(gentle music) >> Welcome back to theCUBE's virtual program, covering VMware Explorer, 2022. The first time since 2019 that the VMware ecosystem is gathered in person. But in the post isolation economy, hybrid is the new format, cube plus digital, we call it. And so we're really happy to welcome Cal Al-Dhubaib who's the founder and CEO and AI strategist of Pandata. And Jonathan Seckler back in theCUBE, the senior director of product marketing at Dell Technologies. Guys, great to see you, thanks for coming on. >> Yeah, thanks a lot for having us. >> Yeah, thank you >> Cal, Pandata, cool name, what's it all about? >> Thanks for asking. Really excited to share our story. I'm a data scientist by training and I'm based here in Cleveland, Ohio. And Pandata is a company that helps organizations design and develop machine learning and AI technology. And when I started this here in Cleveland six years ago, I had people react to me with, what? So we help demystify AI and make it practical. And we specifically focus on trustworthy AI. So we work a lot in regulated industries like healthcare. And we help organizations navigate the complexities of building machine learning and AI technology when data's hard to work with, when there's risk on the potential outcomes, or high cost in the consequences. And that's what we do every day. >> Yeah, yeah timing is great given all the focus on privacy and what you're seeing with big tech and public policy, so we're going to get into that. Jonathan, I understand you guys got some hard news. What's your story around AI and AutoML? Share that with us. >> Yeah, thanks. So having the opportunity to speak with Cal today is really important because one of the hardest things that we find that our customers have is making that transition of experimenting with AI to making it really useful in real life. >> What is the tech underneath that? Are we talking VxRail here? Are you're talking servers? What do you got? >> Yeah, absolutely. So the Dell validated design for AI is a reference framework that is based on the optimized set of hardware for a given outcome. That includes it could be VxRail, VMware, vSphere and Nvidia GPUs and Nvidia software to make all of that happen. And for today, what we're working with is H2O.ai's solution to develop automatic machine learning. So take just that one more step to make it easier for customers to bring AI into production. >> Cool. >> So it's a full stack of software that includes automated machine learning, it includes NVIDIA's AI enterprise for deployment and development, and it's all built on an engineering validated set of hardware, including servers and storage and whatever else you need >> AI out of the box, I don't have to worry about cobbling it all together. >> Exactly. >> Cal, I want to come back to this trusted AI notion. A lot of people don't trust AI just by the very nature of it. I think about, okay, well how does it know it's a cat? And then you can never explain, it says black box. And so I'm like, what are they do with my data? And you mentioned healthcare, financial services, the government, they know everything about me. I just had to get a real ID and Massachusetts, I had to give all my data away. I don't trust it. So what is trusted AI? >> Well, so let me take a step back and talk about sobering statistics. There's a lot of different sources that report on this, but anywhere you look, you'll hear somewhere between 80 to 90% of AI projects fail to yield a return. That's pretty scary, that's a disappointing industry. And why is that? AI is hard. Versus traditional software, you're programming rules hard and fast. If I click this button, I expect A, B, C to happen. And we're talking about recognizing and reacting to patterns. It's not, will it be wrong? It's, when it's wrong, how wrong will it be? And what are it cost to accept related to that? So zooming back in on this lens of trustworthy AI, much of the last 10 years the development in AI has looked like this. Let's get the data, let's race to build the warehouses, okay we did that, no problem. Next was race to build the algorithms. Can we build more sophisticated models? Can we work with things like documents and images? And it used to be the exclusive domain of deep tech companies. You'd have to have teams of teams building the software, building the infrastructure, working on very specific components in this pipeline. And now we have this explosion of technologies, very much like what Jonathan was talking about with validated designs. So it removes the complexities of the infrastructure, it removes the complexities of being able to access the right data. And we have a ton of modeling capabilities and tools out there, so we can build a lot of things. Now, this is when we start to encounter risk in machine learning and AI. If you think about the models that are being used to replicate or learn from language like GPT-3 to create new content, it's training data set is everything that's on the internet. And if you haven't been on the internet recently, it's not all good. So how do you go about building technology to recognize specific patterns, pick up patterns that are desirable, and avoid unintended consequences? And no one's immune to this. So the discipline of trustworthy AI is building models that are easier to interrogate, that are useful for humans, and that minimize the risk of unintended consequences. >> I would add too, one of the good things about the Pandata solution is how it tries to enforce fairness and transparency in the models. We've done some studies recently with IDC, where we've tried to compare leaders in AI technology versus those who are just getting started. And I have to say, one of the biggest differences between a leader in AI and the rest of us is often that the leaders have a policy in place to deal with the risks and the ethics of using data through some kind of machine oriented model. And it's a really important part of making AI usable for the masses. >> You certainly hear a lot about, AI