>>okay. >>Just about a year ago, governments around the world forced shutdowns of their respective economies. We've never seen anything like it. Central banks took immediate action and effective monetary policy like none we've ever seen before. They dropped interest rates to near zero, injected a huge amount of cash into the system, and they fueled this liquidity boom to support those individuals and businesses that were in greatest need. Banks were overwhelmed with the volume of paperwork, for instance, small business P, P P loans and other things. Home buying boomed as mortgage rates hit all time lows for several weeks in the spring, it was complete chaos, but the tech industry stepped up and accommodated work from home. Cloud infrastructure was spun up instantly as access to data centers was really restricted, and Saas companies became a fundamental staple of not only keeping the lights on but helping customers thrive in the face of a pandemic. Automation became a >>mandate >>as humans, they couldn't possibly keep up with the tidal wave of demand, a document overload that was hitting the system. Now, one of the companies that was there to help financial firms in particular, get through the knothole was Oculus, a company that focuses on intelligent automation to deploy the power of machines to allow humans to focus on what they do best. Hello, everyone. And welcome to this cube conversation. My name is Dave Volonte, and we're profiling the most interesting SAS startups that are reimagining how we work. And with me is Sam Bobbly, the co founder and CEO of Oculus. Sam, welcome to the Cube. First time. >>Hey, Dave. Thanks so much for having me excited to have the conversation. >>Yeah, me too. So, listen, I know you've told the story of a zillion times, but I want a community here. How and why did you start the company >>for sure. So when I was in college, I was having a conversation with my dad. Uh, he was telling me about a meeting he just had with his elder law attorney. And the other law attorney was complaining about having to review hundreds or thousands of pages of financial documents for every long term care Medicaid application. When you apply for Medicaid coverage to enter a nursing home, you're required to submit 60 months of financials along with your application. And traditionally the elder law attorney or a nursing home would review those documents literally page by page, line by line to find high value transactions, transfers and other financial trends. And when I heard about this, it just it didn't make sense to me. I said, You know why? In this day and age isn't there? Why isn't there a technology solution that can ingest the documents and spit out a digital report replacing the cumbersome manual page by page review? So it really just started as a research project, trying to learn more about optical character recognition, which is the technology of transforming images into text. And, you know, as we kind of kicked around different products in the market, we we realized that there was an opportunity to build a unique platform that could ingest documents of any format quality and produce perfectly accurate results. And that was the genesis behind what ultimately became Oculus. >>You were a young man at this time. How old were you at that time? >>I was 22 when we started >>so fearless. And, uh, now my friend Eddie Mitchell started a company about 20 years ago. We hacked together a >>Dell >>system and this camera. It was all about the modern operating room in the future, and he showed it to a doctor and and it was just a prototype, she said. How much? He said 10 grand. She wrote a check right there. You have a similar story? How did you see the company? >>So we we we do have a pretty similar experience. You know, Our our concept was we want to get perfect results the customer every time. So if a customer sends us a clean bank statement from Chase or a blurry cell phone image with someone's thumb in the picture from a community bank in Maine, and it's rotated sideways or upside down like we want to give consistent, perfectly active results every single time. And you know, our our view was to completely solve the business problem. So the very first version of the software that we built, we had a rudimentary machine process to extract 60 or 70% of the data, and then we had a little tool built on the back end, where literally, me, myself and some of our early employees would clean up the data output, make sure it's perfect and then return When we couldn't submit, we'd returned to the customer accurate data that could be used at the time for for a Medicaid decision. And what happened is, while we were in our beta period, customers fell in love with the product. They felt it was magical and really just superior from an accuracy standpoint to anything they had ever tested before. And And one of our beta testers said to us, uh, where do I submit credit card information? So at that time, I turned to my colleagues and I said, I think we're ready to I think we're ready. Start charging for this thing and and roll it out for prime time. >>When I was researching the company, I learned that you