101–110 of 170 posts
The model parameters are only available for non-commercial use. That's a shame, as I presume there might be a lot of medical startups that would benefit from having this kind protein-folding tech available.
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why would it put you out of job? Wouldn't it just become one of the tools you use?
The implicit assumption you are making is that the demand increases in lock step with productivity gains. 100x faster drug discovery, 100x more drugs need to be discovered => same number of people employed. These correlations do hold for technical fields, but logically there should be a point beyond which productivity gains outpace, demand growth / demand could even stop growing. One should either retool to solve a n…
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> ... but I don't play that role any more. I was thinking of going into that field. Can you expand a bit on why you left?
Because programming computers is far more lucrative, and I'm better at it. However, if I had an unlimited budget I would return to biology. I spent 15 years trying to be a professor and failed miserably. I was bad at it and didn't like what professors have to do. I then moved to industry to be a random engineer and thrived doing things entirely unrelated to drug discovery. Eventually, I convinced my company to invest…
They hide all of that under "I'm a scientist, you're not".
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Because programming computers is far more lucrative, and I'm better at it. However, if I had an unlimited budget I would return to biology. I spent 15 years trying to be a professor and failed miserably. I was bad at it and didn't like what professors have to do. I then moved to industry to be a random engineer and thrived doing things entirely unrelated to drug discovery. Eventually, I convinced my company to invest…
Most comp-biologists who work directly with programmers are some of the biggest jerks, and the least qualified tech folks. They hide all of that under "I'm a scientist, you're not".
Tech (at its best) hates credentialism (sometimes I think to a point of over-correction).
That said, 80% of the devs in the bay area seem to have gone to Stanford or MIT, so...
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> A disturbing thing is that the architecture is much less novel than I originally thought it would be, so this shows perhaps one of the major difficulties was having the resources to try different things on a massive set of multiple alignments. This is something an industrial lab like DeepMind excels at. Whereas universities tend to suck at anything that requires a directed effort of more than a handful of people. Y…
> high heat-to-light ratio Sorry for the ignorance but what does this mean?
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> A disturbing thing is that the architecture is much less novel than I originally thought it would be, so this shows perhaps one of the major difficulties was having the resources to try different things on a massive set of multiple alignments. This is something an industrial lab like DeepMind excels at. Whereas universities tend to suck at anything that requires a directed effort of more than a handful of people. Y…
> high heat-to-light ratio Sorry for the ignorance but what does this mean?
Earlier quoted context omitted.
why would it put you out of job? Wouldn't it just become one of the tools you use?
The implicit assumption you are making is that the demand increases in lock step with productivity gains. 100x faster drug discovery, 100x more drugs need to be discovered => same number of people employed. These correlations do hold for technical fields, but logically there should be a point beyond which productivity gains outpace, demand growth / demand could even stop growing. One should either retool to solve a n…
This is basically the theory around modern industrial revolution - https://en.wikipedia.org/wiki/Jevons_paradox
Efficiency increases demand more than the efficiency saves.
You could argue that labour does not follow that, but it is more about the technology improving rather than disrupting.
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You can't do intelligent drug design if you don't know what the target protein looks like. We've gotten great at solving protein structures with things like crystallography and cryo-EM microscopy. Unfortunately, many interesting drug targets reside in the membrane of a cell, which means you can't easily work with them in a lab because they aren't soluble in anything but a plasma membrane. For instance, this is an iss…
You should check out Salipro ( https://www.salipro.com/ ) for membrane protein reconstitution.
I am a structural biologist. This is one of the handful of topics that overlaps with my field here. I'm very excited to play with this, although it might eventually put me out of a job.
why would it put you out of job? Wouldn't it just become one of the tools you use?
Alphafold 2 is very very cool, but we need a little dose of reality. It's still a bit away from really solving protein folding as it was marketed. For example, multi-complex proteins are not well predicted yet and these are really important in many biological processes and drug design: https://occamstypewriter.org/scurry/2020/12/02/no-deepmind-h... A disturbing thing is that the architecture is much less novel than I…
>A disturbing thing is that the architecture is much less novel than I originally thought it would be, so this shows perhaps one of the major difficulties was having the resources to try different things on a massive set of multiple alignments. A similar concern has sparked some worries about "AI overhang" https://www.lesswrong.com/posts/75dnjiD8kv2khe9eQ/measuring-... Most of the compute in ML research seems to be g…
No it's not. Only Google spends significant time with automatic architecture search, and many people think this is really to try to sell cloud capacity.
> Once the architecture is found, training and net finetuning/transfer learning is comparatively cheap
Training isn't cheap for significant problems.
Getting the data is very expensive, and compute is a significant expense for large datasets.
> This implies we could see 10-100x gains in AI algorithms using today's hardware
Actually, most of the time we see 10-100% (percent! not times) gains from architecture improvements, whether they be manual or automatic.
But that is very significant, because a 10% improvement can suddenly make something useful that wasn't before.