Discover and read the best of Twitter Threads about #Deepmind

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Read 8 tweets
Last year I congratulated Demis Hassabis, a former chess prodigy who went on to astound the scientific community with his algorithms. His company, @DeepMind who research and build safe #AI systems, has now made a database of human protein structures freely available online.
It is said that scientists have only decoded the structure of a fraction of human proteins in "onerous" lab work, which begun in the 1950s. However, DeepMind's AI program, AlphaFold, has claimed to have predicted the structure of nearly all 20,000 proteins expressed by humans.
“I almost fell off my chair in just excitement and amazement that this longstanding problem of how proteins fold had been solved", Prof Ewan Birney, the director of the EMBL-EBI, working in partnership with the company.…
Read 5 tweets
Thoughts on the #AlphaFold #Deepmind 'we done the proteome' news:
(1) This is great.
(2) This was always going to happen. I'm surprised they did it this fast, but cool.
(3) The dataset will be invaluable for hypothesis generation.
(4) Hypotheses will still need to be proven at the lab bench.
(5) This will advance structural biology, allowing phasing of MX datasets and tracing of domains/proteins into EM maps.
(6) I'm interested to see how their models perform with drug & biologic design.
(7) I'm interested to see how their model aid in construct design. Knowing disordered/unstructured regions is super important for designing well behaved, soluble recombinant proteins for structural study. #Alphafold
Read 18 tweets
30/11 > “Cela va tout changer” : l’#IA de #DeepMind fait un pas de géant dans la résolution des structures protéiques via @nature #LaMethSci Image
30/11 > Plus de 100 visons danois infectés se sont échappés et pourraient propager le #SARSCoV2 à la faune via @ScienceAlert #LaMethSci Image
30/11 > Un bébé est né avec des anticorps protecteurs après que sa mère ait eu la #CoVid19 pendant sa grossesse via @ScienceAlert #LaMethSci Image
Read 5 tweets
#Alphafold by #deepmind used solid interdisciplinary intuitions for algorithm/model design. It wasn't just a rinse-and-repeat machine learning exercise. Details on methods are limited, but here's my best interpretation (+some predictions) so far: [1/n]
Protein sequence databases provide us samples that have defacto passed the fitness test of evolution and are information-rich. "Genetics search" is a retrieval step to find nearest-neighbors as defined by sequence alignment. Why do we need nearest-neighbors (NNs), you ask?
There's a neat principle/intuition called coevolution that can help explain. The mutational variance observed can give clues to protein structure and function. Read more here:
Read 13 tweets
Fabio Ciucci on LinkedIn [1/8]: #Putin said "the nation that leads in #AI will rule the world," not that "a rebel AI will rule the world starting with burning the nation that built such AI." #ArtificialIntelligence
Fabio Ciucci on LinkedIn [2/8]: If #Russia and #China lead on weaponized AI (and they could), other nations including the USA (whatever they deny) do it too, like with atomic bombs.
Fabio Ciucci on LinkedIn [3/8]: News: Google AI (#DeepMind #AlphaStar) finally became able to kill every pro-human at #starcraft2 war game too.
Read 9 tweets

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