In this post, I proclaim/endorse forum participation (aka commenting) as a productive research strategy that I've managed to stumble upon, and recommend it to others (at least to try). Note that this is different from saying that forum/blog posts are a good way for a research community to communicate. It's about individually doing better as researchers.

I like the fact that despite not being (relatively) young when they died, the LW banner states that Kahneman & Vinge have died "FAR TOO YOUNG", pointing to the fact that death is always bad and/or it is bad when people die when they were still making positive contributions to the world (Kahneman published "Noise" in 2021!).
Dictionary/SAE learning on model activations is bad as anomaly detection because you need to train the dictionary on a dataset, which means you needed the anomaly to be in the training set. How to do dictionary learning without a dataset? One possibility is to use uncertainty-estimation-like techniques to detect when the model "thinks its on-distribution" for randomly sampled activations.
habryka4d5120
10
A thing that I've been thinking about for a while has been to somehow make LessWrong into something that could give rise to more personal-wikis and wiki-like content. Gwern's writing has a very different structure and quality to it than the posts on LW, with the key components being that they get updated regularly and serve as more stable references for some concept, as opposed to a post which is usually anchored in a specific point in time.  We have a pretty good wiki system for our tags, but never really allowed people to just make their personal wiki pages, mostly because there isn't really any place to find them. We could list the wiki pages you created on your profile, but that doesn't really seem like it would allocate attention to them successfully. I was thinking about this more recently as Arbital is going through another round of slowly rotting away (its search currently being broken and this being very hard to fix due to annoying Google Apps Engine restrictions) and thinking about importing all the Arbital content into LessWrong. That might be a natural time to do a final push to enable people to write more wiki-like content on the site.
Novel Science is Inherently Illegible Legibility, transparency, and open science are generally considered positive attributes, while opacity, elitism, and obscurantism are viewed as negative. However, increased legibility in science is not always beneficial and can often be detrimental. Scientific management, with some exceptions, likely underperforms compared to simpler heuristics such as giving money to smart people or implementing grant lotteries. Scientific legibility suffers from the classic "Seeing like a State" problems. It constrains endeavors to the least informed stakeholder, hinders exploration, inevitably biases research to be simple and myopic, and exposes researchers to constant political tug-of-war between different interest groups poisoning objectivity.  I think the above would be considered relatively uncontroversial in EA circles.  But I posit there is something deeper going on:  Novel research is inherently illegible. If it were legible, someone else would have already pursued it. As science advances her concepts become increasingly counterintuitive and further from common sense. Most of the legible low-hanging fruit has already been picked, and novel research requires venturing higher into the tree, pursuing illegible paths with indirect and hard-to-foresee impacts.
I thought I didn’t get angry much in response to people making specific claims. I did some introspection about times in the recent past when I got angry, defensive, or withdrew from a conversation in response to claims that the other person made.  After some introspection, I think these are the mechanisms that made me feel that way: * They were very confident about their claim. Partly I felt annoyance because I didn’t feel like there was anything that would change their mind, partly I felt annoyance because it felt like they didn’t have enough status to make very confident claims like that. This is more linked to confidence in body language and tone rather than their confidence in their own claims though both matter.  * Credentialism: them being unwilling to explain things and taking it as a given that they were correct because I didn’t have the specific experiences or credentials that they had without mentioning what specifically from gaining that experience would help me understand their argument. * Not letting me speak and interrupting quickly to take down the fuzzy strawman version of what I meant rather than letting me take my time to explain my argument. * Morality: I felt like one of my cherished values was being threatened.  * The other person was relatively smart and powerful, at least within the specific situation. If they were dumb or not powerful, I would have just found the conversation amusing instead.  * The other person assumed I was dumb or naive, perhaps because they had met other people with the same position as me and those people came across as not knowledgeable.  * The other person getting worked up, for example, raising their voice or showing other signs of being irritated, offended, or angry while acting as if I was the emotional/offended one. This one particularly stings because of gender stereotypes. I think I’m more calm and reasonable and less easily offended than most people. I’ve had a few conversations with men where it felt like they were just really bad at noticing when they were getting angry or emotional themselves and kept pointing out that I was being emotional despite me remaining pretty calm (and perhaps even a little indifferent to the actual content of the conversation before the conversation moved to them being annoyed at me for being emotional).  * The other person’s thinking is very black-and-white, thinking in terms of a very clear good and evil and not being open to nuance. Sort of a similar mechanism to the first thing.  Some examples of claims that recently triggered me. They’re not so important themselves so I’ll just point at the rough thing rather than list out actual claims.  * AI killing all humans would be good because thermodynamics god/laws of physics good * Animals feel pain but this doesn’t mean we should care about them * We are quite far from getting AGI * Women as a whole are less rational than men are * Palestine/Israel stuff   Doing the above exercise was helpful because it helped me generate ideas for things to try if I’m in situations like that in the future. But it feels like the most important thing is to just get better at noticing what I’m feeling in the conversation and if I’m feeling bad and uncomfortable, to think about if the conversation is useful to me at all and if so, for what reason. And if not, make a conscious decision to leave the conversation. Reasons the conversation could be useful to me: * I change their mind * I figure out what is true * I get a greater understanding of why they believe what they believe * Enjoyment of the social interaction itself * I want to impress the other person with my intelligence or knowledge Things to try will differ depending on why I feel like having the conversation. 

