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)
This grand scientific challenge is known as the protein folding problem. To help solve this, we created the latest version of #AlphaFold. We drew inspiration from the fields of biology, physics & ML, as well as the work of many scientists in the field over the past 50 years.(4/6)
We trained #AlphaFold on the sequences and structures of 100,000+ proteins mapped out by scientists around the world. It can now accurately predict a protein’s shape from its sequence of amino acids - unlocking key information that many have sought to understand for years. (5/6)
We hope this breakthrough shows the impact AI can have on scientific discovery & its potential to dramatically accelerate progress in some of the most fundamental fields (i.e drug design & environmental sustainability) that shape our world. Learn more: deepmind.com/alphafold
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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”
We’re back with more suggestions from our researchers for ways to expand your knowledge of AI.
Today’s #AtHomeWithAI recommendations are from research scientist Kimberly Stachenfeld (@neuro_kim) (1/7)
She recommends “The Scientist in the Crib” [longer listen] by @AlisonGopnik, Andrew Meltzoff, & Patricia K. Kuhl for those who are interested in what early learning tells us about the mind.