Gradient Flow #17: RL for Recommenders, AI Assurance, Traffic Prediction

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This edition has 880 words which will take you about 5 minutes to read.

“Juggling is sometimes called the art of controlling patterns, controlling patterns in time and space.”  - Ron Graham

Data Exchange podcast

  • What is AI Assurance?  Ofer Razon and Superwise are part of a community in the early stages of building tools and best practices for scaling AI operations. The goal is to help multiple stakeholders build the necessary solutions to evaluate models, receive alerts and troubleshoot on time, and gather insights to improve efficiency.  AI assurance will ultimately bring together different parts of an organization including business, data science and operational teams, legal and compliance, and privacy and security. 

  • Using machine learning to detect shifts in government policy   Weifeng Zhong is the core maintainer of the open source Policy Change Index (PCI), a framework that uses machine learning and NLP to “process and read” large amounts of text to discern government priorities and policies. The initial PCI uncovers major policy shifts in China by text mining the People’s Daily.

[Image: National Library of China from Wikimedia]

Machine Learning tools and infrastructure


FREE Virtual Conferences

  • Learn about Multi Armed Bandits and RL-based Recommender Systems   This is a tutorial I’ve long advocated for.  Earlier this year I wrote a post on enterprise applications of reinforcement learning and one of the areas I highlighted was recommendation and personalization systems. This industry first tutorial, takes place at the Ray Summit and will be led by one of my favorite teachers, Paco Nathan.

  • How to accelerate NLP deep learning model training across multiple GPUs   New language models like BERT are very expensive to train. It cost OpenAI over $12M to train GPT-3! So instead of training from scratch, most companies focus on fine-tuning these models for their specific applications. A team at Determined AI explains how you can reduce the time it takes to fine-tune BERT from 7 hours to 30 minutes, with very minor changes to your model code.  CTO Neil Conway will be speaking on this topic at the upcoming NLP Summit, the industry gathering for people interested in natural language technologies.

  • Call for Speakers (Data+AI Summit Europe) closes Sep 13th. End 2020 by being part of what will be a very strong roster of speakers.


Work and Hiring

[Image: Wildfire smoke, view from Bernal Hill in San Francisco, 2020-09-09; by Ben Lorica]

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Ben Lorica edits the Gradient Flow newsletter. He is the Program Chair of the Spark+AI Summit, co-chair of the Ray Summit, chair of the NLP Summit, and host of the Data Exchange podcast. You can follow him on Twitter @BigData.  This newsletter is produced by Gradient Flow.