 Okay, welcome back everyone to theCUBE's coverage of AWS ReInvent 2021. I'm John Furrier with Dave Nicholson, my co-host. We're here exploring all the future innovations. We've got a great guest, Walid Negum, who's the EVP Executive President, Chief Research Innovation Officer, Cap Gemini Engineering, Walid. Thanks for coming on theCUBE. Thank you. So I love the title, Chief Research Innovation Engineering Officer. I didn't make it up, they did. You got to love the cloud evolution right now because just more and more infrastructure has codes happening. You got this whole data abstraction layer developing where people are starting to see, okay, I can have horizontally scalable, govern data in a data lake that's smart, somewhat intelligent, and use machine learning. It seems to be the big trend here from AWS, more serverless, more goodness. So engineering, kind of in the front lines here, kind of making it happen. Yeah, so the question that our clients are asking us is how do these data center technologies moving over into cars, planes, trains, construction equipment, industrial, right? And maybe two decades ago it was called IoT, but we're not talking about just sensors, vertical lift aircraft, software-defined cars, manufacturing facilities as a whole. How are these data center technologies going to impact these companies? And it's not a architectural shift for, say, the EV, the electric vehicle money OEM. It's a financial transformation, right? Because if they can make their vehicle containerized, if they can monitor the car's behaviors, they can offer new types of experiences for their clients. So the questions we're asking ourselves is how do you get the cloud into the car? Yeah, and software driving all that. So you got software-defined, everything. Now you got data-driven pun intended with the cars, cloud everywhere. How does that look, what are the concerns? Obviously latency, moving data around, they got outposts, am I moving the cloud to the edge? How are you guys thinking, how are customers thinking through the architectural, I guess, foundational playbook? Is there one? Yeah, you know, coming into this, I did ask my son the question, is hardware or software more important? And then he, you know, he's nine, he said, you know, we're coding our way out of hardware. It was a very interesting insight. Software rules, that is for sure. But when we're talking about physical products, and we're talking about trillions of dollars of investments going into green energy, into autonomous driving, into green aviation, so it's not just the metaverse here, we're dealing real physical products. I think though, the point for us as engineers, or as engineering businesses, how do you co-design hardware and software together? What are the questions you have to ask about that machine learning model being moved over from AWS, for example, into the car? Is the silicon going to be able to support the inferencing rates that are required, right? In real time and whatnot. So some of the questions like that. Well that's been an age old battle between the idea that the flour that's nurtured in a walled garden is always going to be more beautiful than the one that grows out in the meadow. In other words, announcement in Adam's keynote talking about advances in AWS silicon. So what's your view on how important that is? You just sort of alluded to it as being important, the co-development of hardware and software together. Yeah, we're seeing product makers, again, think anybody from a life sciences company building a digital therapeutics product, maybe a blood glucose monitor or an automotive or even an aerospace, going direct to silicon, asking questions around the performance of the silicon and designing their experience around that, right? So if they need low latency, low power, efficiency, green networks, they're taking those questions in-house or asking those questions in-house. So AWS having a portfolio of custom or bespoke silicon, now it's part of the architectural discussion, right? And so I look around here, I see a lot of developers who are going to have to get a little bit more versed in some of these questions around, should I use an ARM based chip? Do I use this silicon partner? What happens when I move it into the vehicle and then I have over-the-air updates? How do I protect that code in an enclave in the car just to continue to use the, so there's a lot of architectural questions that I don't think software engineers typically ask when they just deal in the cloud. Although at the end of the day, over time, a lot of these will be abstracted from the developer to some degree, you know, that is just the nature of the game. It reminds me of the operating system theory of system software, meeting hardware because you have software development, just want a code. Now you're saying, whoa, now I'm responsible for hardware? Well, not if it's programmable, was there a hard top to it? So all these are big questions and important ones, I think, is we're a major inflection point. But it comes back down to, you mentioned aerospace. Space is the same problem. Can't send a break, fix engineer in space. You've got software now. So you've got trust, security, supply chain, right? And who's doing the hardware? Now you've got software supply chain. So a lot of interesting kind of discussions. Yeah, you know, you check them off, back into it, the supply chain problems with silicon. And there are now alternatives to try and get around the bottlenecks using high performance computers versus hundreds of ECUs in a vehicle allows you kind of get away from the supply chain shortage. There's, you know, folks moving from one architecture to another to avoid, you know, kind of getting locked in. And then of course, creating your own silicon or at least having more ownership over the silicon. I think software-defined systems are the way to go regardless of the industry. So you're going to make some decisions on performance characteristics of the hardware, but ultimately you want a software-defined system so you can update it regularly. I was talking with some of the Docker executives. I talked to the marketplace guys here, Deepak over here at Amazon, and containers comes up. You start to see a trend in containers where you're seeing certified containers because, you know, containers are everywhere. You can put malware in containers. So, you know, I think about like just hacking software. It's a surface area now. So you bring the software security model in there. You start to see this kind of like certified containers. I can imagine certified infrastructure now because I mean, what's a processor? It's just a hard and top to a PC. Now you've got the cloud. If I have hardware, how do I know it's workable? How do I trust it? You know, how could it not be hacked? I don't want my car to be hacked driven off the road. So when you're dealing with a payment system or you're dealing with TikTok, different than when you're dealing with a car with life consequence. So we are very active in this software-defined transformation of automotive. And