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内容由GenRAIT Inc. and Razib khan提供。所有播客内容(包括剧集、图形和播客描述)均由 GenRAIT Inc. and Razib khan 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
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Introducing GenRAIT: when technology drives science

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Manage episode 409437107 series 3561016
内容由GenRAIT Inc. and Razib khan提供。所有播客内容(包括剧集、图形和播客描述)均由 GenRAIT Inc. and Razib khan 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

Razib and his cofounders, Santanu Das and Taylor Capito, will talk about what they’ve built at the JPM Healthcare Conference in San Francisco, January 8th-11th, and showcase their products at the Plant and Animal Genomes Conference in San Diego, January 12th-17th.

After an overview of GenRAIT’s business logic with Capito, Razib digs deeper with Dr. Das, a computer scientist by training, and discusses what machine learning is. They drill-down into details like the difference between SVM and random forest models. Dr. Das surveys the multi-decade arc of “Artificial intelligence” (AI), predicts its inevitable utility in biology, and makes the broader case for productizing scientific tools to empower bioinformaticians, ML engineers and data scientists as an integral part of the scientific ecosystem.

  continue reading

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Artwork
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Manage episode 409437107 series 3561016
内容由GenRAIT Inc. and Razib khan提供。所有播客内容(包括剧集、图形和播客描述)均由 GenRAIT Inc. and Razib khan 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

Razib and his cofounders, Santanu Das and Taylor Capito, will talk about what they’ve built at the JPM Healthcare Conference in San Francisco, January 8th-11th, and showcase their products at the Plant and Animal Genomes Conference in San Diego, January 12th-17th.

After an overview of GenRAIT’s business logic with Capito, Razib digs deeper with Dr. Das, a computer scientist by training, and discusses what machine learning is. They drill-down into details like the difference between SVM and random forest models. Dr. Das surveys the multi-decade arc of “Artificial intelligence” (AI), predicts its inevitable utility in biology, and makes the broader case for productizing scientific tools to empower bioinformaticians, ML engineers and data scientists as an integral part of the scientific ecosystem.

  continue reading

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