(contemplative upbeat music) >> Hello and welcome to this CUBE Conversation. I'm John Furrier, your host of theCUBE, here in Palo Alto, California. Got a great topic on expanding capabilities for urgent computing. Dan Stanzione, he's Executive Director of TACC, the Texas Advanced Computing Center, and Rajesh Pohani, VP of PowerEdge, HPC Core Compute at Dell Technologies. Gentlemen, welcome to this CUBE Conversation. >> Thanks, John. >> Thanks, John, good to be here. >> Rajesh, you got a lot of computing in PowerEdge, HPC, Core Computing. I mean, I get a sense that you love compute, so we'll jump right into it. And of course, I got to love TACC, Texas Advanced Computing Center. I can imagine a lot of stuff going on there. Let's start with TACC. What is the Texas Advanced Computing Center? Tell us a little bit about that. >> Yeah, we're part of the University of Texas at Austin here, and we build large-scale supercomputers, data systems, AI systems, to support open science research. And we're mainly funded by the National Science Foundation, so we support research projects in all fields of science, all around the country and around the world. Actually, several thousand projects at the moment. >> But tied to the university, got a lot of gear, got a lot of compute, got a lot of cool stuff going on. What's the coolest thing you got going on right now? >> Well, for me, it's always the next machine, but I think science-wise, it's the machines we have. We just finished deploying Lonestar6, which is our latest supercomputer, in conjunction with Dell. A little over 600 nodes of those PowerEdge servers that Rajesh builds for us. Which makes more than 20,000 that we've had here over the years, of those boxes. But that one just went into production. We're designing new systems for a few years from now, where we'll be even larger. Our Frontera system was top five in the world two years ago, just fell out of the top 10. So we've got to fix that and build the new top-10 system sometime soon. We always have a ton going on in large-scale computing. >> Well, I want to get to the Lonestar6 in a minute, on the next talk track, but... What are some of the areas that you guys are working on that are making an impact? Take us through, and we talked before we came on camera about, obviously, the academic affiliation, but also there's a real societal impact of the work you're doing. What are some of the key areas that the TACC is making an impact? >> So there's really a huge range from new microprocessors, new materials design, photovoltaics, climate modeling, basic science and astrophysics, and quantum mechanics, and things like that. But I think the nearest-term impacts that people see are what we call urgent computing, which is one of the drivers around Lonestar and some other recent expansions that we've done. And that's things like, there's a hurricane coming, exactly where is it going to land? Can we refine the area where there's going to be either high winds or storm surge? Can we assess the damage from digital imagery afterwards? Can we direct first responders in the optimal routes? Similarly for earthquakes, and a lot recently, as you might imagine, around COVID. In 2020, we moved almost a third of our resources to doing COVID work, full-time. >> Rajesh, I want to get your thoughts on this, because Dave Vellante and I have been talking about this on theCUBE recently, a lot. Obviously, people see what cloud's, going on with the cloud technology, but compute and on-premises, private cloud's been growing. If you look at the hyperscale on-premises and the edge, if you include that in, you're seeing a lot more user consumption on-premises, and now, with 5G, you got edge, you mentioned first responders, Dan. This is now pointing to a new architectural shift. As the VP of PowerEdge and HPC and Core Compute, you got to look at this and go, "Hmm." If Compute's going to be everywhere, and in locations, you got to have that compute. How does that all work together? And how do you do advanced computing, when you have these urgent needs, as well as real-time in a new architecture? >> Yeah, John, I mean, it's a pretty interesting time when you think about some of the changing dynamics and how customers are utilizing Compute in the compute needs in the industry. Seeing a couple of big trends. One, the distribution of Compute outside of the data center, 5G is really accelerating that, and then you're generating so much data, whether what you do with it, the insights that come out of it, that we're seeing more and more push to AI, ML, inside the data center. Dan mentioned what he's doing at TACC with computational analysis and some of the work that they're doing. So what you're seeing is, now, this push that data in the data center and what you do with it, while data is being created out at the edge. And it's actually this interesting dichotomy that we're beginning to see. Dan mentioned some of the work that they're doing in medical and on COVID research. Even at Dell, we're making cycles available for COVID research using our Zenith cluster, that's located in our HPC and AI Innovation Lab. And we continue to