engineering talent. What's the best path for someone who has an either advanced degree, or no degree, to get involved in quantum? Is there a certain advice you'd give someone? >> So the reality is, I mean, obviously having taken quantum mechanics in school and understanding the physics behind it to an extent, as much as you can understand the physics behind it, right? I think the other areas, there are programs at universities focused on quantum computing, there's a bunch of them. So, they can go into that direction. But even just regular computer science, or regular mechanical and electrical engineering are all neat. Mechanical around the cooling, and all that other stuff. Electrical, these are electrically-based machines, just like a classical computer is. And being able to code at low level is another area that's tremendously valuable right now. >> Got it. >> You mentioned best fit is coming, that use case. I mean, can you give us a sense of a timeframe? And people will say, "Oh, 10, 15, 20 years." But you're talking much sooner. >> Oh, I don't, I think it's sooner than that, I do. And it's hard for me to predict exactly when we'll have it. You can already do, with some of the annealing machines, like D- Wave, some of the best fit today, right? So it's a matter of people want to use a quantum computer because they need to do something fast, they don't care how much it costs, they need to do something fast. Or it's too expensive to do it on a classical computer, or you just can't do it at all on a classical computer. Today, there isn't much of that last one, you can't do it at all, but that's coming. As you get to around 52, 50, 52 cubits, it's very hard to simulate that on a classical computer. You're starting to reach the edge of what you can practically do on a classical computer. At about 125 cubits, you probably are at a point where you can't just simulate it anymore. >> But you're talking years, not decades, for this use case? >> Yeah, I think you're definitely talking years. I think, and you know, it's interesting, if you'd asked me two years ago how long it would take, I would've said decades. So that's how fast things are advancing right now, and I think that-- >> Yeah, and the computers just getting faster and faster. >> Yeah, but the ability to fabricate, the understanding, there's a number of architectures that are very well proven, it's just a matter of getting the error rates down, stability in place, the repeatable manufacturing in place, there's a lot of engineering problems. And engineering problems are good, we know how to do engineering problems, right? And we actually understand the physics, or at least we understand how the physics works. I won't claim that, what is it, "Spooky action at a distance," is what Einstein said for entanglement, right? And that's a core piece of this, right? And so, those are challenges, right? And that's part of the mystery of the quantum computer, I guess. >> So you're having fun? >> I am having fun, yeah. >> I mean, this is pretty intoxicating, technical problems, it's fun. >> It is. It is a lot of fun. Of course, the whole portfolio that I run over at AWS is just really a fun portfolio, between robotics, and autonomous systems, and IOT, and the advanced storage stuff that we do, and all the edge computing, and all the monitor and management systems, and all the real-time streaming. So like Kinesis Video, that's the back end for the Amazon ghost stores, and working with all that. It's a lot of fun, it really is, it's good. >> Well, Bill, we need an hour to get into that, so we may have to come up and see you, do a special story. >> Oh, definitely! >> We'd love to come up and dig in, and get a special feature program with you at some point. >> Yeah, happy to do that, happy to do that. >> Talk some robotics, some IOT, autonomous systems. >> Yeah, you can see all of it around here, we got it up and running around here, Dave. >> What a portfolio. >> Congratulations. >> Alright, thank you so much. >> Great news on the quantum. Quantum is here, quantum cloud is happening. Of course, theCUBE is going quantum. We've got a lot of cubits here. Lot of CUBE highlights, go to SiliconAngle.com. We got all the data here, we're sharing it with you. I'm John Furrier with Dave Vellante talking quantum. Want to give a shout out to Amazon Web Services and Intel for setting up this stage for us. Thanks to our sponsors, we wouldn't be able to make this happen if it wasn't for them. Thank you very much, and thanks for watching. We'll be back with more coverage after this short break. (upbeat music)

>> Announcer: From the Computer History Museum in Mountain View, California, it's the Cube. On the ground with Accenture Labs 30th Anniversary Celebration. >> Hello and welcome back to our special on the ground coverage of Accenture Labs 30 year celebration. Here's to the next 30 years is their slogan and I'm John Ferry with the Cube and I'm here with Marc Carrel-Billiard who's the Senior Manger that runs R&D Global for Accenture Labs. Welcome to the Cube conversation. Thanks for joining me. >> Marc: Thanks, John. >> So, I got to ask you, Accenture 30 years, they weren't called Accenture back then, it was called Arthur Anderson or Anderson Consulting and then it became Accenture, now you got Accenture Lab. But you have had labs all throughout. >> You're right. I mean, it's pretty amazing. And I think this is absolutely right. So we had this organization for 30 years, believe it or not. And that organization is doing applied research. So what we do is we leverage new technology innovations and everything to really solve business challenges or societal pacts