ultimately, there's algorithms which are built by humans. Although of course, there's algorithms to build algorithms, we know that today. >> Right, exactly. >> But humans are biased, there's inherent bias, and so this is a big problem. Obviously Dell, you have a giant observation space in terms of customers. But I wonder, Cal, if you can share with us how you're working with your customers at Pandata? What kind of customers are you working with? What are they asking? What problems are they asking you to solve? And how does it manifest itself? >> So when I like to talk about AI and where it's useful, it usually has to do with taking a repetitive task that humans are tasked with, but they're starting to act more like machines than humans. There's not much creativity in the process, it's handling something that's fairly routine, and it ends up being a bottleneck to scaling. And just a year ago even, we'd have to start approaching our clients with conversations around trustworthy AI, and now they're starting to approach us. Really example, this actually just happened earlier today, we're partnering with one of our clients that basically scans medical claims from insurance providers. And what they're trying to do is identify members that qualify for certain government subsidies. And this isn't as straightforward as it seems because there's a lot of complexities in how the rules are implemented, how judges look at these cases. Long story short, we help them build machine learning to identify these patients that qualify. And a question that comes up, and that we're starting to hear from the insurance companies they serve is how do you go about making sure that your decisions are fair and you're not selecting certain groups of individuals over others to get this assistance? And so clients are starting to wise up to that and ask questions. Other things that we've done include identifying potential private health information that's contained in medical images so that you can create curated research data sets. We've helped organizations identify anomalies in cybersecurity logs. And go from an exploration space of billions of eventual events to what are the top 100 that I should look at today? And so it's all about, how do you find these routine processes that humans are bottlenecked from getting to, we're starting to act more like machines and insert a little bit of outer recognition intelligence to get them to spend more time on the creative side. >> Can you talk a little bit more about how? A lot of people talk about augmented AI. AI is amazing. My daughter the other day was, I'm sure as an AI expert, you've seen it, where the machine actually creates standup comedy which it's so hilarious because it is and it isn't. Some of the jokes are actually really funny. Some of them are so funny 'cause they're not funny and they're weird. So it really underscored the gap. And so how do you do it? Is it augmented? Is it you're focusing on the mundane things that you want to take humans out of the loop? Explain how. >> So there's this great Wall Street Journal article by Jennifer Strong that she published I think four years ago now. And she says, "For AI to become more useful, it needs to become more boring." And I really truly believe in that. So you hear about these cutting edge use cases. And there's certainly some room for these generative AI applications inspiring new designs, inspiring new approaches. But the reality is, most successful use cases that we encounter in our business have to do with augmenting human decisions. How do you make arriving at a decision easier? How do you prioritize from millions of options, hundreds of thousands of options down to three or four that a human can then take the last stretch and really consider or think about? So a really cool story, I've been playing around with DALL.E 2. And for those of you who haven't heard, it's this algorithm that can create images from props. And they're just painting I really wish I had bought when I was in Paris a few years ago. And I gave it a description, skyline of the Sacre-Coeur Church in Montmartre with pink and white hues. And it came up with a handful of examples that I can now go take to an artist and say paint me this. So at the end of the day, automation, it's not really, yes, there's certain applications where you really are truly getting to that automated AI in action. But in my experience, most of the use cases have to do with using AI to make humans more effective, more creative, more valuable. >> I'd also add, I think Cal, is that the opportunity to make AI real here is to automate these things and simplify the languages so that can get what we call citizen data scientists out there. I say ordinary, ordinary employees or people who are at the front line of making these decisions, working with the data directly. We've done this with customers who have done this on farms, where the growers are able to use AI to monitor and to manage the yield of crops. I think some of the other examples that you had mentioned just recently Cal I think are great. The other examples is where you can make this technology available to anyone. And maybe that's part of the message of making it boring, it's making it so simple that any of us can use it. >> I love that. John Furrier likes to say that traditionally in IT, we solve complexity with more complexity. So anything that simplifies things is goodness. So how do you use automated machine learning at Pandata? Where does that fit in here? >> So really excited that the connection here through H2O that Jonathan had mentioned earlier. So H2O.ai is one of the leading AutoML platforms. And what's really cool is if you think about the traditional way you would approach machine learning, is you need to have data scientists. These patterns might exist in documents or images or boring old spreadsheets. And the way you'd approach this is, okay, get these expensive data scientists, and 80% of what they do is clean up the data. And I'm yet to encounter a situation where