ML4Sci #24: Massive Open Catalyst Project by Facebook and Carnegie Mellon; Trends from ICLR
Also, Google's AI moat and the Department of Justice acts on anti-trust
Hi, I’m Charles Yang and I’m sharing (roughly) weekly issues about applications of artificial intelligence and machine learning to problems of interest for scientists and engineers.
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There was so much news these past few weeks, I’ve decided to just send out an extended “In the News” section. Stay tuned - next issue will be a special collaborative issue on Batteries x AI!
📰In the News
Towards ML Engineering: A Brief History Of TensorFlow Extended (TFX). A nice retrospective by the Google team for TensorFlow Extended on how they are rewriting the rules of “software engineering” for “ML engineering”. For the interested, here are some more DevOps advice from Google: “Rules of ML”. Keep an eye on this field: we’re going to see an explosion of new companies and new techniques in the field of ML DevOps
On the tail of DevOps and Google: “How AI is powering a more helpful Google”(A quick timeline). Kind of insane how Google’s AI expertise+scale of data is creating incredible products with awesome value add to customers in such a short period of time (and to the detriment of any competition).
📈Trends from ICLR submissions - nice meta review of where the cutting-edge of ML is going
Lawrence Berkeley Lab develops self-guided algorithm to autonomously collect data on neutron scattering, with improved fidelity and reduced measuring time (btw, it wasn’t with deep learning). LBL also has a nice website called ml4sci.lbl.gov, so it looks like I’ve got some competition 👀
Facebook, Carnegie Mellon launch Open Catalyst Project, a competition dataset with 1.3M DFT calculations in the hopes of finding new catalysts for energy storage[FB Blog] See below for figure from the dataset paper
🐦Another twitter thread (honestly, I should just become a twitter thread compiler): emerging trends in photovoltaics
Citrine, a ML+materials startup, details their DOE-funded work on determining uncertainty in DFT estimates. A good measure of a field’s maturity is the amount of research done by commercial ventures - it seems the computational materials field is definitely maturing in that area!
Physics Meets ML seminar: “Science is a verb: adopting the scientific method and best practices in AI research” by Michela Paganini@Facebook AI. Really important topic given the huge amount of messy, empirical work being done in AI
🔒Nature Review Materials: AI for 3D printing - using AI for difficult control problems. Silicon Valley enthusiasts want self-driving cars, but for me, I’m much more excited about self-driving printers.
The Science of Science
🎉Nature journals announce first open-access agreement with Max Planck Digital Library. Publishing cost to author is still exorbitant, but the tide is slowly turning toward open-access
🌎Out in the World of Tech
🚗Waymo opens fully driverless ridesharing to Phoenix general public. Another trend (self-driving cars) accelerated by COVID-19
Policy and Regulation
⚖️The Department of Justice has finally filed its antitrust lawsuit against Google. For an in-depth analysis, you can check out Ben Thompson’s Stratechery article on US vs. Google
Thanks for Reading!
I hope you’re as excited as I am about the future of machine learning for solving exciting problems in science. You can find the archive of all past issues here and click here to subscribe to the newsletter.