Experimental designs are critical for obvious reasons but they have a few critical flaws, that mostly all reduce to the fact you can't randomize manipulations with everything. Whether it be due to ethics or practical constraints, you can't conduct a RCT all the time.
This can be more subtly critical than it might seem, in that even if you can manipulate some proxy, often that proxy is insufficient in actually representing the phenomenon of interest, or the conditions under which they actually occur.
I often use the example of videogames and aggression. There were plenty of experimental studies of this but it was always questionable whether lab-induced anger is the same thing as, say, the sort of violence we generally are concerned about societally.
I generally have tried to teach students that experimental designs when done right provide powerful causal evidence of something, but often with limited generalizability; observational designs in contrast provide powerful generalizable evidence of some kind of association, but often with limited certainty about the causal pathways involved.
I've been in a department that was rabidly experimental in its focus and it always seemed sort of short-sighted, because people were idolizing RCTs with proxy manipulations that had questionable generalizability to the real-world phenomena they were trying to model.
Ideally you'd bring both experimental and observational evidence to bear on a question. Your conclusions should be robust to different types of designs.
"De Jonge Akademie, an association of young scientists, warned last year that the fund could end up recruiting academic stars who are not under threat, at a time when Dutch universities were cutting jobs because of government cutbacks. The new cabinet reversed those cuts last month, pledging up to €428 million a year extra for research."
> I'm not sure I understand the difference between "crappy forums" and subreddits. They have all the same features.
There's a lot of differences and they show up all the time with subreddits trying to poorly emulate the full featured organizational flexibility of a traditional forum.
The short answer is there's no subsubreddits, or subsubsubreddits, which are normal in forums, and turn out to be useful or even necessary.
What happens in the subs are classes of content posted repeatedly, members of the subs complaining about this repetitiveness, asking to have it removed, and so forth. The mods are torn because the posts are clearly popular but they do swamp the sub, and so you end up with "daily threads" about x or y. But this doesn't quite work because they're hard to search and aren't what you really need, which are subforums and subsubforums.
See e.g., r/running which was decimated by an attempt to reorganize it with the severe limitations of Reddit. If it was a forum, it would be really obvious how to organize it.
Reddit is pointing in the right direction in emulating traditional forums but doesn't have the same depth.
This doesn't even get into what I see as the harms of downvoting — sometimes I think it works better to just allow emoji reactions to posts, instead of upvoting and downvoting points (although maybe it's not upvoting and downvoting that's the problem, it's the way it's implemented?)
Personally I don't think what's needed really exists yet, or hasn't taken off: a decentralized version of Reddit that allows for more subnesting. Mastodon has features of this too but not really the nesting part at all.
The Tears in Rain monologue occurred to me as well while I was reading the post, but I don't think it's quite the same for one important reason: the replicants have experienced those things and processed them in whatever sense it is, but LLM-style AIs as we have them now are always inferring what those experiences are like.
If you had a fully functioning model in some setting, interacting with the environment and then reporting back to you about it, it might be one thing. But telling you what others have said about it is different.
Humans do this too, but there's real-life experiences informing it also. An LLM hasn't fell in love, it simply reports what others have said and infers what it is like to be in love.
I think too the piece points to another related thing, which is that someone who has actually experienced something firsthand has some knowledge that someone who has not does not. It might take some extensive sampling to find out what that is, but eventually you'll stumble on it.
So e.g., the Sistine Chapel example is sort of telling in this way. Sean basically says "everyone has seen pictures of the Sistine Chapel, if you are asked about it you can tell me what it looks like" but then points out that people don't talk about what it smells like, so if you had been there you might remember it. It's a bit of latent or hidden information, kind of like a secret key, but one that might be informative or useful in some unexpected scenario.
I think ultimately this is what the stochastic parrot idea is about: it's not just about mimicking speech patterns, it's about regurgitating what is said about X from third party Z, without being able to produce some additional information not available from Z except by inference. There's no original uninferred information. The inferences might be powerful and highly accurate in their predictions, but they are not providing anything fundamentally original from the experience in a memory sense.
Maybe that's what it is? LLMs have no firsthand memories, they only have secondhand memories and inference. They're missing information that would be available through firsthand memories, constrained by the scope of sensory channels.
Again, I think you could envision models in some system that are essentially replicant-like, but that's not what our current situation is with standard LLMs.
I measure by mass when I bake, but I've always had the same questions as you (about humidity, for example). That was always the answer I got when discussing volume versus mass measurements — that volume can change due to all sorts of things — but it always seemed to me that mass could change for the same reasons.
I eventually decided mass measurements are most useful when the amount you need in mass is fairly small relative to the volume of the particles of the thing you're measuring. Measuring a small volume of nuts can be tricky, for example, because the nuts are different sizes and shapes, but mass is fairly consistent.
Measurement with baking in general is conducive to replicability assuming the same conditions are met. That is, that you're in the same bakery, with the same oven, same flour, and so forth. It becomes less reliable as you start changing variables.
This is pretty obvious even with flour: two bread flours can absorb really different amounts of water, so you almost have to be aware of texture and so forth. What you want to achieve in a recipe is a certain outcome, in dough characteristics and final loaf. How you get there can be informed by a bunch of things but is never guaranteed unless everything is the same every time.