partner with organizations like TACC and others on research activities to continue to learn about the virus, how it mutates, and then how you treat it. So if you think about all the things, and data that's getting created, you're seeing that distribution and it's really leading to some really cool innovations going forward. >> Yeah, I want to get to that COVID research, but first, you mentioned a few words I want to get out there. You mentioned Lonestar6. Okay, so first, what is Lonestar6, then we'll get into the system aspect of it. Take us through what that definition is, what is Lonestar6? >> Well, as Dan mentioned, Lonestar6 is a Dell technology system that we developed with TACC, it's located at the University of Texas at Austin. It consists of more than 800 Dell PowerEdge 6525 servers that are powered with 3rd Generation AMD EPYC processors. And just to give you an example of the scale of this cluster, it could perform roughly three quadrillion operations per second. That's three petaFLOPS, and to match what Lonestar6 can compute in one second, a person would have to do one calculation every second for a hundred million years. So it's quite a good-size system, and quite a powerful one as well. >> Dan, what's the role that the system plays, you've got petaFLOPS, what, three petaFLOPS, you mentioned? That's a lot of FLOPS! So obviously urgent computing, what's cranking through the system there? Take us through, what's it like? >> Sure, well, there there's a mix of workloads on it, and on all our systems. So there's the urgent computing work, right? Fast turnaround, near real-time, whether it's COVID research, or doing... Project now where we bring in MRI data and are doing sort of patient-specific dosing for radiation treatments and chemotherapy, tailored to your tumor, instead of just the sort of general for people your size. That all requires sort of real-time turnaround. There's a lot AI research going on now, we're incorporating AI in traditional science and engineering research. And that uses an awful lot of data, but also consumes a huge amount of cycles in training those models. And then there's all of our traditional, simulation-based workloads and materials and digital twins for aircraft and aircraft design, and more efficient combustion in more efficient photovoltaic materials, or photovoltaic materials without using as much lead, and things like that. And I'm sure I'm missing dozens of other topics, 'cause, like I said, that one really runs every field of science. We've really focused the Lonestar line of systems, and this is obviously the sixth one we built, around our sort of Texas-centric users. It's the UT Austin users, and then with contributions from Texas A&M , and Texas Tech and the University of Texas system, MD Anderson Healthcare Center, the University of North Texas. So users all around the state, and every research problem that you might imagine, those are into. We're just ramping up a project in disaster information systems, that's looking at the probabilities of flooding in coastal Texas and doing... Can we make building code changes to mitigate impact? Do we have to change the standard foundation heights for new construction, to mitigate the increasing storm surges from these sort of slow storms that sit there and rain, like hurricanes didn't used to, but seem to be doing more and more. All those problems will run on Lonestar, and on all the systems to come, yeah. >> It's interesting, you mentioned urgent computing, I love that term because it could be an event, it could be some slow kind of brewing event like that rain example you mentioned. It could also be, obviously, with the healthcare, and you mentioned COVID earlier. These are urgent, societal challenges, and having that available, the processing capability, the compute, the data. You mentioned digital twins. I can imagine all this new goodness coming from that. Compare that, where we were 10 years ago. I mean, just from a mind-blowing standpoint, you have, have come so far, take us through, try to give a context to the level of where we are now, to do this kind of work, and where we were years ago. Can you give us a feel for that? >> Sure, there's a lot of ways to look at that, and how the technology's changed, how we operate around those things, and then sort of what our capabilities are. I think one of the big, first, urgent computing things for us, where we sort of realized we had to adapt to this model of computing was about 15 years ago with the big BP Gulf Oil spill. And suddenly, we were dumping thousands of processors of load to figure out where that oil spill was going to go, and how to do mitigation, and what the potential impacts were, and where you need to put your containment, and things like that. And it was, well, at that point we thought of it as sort of a rare event. There was another one, that I think was the first real urgent computing one, where the space shuttle was in orbit, and they knew something had hit it during takeoff. And we were modeling, along with NASA and a bunch of supercomputers around the world, the heat shield and could they make reentry safely? You have