there isn't cleaning data. Now, I'll get through the cleaning up the data step, you actually have to consider, all right, am I working with language? Am I working with financial forecasts? What are the statistical modeling approaches I want to use? And there's a lot of creativity involved in that. And you have to set up a whole experiment, and that takes a lot of time and effort. And then you might test one, two or three models because you know to use those or those are the go to for this type of problem. And you see which one performs best and you iterate from there. The AutoML framework basically allows you to cut through all of that. It can reduce the amount of time you're spending on those steps to 1/10 of the time. You're able to very quickly profile data, understand anomalies, understand what data you want to work with, what data you don't want to work with. And then when it comes to the modeling steps, instead of iterating through three or four AutoML is throwing the whole kitchen sink at it. Anything that's appropriate to the task, maybe you're trying to predict a category or label something, maybe you're trying to predict a value like a financial forecast or even generate test. And it tests all of the models that it has at its disposal that are appropriate to the task and says, here are the top 10. You can use features like let me make this more explainable, let me make the model more accurate. I don't necessarily care about interrogating the results because the risk here is low, I want to a model that predicts things with a higher accuracy. So you can use these dials instead of having to approach it from a development perspective. You can approach it from more of an experimental mindset. So you still need that expertise, you still need to understand what you're looking at, but it makes it really quick. And so you're not spending all that expensive data science time cleaning up data. >> Makes sense. Last question, so Cal, obviously you guys go deep into AI, Jonathan Dell works with every customer on the planet, all sizes, all industries. So what are you hearing and doing with customers that are best practices that you can share for people that want to get into it, that are concerned about AI, they want to simplify it? What would you tell them? Go ahead, Cal. >> Okay, you go first, Cal. >> And Jonathan, you're going to bring us home. >> Sure. >> This sounds good. So as far as where people get scared, I see two sides of it. One, our data's not clean enough, not enough quality, I'm going to stay away from this. So one, I combat that with, you've got to experiment, you got to iterate, And that's the only way your data's going to improve. Two, there's organizations that worry too much about managing the risk. We don't have the data science expertise that can help us uncover potential biases we have. We are now entering a new stage of AI development and machine learning development, And I use those terms interchangeably anymore. I know some folks will differentiate between them. But machine learning is the discipline driving most of the advances. The toolkits that we have at our disposal to quickly profile and manage and mitigate against the risk that data can bring to the table is really giving organizations more comfort, should give organizations more comfort to start to build mission critical applications. The thing that I would encourage organizations to look for, is organizations that put trustworthy AI, ethical AI first as a consideration, not as an afterthought or not as a we're going to sweep this on the carpet. When you're intentional with that, when you bring that up front and you make it a part of your design, it sets you up for success. And we saw this when GDPR changed the IT world a few years ago. Organizations that built for privacy first to begin with, adapting to GDPR was relatively straightforward. Organizations that made that an afterthought or had that as an afterthought, it was a huge lift, a huge cost to adapt and adjust to those changes. >> Great example. All right, John, I said bring us home, put a bow on this. >> Last bit. So I think beyond the mechanics of how to make a AI better and more workable, one of the big challenges with the AI is this concern that you're going to isolate and spend too much effort and dollars on the infrastructure itself. And that's one of the benefits that Dell brings to the table here with validated designs. Is that our AI validated design is built on a VMware vSphere architecture. So your backup, your migration, all of the management and the operational tools that IT is most comfortable with can be used to maintain and develop and deploy artificial intelligence projects without having to create unique infrastructure, unique stacks of hardware, and then which potentially isolates the data, potentially makes things unavailable to the rest of the organization. So when you run it all in a VMware environment, that means you can put it in the cloud, you can put it in your data center. Just really makes it easier for IT to build AI into their everyday process >> Silo busting. All right, guys, thanks Cal, John. I really appreciate you guys coming on theCUBE. >> Yeah, it's been a great time, thanks. >> All right. And thank you for watching theCUBE's coverage of VMware Explorer, 2022. Keep it right there for more action from the show floor with myself, Dave Velante, John Furrier, Lisa Martin and David Nicholson, keep it right there. (gentle music)

