(upbeat music) >> Welcome to today's session of the AWS Startup Showcase, featuring Cordial. I'm your host, Lisa Martin. I've got two guests with me here today. Christy Parrish here, she's the director of client success at Cordial, and Hailey Pettit is here as well. She's the CRM manager at Nurx. Ladies, welcome to today's session. >> Thank you. >> Thank you, happy to be here. >> Excited about this conversation. You're going to be talking about personalizing at scale and we're going to look, learn how Nurx is re-inventing the digital patient experience, such a relevant topic. Let's go ahead and get started So the audience understands about each of your companies. Christy, we'll start with you. Give us an overview of Cordial and how you help customers? >> Absolutely. So Cordial is a cross channel messaging and data platform. So our clients can collect all of their unstructured customer and business data from wherever it lives in their tech stack and really use that data to build audience segments, discover trends and insights, and automate super hyper-personalized customer experiences at enterprise scale. So as someone who essentially grew up in the legacy email space, I actually worked at a legacy ESP for about 13 years. I see Cordial as a radical shift from heavy cumbersome data processes and the need for lengthy delays and heavy lift to send messages. We're activating massive amounts of consumer and business data kind of up to the second, regardless of its underlying structure or format. And we're making that available across any outbound channel to deliver these highly personalized messaging. So I think it's important to also mention that Cordial is more than just a platform. All of that power, all of the AWS powered power is backed by some of the best innovators and support teams in the industry. So I'm especially proud of how it partner with our clients like Nurx to help them build and execute on their business goals. We're enabling some of the best brands out there. You know, we have Eddie Bauer, we have 1-800 Contacts, REVOLVE Clothing. We're really bringing them agility and a solid marriage of art and science. >> Oh, I like that, a marriage of art and science Especially as we become more demanding as consumers. Whether we're consuming something in a retail environment or we're patients, we want the information to be really as you said, hyper personalized. So Hailey, let's talk about Nurx. This is a very interesting brand and I think it's very fitting that we're talking about this during Women's History Month as well. Give us an overview of what you guys do and why you're so radically novel? >> Yeah, so Nurx is a telehealth company and we are really focused on sensitive health needs. We're really a leader in the birth control space right now. So we prescribe and deliver online directly to our patients. So we offer not only birth control, but STI home testing prep, which is HIV preventative medication. We just launched acne treatment, migraine treatment. So really expanding within the healthcare space. I think what really sets Nurx apart is our one-to-one relationship with our providers, with our patients, and really at our core we really believe that healthcare should be accessible and affordable to everyone, no matter what their circumstances. So yeah, it's a very exciting space to be in and definitely being data-driven it really impacts that patient care and helps us really care for our patients in a really innovative, exciting way. >> It's very innovative. Christy let's talk a little bit about some of the other customers. We're going to dig into the Nurx story, but talk to me about some of the other customers that Cordial helps in other industries for example. You mentioned a few, but let's kind of open that up. >> Cordial really is helping a lot customers really in the retail space. So retail is that is a large focus on e-comm for us. What really kind of stands out to me about Cordial with retail and maybe even some of our publishing clients is our ability to sort of take that data agnostic approach. Data can come from anywhere, from anywhere in their tech stack and come into Cordial. And then we're really focused on making it accessible to them and meaningful for their outbound communications. So any channel, anytime kind of if they want to do direct mail or Facebook audiences, we really are able to bring in that data and look at their business goals, look at what they're trying to achieve inside of their vertical and then make that data powerful for them, not only for just talking to their customers and growing things like revenue per email or their lifetime value for their customers but really bringing it into their insights. And one of the things that I think Nurx is doing really well is using that data, using those insights to kind of feed the next evolution of their messaging programs. So that's a lot of what we're doing for our clients, and having some really stellar successes across verticals. >> So the data explosion, we have to address that. It's something that we're also helping to create as consumers, as patients, et cetera. But we also have this demand, like I said earlier. We want information on any channel. It's great if a brand can come to the channel that we want, that's rare to get that. But creating a data-driven customer experience is a really challenging thing to accomplish. Christy, how do you, how does Cordial help your customers in any industry actually do that and in a timely fashion so that the messages are relevant and personalized? >> Yeah, so I think in this case and in many cases, the key to creating that great customer experience is really using that data with empathy, being able to not just go out and check a box, look for the next logical data data point but really grabbing that data, making it into maybe thoughtful cohorts, thoughtful automated customer journeys, and using that not just to blast out marketing messages but to potentially, and like in the case of Nurx, address pain points or gaps in the knowledge on the behalf of the consumer. Or even for retail, faults in the buying cycle, right? So are they going to buy again soon? What is going to happen next? So this will really kind of make or break the customer experience. And in this case with Nurx it's the patient experience with the brand. So we want to be of the moment. We don't want to send out something that's wrong, last week's news or something like that, or push them beyond where they're at in their cycle. So being able to have kind of that empathy with the data and looking at it from a holistic standpoint, I think is kind of the data magic that Cordial is able to bring. >> The empathy point is provocative. How do you look at data with empathy and deliver those customer experiences that relay that, so that that customer actually