until they come back to get that problem done, you don't have months or years to really investigate that. And so, what we've sort of learned through some of those, the Japanese tsunami was another one, there have been so many over the years, is that one, these sort of disasters are all the time, right? One thing or another, right? If we're not doing hurricanes, we're doing wildfires and drought threat, if it's not COVID. We got good and ready for COVID through SARS and through the swine flu and through HIV work, and things like that. So it's that we can do the computing very fast, but you need to know how to do the work, right? So we've spent a lot of time, not only being able to deliver the computing quickly, but having the data in place, and having the code in place, and having people who know the methods who know how to use big computers, right? That's been a lot of what the COVID Consortium, the White House COVID Consortium, has been about over the last few years. And we're actually trying to modify that nationally into a strategic computing reserve, where we're ready to go after these problems, where we've run drills, right? And if there's a, there's a train that derails, and there's a chemical spill, and it's near a major city, we have the tools and the data in place to do wind modeling, and we have the terrain ready to go. And all those sorts of things that you need to have to be ready. So we've really sort of changed our sort of preparedness and operational model around urgent computing in the last 10 years. Also, just the way we scheduled the system, the ability to sort of segregate between these long-running workflows for things that are really important, like we displaced a lot of cancer research to do COVID research. And cancer's still important, but it's less likely that we're going to make an impact in the next two months, right? So we have to shuffle how we operate things and then just, having all that additional capacity. And I think one of the things that's really changed in the models is our ability to use AI, to sort of adroitly steer our simulations, or prune the space when we're searching parameters for simulations. So we have the operational changes, the system changes, and then things like adding AI on the scientific side, since we have the capacity to do that kind of things now, all feed into our sort of preparedness for this kind of stuff. >> Dan, you got me sold, I want to come work with you. Come on, can I join the team over there? It sounds exciting. >> Come on down! We always need good folks around here, so. (laughs) >> Rajesh, when I- >> Almost 200 now, and we're always growing. >> Rajesh, when I hear the stories about kind of the evolution, kind of where the state of the art is, you almost see the innovation trajectory, right? The growth and the learning, adding machine learning only extends out more capabilities. But also, Dan's kind of pointing out this kind of response, rapid compute engine, that they could actually deploy with learnings, and then software, so is this a model where anyone can call up and get some cycles to, say, power an autonomous vehicle, or, hey, I want to point the machinery and the cycles at something? Is the service, do you guys see this going that direction, or... Because this sounds really, really good. >> Yeah, I mean, one thing that Dan talked about was, it's not just the compute, it's also having the right algorithms, the software, the code, right? The ability to learn. So I think when those are set up, yeah. I mean, the ability to digitally simulate in any number of industries and areas, advances the pace of innovation, reduces the time to market of whatever a customer is trying to do or research, or even vaccines or other healthcare things. If you can reduce that time through the leverage of compute on doing digital simulations, it just makes things better for society or for whatever it is that we're trying to do, in a particular industry. >> I think the idea of instrumenting stuff is here forever, and also simulations, whether it's digital twins, and doing these kinds of real-time models. Isn't really much of a guess, so I think this is a huge, historic moment. But you guys are pushing the envelope here, at University of Texas and at TACC. It's not just research, you guys got real examples. So where do you guys see this going next? I see space, big compute areas that might need some data to be cranked out. You got cybersecurity, you got healthcare, you mentioned oil spill, you got oil and gas, I mean, you got industry, you got climate change. I mean, there's so much to tackle. What's next? >> Absolutely, and I think, the appetite for computing cycles isn't going anywhere, right? And it's only going to, it's going to grow without bound, essentially. And AI, while in some ways it reduces the amount of computing we do, it's also brought this whole new domain of modeling to a bunch of fields that weren't traditionally computational, right? We used to just do engineering, physics, chemistry, were all super computational, but then we got into genome sequencers and imaging and a whole bunch of data, and that made biology