AI has been extremely successful in real world games (GO, DOTA, StarCraft) with results coming from relatively simple multi-agent algorithms. In this paper, we hypothesise that they share a common geometry - Spinning Tops. Learn more: bit.ly/3qI8RrD#NeurIPS2020
I’ve always loved biology. During my masters I decided to take a handful of neurophysiology courses - which I found to be interesting. But eventually I realised that my true strengths were in mathematical sciences. A career in ML and AI became a natural way to combine the two.
I wish I knew that having a doctorate wasn’t essential for a successful career. Nowadays you can join an AI lab as a research engineer (or another similar role) which can provide you with the experience and opportunities needed to succeed!
I want to relearn some of the mathematics that I’ve forgotten. A year isn’t long enough to revisit the whole domain, so maybe I’ll start with set theory, topology and some functional analysis. There are also a few projects I'm working on - maybe something to share at #NeurIPS2021
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Yesterday we shared the news that #AlphaFold has been recognised as a solution to the ‘protein folding problem’ by #CASP14, the biennial Critical Assessment of Protein Structure Prediction. But what exactly is protein folding, and why is it important? A thread… (1/6)
Proteins are the building blocks of life - they underpin the biological processes in every living thing. If you could unravel a protein you would see that it’s like a string of beads made of a sequence of different chemicals known as amino acids. (2/6)
Interactions between these amino acids make the protein fold, as it finds its shape out of almost limitless possibilities. For decades, scientists have been trying to find a method to reliably determine a protein’s structure just from its sequence of amino acids. (3/6)
He suggests the Deep Learning Book from @mitpress for a comprehensive introduction to the fundamentals of DL: bit.ly/351qMzb (1/7)
Overwhelmed with the number of available machine learning courses? @seb_ruder recommends taking a look through @venturidb’s curated - and ranked - list available on @freeCodeCamp.
Are you a beginner looking for a lesson on the Monte Carlo method?
Taylan’s own, “A Tutorial Introduction to Monte Carlo methods, Markov Chain Monte Carlo and Particle Filtering” is available here: bit.ly/3cAQ8XG#AtHomeWithAI
Looking for something challenging and fun? @KerenGu suggests Project Euler, a series of complex mathematical/computer programming problems hosted in a fun and recreational context. Test your skills and play along here: bit.ly/3bxBmAj#AtHomeWithAI (3/5)
Looking to learn more about AI? Our researchers are continuing to share their #AtHomeWithAI recommendations!
Today’s choices come from William Isaac (@wsisaac), a senior research scientist who specialises in ethics, bias and fairness. (1/5)
For an overview on fairness & how it applies to machine learning, William suggests diving into this freely available book [long read] by @s010n@mrtz and @random_walker!
@random_walker also discusses the various definitions of fairness and the tradeoffs they present for society in the video tutorial “21 definitions of fairness and their politics”