>>Thank you and good morning or afternoon, everyone, depending on where you're coming to us from and welcome to today's breakout session, fast data, a retail industry business imperative. My name is Brent Bedell, global managing director of retail, consumer bids here at Cloudera and today's hosts joining today. Joining me today is our feature speaker Brian Hill course managing partner from RSR. We'll be sharing insights and implications from recently completed research across retailers of all sizes in vertical segments. At the end of today's session, I'll share a brief overview on what I personally learned from retailers and how Cloudera continues to support retail data analytic requirements, and specifically around streaming data, ingest analytics, automation for customers around the world. There really is the next step up in terms of what's happening with data analytics today. So let's get started. So I thought it'd be helpful to provide some background first on how Clare to Cloudera is supporting and retail industry leaders specifically how they're leveraging Cloudera for leading practice data analytics use cases primarily across four key business pillars. >>And these will be very familiar to, to those in the industry. Personalize interactions of course, plays heavily into e-commerce and marketing, whether that's developing customer profiles, understanding the OB omni-channel journey, moving into the merchandising line of business focused on localized promotional planning, forecasting demand, forecast accuracy, then into supply chain where inventory visibility is becoming more and more critical today, whether it's around fulfillment or just understanding where your stuff is from a customer perspective. And obviously in and outbound route optimization right now, as retailers are taking control of actual delivery, whether it's to a physical store location or to the consumer. And then finally, uh, which is pretty exciting to me as a former store operator, you know, what's happening with physical brick and mortar right now, especially for traditional retailers. Uh, the whole re-imagining of stores right now is on fire in a lot of focus because, you know, frankly, this is where fulfillment is happening. >>Um, this is where customers, you know, still 80% of revenue is driven through retail, through physical brick and mortar. So right now store operations is getting more focused and I would say it probably is had and decades. Uh, and a lot of has to do for us with IOT data and analytics in the new technologies that really help, uh, drive, uh, benefits for retailers from a brick and mortar standpoint. And then, and then finally, um, you know, to wrap up before handing off to Brian, um, as you'll see, you know, all of these, these lines of businesses are raw, really experiencing the need for speed, uh, you know, fast data. So we're, we're moving beyond just discovery analytics. You don't things that happened five, six years ago with big data, et cetera. And we're really moving into real time capabilities because that's really where the difference makers are. >>That's where the competitive differentiation as across all of these, uh, you know, lines of business and these four key pillars within retail, um, the dependency on fast data is, is evident. Um, and it's something that we all read, you know, you know, in terms of those that are students of the industry, if you will, um, you know, that we're all focused on in terms of bringing value to the individual, uh, lines of business, but more importantly to the overall enterprise. So without further ado, I, I really want to, uh, have Brian speak here as a, as a third party analyst. You know, he, he's close in touch with what's going on, retail talking to all the solution providers, all the key retailers about what's important, what's on their plate. What are they focusing on right now in terms of fast data and how that could potentially make a difference for them going forward? So, Brian, uh, off to you, >>Well, thanks, Brent. I appreciate the introduction. And I was thinking, as you were talking, what is fast data? Well, data is fast. It is fast data it's stuff that comes at you very quickly. When I think about the decision cycles in retail, they were, they were, they were time phased and there was a time when we could only make a decision perhaps once a month and then met once a week and then once a day, and then intraday fast data is data that's coming at you and something approaching real time. And we'll explain why that's important in just a second. But first I want to share with you just a little bit about RSR. We've been in business now for 14 years. And what we do is we studied the business use cases that drive the adoption of technology in retail. We come from the retail industry, I was a retail technologist, my entire working life. >>And so we started this company. So I'm, I have a built in bias, of course, and that is that the difference between the winners in the retail world and in fact, in the entire business world and everybody else is how they value the strategic importance of information, and really that's where the battle is being fought today. We'll talk a little bit about that. So anyway, uh, one other thing about RSR research, our research is free to the entire world. Um, we don't, we don't have a paywall. You have to get behind. All you have to do is sign into our website, uh, identify yourself and all of our research, including these two reports that we're showing on the screen now are available to you. And we'd love to hear your comments. So when we talk about data, there's a lot of business implications to what we're trying to do with fast data and as being driven by the real world. >>Uh, we saw a lot of evidence of that during the COVID pandemic in 2020, when people had to make many decisions very, very quickly, for example, a simple one. Uh, do I redirect my replenishments to store B because store a is impacted by the pandemic, those kinds of things. Uh, these two drawings are actually from a book that came out in 1997. It was a really important book for me personally is by a guy named Steven Hegel. And it was the name of the book was the adaptive enterprise. When you think about your business model, um, and you think about the retail business model, most of those businesses are what you see on the left. First of all, the mission of the business doesn't change much at all. It changes once in a generation or maybe once in a lifetime, um, but it it's established quite early. >>And then from that point on it's, uh, basically a wash rinse and repeat cycle. You do the things that you do over and over and over again, year in and year out season in and season out. And the most important piece of information that you have is the transaction data from the last cycle. So a Brent knows this from his experience as a, as a retailer, the baseline for next year's forecast is last year's performance. And this is transactional in nature. It's typically pulled from your ERP or from your best of breed solution set on the right is where the world is really going. And before we get into the details of this, I'll just use a real example. I'm I'm sure like, like me, you've watched the path of hurricanes as they go up to the Florida coast. And one of the things you might've noticed is that there's several different possible paths. >>These are models, and you'll hear a lot about models. When you talk to people in the AI world, these are models based on lots and lots of information that they're getting from Noah and from the oceanographic people and all those kinds of folks to understand the likely path of the hurricane, based on their analysis, the people who watch these things will choose the most likely paths and they will warn communities to lock down and do whatever they need to do. And then they see as the, as the real hurricane progresses, they will see if it's following that path, or if it's varying, it's going down a different path and based on that, they will adapt to a new model. And that is what I'm talking about here now that not everything is of course is life and death as, as a hurricane. But it's basically the same concept what's happening is you have your internal data that you've had since this, a command and control model that we've mentioned on the left, and you're taking an external data from the world around you, and you're using that to make snap decisions or quick decisions based on what you see, what's observable on the outside, back to my COVID example, um, when people were tracking the path of the pandemic through communities, they learn that customers or consumers would favor certain stores to pick up their, what they needed to get. >>So they would avoid some stores and they would favor other stores. And that would cause smart retailers to redirect the replenishments on very fast cycles to those stores where the consumers are most likely to be. They also did the same thing for employees. Uh, they wanted to know where they could get their employees to service these customers. How far away were they, were they in a community that was impacted or were they relatively safe? These are the decisions that were being made in real time based on the information that they were getting from the marketplace around them. So, first of all, there's a context for these decisions. There's a purpose and the bounds of the adaptive structure, and then there's a coordination of capabilities in real time. And that creates an internal feedback loop, but there's also an external feedback loop. This is more of an ecosystem view. >>And based on those two, those two inputs what's happening internally, what your performance is internally and how your community around you is reacting to what you're providing. You make adjustments as necessary. And this is the essence of the adaptive enterprise. Engineers might call this a sense and respond model. Um, and that's where retail is going. But what's essential to that is information and information, not just about the products that you sell or the stores that you sell it in, or the employees that you have on the sales floor or the number of market baskets you've completed in the day, but something much, much more. Um, if you will, a twin, a digital twin of the physical assets of your business, all of your physical assets, the people, the products, the customers, the buildings, the rolling stock, everything, everything. And if you can create a digital equivalent of a physical thing, you can then analyze it. >>And if you can analyze it, you can make decisions much, much more quickly. So this is what's happening with the predict pivot based on what you see, and then, because it's an intrinsically more complicated model to automate, decision-making where it makes sense to do so. That's pretty complicated. And I talk about new data. And as I said earlier, the old data is all transactional in nature. Mostly about sales. Retail has been a wash in sales data for as long as I can remember throw, they throw most of it away, but they do keep enough to create the forecast the next for the next business cycle. But there's all kinds of new information that they need to be thinking about. And a lot of this is from the outside world. And a lot of this is non-transactional nature. So let's just take a look at some of them, competitive information. >>Those are always interested in what the competitor is up to. What are they promoting? How well are they they doing, where are they? What kind of traffic are they generating sudden and stuff, significant changes in customer behaviors and sentiment COVID is a perfect example of something that would cause this consumers changing their behaviors very quickly. And we have the ability to, to observe this because in a great majority of cases, nowadays retailers have observed that customers start their, uh, shopping journey in the digital space. As a matter of fact, Google recently came out and said, 60%, 63% of all, all sales transactions begin in the digital domain. Even if many of them end up in the store. So we have the ability to observe changes in consumer behavior. What are they looking at? When are they looking at it? How long do they spend looking at it? >>What else are they looking at while they're, while they're doing that? What are the, what is the outcome of that market metrics? Certainly what's going on in the marketplace around you? A good idea. Example of this might be something related to a sporting event. If you've planned based on normal demand and for, for your store. And there's a big sporting event, like a football match or a baseball game, suddenly you're going to see a spike in demand. So understanding what's going on in the market is really important. Location, demographics and psychographics, demographics have always been important to retailers, but now we're talking about dynamic demographics, what customers, or what consumers are, are in your market, in something approaching real time, psychographics has more to do with their attitudes. What kind of folks are, are, are in them in a particular marketplace? What do they think about what do they favor? >>And all those kinds of interesting deep tales, real-time environmental and social incidents. Of course, I mentioned hurricanes. And so that's fairly, self-evident disruptive events, sporting events, et cetera. These are all real. And then we get the real time internet of things. These are, these are RFID sensors, beacons, video, et cetera. There's all kinds of stuff. And this is where, yeah, it's interesting. This is where the supply chain people will start talking about the difference, little twin to their physical world. If you can't say something, you can manage it. And retailers want to be able to manage things in real time. So IOT, along with it, the analytics and the data that's generated is really, really important for them going forward, community health. We've been talking a lot about that, the progression of the flu, et cetera, et cetera, uh, business schedules, commute patterns, school schedules, and whether these are all external data that are interesting to retailers and can help them to make better operational in something approaching real time. >>I mentioned the automation of decision making. This is a chart from Gardner, and I'd love to share with you. It's a really good one because it describes very simply what we're talking about. And it also describes where the inflection of new technology happens. If you look on the left there's data, we have lots and lots of data. We're getting more data all the time, retailers for a long time. Now, since certainly since the seventies or eighties have been using data to describe what happened, this is the retrospective analysis that we're all very familiar with, uh, data cubes and those kinds of things. And based on that, the human makes some decisions about what they're going to do going forward. Um, sometime in the not too distant past, this data was started to be used to make diagnostic decisions, not only what happened, but why did it happen? >>And me might think of this as, for example, if sales were depressed for a certain product, was it because we had another product on sale that day, that's a good example of fairly straightforward diagnostics. We then move forward to what we might think of as predictive analytics. And this was based on what happened in the past and why it happened in the past. This is what's likely to happen in the future. You might think of this as, for example, halo effect or, or the cannibalization effect of your category plans. If you're, if you happen to be a grocer and based on that, the human will make a decision as to what they need to do next then came along AI, and I don't want to oversell AI here. AI is a new way for us to examine lots and lots of data, particularly unstructured data AI. >>If I could simplify it to its maximum extent, it essentially is a data tool that allows you to see patterns in data, which might be interesting. It's very good at sifting through huge data sets of unstructured data and detecting statistically significant patterns. It gets deeper than that, of course, because it uses math instead of rules. So instead of an if then, or else a statement that we might've used with our structured data, we use the math to detect these patterns in unstructured data. And based on those, we can make some models. For example, uh, my guy in my, in my, uh, just turned 70 on my 70 year old man, I'm a white guy. I live in California. I have a certain income and a certain educational level. I'm likely to behave in this way based on a model that's pretty simplistic. But based on that, you can see that. >>And when another person who meets my psychographics, my demographics, my age group, my income level and all the rest, um, you, they might, they might be expected to make a certain action. And so this is where prescriptive really comes into play. Um, AI makes that possible. And then finally, when you start to think about moving closer to the customer on something, approaching a personalized level, a one-to-one level, you, you suddenly find yourself in this situation of having to make not thousands of decisions, but tens of millions of decisions. And that's when the automation of decision-making really gets to be pretty important. So this is all interesting stuff, and I don't want to oversell it. It's exciting. And it's new. It's just the latest turn of the technology screw. And it allows us to use this new data to basically automate decision-making in the business, in something approaching real time so that we can be much, much more responsive to real-time conditions in the marketplace. >>Very exciting. So I hope this is interesting. This is a piece of data from one of our recent pieces of research. Uh, this happens to be from a location analytics study. We just published last week and we asked retailers, what are the big challenges what's been going on in the last 12 months for them? And what's likely to be happening for them in the next few years. And it's just fascinating because it speaks to the need for faster decision-making there. The challenges in the last 12 months were all related to COVID. First of all, fulfilling growing online demand. This is a very, very real time issue that we all had to deal with. But the next one was keeping forecasts in sync with changing demand. And this is one of those areas where retailers are now finding themselves, needing to look at that exoticness for that external data that I mentioned to you last year, sales were not a good predictor of next year of sales. >>They needed to look at sentiment. They needed to look at the path of the disease. They needed to look at the availability of products, alternate sourcing, global political issues. All of these things get to be pretty important and they affect the forecast. And then finally managing a supply them the movement of the supply through the supply chain so that they could identify bottlenecks now, point to one of them, which we can all laugh at now because it's kind of funny. It wasn't funny at the time we ran out of toilet paper, toilet paper was a big problem. Now there is nothing quite as predictable as toilet paper, it's tied directly to the size of the population. And yet we ran out and the thing we didn't expect when the COVID pandemic hit was that people would panic. And when people panic, they do funny things. >>One of the things I do is buy up all the available toilet paper. I'm not quite sure why that happened. Um, but it did happen and it drained the supply chain. So retailers needed to be able to see that they needed to be able to find alternative sources. They needed to be able to do those kinds of things. This gets to the issue of visibility, real time data, fast data tomorrow's challenge. It's kind of interesting because one of the things that they've retailers put at the top of their list is improved inventory productivity. Uh, the reason that they are interested in this is because then we'll never spend as much money, anything as they will on inventory. And they want the inventory to be targeted to those places where it is most likely to be consumed and not to places where it's least likely to be consumed. >>So this is trying to solve the issue of getting the right product at the right place at the right time to the right consumer and retailers want to improve this because the dollars are just so big, but in this complex, fast moving world that we live in today, it's this requires something approaching real-time visibility. They want to be able to monitor the supply chain, the DCS and the warehouses. And they're picking capacity. We're talking about each of us, we're talking about each his level. Decision-making about what's flowing through the supply chain all the way from the, from the manufacturing doctor, the manufacturer through to consumption. There's two sides of the supply chain and retailers want to look at it, you'll hear retailers and, and people like me talk about the digital twin. This is where this really becomes important. And again, the digital twin is, is enabled by IOT and AI analytics. >>And finally they need to re to increase their profitability for online fulfillment. Uh, this is a huge issue, uh, for some grocers, the volume of online orders went from less than 10% to somewhere north of 40%. And retailers did in 2020, what they needed to do to fulfill those customer orders in the, in the year of the pandemic, that now the expectation that consumers have have been raised significantly. They now expect those, those features to be available to them all the time. And many people really liked them. Now retailers need to find out how to do it profitably. And one of the first things they need to do is they need to be able to observe the process so that they can find places to optimize. This is out of our recent research and I encourage you to read it. >>And when we think about the hard one wisdoms that retailers have come up with, we