computational. And with AI, now we're making things like the behavior of human society and things, computational problems, right? So there's this sort of growing amount of workload that is, in one way or another, computational, and getting bigger and bigger. So that's going to keep on growing. I think the trick is not only going to be growing the computation, but growing the software and the people along with it, because we have amazing capabilities that we can bring to bear. We don't have enough people to hit all of them at once. And so, that's probably going to be the next frontier in growing out both our AI and simulation capability, is the human element of it. >> It's interesting, when you think about society, right? If the things become too predictable, what does a democracy even look like? If you know the election's going to be over two years from now in the United States, or you look at these major, major waves >> Human companies don't know. >> of innovation, you say, "Hmm." So it's democracy, AI, maybe there's an algorithm for checking up on the AI 'cause biases... So, again, there's so many use cases that just come out of this. It's incredible. >> Yeah, and bias in AI is something that we worry about and we work on, and on task forces where we're working on that particular problem, because the AI is going to take... Is based on... Especially when you look at a deep learning model, it's 100% a product of the data you show it, right? So if you show it a biased data set, it's going to have biased results. And it's not anything intrinsic about the computer or the personality, the AI, it's just data mining, right? In essence, right, it's learning from data. And if you show it all images of one particular outcome, it's going to assume that's always the outcome, right? It just has no choice, but to see that. So how we deal with bias, how do we deal with confirmation, right? I mean, in addition, you have to recognize, if you haven't, if it gets data it's never seen before, how do you know it's not wrong, right? So there's about data quality and quality assurance and quality checking around AI. And that's where, especially in scientific research, we use what's starting to be called things like physics-informed or physics-constrained AI, where the neural net that you're using to design an aircraft still has to follow basic physical laws in its output, right? Or if you're doing some materials or astrophysics, you still have to obey conservation of mass, right? So I can't say, well, if you just apply negative mass on this other side and positive mass on this side, everything works out right for stable flight. 'Cause we can't do negative mass, right? So you have to constrain it in the real world. So this notion of how we bring in the laws of physics and constrain your AI to what's possible is also a big part of the sort of AI research going forward. >> You know, Dan, you just, to me just encapsulate the science that's still out there, that's needed. Computer science, social science, material science, kind of all converging right now. >> Yeah, engineering, yeah, >> Engineering, science, >> slipstreams, >> it's all there, >> physics, yeah, mmhmm. >> it's not just code. And, Rajesh, data. You mentioned data, the more data you have, the better the AI. We have a world what's going from silos to open control planes. We have to get to a world. This is a cultural shift we're seeing, what's your thoughts? >> Well, it is, in that, the ability to drive predictive analysis based on the data is going to drive different behaviors, right? Different social behaviors for cultural impacts. But I think the point that Dan made about bias, right, it's only as good as the code that's written and the way that the data is actually brought into the system. So making sure that that is done in a way that generates the right kind of outcome, that allows you to use that in a predictive manner, becomes critically important. If it is biased, you're going to lose credibility in a lot of that analysis that comes out of it. So I think that becomes critically important, but overall, I mean, if you think about the way compute is, it's becoming pervasive. It's not just in selected industries as damage, and it's now applying to everything that you do, right? Whether it is getting you more tailored recommendations for your purchasing, right? You have better options that way. You don't have to sift through a lot of different ideas that, as you scroll online. It's tailoring now to some of your habits and what you're looking for. So that becomes an incredible time-saver for people to be able to get what they want in a way that they want it. And then you look at the way it impacts other industries and development innovation, and it just continues to scale and scale and scale. >> Well, I think the work that you guys are doing together is scratching the surface of the future, which is digital business. It's about data, it's about out all these new things. It's about advanced computing meets the right algorithms for the right purpose. And it's a really amazing operation you guys got over there. Dan, great to hear the stories. It's very provocative, very enticing to just want to jump in and hang out. But I got to do theCUBE day job here, but congratulations on success. Rajesh, great to see you and thanks for coming on theCUBE. >> Thanks for having us, John. >> Okay. >> Thanks very much. >> Great conversation around urgent computing, as computing becomes so much more important, bigger problems and opportunities are around the corner. And this is theCUBE, we're documenting it all here. I'm John Furrier, your host. Thanks for watching. (contemplative music)

