>>law from Las Vegas. It's the Q covering a ws re invent 2019. Brought to you by Amazon Web service is and in along with its ecosystem partners. >>Hey, welcome back. Everyone's two cubes. Live coverage I'm John for with the Cube were here reinvent date, too, as it winds down Walter Wall interviews two sets here. We want to think Intel, big sponsor of this, said we without Intel, we wouldn't have this great content. They support our mission at the Q. We really appreciate it. We're here and strengthen the signal the noise on our seventh reinvent of the eight years that they've been here. We've been documenting history, and we got a great panel lined up here. They got Sebastian to holler Who's the CEO? Sale Drone. Henry Stalls, Stool The VP of Science and Technology and Bowery Farming. Great use case around the food supply and Janet his era space weather scientists at NASA. The Kilo Physics division. We got a great lineup here. Great panel. Welcome to the Cube. Thanks for coming. Thank you. Okay. We'll start with you, Jen. And you're doing some super cool space exploration. You're looking at super storms in space. What's your story? >>Yeah, I work at NASA and NASA has in its mandate to understand how to protect life on Earth and in space from events like space, weather and other things. And I'm working with Amazon right now to understand how storms in space get amplified into super storms in space, which now people understand, can have major impacts on infrastructures head earth like power grits. >>So there's impact. >>There's a >>guy's measuring that, not like a supernova critical thing like >>that >>of, like, practical space. >>Actually, the idea that the perception of the world of the other risks of space weather changed dramatically in 1989 when Superstorm actually caused the collapse of a power grid in Canada and the currents flowing in the ground from the storm entered the power grid and it collapsed in 90 seconds. It couldn't even intervene. >>Wow, some serious issues. We want to get into the machine learning and how you guys are applying. But let's get through here, and we're doing some pretty cool stuff that's really important. Mission. Food supply and global food supply something that you're doing. What I think it might explain. >>Yeah, Bowery were growing food for a better future by revolutionizing agriculture. And to do that, we're building these ah network of large warehouse scale indoor farms where we go all sorts of produce indoors 365 days a year, using zero pesticides using hydroponic systems and led technology. So it's really exciting. And at the core of it is some technology we call the Bowery operating system, which is how we leverage software hardware in a I tow, operate and learn from our farm. >>I'm looking forward to digging into that Sebastian sale drone. You're doing some stuff you're sailing around the world. You got nice chance that you now tell your story. >>Sadly, no way. Use wind powered robots to study the 20% of the planet that's currently really data scarce. And that's the oceans on. So we measure things like biomass, which is how many fish down in the ocean. We measure the input of energy, which impacts weather and climate. We mapped the seabed on. We do all kinds of different tasks which are very, very expensive to do with few ships >>and to report now that climate change is on everyone's agenda, understanding potentially blind spots. Super important, right? >>That's what I'm trying to, You know, this whole question of if it's a question of what? When and what and how much. And so, you know, the ice is melting, the Gulf Stream is changing, and Nina is wrecking havoc. But we just do not understand this because we just don't have the data. In city, we use satellites where they have very low resolution. They cannot see through the water where you ships. No, has 16 ships he in the U. S. So we have to do better. We have to translate this into a big data problem. So that's what we're doing. We have 1000 sale drones on our plan with 100 water right now. And so we're trying to instrument old oceans all the time, >>you know, and data scales your friend because you don't want more data. Yes. Talk about what you're working on. What kind of a I in machine learning are you doing? You just gathering day. Then you're pumping it up to the cloud via satellites or what's going on there? >>One of the one of the use cases trying to understand you know who's out there. What are they doing? Another doing anything illegal. So to do this, you need to use cameras and look at the horizon and detect. You know whether you have vessels. And if those vessels are not transmitting the position, it means that they're trying to stay hidden on the ocean. And so we use machine learning and I that we train on on AWS to try to understand what where those things are. It's hard enough on land at sea. It's very hard because every pixel is moving. You have waves. The horizon is moving, the skies moving, the ship is moving. And so trying to solve this problem is a completely new thing that's called maritime domain awareness on, and it's something that has never been done before. >>And what's the current status of the project? >>So wave been live for about four years now we have 100 sail drones were building one a day towards the goal of having 1000 which we covered all the planet in a six by six degrees squares on. We are operationally active in the Arctic in the tropical Pacific. In the Atlantic. We just circumnavigated Antarctica, So it's the thing. That's really it's out there. But it's very far from from from land, >>So the spirit of cloud and agility static buoy goes away. You want to put the sale drones out there to gather and move around and capture. >>That's what the buoy is. You know, a massive steel thing, which has a full mile long cable, and it's it's headed to the silo in a fix stations one point and the ocean goes by. You having and robots means that you can go where you know something interesting is happening where you have a hurricane where you might have an atmospheric river where you might have a natural catastrophe or man made catastrophe. So this intelligence of the platform is really important in the navigation. That platform requires intelligence. And on the other side, getting 1000 times more data allows you to understand things better, just like Michael is doing. >>It isn't a non profit of four profit venture. >>It's a for profit company. So we said raw data a fraction of the cost of existing solution to try to create this kind of transformative impact on understanding what's happening >>that's super exciting for all the maritime folks out there because I love the ocean myself. Henry, you you're tackling real big mission. How using technology. I can almost imagine the instrumentation must be off the charts. What's your opportunity? Looked like? A tech perspective >>s o The level of control we have in our farms is really unparalleled. Weaken tune Just about every parameter that goes into growing our plans from temperature humidity Co Two light intensity day night cycles list keeps going on. And so to do Maur with fewer resource is to grow Maurin our farms. We're doing something called science a scale where