think about these things better visibility has led to better understanding, which increases their reaction time, which increases their profitability. So what are the opportunities? This is the first place that you'll see something that's very common. And in our research, we separate over performers, who we call retail winners from everybody else, average and under-performers, and we've noticed throughout the life of our company, that retail winners, don't just do all the same things that others do. They tend to do other things. And this shows up in this particular graph, this again is from the same study. So what are the opportunities to, to address these challenges? I mentioned to you in the last slide, first of all, strategic placement of inventory throughout the supply chain to better fulfill customer needs. This is all about being able to observe the supply chain, get the inventory into a position where it can be moved quickly to fast changing demand. >>And on the consumer side, a better understanding and reacting to unplanned events that can drive a dramatic change in customer behavior. Again, this is about studying the data, analyzing the data and reacting to the data that comes before the sales transaction. So this is observing the path to purchase observing things that are happening in the marketplace around the retailer, so that they can respond very quickly, a better understanding of the dramatic changes in customer preference and path to purchase. As they engage with us. One of the things we, all we all know about consumers now is that they are in control and the literally the entire planet is the assortment that's available to them. If they don't like the way they're interacting with you, they will drop you like a hot potato and go to somebody else. And what retailers fear justifiably is the default response to that is to just see if they can find it on Amazon. >>You don't want this to happen if you're a retailer. So we want to observe how we are interacting with consumers and how well we are meeting their needs, optimizing omni-channel order fulfillment to improve profitability. We've already mentioned this, uh, retailers did what they needed to do to offer new fulfillment options to consumers. Things like buy online pickup curbside, buy online pickup in store, buy online, pick up at a locker, a direct to consumer all of those things. Retailers offer those in 2020 because the consumers demand it and needed it. So when retailers are trying to do now is to understand how to do that profitably. And finally, this is important. It never goes away. Is the reduction of waste shrink within the supply chain? Um, I'm embarrassed to say that when I was a retail executive in the nineties, uh, we were no more certain of consumer demand than anybody else was, but we, we wanted to commit to very high service levels for some of our key county categories somewhere approaching 95%. >>And we found the best way to do that was to flood the supply chain with inventory. Uh, it sounds irresponsible now, but in those days, that was a sure-fire way to make sure that the customer had what she was looking for when she looked for it. You can't do that in today's world. Money is too tight and we can't have that, uh, inventory sitting around and move to the right places. Once we discovered what the right place is, we have to be able to predict, observe and respond in something much closer to your time. One of the next slide, um, the simple message here, again, a difference between winners and everybody else, the messages, if you can't see it, you can't manage it. And so we asked retailers to identify, to what extent an AI enabled supply chain can help their company address some issues. >>Look at the differences here. They're shocking identifying network bottlenecks. This is the toilet paper story I told you about over half of retail winners, uh, feel that that's very important. Only 19% of average and under performers, no surprise that their average and under-performers visibility into available to sell inventory anywhere within the enterprise, 58% of winners and only 32% of everybody else. And you can go on down the list, but you get the just retail winners, understand that they need to be able to see their assets and something approaching real time so that they can make the best decisions possible going forward in something approaching real time. This is the world that we live in today. And in order to do that, you need to be able to number one, see it. And number two, you need to be able to analyze it. And number three, you have to be able to make decisions based on what you saw, just some closing observations on. >>And I hope this was interesting for you. I love talking about this stuff. You can probably tell I'm very passionate about it, but the rapid pace of change in the world today is really underscoring the importance. For example, of location intelligence, as a key component of helping businesses to achieve sustainable growth, greater operational effectiveness and resilience, and ultimately your success. So this is really, really critical for retailers to understand and successfully evolving businesses need to accommodate these new consumer shopping behaviors and changes in how products are brought to the market. So that, and in order to do that, they need to be able to see people. They need to be able to see their assets, and they need to be able to see their processes in something approaching real time, and then they need to analyze it. And based on what they've uncovered, they need to be able to make strategic and operational decision making very quickly. This is the new world we live in. It's a real-time world. It's a, it's a sense and respond world and it's the way forward. So, Brent, I hope that was interesting for you. I really enjoyed talking about this, as I said, we'd love to hear a little bit more. >>Hey, Brian, that was excellent. You know, I always love me love hearing from RSR because you're so close to what retailers are talking about and the research that your company pulls together. Um, you know, one of the higher level research articles around, uh, fast data frankly, is the whole notion of IOT, right? And he does a lot of work in this space. Um, what I find fascinating based off the recent research is believe it or not, there's $1.2 trillion at stake in retail per year, between now and 2025. Now, how is that possible? Well, part of it is because the Kinsey captures not only traditional retail, but also QSRs and entertainment then use et cetera. That's considered all of retail, but it's a staggering number. And it really plays to the effect that real-time can have on individual enterprises. In this case, we're talking of course, about retail. >>So a staggering number. And if you think about it from streaming video to sensors, to beacons, RFID robotics, autonomous vehicles, retailers are testing today, even pizza delivery, you know, autonomous vehicle. Well, if you think about it, it shouldn't be that shocking. Um, but when they were looking at 12 different industries, retail became like the number three out of 12, and there's a lot of other big industries that will be leveraging IOT in the next four years. So, um, so retailers in the past have been traditionally a little stodgy about their spend in data and analytics. Um, I think retailers in general have got the religion that this is what it's going to take to compete in today's world, especially in a global economy. And in IOT really is the next frontier, which is kind of the definition of fast data. Um, so I, I just wanted to share just a few examples or exemplars of, of retailers that are leveraging Cloudera technology today. >>So now, so now the paid for advertisement at the end of this, right? So, so, you know, so what bringing to market here. So, you know, across all retail, uh, verticals, you know, if we look at, you know, for example, a well-known global mass virtual retailer, you know, they're leveraging Cloudera data flow, which is our solution to move data from point to point in wicked fast space. So it's open source technology that was originally developed by the NSA. So, um, it is best to class movement of data from an ingest standpoint, but we're also able to help the roundtrip. So we'll pull the sensor data off all the refrigeration units for this particular retailer. They'll hit it up against the product lifecycle table. They'll understand, you know, temperature fluctuations of 10, 20 degrees based on, you know, fresh food products that are in the store, what adjustments might need to be made because frankly store operators, they'll never know refrigeration don't know if a cooler goes down and they'll have to react quickly, but they won't know that 10, 20 degree temperature changes have happened overnight. >>So this particular customer leverages father a data flow understand temperature, fluctuations the impact on the product life cycle and the round trip communication back to the individual department manager, let's say a produce department manager, deli manager, meat manager, Hey, you had, you know, a 20 degree drop in temperature. We suggest you lower the price on these products that we know are in that cooler, um, for the next couple of days by 20%. So you don't have to worry, tell me about freshness issues and or potential shrink. So, you know, the grocery with fresh product, if you don't sell it, you smell it, you throw it away. It's lost to the bottom line. So, you know, critically important and, you know, tremendous ROI opportunity that we're helping to enable there, uh, from a, a leading global drugstore retailer. So this is more about data processing and, you know, we're excited to, you know, the recent partnership with the Vidia. >>So fast data, isn't always at the edge of IOT. It's also about workloads. And in retail, if you are processing your customer profiles or segmentation like intra day, you will ever achieve personalization. You will never achieve one-on-one communications with readers killers or with customers. And why is that? Because customers in many cases are touching your brand several times a week. So taking you a week or longer to process your segmentation schemes, you've already lost and you'll never achieve personalization in frack. In fact, you may offend customers by offering. You might push out based on what they just bought yesterday. You had no idea of it. So, you know, that's what we're really excited about. Uh, again, with, with the computation speed, then the video brings to, to Cloudera, we're already doing this today already, you know, been providing levels, exponential speed and processing data. But when the video brings to the party is course GPU's right, which is another exponential improvement, uh, to processing workloads like demand forecast, customer profiles. >>These things need to happen behind the scenes in the back office, much faster than retailers have been doing in the past. Um, that's just the world we all live in today. And then finally, um, you know, proximity marketing standpoint, or just from an in-store operation standpoint, you know, retailers are leveraging Cloudera today, not only data flow, but also of course our compute and storage platform and ML, et cetera, uh, to understand what's happening in store. It's almost like the metrics that we used to look at in the past in terms of conversion and traffic, all those metrics are now moving into the physical world. If you can leverage computer vision in streaming video, to understand how customers are traversing your store, how much time they're standing in front of the display, how much time they're standing in checkout line. Um, you can now start to understand how to better merchandise the store, um, where the hotspots are, how to in real time improve your customer service. >>And from a proximity marketing standpoint, understand how to engage with the customer right at the moment of truth, right? When they're right there, um, in front of a particular department or category, you know, of course leveraging mobile devices. So that's the world of fast data in retail and just kind of a summary in just a few examples of how folks are leveraging Cloudera today. Um, you know, from an overall platform standpoint, of course, father as an enterprise data platform, right? So, you know, we're, we're helping to the entire data life cycle. So we're not a data warehouse. Um, we're much more than that. So we have solutions to ingest data from the edge from IOT leading practice solutions to bring it in. We also have experiences to help, you know, leverage the analytic capabilities of, uh, data engineering, data science, um, analytics and reporting. Uh, we're not, uh, you know, we're not, we're not encroaching upon the legacy solutions that many retailers have today. >>We're providing a platform, this open source that helps weave all of this mess together that existed retail today from legacy systems because no retailer, frankly, is going to rip and replace a lot of stuff that they have today. Right. And the other thing that Cloudera brings to market is this whole notion of on-prem hybrid cloud and multi-cloud right. So our whole, our whole culture has been built around open source technology as the company that provides most of the source code to the Apache network around all these open source technologies. Um, we're kind of religious about open source and lack of vendor lock-in, uh, maybe to our fault. Uh, but as a company, we pull that together from a data platform standpoint. So it's not a rip and replace situation. It's like helping to connect legacy systems, data and analytics, um, you know, weaving that whole story together to be able to solve this whole data life cycle from beginning to end. >>And then finally, you know, just, you know, I want to thank everyone for joining today's session. I hope you found it informative. I can't say Brian killed course enough. Um, you know, he's my trusted friend in terms of what's going on in the industry. He has much broader reach of course, uh, in talking to a lot of our partners in, in, in, in other, uh, technology companies out there as well. But I really appreciate everyone joining the session and Brian, I'm going to kind of leave it open to you to, you know, any closing comments that you might have based on, you know, what we're talking about today in terms of fast data and retail. >>First of all, thank you, Brent. Um, and this is an exciting time to be in this industry. Um, and I'll just leave it with this. The reason that we are talking about these things is because we can, the technology has advanced remarkably in the last five years. Some of this data has been out there for a lot longer than that in it, frankly wasn't even usable. Um, but what we're really talking about is increasing the cycle time for decisions, making them go faster and faster so that we can respond to consumer expectations and delight them in ways that that make us a trusted provider of their life, their lifestyle needs. So this is really a good time to be a retailer, a real great time to be servicing the retail technology community. And I'm glad to be a part of it. And I was glad to be working with you. So thank you, Brian. >>Yeah, of course, Brian, and one of the exciting things for me to not being in the industry, as long as I have and being a former retailer is it's really exciting for me to see retailers actually spending money on data and it for a change, right? They've all kind of come to this final pinnacle of this is what it's going to take to compete. Um, you know, you know, and I talked to, you know, a lot of colleagues, even, even salespeople within Cloudera, I like, oh, retail, very stodgy, you know, slow to move. That's not the case anymore. Um, you know, religion is everyone's, everyone gets the religion of data and analytics and the value of that. And what's exciting for me to see as all this infusion of immense talent within the industry years ago, Brian, I mean, you know, retailers are like, you know, pulling people from some of the, you know, the greatest, uh, tech companies out there, right? From a data science data engineering standpoint, application developers, um, retail is really getting this legs right now in terms of, you know, go to market and in the leverage of data and analytics, which to me is very exciting. Well, >>You're right. I mean, I, I became a CIO around the time that, uh, point of sale and data warehouses were starting to happen data cubes and all those kinds of things. And I never thought I would see a change that dramatic, uh, as the industry experience back in those days, 19 89, 19 90, this changed doors that, but the good news is again, as the technology is capable, uh, it's, it's, we're talking about making technology and information available to, to retail decision-makers that consumers carry around in their pocket purses and pockets is there right now today. Um, so the, the, the question is, are you going to utilize it to win or are you going to get beaten? That's really what it boils down to. Yeah, >>For sure. Uh, Hey, thanks everyone. We'll wrap up. I know we ran a little bit long, but, uh, appreciate, uh, everyone, uh, hanging in there with us. We hope you enjoyed the session. The archive contact information is right there on the screen. Feel free to reach out to either Brian and I. You can go to cloudera.com. Uh, we even have, you know, joint sponsored papers with RSR. You can download there as well as eBooks and other assets that are available if you're interested. So thanks again, everyone for joining and really appreciate you taking the time. >>Hello everyone. And thanks for joining us today. My name is Brent Bedell, managing director retail, consumer goods here at Cloudera. Cloudera is very proud to be partnering with companies like three soft to provide data and analytic capabilities for over 200 retailers across the world and understanding why demand forecasting could be considered the heartbeat of retail. And what's at stake is really no mystery to most, to most retailers. And really just a quick level set before handing this over to my good friend, uh, Camille three soft, um, you know, IDC Gartner. Um, many other analysts have kind of summed up an average, uh, here that I thought would be important to share just to level set the importance of demand forecasting or retail. And what's at stake. I mean the combined business value for retailers leveraging AI and IOT. So this is above and beyond. What demand forecasting has been in the past is a $371 billion opportunity. >>And what's critically important to understand about demand forecasting. Is it directly impacts both the top line and the bottom line of retail. So how does it affect the top line retailers that leverage AI and IOT for demand forecasting are seeing average revenue increases of 2% and think of that as addressing the in stock or out of stock issue in retail and retail is become much more complex now, and that is no longer just brick and mortar, of course, but it's fulfillment centers driven by e-commerce. So inventory is now having to be spread over multiple channels. Being able to leverage AI and IOT is driving 2% average revenue increases. Now, if you think about the size of most retailers or the average retailer that on its face is worth millions of dollars of improvement for any individual retailer on top of that is balancing your inventory, getting the right product in the right place and having productive inventory. >>And that is the bottom line. So the average inventory reduction, leveraging AI and IOT as the analyst have found, and frankly, having spent time in this space myself in the past a 15% average inventory reduction is significant for retailers not being overstocked on product in the wrong place at the wrong time. And it touches everything from replenishment to out-of-stocks labor planning and customer engagement for purposes of today's conversation. We're going to focus on inventory and inventory optimization and reducing out-of-stocks. And of course, even small incremental improvements. I mentioned before in demand forecast accuracy have millions of dollars of direct business impact, especially when it comes to inventory optimization. Okay. So without further ado, I would like to now introduce Dr. Camille Volker to share with you what his team has been up to. And some of the amazing things that are driving at top retailers today. So over to you, Camille, >>Uh, I'm happy to be here and I'm happy to speak to you, uh, about, uh, what we, uh, deliver to our customers. But let me first, uh, introduce three soft. We are a 100 person company based in Europe, in Southern Poland. Uh, and we, uh, with 18 years of experience specialized in providing what we call a data driven business approach, uh, to our customers, our roots are in the solutions in the services. We originally started as a software house. And on top of that, we build our solutions. We've been automation that you get the software for biggest enterprises in Poland, further, we understood the meaning of data and, and data management and how it can be translated into business profits. Adding artificial intelligence on top of that, um, makes our solutions portfolio holistic, which enables us to realize very complex projects, which, uh, leverage all of those three pillars of our business. However, in the recent time, we also understood that services is something which only the best and biggest companies can afford at scale. And we believe that the future of retail, uh, demon forecasting is in the product solutions. So that's why we created occupy our AI platform for data driven retail. That also covers this area that we talked about today. >>I'm personally proud to be responsible for our technology partnerships with other on Microsoft. Uh, it's a great pleasure to work with such great companies and to be able to, uh, delivered a solution store customers together based on the common trust and understanding of the business, uh, which cumulates at customer