and social changes and everything. >> State of the art back then, if I remember correctly my history was converting an S&A gateway to a technet to a TCP/IP network. >> Yeah we just improved a little bit. We went to quantum computing, to Blockchain, to different type of things like that. >> What a magical time it is right now >> It is magic. >> Share some color on today's culture, the convergence of all this awesomeness happening. Open source, booming. Cloud, unlimited compute. You have now more developers than ever, Enterprise is looking more and more like consumers. So a lot of action. What's the excitement? Share the cutting edge lab's activity. I think you said something absolutely right. I mean, I think there's a combinatorial effect of two different technology working very well together, and is a compression on time, all those technology waves that are maturing very fast. So one thing that we been doing is a great example for that, is quantum computing. You heard about quantum computing, you know? >> Of course. >> That's the new Paradigm of computing power. Leveraging like, quantum mechanics, you know? I mean it's really amazing stuff. And believe it or not, we've been working with D-Wave, they have a quantum computer in Vancouver, and a companies called 1QBit, it's a software company, and we've built, on top of that, an algorithm that has molecule comparison. And we worked with Biogen, a pharmaceutical company, to work on this. Now, the really staggering thing about it, is that we talked about it like six months ago, we build the pilot in two months time. Done. And then now, I mean, it's already made. >> Well, this is amazing. This is what highlights to me what's exciting. What you just described is a time frame that's really short. >> That's right! >> Back in the old days, it was these projects were months and months, and potentially years. >> Absolutely. >> What is the catalyst for that? Is it the technology leverage? Is it the people? Is it the process? All three? What's the take? >> I think it's all three. I would say that definitely the technology, as I said, get combined faster. You said very right, there's a lot of capability in term of high performance computing we can get through the Cloud, the storage as well. The data that we're going to be accessing, and then I think the beauty is that, putting all the people together for the quantum work. We had mathematicians, we have from Biogen, we have our own labs, and all people together, they make the magic happen. >> 30 years ago, just a little history 'cause I'm old enough to actually talk about 30 years ago, the Big Six Accounting Firms, accounting firms, ran all the big software projects. How ironic is that, that today Blockchain disrupts the even need for an accounting firm, because with Smart Contracts, Blockchain is turning out to be a very, very disruptive operation in technology, because you don't need an accounting firm to clear out contracts. Blockchain is very disruptive. What are you guys doing on Blockchain? >> You're absolutely right, John. And you know, the first thing. So, we have seven labs in Accenture Labs. And we have one lab didn't get it on Blockchain, and it's Sophia Antipolis inside of France, where I'm from, by the way. We're doing a lot of things with Blockchain. A lot of people are thinking about Blockchain as a system that's going to regulate, basically, transfer a transaction, financial transaction. We want to take Blockchain to the next level. And one thing we're doing, for example, We're using Blockchain for Angels. How we're track, basically, donation you're going to do. We going to use Blockchain for-- >> Well that's because people want to know their money's actually going to good. >> That's right! That's right! >> Not to scams that have been out there. >> You got it. >> We going to use Blockchain as a DRM system, Digital Rights Management system. We're going to use that in manufacturing industry, in many industry, and it goes on and on and on. >> What is the big buzz right now with Cryptocurrency? You're seeing a lot of these ICOs out there. Are those legit? In your mind, is it just a bubble? Is it just a normalization's going to come, what's your take on Initial Coin Offerings? >> I think, to be honest with you, I think this is a progress with thing. I mean, we discuss about Blockchain and everything. We see some trains going there. I think it's accelerating as well, because it's got a lot of take up and everything. We see, also, the world changing, and I think we need to look at the geo-political context of the world and what could happen. So I think those kind of new regulation, the way it's going to work. I mean, it's coming on time, people's going to leverage it, so I think it's not some fad stuff. This is something that's going to stay. >> It's just a Wild West. >> But it was, exactly. Right now, we need to work on the right standard, we need to figure out how it's going to work and everything. >> What is the exciting things that you see out there right now? I mean, Blockchain just kind of gets us excited 'cause you can imagine different new things happening. But the clients that I talk to, customers, your clients, or CIOs, they have to reimagine the future. >> That's right. >> With preexisting conditions called legacy infrastructure. >> Exactly >> Legacy software. How do they get the best of the magic and manage the preexisting conditions? >> So, there's a lot of innovation in term of software development. You take energy in everything that we have, basically, to connect to your legacy, and leverage it as much as you can. You know, there's a big progress in artificial intelligence