leveraged. At least some of the idea came from the capture, and I never knew this, But the capture that we all hate came from Google where they write, they had at one point you could maybe you still can. You can go online. You can read books and have to It's just scanned. You can't even read the stuff half the time. So they were putting the capture in front of us, quite brilliant to try to solve for those those those white spaces that they didn't know. So So how did you learn from that? Was there an A P I that you could plug into Google's data set, or did you do your own? What was that? How did that all work? >>The the concept of humans in the loop is super powerful, right? So when we first started, we recognize that OCR and machine data capture couldn't do the job completely. OCR, generally speaking, can process financial documents with roughly 80 to 85% accuracy plus or minus machines, particularly struggled with semi structured and unstructured documents where the format is unpredictable as well as lower quality images. So pretty early on, we recognized that we needed human intervention in the process in order to achieve perfect accuracy every single time, and also to create training data to constantly teach our machine learning models to get smarter and drive additional automation. So, as I mentioned, the very first version was myself and other employees verifying the data on our own. But as we started thinking about how to scale this up and, you know, take on millions and millions of documents, we needed to, uh, you know, learn how to better parallelized task and really build the system for for efficiency and for scanning. So we we we learned about the Google Books Initiative and their ability to leverage capture technology and a distributed workforce to verify pieces of information that their systems couldn't automatically read from books. And we took a lot of those learnings into building our human in the loop infrastructure. And, you know, a way to think about our our product is it's the marriage of machines and humans that makes us unique. As much of the heavy lifting as we can do with machines we do. But whatever we can't do automatically, we slice into smaller tasks and intelligently route those tasks to humans to perform verification. We then layer in algorithmic checks to make sure our humans did the review correctly. The customer gets perfect results, and that same perfect output is used in a feedback loop to train our machine learning models to get smarter and smarter, which dynamically improves the product on an ongoing basis. And, you know, the folks at Google were we're onto this pretty early with the capture technology, and we were following in their footsteps with our own unique take on it, but specifically applying it to financial documents. >>I mean on the Cube. We know a lot about this because we were looking at transcriptions of video all the time, and it just keeps getting better and better and better in our systems. Get smarter and smarter, smarter. So we're sort of closing that gap between what humans can can do and machines can't. And I would expect that you're seeing the same thing. I mean, you think there's always going to be kind of humans in the loop in terms of the quality or is that gap going to be, you know, six nines in the, you know, near near term. >>I think it's gonna take a while to get rid of all the edge cases. You know, you mentioned the PPB program like we've been on the back end processing P p P loans for some of the leading players, like Cross River Bank, blue Vine, Square Capital and others. And you know, what we've seen during the ppb process is just a a wide variety of different documents and inputs, Uh, and a lot of difficult to read documents that are, you know, very challenging to automate. So I think we will, you know, incrementally continue to automate more and more of the process. But the value of having humans plus machines is much more powerful than just having machines alone or just having humans alone. And as it relates to the end customer, our our goal is to do as much of the mundane work as possible to free our customer up to do the more cerebral analysis. So in a lending context and and for the record, you know, our our biggest market opportunity is in the limbic space. Despite the fact that we started with medicating attorneys, we quickly pivoted and realized that our technology was super valuable to to lenders to help them automate the underwriting process. And our our thesis is, if we can take out all of the necessary evils like document review and allow underwriters to focus on the actual analysis of financial health, it's a win win win and creates a really fantastic, complementary relationship between us and our customers. >>Yes, I want to ask you about the pivot to financial services. You said you started well, you have the inspiration from elder law because Jimmy McGill. Okay. Saul Goodman breaking bad. You got started. An elder law. But then you made the pivot to financial services. Really pretty early on. You had really good, great product market fit, but you kind of went for it. I get early twenties. You know, you