Cool to see this from Brian Hie, who was doing interesting computational bio research at Meta's FAIR before they axed it. Interesting that this is work on the more physical/testing/manufacturing level than the computational, but it seems very useful.
It's hard to quantify the impact of new foundational tools like this at launch. Most of the time it falls flat, but even the successes are difficult. For example, CRISPR has led to interesting experiments and treatments on the way, but the effect does feel muted compared to the initial predictions. But there are many other related techniques that can be pulled out of this original research (e.g. dCas9 which lets you operate without cutting).
Similar story with cellular reprogramming.
Eventually one of these things will surface that will be GPU/transistor type innovations.
Yeah, it feels like we need a phase transition in the speed and practicality of the process. But I don't believe we need a single concrete lab tech.
Years ago when I did research, my impression was that there was complexity galore. A researcher on Drosophila developmental signaling would have a very disjoint knowledge domain than that of a researcher in horizontal gene transfer and antibiotic resistance. Both would exist in a different planet altogether than a clinician prescribing a cancer treatment. And the three of them would generally lack the tooling that somebody doing systems biology was used to.
So, to me, the key thing we need is some sort of "domain cement", or a good way to pull operative knowledge and usable skills from everywhere.
> the key thing we need is some sort of "domain cement", or a good way to pull operative knowledge and usable skills from everywhere.
Isn't that what LLMs are shaping up to be? Once we manage to divorce the knowledge from the weights in some way we could have in effect a frontier model whose awareness was limited to the sum total of the scientific literature.
> that predictive models are now producing faster than anyone can construct them.
Erm ... you have A T C G. You can have a gazillion of combinations there.
Of course BY DEFAULT it will always be slower than ANY combination you would desire to have - and
you most definitely do not need AI slop to have that either. Do we need AI slop for generating any permutation of those 4 letters now? So what is the
point of stating "can construct".
IF the synthesis method works, then that is the focus to be debated, not the AI slop is our master-thinker now.
> “We really want this to be an enabling platform,” says Robinson. “We want people to do cool things with the technology.”
And I think they patented this (if it really works), so ... enabling platform, right.
Interestingly the article omits many key questions to be asked here. If the method already works as-is, why isn't everyone using it? If it is cheaper and faster, then logically it would already be used or usable.
Cool to see this from Brian Hie, who was doing interesting computational bio research at Meta's FAIR before they axed it. Interesting that this is work on the more physical/testing/manufacturing level than the computational, but it seems very useful.
It's hard to quantify the impact of new foundational tools like this at launch. Most of the time it falls flat, but even the successes are difficult. For example, CRISPR has led to interesting experiments and treatments on the way, but the effect does feel muted compared to the initial predictions. But there are many other related techniques that can be pulled out of this original research (e.g. dCas9 which lets you operate without cutting).
Similar story with cellular reprogramming.
Eventually one of these things will surface that will be GPU/transistor type innovations.
> but even the successes are difficult.
Yeah, it feels like we need a phase transition in the speed and practicality of the process. But I don't believe we need a single concrete lab tech.
Years ago when I did research, my impression was that there was complexity galore. A researcher on Drosophila developmental signaling would have a very disjoint knowledge domain than that of a researcher in horizontal gene transfer and antibiotic resistance. Both would exist in a different planet altogether than a clinician prescribing a cancer treatment. And the three of them would generally lack the tooling that somebody doing systems biology was used to.
So, to me, the key thing we need is some sort of "domain cement", or a good way to pull operative knowledge and usable skills from everywhere.
> the key thing we need is some sort of "domain cement", or a good way to pull operative knowledge and usable skills from everywhere.
Isn't that what LLMs are shaping up to be? Once we manage to divorce the knowledge from the weights in some way we could have in effect a frontier model whose awareness was limited to the sum total of the scientific literature.
I can't be the only one reading this who doesn't have alarm bells going off in their heads.
> that predictive models are now producing faster than anyone can construct them.
Erm ... you have A T C G. You can have a gazillion of combinations there.
Of course BY DEFAULT it will always be slower than ANY combination you would desire to have - and you most definitely do not need AI slop to have that either. Do we need AI slop for generating any permutation of those 4 letters now? So what is the point of stating "can construct".
IF the synthesis method works, then that is the focus to be debated, not the AI slop is our master-thinker now.
> “We really want this to be an enabling platform,” says Robinson. “We want people to do cool things with the technology.”
And I think they patented this (if it really works), so ... enabling platform, right.
Interestingly the article omits many key questions to be asked here. If the method already works as-is, why isn't everyone using it? If it is cheaper and faster, then logically it would already be used or usable.
Oh cool! Can we introduce a gene making Indians less smelly and yucky?