success at the end. So why, why should you analyze data at retail? Why is it so important? Um, it's kind of obvious that there is a lot of potential in the data per se, but also understanding the different areas where it can be used in retail is very important. We believe that thanks to using data, it's basically easier to the right, uh, the good decisions for the business based on the facts and not intuition anymore. Those four areas that we observe in retail, uh, our online data analysis, that's the fastest growing sector, let's say for those, for those data analytics services, um, which is of course based on the econ and, uh, online channels, uh, availability to the customer. >>Pandemic only speeds up this process of engagement of the customers in that channel, of course, but traditional offline, um, let's say brick and mortar shops. Uh, they still play the biggest role for most of the retailers, especially from the FMCG sector. However, it's also very important to remember that there is plenty of business, uh, related questions that meet that need to be answered from the headquarter perspective. So is it actually, um, good idea to open a store in a certain place? Is it a good idea to optimize a stock with Saturday in producer? Is it a good idea to allocate the goods to online channel in specific way, those kinds of questions they are, they need to be answered in retail every day. And with that massive amount of factors coming into that question, it's really not, not that easy to base, only on the intuition and expert knowledge, of course, uh, as Brent mentioned at the beginning, the supply chain and everything who's relates to that is also super important. We observe our customers to seek for the huge improvements in the revenue, just from that one single area as well. Okay. >>So let me present you a case study of one of our solutions, and that was the lever to a leading global grocery retailer. Uh, the project started with the challenge set of challenges that we had to conquer. And of course the most important was how to limit overstocks and out of stocks. Uh, that's like the holy grail in of course, uh, how to do it without flooding the stores with the goods and in the same time, how to avoid empty shelves, um, from the perspective of the customer, it was obvious that we need to provide a very well, um, a very high quality of sales forecast to be able to ask for, uh, what will be the actual sales of the individual product in each store, uh, every day, um, considering huge role of the perishable goods in the specific grocery retailer, it was a huge challenge, uh, to provide a solution that was able to analyze and provide meaningful information about what's there in the sales data and the other factors we analyzed on daily basis at scale, however, uh, our holistic approach implementing AI with data management, uh, background, and these automation solutions all together created a platform that was able to significantly increase, uh, the sales for our customer just by minimizing out of stocks. >>In the same time we managed to not overflow the stock, the shops with the goods, which actually decreased losses significantly, especially on the fresh fruit. >>Having said that this results of course translate into the increase in revenue, which can be calculated in hundreds of millions of dollars per year. So how the solution actually works well in its principle, it's quite simple. We just collect the data. We do it online. We put that in our data lake, based on the cloud, there are technology, we implement our artificial intelligence models on top of it. And then based on the aggregated information, we create the forecast and we do it every day or every night for every single product in every single store. This information is sent to the warehouses and then the automated replenishment based on the forecast is on the way the huge and most important aspect of that is the use of the good tools to do the right job. Uh, having said that you can be sure that there is too many information in this data, and there is actually two-minute forecast created every night that any expert could ever check. >>This means our solution needs to be, uh, very robust. It needs to provide information with high quality and high porosity. There is plenty of different business process, which is on our forecast, which need to be delivered on time for every product in each individual shop observing the success of this project and having the huge market potential in mind, we decided to create our QB, which can be used by many retailers who don't want to create a dedicated software for that. We'll be solving this kind of problem. Occupy is, uh, our software service offering, which is enabling retailers to go data driven path management. >>We create occupant with retailers, for retailers, uh, implementing artificial intelligence, uh, on top of data science models created by our experts, uh, having data, data analysis in place based on data management tools that we use we've written first, um, attitude. The uncertain times of pandemic clearly shows that it's very important to apply correction factors, which are sometimes required because we need to respond quickly to the changes in the sales characteristics. That's why occupy B is open box solution, which means that you basically can implement that in your organization. We have without changing the process internally, it's all about mapping your process into this into the system, not the other way around the fast trends and products, collection possibilities allow the retailers to react to any changes, which are pure in the sales every day. >>Also, it's worth to mention that really it's not only FMCG. And we believe that different use cases, which we observed in fashion health and beauty, common garden pharmacies and electronics, flavors of retail are also very meaningful. They also have one common thread. That's the growing importance of e-commerce. That's why we didn't want to leave that aside of occupant. And we made everything we can to implement a solution, which covers all of the needs. When you think about the factors that affect sales, there is actually huge variety of data and that we can analyze, of course, the transactional data that every dealer possesses like sales data from sale from, from e-commerce channel also, uh, averaging numbers from weeks, months, and years makes sense, but it's also worth to mention that using the right tool that allows you to collect that data from also internal and external sources makes perfect sense for retail. Uh, it's very hard to imagine a competitive retailer that is not analyzing the competitor's activity, uh, changes in weather or information about some seasonal stores, which can be very important during the summer during the holidays, for example. Uh, but on the other hand, um, having that information in one place makes the actual benefit and environment for the customer. >>Okay. Demon forecasting seems to be like the most important and promising use case. We can talk about when I think about retail, but it's also their whole process of replenishment that can cover with different sets of machine learning models. And they done management tools. We believe that analyzing data from different parts of the retail, uh, replenishment process, uh, can be achieved with implementing a data management solution based on caldera products and with adding some AI on top of it, it makes perfect sense to focus on not only demand forecasting, but also further use cases down the line when it comes to the actual benefits from implementing solutions for demand management, we believe it's really important to analyze them holistically. First is of course, out of stocks, memorization, which can be provided by simply better sales focus, but also reducing overstocks by better inventory management can be achieved in, in the same time. Having said that we believe that analyzing data without any specific new equipment required in point of sales is the low hanging fruit that can be easily achieved in almost every industry in almost every regular customer. >>Hey, thanks, Camille, having worked with retailers in this space for a couple of decades, myself, I was really impressed by a couple of things and they might've been understated, frankly. Um, the results of course, I mean, you, you know, as I kind of set up this session, you doubled the numbers on the statistics that the analysts found. So obviously in customers you're working with, um, you know, you're, you're doubling average numbers that the industry is having and, and most notably how the use of AI or occupy has automated so many manual tasks of the past, like tour tuning, item profiles, adding new items, et cetera. Uh, and also how quickly it felt like, and this is my, my core question. Your team can cover, um, or, or provide the solution to, to not only core center store, for example, in grocery, but you're covering fresh products. >>And frankly, there are, there are solutions out on the market today that only focus on center store non-perishable department. So I was really impressed by the coverage that you're able to provide as well. So can you articulate kind of what it takes to get up and running and your overall process to roll out the solution? I feel like based on what you talked about, um, and how you were approaching this in leveraging AI, um, that you're, you're streamlining processes of legacy demand, forecasting solutions that required more manual intervention, um, how quickly can you get people set up and what is the overall process like to get started with soft? >>Yeah, it's usually it takes three to six months, uh, to onboard a new customer to that kind of solution. And frankly it depends on the data that the customer, uh, has. Uh, usually it's different, uh, for smaller, bigger companies, of course. Uh, but we believe that it's very important to start with a good foundation. The platform needs to be there, the platform that is able to, uh, basically analyze or process different types of data, structured, unstructured, internal, external, and so on. But when you have this platform set, it's all about starting ingesting data there. And usually for a smaller companies, it's easier to start with those, let's say, low hanging fruits. So the internal data, which is there, this data has the highest veracity is already easy to start with, to work with them because everyone in the organization understands this data for the bigger companies. It might be important to ingest also kind of more unstructured data, some kind of external data that need to be acquired. So that may, that may influence the length of the process. But we usually start with the customers. We have, uh, workshops. That's very important to understand their business because not every deal is the same. Of course, we believe that the success of our customers comes also due to the fact that we train those models, those AI models individually to the needs of our >>Totally understand and POS data, every retailer has right in, in one way shape or form. And it is the fundamental, uh, data point, whether it's e-comm or the brick and mortar data, uh, every retailer has that data. So that, that totally makes sense. But what you just described was bunts. Um, there are, there are legacy and other solutions out there that this could be a, a year or longer process to roll out to the number of stores, for example, that you're scaling to. So that's highly impressive. And my guess is a lot of the barriers that have been knocked down with your solution are the fact that you're running this in the cloud, um, you know, on, from a compute standpoint on Cloudera from a public cloud stamp point on Microsoft. So there's, there's no, it intervention, if you will, or hurdles in preparation to get the database set up and in all of the work, I would imagine that part of the time-savings to getting started, would that be an accurate description? >>Yeah, absolutely. Uh, in the same time, this actually lowering the business risks, because we simply take data and put that into the data lake, which is in the cloud. We do not interfere with the existing processes, which are processing this data in the combined. So we just use the same data. We just already in the company, we ask some external data if needed, but it's all aside of the current customers infrastructure. So this is also a huge gain, as you said, right? >>And you're meeting customers where they are. Right. So, as I said, foundationally, every retailer POS data, if they want to add weather data or calendar event data or, you know, want incorporate a course online data with offline data. Um, you have a roadmap and the ability to do that. So it is a building block process. So getting started with, for data, uh, as, as with POS online or offline is the foundational component, which obviously you're very good at. Um, and then having that ability to then incorporate other data sets is critically important because that just improves demand, forecast accuracy, right. By being able to pull in those, those other data sources, if you will. So Camille, I just have one final question for you. Um, you know, there, there are plenty of not plenty, but I mean, there's enough demand forecasting solutions out on the market today for retailers. One of the things that really caught my eye, especially being a former retailer and talking with retailers was the fact that you're, you're promoting an open box solution. And that is a key challenge for a lot of retailers that have, have seen black box solutions come and go. Um, and especially in this space where you really need direct input from the, to continue to fine tune and improve forecast accuracy. Could you give just a little bit more of a description or response to your approach to open box versus black box? >>Yeah, of course. So, you know, we've seen in the past the failures of the projects, um, based on the black box approach, uh, and we believe that this is not the way to go, especially with this kind of, uh, let's say, uh, specialized services that we provide in meaning of understanding the customer's business first and then applying the solution, because what stands behind our concept in occupy is the, basically your process in the organization as a retailer, they have been optimized for years already. That's where retailers put their, uh, focus for many years. We don't want to change that. We are not able to optimize it properly. For sure as it combined, we are able to provide you a tool which can then be used for mapping those very well optimized process and not to change them. That's our idea. And the open box means that in every process that you will map in the solution, you can then in real time monitor the execution of those processes and see what is the result of every step. That way we create truly explainable experience for our customers, then okay, then can easily go for the whole process and see how the forecast, uh, was calculated. And what is the reason for a specific number to be there at the end of the day? >>I think that is, um, invaluable. Um, can be, I really think that is a differentiator and what three soft is bringing to market with that. Thanks. Thanks everyone for joining us today, let's stay in touch. I want to make sure to leave, uh, uh, Camille's information here. Uh, so reach out to him directly or feel free at any, any point in time, obviously to reach out to me, um, again, so glad everyone was able to join today, look forward to talking to you soon.

>>Hello, and welcome to today's event, dealing government emergency responses beyond the pandemic. This is Bob Wooley, senior fellow for the center for digital government and formerly the chief tech clerk for the state of Utah. I'm excited to serve as moderator for today's event. And just want to say, thank you for joining us. I know we're in for an informative session over the next 60 minutes before we begin a couple of brief housekeeping notes or recording of this presentation will be emailed to all registrants within 48 hours. You can use the recording for your reference or feel free to pass it along to colleagues. This webcast is designed to be interactive and you can participate in Q and a with us by asking questions at any time during the presentation, you should see a Q and a box on the bottom left of the presentation panel. >>Please send in your questions as they come out throughout the presentation, our speakers will address as many of these questions as we can during the Q and a portion of the close of our webinar today, if you would like to download the PDF of the slides for this presentation, you can do so by clicking the webinar resources widget at the bottom of the console. Also during today's webinar, you'll be able to connect with your peers by LinkedIn, Twitter and Facebook. Please use the hashtag gov tech live to connect with your peers across the government technology platform, via Twitter. At the close of the webinar, we encourage you to complete a brief survey about the presentation. We would like to hear what you think if you're unable to see with us for the entire webinar, but we're just like to complete the survey. As much as you're able, please click the survey widget at the bottom of the screen to launch the survey. Otherwise it will pop up once the webinar concludes at this time, we recommend that you disable your pop-up blockers, and if you experiencing any media player issues or have any other problems, please visit our webcast help guide by clicking on the help button at the bottom of the console. >>Joining me today to discuss this very timely topic are Karen revolt and Tim Burch, Kim Berge currently serves as the administrator of human services for Clark County Nevada. He's invested over 20 years in improving health and human service systems of care or working in the private public and nonprofit sectors. 18 of those years have been in local government in Clark County, Las Vegas, where you served in a variety of capacities, including executive leadership roles as the director of department of social services, as well as the director for the department of family services. He has also served as CEO for provider of innovative hosted software solutions, as well as chief strategy officer for a boutique public sector consulting firm. Karen real-world is the social program management offering lead for government health and human services with IBM Watson health. Karen focuses delivering exciting new offerings by focusing on market opportunities, determining unmet needs and identifying innovative solutions. >>Much of her career has been in health and human services focused on snap, TANIF, Medicaid, affordable care act, and child welfare prior to joining IBM. Karen was the senior director of product management for a systems integrator. She naturally fell in love with being a project manager. She can take her user requirements and deliver offerings. Professionals would use to make their job easier and more productive. Karen has also found fulfillment in working in health and human services on challenges that could possibly impact the outcome of people's lives. Now, before we begin our discussion of the presentation, I want to one, we'd like to learn a little more about you as an audience. So I'm going to ask you a polling question. Please take a look at this. Give us an idea of what is your organization size. I won't bother to read all these to you, but there are other a range of sizes zero to 250 up to 50,000. Please select the one that is most appropriate and then submit. >>It looks like the vast majority are zero to two 50. Don't have too many over 250,000. So this is a very, very interesting piece of information. Now, just to set up our discussion today, what I want to do is just spend just a moment and talk about the issue that we're dealing with. So when you look the COVID-19 pandemic, it's put immense pressure on States. I've been a digital state judge and had been judging a lot of the responses from States around the country. It's been very interesting to me because they bifurcate really into two principle kinds of reactions to the stress providing services that COVID environment present. One is we're in a world of hurt. We don't have enough money. I think I'm going to go home and engage as little as I have to. Those are relatively uncommon. Thankfully, most of them have taken the COVID-19 pandemic has immense opportunity for them to really do a lot more with telework, to do more with getting people, employees, and citizens involved with government services. >>And I've done some really, really creative things along the way. I find that to be a really good thing, but in many States systems have been overloaded as individuals and families throughout the country submitted just an unprecedented number of benefit applications for social services. At the same time, government agencies have had to contend with social distance and the need for a wholly different approach to engage with citizens. Um, overall most public agencies, regardless of how well they've done with technology have certainly felt some strain. Now, today we have the opportunity to go into a discussion with our speakers, have some wonderful experience in these areas, and I'm going to be directing questions to them. And again, we encourage you as you hear what they have to say. Be sure and submit questions that we can pick up later at the time. So Tim, let's start with you. Given that Las Vegas is a hub for hospitality. An industry hit severely as a result of this pandemic. How's the County doing right now and how are you prioritizing the growing needs of the County? >>Thanks Bob. Thanks for having me. Let me start off by giving just a little, maybe context for Clark County too, to our audience today. So, uh, Clark County is, you know, 85% of the state of Nevada if we serve not just as a regional County by way of service provision, but also direct municipal services. Well, if, uh, the famous Las Vegas strip is actually in unincorporated