Generations of data, fewer differences in environmental factors between members (diets, lifestyle, diseases, etc) to obscure the effect. For long-term effects like this, 'modern science' hasn't really existed long enough to get much data in comparison to centuries of generational trial-and-error
Edit: also, long-term effects measured now have a bunch of confounders due to lifestyle change and rapid technological and medical development, while their conditions were basically stationary. Scientists would kill for that kind of data now!
Peer review usually results in papers being accepted with minor or major revisions, and very much can and does effect serious changes in the study design. You can read the peer review back-and-forth in many journals, they are often pretty interesting. Machine learning and computer science are different because they usually publish in conference proceedings. That means there are very tight deadlines, so it's more common to rebut the reviewer's comments outside of very minor changes. In my opinion it's why peer review is seen so poorly in ML, because there's not much paper-improvement going on as a result of the process.
The editor of an article is the person who decides whether to desk-reject or seek reviewers, find and coordinate the reviewers, communicate with the authors during the process and so on. That's standard at all journals afaik. The editor decides on publication according to the journal's criteria. PNAS does have this special track but one of the authors must be in NAS, and as that author you can't just submit a bunch of papers in that track, you can use it once a year or something. And most readers of PNAS know this and are suitably sceptical of those papers...
Chris Olah was absolutely not the first person to discuss the idea of using interpretability to better understand the underlying data-generating process. Most statistical modelling has been driven by that aim, and that way of thinking wasn't just abandoned as ML research progressed - Breiman (of random forest fame) discusses it in his 2001 paper on 'the two cultures', for example. While a lot of explainability/interpretability research has focussed on the goal of understanding the model, there has been plenty written and discussed about using those methods...
Explainable AI and interpretable ML research and methods, aside from the researchers affiliated with the rationalist scene, are for some reason excluded from the narrative. Is it really your view that 'mechanistic interpretability' is so different that it is an entirely different field? Doesn't it seem a bit questionable that the term 'mechanistic interpretability' was coined in order to distance Olah's research from other explanation approaches that had been found to have fundamental weaknesses - especially when mechanistic interpretability methods repeat...
I'd like to comment on your discussion of peer review.
'Tyler Cowen’s presentation of the criticism then compounds this, entitled ‘Modeling errors in AI doom circles’ (which is pejorative on multiple levels), calling the critique ‘excellent’ (the critique in its title calls the original ‘bad’), then presenting this as an argument for why this proves they should have… submitted AI 2027 to a journal? Huh?'
To me, this response in particular suggests you might misunderstand the point of submitting to journals and receiving peer review. The reason Tyler says the...
Why not read and review the IPCC report? I am confused by why this seems to not be the most popular recommendation for people on this forum who want to understand the most up-to-date scientific consensus on climate change risk. It's written by an international community of scientists, it's very accessible with further higher-level overviews aimed at decision-makers and the broader public, all claims include an estimated level of uncertainty (and are very conservative) and you can follow the citations for any particular claim made. The website is great, the...
This weakness of SAEs is not surprising, as this is a general weakness of any interpretation method that is calculated based on model behaviours for a selected dataset. The same effect has been shown for permutation feature importances, partial dependence plots, Shapley values, integrated gradients and more. There is a reasonably large body of literature on the subject from the interpretable ML / explainable ML research communities in the last 5-10 years.
'Have you ever tried to explain the difference between correlation and causation to someone who didn't understand it? I'm not convinced that this is even something humans innately have, rather than some higher-level correction by systems that do that.'
You are outside and feel wind on your face. In front of you, you can see trees swaying in the wind. Did the swaying of the trees cause the wind? Or did the wind cause the trees to sway?
The cat bats at a moving toy. Usually he misses it. If he hits it, it usually makes a noise, but not always. The presence of ...
You submit a finished product, yes, and it can be accepted without revisions, but I have never heard of that happening actually and nobody I know has had that happen to them, I believe. Or, it might get rejected (but if so, no, you don't have to start over. If it was sent for review, you will receive feedback you can use to improve the study, and you may be invited to resubmit after making those changes, or you might submit the same paper to a different journal). Hopefully, it is accepted with major or minor revisions, so you go away and make the requested... (read more)