6 ms·

> There's literally nothing new about the idea. The real trick is being incredibly lucky and finding something that actually works in humans after multiple tria

> There's literally nothing new about the idea. The real trick is being incredibly lucky and finding something that actually works in humans after multiple trials. I guess I'm not sure where the dismissiveness is coming from here. Are claiming this could have been trivially done before? If so, why didn't you or someone else do it already? Or are you claiming it's an uninteresting result that is not worthy of publication or attention?

xzel 7y ago
I'm saying I'm tired of the puff piece articles and reddit style headlines. I was saying NN's and other "AI" style models has been used in this way for decades for these types of things. 0 disrespect to the actual science; the paper is great and I truly hope it works in humans. We need more drugs against the inevitable fight against drug resistance.
- redsymbol 7y ago
  Okay, fine... what do you want to be different about the situation, then? Do you not want any non-technical summary articles like this to be written? So that only those with the training to understand a Cell journal article would be able to learn anything about the result? Or do you prefer that no journal articles be published that rely on 2020-era NN models, because older articles based on less state-of-the-art NNs have been published already?
  - sp332 7y ago
    The story could be about perseverance paying off, instead of a breakthrough portending big changes in near-future drug development.
  - xzel 7y ago
    I'd much prefer there be more detail in the article about AI research in drug discovery, how it still needs clinical trials in humans. I agree with the sub-comment as well. I think your last question doesn't make sense to me though as I haven't said anything similar to that. But I think you raise an interesting point about non-technical summary articles: what do they do and who are they for? Does the non-technical public need to know about this research? Do they gain anything vs. reading the study's actual summary? I'm not sure. I do think there isn't much for non-technical people to get from this article that would be useful. I think the best reason would be for younger people to pique their interest in the field. Though, I honestly don't know the answer.
    - redsymbol 7y ago
      Yeah, good points. I guess such non-tech articles serve several purposes. I do think this one gives non-technical people get something useful, though. More broadly, it'll increase science understanding among the non-scientist/non-technical public, at least on this topic, and good does tend to come out of that.
Bartweiss 7y ago
This is a novel and important result in antibiotics. It's also a proof-of-concept for using ML to produce vital drugs with novel mechanisms, rather than incidental alterations or discoveries in noncompetitive spaces. It might be an incremental speedup or computing-power advance in ML drug discovery also, but it could equally just be the result of a lucky break or a particularly large lab-test budget. (In which case, "why didn't someone do it already?" is closer to asking why nobody else bothered to win the lottery.) It's not a major theoretical advance in ML drug-discovery techniques or the first big step in ML drug discovery. It's certainly not the invention of ML drug discovery or neural nets as an ML technique, both things I've seen implied in news stories on this work. This is attention-worthy, absolutely. (I'll leave "publication-worthy methodology" to experts.) But it's newsworthy on actual merits, as a drug breakthrough and a demonstration of an increasingly-important technique. So I share the frustration when lazy or confused reporting implies this is the same style of ML-theory breakthrough as CNNs, Transformers, or even neural nets themselves.

xzel 7y ago

I'm saying I'm tired of the puff piece articles and reddit style headlines. I was saying NN's and other "AI" style models has been used in this way for decades for these types of things. 0 disrespect to the actual science; the paper is great and I truly hope it works in humans. We need more drugs against the inevitable fight against drug resistance.

redsymbol 7y ago
Okay, fine... what do you want to be different about the situation, then? Do you not want any non-technical summary articles like this to be written? So that only those with the training to understand a Cell journal article would be able to learn anything about the result? Or do you prefer that no journal articles be published that rely on 2020-era NN models, because older articles based on less state-of-the-art NNs have been published already?
- sp332 7y ago
  The story could be about perseverance paying off, instead of a breakthrough portending big changes in near-future drug development.
- xzel 7y ago
  I'd much prefer there be more detail in the article about AI research in drug discovery, how it still needs clinical trials in humans. I agree with the sub-comment as well. I think your last question doesn't make sense to me though as I haven't said anything similar to that. But I think you raise an interesting point about non-technical summary articles: what do they do and who are they for? Does the non-technical public need to know about this research? Do they gain anything vs. reading the study's actual summary? I'm not sure. I do think there isn't much for non-technical people to get from this article that would be useful. I think the best reason would be for younger people to pique their interest in the field. Though, I honestly don't know the answer.
  - redsymbol 7y ago
    Yeah, good points. I guess such non-tech articles serve several purposes. I do think this one gives non-technical people get something useful, though. More broadly, it'll increase science understanding among the non-scientist/non-technical public, at least on this topic, and good does tend to come out of that.

Bartweiss 7y ago

This is a novel and important result in antibiotics. It's also a proof-of-concept for using ML to produce vital drugs with novel mechanisms, rather than incidental alterations or discoveries in noncompetitive spaces. It might be an incremental speedup or computing-power advance in ML drug discovery also, but it could equally just be the result of a lucky break or a particularly large lab-test budget. (In which case, "why didn't someone do it already?" is closer to asking why nobody else bothered to win the lottery.) It's not a major theoretical advance in ML drug-discovery techniques or the first big step in ML drug discovery. It's certainly not the invention of ML drug discovery or neural nets as an ML technique, both things I've seen implied in news stories on this work. This is attention-worthy, absolutely. (I'll leave "publication-worthy methodology" to experts.) But it's newsworthy on actual merits, as a drug breakthrough and a demonstration of an increasingly-important technique. So I share the frustration when lazy or confused reporting implies this is the same style of ML-theory breakthrough as CNNs, Transformers, or even neural nets themselves.