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Today, Razib cross-posts an episode of his other podcast. When not working on this Substack, Razib devotes his time to GenRAIT, a startup accelerating scientific discovery by providing infrastructure and tools to researchers. GenRAIT fosters science and discovery by making biological data accessible, usable, and minable.
Razib and his cofounders, Dr. 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, drilling 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.
By Razib Khan4.8
206206 ratings
Today, Razib cross-posts an episode of his other podcast. When not working on this Substack, Razib devotes his time to GenRAIT, a startup accelerating scientific discovery by providing infrastructure and tools to researchers. GenRAIT fosters science and discovery by making biological data accessible, usable, and minable.
Razib and his cofounders, Dr. 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, drilling 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.

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