>> Hi, welcome once again to Action Item. (funky electronic music) I'm Peter Burris and this is Wikibon's weekly research meeting where we bring together some of the best minds in Silicon Valley to talk about some of the trends that are most important. We're broadcasting from here in the Cube studios in beautiful Palo Alto, California. And in the studio, I'm being joined by George Gilbert and David Floyer and on the phone we have Neil Raden, Jim Kobielus, Dave Vellante. Team, thanks very much for being part of this conversation today. What we're going to do today is we're going to bring forward some of Wikibon's predictions for 2018. In a previous show, we discussed what we learned in 2017, so some of the trends and some of the expectations that didn't play out as expected. This year we're going to dig a little bit deep into what we think is going to happen in 2018 and it all starts with a proposition that even as we go through significant industry change, we're not necessarily going to see the economics of the industry change as fast, which leads to prediction number one. David Floyer, what is it? >> So, my prediction is that volume is going to take a key role in the evolution of disruptive technologies. So for example, in AI and IOT and in true private cloud, volume is going to be the key determination of when it starts to take off, when it starts to hockey stick. >> So this has been something that's been featured in the industry for a while, Dave, but give us an example. What's the relationship between volume and AI? >> So if we take the relationship between AI and volume, AI is going sideways, and I would predict that it's going to go sideways in 2018 because every implementation is a snowflake until there are solutions out there which can be delivered in volume by vendors. Then that will the point at which things will take off. So an example, for example, automated cars. They are AI, when they start to come out in volume, there'll be volume manufacturers, volume of the census, volume of the processes, the on-processes, volume of everything that will drive down costs and make those implementations so quickly. >> And it's still software, so we're still worried about support and service on a very, very broad scale. >> David: Yeah. >> So that leads to our second quick prediction. Dave Vellante, build on this notion of volume. What's going to be the impact on a lot of the innovative smaller companies in 2018? >> Dave: So Peter, my prediction is got to go scale or go home, AKA go out of business. So we expect massive industry consolidation is going to take place in the next two years, certainly through 2019 as the business models of VC-backed tech startups are getting smashed by cloud and, to a great extent, open source. In a turnabout from the historical norms, innovations and cost reductions from the largest cloud players are moving at a pace that's faster than many, if not most startups are able to deliver. So finding white space is much, much harder. We see private equity as playing a key role here, providing capital for M and A and doing roll-ups that are going to create scale and large portfolios that can compete. >> So Neil Raden, as we think about what Dave just said, one of the key things that's happening is a lot of money's being put into some of the new technologies that are intended to provide more intelligence in a lot of different places. One of the large company leaders indicating or describing how this was going to play out was IBM with its Watson story. What's been going on with Watson? What's our prediction for how that's playing out and likely, what's a likely 2018 scenario for IBM and Watson? >> Neil: Well, not to sugarcoat it but Watson's been a dismal failure, and I think that IBM is going to reassess their whole approach to cognitive computing in 2018. Numbers don't lie, let me give you some numbers from 2016. They obviously don't have '17 yet. But these are reliable numbers from some institutional clients of mine. Their goal for 2016 was over 8,000 clients. They achieved 500. Their goals for business partners was over 4,000 and they achieved 329. So, you know, the numbers speak for themselves, but Watson hasn't caught on. It's a solution in search of a problem. It was a marketing stunt, really, that someone thought to be turned into a 20 billion dollar per year business. It's not even a product, really. It's dozens of subsystems that are linked with APIs. Some of them are interesting, but most already are available in the open source world. >> Well one of the things we talked about last week, Neil, was the idea that we're