feels the empathy coming from the vendor? >> Yeah, I love that point as well, especially in the healthcare space it's all about patient care and understanding how each patient is different in their needs. And so utilizing the data, understanding where they are in their journey with healthcare is so important and Cordial really does allow us to do that. And we use that data to craft really empathetic messaging. So we know where they are in the flow. We know what pain points they may have or what questions they may have at that stage. And so addressing those head on is super important and it's like a key strategic goal of decisions that we've made and everything that we do. I also think there's a lot of stigma in the healthcare space and so education is also a very key factor around these service lines. And yeah, it's really exciting to be able to have a voice in this space and really educate our patients and address those needs. >> And meet them where they are. I think, again, as consumers we're more and more demanding. We can get anything anywhere, any time. And we want you to come where we are, rather than us have to go to where you are. And certainly with healthcare that's been a big challenge in the last year or so. But let's talk about some of the, Christy I want to get your perspective on some of the challenges and the roadblocks when businesses are trying to really form synergistic, empathetic customer relationships at scale, what are some the roadblocks that you help customers move out of the way? >> Right, yeah. So every day at Cordial the volume of data increases, right? So data's coming from all places and we're trying to be smart about using it. We're really working on helping marketers figure out ways to apply insights and meaningful communication strategies to get past this concept of data paralysis, right? It's making that data accessible and meaningful and then giving marketers tools to distill that data into more actionable views so that they can take what they have learned from it and then again iterate on it. So building out customer attributes, cohorts, different ways of slicing the data to make sure that it's as meaningful as possible for their program. And then we partner that with offline insights. So best practices, program strategy trends, to push that distilled data even farther on behalf of their marketing programs. >> You mentioned data paralysis, and that's certainly something that no business, especially in the last year as we've seen a demand for real time is no longer a nice to have, it's really table stakes, but that data paralysis can be a big challenge in terms of how to work around it. How do you pull actionable nuggets from the data to make decisions in the fast enough time that are still relevant for your audience? Can you walk us through how you're doing that at Nurx? >> Yeah, at X we definitely stay focused on the patient and when you have that clarity it's easier to navigate through the data and not getting caught up in that paralysis. I mean, I'm not saying we're perfect, because I've definitely experienced that where there's just so much information that you have. And if you were to touch on each point, a lot of your automations would get really thin. So using the data smart and with, but also you're creative is really important. Another roadblock that we've had is, when you have increasing demand, when you're at scale, really automating some of the one-to-one interactions that you have is so important. And digging down into what data is important to automate those interactions. And I think a great example for us is we launched a post prescription flow. So our patients are notified when their prescription is on their way. And our providers told us, "Like clockwork we get these questions once patients are notified. And so we validated that in the data, we put that data within Cordial and we were able to build out a really successful automation that proactively address those questions. And we saw a direct decrease in those types of tickets to our providers asking those questions." So, yeah, that was really great to see too. >> So taking a look at the data and seeing the most common questions for certain types of prescriptions that providers are getting, which I imagine takes time from the provider being able to treat somebody else, condensing those down, automating those and then you're freeing up the provider as well because you know the common questions they could ask. So in terms of the patient then they sort of proactively got messages about questions that they might have. >> Yes, so more specifically how to start their medication. And then also some of the side effects that may be involved with that medication and what would be normal versus abnormal and what you should pay attention to. So just putting that in a very user-friendly format within an email worked really well. And addressing that question that our providers were taking a lot of time to answer. >> So, Hailey, so a prospective patient would go online, order what they want prescription wise, gets to a provider. They write the prescription and then is that sent to the patient's home? So there's no like physical interaction, it's all digital? >> Correct, yeah. It's all digital. We have our own pharmacy that fills the prescriptions and sends it right to your door. >> Yeah, excellent, on demand. So if we look at the last year there's been so many challenges, too many to count. But I'm wondering how, as the channels expand, we're all dependent on text and email and mobile, as the channels expand, Christy, how does the Cordial data architecture allow customers like Nurx to be able to flex as data sources expand, as data volumes grow, as channels expand, how do you allow them to have an architecture that will allow them to grow and continue to scale? >> Yeah, it's really important to ask that we be able to bring in all of this data and then like you said, a really critical point to Nurx and to a lot of our customers and our clients is, "Hey, we want to send it out across any channel." So Nurx, Hailey didn't mention that they're sending not only prescription information and follow up out by SMS or MMS marketing. They're also sending marketing messages too. So they're able to really leverage what we've built in terms of making that data accessible through all of these different channels, this channel agnostic at this point. So leveraging all of the bells and whistles of the platform, and also then using their data smartly, that's really where the clients are seeing a huge lift with the Cordial platform. They're able to visualize their data, see it, access it, even manipulate it, where in a legacy ESP, it's very limited in terms of manipulating data, aggregating it, looking at it from different angles and then being able to actually