Been hard at work at a seven part series on AI. I’ll most more details soon. For now:

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Been hard at work at a seven part series on AI. I’ll most more details soon. For now:

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This year I produced two videos with my friends at IEEE (Information Theory Society) which cover two highly influential academic papers from the 20th Century. What’s really exciting about these videos is we had the original authors of the papers review the script during development (Abraham Lempel and Robert Gallager). On average we spent about 4 months writing & iterating on each script until we had something that is clear and correct.

Here they are:

Lempel-Ziv compression – One of the most influential compression algorithms of the 20th Century:

LDPC codes – One of the most versatile and widely applicable error correction codes which was about 40 years ahead of its time:

In 2019 I will continue this project with IEEE.

I spent a long time thinking about a way to explain the what/why/how’s of Bitcoin to general audiences (my Mom) in a way that doesn’t frustrate Engineers (no hand waving). I feel that all existing Bitcoin videos contain jargon that scares away the typical viewer. In this video I made a point to avoid using a single word that might confuse people. I don’t know if I succeeded but I tried my very very best…

Please share this video with anyone who still doesn’t “get it” – as it might fill in some gaps.

This year marked the beginning of a long term collaboration with IEEE Information Theory Society. The goal of our ‘Information Age’ series is to produce short videos which bring to life the most impactful ideas from Information Theory and show how they play a role in our lives today. The first two pilot videos we produced in 2017 were on Network Coding and Space-Time codes. In 2018 we’ll be exploring source coding, channel coding, quantum information theory and security. Below are the 2017 pilot videos. I’ve enjoyed working with Matthieu Bloch, Michelle Effros, Christina Fragouli & Suhas Diggavi on this project.

Network Coding:

Space-Time codes (multi-input multi-output networks):

I spent many years pondering (making sense of) this question. I ended up building a entire Computer Science series just to get to it. This video explores complexity theory and the (P vs. NP question) and is the conclusion to the CS series. I hope it helps others get the the key realizations faster than i…

For years I struggled to clarify exactly what a Turing machine needs to do, and more importantly, how Turing conceived of it. Even after I finished a CS degree I wasn’t able to “build one from scratch” because I hadn’t yet *independently realized* what he had…

Recently I had a moment of insight and hit on an improved analogy for the “program” of a Turing machine, which Turing describes as “a book”. I took this one step further and clarified that each *page* in this book can be thought of as a unique *state*. A page contains a single *instruction* to follow (which takes the form of a *conditional statement*). This subtle step is something Turing didn’t include in his paper (instead he skipped ahead and simply refers to it as a ‘big table’, which can be tough to digest at first)

I feel this is the key to make the mechanism behind Turing machines more concrete and intuitive for the new learner. I hope Turing would approve of my modification to his analogy…and after reading his paper some 20 times, I can say with certainty that he would.

Here is the video on how it all works (this is also the 2nd last video in the CS series)

This video was on the tip of my tongue for years, it feels wonderful to finally move on. It features Aristotle, Leibniz, Adam Smith and Charles Babbage. It’s the main “case study” for this series, after which we’ll move into more modern views on computers and computability.