When genetic engineering meets machine learning
Heads-up on an amazing conversation I had
|Azeem Azhar||May 8, 2019||37|
Forgive me for interrupting your Wednesday—every once in a while, I have a conversation that is so interesting that I want to share it with all readers of Exponential View.
I spent a while talking to genomics entrepreneur & theoretical physicist, Stephen Hsu, about the intersection of genetic engineering and machine learning. Stephen has pioneered research—and now commercial business—in polygenic risk scoring.
These techniques combine cheap genome sequencing with advanced machine learning to predict the risk, or likely prevalence of severe disease, but also traits like height or general cognitive ability.
Genomics is quite the technology. And rapidly improving sequencing and computation are making it cheaper and more accessible. With its power come some challenging questions: When is it appropriate to select for traits? Where does the boundary between preventing severe disease conditions and selecting for enhancement lie? Who should make those decisions?
I explore these issues in an amazing conversation with Stephen. I don’t want you to miss out on it.
You can listen to it via
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