thecube presents dell technologies world brought to you by dell good afternoon everyone welcome back to thecube's third day of coverage live from the show floor at dell technologies world 2022 lisa martin here with dave vellante we've been having lots of great conversations the last day and a half one of the things we love to do is really hear from the voice of dell's customers and we're going to do that next please welcome jonathan suckler the senior director of product marketing for dell and keith bradley the vp of i.t at nature fresh farms guys welcome hey great to be here thank you great thank you for letting us be here of course thanks for joining us so jonathan we're going to start with you we've been hearing a lot about we've been talking about ai for decades we've been hearing a lot about ai at the show it's it's so it's pervasive right it's in our refrigerators and our thermostats and our cars and that hockey puck thing that's in the kitchen that plays music when you're cooking right what's going on what is do you think from dell's perspective is fueling the adoption of ai now you know there's it i think that there's this huge interest in ai right now and you and you you're definitely pointed out a lot of the great success stories around ai but the the real benefit of is that you know with with with artificial intelligence applied to a lot of business problems you can solve them in ways that are that are much quicker than you would expect you know and you can solve them in ways you wouldn't have expected uh uh you know then than than you do what's really surprising though is as a as many as many people are interested in in using it and and all of the benefits that come from it though is that we really don't see the adoption being as quick as we would like to right i mean i want to say that like 80 percent of companies out there want to use ai they're testing ai you know they're they're they're planning uh projects around ai applications but when you ask them what's in production it really is still it's an innovator's game like you know companies like like nature fresh farms with uh what they're doing is truly at the tip of the spear what are some of the challenges jonathan that you're seeing from an adoption perspective of 80 say we want to actually be able to leverage this emerging technology in production the challenges are i think the pers it's a perceived challenge issue right i think there's like three big issues that people perceive as being uh barriers to adoption um the first one is pretty obvious it's cost right they they see artificial intelligence you they hear about all of the uh you know specialized hardware and and the software and the new and the people and the talent you've got to acquire to uh as being a barrier to that and they don't see the benefit or they they balance that against the benefit i think there's an issue also with uh complexity right because at the same time that you know you're building these these infrastructures around what you need to do for an artificial intelligence-enabled application there's this expectation that it needs to be separate and different and special and that becomes an issue from a management perspective right uh and i think finally uh it's uh it's change right i mean you you're you're bringing in new talent new new skill sets you're bringing in new technology and i think a lot of companies still today you know look at that as being like well what if if i do this am i really going to see the benefit if i am i stuck going down a path that i that i'm going to change later on and i think that's really the issue uh you know those but they're all perceived issues they're they're in in reality they're really not that true i mean keith has this done that nature fresh farms has done some incredible stuff right with with ai in an area that i i would never have guessed being a ripe for that kind of innovation you know so lisa keith knows that i love you know fresh tomatoes i live in the northeast where it's cold six months a year so we plant our tomatoes at memorial day weekend yeah right and then maybe you're lucky if you get tomatoes late august september and then you're done however you and i met a couple years ago you sent me all these vegetables i think i was popping the tomatoes like candy and then i interviewed you you were live in the giant greenhouse and it's just amazing what you guys have going to jonathan's point you're using ai to really create you know sustainable continuing flow of awesome vegetables tell us more about nature fresh so at nature fresh farms we're a 200 acre greenhouse just shy of 200 acres growing bell peppers and tomatoes and one of the biggest use cases for us in our ai is everything we do we need to be proactive so we need that ai to not be reactive to climate change to what happens to the weather to be proactive so it changes before the plant reacts because every time the plant will doesn't do as great we've lost production from it so we're always using our ai to help increase the yield per square meter inside of our greenhouses so everything from the growth the length the weight of the plant we monitor everything we want to know every aspect of that plant's life it's almost like doing an ekg on a plant 24 by 7 and wanting to know everything out of it how old is is the company nature fresh farms started in 1999 so we're just hitting 23 years now so we started off as a 16 acre little greenhouse our owner kind of got into it saying i think this is going to be new and he was one of the first ones to say i want to be all computers i want to do it culturally this is this was not an upsell or a hard sell for you from the vp of i.t perspective no no he's always been one saying that technology will change the greenhouse industry and that by adding technology the expertise is in the growers and letting technology help them do more because when we first started in the greenhouse industry you'd need a grower for every range so every 16 acre range would need a very senior grower now we have one grower that does 64 or almost 100 acres of greenhouse he'll have junior growers but he's able to do so much more so where do you specifically apply the ai can you talk about that uh so we talk specifically we apply the ai in almost all areas anything from picking the plant to the climate of the plant we'll do all those areas even on the packing line we actually have uh one robot well not a robot story a machine that looks at a box of tomatoes and basically tells us which one doesn't match the proper red because how you see red how you guys