didn't have a big family at the time. I didn't have a lot of a lot of risks. So you went for it, right? But talk about that pivot because a lot of companies wouldn't do that. They get comfortable and just, you know, stay where they're at. But you made that >>call. It was a big risk, for sure. I mean, look, the product was working. We launched the paid version of our product in 2016. Pretty quickly were onboarding dozens of accountants and attorneys, you know, doing Medicaid work. Um, in mid to late 2016, we got introduced to a large small business lender in New York City called strategic funding Source. They've since renamed them their company Capital as the current name, but we met with the CEO and the head of product and showed them a demo of the technology. And they said, You know, quote unquote, we've been looking for this for years. We've been looking for something exactly like this for years, and we said back to them about how many pages of financial documents to review every single month. They pointed out to a bullpen of dozens of people sitting there tearing through bank statements, page by page, line by line. And they said, You know, it's hundreds of thousands. My eyes almost fell out of my head. I couldn't believe the volume, and it was much bigger than what the, you know, single accountants or attorneys were doing. Uh, so we made the strategic decision to pivot at that time and focus on FINTECH. Lenders continue to tailor the product and build additional features for the fintech lending space. And and, you know, lending in general had the perfect mix of short sale cycle and high average customer value that allowed a company like ours to scale and ramp our revenue quite quite quickly. Um, and then the other thing that happened is kind of as we were getting deeper and deeper into the space, the fintech space as a whole started growing massive. So we we kind of had the perfect storm of product market fit, plus the market growing that allowed us to really ramp significantly grow revenue. And, uh, you know, despite the fact that it was the risk it was, it was totally right. Decision to to focus the business on financial services >>much bigger Tam. And you could subjectively measure it by the size of the stack of papers. Um, how how does this relate to our p A. As you know, the R p. A hot space. You probably get this question a lot, and it sounds like there are some similarities with software bots. What's the similarity? What's the difference? >>Good question. It's It's totally a synergistic offering, right? So rrp a companies like UI path and automation anywhere they typically provide a horizontal toolkit to allow you know, banks and lenders to automate much of the mundane work like, for example, collecting information from emails or doing onboarding for a new employee. Or, you know, different types of tasks that a manual worker would have done but could be automated with relatively simple code. Um, what happens in our p a. Workflows is they get hung up on tasks that can't be completely automated. So, for example, a robot might be, uh, trying to complete an intend lending flow. But when a bank statement is submitted as part of that flow, the robot can't parse it. So what they do instead, is they routed to an underwriter who performs a manual analysis, keys information into a back office system that the bank is using and that information then gets handed back to a robot and continues the automation flow. What we do is we plug the gaps that used to be manual so a robot can pass US documents like bank statements or pay stubs or tax stops. We run our unique human in the loop process. We return structure Jason output directly to a robot, and it continues into the, you know, to the next step of the flow. And, you know, in in summary, the combination of robotic process automation and human in the loop, which is what we're doing, creates true and and automated flows rather than R P. A mite by itself might get you 80% of the way there. >>So do you have, like, software integrations or partnerships with those companies. How are you integrating with them? >>We do. We have software integrations with both UI path and automation anywhere in our core fintech lending business. R P A isn't as prevalent, but we are now expanding beyond fintech lenders into mortgage lending and traditional banks. And we're also expanding use cases, right? Like historically small business lending was the core of our business. More recently, we've moved into consumer auto mortgage, additional asset classes. And as we've gotten deeper with financial institutions, we've seen even more opportunity to partner and coexist with broader r p a player's >>Yeah, great. I mean, I was just staring at their s one. I guess it was came up Monday. Half over half a billion dollars in a are are they're actually cash flow positive as you iPad. So we're going to see we're going to see them hit the public market shortly. Um, hang on, folks. Uh, now it's okay. So this is you sell a sas, right? A SAS service. Even though there's that human in the loop, that's all part of the service. How do you How do you price? >>So usage based model. So we we