make it useful inside the platform for them. >> And Hailey, question for you. We talked about that automated prescription workflow a second ago. You also talked about this, each patient patient's journey being unique, wanting to deliver personalized, hyper-personalized actually is the word, Christy that you use. How does Cordial's platform allow you to respect the individual patient journey, customize it, and also do automation at scale? >> Yeah, I think with Cordial it's an incredible platform. We're able to pull in data from multiple sources and then it's very user-friendly in the way that you can interact with that data and manipulate it and really get at the cohorts that you were trying to reach. I think it's really a special platform. Honestly I think I haven't seen a lot of other platforms like this where it does make it really visually accessible to a brand or a company. >> Something, Christy that I wanted to ask you. I saw in the marketing messaging that what you're aiming to do is making marketing not personalized. And I thought, "Ooh, that's an interesting statement." What's the difference between personal and personalized from Cordial's point of view? >> It really goes back to that whole checking a box, right? So the traditional way of doing outbound communications marketing, even going back to the days of direct mail is to sort of wedge our customers into little boxes or even big boxes, and then send out messages that we think will resonate with them. Now we're really looking at it in real time as the messages are being generated and sent out of a platform where at the moment of send we're reading some signals that the customer is giving us, like what did they do on a website? Or did they respond to an SMS message or a text message? And at the moment of said we're actually sending content that is relevant at that time. It's vastly different from the way that we've traditionally marketed in outbound communications across all channels. So looking at real time, Hailey mentioned that she can visualize, we have a feature called orchestration builder that allows Hailey to come in and say, "Okay, based on these signals or triggers, I want to send this message to these users or these patients, but they need to be in that moment ready for that message. Or she could say, "If they're not ready for that message, let's skip them and come back to them later." And be able to really kind of narrow in and get super personal with those messages. Nurx is incredible, the way that they've used the platform and the way that she's built out these orchestrations, all credit to Haley on this. The way that she's smartly used her tools, it's not only effective, but it's sort of revolutionary, just in the way that she's able to find the right message at the right time. And in email we've said that for years. Right message at the right time. But really we haven't said, "Let's make it personal. Let's use the data that we just got 10 seconds ago and send the message now." So it's been great. >> Yeah, that's a game changer. Using the data that we just got about this person. Speaking of that, on-demand culture, that's a game changer for retail, for healthcare, to be able to tune that in an automated way. I imagine that the campaign ROI numbers, Christy are probably pretty much off the wall for your customers? >> In a lot of cases, they are. Yeah, they're doing really well. They're leveraging data in ways that I've never seen before. We've got some clients who are looking across periods of time, especially in retail, looking across periods of time at their customer's behavior. And then looking for ways to communicate them when maybe there wouldn't be a way to communicate with them that day. So it's the day that they send out a sale but they're are a person who doesn't like sales or doesn't respond to sales. So they may send them a different piece of content something lifestyle, or, you know, curating content, that kind of thing. So it's really been it has been a bit revolutionary in the way that the clients have leveraged the ability to let you know to use their data in new and kind of special ways. >> I can only imagine the last year has affected this in a good way, because we've become even more demanding as a society. Everybody everywhere struggling to get certain supplies for example, but Christy, how has the last year affected Cordial's growth and that of your customers? >> Yeah, so I say this frequently, we have sort of trained a new generation of e-com buyers in the last year. We've taught people how to buy online and that has affected a lot of our brick and mortar clients who also have e-comm business. You know, so we have a large group of furniture clients. And so they've really seen some incredible success, retraining their their customers to buy large items online. It's not an easy thing, but they've really become sort of renegade in the way that they're pushing out messaging and finding the right people to send those to these new econ buyers. So it's been really interesting and they've come back and invested in technology that has enabled them to build trust and build out these individualized brand experiences so that they can actually scale those programs. Now this year, as we're reopening, the strategy is shifting, right? We're looking at, "Okay, we had an incredible year in some cases last year with e-com. Now we're going to have a store that's opened." How do we make that experience special? How do we continue the dialogue with these customers? >> Such an interesting thing that sounds like it's Cordial's been a facilitator of the many pivots that so many businesses have needed to go through. And to your point, sort of re-pivoting back towards some mixture of online e-com and in physical retail or store rather experience, it'll be interesting to see how that happens. And then, Hailey, some pretty big statistics you have to share in terms of some of the things that Nurx saw in the last 12 months with respect to use of your platform and personalization. Talk to us about that? >> Yeah, it's been a very interesting year for Nurx for sure. We've seen a 50% increase in our demand for birth control, with medical providers, brick and mortar medical providers, having limited capacity. We've been able to really step up and serve these patients and make sure they have their healthcare needs met during this really difficult time. We've also seen about a three X increase in our demand for emergency contraception, 130% in STI home test kits. Yeah, it's just across the board it's been really incredible to be able to really fill this need in such a difficult time. So it's been exciting. I love being able to help serve these patients during this time, yeah. >> Yeah, and that need to do something so personalized during such an incredibly difficult time. That's