Evidence receipt / belief
Published · transcript-backedEd Boyden: belief
10 Apr 2019 Conversations with Tyler Ed Boyden on Minding your Brain
“What has changed since then is, no doubt, some improvements in the mathematics, but largely, I think we’d all agree, better compute power and a lot more data.”
Source trail
Everything needed to verify it.
- Speaker
- Ed Boyden
- Attribution
- Verified speaker
- Claim type
- belief
- Recorded
- 10 Apr 2019
- Publisher
- Conversations with Tyler
- Episode
- Ed Boyden on Minding your Brain
Transcript context
…Two short takes. Then get to that. Okay, just very briefly. The second thing I would do is to be more dynamic in my funding. Right now, maybe there’s a grant that you apply for, and then a year later you get the money. But what if somebody tries something out one Friday afternoon, and whoa, that could cure disease, or that could yield an amazing new insight into biology, or that could allow us to diagnose brain diseases early, or whatever? Why wait a year? What if one could dynamically allocate funding up and down based upon the real-time metrics of science? In my own group, sometimes we get a project out of the blue, and hey, that’s pretty cool. Then we’ll dynamically try to understand if we can reallocate resources. That’s another thing I would do. The third thing I would do is I would go looking for trouble. I would go looking for serendipity. If you look at CRISPR for genome editing — that was found by some scientists working on yogurt. If you look at fluorescent proteins — that was identified by a person who just was obsessed with jellyfish. In my own field, if you look at our optogenetics work or our expansion microscopy work — these fields owe a debt to basic curiosity about critters living in bodies of water for optogenetics, and expansion microscopy goes back to the 1980s where people were wondering why do certain polymers swell so hugely, with no practical-purpose implications of it. One idea is, how do we find the diamonds in the rough, the big ideas but they’re kind of hidden in plain sight? I think we see this a lot. Machine learning, deep learning, is one of the hot topics of our time, but a lot of the math was worked out decades ago — backpropagation, for example, in the 1980s and 1990s. What has changed since then is, no doubt, some improvements in the mathematics, but largely, I think we’d all agree, better compute power and a lot more data. So how could we find the treasure that’s hiding in plain sight? One of the ideas is to have sort of a SWAT team of people who go around looking for how to connect the dots all day long in these serendipitous ways. Does that mean fewer committees and more individuals?…
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