Clark County, and if we were incorporated, we would be the largest city in the state. So I say all of that to kind of help folks understand that we provide a mix of services, not just regional services, like health and human services, the direct and, and missable, uh, services as well as we work with our other five jurisdiction partners, uh, throughout the area. Uh, we are very much, um, I think during the last recession we were called the Detroit of the West. >>And, uh, that was because we're very much seen as a one industry town. Uh, so most like when the car plants, the coal plants closed back East and in the communities fuel that very rapidly, the same thing happens to us when tourism, uh, it's cut. Uh, so of course, when we went into complete shutdown and March, uh, we felt it very rapidly, not just on, uh, uh, tax receipts and collectibles, but the way in which we could deliver services. So of course our first priority was to, uh, like I think you mentioned mobilized staff. We, we mobilized hundreds of staff overnight with laptops and phones and cars and the things they needed to do to get mobile and still provide the priority services that we're mandated to provide from a safety standpoint. Um, and then we got busy working for our clients and that's really where our partnership with IBM and Watson, uh, came in and began planning that in July. And we're able to open that portal up in October to, to really speed up the way in which we're giving assistance to, to our residents. Um, re focus has been on making sure that people stay housed. We have, uh, an estimated, uh, 2.5 million residents and over 150,000 of those households are anticipated to be facing eviction, uh, as of January one. So we, we've got a, a big task ahead of us. >>All of this sounds kind of expensive. Uh, one of the common threads as you know, runs throughout government is, ah, I don't really have the money for that. I think I'd be able to afford that a diaper too, as well. So what types of funding has been made available for counties, a result of a pandemic, >>Primarily our funding stream that we're utilizing to get these services out the door has been the federal cares act. Uh, now we had some jurisdictions regionally around us and even locally that prioritize those funds in a different way. Um, our board of County commissioners, uh, took, um, a sum total of about $85 million of our 240 million that said, this will go directly to residents in the form of rental assistance and basic needs support. No one should lose their home or go hungry during this pandemic. Uh, so we've really been again working through our community partners and through our IBM tools to make sure that happens. >>So how does, how does, how does the cares act funding then support Clark County? Cause it seems to me that the needs would be complex, diverse >>Pretty much so. So as you, as folks may know him a call there's several tronches of the cares act, the original cares act funding that has come down to us again, our board, uh, identified basic needs or rental assistance and, and gave that the department of social service to go to the tunicate, uh, through the community. We then have the cares act, uh, uh, coronavirus relief funds that have, uh, impacted our CDBG and our emergency solutions grants. We've taken those. And that's what we was going to keep a lot of the programs and services, uh, like our IBM Watson portal open past January one when the cares act dollars expire. Uh, our initial response was a very manual one, uh, because even though we have a great home grown homeless management information system, it does not do financials. Uh, so we had 14 local nonprofits adjudicating, uh, this rental assistance program. >>And so we could get our social service visitor portal up, uh, to allow us to take applications digitally and run that through our program. Uh, and, uh, so those partners were obviously very quickly overwhelmed and were able to stand up our portal, uh, which for the reason we were driving so hard, even from, uh, beginning of the conversations where after going into lockdown into contracting in July and getting the portal open in October, which was an amazing turnaround. Uh, so the kudos that IBM team, uh, for getting us up and out the door so quickly, uh, was a tie in, uh, to our, uh, Curam IBM, uh, case management system that we utilize to adjudicate benefits on daily basis in Clark County for all our local indigent population, uh, and high needs folks. Uh, and then that ties into our SAP IBM platform, which gets the checks out the door. >>So what, what we've been able to do with these dollars is created in Lucian, uh, that has allowed us in the last 60 days to get as much money out the door, as our nonprofits were able go out the door in the first six months pandemic. So it really has helped us. Uh, so I'm really grateful to our board of County commissioners for recognizing the investment in technology to, to not only get our teams mobile, but to create ease of access for our constituents and our local residents to give them the help they need quickly and the way that they need it. >>Just to follow up question to that, Tim, that I'm curious about having done a lot of work like this in government, sometimes getting procurement through in a timely way is a bit challenging. How were you able to work through those issues and getting this up and provision so quickly? >>Uh, yeah, so we, we put together a, what we call a pandemic playbook, which is kind of lessons learned. And what we've seen is the folks who were essential workers in the first 60 days of the, uh, pandemic. We were able to get a lot done quickly because we were taking full advantage of the emergency. Uh, it may sound a little crass to folks not inside the service world, but it was, uh, you know, don't want you to crisis. It was things we've been planning or trying to do for years. We need them yesterday. We should have had them yesterday, but let's get them tomorrow and get it moving very quickly. Uh, this IBM procurement was something we were able to step through very quickly because of our longstanding relationship. Our countywide, uh, system of record for our financials is SAP. Uh, we've worked with Curam, uh, solution, uh, for years. >>So we've got this long standing relationship and trust in the product and the teams, which helped us build the business case of why we did it, no need to go out for competitive procurement that we didn't have time. And we needed something that would integrate very quickly into our existing systems. Uh, so that part was there. Now when the folks who were non essential came back in June and the reopening, it was whiplash, uh, the speed at which we were moving, went back to the pace of normal business, uh, which feels like hitting a wall, doing a hundred miles an hour when you're used to having that, uh, mode of doing business. Uh, so that's certainly been a struggle, uh, for all of those involved, uh, in trying to continue to get things up. Um, but, uh, once again, the teams have been great because we've probably tripled our licensure on this portal since we opened it, uh, because of working with outside vendors, uh, to, uh, literally triple the size of our staff that are processing these applications by bringing on temporary staff, uh, and short-term professionals. Uh, and so we've been able to get those things through, uh, because we'd already built the purchasing vehicle during the early onset of the crisis. >>That's very helpful. Karen, IBM has played a really pivotal role in all of this. Uh, IBM Watson health works with a number of global government agencies, raging from counties like Clark County to federal governments. What are some of the major challenges you've seen with your clients as a result of the pandemic and how is technology supporting them in a time of need and give us some background Watson health too. So we kind of know a little more about it because this is really a fascinating area. >>Yeah. Thank you, Bob. And thanks Tim for the background on Clark County, because I think Clark County is definitely also an example of what federal governments and global governments are doing worldwide today. So, um, Watson health is our division within IBM where we really focus on health and human services. And our goal is to really focus in on, um, the outcomes that we're providing to individuals and families and looking at how we use data and insights to really make that impact and that change. And within that division, we have our government health and human services area, which is the focus of where we are with our clients around social program. But it also allows us to work with, um, different agencies and really look at how we can really move the ball in terms of, um, effecting change and outcomes for, um, really moving the needle of how we can, uh, make an impact on individuals and families. >>So as we look at the globe globally as well, you know, everything that Tim had mentioned about how the pandemic has really changed the way that government agencies operate and how they do services, I think it's amazing that you have that pandemic playbook because a lot of agencies in the same way also had these set of activities that they always wanted to go and take part on, but there was no impetus to really allow for that to happen. And with the pandemic, it allowed that to kind of open and say, okay, we can try this. And unfortunately I'm in a very partial house way to do that. And, um, what Tim has mentioned about the new program that they set up for the housing, some of those programs could take a number of years to really get a program online and get through and allowing, uh, the agencies to be able to do that in a matter of weeks is amazing. >>And I think that's really gonna set a precedent as we go forward and how you can bring on programs such as the housing and capability in Canada with the economic, uh, social, um, uh, development and, and Canada need that the same thing. They actually had a multi benefit delivery system that was designed to deliver benefits for three programs. And as part of the department of fisheries and oceans Canada, the, um, the state had an emergency and they really need to set up on how they could provide benefits to the fishermen who had been at that impacted, um, from that. And they also did set up a digital front-end using IBM citizen engagement to start to allow the applications that benefits, um, and they set it up in a matter of weeks. And as I mentioned, we, uh, Clark County had a backend legacy system where they could connect to and process those applications. And this case, this is a brand new program and the case management system that they brought up was on cloud. And they had to set up a new one, but allow them to set up a, what we used to call straight through processing, I think has been now turned, turned or coined contact less, uh, processing and allowing us to really start to move those benefits and get those capabilities out to the citizens in even a faster way than has been imagined. Uh, pre pandemic. >>Karen, I have one follow-up question. I want to ask you, having had a lot of experience with large projects in government. Sometimes there's a real gap between getting to identified real requirements and then actions. How do you, how do you work with clients to make sure that process time to benefit is shortened? >>So we really focus on the user themselves and we take a human centered design focus and really prioritizing what those needs are. Um, so working with the clients, uh, effectively, and then going through agile iterations of brain, that capability out as, um, in, in a phased approach to, so the idea of getting what we can bring out that provides quality and capability to the users, and then over time starting to really roll out additional functions and, um, other, uh, things that citizens or individuals and families would need >>Very helpful. Tim, this is an interesting partnership. It's always good to see partnerships between private sector and government. Tell us a little bit about how the partnership with IBM Watson health was established and what challenges or they were brought into assist, where they brought into assist with back to requirements. Again, within the requirements definitely shifted on us. You know, we had the con looking at, uh, Watson on our child welfare, uh, side of the house that I'm responsible for and how that we could, uh, increase access to everything from tele-health to, to, uh, foster parent benefit, uh, kinship, placement benefits, all those types of things that, that right now are very manual, uh, on the child welfare side. Uh, and then the pandemic kid. And we very quickly realized that we needed, uh, to stand up a, um, a new program because, uh, a little bit for context, uh, the park County, we don't administer TANIF or Medicaid at the County level. >>It is done at the state level. So we don't have, uh, unemployment systems or Medicaid, 10 of snap benefits systems to be able to augment and enroll out. We provide, uh, the indigent supports the, the homelessness prevention, referee housing continuum of care, long-term care, really deep emergency safety net services for our County, which is a little bit different and how those are done. So that was really our focus, which took a lot of in-person investigation. We're helping people qualify for disability benefits so they can get into permanent supportive housing, uh, things that are very intensive. And yet now we have a pandemic where we need things to happen quickly because the cares act money expires at the end of December. And people were facing eviction and eviction can help spread exposure to, to COVID. Uh, so, uh, be able to get in and very rapidly, think about what is the minimal pelvis to MVP. >>What's the minimum viable product that we can get out the door that will help people, uh, entrance to a system as contactless as possible, which again was a complete one 80 from how we had been doing business. Um, and, uh, so the idea that you could get on and you have this intelligent chat bot that can walk you through questions, help you figure out if you look like you might be eligible, roll you right into an application where you can upload the few documents that we're going to require to help verify your coat would impact and do that from a smartphone and under, you know, 20 minutes. Um, it, it, it is amazing. And the fact that we've stood that up and got it out the door in 90 days, it's just amazing to me, uh, when it shows the, uh, strength of partnership. Um, I think we can, we have some shared language because we had that ongoing partnership, but we were able to actually leverage some system architects that we had that were familiar with our community and our other products. So it really helped expedite, uh, getting this, uh, getting this out to the citizens. >>So, uh, I assume that there are some complexities in doing this. So overall, how has this deployment of citizen engagement with Watson gone and how do you measure success other than you got it out quick? How do you know if it's working? >>Yeah. Right. So it's the adage of, you know, quick, fast and good, right. Um, or fast, good and cheap. So, uh, we measure success in this way. Um, how are we getting access as our number one quality measurement here? So we were able to collect, uh, about 13,000 applications, uh, manual NRC, manually folks had to go onto our website, download a PDF, fill it out, email it, or physically drop it off along with their backup. One of their choice of 14 non-profits in town, whichever is closest to them. Um, and, uh, and then wait for that process. And they were able to get 13,000 of those, uh, process for the last six months. Uh, we have, I think we had about 8,000 applications the first month come into the portal and about an equal amount of folks who could not provide the same documentation that it was needed. >>And self-selected out. If we had not had the, the tool in place, we would have had 16,000 applications, half of which would have been non-eligible would have been jamming up the system, uh, when we don't have the bandwidth to deal to deal with that, we, we need to be able to focus in on, uh, Judy Kenny applications that we believe are like a 95% success rate from the moment our staff gets them, but because we have the complex and he was on already being dependent upon the landlord, having to verify the rent amount and be willing to work with us, um, which is a major hurdle. Um, but, uh, so w we knew we could not do is go, just reinvent the manual process digitally that that would have been an abject failure on our behalf. So, uh, the ideas that, uh, folks had can go on a very, had this very intuitive conversation to the chat bot, answer some questions and find out if they're eligible. >>And then self-select out was critical for us to not only make sure that the citizens got the help they needed, but not so burnt out and overload our workforce, which is already feeling the strain of the COVID pandemic on their own personal lives and in their homes and in the workplace. Um, so that was really critical for us. So it's not just about speed, ease of access was important. Uh, the ability to quickly automate things on the fly, uh, we have since changed, uh, the area median income, a qualifier for the rental assistance, because we were able to reallocate more money, uh, to the program. So we were able to open it up to more people. We were able to make that, uh, change to the system very quickly. Uh, the idea that we can go on the home page and put updates, uh, we recognized that, uh, some of our monolingual Hispanic residents were having difficulty even with some guidance getting through the system. >>So we're able to record a, a Spanish language walkthrough and get done on the home page the next day, right into the fordable, there'll be a fine, so they could literally run the YouTube video while they're walking through their application. Side-by-side so things like that, that those are how we are able to, for us measured success, not just in the raw dollars out the door, not just in the number of applications that have come in, but our ability to be responsive when we hear from our constituents and our elected officials that, Hey, I want, I appreciate the 15,000 applications as you all, a process and record time, I've got three, four, five, six, 10 constituents that having this type of problem and be able to go back and retool our systems to make them more intuitive, to do, be able to keep them responsive for us is definitely a measure of success and all of this, probably more qualitative than here we're looking >>For, but, uh, that's for us, that's important. Actually the qualitative side is what usually gets ignored. Uh, Karen, I've got a question that's a follow up for you on the same topic. How does IBM facilitate reporting within this kind of an environment given the different needs of stakeholders, online managers and citizens? What kinds of things do you, are you able to do >>So with, um, the influx of digitalization? I think it allows us to really take a more data-driven approach to start looking at that. So, as, as Tim was mentioning, you can see where potentially users are spending more time on certain questions, or if they're stuck on a question, you can see where the abandoned rate is. So using a more data-driven approach to go in to identify, you know, how do we actually go and, um, continue to drive that user experience that may not be something that we drive directly from the users. So I would say that analytics is really, uh, I think going to continue to be a driving force as government agencies go forward, because now they are capturing the data. But one thing that they have to be careful of is making sure that the data that they're getting is the right data to give them the information, to make the right next steps and decisions. >>And Tim, you know, use a really good example with, um, the chatbot in terms of, you know, with the influx of everything going on with COVID, the citizens are completely flooded with information and how do they get the right information to actually help them decide, can I apply for this chap program? Or should I, you know, not even try and what Tim mentioned just saved the citizens, you know, the people that may not be eligible a lot of time and going through and applying, and then getting denied by having that upfront, I have questions and I need answers. Um, so again, more data-driven of how do we provide that information? And, you know, we've seen traditionally citizens having to go on multiple website, web pages to get an answer to the question, because they're like, I think I have a question in this area, but I'm not exactly sure. And they, then they're starting to hunt and hunt and hunt and not even potentially get an answer. So the chocolate really like technology-wise helps to drive, you know, more data-driven answers to what, um, whether it's a citizen, whether it's, um, Tim who needs to understand how and where my citizens getting stuck, are they able to complete the application where they are? Can we really get the benefits to, um, this individual family for the housing needs >>Too many comments on the same thing. I know you have to communicate measures of success to County executives and others. How do you do that? I mean, are you, do you have enough information to do it? Yeah, we're able to, we actually have a standup meeting every morning where the first thing I learn is how many new applications came in overnight. How many of those were completed with full documentation? How many will be ported over into our system, assigned the staff to work, where they're waiting >>On landlord verification. So I can see the entire pipeline of applications, which helps us then determine, um, Oh, it's, it's not, you know, maybe urban legend is that folks are having difficulty accessing the system. When I see really the bottleneck there, it got gotten the system fine, the bottlenecks laying with