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SVR Ep. 3 | Descartes Labs Using AI + ML for Crop Predictions

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Manage episode 159152976 series 1243503
内容由Space Ventures Radio提供。所有播客内容(包括剧集、图形和播客描述)均由 Space Ventures Radio 或其播客平台合作伙伴直接上传和提供。如果您认为有人在未经您许可的情况下使用您的受版权保护的作品,您可以按照此处概述的流程进行操作https://zh.player.fm/legal
This week's episode of Space Ventures Radio examines Descartes Labs, a machine learning and predictive analytics startup using satellite imagery to predict crop yields, starting with corn in the U.S. Important Update on the "Team" Section: I missed mentioning Mark Mathis (Software Architect), Rick Chartrand (Mathematician) and Tim Kelton (Cloud Architect) in the founding team. MARK MATHIS — For the past decade Mark has worked to help put great science into the hands of decision makers, a role he continues to pursue at Descartes Labs creating and curating rich customer experiences. He studied computer science and engineering at Texas A&M University before moving to Los Alamos National Laboratory as a DOE High Performance Computer Science Graduate Fellow. RICK CHARTRAND — was once a pure mathematician, with a PhD from University of California, Berkeley. He now much prefers to be useful. Rick’s applied mathematics expertise includes image processing, machine learning, compressive sensing, and the iconoclasm of non-convex continuous optimization. TIM KELTON — focuses on building distributed systems using cloud architecture. Prior to joining Descartes Labs, Tim was a Research and Development engineer for 15 years at Los Alamos National Laboratory working on problem areas such as deep learning, space systems, nuclear non-proliferation, and counterterrorism. http://descarteslabs.com/team.html ------------------------ EPISODE SECTIONS ------------------------ 2:32 - The Upshot 4:18 - The Problem 5:19 - The Solution 8:33 - The Business Model 10:53 - The Team 12:43 - The Competition 15:17 - The Roadmap 16:36 - The Conclusion ------------------------ RELEVANT LINKS ------------------------ Descartes Labs 2016 Corn Forecast: http://descarteslabs.com/forecast.html ------------------------ Intro music: “Take Me Higher” by Menya Hinga
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Manage episode 159152976 series 1243503
内容由Space Ventures Radio提供。所有播客内容(包括剧集、图形和播客描述)均由 Space Ventures Radio 或其播客平台合作伙伴直接上传和提供。如果您认为有人在未经您许可的情况下使用您的受版权保护的作品,您可以按照此处概述的流程进行操作https://zh.player.fm/legal
This week's episode of Space Ventures Radio examines Descartes Labs, a machine learning and predictive analytics startup using satellite imagery to predict crop yields, starting with corn in the U.S. Important Update on the "Team" Section: I missed mentioning Mark Mathis (Software Architect), Rick Chartrand (Mathematician) and Tim Kelton (Cloud Architect) in the founding team. MARK MATHIS — For the past decade Mark has worked to help put great science into the hands of decision makers, a role he continues to pursue at Descartes Labs creating and curating rich customer experiences. He studied computer science and engineering at Texas A&M University before moving to Los Alamos National Laboratory as a DOE High Performance Computer Science Graduate Fellow. RICK CHARTRAND — was once a pure mathematician, with a PhD from University of California, Berkeley. He now much prefers to be useful. Rick’s applied mathematics expertise includes image processing, machine learning, compressive sensing, and the iconoclasm of non-convex continuous optimization. TIM KELTON — focuses on building distributed systems using cloud architecture. Prior to joining Descartes Labs, Tim was a Research and Development engineer for 15 years at Los Alamos National Laboratory working on problem areas such as deep learning, space systems, nuclear non-proliferation, and counterterrorism. http://descarteslabs.com/team.html ------------------------ EPISODE SECTIONS ------------------------ 2:32 - The Upshot 4:18 - The Problem 5:19 - The Solution 8:33 - The Business Model 10:53 - The Team 12:43 - The Competition 15:17 - The Roadmap 16:36 - The Conclusion ------------------------ RELEVANT LINKS ------------------------ Descartes Labs 2016 Corn Forecast: http://descarteslabs.com/forecast.html ------------------------ Intro music: “Take Me Higher” by Menya Hinga
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