DeepMind Profile picture
30 Nov, 3 tweets, 2 min read
In a major scientific breakthrough, the latest version of #AlphaFold has been recognised as a solution to one of biology's grand challenges - the “protein folding problem”. It was validated today at #CASP14, the biennial Critical Assessment of protein Structure Prediction (1/3)
CASP is both the gold standard for assessing predictive techniques and a unique global community built on shared endeavour. Accuracy is measured on a range of 0-100 “GDT”. #AlphaFold has a median score of 92.4 GDT across all targets - its average error about the width of an atom.
We’re excited about the potential impact #AlphaFold may have on the future of biological research and scientific discovery. Thank you to the CASP organisers & the whole community - we look forward to the many years of hard work and discovery ahead: bit.ly/3qdko1Q

• • •

Missing some Tweet in this thread? You can try to force a refresh
 

Keep Current with DeepMind

DeepMind Profile picture

Stay in touch and get notified when new unrolls are available from this author!

Read all threads

This Thread may be Removed Anytime!

PDF

Twitter may remove this content at anytime! Save it as PDF for later use!

Try unrolling a thread yourself!

how to unroll video
  1. Follow @ThreadReaderApp to mention us!

  2. From a Twitter thread mention us with a keyword "unroll"
@threadreaderapp unroll

Practice here first or read more on our help page!

More from @DeepMind

1 Dec
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)
Read 6 tweets
9 Jun
We have research scientist @seb_ruder up next with more #AtHomeWithAI recommendations!

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.

bit.ly/3erZEN4 #AtHomeWithAI
Do you have a technical background? Are you looking for an introduction to natural language processing?

Sebastian recommends the @fastdotai course, “A Code-First Introduction to Natural Language Processing”.

bit.ly/3esFtP8 #AtHomeWithAI
Read 7 tweets
27 May
Looking for a few more favourite resources from the team? Today’s #AtHomeWithAI picks are from research scientist @TaylanCemgilML! (1/6)
His first recommendation is for those looking to learn about the basics of probabilistic reasoning and modelling.

He suggests “Bayesian Reasoning and Machine Learning” [longer read] by @davidobarber. Read it for free here: bit.ly/3cG99rS #AtHomeWithAI
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
Read 6 tweets
21 May
We’re back with the latest set of #AtHomeWithAI researcher recommended resources, this time from research scientist @AdamMarblestone! (1/7) Image
Adam suggests class materials from @Stanford if students are looking for ideas on computational models of the neocortex.

Follow along here: stanford.io/2XWiNlB #AtHomeWithAI
Need a resource that covers the essentials of linear algebra for AI? This online lecture by #gilbertstrang and @broadinstitute does just that.

Watch it here: bit.ly/3buHbi6 #AtHomeWithAI
Read 7 tweets
18 May
We’re back with more researcher recommended resources available to use #AtHomeWithAI. Today is the turn of research engineer @KerenGu! (1/5) Image
Her first two recommendations come from @MITDeepLearning and @MITOCW . Both are intro courses - one for machine learning & one for deep learning. Find them here: bit.ly/2VSb4lJ & bit.ly/2Vx5ZQI (2/5)
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)
Read 5 tweets
15 May
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) Image
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!

See here: bit.ly/350VoAO #AtHomeWithAI
@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”

Watch it here: bit.ly/2S21KuE #AtHomeWithAI
Read 5 tweets

Did Thread Reader help you today?

Support us! We are indie developers!


This site is made by just two indie developers on a laptop doing marketing, support and development! Read more about the story.

Become a Premium Member ($3/month or $30/year) and get exclusive features!

Become Premium

Too expensive? Make a small donation by buying us coffee ($5) or help with server cost ($10)

Donate via Paypal Become our Patreon

Thank you for your support!

Follow Us on Twitter!