we can pull different levers and make changes to recipes in real time. And we're using a I tow, understand the impact that those changes have and to guide us going from millions of different permutations. Trillions of permutations, really too. The perfect outdone >>converging. You jittery? Look at the product outcome. You circle that dated back is all on Amazon >>way. Do operate on Amazon. Yeah, and we're using deep learning technology to analyze pictures that come from cameras all over our farms. So we actually have eyes on every single crop that grows in our facilities and So we process those, learn from the data and and funnel that back into the >>like, Maybe put more light on this or do that kind of make a just a conditions. Is that that thing? That's >>exactly it. And we grow lots of different types of plants. We grow butter, head lettuce, romaine, kale, spinach, arugula, basil, cilantro. So there's a lot of different things we grow, and each of them require different, different little tweaks here and there. Toe produced over the best tasting and most nutritious product. >>That's cool, Janet Space. Lastly, on one inspection, we're gonna live on Mars someday. So you might be a weather forecaster for what route to take to Mars. But right now, the practical matter is Israel correlation between these storms. What kind of data problem are you looking at? What is the machine learning? What are some of the cool things you're working on? >>It? We have a big date, a problem because storms of that magnitude are very rare. So it's hard for us to find enough data to train a I we can't actually train a we have to use, you know, learning that doesn't require us to train it, but we've decided to take the approach that these super storms are like anomalies on the normal weather patterns. So we're trying to use the kind of a I that you used to detect anomalies like people who are trying to break into to do bank fraud or, you know, do a Web server tax. We use that same kind of software to tryto identify anomalies that are the space weather and look at the patterns between sort of a normal, more of a normal storm and a space with a huge space weather event to see how they patterns. Comparing how you're amplifying the regular storm into this big Superstorm activity. >>So it sounds like you have to be prepared for identifying the anomaly. See you looking at anomalies to figure out where the anomaly might be ready to be ready to get the anomaly. >>Yeah, you look at the background, and then what sticks out of the background that doesn't look like the background is is identified as the anomaly. And that's the storms that air happening, which are quite rare, >>all three of you guys to do some real cutting edge cool projects. I guess my question would be for the folks that are putting their toe in the water for machine learning. They tend to be new use cases like what you guys are doing, whether it's just a company tryingto read, factor themselves or we become reborn in the cloud ran legacy stuff. When you hear it, Amazon reinvent. This is the big question for these folks that are here. You guys are on the front end of a really cool projects. What's your advice that the people are trying to get in that mindset? >>So I think I think you know the way the way to think about this is if you're good at something and if you think you have the solution for something, how can you make that a 1,000,000 times more efficient? And so the problem is, there's just not enough capacity in the world, usually to treat data sets that a 1,000,000 times larger. And this is where machine learning should be thought about it as an extension of what humans really good at using a pair of eyes, ears or whatever or the sense. And so in our case. For example, counting fish acoustician, train acoustician, look at sonar data and understand schools of fish and can recognize them. And by using this knowledge base, we can train machines to do this on a much grander scale. And when you're doing a much grander scale, you derive. Ah, holding tight to >>your point is that humans are critical. I'm the process. So scaling the human capabilities and maybe filling in another scale issues or >>that's what a machine learning is. It's the greatest enabler of our time. It enables us to do things which are impossible to do before because we just didn't have enough people to do them at scale. >>AKI is being able to ask questions, right? And so if you have the questions to ask, you can apply this technology in a way that's never really been before possible. >>You're Jake. >>Yeah, I am actually someone who didn't know anything about a Ira ml when I started. I'm on. I'm a research scientist. That space weather. So coming into this, I'm working with E m L Solutions Lab here and putting a I experts with with experts and space brother we're getting we're doing things that are gonna give us new advances. I mean, We're already seeing things we didn't know before. So I think that if you partner with people who really have strong a I knowledge, you can use your knowledge of science to really get to the really important issues. >>Okay, I have to ask the final lightning round question. What is the coolest thing that you've done with your project that you've either observed implemented? That is super cool. Super cool. What's the coolest thing >>well in in terms of us were using anomaly detection to identify storms and in the first round through it actually identified every single Superstorm, which was not the major super storms, but it did. But it also started identifying other anomalous events, and when you went looked at him, they were anomalous events. So we're seeing things. It's picking out the weird things that are happening in space weather. It's kind of exciting and interesting. >>I worked for a day with you. I would love to just leave these anomalies every what's the coolest thing that you've seen or done with your project? >>I think the fact that we've built our own custom hardware own camera systems, uh, and that we feed those through algorithms that tell us something about what's happening minute by minute with plans as they grow to see pictures of plants minute by minute, they dance and it's truly it's It's remarkable. >>Wow! Fascinating Machin >>We've counted every single fish on the West Coast, the United States, every single air from Canada to Mexico. I thought I >>was pretty >>good. I didn't think it was possible. >>Very cool. But what's the number? >>Yeah, If I could tell you, I would. But I'm not allowed to tell you the jam. >>And you know where the salmon are, where they're running all that good stuff. Awesome. Well, congratulations, You guys doing some amazing work is pioneering a great example of just what's coming. And I love this angle of making larger human impact using technology. Where you guys a shaping technology for good things. Really, really exciting. Thanks for coming on, John Kerry. We're here live in Vegas for re invent 2019. Stay with more coverage. Day three coming tomorrow back with more After this break, when a fake intel for making it all happened presented by Intel Without their sponsorship, we wouldn't be able to bring this great content. Thanks for watching