> But if you joined a protest group where the organizer shoots a policeman and you’ve all got weapons at home and then you tell people to move the material you have at home that indicates you’re part of the group then I think the convictions are pretty accurate.
30 years though? And for moving what material exactly?
I think you can have some kind of rationale for why these people should be prosecuted. I'm not sure that's the issue for many though — the issue is the proportionality of the sentence.
A lot of focus has been put on the freedom of speech aspects of this — and rightly so — but my concern is that this is a much broader attempt to simply put control of research monies in the hands of the Trump administration for whatever purposes it serves those individuals.
It's essentially an assertion that they can do whatever they like with funds allocated for research, giving it to whoever they like, under whatever conditions they like, whenever they want.
I didn't know what Harajuku was, and thought it was going to be some Japanese term for some psychological concept, like ikigai, kaizen, wabi-sabi, or something like that.
I think there's some evidence for this, and it's consistent with my experiences with myself and what I've seen in others.
It's basically the idea behind the motivation to change literature, that there has to be some point at which the person has to be on board and interested in the change. It may be the desire to change isn't a discrete thing, that something builds over time, and we just become conscious of it at a particular time, or only remember certain moments, or whatever.
There has to be an opportunity though as well, which is another point people get tripped up on and why they lose motivation. Even if someone wants to change, if they don't perceive it as being possible for whatever reason, correctly or incorrectly, the desire for change doesn't have an outlet. It may rise to consciousness and then be immediately quashed because there's nowhere to go.
A lot of the time I think that's the bigger obstacle; it's not being aware of some desire to change, it's having some sense that the change isn't possible or that they don't know how to go about it, which amounts to the same thing.
More decentralized decision-making (in the hands of universities and institutes), requirements for permanent positions as a requirement for funding, more funding of individuals and programs rather than projects, more randomized funding (lotteries etc), requirements for trainee positions to be tied to projected permanent positions, more early career development and retraining funding, etc etc etc.
The traditional model was actually pretty solid: a decentralized system focused on universities and institutes with lots of independent funding of individuals, with protection of those individuals. The shift toward funding of universities by a centralized institution (the US federal government) based on temporary projects with no protection of the individuals behind the projects, led to the pyramid schemes and vulnerable attention-seeking that you see now.
Academics in the US has been a tinder box for years. This past year or so has been like setting fire to the tinder box rather than cleaning up the fire hazard.
This is a narrative that doesn't match the reality.
Reported failures to replicate are pretty similar in other fields, including in biomedicine. The replication crisis was just first highlighted in social psychology; lots of other fields just don't report these failures to replicate (https://en.wikipedia.org/wiki/Replication_crisis). It's a bit like shooting the messenger.
It's funny reading through the comments in this thread because there are people claiming that sociology both (1) is so obvious that the effects replicate at such rates that the research is useless because everything is known a priori and (2) that nothing replicates at all.
Also, criticizing fields like social psychology and sociology for focusing on social phenomena seems a bit strange because that's what the fields are about. It's like criticizing biology for having too much of a bias toward living things.
Other people answered better than I could, although some of their examples were what I had in mind (China, much of Europe over the last 150 years or so, maybe the US during the 19th century).
I love it also, but I think the comments are pointing to an unmet need for firewood splitting simulators.
The comments are suggesting that someone could go to town adding different kinds of hatchets, mauls, axes, woods, and different swings, and people would eat it up.
This makes an important point, and is important to think about, but I'm also interested in how societies go to that brink and come back.
I suspect there's a bit of bias in this, as you don't hear as much about the nations that come to the point of collapse and then somehow immediately recover, you hear more about those that disintegrate into decades of chaos and disorganization.
The essay also points to something else on my mind a lot lately, which is, when does that continuation of the status quo stop, and why? At what point did these societies start to see themselves as something else, and why? Is it always due to some fundamental breaking down of some governmental or military covenant?
You might be completely correct, although my hunch is this is something that would require a change in architecture rather than increases in scale.
The failure points happen in a fairly simple task (Stroop) with increases in repetition of trials. It's not like the number of colors or color words is increasing, which is the sort of thing I might expect if it had to do with the size of the LLM.
On the other hand who knows. I agree that model scale changes make a lot of things a moving target.
At first I thought this paper was kind of odd, but then I felt like it was maybe possibly onto something important. Intuitively I could see the possibility that whatever is causing this failure in the Stroop task might be related to the tendency of LLMs to be "derailable".
This can be more subtly critical than it might seem, in that even if you can manipulate some proxy, often that proxy is insufficient in actually representing the phenomenon of interest, or the conditions under which they actually occur.
I often use the example of videogames and aggression. There were plenty of experimental studies of this but it was always questionable whether lab-induced anger is the same thing as, say, the sort of violence we generally are concerned about societally.
I generally have tried to teach students that experimental designs when done right provide powerful causal evidence of something, but often with limited generalizability; observational designs in contrast provide powerful generalizable evidence of some kind of association, but often with limited certainty about the causal pathways involved.
I've been in a department that was rabidly experimental in its focus and it always seemed sort of short-sighted, because people were idolizing RCTs with proxy manipulations that had questionable generalizability to the real-world phenomena they were trying to model.
Ideally you'd bring both experimental and observational evidence to bear on a question. Your conclusions should be robust to different types of designs.