today. I mean, I've live a lot of winters of artificial intelligence. I think finally, maybe there's going to be some spring. Why? Because of what we talk about. The iPad from one's computing the data available, and then also, some new type of algorithm like deep learning and everything. That data that is somewhere into this company called the Dark Data, people is going to be able to leverage it, and then make those artificial intelligence systems even more intelligence, smarter, and everything. So, legacy's here, but we're going to leverage it, and we're going to give a second life to those legacy environment. So those technology like artificial intelligence, new analytics and all those different things. >> So I got to ask you a kind of politically hot question, which is the digital transformation. >> Yes. >> So there's doubt we're in a digital transformation. No brainer. Yet, I go to conferences over and over again, and I see Gartner Magic Quadrant. I'm number one on the Magic Quadrant, and everybody's number one in the Magic Quadrant. So, the question is, what's the scoreboard of the new environment? Because, if you use the old scoreboard, and the world's horizontally scalable, you're going to have a blending of Magic Quadrants. So there's going to be a disruption, and that's causing confusion to the CIOs and CXOs because you got Chief Data Officer, Chief Security Officer, you got no perimeter for security, you have quantum computing, you have Cloud. So, people are trying to squint through all the nonsense and saying, how do you measure success? >> Yeah. >> Certainly customers is a good one. >> I think this is the typical question. I mean, this whole digital transformation, I understand that is important, and we need to understand. I mean, Accenture, and especially the lab, it's all about result. And you know what? The mission of the lab is new, it's applied, is now. New technology applied for real challenges, and I want to deliver it now, and I want to work for six months. So my word is that our research is outcome driven, and that's exactly what we're seeing. So, I told you about the quantum computing, and I have other example where we are really laser-focused on making an outcome. I think that's where-- >> So, to your point, people shouldn't buy promises. >> No. >> They should buy results. >> That's right. >> So, Peter Barris, who runs our research, said to me, and I asked him the question, he goes, ah, that's just a bunch of BS. The ultimate metric is how many customers you have. So, someone should be touting their customers. >> Sorry? >> They should be touting their customers, not some survey. >> No, absolutely. And I'm really for that. >> I want to tell you something, that I'm a very pragmatic person. I'm coming from the field, where I was serving 400 clients doing, every day, project delivery, you know? >> John: God bless you. >> And I've always been doing innovation at the same time, but my view was that innovation needs to be scalable, it needs to be tangible, it needs to be outcome driven. So again, this is really the matter of the lab, and if you look at how the lab works with the rest of the organization of Accenture, this is exactly what we're doing. We connect with our studio, where we can do prototyping front of the eyes of our client. We connect with Open Innovation, where we connect with the best start ups in the world. I think, you remember when I told you combinatorial effect. There's a combinatorial effect with technology that is a combinatorial effect with people. If you put the people from start up, the best guys from the lab, the best guys from the studios and everything, that's where the magic happens. >> So this is a new configuration? >> We collect the innovation architecture. >> So this is a scalable model for being agile, and the results are what? Faster performance? >> Faster performance, innovative performance, and tangible outcome. >> Okay Marc, you're an excitable guy, I like talkin' with you, what are you most excited about right now in this world that you're living in? So, I told you about the technology, and there's one thing that the lab is doing, and we'll be launching that this year, and we'll continue expanding. It's what we call Tech For Good. Tech For Good is how we're going to apply technology to change society. What we're going to do for fighting hunger in India. How we're going to give situational awareness to blind people using augmented reality immersion learning. That keeps me awake at night, because this is technology for best usage, it allows for our people to sleep well at night. My kids are proud of me, and I think we can-- >> Change the world! >> That's right! We can attract great people. >> Alright, final question. Here at the celebration, at the Computer History Museum in Silicon Valley, what's the big scene here? Share with the folks who are watching, who aren't here, what's happening. >> I think, first of all, the venue is amazing. Computer Historic Museum is probably one of my favorite museum here in Silicon Valley. I mean, you need to understand that, 15 years old I started to work on a IBM 360 of my uncle, so the machine over there, I know it. I worked on it. And when I see the completed progress where we are today, when we see the Cray, when we see the quantum and everything, I feel so lucky that we're celebrating 30 years. Now I'd to go for the next 30 years of the lab. That's what I want to do. >> Let's get that on our next interview. Marc, thanks for sharing, here's to the next 30 years. This is the Cube coverage of Accenture Lab's 30 year celebration. The Computer History Museum, I'm John Ferry. Thanks for watching.