a really interesting mix there. And congratulations on the success that you guys have had. I want to wrap things up, Christy with you. Let's just talk a little bit about the Cordial AWS relationship. You guys started on day one back in 2014, or you built on AWS, tell me about that? >> Yep, so definitely built on AWS. We leveraged the AWS system extensively and yeah, we started in 2014. I think the founders were looking for, where do we go for stability? Where do we go for efficiency and reliability? And so came over to AWS and since then we've become an advanced tier independent software vendor. And then I think more recently in the last couple of years, we've kind of gained a couple of competencies, retail and digital customer experience. So super embedded there. And really, I think AWS not only contributed to the foundation of the platform, it allows us to store and manage that massive volume of customer and business data for our clients and be able to actually house it in the cloud. Really it kind of empowers us to do that, to deliver that messaging at scale that we've been talking about. >> So AWS is an enabler of the way that you help your customers create this personalized experience at scale? >> Yeah, absolutely. And it really it helps us solve these challenging problems that we have where we're working with a client like Nurx and we have a few others where we have to be HIPAA compliant. So we rely on the AWS architecture to not only enable our scalability and reliability, but also with those high security and compliance standards. This is incredibly important to us in servicing our clients. >> Well, ladies, thank you so much for joining me today on the Showcase and sharing what your companies are each doing and what they're doing together. Big changes, big opportunities, and that personalized experience that I think we all crave. Thank you so much for joining me today. >> Thanks Lisa. >> Thank you. For my guests, Christy Parrish and Hailey Pettit. I'm Lisa Martin. Thanks for watching. (gentle music)

>>From San Francisco. It's the cube covering get lab commit 2020 Rocky you buy get lab. >>Hi, I'm Stu Miniman and this is the cubes coverage of get lab commit 2020 here in San Francisco. Kicking off our coverage of 2020 a great developers show talking about the, the platform they get. Lab is building and have one of the keynote presenters from this morning. Christy Lineville who is the user experience director at get lab. Thanks so much for joining us. My pleasure. All right, so th one of the things that uh, you know you and was talking, talking in the keynote this morning was we have the scroll of tools and of course one of the challenges people know is if you're talking different tools in different environments, the user interface is going to be different. And therefore the stat I heard in the keynote was over 50% of dev ops time is wasted on logistics and repetitive tasks and all these environments. Before we dig into it. Christie, I'd love just a little bit about your background because you hinted at it a little bit in your keynote that some past experiences you've had. So what, what led you to this role at get lab? >>Yeah, so I've been in tech for about 20 years. Um, didn't go to school thinking that I would be a UX or one day because 20 years ago, frankly it wasn't even a thing. Um, but over the years I've gotten to work at Dell and general motors, Rackspace and then a regional company that's still huge called HEB. So lots of enterprise, lots of tech, um, which is areas that I'm just really passionate about. UX and, >>yeah, well we, we, we talk so much about Keck. Um, I love, one of the things I've looked at my career is there's the cool tech button, you know, what is, how does design fit into these, you know, there's of course the easy examples of Apple, but you know, so many of the products, when you talk about the difference between it being a utility, and I love this thing often is the design. And that, that, that user experience piece of it, um, in the dev ops software world, give us a little bit of your world challenges that you're seeing. What differentiates a, you know, an okay product versus something that customers are going to be like, you know, I love this. I want everybody to use it and, you know, want to spread, spread the gospel. >>Yeah. Um, so building the types of tools that we are building at get lab isn't sexy or like working at Apple. Um, but I'll tell you this, the designers who work on these types of tools are really deeply passionate about creating great experiences for people to do their jobs every day. Um, which is actually really exciting work. So what's interesting though is oftentimes people are coming from using these very outdated legacy tools. Oftentimes their internal tools, uh, they just don't have a great experience. So we get really excited about being able to take the type of tool that someone is kind of like, these folks don't have a choice. They're not getting to decide which tool they get to use to do their job. They have to use it. And we're really respectful of that. Just because they have to use it doesn't mean that we want to take advantage of that. Uh, we want it to be a really excellent experience. >>All right, so Christie, I had heard before when you talk about get lab there, there's dev, there's sec, there's ops in the keynote, you talked, even groups like finance and marketing need to get involved. There's very different expectations and skill set when you talk about those roles. So it helped me understand a little bit, are there different interfaces based on my roles? Is it just so simple that anybody should be able to understand it? Help us understand. >>Yeah. So that's the goal. I'm not going to tell you that they're there yet today, but that's the idea. So yeah, having worked in tech for such a long time, um, it's, I've got a lot of, uh, experience with watching different roles, try to interact with these technical teams that need the tech teams for them. This is, this is bread and butter stuff. They know exactly what's going on. And, uh, other roles really try to kind of bring themselves to the developers and that's what we're trying to make easy. So things like taxonomy play a huge role in that. The way that, uh, deeply technical people talk about the work that they do is very different from how people in other roles do. And we're starting to think about how we can converge those two things just to make it easy for everyone. >>No, I love that because a few years ago there was Oh, developers and the new kingmakers and they're going to do off their thing, but it kind of seemed like the developers were off on the side and they were going to choose their tools and figure things out and then somebody eventually needed to pay for something and figure out how it works in