My understanding is that peer review is somewhat less common in computer science fields because research is often published in conference proceedings without extensive peer review. Of course, you could say that the conference itself is doing the vetting here, and computer science often has the advantage of easy replication by running the supplied code. This applies to some of the papers people are providing... but certainly not all of them. 

Peer review is far from perfect, but if something isn't peer reviewed I won't fully trust it unless it's gone through an equivalent amount of vetting by other means. I mean, I won't fully trust a paper that has gone through external peer review, so I certainly won't immediately trust something that has gone through nothing. 

I'm working on an article about this, but I consider the lack of sufficient vetting to be one of the biggest epistemological problems in EA. 

Why? What advantage does this have over a lower stakes bet of $50:$250? 

Do you have an alternate suggestion for how flaws and mistakes made by projects in the EA sphere can be discovered?

As a scientist, one of the reasons people trust our work is the expectation that the work we publish has been vetted and checked by other experts in the field (and even with peer review, sloppy work gets published all the time). Isn't one of the goals of the EA forum to crowdsource at least some of this valuable scrutiny? 

Scaled voting power is part of why moderation on the Forum is sustainable

On a typical day the forum has like 10 posts and like 30 comments. A subreddit like r/excel is roughly 3 times as active as this forum, and is moderated entirely by volunteers. I do not think it would be very difficult to moderate the forum without a karma system if people chose to do so. 

Therefore, high karma correlates pretty well with people having good Forum content taste. 

I would say the people with the most karma are the people who comment a lot with a content that is in line with the tone of the forum. This seems like it serves to reinforce the status quo of forum norms. Whether this is a good or bad thing will depend on your opinion of said norms: for example, I would prefer this place be a bit  more tolerant of humour and levity.  

I am having trouble understanding why AI safety people are even trying to convince the general public that timelines are short. 

If you manage to convince an investor that timelines are very short without simultaneously convincing them to care a lot about x-risk, I feel like their immediate response will be to rush to invest briefcases full of cash into the AI race, thus helping make timelines shorter and more dangerous. 

Also, if you make a bold prediction about short timelines and turn out to be wrong, won't people stop taking you seriously the next time around? 

As I said, I don't think your statement was wrong, but I want to give people a more accurate perception as to how AI is currently affecting scientific progress: it's very useful, but only in niches which align nicely with the strengths of neural networks. I do not think similar AI would produce similarly impressive results in what my team is doing, because we already have more ideas than we have the time and resources to execute on. 

I can't really assess how much speedup we could get from a superintelligence, because superintelligences don't exist yet and may never exist. I do think that 3xing research output with AI in science is an easier proposition than building digital super-einstein, so I expect to see the former before the latter. 

I found this article well written, although of course I don't agree that AGI by 2030 is likely. I am roughly in agreement with this post by an AI expert responding to the other (less good) short- timeline article going around. 

I thought instead of critiquing the parts that I'm not an expert in, I might take a look at the part of this post that intersects with my field, when you mention material science discovery, and pour just a little bit of cold water on it. 

A recent study found that an AI tool made top materials science researchers 80% faster at finding novel materials, and I expect many more results like this once scientists have adapted AI to solve specific problems, for instance by training on genetic or cosmological data.

So, an important thing to note is that this was not an LLM (neither was alphafold), but a specially designed deep learning model for generating candidate material structures. I covered a bit about them in my last article, this is a nice bit of evidence for their usefulness. The possibility space for new materials is ginormous and humans are not that good at generating new ones: the paper showed that this tool boosted productivity by making that process significantly easier. I don't like how the paper described this as "idea generation": it evokes the idea that the AI is making it's own newtonian flashes of scientific insight, but actually it's just mass generating candidate materials that an experienced professional can sift through. 

I think your quoted statement is technically true, but it's worth mentioning that the 80% faster figure was just for the people previously in the top decile of performance (ie the best researchers), for people who were not performing well there was not evidence of a real difference. In practice the effect of the tool on progress was less than this: it was plausibly attributed to increasing the number of new patents at a firm by roughly 40%, and increasing the number of actual prototypes by 20%. You can also see that the productivity is not continuing to increase: they got their boost from the improved generation pipeline, and now the bottleneck is somewhere else. 

To be clear, this is still great, and a clear deep learning success story, but it's not really in line with colonizing the mars in 2035 or whatever the ASI people are saying now. 

In general, I'm not a fan of the paper, and it really could have benefited from some input from an actual material scientist. 

I think if you surveyed any expert on LLMs and asked them "which was a greater jump in capabilities, Gpt2 to GPT3 or GPT3 to GPT4?" the vast majority would say the former, and I would agree with them. This graph doesn't capture that, which makes me cautious about overelying on it. 

I feel like this should be caveated with a "long timelines have gotten short... within people the author knows about in tech circles". 

I mean, just two months ago someone asked a room full of cutting edge computational physicists whether their job could be replaced by an AI soon, and the response was audible laughter and a reply of "not in our lifetimes". 

On one side you could say that this discrepancy is because the computational physicists aren't as familiar with state of the art genAI, but on the flipside, you could point out that tech circles aren't familiar with state of the art physics, and are seriously underestimating the scale of task ahead of them. 

I'd be worried about getting sucked into semantics here. I think it's reasonable to say that it passes the original turing test, described by Turing in 1950:

I believe that in about fifty years’ time it will be possible to programme computers, with a storage capacity of about 10⁹, to make them play the imitation game so well that an average interrogator will not have more than 70 percent chance of making the right identification after five minutes of questioning. … I believe that at the end of the century the use of words and general educated opinion will have altered so much that one will be able to speak of machines thinking without expecting to be contradicted.

I think given the restrictions of an "average interrogator" and "five minutes of questioning", this prediction has been achieved, albeit a quarter of a century later than he predicted.  This obviously doesn't prove that the AI can think or substitute for complex business tasks (it can't), but it does have implications for things like AI-spambots.