our landlord. So let's do a landlord, a town hall and iterate and reeducate them about what their responsibilities are and how easy it is for them to respond with the form they need to attest to. And so it lets us see in real time where we're having difficulties, uh, because, uh, there's a constant pressure on this system. Not just that, uh, we don't want anyone to lose their home, uh, but these dollars also go away within a December. So we've got this dual pressure of get it right and get it right now. >>Uh, and so th the ability to see these data and these metrics on, on a daily basis is critical for us to, to continue to, uh, ModuLite our response. Um, and, and not just get comfortable are baked into well, that's why we developed the flowchart during requirements, and that's just the way things are gonna stay. Uh, that's not how you respond to a pandemic. Uh, and so having a tool and a partner that helps us, uh, stay flexible, state agile, I guess, to, to, to leverage some terminology, uh, is important. And, and it's, it's paid dividends for our citizens. Karen, again, is another up to the same thing. I'm kind of curious about one of the problems of government from time to time. And Tim, I think attest to this is how do you know when Dunn has been reached? How did you go about defining what done would look like for the initial rollout with this kind of a customer? >>So I think Doug, I guess in this case, um, is, is this, isn't able to get the benefits that they're looking for and how do we, uh, you know, starting from, I think what we were talking about earlier, like in terms of requirements and what is the minimum viable, um, part of that, and then you start to add on the bells and whistles that we're really looking to do. So, um, you know, our team worked with him to really define what are those requirements. I know it's a new program. So some of those policy decisions were still also being worked out as the requirements were being defined as well. So making sure that you are staying on top of, okay, what are the key things and what do we really need to do from a compliance standpoint, from a functionality, and obviously, um, the usability of how, uh, an assistant can come on and apply and, um, have those, uh, requirements, make sure that you can meet that, that version before you start adding on additional scope. >>Very helpful. Jim, what's your comment on this since I know done matters to you? Yeah. And look, I I've lived through a, again, multiple, uh, county-wide it implementations and some department wide initiatives as well. So I think we know that our staff always want more so nothing's ever done, uh, which is a challenge and that's on our side of the customer. Um, but, uh, for this, it really was our, our experience of recognizing the, the time was an essence. We didn't have a chance. We didn't have, uh, the space to get into these endless, uh, conversations, uh, the agile approach, rather than doing the traditional waterfall, where we would have been doing requirements tracking for months before we ever started coding, it was what do we need minimally to get a check in the hands of a landlord on behalf of a client, so they don't get evicted. >>And we kept just re honing on that. That's nice. Let's put that in the parking lot. We'll come back to it because again, we want to leverage this investment long term, uh, because we've got a we, and we've got the emergency solutions and CDBG, and then our, uh, mainstream, uh, services we brought on daily basis, but we will come back to those things speed and time are of the essence. So what do we need, uh, to, to get this? So a chance to really, um, educate our staff about the concepts of agile iteration, um, and say, look, this is not just on the it side. We're gonna roll a policy out today around how you're doing things. And we may figure out through data and metrics that it's not working next week, and we'll have to have that. You want it. And you're going to get the same way. >>You're getting updated guidance from the CDC on what to do and what not to do. Uh, health wise, you're getting the same from us, uh, and really to helping the staff understand that process from the beginning was key. And, uh, so, and, and that's, again, partnering with, with our development team in that way was helpful. Um, because once we gave them that kind of charter as I am project champion, this is what we're saying. They did an equally good job of staying on task and getting to the point of is this necessary or nice. And if it wasn't necessary, we put it in the nice category and we'll come back to it. So I think that's really helpful. My experience having done several hundred sheet applications also suggest the need for MBP matters, future stages really matter and not getting caught. My flying squirrels really matters. So you don't get distracted. So let's move on to, let's do a polling question before we go on to some of our other questions. So for our audience, do you have a digital front ends for your benefit delivery? Yes, no. Or we're planning to a lot of response here yet. There we go. Looks like about half, have one and half note. So that's an interesting question. What's going to one more polling question, learn a little more here. Has COVID-19 >>Accelerated or moved cloud. Yes, no. We already run a majority of applications on cloud. Take a moment and respond if you would, please. So this is interesting. No real acceleration was taken place and in terms of moving to cloud is not what I was expecting, but that's interesting. So let's go onto another question then. And Karen, let me direct this one to you, given that feedback, how do you envision technologies such as citizen engagement and watching the system will be used, respond to emergency situations like the pandemic moving forward? I mean, what should government agencies consider given the challenges? This kind of a pandemic is brought upon government and try to tie this in, if you would, what, what is the role of cloud in all of this for making this happen in a timely way? Karen, take it away. >>Okay. Thanks Bob. So as we started the discussion around the digital expansion, you know, we definitely see additional programs and additional capabilities coming online as we continue on. Um, I think, uh, agencies have really seen a way to connect with their citizens and families and landlords, um, in this case an additional way. And he prepared them like there were, uh, presuppose assumptions that the, um, the citizens or landlords really wanted to interact with agency face-to-face and have that high touch part. And I think, um, through this, the governments have really learned that there is a way to still have an impact on the citizen without having a slow, do a face to face. And so I think that's a big realization for them to now really explore other ways to digitally explain, expand their programs and capabilities. Another area that we touched on was around the AI and chat bot piece. >>So as we start to see capabilities like this, the reason why Clark County was able to bring it up quickly and everything was because it was housed on cloud, we are seeing the push of starting to move some of the workloads. I know from a polling question perspective that it's been, um, lighter in terms of getting, uh, moving to the cloud. But we have seen the surge of really chatbots. I think we've been talking about chatbots for a while now. And, um, agencies hadn't really had the ability to start to implement that and really put it into effect. But with the pandemic, they were able to bring things up and, you know, very short amount of time to solve, um, a big challenge of not having the call center be flooded and have a different way to direct that engagement between the citizen and the government. >>So really building a different type of channel for them to engage rather than having to call or to come into an office, which wasn't really allowed in terms of, um, the pandemic. Um, the other thing I'll touch on is, um, 10 mentioned, you know, the backlog of applications that are coming in and we're starting to see the, um, the increase in automation. How do we automate areas where it's administratively highly burdened, but it's really a way that we can start to automate those processes, to give our workers the ability to focus on more of those complex situations that really need attention. So we're starting to see where the trends of trying to push there of can we automate some of those processes, um, uh, uploading documents and verification documents is another way of like, trying to look at, is there a way that we can make that easier? >>Not only for the applicant that's applying, but also for the caseworker. So there's not having to go through that. Um, does the name match, um, the applicant, uh, information and what we're looking on here, and Bob, you mentioned cloud. So behind the scenes of, you know, why, uh, government agencies are really pushing the cloud is, um, you heard about, I mean, with the pandemic, you see a surge of applicants coming in for those benefits and how do we scale for that kind of demand and how do you do that in an inappropriate way, without the huge pressures that you put on to your data center or your staff who's already trying to help our citizens and applicants, applicants, and families get the benefits they need. And so the cloud, um, you know, proposition of trying, being able to be scalable and elastic is really a key driver that we've seen in terms of, uh, uh, government agencies going to cloud. >>We haven't really seen during a pandemic, the core competencies, some of them moving those to cloud, it's really been around that digital front end, the chat bot area of how do we start to really start with that from a cloud perspective and cloud journey, and then start to work in the other processes and other areas. Um, security is also huge, uh, focus right now with the pandemic and everything going online. And with cloud allows you to be able to make sure that you're secure and be able to apply the right security so that you're always covered in terms of the type of demand and, um, impact, uh, that is coming through >>Very helpful. Tim, I'm going to ask to follow up on this of a practical nature. So you brought this up very quickly. Uh, there's a certain amount of suspicion around state government County government about chatbots. How did you get a chat much and be functional so quickly? And were you able to leverage the cloud in this process? Yeah, so on the trust is important. Uh, and I'll go back to my previous statement about individuals being able to see upfront whether they believe they're eligible or not, because nothing will erode trust more than having someone in hours applying and weeks waiting to find out they were denied because they weren't eligible to begin with, uh, that erodes trust. So being able to let folks know right up front, here's what it looks like to be eligible, actually help us build some of that, uh, cause they don't feel like, uh, someone in the bureaucracy is just putting them through the ringer for no reason. >>Um, now in regard to how do we get the chat bot out? I will say, uh, we have a, uh, dynamic it and leadership, uh, team at the highest level of County government who we have been already having conversations over the last year about what it meant to be smart government, uh, the department of social service and family services that I'm responsible for. We're already, uh, hands up first in line, you know, Guinea pigs volunteering to be on the front end of, uh, certain projects. So w we have primed ourselves for, for some of this readiness in that aspect. Um, but for citizen trust, um, the timeliness of application right now is the biggest element of trust. Uh, so I've applied I've I feel like I put my housing future in your hands. Are you going to deliver and having the ability for us to rapidly scale up? >>Uh, we typically have 120 staff in the department of social service that, that are adjudicating benefits for programs on daily basis. We've doubled that with temporary staff, uh, through some partnerships, uh, we're, we're gonna, as of next week, probably have more temporary per professional staff helping an adjudicator applications. No, do full-time County staff, because again, this rush to get the dollars out, out the door. So having a system where I can easily, uh, ramp on new users and manage them without having to be solely dependent upon an already, uh, overworked it staff who were trying to support 37 other departments in the County, um, around infrastructure needs has been greatly helpful. Sounds to me like a strong outcome focus and one that seems to work. Let's move on now to our audience questions. We're getting close to the end of our time. So let's jump into some questions from the audience. A number of you have been asking about getting copies of today's presentation within the next 48 hours. Government technology will provide all attendees with the link to the recording for your reference, or to share with colleagues. Well, let's go to our first question. So this is an interesting one. And Karen, this is for you did IBM work with other counties and States to provide digital engagement portals. >>We did Bob, uh, we've worked, um, so globally we've provided guidance on this. We work closely with New York city. They've been the integral part of the development also with our citizen engagement offering. Um, we work closely with the States. So we worked with New York city. Um, North Carolina was also another state who, um, improved their, uh, citizen engagement piece, bring up their Medicaid and snap, um, applications along with Medicaid. COVID testing along that. And I mentioned, um, the economic and social development in Canada as well. And we also work with the ministry of social development in Singapore. So a number of our customers had put up, uh, a global, uh, or sorry, a citizen engagement frontend. And during this timeframe, >>Very helpful. I don't know how much did you hear your mom provide you, but how much did it cost for initial deployment and what are the ongoing costs in other words, is this thing going to be sustainable over time? >>Yeah, absolutely. So total, uh, to date, we've spent about a $1.8 million on development implementations and licensure. A big chunk of that again has been the rapid extended of licensure, uh, for this program. Um, I think over a third of that is probably licensing because again, we need to get the dollars out and we need staff to do that and making the short term several hundred thousand dollar investment in a professional support staff and having them be able to work this portal is much cheaper than the long-term investment of bringing on a staff, printing a job, uh, during a financial difficulty that we're facing, uh, the single largest fiscal cliff let's get into that us history. Um, so it's not smart to create jobs that have a 30 year, one way to retirement, uh, inside our in unionized government environment here. So having this, the staff that would come on and do this and get out the door on these federal dollars was critical for us. Um, and there is a $800,000 a year, I believe so ongoing costs associated with licensure and, and the programming support. Uh, but once again, we're going to be moving, um, our traditional services into this digital front end. We'll be continuing this because we're, we're, we're facing, it took us, I think, six and a half, seven years to come back from the previous recession. Undoubtedly, take a little longer to get back >>From this one. Here's another interesting question, I guess really primarily Tim Tim was the solution on primarily on premise or in the cloud. >>So we'll, we've done a mix. Uh, the, and I'm starting a lot of feedbacks. I don't know if you all can hear that or not, but the, uh, I think we went on prem for, uh, some people because of the, uh, bridge into our service case manager system, which is on prem. So we did some management there. I do believe the chat bot piece of it though is in the cloud. So we're bringing it down to, from one system to the other. Uh, and, and part of that was a student negotiations and costs and worrying about what long-term is that we have a very stated goal of moving, uh, our Curam platform, which is on-prem, this is the backend. So how are we? We, we set our IBM Watson, uh, portal up, uh, and moving all of that on cloud, uh, because I mean, we've got, uh, a workforce who, uh, has the ability to retire at a very high rate over the next five years. >>And, uh, having 24 seven support in the cloud is, is as a, someone who would be called to respond to emergency situations like the is, is a much better Cod deal for, for myself and the citizen. So migrating, uh, and, um, our typical on-prem stuff up into the cloud, uh, as we continue on this, uh, evolution of what IBM Watson, uh, and the plug into our Curam, uh, system looks like Karen related question for another user is the portal provided with Clara County and others linked to other third-party backend office apps, or can it be, >>Yeah, the answer is it can be it's interoperable. So through APIs, uh, rest, uh, however, um, assistance that they need to be integrated with can definitely be integrated with, uh, like, uh, Tim mentioned, we, we went to the case management solution, but it can be integrated with other applications as well. >>Tim, did you use some other backend third party apps with yours? Uh, we did not. Uh, again, just for speed of getting, uh, this MVP solution out the door. Uh, now what we do with that on the go forward, it is going to look different and probably will include some, another practical question. Given the cares funding should be expended by December. Can this application even be employed at this late date? And you want to take a cut at that? Yeah, for us, uh, once again, we brought up earlier, um, the emergency solutions grants and the community development block grants, which have a Corona virus, uh, CV traunch, each one of those, and those have two to three year expenditure timeframes on them. Uh, so we were going to leverage those to keep this system and some of these programs going once again, that the housing needs, uh, will outstrip our capacity for years to come. >>I guess probably I should have said upfront Las Vegas has one of the worst affordable housing inventories in the nation. Uh, so we know we're going to be facing a housing issue, um, because of this for, for a long time. So we'll be using those two traunches of dollars, ESE, ESPs, uh, CV CDBG, CB funds, uh, in addition to dollars earmarked through some, uh, recreational marijuana license fees that have been dedicated to our homelessness. And when you consider this housing, uh, stability program was part of that homelessness prevention. That's our funding mix locally. Very helpful. So questions maybe for bolts for you on this one, you can probably also teach respond is the system has been set up helping the small business community. Um, this user's been canvassing and the general feeling is that small businesses have been left behind and they've been unable to access funds. What's your response on that? Karen, do you want to take that first? >>Um, yes. So in terms of, uh, the security and sorry. Um, but, uh, can you repeat the last part of that? I just missed the last part when you >>Behind it, but unable to access funds. >>Uh, yeah, so I think from a funding perspective, there's different types of, I think what Tim mentioned in terms of the cares funding, there was different types of funding that came out from a government perspective. Uh, I think there were also other grants and things that are coming out one, uh, that we're still looking at. And I think as we go into the new year, it'll be interesting to see, you know, what additional funding, um, hopefully is, is provided. Uh, but in terms of creativity, we've seen other creative ways that organizations come together to kind of, uh, help with the different agencies, to provide some, some guidance to the community, um, and helping to, uh, provide efforts and, uh, maybe looking at different ways of, um, providing, uh, some of the capabilities that the, either at the County or at the state level that they're able to leverage. But Tim happy to maybe have you chime in here too. >>Yeah. So I'll first start with my wheelhouse and I'll expand out to, to some of my partners. Uh, so the primary, small business, we knew the idea was a daily basis inside this realm is going to be landlords. Uh, so actually this afternoon, we're doing a town hall with folks to be able to roll out, uh, which they will go to our portal to find a corporate landlord program. Uh, so that I seem a landlord for Camille the application pack and on behalf of a hundred residents, rather than us having to adjudicate a hundred individual applications and melon a hundred checks. Uh, so that is because we were listening to that particular segment of the, uh, the business community. Now I know early on, we were, we were really hoping that the, the paycheck protection program federally would have, uh, been dispersed in a way that helped our local small businesses. >>Uh, more we did a, our economic development team did a round of small business supports through our cares act. Uh, our quarterly unfortunate was not open yet. It was just about 15, 20 days shy. So we use, uh, another traditional grant mechanism that we have in place to dedicate that. Uh, but on a go forward board, willing to Congress passes something over the next 30 days, um, that if there's a round two of cares or some other programs, we absolutely now have a tool that we know we can create a digital opening for individuals to come figure out if they're eligible or not for whatever program it is, the it housing, the it, uh, small business operations supports, uh, and it would apply through that process and in a very lightweight, so we're looking forward to how we can expand our footprint to help all of the needs that are present