kind of try to model are themselves nerve. A massive company is super powerful. We apply that same concept document processing, so it's a usage based model. Customers will pay us either per application per document or per page, and if they want to subscribe for, you know, one document per month or millions of documents per month, it's up to them. And we're able to flex up and flex down to meet the supply and demand. Um and that that concept that scalability and flexibility was particularly powerful in the P P P program, right? P P. P. Was kind of a very unique situation in the sense that lenders weren't able to predict the amount of loans they needed to process in normal lending. A small business lender can tell you Hey, we expect to get roughly 10,000 applications in the month of April with P P p. They could tell us, Hey, we're going to send out 200,000 marketing emails and we expect 30% of people might reply, but we really don't have any idea, right? So what happened is the big banks ended up hiring without exaggeration. Thousands of temporary employees to come in and review documents and kind of scrambled to do this in a work from home setting during the pandemic. Whereas Cross River, they took a technology first approach. They implemented our A P I in the back end, and it enabled them to instantly scale up their resources. And the result of that is Cross River ended up becoming a top three pp, a top three p p. P lender nationally, outperforming many of the big banks with a super efficient and fast document review process. Because we were able to help them on the back end with the automation. >>That's awesome. I love the pricing model you mentioned. You mentioned Amazon. Is that the cloud you use or >>we do Our Our product is hosted in AWS and we, you know, take a lot of learnings from them from a business model and and positioning point of view. >>Yeah, and and I'm thrilled to hear you say I mean, I think a lot of forward thinking startups are doing the consumption model. I mean, you certainly see that with companies like snowflake and data dog and stripe. I mean, I think that that SAS model of okay, we're gonna lock you into a one year, two year, three year term. Sorry if if you get acquired, you're stuck with some, you know, stranded licenses. That's your problem. I think that, you know, you really thought that out. Well, um, you mentioned you're sort of expanding your your your total available market now, looking at at new markets, what are some of the big trends that you want to ride over the coming decade as you scale your company? >>The biggest one for us is mortgage automation. You know, the kind of the one of fintech small business and consumer loans were optimized, and we went from a place where, uh, you know, you would deal with a loan officer and have an in person transaction to modern day. You can get a loan from small business. If you're a small business, you can get a loan from PayPal online effectively instantly. If you're a consumer, you can get a loan from Sofia or lending club super smooth digital experience and really revolutionized the way that you know, the market thinks about financial products. I think the next wave of that is mortgage, and that's what we're focused on. Uh, you know, mortgage is a massive market in the sense of thousands of lenders. The average application contains a couple 100 pages worth of financial documents, and the pain points of the back end of the mortgage process were really accentuated. During covid, right refi Valium went way up and mortgage lenders were forced to process that volume in a work from home setting. So what happened is mortgage lenders were struggling with the concept of sending personally identifiable financial information to underwriters who aren't working in an office there, working at home and, you know, kids running around a million things going on. And it's just more difficult to manage than ever before. Um, and you know, as as the the volume kind of normalized debate and mortgage lenders thought about their own future of automation, I think there was just clear recognition across the board that these these mortgage lenders needed to learn from some of the fintech and really focus on automating the back office peace and you know, to your point earlier about business model, what we think about is translating cost that used to be a fixed cost and turning them into a variable costs So now, instead of worrying about having to match supply and demand and hire or fire people, depending on the volume that's coming in on any given month, a mortgage lender can instantly flex up, reflects down and have a super fast, accurate process to handle the darks. Um, and you know, we're seeing just awesome traction in the market with that with the mortgage space and we're excited to push >>forward there. That's great. Thank you. I mean you, Sam. You describe the chaos that work from home. The financial industry is very overly officious. If you know it's very security conscious. How do you handle security? Maybe you could comment on that. How you think about that? >>Sure. I mean, we we take a compliance first approach. We built the product