Buomsoo Kim

Deep learning state of the art 2020 (MIT Deep Learning Series) - Part 3

08 Apr 2020 | deep learning data science

This is the third and last part of Lex Fridman’s Deep learning state of the art 2020 talk. In this posting, let’s review the remaining part of his talk, starting with Government, Politics, and Policy.

Government, Politics, and Policy

AI in Political Discourse - Andrew “Yang”

First presedential candidate to discuss AI extensively as part of his platform

  • Department: new executive department (the Department of Technology)
  • Focus on AI
  • Companies: create a public-private partnership

American AI Initiative

In Feb 2019, the president signed Executive Order 13859 announcing the American AI Initiative

  • Investment in long-term research
  • Support research in academia and industry
  • Access to federal data
  • Promote STEM education
  • Develop AI in “a manner consistent with our Nation’s values, policies, and priorities”
  • AI must also be developed in a way that does not compromise our American values, civil liberties, or freedoms.

Ethics of recommender systems

Most of the recommender systems used by large tech companies such as FAANG use DL. There should be some effort to inform public/government about the details in the system.

Play Store App discovery (DeepMind + Google)

Hopes for 2020

Less fear of AI

More balanced, informed discussion on the impact of AI in society

Experts

Continued conversations by government officials about AI, privacy, cybersecurity with experts in academia and industry

Recommender system transparency

More open discussion and publication behind recommender systems used in industry

Courses, Tutorials & Books

If you are interested, please refer to my curation on data science study materials for a more comprehensive list of courses, tutorials, and books!

Online DL courses

Deep Learning

  • Fast.ai: Practical deep learning for coders
  • Stanford CS231n: CNN for visual recognition
  • Stanford CS224n: NLP with DL
  • Deeplearning.ai (coursera): Deep Learning (Andrew Ng)

Reinforcement Learning

  • David Silver: Intro to RL
  • OpenAI: Spinning Up in Deep RL

Tutorials

Over 200 of the best ML, NLP, and Python tutorials (by Robbie Allen)

Deep learning books

  • Deep Learning with Python (by F. Chollet)
  • grokking Deep Learning (by A. W. Trask)
  • Deep Learning (by I. Goodfellow)

General hopes for 2020

Summary & key points

  • Reasoning
  • Active learning and life-long learning
  • Multi-modal and multi-task learning
  • Open-domain conversations
  • Applications: medical, autonomous vehicles
  • Algorithmic ethics
  • Robotics
  • Recommender systems

Recipe for progress in AI

Again, it is a great talk and I really enjoyed watching the video and summarizing it. So if you are interested, please check out the Youtube link to the video! Even though you don’t watch the whole video and just focus on the part that you are interested in, I bet you can benefit from it greatly.

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