in our community. This leads to another question which may be our last one, but this is an interesting question. How can agencies use COVID-19 as a proof point providing a low cost configurable solutions that can scale across government. Karen, do you want to respond to that? And then Tim also, >>Thanks, Bob. So I believe like, you know, some of the things that we've said in terms of examples of how we were able to bring up the solution quicker, I definitely see that scaling as you go forward and trying to really, um, focus in on the needs and getting that MVP out the door. Uh, and then Tim alluded to this as well. A lot of the change management processes that went into re-imagining what these processes look like. I definitely see a additional, you know, growth mindset of how do we get better processes in place, or really focusing on the core processes so that we can really move the ball forward and continuing to go that path of delivering on a quicker path, uh, leveraging cloud, as we mentioned of, um, some, some of the capabilities around the chat bot and other things to really start to push, um, uh, the capabilities out to those citizens quicker and really reduce that timeline that we have to take on the backend side, um, that that would be our hope and goal, um, given, you know, sort of what we've been able to accomplish and hoping using that as a proof point of how we can do this for other types of, uh, either programs or other processes. >>Yeah, I think, um, the, you know, the tool has given us capability now there, whether we use local leaders leverage that to the fullest really becomes a coming upon us. So do we take a beat, uh, when we can catch our breath and then, you know, work through our executive leadership to say, look, here's all the ways you can use this tool. You've made an enterprise investment in. Um, and I know for us, uh, at Clark County, we've stood up, uh, enterprise, uh, kind of governance team where we can come and talk through all of our enterprise solutions, uh, encourage our other department head peers, uh, to, to examine how you might be able to use this. Is there a way that, um, you know, parks and rec might use this to better access their scholarship programs to make sure that children get into youth sports leagues and don't get left out, uh, because we know youth suicide on the rise and they need something positive to do when this pandemic is clear, I'm there for them to get out and do those things. >>So the possibilities really are out there. It really becomes, um, how do we mind those internally? And I know that being a part of listservs and, uh, you know, gov tech and all the magazines and things are out there to help us think about how do we better use our solutions, um, as well as our IBM partners who are always eager to say, Hey, have you seen how they're using this? Um, it is important for us to continue to keep our imaginations open, um, so that we continue to iterate through this process. Um, cause I, I would hate to see the culture of, um, iteration go away with this pandemic. >>Okay. We have time for one final question. We've already addressed this in part two, and this one is probably for you and that you've used the cares act to eliminate some of the procurement red tape that's shown up. Well, how do you somehow that's been very positive. How do you see that impacting you going forward? What happens when the red tape all comes back? >>Yeah, so I think I mentioned a little bit, uh, about that when some of the folks who are deemed non essential came back during our reopening phases and they're operating at the speed of prior business and red tape where we had all been on this, these green tape, fast tracks, uh, it, it was a bit of a organizational whiplash. Uh, but it, for us, we've had the conversation with executive management of like, we cannot let this get in the way of what our citizens need. So like keep that pressure on our folks to think differently. Don't and, uh, we've gone so far as to, uh, even, uh, maybe take it a step further and investigate what had been done in, in, in Canada. Some other places around, um, like, like going right from in a 48 hour period, going from a procurement statement through a proof of concept and doing purchasing on the backside, like how can we even get this even more streamlined so that we can get the things we need quickly, uh, because the citizens don't understand, wait, we're doing our best, uh, your number 3000 and queue on the phone line that that's not what they need to hear or want to hear during times of crisis. >>Very helpful. Well, I want to be respectful of our one hour commitment, so we'll have to wrap it up here in closing. I want to thank everyone for joining us for today's event and especially a big, thank you goes to Karen and Tim. You've done a really great job of answering a lot of questions and laying this out for us and a special thanks to our partners at IBM for enabling us to bring this worthwhile discussion to our audience. Thanks once again, and we look forward to seeing you at another government technology event,

>>from around the globe. It's the queue with digital coverage of Docker Con Live 2020 brought to you by Docker and its ecosystem partners. Welcome to the Special Cube coverage of Docker Con 2020. It's a virtual digital event co produced by Docker and the Cube. Thanks for joining us. We have great segment here. Precision cancer medicine really is evolving where the personalization of the data are really going to be important to personalize those treatments based upon unique characteristics of the tumors. This is something that's been a really hot topic, talking point and focus area in the industry. And technology is here to help with two great guests who are using technology. Docker Docker containers a variety of other things to help the process go further along. And we got here spring and who's the bioinformatics research assistant and Camille took Who's a student and in turn, you guys done some compelling work. Thanks for joining this docker con virtualized. Thanks for coming on. >>Thanks for having me. >>So first tell us about yourself and what you guys doing at the Children's Cancer Institute? That's where you're located. What's going on there? Tell us what you guys are doing there? >>Sure, So I built into Cancer Institute. As it sounds, we do a lot of research when it comes to specifically the Children's cancer, though Children a unique in the sense that a lot of the typical treatment we use for adult may or may not work or will have adverse side effects. So what we do is we do all kinds of research. But what lab and I love, which we call a dry love What we do research in silica, using computers at the develop pipelines in order to improve outcomes for Children. >>And what are some of the things you get some to deal with us on the tech side, but also there's the workflow of the patients survival rates, capacity, those constraints that you guys are dealing with. And what are some of the some of the things going on there that you have to deal with and you're trying to improve the outcomes? What specific outcomes were you trying to work through? >>Well, at the moment off of the past decade and all the work you've done in the past decade, we've made a substantial impact on the supply of ability off several high risk cancers in Pediatrics on and we've Got a certain Program, which spent I'll talk about in more depth called the Zero Childhood Cancer Program and essentially that aims to reduce childhood cancer in Children uh, zero. So that, in other words, with the previous five ability 100% on hopefully, no lives will be lost. But that's >>and what do you guys doing specifically? What's your your job? What's your focus? >>Yes, so part of our lab Old computational biology. Uh, we run a processing pipeline, the whole genome and our next guest that, given the sequencing information for the kids, though, we sequence the healthy cells and we sequence there. Two missiles. We analyze them together, and what we do is we find mutations that are causing the cancel that help us determine what treatment. So what? Clinical trials might be most effective for the kids and so specifically Allah books on that pipeline where we run a whole bunch of bioinformatics tools, that area buying thematic basically biology, informatics, and we use the data generated sequel thing in order to extract those mutations that will be the cancer driving mutations that hopefully we can target in order to treat the kids. >>You know, you hear about an attack and you hear Facebook personalization recommendation engines. What the click on you guys are really doing Really? Mawr personalization around treatment recommendations. These kinds of things come into it. Can you share a little bit about what goes on there and and tell us what's happening? >>Well, as you mentioned when you first, some brought us into this, which we're looking at, the the profile of the team itself and that allows us to specialize the medication on the young treatment for that patient on. Essentially, that lets us improve the efficiency and the effectiveness off the treatment, which in turn has an impact on this probability off. >>What are some of the technical things? How did you guys get involved with Docker with Docker fit into all this? >>Yeah, I'm sure Camille will have plenty to bring up on this as well. But, um, yes, it's been quite a project to the the pipeline that we have. Um, we have built on a specific platforms and is looking great. But as with most tools in a lot of things that you develop when your engineers eyes pretty easy for them to become platform specific. And then that kind of stuck there. And you have to re engineer the whole thing kind of of a black hole. That's such a pain to there. So, um, the project that Mikhail in my field working on was actually taking it to the individual's pools we used in the pipeline and Docker rising them individually containing them with the dependencies they need so that we could hook them up anyway. We want So we can configure the pipeline, not just customized based off of the data like we're on the same pipeline and every it even being able to change the pipeline of different things to different kids. Be able to do that easily, um, to be able to run it on different platforms. You know, the fact that we have the choice not only means that we could save money, but if there's a cloud instance that will run an app costal. If there's a platform that you know wanted to collaborate with us and they say, Oh, we have this wholesome data we'd love for you to analyze. It's over hell, like a lot of you know, >>use my tool. It's really great. >>Yeah. And so having portability is a big thing as well. And so I'm sure people can go on about, uh, some of the pain point you having to do authorize all of the different, But, you know, even though they Austin challenges associated with doing it, I think the payoff is massive. >>Dig into this because this is one of the things where you've got a problem statement. You got a real world example. Cancer patients, life or death gets a serious things going on here. You're a tech. You get in here. What's going on? You're like, Okay, this is going to be easy. Just wrangle the data. I throw some compute at it. It's over, right? You know what? How did you take us through the life? They're, you know, living >>right. So a supreme I mentioned before, first and foremost well, in the scale of several 100 terabytes worth of data for every single patient. So obviously we can start to understand just how beneficial it is to move the pipeline to the data, rather the other way around. Um, so much time would be saved. The money costs as well, in terms of actually Docker rising the but the programs that analyze the data, it was quite difficult. And I think Sabrina would agree mate would agree with me on this point. The primary issue was that almost all of the apps we encountered within the pipeline we're very, very heavily dependent on very specific versions off some dependencies, but that they were just build upon so many other different APS on and they were very heavily fined tuned. So docker rising. It was quite difficult because we have to preserve every single version of every single dependency in one instance just to ensure that that was working. And these apps get updated quite Simpson my regularly. So we have to ensure that our doctors would survive. >>So what does it really take? The doc arise your pipeline. >>I mean, it was a whole project. Well, um, myself, Camille, we had a whole bunch of, um, automatic guns doing us over the summer, which was fantastic as well. And we basically have a whole team of lost words like, Okay, here's another automatic pull in the pipeline. You get enterprise, you get to go for a special you get enterprise, they each who individually and then you've been days awake on it, depending on the app. Easier than others. Um, but particularly when it comes to things a lot by a dramatic pools, some of them are very memory hungry. Some of them are very finicky. Some of the, um ah, little stable than others. And so you could spend one day characterizing a tool. And it's done, you know, in a handful of Allah's old. Sometimes it could make a week, and he's just getting this one tool done. And the idea behind the whole team working on it was eventually use. Look through this process, and then you have, um, a docker file set up. Well, anyone to run it on any system. And we know we have an identical set up, which was not sure before, because I remember when I started and I was trying to get the pipeline running on my own machine. Ah, lot of things just didn't look like Oh, you don't have the very specific version of ah that this developer has. 00 that's not working because you don't have this specific girl file that actually has a bug fixes in it. Just for us like, Well, >>he had a lot of limitations before the doctor and doctor analyzing docker container izing it. It was tough. What was it like before and after? >>And we'll probably speak more people full. It was basically, uh, yeah, days or weeks trying to set up on in. Stole everything needed around the whole pipeline. Yeah, it took a long time. And even then, a lot of things, But how you got to set up this? You know, I think speculation of pipeline, all the units, these are the three of the different programs. Will you need this version of obligation? This new upgrade of the tools that work with that version of Oz The old, all kinds of issues that you run into when they schools depend on entirely different things and to install, like, four different versions of python. Three different versions of our or different versions of job on the one machine, you know, just to run it is a bit of >>what has. It's a hassle. Basically, it's a nightmare. And now, after you're >>probably familiar with that, >>Yeah. So what's it like after >>it's a zoo? It supports ridiculously efficient. Like it. It's It's incredible what Michael mentioned before, as soon as we did in stone. Those at the versions of the dependencies. Dhaka keeps them naturally, and we can specify the versions within a docker container. So we can. We can absolutely guarantee that that application will run successfully and effectively every single time. >>Share with me how complicated these pipelines are. Sounds like that's a key piece here for you guys. And you had all the hassles that you do. Your get Docker rised up and things work smoothly. Got that? But tell >>me about >>the pipelines. What's what's so complicated about them? >>Honestly, the biggest complication is all of the connection. It's not a simple as, um, run a from the sea, and then you don't That would be nice, but that know how these things work if you have a network of programs with the output of this, input for another, and you have to run this program before this little this one. But some of the output become input for multiple programs, and by the time you hook the whole thing up, it looks like a gigantic web of applications. The way all the connections, so it's a massive Well, it almost looks like a massive met when you look at it. But having each of the individual tools contained and working means that we can look them all up. And even though it looks complicated, it would be far more complicated if we had that entire pipeline. You know, in a single program like having to code, that whole thing in a single group would be an absolute nightmare. Where is being able to have each of the tools as individual doctors means we just have the link, the input on that book, which is the top. But once you've done that, it means that you know each of the individual pools will run. And if an individual fails, or whatever raised in memory or other issues run into, you can rerun that one individual school re hooks the output into whatever the next program is going without having one massive you know, program will file what it fails midway through, and there's nothing you can do. >>Yeah, you unpack. It really says, Basically, you get the goodness to the work up front, and a lot of goodness come out of it. So this lets comes to the future of health. What are the key takeaways that you guys have from this process? And how does it apply to things that might be helpful to you right around the corner? Or today, like deep learning as you get more tools out there with machine learning and deep learning? Um, we hope there's gonna be some cool things coming out. What do you guys see here? And the insights? >>Well, we have a section of how the computational biologist team that is looking into doing more predictive talks working out, um, basically the risk of people developing can't the risks of kids developing cancel. And that's something you can do when you have all of this data. But that requires a lot of analysis as well. And so one of the benefits of you know being able to have these very moveable pipelines and tools makes it easier to run them on. The cloud makes it easier to shale. You're processing with about researches to the hospitals, just making collaboration easier. Mainz that data sharing becomes a possibility or is before if you have three different organizations. But the daughter in three different places. Um, how do you share that with moving the daughter really feasible. Pascal, can you analyze it in a way that practical and so I don't want one of the benefits of Docker? Is all of these advanced tools coming out? You know, if there's some amazing predicted that comes out that uses some kind of regression little deep learning, whatever. If we wanted to add that being able to dock arise a complex school into a single docker ice makes it less complicated that highlighted the pipeline in the future, if that's something we'd like to do, >>Camille, any thoughts on your end on this? >>Actually, I was Sabrina in my mind for the last point. I was just thinking about scalability definitely is very. It's a huge point because the part about the girls as a technology does any kind of technology that we've got to inspect into the pipeline. As of now, it be significantly easier with the use of Docker. You could just docker rise that technology and then implant that straight into the pipeline. Minimal stress. >>So productivity agility doesn't come home for you guys. Is that resonate? >>Yeah, definitely. >>And you got the collaboration. So there's business benefits, the outcomes. Are there any proof points you could share on some results that you guys are seeing some fruit from the tree, if you will, from all this Goodness. >>Well, one of the things we've been working on is actually a collaboration with those Bio Commons and Katica. They built a platform, specifically the development pipelines. We wanted to go out, and they have support for Docker containers built into the platform, which makes it very easy to push a lot of containers of the platform, look them up and be able to collaborate with them not only to try a new platform without that, but also help them look like a platform to be able to shoot action access data that's been uploaded there as well. But a lot of people we wouldn't have been able to do that if we hadn't. Guys, they're up. It just wouldn't have. Actually, it wouldn't be possible. And now that we have, we've been able to collaborate with them in terms of improving the platform. But also to be able to share and run our pipelines on other data will just pretty good, >>awesome. Well, It's great to have you on the Cube here on Docker Con 2020 from down under. Great Internet connections get great Internet down. They're keeping us remote were sheltering in place here. Stay safe and you guys final question. Could you eat? Share in your own words from a developer? From a tech standpoint, as you're in this core role, super important role, the outcomes are significant and have real impact. What has the technology? What is docker ization done for you guys and for your work environment and for the business share in your own words what it means. A lot of other developers are watching What's your opinion? >>But yeah, I mean, the really practical point is we've massively increased capacity of the pipeline. One thing that been quite fantastic years. We've got a lot of increased. The Port zero child who can program, which means going into the schedule will actually be able to open a program. Every child in Australia that, uh, has cancel will be ableto add them to the program. Where is currently we're only able to enroll kids who are low survivability, right? So about 30% the lowest 30% of the viability we're able to roll over program currently, but having a pipeline where we can just double the memory like that double the amount of battle. Uh, and the fact that we can change the instance is really to just double the capacity trip. The capacity means that now that we have the support to be able to enroll potentially every kid, Mr Leo, um, once we've upgraded the whole pipeline, it means will actually be a code with the amount of Children being enrolled, whereas on the existing pipeline, we're currently that capacity. So doing the upgrade in a really practical way means that we're actually going to be a triple the number of kids in Australia. We can add onto the program which wouldn't have been possible otherwise >>unleashing the limitations and making it totally scalable. Your thoughts as developers watching you're in there, Your hand in your hands, dirty. You built it. It's showing some traction. What's what's your what's your take? What's your view? >>Well, I mean first and foremost locks events. It just feels fantastic knowing that what we're doing is as a substantial and quantify who impact on the on a subset of the population and we're literally saving lives. Analyze with the work that we're doing in terms off developing with With that technology, such a breeze especially compared Teoh I've had minimal contact with what it was like without docker and from the horror stories I've heard, it's It's It's a godsend. It's It's it's really improved The quality of developing. >>Well, you guys have a great mission. And congratulations on the success. Really impact right there. You guys are doing great work and it must feel great. I'm happy for you and great to connect with you guys and continue, you know, using technology to get the outcomes, not just using technology. So Fantastic story. Thank you for sharing. Appreciate >>you having me. >>Thank you. >>Okay, I'm John for we here for Docker Con 2020 Docker con virtual docker con digital. It's a digital event This year we were all shale three in place that we're in the Palo Alto studios for Docker con 2020. I'm John furrier. Stay with us for more coverage digitally go to docker con dot com from or check out all these different sessions And of course, stay with us for this feat. Thank you very much. Yeah, yeah, yeah, yeah, yeah, yeah