from the ground up with compliance in mind, knowing that we were selling into financial institutions. We have a sock to type one and type two certification, which is, you know, an industry standard. All of our our verification happens with the Oculus employees. So there's no third parties involved in our process whatsoever. Um and then lastly, But perhaps most importantly, our product in and of itself is innately, um, you know, innately drives compliance. So every data point that we process from a financial document, we not only return the data, we return an exact bounding box coordinates of where that data field appeared on the original source so that that audit trail lives with the loan throughout its life cycle. What we saw prior to Oculus is a mortgage would go through an underwriting process. They make a decision, and then that loan might be sold downstream and a diligence firm as to come in. And they don't have the resources to review all the loans. So they review 15% of the loan tape and then they say, you know, they give a rating and what we do is we proactively tackle that by creating a a perfect audit trail upon origination that can live with the loan throughout its life cycle and that that process and that traceability has been super valuable to our mortgage and banking partners. >>So you can ensure the providence there. So let me end just by talking about the company a little bit. So you incubated you nailed the product market fit the and you pivoted and re nailed the product market fit. And like a lot of companies in your position, I would imagine you saw your growth come from just having a great product. You know, initially, word gets around, but then you got a scale. Uh, maybe you could talk a little bit about how how you did that. How you're doing that. You know where your hiring how you're hiring, what your philosophy is on on scaling. >>Sure. Look, I think the key for us is just surrounding ourselves with the right people. You know, the right mentors, advisors and investors to help us really take the business to the next level. Uh, you know, we had no pride of authorship. We're building this and recognize that there are a lot of people out there who have been there, done that and can really guide us and show us the way. I know you had interviewed Marc Roberge on on the show previously. Formerly the C r. O of hubspot. Mark was someone that we you know, we we read his book and had taken sales advice from him from an early age. And over the over time, we got him a little bit more familiar with the company. And and ultimately, Mark and his partner, J Po at Stage two Capital ended up investing in Oculus and really helping us understand how to build the right go to market engine. Um, as the company got bigger, we took on investments from really reputable firms in the financial services space. So our largest investors are okay, H C F T fintech collective and and QED investors. Uh, you know, QED was was founded by Nigel Morris, who is the co founder of Capital One. They backed Sophia and Prosper and a lot of big fintech lenders and, you know, bringing the collective expertise from the fintech sector as well as you know, from a sales and go to market strategy. Point of view created the right mix of ingredients for us to to really ramp up significantly. Uh, we had an awesome run over the years. We were pretty recently recognized by magazine as the number one fastest growing fintech company. And, you know, as the momentum is increased and the market conditions have been very favorable to us, we we just want to double down and expand. Mortgage is the biggest area of opportunity for us. And what we're seeking from a hiring perspective is, you know, go to market sales account executive type resources on the mortgage side as well as you know, deeper products expertise both on the mortgage side as well as with machine learning our product. Because we have the human in the loop piece, we create massive amounts of training data on a daily basis. So it's a, I think, a really exciting place for cutting edge machine learning developers to come and and innovate. >>What can you share with our audience about, you know, your company, any metrics and whatever you're comfortable with, how much money you've raised on my head count? If you want to get some companies comfortable giving a r r others on. But what what do you What can you share with us? >>Sure. Um, you know, we we've raised about 50 million in venture capital. We have grown from one to north of 20 million in revenue in the in the last three years. So particularly since you know, 2017, 2018 is what we really started to see. The growth take off, uh, company size. We have about 800 to 900 employees globally. Now we have about 200 corporate employees who perform the, you know, the the day to day functions of Oculus. And then we have a long tail of about 600 or so verifiers who perform data verification and quality control work again, Speaking to the human in the loop piece of the bottle. Uh, we're, you know, we're focused on expanding beyond the fintech customer base, where we serve customers like plaid PayPal lending club so fi square, etcetera into the mortgage space and ultimately into the traditional banking space where you know, the problems, frankly, are extremely similar. Just on a much larger scale. >>San Bobbly. Congratulations on all the success. You You've got a great road ahead. I really appreciate you coming on the Cube, >>Dave. Thanks so much. It's been a great chat. Look forward to keeping in touch. >>Alright, Did our pleasure. Thank you for watching everybody. This is Dave Volonte for the Cube. We'll see you next time

