The following are some points with best available science and data on where we stand and what we should be doing as a society and as individuals. I’ll keep it updated regularly.

Where We Stand

  • Health numbers (as of April 8, 2020 — Source)
    - Global deaths:
    82,421
    -
    US deaths: 12,857
    - US tests: 2,082,443 (0.6% of population)
  • US unemployment: Rose from 3.5% to 13%, 16 million increase (Source)
  • Shortages:
    - Masks: N95 respirator masks and surgical masks
    - Testing: Testing infrastructure and supplies
    - Beds: Hospital and ICU beds
    - Tracing: Contact tracing infrastructure
  • Treatment and prevention: Candidate vaccines & antiviral…


I fine-tuned the GPT-2 language model (345 million parameters) on tweets from people’s Twitter accounts to create AI versions of them, and then had the bots rewrite real tweets or complete tweets based on various prompts (short sentence starters like “The meaning of life is”). To make it end-to-end (prompt to pic), I add 5 candidates from each bot to a pool of tweets for A/B voting. Once the top tweets are selected I make an image out of them (see below) with OpenCV.

Some of these are funny, some are profound, some are dark in a way that gives…


MIT Deep Learning series of courses (6.S091, 6.S093, 6.S094). Lecture videos and tutorials are open to all.

As part of the MIT Deep Learning series of lectures and GitHub tutorials, we are covering the basics of using neural networks to solve problems in computer vision, natural language processing, games, autonomous driving, robotics, and beyond.

This blog post provides an overview of deep learning in 7 architectural paradigms with links to TensorFlow tutorials for each. It accompanies the following lecture on Deep Learning Basics as part of MIT course 6.S094:

Deep learning is representation learning: the automated formation of useful representations from data. …

Lex Fridman

Research scientist at MIT working on human-centered AI and deep learning approaches to shared autonomy in self-driving cars. https://deeplearning.mit.edu

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