see red is slightly different so it'll tell us that this red tomato doesn't match so change out the right one so when it goes down the line into the consumers they're all exactly the same so it looks unified it looks beautiful like that how about that you're sending out red tomatoes yeah yeah that's what we do now what is dell's role in all this so dell's role has helped us grow what we do we started off with power scale and vxrail and stuff like that so everything's hosted on that and they have been a great partner at finding that solution to them i've been able to go to them and say hey i'm running into a storage problem i'm running into a compute problem they've been able to find a validated solution for us to use and to put out there and help us grow and then the next part that was really great that we've really now done is it's scalable as we're growing we've been able to community add more compute and more storage but not have to take things down to do it and that's what we really wanted to do yeah no i i think and i think what you're talking about there is really the one of the big issues that i was talking about earlier which is around complexity and cost right you know one of the answers to doing artificial intelligence in the enterprise is making sure that you can maintain and have an infrastructure that scales that's part of everything else and and to do that you've got to virtualize it and you know with power uh with a dell vxrail and power scale which it's all running vmware uh with with the uh with the containers and the vms on top of that actually managing you know and running those applications it takes a lot of the complexity of of worrying about where you're going to how you're going to manage that infrastructure and who's going to do it who's going to back it up how are you going to how you're going to you know keep costs down so it really really helps i think yeah yep and we just love it because we're able to take that solution make it better and make it do more and more every day and it's it's allowed our growers to see exponential time where they did it years ago it used to be overnight to get results sometimes from our system doing it now we're seeing it in real time and that's where i it really got to that point now where we're being reactive proactive to the to the plant the weather to stuff we know exactly what needs to happen before happens and that makes the plant grow more and that's what we're always aiming to do you know if you don't mind one of the things that i you were telling me about i think is really fascinating so is this idea that you know you need to have a data scientist you need a whole new staff to manage these applications these these technologies but you were talking about your growers are actually yeah they're actually data scientists that way right that's what we like to call them we call them grower scientists right now green sciences data scientists yeah because they've researched this data they know what the plant does and it's it's been a neat transition we talked about that how they went from being out in the greenhouse so much to being in front of the computer now but now with the help of ai they're more able to get back out into the greenhouse to now watch the plants see what's going on and be a part of the growth again and they said it's been great but they're the ones that are looking at these numbers every day every second if it's not remotely from home it's remote on the greenhouse they're launching everything because yeah think about they're watching 64 acres of land and making sure that does everything it needs to do so lisa this is a really good example of sort of distributed data at work right about this whole notion of data mesh where you have domain experts actually own the data you know they know they can bring context to the data it's not somebody who's just oh it's just data i don't really know what to do with it it's somebody who actually knows what it what it means that to me is a future use case that's going to explode yep it's like me i i look at their data and they always tease me because i'll look at it and i'll go yeah i have no idea but it's giving you numbers so are they right or not and it's a it's always a joke in the in the plant that i like ah you don't got question marks so it's working and then i'll go to them and say is this right and then they'll say yep we're on we're getting what we need i love the idea that you know we've we've heard of this term citizens citizen scientists or citizen data scientists and you have a grower data scientist yeah and i think that eliminates you talk again those problems like or challenges i mentioned earlier that kind of eliminates the complexity issue you know the uncertainty issue the fear of change when you've got your own uh teams who are who know what they need to do and they have the data to do it it just changes the game right yeah and the other two we found is i've always believed in it myself if you love what you do yeah you commit so much more to it and our growers they love what they do so their passion just exudes into the data and then it comes right back into the product well the technology is an enabler of their passion really i'm curious keith how the obviously the events of the last two years have been quite challenging how has ai been a facilitator of what seems like a competitive differentiation for your company uh it actually really accelerated it because we really had to invest in it that's when we started the the big journey to the vx rail the power protect data management we really had to invest in and then we heavily invested in the ai we've always had some lingerie in the background and it's always been there and we've been using it for years and years now but it really brought it right to the forefront though we have to do this better and we had to really push everything and as we grew it became more and more apparent that we were taking that road that investment was paying off for us now yeah how do i buy ai