>> Narrator: From the Computer History Museum in the heart of Silicon Valley it's the Cube, covering food I.T., fork to farm, brought to you by Western Digital. >> Hey, welcome back, everybody. Jeffrey here with The Cube. We're at the Computer History Museum in Mountain View, California, at the Food I.T. show. About 350 people from academe, from food producers, somebody came all the way from New Zealand for this show. A lot of tech, big companies and start-ups talking about applying IT to food, everything from ag to consumption to your home kitchen to what do you do with the scraps that we all throw away. We're excited now to get to the "Big Brain" segment. We've got our Ph.D.s on here. We're excited to have Doctor Glenda Humiston. She's the V.P. of agriculture and natural resources for the University of California. Welcome. And also, Doctor Helene Dillard. She's the dean of the College of Agricultural and Environmental Sciences at UC Davis. Welcome. >> Thank you. >> So first off, we were talking a little bit before we turned the cameras on. Neither of you have been to this event before. Just kind of your impressions of the event in general? >> Glenda: I love seeing the mix of the folks here as you were saying in your intro. There's quite a diverse array of people, and I personally believe that's what's really going to help us find solutions moving forward, that cross-pollination. >> Helene: And I've enjoyed it, just seeing all the different people that are here, but then the interaction with the audience was very uniquely done, and I just think that's a real big positive for the show. >> So you guys were on a panel earlier today, and I thought one of the really interesting topics that came up on that panel was, what is good tech? You know, everybody wants it all, but unfortunately there's no free lunch, right? Something we all learned as kids. There's always a trade-off, and so people want perfect, organic, this-free, that-free, cage-free, at the same time they want it to look beautiful, be economical and delivered to their door on Amazon Prime within two hours. So it's interesting when we think of the trade-offs that we have to make in the food industry to kind of hit all these pieces, or can we hit all these pieces or how does stuff get prioritized? >> Well I think that for us, it's going to be a balance, and trying to figure out how do you provide the needs for all these different audiences and all the different things that they want and I don't think one farmer can do it for all these different groups that have different demands on what they're looking for. And some of the tradeoffs could be, as we go away from pesticides and from other things, we might have more blemishes. And those are still edible pieces of fruit and vegetables, it's just that maybe it's curly, maybe the carrot's not straight, you know, maybe it's forked, but it's still very edible. And so I think that we have to do a lot more to help educate consumers, help people understand that it doesn't have to look perfect to give you perfect nutrition. >> Right, right. >> Glenda: Yeah, yeah, Helene is absolutely right. Some of it's just education, but some of it's also us finding the new technology that is acceptable to the public. Part of the problem is we sometimes have researchers working on their own, trying to find the best solution to a problem and we're not socializing that with the public as we're moving forward. So then all of a sudden, here's this new type of technology and they're like, where did this come from? What does it mean to me? Do I need to worry about it? And that's one reason--we talked earlier on the panel too, about the need to really engage more of our citizens in the scientific process itself, and really start dealing with that scientific illiteracy that's out there. >> Because there was a lot of talk about transparency in the conversation-- >> Yes. >> Earlier today about what is transparency. Cause you always think about people complaining about genetically modified foods. Well what is genetically modified? Well, all you have to do is look at the picture of the first apple ever, and it was a tiny little nasty-looking thing that nobody would want to eat compared to what we see at the grocery store today. A different type of genetic modification, but still, you don't plant the ugly one, and you plant the ones that are bigger and have more fruit. Guess what, the next round has more fruit. So it does seem like a big education problem. >> It is, and yet, for the average human being out there, all you have to do is look at a chihuahua next to a Saint Bernard. None of that was done with a genetically modified