the environment. The story I'm hearing and the maturation of that is developers are closer to the business and these roles need to talk and communicate and fit together. Is that what you're seeing? >>Yeah, that's absolutely right. >>All right, so get lab also your, your product line spans of just a broad spectrum. There's, yeah, I don't have memorize the 10 categories that you need to fit. Um, I believe there was a couple of acquisitions, uh, that helped grow here. But you start with SCM and CII. Those alone, making sure that those work together is a certain bit of work. But how do you, how do you span the gamut and make sure that all these various pieces, uh, I'm going to have some kind of coherent experience. >>Yeah. So we're also thinking about project planning that happens before SCM NCI ever starts. Um, and so we're thinking about how do we make it easy to take something from an idea, an issue directly into that build process. Um, and then after that it's like, okay, so then what happens next? Keeping it secure, um, and then watching it to see what's going on it and then just getting it out onto infrastructure through our ops features. Okay. >>Talk a little bit about how you interact with the ecosystem in the community. Also, it's everything is open to, you know, understand, you know, I want them to see the meeting minutes. I can dive right in and do it. And we heard lots of examples in the presentations about, Oh, some change has been made or you know, your CEO joke to somebody corrects my grammar. And that not necessarily, Oh, maybe it is someone inside the company, but uh, you know, that dynamic is to make sure you have something that is coherent when you have so many different internal and external constituencies that will be opinionated as how things should go. >>Yeah. Um, so let's see here. Um, ask me again. Sorry. >>Yeah. So you get all these other constituents that, that want to kind of have a stake and probably have an opinion as to how things should go. How do you make sure it works, not just forget lab but all of your customers and the partner ecosystem that you're building around it. >>Thank you. And so we do take the comments that come in on issues very seriously. Uh, my team is looking at that. Our product managers are certainly looking at that. Um, and we look at that as directional information. Where my team really takes that though is then we dive in and we do UX research. Um, so we are very mindful of the fact that the comments that are coming in, um, we don't take them literally, uh, we take them as kind of advice about where do you dig in next? And so what my team is doing is figuring out, uh, what roles are really interested in this future going out and either doing surveys or talking directly to customers doing qualitative interviews, or we're sitting down and saying, okay, so we get it. You have some feedback here and that's wonderful, but what were you trying to do? How did you even get here? Where did you want to go next? What things are working well for you? What things aren't working as well? And then that's a lot of what we do. >>Um, you've got a global environment that this is going into. What, what challenges does that put on what you're doing? >>Yeah, it brings a lot of challenges. Uh, one of the bigger challenges that it brings is in our UI copy, right? Um, so field labels, things like that. We really try to be mindful about that. Uh, so in a couple of different ways. So, um, the way that people talk about things a is different throughout the world. We try to be mindful about not using things like jargon. Um, so that everything is clear and easy to understand no matter where you are. We also think about things though like length of text, which can have a really big impact. So we know German tends to have some long words. We have to be mindful of that as we're writing UX copy. Cause in the end we want this to be as easy for everyone to understand as possible the moment that they look at it. All right. >>Uh, how about announcements? Uh, we, I understand the 22nd of every month is when a code drops. So just bring us up to speed as to what people should know about boat get lab product today. >>Yeah. So we, we released features at an industry changing velocity. I have never seen anything like it. Um, and from a, I'm always gonna think from a UX perspective, UX is deeply involved in that. So there is not a release that goes by where you as a customer or a user can't actually see the impact of the release. Yeah. Things are happening behind the scenes and we're shoring things up and strengthening the backend, but we're doing things on the front end constantly. Um, and my designers and researchers know that that's like they're on the hook for that. And so they're always thinking about like, what's that next thing that we can deliver? >>All right, so Kristi, dark mode for everything. >>dark mode has definitely been something that we have heard from our user base that they really want. Um, something that we're working on is a good design system so that we have single source of truth components we'll make that'll make it much easier for us to do the dark mode that we know is a legitimate ask from our user base. >>Yeah, absolutely. Anything else? Uh, just trends or things that you're looking at for 2020 >>trends that we're looking at? No, it's interesting. I'll be honest, I don't think that we think a lot about trends. What we're really doing is we're looking at the feedback that's coming in directly from our user base and then we're trying to make decisions based on that. Um, so actually I don't, I couldn't say that we have any trends. >>Well, you know, mobile drove a lot of the last decade or so. Are any of the voice or interactive, you know, type of platforms have any impact on what you're doing yet? >>Yeah, so we, uh, we are thinking about mobile. We're not thinking about in the term about it in terms of, uh, native mobile apps. We're really trying to think about it in terms of just making a really good responsive experience. Uh, we're trying to get a better sense of which jobs, um, are most commonly done on mobile devices so that we can focus first on making those better. Um, but that's also something we're trying to think about with every design. So I see my designers doing a really good job these days. So they, you know, they put together a design, they're thinking about it in terms of desktop, and then I see them pivot and think, okay, so what does this now look like on a mobile device? So we have a lot of work to do in this area. I'm not going to tell you that we don't, but I see us getting better and better all the time. >>All right, Christie, thanks so much for giving us all the updates. Really great to dig into it. It's been my pleasure. Alright, I'm Stu Miniman and thank you for watching the cube.