from you so you know it's interesting like i said we want to make it easy for for customers to implement an ai solution at dell and it's not so much that you go out and you buy an ai right or something like that what you're doing is is you're you're making your infrastructure ready for the applications that you need to run right and so at dell we have this uh these predefined uh architectures that we call validated designs they're validated uh to work in you know in a co in any a common environment we take the you know we take the guesswork out of uh how to put these systems together uh and in the case of artificial intelligence you know we we validate with our partners like uh uh vmware and like nvidia to make sure that the technologies work together so that they fit into the existing infrastructure they already have and uh you know in a way it's i think of it as virtualized ai but i think even more importantly it's it's ai for for any company it's not not for the not for the special scientists and you know not for the not for the uh the researcher at the university it's it's for you know it's for nature fresh farms with vxrail it's software defined you're able to bring in a gpu you've got the flexibility to do that for example yeah whereas with the traditional you know the old days you wouldn't be able to do that you'd be you'd have a lot of time on your hands and a lot of compute power you spent a lot of money doing what you need to do yeah oh yeah we'd be spending all the time working at it growing it and doing more and it just made our life easier not to manage the life the managed life cycle of the ai systems that we have is so much easier now because it's all predefined it's all it's all ready to go upgrade process all that is built into it yeah so life cycle is much easier from the i.t side so keith talk to talk to those folks in the audience who might have those those perceived challenges or limitations that jonathan was talking about because you're making it sound like this has been such an enabler of a business that's 23 years old we're taking growers who are experts at growing and they're playing and loving playing with data and ai how do you how do you advise folks to really eliminate some of those preconceived challenges that are out there i would say you have to sit there and just dive in you have to actually start to do it but you have to think about not where you the first two steps say where we want to be five steps from now and then say talk to a partner like dell with us and say this is where we want to get to this is and then figure out a way how to get there and committing to that path you can't get frustrated the first few times ai is very flustering sometimes the first few pass don't work and just saying going back to the drawing board each time we'll do it we've had a couple experiments where it didn't work and we didn't get the results we wanted and we had to just say let's change our thought process and how do we optimize this ai and then all of a sudden we started getting the right results but that it's it's like uh falling over the first time you fall over as a child it's gonna hurt but each time he gets a little less each time failure is progress yeah that's right that's right fail fast yeah failure can be a good f word yeah if you but you have to be open-minded yep oh yes every minute every minute you have to be open-minded and you have to you have to think outside the box too and that's the biggest part of things it's just not accepting things and just saying we have to do it but you have to have the culture that will embrace that and it sounds like the growers these are people that are expert and growing how it sounds like it wasn't an uphill battle to get them to come on board and become these citizen growers data scientists well you know it was funny because with the technology it kind of gave them that work-life balance that they didn't have before their life was inside the greenhouse because the plants grow 24 by 7. so now all of a sudden they just kept growing they could they could go home they kept doing their thing they could go home at five o'clock and because of the vdi solutions and stuff like that and the ai that's helping them grow they can kind of turn off and instead of having to come in sunday morning and that the the one joke we used to have is that on sundays if you're in church and there's clouds had come rolling out all the growers would stand up and leave because they had to go to their church they had to go back to their farm now the system does that automatically for them so they're able to get their work life home balance back so it was different for them it was a jump for them anybody that's not used to technology and jumping into it is hard but once they started to see the benefits and what more yield they can get and the home work life balance it was amazing there's no i can't underestimate the work-life balance enough i think it's challenge it's a very challenging thing for people in any industry to achieve we've we've seen that in the last two years with you know do i live at work do i work from home so achieving that is kudos to you and for del for enabling that because that's that's big that that affects everybody guys thank you so much for joining us talking about ai what you're doing at nature fresh the future what's possible yeah and how you buy ai from dell no i think it's great i think you know nature fresh farms is a great euro you've been a great like a great partner for sure but also this great kind of beacon to show people how it can be done and i think it's just a thank you very much we really enjoyed it excellent well thanks for thanks for bringing the beacon on the show we appreciate it we want to thank you for watching for our guests i'm lisa martin for dave vellante i'm lisa martin i should say you're watching thecube day three of our coverage live from the show floor of dell tech world 2022 stick around we'll be right back with our next guest after a short break [Music] you