going to see more buy, as opposed to build, and we talked about the volume play there, and then we asked the question, is there going to be more software or is there going to be more services? It sounds like IBM's play to be a dominate player in AI-related services has not gone as well as expected. Is that kind of where we are right now? >> Neil: Well, yeah. If you look at one of the more public failures of Watson, which was MD Anderson Cancer Center, they pulled the plug on the project after 62 million dollars, but IBM only got about 20 million dollars of that, the rest of it went to PWC. So how they intend to split that business between global services and their partners, I really don't know. And the failure of Watson at MD Anderson wasn't entirely IBM's fault. A lot of it had to do with PWC's project management, and a lot of it had to do with the people at Anderson who basically started the project by looking at a very well-understood type of leukemia that had a well-understood etiology and treatment options. So when the auditors looked at it, they said we haven't learned anything for 62 million dollars, and that's been repeated at other projects. >> So it sounds like this is, again, tied back to the idea of scale, volume, and related issues. But it also sounds like there's a lot of question, ultimately, about what is AI? What isn't AI? What role is Watson going to play? Is it going to be private data? Is it going to be public data? A lot of questions are going to emerge over the course of next year. But there are domains where AI, ML, DL are likely to have some important success. And George, we've got a prediction about where they're likely to be successful in 2018. What are we thinking, what's one domain where we think at least machine learning is going to have a significant impact in 2018? >> Well, keying off David's point about volume, volume economics, we think that IT operations management is going to be one of the first horizontal applications that embeds machine learning. It's not about presenting, modeling, and tools to developers, it's just part of the application. The reason it's important, there's really two key reasons. We're building out shared ephemeral infrastructure, which is very different from the dedicated silos that we had for mission-critical applications. And this infrastructure, and the application landscape on top of it, is extremely hard to manage, and machine learning can help greatly. And I think investment in that will be driven also by a realization that this is training wheels for IOT in the sense that you're monitoring machines through data telemetry that they throw off, and you're using models to figure out how they should be operating versus how they are operating. >> So this has significant applications across IOT, ML, and how we get to volume because it's a controlled and pretty well-defined space. By that I mean, but nonetheless, it's related to the problem space, and by that I mean that bespoke applications, whether they're from AI or whatnot, are going to create new needs for new types of monitoring. But the classification of the tools and the classifications of the devices that will be monitored are pretty well-understood and they're controlled by the IT industry, so they ought to have pretty good definitions. Is that what we're thinking here, George? >> Yes, precisely, and the bespoke pieces can be modeled because they fall within a well-known domain. But I just want to add on the go to market side that keys off of what Dave Vellante said, which is that these IT operations management applications, they can come from cloud vendors, they can come from enterprise software vendors, but especially the ones that are going to be hybrid cloud are going to need enterprise sales forces to get them to market. You hear millions of, virtually millions of startups say our go to market strategy is land and expand. That doesn't get you enterprise wide, and for that you need an enterprise sales force, most expensive migratory workforce in the world, and startups don't have them. And that's why, one of the reasons, we will see roll-ups for scale. >> So we've talked about the need for scale, the impact on start-ups, the impact on big companies like IBM. One of the domains we think this is going to play out most successfully is in ITOM, IT operations management for some of these new technologies. But underneath all of this is a lot of new complexity because of distribution of function, distribution of data, distribution of application, and there needs to be a new technology concept that allows for that distribution to take place under control. And we talked about this a few weeks ago, but Jim Kobielus, what's our prediction for the world of blockchain or blockchain-like technologies are going to take in facilitating this new distribution of capability around digital business? >> Jim: Yeah, blockchain, we're predicting, it will be as fundamental to the growth of the worldwide digital infrastructure and digital markets as 40 plus, 30 to 40 years ago TCPIP was to the growth of what became the web and the internet. And why is that? Well, you know, when you