>>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.

>> from the Cosmopolitan Hotel in Las Vegas, Nevada. It's the Cube covering Cooper inspired 2019. Brought to you by Cooper. >> Hey, welcome to the Cube. Lisa Martin on the ground in Las Vegas at Cooper Inspired 19. Excited to welcome to the program. And gentlemen from Staples, a place I go to all the time we have Christy Oreo, VP of strategic sourcing. Hey, Chris, welcome to the Cube. >> Thank you. Glad to be here, >> So I was just a staples the other day getting office supplies. It's a go to Penn's files Folders, Inc et cetera. You name it. That is a place I think everybody on the planet knows. But I >> want to >> talk to you about the Staples business and how you guys now have control over 8000 suppliers. You've got this visibility in control, which I think every human wants and every element of life you know of 100% of your indirect spend under management. So given those big business outcomes, let's dissect that. Obviously, you're a cool customer. That's why you're here. Talk to us about a little bit about Staples. All the different suppliers you guys have, and some of the challenges that you came to Cooper to help erase >> Well, we had a lot of issues with Roque spend. Everybody was doing what they wanted in every location. We had no verification. We weren't consolidating our spend to get the best deals and get the best outcomes, lack of consistency, all the stuff you hear about. And since we've ruled out Cooper, we've got a lot of structure in place now, and we've got much better uniformity, much better consistency. We've dramatically lowered our costs through the use of the tool and some of the some of the rules that we've put in place as a result of of launching Cooper a couple years ago now. So we're really pleased with how it's helped us organize our business and really bring visibility to where we're spending money and showing us the opportunities and where we could go after her and save even more money. >> You know, you talk about rogue spending. >> One of >> the interesting disrupters of procurement and finance is consumer ization we all have. Whether we're going on staples dot com or something else, we're on Amazon. I need to buy this. We have this expectation as consumers in our private lives that we can get anything we want. We have to check with anybody. I one click. So then when we go in as business buyers, we sort of have the same mentality. But obviously the challenge there is, a lot of organizations don't have visibility into. Where has every single dollar going? How many different suppliers are we working with? Do we have duplicates triple kits everywhere to talk to me >> a little >> bit about the with a kind of cultural strategic shift that you guys are making now that you have this visibility? Well, >> everybody's happy with the results. That's >> always good right >> when he had his. When you have some success and you start to tell the story, then all of a sudden people's eyes get opened, and what's interesting is I don't think anyone does anything with malice. But if you have a general manager of a warehouse who believes that ex widget is what he needs to really Pel perform and do better, he's doing that with the right intentions. What he doesn't understand is everything else that's going on behind the scenes, and we have deals in place with suppliers and there's a level of consistency that we expect that our suppliers expecting that our customers expect and we can't have that experience be different. So once we can't explain that story and the tool helps us see where that spend is coming from, we go back. We have a conversation, and all of a sudden it's enlightening like, Oh, I didn't know. Now that I know Okay, I get it. Let me do what you want me to do or what you need me to do. So that's been the biggest shift I think is just sharing information and putting a spotlight on things when they come up and it happens even still. You know, we've rolled out now a little over two and 1/2 years ago, and we still have these things come up because you get new people and people change roles and, you know, as a business person there's folks I've done business with in the past that have earned my trust, and I want to do business with them again because I know what. When people get new roles, they do the same thing, and sometimes that's not what we need them to do. So once you explain the story and you tell them about it and you show him the results, they come onboard. It's phenomenal. >> Everything goes back to the user experience with customer, whether your customer is an individual buyer or a business of 20 people to a Fortune 500. Everybody in an organization is ultimately, in some form or fashion touching the customer. The customer experience is critical to delight the customer to drive higher customer lifetime value from that customer. Um, so having the employees onboard understanding we still want you to be able to manage your Ware house even more efficiently. But we need you to understand how we're gonna give you the tools to do it better. Ultimately, the end of the day, it's goes back to that customer and making sure you can keep extracting value from them. >> One of our core values is put the customer first, always, and that's at the heart of everything we do. It's not about buying things cheap. It's about buying things at the right value and giving the customer the best possible experience they can, so there may be less expensive ways to do it, but it may not deliver the outcomes we want, so it's not always about buying cheap. It's about buying and getting the best value for us so that we can deliver the right experience to our customers. >> Was that a >> mentality that staples had prior to bringing on Cooper? Or now? Because suddenly you're starting to You have visibility into everything you're going? Oh, cheaper isn't necessarily better in some of these areas. I think it's >> a It's a corporate philosophy that we've had. I think we we realize that people can shop anywhere for anything they wanted. Anytime. Cooper has helped highlight some some discrepancies that we've been able to kind of take out. I would say that Cooper's help with that, but it's also been just a core philosophy of the company for a long time. Cooper's helping us execute against >> that now, but you're right. Consumers can buy >> whatever it >> is. If it's a product like something you want to buy on Amazon or service. Maybe it's your Internet service provider. We have so much choice. Think vendors of any product testers that recognize that and sounds like Staples does. From a core cultural perspective. You're already in a better position to understand. I really need to find Tune everything under the hood here because they could go somewhere else like that. They can't. It's