Issue #6: Life on Lockdown, Next-gen Simulation Tools, and the Misinformation Apocalypse

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Data Exchange podcast

Next-generation simulation software will incorporate deep reinforcement learning

Chris Nicholson, founder and CEO of Pathmind, a startup applying deep reinforcement learning (DRL) to business simulations. Through early previews from Pathmind, I’ve already seen early glimpses of how DRL is being incorporated into simulation modeling software. I expect this to be an arena where RL will be extensively used (albeit in the background). 

Business at the speed of AI: Lessons from Shopify

Solmaz Shahalizadeh, VP and Head of Data Science and Data Platform Engineering at Shopify, and she has played a critical role in helping Shopify scale its data and machine learning infrastructure.

How deep learning is being used for search and information retrieval

Edo Liberty, is the founder of Hypercube, a startup building tools for deploying deep learning models in search and information retrieval involving large collections. When I spoke at AI Week in Tel Aviv last November several friends encouraged me to learn more about Hypercube – I’m glad I took their advice! 

Enterprise applications of reinforcement learning

The success of reinforcement learning in game play (Atari, Go, multiplayer video games) has led to considerable interest from industrial data scientists and machine learning engineers. I recently wrote a post describing use cases in recommendations, personalization, and business simulation modeling.

Machine Learning tools and infrastructure

  • Open source database management systems: Andy Pavlo takes a look at the code repo sizes of some popular systems.
  • New JAX swing: Created by researchers at Google Brain, JAX seems to be taking hold in the machine learning research community. DeepMind recently released two new libraries –   RLax (RL on JAX) and Haiku (a simple DL library on JAX).
  • Ray Summit has been postponed until the Fall. In the meantime, enjoy an amazing series of virtual conferences beginning in mid May on the theme “Scalable machine learning, scalable Python, for everyone”.  The first event features a talk on the state of AI and ML by Michael Jordan.
  • A Tour of End-to-End Machine Learning Platforms
  • Easy Distributed Scikit-Learn with Ray: Ameer Haj Ali of RISELab  describes an easy way to scale your scikit-learn applications to a cluster with Ray’s implementation of joblib’s backend.


  • Life on lockdown in China: a New Yorker piece by one of my favorite writers, Peter Hessler.
  • The power of data in a pandemic: data unification, refinement, and cleaning for a data platform that will provide UK organizations responsible for coordinating the response with secure, reliable and timely data.
  • Epidemic Modeling 101: My friend Bruno Gonçalves explains fundamental concepts in mathematical biology that are relevant to modeling COVID-19.
  • A recent paper investigates how air temperature and humidity influence the transmission of COVID-19. My home region of Southeast Asia will be an important test case for the importance of these factors.
  • MIT Webinar:  check if video is available

Work and hiring:


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