technology and yet people just--they forget that we've been doing this for thousands of years. >> Jeffrey: Right, right. You talked about, Glenda, the VINE earlier on in the panel. What is the VINE? What's the VINE all about? >> Well, it's brand new. It's still getting rolled out. In fact, we announced it today. It's the Verde Innovation Network for Entrepreneurship. You know, you've got to think of a clever way to get that acronym in there >> Which comes first, the chicken or the egg? >> Basically it's our intent from University of California to catalyze regional innovation and entrepreneurship ecosystems. Part of what's driving that is we've got a fairly good amount of resources scattered around the state, even in some of our rural areas, on small business development centers, our community colleges, our county cooperative extension offices, and a host of other resources including lately, the last several years, incubators, accelerators, maker's labs. But they don't talk to each other, they don't work together. So we're trying to go in, region by region, and catalyze a coalition so that we can make sure that our innovators, our inventors out there, are able to go from idea to commercialization with all the support they need. Via just basic legal advice, on should they be patenting something. Access to people to discuss finances, access to people that can help them with business plans. Opportunities to partner with the University in joint research projects. Whatever it takes, make sure that for anybody in California they can access that kind of support. >> That's interesting. Obviously at Haas, and at Stanford, not far from here, you know, a lot of the technologies of such companies come out of, you know, kind of an entrepreneurial spin with a business-focused grad and often a tech grad in a tech world. You know, ton of stuff at Berkeley on that, but >> Yeah, but those folks this is really for ag >> are in urban areas >> If you're in a large urban area or you're near a major campus you've probably got access to most of that. If you're in agriculture, natural resources, and in particular, our more remote, rural communities, you typically have no access, or very little. >> Right. So biggest question is, Helene, so you're at Davis, right, obviously known as one of the top agricultural-focused schools certainly in the UC system, if not in the world. I mean, how is the role of academic institutions evolving in this space, as we move forward? >> I would say it's evolving in that we're getting more entrepreneurship on campus. So professors are being encouraged to look at what they're working on and see if there's patent potential for this. And also, we have a group on UC Davis campus called Innovation Access, but looking at how can they access this population of people with money and, you know, the startups to help them bring their thing to market? So that's becoming-- that's a very different campus than years ago. I think the other thing is, we're also encouraging our students to look at innovation. And so we have a competition called the Big Bang, and students participate in that. They do Hag-a-thon, they do all these kinds of things that we tend to think that only the adults are doing those but now the students are doing them as well. And so we're trying to push that entrepreneurship spirit out onto all of our campus, onto everyone on the campus. >> And I do want to emphasize that this isn't just for our students or our faculty. One of the key focuses of the VINE is all of our external partners, too. Just the farmers, the landowners, the average citizens we're working with out there. If they've got a great idea, we'd like to help them. >> Jeffrey: And what's nice about tech is, you know, tech is a vehicle you can change the world without having a big company. And I would imagine that ag is kind of-- big ag rolled up a lot of the smaller, midsize things, and there probably didn't feel like there was an opportunity that you could have this huge impact. But as we know, sitting across the street from Google, that via software and technology, you can have a huge impact far beyond the size and scope of your company. And I would imagine that this is a theme that you guys are playing off of pretty aggressively. >> Absolutely. I think that there are people on campus that are looking for small farm answers and mechanization as well as large farm answers. We have people that are working overseas in developing countries with really, really small farm answers. We have people that are working with the Driscolls and partnering up with some of these other big companies. >> We talked a little bit before we went on air about kind of the challenges of an academic institution, with some of the resources and scale. These are big, complicated problems. I mean, obviously water is kind of the elephant in the room at this conference, and it's not being talked about specifically I think they've got other water shows. Just drive up and down the valley by Turlock and Merced and you can see the signs. We want the water for the farms, not for the salmon in the streams, so where do the--the environmental impacts. So these