>>from Las Vegas. It's the Q covering. Next work. 2019 America's Do You buy Juniper Networks? Come back already. Jeffrey here with the Cube were in Las Vegas at Caesar's at the Juniper. Next work event. About 1000 people kind of going over a lot of new cool things. 400 gigs. Who knew that was coming out of new information for me? But that's not what we're here today. We're here for the fourth installment of around the Cube unpacking. I were happy to have all the winners of the three previous rounds here at the same place. We don't have to do it over the phone s so we're happy to have him. Let's jump into it. So winner of Round one was Bob Friday. He is the VP and CTO at Missed the Juniper Company. Bob, Great to see you. Good to be back. Absolutely. All the way from Seattle. Sharna Parky. She's a VP applied scientist at Tech CEO could see Sharna and, uh, from Google. We know a lot of a I happen to Google. Rajan's chef. He is the V p ay ay >>product management on Google. Welcome. Thank you, Christy. Here >>All right, so let's jump into it. So just warm everybody up and we'll start with you. Bob, What are some When you're talking to someone at a cocktail party Friday night talking to your mom And they say, What is a I What >>do you >>give him? A Zen examples of where a eyes of packing our lives today? >>Well, I think we all know the examples of the south driving car, you know? Aye, aye. Starting to help our health care industry being diagnosed cancer for me. Personally, I had kind of a weird experience last week at a retail technology event where basically had these new digital mirrors doing facial recognition. Right? And basically, you start to have little mirrors were gonna be a skeevy start guessing. Hey, you have a beard, you have some glasses, and they start calling >>me old. So this is kind >>of very personal. I have a something for >>you, Camille, but eh? I go walking >>down a mall with a bunch of mirrors, calling me old. >>That's a little Illinois. Did it bring you out like a cane or a walker? You know, you start getting some advertising's >>that were like Okay, you guys, this is a little bit over the top. >>Alright, Charlotte, what about you? What's your favorite example? Share with people? >>Yeah, E think one of my favorite examples of a I is, um, kind of accessible in on your phone where the photos you take on an iPhone. The photos you put in Google photos, they're automatically detecting the faces and their labeling them for you. They're like, Here's selfies. Here's your family. Here's your Children. And you know, that's the most successful one of the ones that I think people don't really think about a lot or things like getting loan applications right. We actually have a I deciding whether or not we get loans. And that one is is probably the most interesting one to be right now. >>Roger. So I think the father's example is probably my favorite as well. And what's interesting to me is that really a I is actually not about the Yeah, it's about the user experience that you can create as a result of a I. What's cool about Google photos is that and my entire family uses Google photos and they don't even know actually that the underlying in some of the most powerful a I in the world. But what they know is they confined every picture of our kids on the beach whenever they whenever they want to. Or, you know, we had a great example where we were with our kids. Every time they like something in the store, we take a picture of it, Um, and we can look up toy and actually find everything that they've taken picture. >>It's interesting because I think most people don't even know the power that they have. Because if you search for beach in your Google photos or you search for, uh, I was looking for an old bug picture from my high school there it came right up until you kind of explore. You know, it's pretty tricky, Raja, you know, I think a lot of conversation about A They always focus the general purpose general purpose, general purpose machines and robots and computers. But people don't really talk about the applied A that's happening all around. Why do you think that? >>So it's a good question. There's there's a lot more talk about kind of general purpose, but the reality of where this has an impact right now is, though, are those specific use cases. And so, for example, things like personalizing customer interaction or, ah, spotting trends that did that you wouldn't have spotted for turning unstructured data like documents into structure data. That's where a eyes actually having an impact right now. And I think it really boils down to getting to the right use cases where a I right? >>Sharon, I want ask you. You know, there's a lot of conversation. Always has A I replace people or is it an augmentation for people? And we had Gary Kasparov on a couple years ago, and he talked about, you know, it was the combination if he plus the computer made the best chess player, but that quickly went away. Now the computer is actually better than Garry Kasparov. Plus the computer. How should people think about a I as an augmentation tool versus a replacement tool? And is it just gonna be specific to the application? And how do you kind of think about those? >>Yeah, I would say >>that any application where you're making life and death decisions where you're making financial decisions that disadvantage people anything where you know you've got u A. V s and you're deciding whether or not to actually dropped the bomb like you need a human in the loop. If you're trying to change the words that you are using to get a different group of people to apply for jobs, you need a human in the loop because it turns out that for the example of beach, you type sheep into your phone and you might get just a field, a green field and a I doesn't know that, uh, you know, if it's always seen sheep in a field that when the sheep aren't there, that that isn't a sheep like it doesn't have that kind of recognition to it. So anything were we making decisions about parole or financial? Anything like that needs to have human in the loop because those types of decisions are changing fundamentally the way we live. >>Great. So shift gears. The team are Jeff Saunders. Okay, team, your mind may have been the liquid on my bell, so I'll be more active on the bell. Sorry about that. Everyone's even. We're starting a zero again, so I want to shift gears and talk about data sets. Um Bob, you're up on stage. Demo ing some some of your technology, the Miss Technology and really, you know, it's interesting combination of data sets A I and its current form needs a lot of data again. Kind of the classic Chihuahua on blue buried and photos. You got to run a lot of them through. How do you think about data sets? In terms of having the right data in a complete data set to drive an algorithm >>E. I think we all know data sets with one The tipping points for a I to become more real right along with cloud computing storage. But data is really one of the key points of making a I really write my example on stage was wine, right? Great wine starts a great grape street. Aye, aye. Starts a great data for us personally. L s t M is an example in our networking space where we have data for the last three months from our customers and rule using the last 30 days really trained these l s t m algorithms to really get that tsunami detection the point where we don't have false positives. >>How much of the training is done. Once you once you've gone through the data a couple times in a just versus when you first started, you're not really sure how it's gonna shake out in the algorithm. >>Yeah. So in our case right now, right, training happens every night. So every night, we're basically retraining those models, basically, to be able to predict if there's gonna be an anomaly or network, you know? And this is really an example. Where you looking all these other cat image thinks this is where these neural networks there really were one of the transformational things that really moved a I into the reality calling. And it's starting to impact all our different energy. Whether it's text imaging in the networking world is an example where even a I and deep learnings ruling starting to impact our networking customers. >>Sure, I want to go to you. What do you do if you don't have a big data set? You don't have a lot of pictures of chihuahuas and blackberries, and I want to apply some machine intelligence to the problem. >>I mean, so you need to have the right data set. You know, Big is a relative term on, and it depends on what you're using it for, right? So you can have a massive amount of data that represents solar flares, and then you're trying to detect some anomaly, right? If you train and I what normal is based upon a massive amount of data and you don't have enough examples of that anomaly you're trying to detect, then it's never going to say there's an anomaly there, so you actually need to over sample. You have to create a population of data that allows you to detect images you can't say, Um oh, >>I'm going to reflect in my data set the percentage of black women >>in Seattle, which is something below 6% and say it's fair. It's not right. You have to be able thio over sample things that you need, and in some ways you can get this through surveys. You can get it through, um, actually going to different sources. But you have to boot, strap it in some way, and then you have to refresh it, because if you leave that data set static like Bob mentioned like you, people are changing the way they do attacks and networks all the time, and so you may have been able to find the one yesterday. But today it's a completely different ball game >>project to you, which comes first, the chicken or the egg. You start with the data, and I say this is a ripe opportunity to apply some. Aye, aye. Or do you have some May I objectives that you want to achieve? And I got to go out and find the >>data. So I actually think what starts where it starts is the business problem you're trying to solve. And then from there, you need to have the right data. What's interesting about this is that you can actually have starting points. And so, for example, there's techniques around transfer, learning where you're able to take an an algorithm that's already been trained on a bunch of data and training a little bit further with with your data on DSO, we've seen that such that people that may have, for example, only 100 images of something, but they could use a model that's trained on millions of images and only use those 100 thio create something that's actually quite accurate. >>So that's a great segue. Wait, give me a ring on now. And it's a great Segway into talking about applying on one algorithm that was built around one data set and then applying it to a different data set. Is that appropriate? Is that correct? Is air you risking all kinds of interesting problems by taking that and applying it here, especially in light of when people are gonna go to outweigh the marketplace, is because I've got a date. A scientist. I couldn't go get one in the marketplace and apply to my data. How should people be careful not to make >>a bad decision based on that? So I think it really depends. And it depends on the type of machine learning that you're doing and what type of data you're talking about. So, for example, with images, they're they're they're well known techniques to be able to do this, but with other things, there aren't really and so it really depends. But then the other inter, the other really important thing is that no matter what at the end, you need to test and generate based on your based on your data sets and on based on sample data to see if it's accurate or not, and then that's gonna guide everything. Ultimately, >>Sharon has got to go to you. You brought up something in the preliminary rounds and about open A I and kind of this. We can't have this black box where stuff goes into the algorithm. That stuff comes out and we're not sure what the result was. Sounds really important. Is that Is that even plausible? Is it feasible? This is crazy statistics, Crazy math. You talked about the business objective that someone's trying to achieve. I go to the data scientist. Here's my data. You're telling this is the output. How kind of where's the line between the Lehman and the business person and the hard core data science to bring together the knowledge of Here's what's making the algorithm say this. >>Yeah, there's a lot of names for this, whether it's explainable. Aye, aye. Or interpret a belay. I are opening the black box. Things like that. Um, the algorithms that you use determine whether or not they're inspect herbal. Um, and the deeper your neural network gets, the harder it is to inspect, actually. Right. So, to your point, every time you take an aye aye and you use it in a different scenario than what it was built for. For example, um, there is a police precinct in New York that had a facial recognition software, and, uh, victim said, Oh, it looked like this actor. This person looked like Bill Cosby or something like that, and you were never supposed to take an image of an actor and put it in there to find people that look like them. But that's how people were using it. So the Russians point yes, like it. You can transfer learning to other a eyes, but it's actually the humans that are using it in ways that are unintended that we have to be more careful about, right? Um, even if you're a, I is explainable, and somebody tries to use it in a way that it was never intended to be used. The risk is much higher >>now. I think maybe I had, You know, if you look at Marvis kind of what we're building for the networking community Ah, good examples. When Marvis tries to do estimate your throughput right, your Internet throughput. That's what we usually call decision tree algorithm. And that's a very interpretive algorithm. and we predict low throughput. We know how we got to that answer, right? We know what features God, is there? No. But when we're doing something like a NAMI detection, that's a neural network. That black box it tells us yes, there's a problem. There's some anomaly, but that doesn't know what caused the anomaly. But that's a case where we actually used neural networks, actually find the anomie, and then we're using something else to find the root cause, eh? So it really depends on the use case and where the night you're going to use an interpreter of model or a neural network which is more of a black box model. T tell her you've got a cat or you've got a problem >>somewhere. So, Bob, that's really interested. So can you not unpacking? Neural network is just the nature of the way that the communication and the data flows and the inferences are made that you can't go in and unpack it, that you have to have the >>separate kind of process too. Get to the root cause. >>Yeah, assigned is always hard to say. Never. But inherently s neural networks are very complicated. Saito set of weights, right? It's basically usually a supervised training model, and we're feeding a bunch of data and trying to train it to detect a certain features, sir, an output. But that is where they're powerful, right? And that's why they basically doing such good, Because they are mimicking the brain, right? That neural network is a very complex thing. Can't like your brain, right? We really don't understand how your brain works right now when you have a problem, it's really trialling there. We try to figure out >>right going right. So I want to stay with you, bought for a minute. So what about when you change what you're optimizing? Four? So you just said you're optimizing for throughput of the network. You're looking for problems. Now, let's just say it's, uh, into the end of the quarter. Some other reason we're not. You're changing your changing what you're optimizing for, Can you? You have to write separate algorithm. Can you have dynamic movement inside that algorithm? How do you approach a problem? Because you're not always optimizing for the same things, depending on the market conditions. >>Yeah, I mean, I think a good example, you know, again, with Marvis is really with what we call reinforcement. Learning right in reinforcement. Learning is a model we use for, like, radio resource management. And there were really trying to optimize for the user experience in trying to balance the reward, the models trying to reward whether or not we have a good balance between the network and the user. Right, that reward could be changed. So that algorithm is basically reinforcement. You can finally change hell that Algren works by changing the reward you give the algorithm >>great. Um, Rajan back to you. A couple of huge things that have come into into play in the marketplace and get your take one is open source, you know, kind of. What's the impact of open source generally on the availability, desire and more applications and then to cloud and soon to be edge? You know, the current next stop. How do you guys incorporate that opportunity? How does it change what you can do? How does it open up the lens of >>a I Yeah, I think open source is really important because I think one thing that's interesting about a I is that it's a very nascent field and the more that there's open source, the more that people could build on top of each other and be able to utilize what what others others have done. And it's similar to how we've seen open source impact operating systems, the Internet, things like things like that with Cloud. I think one of the big things with cloud is now you have the processing power and the ability to access lots of data to be able to t create these thes networks. And so the capacity for data and the capacity for compute is much higher. Edge is gonna be a very important thing, especially going into next few years. You're seeing Maur things incorporated on the edge and one exciting development is around Federated learning where you can train on the edge and then combine some of those aspects into a cloud side model. And so that I think will actually make EJ even more powerful. >>But it's got to be so dynamic, right? Because the fundamental problem used to always be the move, the computer, the data or the date of the computer. Well, now you've got on these edge devices. You've got Tanya data right sensor data all kinds of machining data. You've got potentially nasty hostile conditions. You're not in a nice, pristine data center where the environmental conditions are in the connective ity issues. So when you think about that problem yet, there's still great information. There you got latent issues. Some I might have to be processed close to home. How do you incorporate that age old thing of the speed of light to still break the break up? The problem to give you a step up? Well, we see a lot >>of customers do is they do a lot of training on the cloud, but then inference on the on the edge. And so that way they're able to create the model that they want. But then they get fast response time by moving the model to the edge. The other thing is that, like you said, lots of data is coming into the edge. So one way to do it is to efficiently move that to the cloud. But the other way to do is filter. And to try to figure out what data you want to send to the clouds that you can create the next days. >>Shawna, back to you let's shift gears into ethics. This pesky, pesky issue that's not not a technological issue at all, but right. We see it often, especially in tech. Just cause you should just cause you can doesn't mean that you should. Um so and this is not a stem issue, right? There's a lot of different things that happened. So how should people be thinking about ethics? How should they incorporate ethics? Um, how should they make sure that they've got kind of a, you know, a standard kind of overlooking kind of what they're doing? The decisions are being made. >>Yeah, One of the more approachable ways that I have found to explain this is with behavioral science methodologies. So ethics is a massive field of study, and not everyone shares the same ethics. However, if you try and bring it closer to behavior change because every product