good to >> understand that. But Cooper gives us the data and the facts and the analytics to help prove out where we can make a change and where we can help the company and help our customers. So it's a combination of both. >> Let's Dig into that data was in one of the things that Robert seemed shared this morning was about. Since Cooper's been public, which was 2016 they have a five x increase in the amount of spend that is being managed in the Cooper platform. I think the number was is now 1.2 trillion dollars, a tremendous amount of data in this group of community that everybody can leverage and share. We often hear data is gold. It's the new oil it is and you're smiling if you can actually see it, right extracted value, Yes, talk to us about the amount of value that Staples is getting by this group of community with a ton of valuable data. >> I would say we're at the infancy of going into the Cooper community in terms of sharing information and gaining information. I'm excited about the little bit that I've seen, and I'm one of things I want to learn. Here is more about how it will work and how it can help us. What Rob shared this morning was very interesting to me, and I'm very excited to learn more about >> it. Sounds and you're right And even Cooper says, they're at the infancy of it. I think they have. A couple of 100 customers are starting to use the community to share intelligence. Eso It is early days, but it's also something that I think of when I go to events and we talk about, you know, devil's Community. It's a very collaborative that not only is it customer centric, also, supplier centric Staples is a supplier of a lot of other businesses. So imagine there's kind of double and did benefit. It's that could be gleaned by you guys from them. We hope so. >> I think we're you know, we're probably a more unique customer than many that Cooper has and that we are. We are a customer. We use the tools, we love the tool, but we're also a cellar to you guys and two other Cooper uses in the community. So we see both sides of the equation with Cooper, and it is interesting. T gain those insights and see how we can help both sides of the company. Help group is customers and our customers more >> if you look at >> the platform for procurement invoices, expenses. Heymans, where did you start a few years ago with Cooper and where are you now? In terms of all the different elements that are running through it? >> We started with a simple PIO management secure to pay. Then we instituted a no P o no pay policy, and everyone started using the tools. It really helped us change things We don't use it for. Expenses wear starting like, as I said, to start to use some of the analytics. I'm very interested in learning more about Cooper pay or out here virtual card usage. That's very interesting to me, so I'm curious to learn about that on. We'll see where we go from there. >> Cooper Pay was, I think I know it's just a few months ago in London, and we are excited to hear some more news about that tomorrow, how they're expanding that. But there's this visibility and control idea is so critical because of any type of organization. Whether it's a retailer manufacturer, it's a hospital. There's so much shatter, weighty going. But I t is really big challenge of reining in the cats, if you will in all these cats. Because we all know now that Robert likes cats. But it's one of the things that they're announced with Amazon is wow. I t can have access to buy all of this software, control it, deploy it, manage it through the Amazon marketplace. And you suddenly think, Wow, how procurement and t are gonna be aligning, joining forces and really affecting top line of of any industry. >> Yeah, I think in Staples are our relationship between procurement and our i t S t s department has been strong from day one. They were the biggest advocates of us getting the tool to help them gain control and kind of eliminates a lot of the shadow I t organizations issue. Does you mentioned so in our environment, we are excited about that. We embrace that we're trying. Thio forced that out. So we've always had that sort of very strong partnership with our I T team, and that's really what's helped progress the tool through the company with great success with them in the beginning. And then you start to tell the story, and more and more people are interested in. Wait a minute. You can help them save how much into the budget and where we can reallocate that money and what can I do with it? So it's been really exciting and sort of fun to be part of the transformation. >> And you guys have, what north of 17,000 users on the platform, >> today's wave? A lot, A lot. >> That's pretty quick >> adoption in a few years, a lot of people to train, to educate and and to have it become part of their normal everyday activities. >> Well, we're going through a relaunch now, and the Cooper team has been phenomenal in terms of training and helping my team with all the work that goes on behind the scenes that nobody sees and helping us develop training for all of our associates as we relaunch it, because we're really gonna change the tool. We were a couple of revisions behind Ah, now we're getting caught up. So there's a lot of change coming in September to my company and to Cooper and thrilled with the help that the Cooper team has given us the launch. This >> last question for you. Chris Staples, a 34 year young business. I was just talking with a gentleman from procurement and Lulu Lemon and much younger business. And you >> kind of think, Well, a younger business Have more nimble mind sets. Give your advice your best lessons learned to your peers >> at older, more established organizations, going through a change of really looking at getting complete visibility and all your spent advice to them. >> It's a bit of a cliche, but don't do what you did yesterday. You know, you've got to be open to change. You've got to let the you know, I always say, the month the numbers tell the story, and where is where you're spending too much and how do you fix that? And just because you love a supplier today doesn't mean you can't love somebody else just as much tomorrow. If they can deliver a better value, and a lot of times you can find out that your current supplier can give you a better value than you. Then you had before if you just start poking around a little bit. So my advice would be not to stick with the status quo. Just cause it's easy. Challenge yourself. Challenger team. Challenge the people you work with. Change is good. >> Change is good. Chorus. What a pleasure to have you on the Cube. Big. Thanks. So much for joining me. >> Thank you. Very nice. I appreciate it. >> All right. For Christie. Oreo. I'm Lisa Martin. You're watching the Cube from Kucha. Inspire 19. Thanks for watching.

