[FRIAM] Can current AI beat humans at doing science?

[Marcus Daniels]

What do you mean?  It will be the great equalizer.

From: Friam <friam-bounces at redfish.com> On Behalf Of Pieter Steenekamp
Sent: Tuesday, July 20, 2021 12:12 PM
To: The Friday Morning Applied Complexity Coffee Group <friam at redfish.com>
Subject: [FRIAM] Can current AI beat humans at doing science?

A year or so ago, Deepmind's AlphGo defeated the then world Go-champion Lee Sedol at a time when leading Ai researchers predicted it will be at least 10 years before AI can reach that level. But the valid question then was - why so excited? It's just a game. There is an interesting documentary on youtube about this at https://www.youtube.com/watch?v=WXuK6gekU1Y

What's happening now is that AI makes scientific discoveries beyond human ability.

Is anybody worried where it will end?

I quote from https://www.nature.com/articles/s41586-021-03819-2
Highly accurate protein structure prediction with AlphaFold
Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort1–4, the structures of around 100,000 unique proteins have been determined5, but this represents a small fraction of the billions of known protein sequences6,7. Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the 3-D structure that a protein will adopt based solely on its amino acid sequence, the structure prediction component of the ‘protein folding problem’8, has been an important open research problem for more than 50 years9. Despite recent progress10–14, existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even where no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14)15, demonstrating accuracy competitive with experiment in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm.




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