look at the basic principles for development of any infrastructure where there's an innovation on the infrastructure side that is shared or standardized, robust, meaning secure, and distributed, it quickly becomes a common bond enabling growth of sharing and teaming and markets and so forth. So really, it's a layering process where we have TCPIP and you know, DNS and URL providing this shared address space. Layered on top of that was public key infrastructure, which is the foundation of the security that makes blockchain so strong. You know, PKI and SSL and all that is an enabler, that's another robust, shared common infrastructure. And then on top of that, what we see on top of that is they distributed robust shared record of transactions. That's blockchain, and really blockchain as an enabler for the new generation of digital crypto currencies such as bitcoin, enabling a shared robust and distributed currency or means of payment across the worldwide economy. So, in many ways, blockchain is an enabler for this new generation of truly robust and shared currency and transactions with a mutable, secured, shared record. It's just going to be a growth accelerator for the world economy in the 21st century going forward. >> So in many respects, technology takes off when network formation occurs. TCPIP was a foundation for network formation for distributed computing. What we're basically saying is a blockchain becomes a crucial feature of how application networks get constructed over the course of the next 10 years. Have I got that right, David Floyer? >> Absolutely, that's the key. The guy who sold the first telephone was a genius, the second was easier, and it gets easier and easier as that work grows, and blockchain is a key contributor to the development of those networks, and a one-to-one relationship, many, many one-to-one relationships that can occur from that, away from centralization and to a much more distributed environment. >> So I think we've got time for one more prediction really quickly, and I'll bring it up, and then I want to open it up for conversation because this is an interesting one. We come back to this notion of global network formation, blockchain being what we think, or blockchain-like technologies being a crucial element of that. But let's talk about how the relationship between technology, the cloud, and global economies are likely to evolve. For the most part, when people think about the cloud today, we think about US-based companies: Amazon, Microsoft, Google, Facebook, IBM also in there. But there's some other companies are going to have a say on how the cloud industry evolves over the course of the next five years: Alibaba, Tencent, Baidu. So our prediction is that in 2018, we're going to see a lot more conversation about the role that China plays in establishing some of the new rules for how cloud, application networks, and security plays on a global basis, and that's going to facilitate the emergence of Alibaba, Tencent, and Baidu, also on the global stage as cloud-computing companies. What are you guys' thoughts? Dave Vellante, let me start with you. >> Dave: Well I think we're going to see the emergence of, we've seen the emergence of the China cloud and we're going to see that seep through other parts of Asia Pacific. As we discussed earlier as a team in our private meeting, Europe is going to be a very interesting pivot point because if China can control at least portions of Europe and use that as a lure for China, that's going to give them a leg up on global cloud. >> So that leads ultimately to a series of questions about what will be the relationship between formation of cloud industries, the evolution of the cloud industries, and geopolitical concerns. And I think what we need to do, guys, is dedicate an entire research meeting to that question because it's going to be one of the most important dictators of how the industry evolves over the next few years, and ultimately how businesses and enterprises need to start establishing crucial partnerships with their key and strategic suppliers. So look in the last couple minutes we want to do our Action Item round. Now, what we do here at the Action Item show is we start off having a conversation and then we go into the Action Item, what are you going to do differently Monday as a consequence of the information we're talking about? So let's do that now, hit some Action Items, what you heard from the five, six predictions that we talked about. David Floyer, what's your Action Item? >> So my Action Item is for CIOs and CTOs, is to take a pause on IOT and look for vendors that have solutions which can be put in easily and quickly and span OT and IT in the IOT space. >> Neil Raden, what's your Action Item? >> Neil: Well, I think there's a lot of activity around AI and there's going to be an explosion of it in 2018 but most of it's not really going to be AI, it's going to be machine learning, and machine learning is really just math and floating points. AI is different. AI is neuroscience, it's neurology, it's biology and physics and sociology, it's more science. I think that some machine learning is there