Gradient Flow #37: Automation in DataOps, Neural RecSys, Self-Supervision

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This edition has 485 words which will take you about 3 minutes to read.

“Moses was technically the first person to download files to his tablet from the cloud.” – @ADDiane

Data Exchange podcast

  • Automation in Data Management and Data Labeling  Hyun Kim is co-founder and CEO of Superb AI, a startup building tools to help companies manage data across the entire machine learning application lifecycle. This includes tools to label, store, and monitor data assets that power computer vision applications. We also discussed emerging trends in machine learning and AI including synthetic data, reinforcement learning, and self-supervised learning.
  • Questioning the Efficacy of Neural Recommendation Systems  I speak with Paolo Cremonesi, Professor of Computer Science, and Maurizio Ferrari Dacrema a postdoc at Politecnico di Milano, where they are both part of the RecSys research group. We discussed two survey papers they recently completed on the use of deep learning in recommendation systems, as well as broader trends in RecSys systems.
[Image by Peggy und Marco Lachmann-Anke from Pixabay.]

Data & Machine Learning tools and infrastructure

2021 NLP Survey

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  • The Top Trends in Tech   A must-read new report and interactive website from the McKinsey Technology Council
  • Self-supervision from the bottom up  Alyosha Efros explains why he’s excited about the potential of self-supervised learning methods in computer vision.
  • Neural Network Verification  Given challenges facing ML teams in areas like safety, robustness, consistency, this is a must-read (in progress) book on how to apply ideas from formal methods to verifying properties of neural networks. 
  • The SaaS CTO Security Checklist  A nice list, but as with anything pertaining to managing risk, we need a section dedicated to organizational best practices. 
  • We Are AI   A five-module NYU course on the basics of AI that comes with comic books

Closing Short: Big Tech in China.

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