that we're building is seeking to change of behavior. We need to ask questions like, What is the gap between the person's intention and the goal we have for them? Would they choose that goal for themselves or not? If they wouldn't, then you have an ethical problem, right? And this this can be true of the intention, goal gap or the intention action up. We can see when we regulated for cigarettes. What? We can't just make it look cool without telling them what the cigarettes are doing to them, right so we can apply the same principles moving forward. And they're pretty accessible without having to know. Oh, this philosopher and that philosopher in this ethicist said these things, it can be pretty human. The challenge with this is that most people building these algorithms are not. They're not trained in this way of thinking, and especially when you're working at a start up right, you don't have access to massive teams of people to guide you down this journey, so you need to build it in from the beginning, and you need to be open and based upon principles. Um, and it's going to touch every component. It should touch your data, your algorithm, the people that you're using to build the product. If you only have white men building the product, you have a problem you need to pull in other people. Otherwise, there are just blind spots that you are not going to think of in order to still that product for a wider audience, but it seems like >>they were on such a razor sharp edge. Right with Coca Cola wants you to buy Coca Cola and they show ads for Coca Cola, and they appeal to your let's all sing together on the hillside and be one right. But it feels like with a I that that is now you can cheat. Right now you can use behavioral biases that are hardwired into my brain is a biological creature against me. And so where is where is the fine line between just trying to get you to buy Coke? Which somewhat argues Probably Justus Bad is Jule cause you get diabetes and all these other issues, but that's acceptable. But cigarettes are not. And now we're seeing this stuff on Facebook with, you know, they're coming out. So >>we know that this is that and Coke isn't just selling Coke anymore. They're also selling vitamin water so they're they're play isn't to have a single product that you can purchase, but it is to have a suite of products that if you weren't that coke, you can buy it. But if you want that vitamin water you can have that >>shouldn't get vitamin water and a smile that only comes with the coat. Five. You want to jump in? >>I think we're going to see ethics really break into two different discussions, right? I mean, ethics is already, like human behavior that you're already doing right, doing bad behavior, like discriminatory hiring, training, that behavior. And today I is gonna be wrong. It's wrong in the human world is gonna be wrong in the eye world. I think the other component to this ethics discussion is really round privacy and data. It's like that mirror example, right? No. Who gave that mirror the right to basically tell me I'm old and actually do something with that data right now. Is that my data? Or is that the mirrors data that basically recognized me and basically did something with it? Right. You know, that's the Facebook. For example. When I get the email, tell me, look at that picture and someone's take me in the pictures Like, where was that? Where did that come from? Right? >>What? I'm curious about to fall upon that as social norms change. We talked about it a little bit for we turn the cameras on, right? It used to be okay. Toe have no black people drinking out of a fountain or coming in the side door of a restaurant. Not that long ago, right in the 60. So if someone had built an algorithm, then that would have incorporated probably that social norm. But social norms change. So how should we, you know, kind of try to stay ahead of that or at least go back reflectively after the fact and say kind of back to the black box, That's no longer acceptable. We need to tweak this. I >>would have said in that example, that was wrong. 50 years ago. >>Okay, it was wrong. But if you ask somebody in Alabama, you know, at the University of Alabama, Matt Department who have been born Red born, bred in that culture as well, they probably would have not necessarily agreed. But so generally, though, again, assuming things change, how should we make sure to go back and make sure that we're not again carrying four things that are no longer the right thing to do? >>Well, I think I mean, as I said, I think you know what? What we know is wrong, you know is gonna be wrong in the eye world. I think the more subtle thing is when we start relying on these Aye. Aye. To make decisions like no shit in my car, hit the pedestrian or save my life. You know, those are tough decisions to let a machine take off or your balls decision. Right when we start letting the machines Or is it okay for Marvis to give this D I ps preference over other people, right? You know, those type of decisions are kind of the ethical decision, you know, whether right or wrong, the human world, I think the same thing will apply in the eye world. I do think it will start to see more regulation. Just like we see regulation happen in our hiring. No, that regulation is going to be applied into our A I >>right solutions. We're gonna come back to regulation a minute. But, Roger, I want to follow up with you in your earlier session. You you made an interesting comment. You said, you know, 10% is clearly, you know, good. 10% is clearly bad, but it's a soft, squishy middle at 80% that aren't necessarily super clear, good or bad. So how should people, you know, kind of make judgments in this this big gray area in the middle? >>Yeah, and I think that is the toughest part. And so the approach that we've taken is to set us set out a set of AI ai principles on DDE. What we did is actually wrote down seven things that we will that we think I should do and four things that we should not do that we will not do. And we now have to actually look at everything that we're doing against those Aye aye principles. And so part of that is coming up with that governance process because ultimately it boils down to doing this over and over, seeing lots of cases and figuring out what what you should do and so that governments process is something we're doing. But I think it's something that every company is going to need to do. >>Sharon, I want to come back to you, so we'll shift gears to talk a little bit about about law. We've all seen Zuckerberg, unfortunately for him has been, you know, stuck in these congressional hearings over and over and over again. A little bit of a deer in a headlight. You made an interesting comment on your prior show that he's almost like he's asking for regulation. You know, he stumbled into some really big Harry nasty areas that were never necessarily intended when they launched Facebook out of his dorm room many, many moons ago. So what is the role of the law? Because the other thing that we've seen, unfortunately, a lot of those hearings is a lot of our elected officials are way, way, way behind there, still printing their e mails, right? So what is the role of the law? How should we think about it? What shall we What should we invite from fromthe law to help sort some of this stuff out? >>I think as an individual, right, I would like for each company not to make up their own set of principles. I would like to have a shared set of principles that were following the challenge. Right, is that with between governments, that's impossible. China is never gonna come up with same regulations that we will. They have a different privacy standards than we D'oh. Um, but we are seeing locally like the state of Washington has created a future of work task force. And they're coming into the private sector and asking companies like text you and like Google and Microsoft to actually advise them on what should we be regulating? We don't know. We're not the technologists, but they know how to regulate. And they know how to move policies through the government. What will find us if we don't advise regulators on what we should be regulating? They're going to regulate it in some way, just like they regulated the tobacco industry. Just like they regulated. Sort of, um, monopolies that tech is big enough. Now there is enough money in it now that it will be regularly. So we need to start advising them on what we should regulate because just like Mark, he said. While everyone else was doing it, my competitors were doing it. So if you >>don't want me to do it, make us all stop. What >>can I do? A negative bell and that would not for you, but for Mark's responsibly. That's crazy. So So bob old man at the mall. It's actually a little bit more codified right, There's GDP are which came through May of last year and now the newness to California Extra Gatorade, California Consumer Protection Act, which goes into effect January 1. And you know it's interesting is that the hardest part of the implementation of that I think I haven't implemented it is the right to be for gotten because, as we all know, computers, air, really good recording information and cloud. It's recorded everywhere. There's no there there. So when these types of regulations, how does that impact? Aye, aye, because if I've got an algorithm built on a data set in in person, you know, item number 472 decides they want to be forgotten How that too I deal with that. >>Well, I mean, I think with Facebook, I can see that as I think. I suspect Mark knows what's right and wrong. He's just kicking ball down tires like >>I want you guys. >>It's your problem, you know. Please tell me what to do. I see a ice kind of like any other new technology, you know, it could be abused and used in the wrong waste. I think legally we have a constitution that protects our rights. And I think we're going to see the lawyers treat a I just like any other constitutional things and people who are building products using a I just like me build medical products or other products and actually harmful people. You're gonna have to make sure that you're a I product does not harm people. You're a product does not include no promote discriminatory results. So I >>think we're going >>to see our constitutional thing is going applied A I just like we've seen other technologies work. >>And it's gonna create jobs because of that, right? Because >>it will be a whole new set of lawyers >>the holdings of lawyers and testers, even because otherwise of an individual company is saying. But we tested. It >>works. Trust us. Like, how are you gonna get the independent third party verification of that? So we're gonna start to see a whole terrorist proliferation of that type of fields that never had to exist before. >>Yeah, one of my favorite doctor room. A child. Grief from a center. If you don't follow her on Twitter Follower. She's fantastic and a great lady. So I want to stick with you for a minute, Bob, because the next topic is autonomous. And Rahman up on the keynote this morning, talked about missed and and really, this kind of shifting workload of fixing things into an autonomous set up where the system now is, is finding problems, diagnosing problems, fixing problems up to, I think, he said, even generating return authorizations for broken gear, which is amazing. But autonomy opens up all kinds of crazy, scary things. Robert Gates, we interviewed said, You know, the only guns that are that are autonomous in the entire U. S. Military are the ones on the border of North Korea. Every single other one has to run through a person when you think about autonomy and when you can actually grant this this a I the autonomy of the agency toe act. What are some of the things to think about in the word of the things to keep from just doing something bad, really, really fast and efficiently? >>Yeah. I mean, I think that what we discussed, right? I mean, I think Pakal purposes we're far, you know, there is a tipping point. I think eventually we will get to the CP 30 Terminator day where we actually build something is on par with the human. But for the purposes right now, we're really looking at tools that we're going to help businesses, doctors, self driving cars and those tools are gonna be used by our customers to basically allow them to do more productive things with their time. You know, whether it's doctor that's using a tool to actually use a I to predict help bank better predictions. They're still gonna be a human involved, you know, And what Romney talked about this morning and networking is really allowing our I T customers focus more on their business problems where they don't have to spend their time finding bad hard were bad software and making better experiences for the people. They're actually trying to serve >>right, trying to get your take on on autonomy because because it's a different level of trust that we're giving to the machine when we actually let it do things based on its own. But >>there's there's a lot that goes into this decision of whether or not to allow autonomy. There's an example I read. There's a book that just came out. Oh, what's the title? You look like a thing. And I love you. It was a book named by an A I, um if you want to learn a lot about a I, um and you don't know much about it, Get it? It's really funny. Um, so in there there is in China. Ah, factory where the Aye Aye. Is optimizing um, output of cockroaches now they just They want more cockroaches now. Why do they want that? They want to grind them up and put them in a lotion. It's one of their secret ingredients now. It depends on what parameters you allow that I to change, right? If you decide Thio let the way I flood the container, and then the cockroaches get out through the vents and then they get to the kitchen to get food, and then they reproduce the parameters in which you let them be autonomous. Over is the challenge. So when we're working with very narrow Ai ai, when use hell the Aye. Aye. You can change these three things and you can't just change anything. Then it's a lot easier to make that autonomous decision. Um and then the last part of it is that you want to know what is the results of a negative outcome, right? There was the result of a positive outcome. And are those results something that we can take actually? >>Right, Right. Roger, don't give you the last word on the time. Because kind of the next order of step is where that machines actually write their own algorithms, right? They start to write their own code, so they kind of take this next order of thought and agency, if you will. How do you guys think about that? You guys are way out ahead in the space, you have huge data set. You got great technology. Got tensorflow. When will the machines start writing their own A their own out rhythms? Well, and actually >>it's already starting there that, you know, for example, we have we have a product called Google Cloud. Ottawa. Mel Village basically takes in a data set, and then we find the best model to be able to match that data set. And so things like that that that are there already, but it's still very nascent. There's a lot more than that that can happen. And I think ultimately with with how it's used I think part of it is you have to start. Always look at the downside of automation. And what is what is the downside of a bad decision, whether it's the wrong algorithm that you create or a bad decision in that model? And so if the downside is really big, that's where you need to start to apply Human in the loop. And so, for example, in medicine. Hey, I could do amazing things to detect diseases, but you would want a doctor in the loop to be able to actually diagnose. And so you need tohave have that place in many situations to make sure that it's being applied well. >>But is that just today? Or is that tomorrow? Because, you know, with with exponential growth and and as fast as these things are growing, will there be a day where you don't necessarily need maybe need the doctor to communicate the news? Maybe there's some second order impacts in terms of how you deal with the family and, you know, kind of pros and cons of treatment options that are more emotional than necessarily mechanical, because it seems like eventually that the doctor has a role. But it isn't necessarily in accurately diagnosing a problem. >>I think >>I think for some things, absolutely over time the algorithms will get better and better, and you can rely on them and trust them more and more. But again, I think you have to look at the downside consequence that if there's a bad decision, what happens and how is that compared to what happens today? And so that's really where, where that is. So, for example, self driving cars, we will get to the point where cars are driving by themselves. There will be accidents, but the accident rate is gonna be much lower than what's there with humans today, and so that will get there. But it will take time. >>And there was a day when will be illegal for you to drive. You have manslaughter, right? >>I I believe absolutely there will be in and and I don't think it's that far off. Actually, >>wait for the day when I have my car take me up to Northern California with me. Sleepy. I've only lived that long. >>That's right. And work while you're while you're sleeping, right? Well, I want to thank everybody Aton for being on this panel. This has been super fun and these air really big issues. So I want to give you the final word will just give everyone kind of a final say and I just want to throw out their Mars law. People talk about Moore's law all the time. But tomorrow's law, which Gardner stolen made into the hype cycle, you know, is that we tend to overestimate in the short term, which is why you get the hype cycle and we turn. Tend to underestimate, in the long term the impacts of technology. So I just want it is you look forward in the future won't put a year number on it, you know, kind of. How do you see this rolling out? What do you excited about? What are you scared about? What should we be thinking about? We'll start with you, Bob. >>Yeah, you know, for me and, you know, the day of the terminus Heathrow. I don't know if it's 100 years or 1000 years. That day is coming. We will eventually build something that's in part of the human. I think the mission about the book, you know, you look like a thing and I love >>you. >>Type of thing that was written by someone who tried to train a I to basically pick up lines. Right? Cheesy pickup lines. Yeah, I'm not for sure. I'm gonna trust a I to help me in my pickup lines yet. You know I love you. Look at your thing. I love you. I don't know if they work. >>Yeah, but who would? Who would have guessed online dating is is what it is if you had asked, you know, 15 years ago. But I >>think yes, I think overall, yes, we will see the Terminator Cp through It was probably not in our lifetime, but it is in the future somewhere. A. I is definitely gonna be on par with the Internet cell phone, radio. It's gonna be a technology that's gonna be accelerating if you look where technology's been over last. Is this amazing to watch how fast things have changed in our lifetime alone, right? Yeah, we're just on this curve of technology accelerations. This in the >>exponential curves China. >>Yeah, I think the thing I'm most excited about for a I right now is the addition of creativity to a lot of our jobs. So ah, lot of we build an augmented writing product. And what we do is we look at the words that have happened in the world and their outcomes. And we tell you what words have impacted people in the past. Now, with that information, when you augment humans in that way, they get to be more creative. They get to use language that have never been used before. To communicate an idea. You can do this with any field you can do with composition of music. You can if you can have access as an individual, thio the data of a bunch of cultures the way that we evolved can change. So I'm most excited about that. I think I'm most concerned currently about the products that we're building Thio Give a I to people that don't understand how to use it or how to make sure they're making an ethical decision. So it is extremely easy right now to go on the Internet to build a model on a data set. And I'm not a specialist in data, right? And so I have no idea if I'm adding bias in or not, um and so it's It's an interesting time because we're in that middle area. Um, and >>it's getting loud, all right, Roger will throw with you before we have to cut out, or we're not gonna be able to hear anything. So I actually start every presentation out with a picture of the Mosaic browser, because what's interesting is I think that's where >>a eyes today compared to kind of weather when the Internet was around 1994 >>were just starting to see how a I can actually impact the average person. As a result, there's a lot of hype, but what I'm actually finding is that 70% of the company's I talked to the first question is, Why should I be using this? And what benefit does it give me? Why 70% ask you why? Yeah, and and what's interesting with that is that I think people are still trying to figure out what is this stuff good for? But to your point about the long >>run, and we underestimate the longer I think that every company out there and every product will be fundamentally transformed by eye over the course of the next decade, and it's actually gonna have a bigger impact on the Internet itself. And so that's really what we have to look forward to. >>All right again. Thank you everybody for participating. There was a ton of fun. Hope you had fun. And I look at the score sheet here. We've got Bob coming in and the bronze at 15 points. Rajan, it's 17 in our gold medal winner for the silver Bell. Is Sharna at 20 points. Again. Thank you. Uh, thank you so much and look forward to our next conversation. Thank Jeffrey Ake signing out from Caesar's Juniper. Next word unpacking. I Thanks for watching.