on the event horizon of AI, but it's not. So we need to make sure we're clear about what announcements and what technology is machine-learning versus artificial intelligence. >> Jim Kobielus, what's your Action Item? >> Jim: I think my Action Item is to revisit IBM's prospects in the AI market in deep learning going forward. And revisit on a positive note actually because IBM officially turned around their cognitive strategy in the last year. Do they focus on the power AI flight form which is really framework agnostic and so forth. And really the AI space that's actually shaping up is different from the one that IBM and others envisioned at the start of this decade, and so it really is 2018, we're going to see IBM come out strong, I believe, as a provider of, one of the providers of the core framework agnostic data deep learning development platforms in the industry, that's my prediction. >> David Vellante, what's your Action Item? >> Dave: I think if you're a startup, you really have to take a hard look at your business and the value that you're bringing to market and be honest, if you're not delivering something that the cloud guys can't deliver or don't want to deliver, then I think you've really got to think about pivoting or exiting the business that you're in. And as part of that, I think you've got to find, to George's point, distribution channels and distribution partners that can help you with go to market at scale or you're in big trouble. >> George Gilbert, Action Item. >> We've been talking about sort of the cloud wars and my recommendation to CIOs and senior IT leaders would be that if you want to hedge your bets, you don't want to be all in on one cloud, it's not dividing a workload across different clouds. Pick a cloud for a workload or for an application because its portability is, it's sort of more of a dream than a reality. It's not about moving containers around, you're in an API ecosystem, you're subject to data gravity, so it's almost like if you're going to do the equivalent of distributed computing, you're going to put some part of the application on one cloud and some part in another cloud. >> So the Action Item is be smart about the relationship between new style of applications and architecture and cloud choices. Okay, let me summarize the meeting very quickly. This has been a great conversation about predictions in 2018, you expect to see more from us over the course of the next month, this is going to be a major theme of ours in November and into December. So, quickly the findings are these. The technology industry made a major mistake with the dot com boom, and the mistake was a presumption that technology change necessarily meant economic change. That is a false assumption. The economics of technology have been pretty well understood for quite some time and they're going to assert themselves even as we go through this significant transformative period in the technology industry. And the economics of volume are going to continue to be important. And we expect that those economics, coupled with the three factors of what's driving cloud architecture decisions, the realities of physics, geopolitical concerns, and literature property concerns, are going to lead to some significant changes in 2018 that we've only just conceived of. One, we expect that we're going to see an emergence of true private cloud that will continue to be crucial to how businesses think about their information technology overall infrastructure and plant, and that's going to have an impact ultimately on where AI gets developed, more from software vendors based on volume. Two, we expect to see a significant impact on, ultimately, what happens in the VC fronted world as startups, which have historically just presumed that there was no need for go to market, that everything was going to be try and buy and then we'd scale from there, start to hit the business realities of the consistency of the economics of volume. Three, IBM we think is repositioning, and somewhat paradoxically is likely to become more successful as a consequence, as a provider of the technologies that make possible some of these new comprehensive, complex AI and related oriented technologies, and not just as a service provider. Very importantly, ITOM is going to become increasingly important and we'll see AI, machine learning be an essential feature of that, in fact, one of the places where we learn how to do it right. And the final one is lots going on with blockchain, but we expect greater distribution of applications, greater distribution of data, and the security technologies and the technologies for bringing that together and supporting the network formation of data and applications must be in place, and that's going to be a major area of technology and innovation in 2018. Alright, so this closes out our Action Item for this week. Once again, I'm Peter Burris. I'd like to, as always, thank the Wikibon team for participating with me today and we look forward to once again visiting with you from the Cube studios here in Palo Alto, California on the next Action Item. Thank you very much. (funky electronic music)