Are your scientist friends intimidated by machine learning? They might be inspired by a primer from one of the world’s premier tech titans.

What’s new: Former Google CEO Eric Schmidt and Cornell PhD candidate Maithra Raghu school scientists in machine learning in a sprawling overview.

Scientific Revolution 2.0: Science produces mountains of data, and machine learning can help make sense of it. Schmidt and Raghu offer a brisk tour of architectures and techniques, explaining how neural networks have served disciplines from astronomy to radiography.

  • Image classifiers are showing great potential in medicine, where they can predict, say, whether viruses appear in a picture from a cryo-electron microscope. Object detection has spotted individual cancer cells in microscope images, and semantic segmentation has differentiated various types of brain tissue in MRIs.
  • Graph neural networks, which learn relationships between nodes and connections, have been used to analyze how atoms and bonds determine molecular structure. They’ve also been used to design molecules to match particular chemical properties.
  • The qualities that make recurrent neural networks good at figuring out grammar helps them find patterns in a variety of sequential data. This includes finding patterns in gene sequences.
  • Weakly supervised learning is handy for scientists with lots of data but few grad students to label and organize it. If has been applied widely in biomedicine, but also to track penguins in satellite photos.
  • Reinforcement learning shows promise in accelerating simulations in astronomy, chemistry, climate science, high energy-density physics, and seismology.

Behind the news: Maithra Raghu isn’t as famous as her co-author, but her star is on the rise. Named among Forbes’ “30 Under 30” last year, she focuses on improving human-machine collaboration.

Why it matters: The range of mysteries that machine learning can help solve is limited by the number of scientists who are proficient in machine learning.

We’re thinking: We’d like to see more CEOs publish technical papers on!


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