One of the reasons artificial intelligence is such an interesting field is that pretty much no one knows what it might turn out to be good at. Two papers by leading labs published in the journal Nature today show that machine learning can be applied to tasks as technically demanding as protein generation and as abstract as pure mathematics.
The protein thing may not sound like much of a surprise given the recent commotion around AI’s facility in protein folding, as demonstrated by Google’s DeepMind and the University of Washington’s Baker Lab, not coincidentally also the ones who put out the papers we’re noting today.
The study from the Baker Lab shows that the model they created to understand how protein sequences are folded can be repurposed to essentially do the opposite: create a new sequence meeting certain parameters and which acts as expected when tested in vitro.
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