>>Hi. Welcome to the Cube. I'm Lisa Martin on the ground at the Computer History Museum with the Association for Corporate Go Silicon Valley. Tonight is their 13th annual grow worth, and we're very excited to be with one of their pick sponsors. Deloitte Christie. Simon's from Deloitte. Welcome. Thank you. Great to have you here. So you are a veteran and technology. You've been in the tech industry over 25 years. You've probably seen incredible transformation. Tell us about what you're doing with Deloitte and the advisory service is not you. Offer way. Offer a number of service is advisory audit tax too in Silicon Valley to a lot of these emerging growth companies. So it's been very exciting >>in my >>career to see the evolution of what I call old technology right where we kind of got the traditional software semiconductor box companies to what is now digitally what I call a new technology and what is propelling the economy in the throat that we're seeing. Not only in Bali. Exactly. So right now you are working, leading hurt and development of Deloitte's technology practice up in San Francisco. You're working with clients and you mentioned digital and clown Internet media sectors tell us about that, especially as you mentioned new technology. So a lot of them are startup companies, which is really sweet spotted, A C G. And that's why we're so involved with a G. But a lot of these new technology companies that you mentioned, you know, cloud software service, Internet media, data security, those types of companies, eyes really propelling the digital economy. So we see a lot of growth in that sector, primarily in San Francisco but also in the broader Bay Area. Silicon being checked better and as you are you mentioned out of what's going on domestically but also internationally. How do you see the influence of Silicon Valley here in Silicon Valley as well as across the globe? You know, there's a lot of factors weigh serve companies all over the globe. So primarily, Silicon Valley is propelling a lot of those. And to the extent that companies here are international, most of a lot of multinational companies and do sell their products lovely there, developed here with products are actually sold. Are you seeing kind of the inverse where companies may be headquartered in in Europe or Asia? are influencing and bringing technology over to the Silicon Valley. Next thing, let us here. Yeah, some of that, especially as we think about, uh, engineers and the aspects and some of that development that happens there, obviously sourcing that from around interest of an industry perspective in 2017. It's like every company's tech way. Look at tests around the street. Look at Walmart Labs and what they're doing there. How are you seeing some of the clients you advise for? What are some of the industries that you're seeing are now technology? There's definitely a convergence says you mentioned Too many industries, actually, all industries. So when we think about financial service is no fintech. When you think about life sciences, health, when you think about retail, right, you got Internet. So definitely saying convergence and technology is impacting our daily lives and almost everything that we d'oh and in almost every product and service that we buy, there's some form or elements of technology. Exactly. It's really remarkable. Speaking of remarkable, tonight we're here with a C G to recognize two Fantastic Cos Twilio is the emerging growth winner, 2017 and video the Outstanding Growth Award winner. If you look at and video, for example, inventor of the GPS, which is really catalyzed a tremendous amount of technology across industries. If we were just talking about you, look at their market kind of what you see them over the next couple of years. The market drivers you think they're gonna impact mentioned and video write graphic way historically have been known for games and films and virtual reality kind of thing. Now they're actually moving more into artificial intelligence. Artificial intelligence? A. I knew Buzz Word, right? So there's probably a lot of opportunities for a video that technology evolves and develops over the next. Several questions for Twilio. Who's winning the emerging world? What would you do for them? So they're, you know, cloud platform company for software developers. So you think that part of the new technology is and a cloud, so providing an opportunity for engineers to develop software and software is involved in almost everything that we do as well in our daily lives. So you know that convergence of all the industries that's happening, a lot of that is a result of software and the developers who are creating that software Twilio is providing a platform for that communicated a tremendous opportunity. Companies in this new technology. Christy, thank you so much for joining us on the Cuban. Sharing your insight. Have a great evening tonight. Yes, it's, uh, it's a great turn out Isn't a lot of fun. It is. I want to thank you for watching way around the museum with a c D E f G. I'm Lisa Martin. Thanks.