Gradient Flow #18: Forecasting & Groupthink, Interpreting NLP, Ray Ecosystem

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This edition has 640 words which will take you about 4 minutes to read.

“If you want to go fast, go alone. If you want to go far, go together.” – African Proverb.

Data Exchange podcast

  • Using machine learning to modernize medical triage and monitoring systems  Kira Radinsky is Chairwoman & Chief Technology Officer at Diagnostic Robotics, a startup using AI to build a medical-grade triage and clinical-predictions platform. She was one of the pioneers in using alternative data sources to augment forecasting models. Her earlier work includes models to predict social unrest as well as disease outbreaks. Diagnostic Robotics was already building tools for clinical triage and they very quickly were able to tune their systems for the battle against COVID-19.  
  • Connecting Reinforcement Learning to Simulation Software   Max Pumperla, deep learning engineer at Pathmind and a contributor to many open source projects in data science and machine learning (including Hyperopt and Keras). At Pathmind, he is helping bring reinforcement learning to software used for simulations. Many companies already use simulation software and RL lets them tackle more complex real-world scenarios.

 [Image: reykjavik, harpa, hall from pikist]

Machine Learning tools and infrastructure

FREE Virtual Conferences

  • Horovod on Ray   Initially released in 2017, Horovod has become one of the most popular open source packages out of Uber. It’s used for scaling deep learning training, specifically data-parallel distributed training.  As of the latest release, Ray can now be used to execute Horovod jobs “without needing to coordinate the workers by hand”. Travis Addair who leads the Horovod project at Uber, will give a talk at the Ray Summit on Distributed Deep Learning with Horovod on Ray.  Horovod and Seldon are among a growing number of popular libraries that have integrations with Ray for distributed execution.
  • Interpreting Natural Language Processing Models   In this recent Simons Institute talk, Yonatan Belinkov of Technion describes much needed interpretability tools in an age when end-to-end learning using neural models dominates most NLP tasks. He goes through recent research into how linguistic structures (syntax, POS, morphology, …) can be used to understand neural language models. Reminiscent of work in explaining computer vision and speech models, layers of deep language models appear to correspond to a hierarchy of language properties (higher layers correspond to more sophisticated linguistic units). For more on recent research in NLP, the upcoming NLP Summit features recent progress in speech recognition (Bo Li of Google) and in testing NLP models (Marco Túllio Ribeiro of MSR).

Work and Hiring

[Image by Hillary Wimsatt from Pixabay]