are big, hairy problems. These are not simple solutions. So it does take a lot of the systems approach to think through, what are the tradeoffs of a free lunch? >> It really does take a systems approach, and that's one thing here in California, we're doing some very innovative work on. A great example that both UC Davis, my division, and other parts of the UC system are working on is Central Valley AgPlus Food and Beverage Manufacturing Consortium, which is 28 counties, the central valley and up into the Sierra. And what's exciting about it is, it is taking that holistic approach. It's looking at bringing around the table the folks from research and development, workforce, trained workforce, adequate infrastructure, financing, access to capital, supply chain infrastructure, and having them actually work together to decide what's needed, and leverage each other's resources. And I think that offers a lot of possibility moving forward. >> And I would say that at least in our college, and I would call the whole UC Davis, there's a lot of integration of that whole agriculture environmental space. So we've been working with the rice farmers on when can you flood the rice fields so that there's landing places for the migrating birds? Cause this is the Pacific flyway. And can we grow baby salmonids in that ricewater and then put them back in the bay? And they figured out a way to do that, and have it actually be like a fish hatchery, only even better, because we're not feeding them little tiny pellets, they're actually eating real food, (laughs) whole foods. >> And how has an evolution changed from, again, this is no different than anyplace else, an old school intuition, the way we've always done it versus really a more data driven, scientific approach where people are starting to realize there's a lot of data out there, we've got all this cool technology with the sensors and the cloud and edge computing and drones and a whole lot of ways to collect data in ways that we couldn't do before and analyze it in ways that we couldn't do before to start to change behavior, and be more data-driven as opposed to more intuition driven. >> I would say that what we're seeing is as this data starts to come in precision gets better. And so now that we understand that this corner of the field needs more water than the other side, we don't have to flood the whole thing all at once. You can start on the dry side and work over to the other side. So I think the precision is getting much, much better. And so with that precision comes water efficiency, chemical efficiency, so to me it's just getting better every time. >> And frankly, we're just at the beginning of that. We're just starting to really use drones extensively to gather that type of data. New ways of using satellite imagery, new way of using soil sensors. But one of the problems, one of the big challenges we have, back to infrastructure, is in many parts of your agricultural areas, access to the internet. That pipeline, broadband. If you've got thousand of sensors zapping information back you can fill up that pipeline pretty fast. It becomes a problem. >> Jeffrey: That pesky soft underbelly of the cloud, right? You've got to be connected. Well, we're out of time, unfortunately. I want to give you the last word for people that aren't as familiar with this, basically, myself included, what would you like to share with people that could kind of raise their awareness of what's happening with technology and agriculture? >> Well, I guess that I would start out saying not to be afraid of it, and to look at the technology that has come. Remember when we had the rotary dial phone? My son doesn't even know what that is! (laughs) >> Jeffrey: Mom, why do you say dial them up? >> Yeah, why do you say dial people up? So I think, looking at your rotary phone, now, looking at your smart phone, which has more computing power than your first Macintosh. It's very--the world is changing, and so why do we expect agriculture to stay in the 1800s mindset? It's moving too, and it's growing too, and it's getting better just like that iPhone that you have in your hand. >> I think I would add that to that, back to the citizen science, I would love people out there, anybody, average citizens young or old to know that there's opportunities for them to engage. If they're concerned about the science or the technology come work with us! We have over twenty thousand volunteers in our programs right now. We will happily take more. And they will have a chance to see, up close and personal, what this technology is and what it can do for them. >> Alright. Well that's great advice. We're going to leave it there, and Dr. Humiston, Dr. Dillard, thank you for taking a few moments out of your day. I'm Jeffrey. You're watching the Cube. We're at the Computer History Museum. Food IT. Learning all about the IT transformation in the agriculture industry. Also to the kitchen, your kitchen, the kitchen of the local restaurant and all the stuff that can happen with those scraps that we throw away at the end of the day. Thanks for watching, and we'll be right back after this short break. (electronic music)