it's easy to say I'm just going to load it up with all this data center technology, but there's safety, criticality issues that you have to take into consideration. Containers are well suited for that. Just requires some thought. I mean, my excitement enthusiasm about this product engineering is if you just take any of these products and apply them into a product engineering context, so much invention and creativity can happen. But on the safety side, we're working through security enclaves using containers and hardware-based roots of trust. So there's ways around, you know, malware and bad actors at the edge. Yep. What's your take on explainable AI where I got you might as well ask because this comes up a lot. Explainable AI is hot in college right now. AI that can be explained, it's kind of got some policy to it. What's your thoughts on this AI trend? Because obviously it's everywhere. I mean, what is explainable AI? Is that even real or how do you explain AI? Is that more democratized? You know, computer vision is a great example, I think, to bring it to life. And a lot of the audience probably knows this, but you can tell your kid that this is a cat once. And they'll know every single cat out there is a cat. You need thousands of images for a computer vision model to learn that this is a cat. And even, you know, you can probably give it an example out of, say, a remote region of the world and it's going to get confused. So to me, explainability is about adding some sort of certainty to the decision-making process. And when there's some confusion, be able to understand why that happened. I think in automotive or any even quality assurance, being able to know that this product was definitively defective or this pedestrian definitively did cross the crosswalk or not, you know, is very important because there are consequences. So just being able to understand why the algorithm or the model said what it said, why did it make that judgment is super important, super important. So I got to ask you, now that we're here, reinvent, from your engineering perspective, as you look at the landscape of AWS, the announcements, how do you think about other engineers out there trying to grok all the technology who really want to put innovation in place? Whether it's creating new markets, new categories, or innovating their existing business, how do you grab the cloud and make it work for you? I mean, from an engineering standpoint, how do you look at AWS and say, how do I make this work better for me? So, I mean, over the years, I think it's true, AWS has started to really look like a utility, you know, the days where it was called utility as a service. And, you know, I did attend a workshop on, I think it was called Lightsail or something like that, but they are simplifying the way that you can consume this infrastructure to a degree that is somewhat phenomenal. And they're building an, yeah, they continue to expand the ecosystem. So, I mean, for me, it's a utility, it's consumable, you've got an idea. You can roll your own. You can roll your own. Okay, so back to the concept of AI and explainability. One of my cars won't allow me to unlock certain functions because of the way that I drive. No one needs to explain to me why, because I know what I'm doing wrong, but I'm still frustrated by it. So that sort of leads to kind of the larger philosophical question to you about what you're seeing. Where are we in this kind of leapfrog, constant pace of the technology exists, but people aren't culturally ready to accept it? Because it feels like right now to me that there isn't anything we can't do with cloud technology. From a technical perspective, it can all be done. Swami's keynote today, talking about integrating all sorts of sources of data and actually leveraging them in the cloud. Technically possible, yet 85% of IT spend is still on prem. So what's your thought there? What are the real inhibitors from a technology perspective versus the cultural ones, setting aside my lack of adherence to driving law? It's industry by industry. I think if you're trying to do a diagnostic on an MRI in an automated way, and there's going to be false positives, false negatives, and yes, we know that there's going to be a physician participating in the final judgment call. I think just getting a really good comfort level on the trustworthiness of these decision points is really important. And so I don't blame folks for being reticent about trusting or asking some questions about, does this really work? Are these autonomous systems as they become more and more present? Are they doing the right thing? I think there's research that has to be done on agency, am I in control? What happened? Did I lose control? I think there's questions around handoffs and participation in decision-making. So I think just overall just the broad area of trust and the relationship between the participants, the humans and the machines. Still, I think there's some work to do, to be honest with you. I think there's some work to do. Maybe in a manufacturing facility where everything's automated, maybe it's a solved problem, but in an open road where the vehicle's driving in the middle afternoon, you probably should ask some more questions. Well, I want to ask you what we got, so a couple of minutes left. Real-time data. Near real-time, real-time, always a big hot topic, seeing more and more databases out there in the keynote today from Swami. Real-time, are we there yet? How are we doing with real-time data? Software, consuming the data, you can't see cars and things that are moving. You know, real-time versus near real-time, could be life or death. I mean, this is big time, where are we? So I was trying to conduct a web conference, I won't tell the vendor, it had nothing to do with the vendor, and I couldn't get a connection. I couldn't get a connection at re-invent. I'm sorry guys, I couldn't get a connection. So, we're talking about real-time operating systems and real-time data collection at the edge. Yeah, we're there, we can collect the data and we can deploy a model in the aircraft, on the train to do predictive analytics. If we got to stream that data back home to the cloud, we better figure out how to make sure we have a reliable and stable connection. 5G is will be deployed, right? And it has ultra, low latency, and can achieve those types of requirements. But it has to be in the right setting, right? It has to be in the right setting. In a facility, very well-controlled, where you understand the density of the cell sites, small cells, narrow cells, and you really can deploy a mobile robot wirelessly. Yes, you could do that, but in other scenarios, we have to ask a lot of questions about it. It's all about the connections and making that false, huh? Well, thanks for coming on, great insight, great conversation, very deep, awesome work. Thanks for coming on and sharing your insights from Capgemini. We're here in theCUBE, the worldwide leader in tech coverage live on the floor here at Reinvent. I'm John Furrier with Dave Nicholson, we'll be right back.