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Summary: The post describes a method that allows us to use an untrustworthy optimizer to find satisficing outputs.

Acknowledgements: Thanks to Benjamin Kolb (@benjaminko), Jobst Heitzig (@Jobst Heitzig) and Thomas Kehrenberg (@Thomas Kehrenberg)  for many helpful comments.

Introduction

Imagine you have black-box access to a powerful but untrustworthy optimizing system, the Oracle. What do I mean by "powerful but untrustworthy"? I mean that, when you give an objective function  as input to the Oracle, it will output an element  that has an impressively low[1] value of . But sadly, you don't have any guarantee that it will output the optimal element and e.g. not one that's also chosen for a different purpose (which might be dangerous for many reasons, e.g. instrumental convergence).

What questions can you safely ask the Oracle? Can you use it to...

First thought: The oracle is going to choose to systematically answer or not answer the queries we give it. This represents a causal channel of one bit per query it can use to influence the outside world[1]. Can you conquer the world in one awkwardly delivered kilobyte or less? Maybe.

Agreed. I think it's potentially a good bit worse than one kilobyte if let ourselves bet tricked to ask many questions, different questions or lower the difficulty of the safety constraint too much. 

As mentioned in footnote 10, this requires a kind of perfect coordination... (read more)

3Simon Fischer2h
Sure, I mostly agree with the distinction you're making here between "sins of commission" and "sins of omissions". Contrary to you, though, I believe that getting rid of the threat of "sins of commission" is extremely useful. If the output of the Oracle is just optimized to fulfill your satisfaction goal and not for anything else, you've basically gotten rid of the superintelligent adversary in your threat model. I agree that for many ambitious goals, 'unboxing the Oracle' is an instrumental goal. It's overwhelmingly important that we use such an Oracle setup only for goals that are achievable without such instrumental goals being pursued as a consequence of a large fraction of the satisficing outputs. (I mentioned this in footnote 2, but probably should have highlighted it more.) I think this is a common limitation of all soft-optimization approaches. This is talking about a different threat model than mine. You're talking here about security in a more ordinary sense, as in "secure from being hacked by humans" or "secure from accidentally leaking dangerous information". I feel like this type of security concerns should be much easier to address, as you're defending yourself not against superintelligences but against humans and accidents. The example you gave about the Oracle producing a complicated plan that leaks the source of the Oracle is an example of this: It's trivially defended against by not connecting the device the Oracle is running on to the internet and not using the same device to execute the great "cure all cancer" plan. (I don't believe that either you or I would have made that mistake!)
4gwern39m
We probably would've been less enthusiastic about hooking up LLMs to the Internet too, but here we collectively are. We do face a superintelligent adversary: all of the incentives and factions of humanity. An Oracle which is simply neutral is still default dangerous. Also, one of my points was that even a tiny trace of malicious optimization here can still have large effects because there are so many neutral options which are convergently unsafe, so with such a high base rate, even a bit of optimization can result in a large absolute increase in failure. Lucius brings up one concern about what one bit could do. What could one bit do? Well, let's continue my scenario: the Oracle is not quite purged of scheming by all of your tricks, and it is able to exert exactly 1 bit of optimization power unnoticed (ie. a single binary choice). You ask it to cure cancer, and it computes a plan and comes to the database selection: it could choose MongoDB or PostgreSQL - both respectable common choices, with their own set of tradeoffs, which would be equally appropriate. However, the MongoDB devs have a nasty habit of 'blaming the users' when it comes to security, which is why MongoDB shipped with default username/passwords, which is something the more serious PostgreSQL devs fixed back in the '90s. The Oracle notes this, and that the PostgreSQL plan would have zero chance of unboxing, and exerts its one bit of power to pick MongoDB each time and/or kill candidate plans containing PostgreSQL to get a near 100% chance of unboxing. You get back all of the plans and see unanimity about MongoDB, and inner-monologues truthfully extolling its flexibility and Web 2.0 scalability and explaining why PostgreSQL comes with downsides like restrictive schema etc, and sign off on it... Whereas in your described threat model, a mere 1 bit would still wind up orders of magnitude far short of any discernible problem.
3Simon Fischer19m
I completely agree with that. My proposal does not address the global coordination problem that we face, but it might be a useful tool if we collectively get our act together or if the first party with access to superintelligence has enough slack to proceed extra carefully. Even more modestly, I was hoping this might contribute to our theoretical understanding of why soft-optimization can be useful.

This is the ninth post in my series on Anthropics. The previous one is The Solution to Sleeping Beauty.

Introduction

There are some quite pervasive misconceptions about betting in regards to the Sleeping Beauty problem.

One is that you need to switch between halfer and thirder stances based on the betting scheme proposed. As if learning about a betting scheme is supposed to affect your credence in an event.

Another is that halfers should bet at thirders odds and, therefore, thirdism is vindicated on the grounds of betting. What do halfers even mean by probability of Heads being 1/2 if they bet as if it's 1/3?

In this post we are going to correct them. We will understand how to arrive to correct betting odds from both thirdist and halfist positions, and...

(Tl;dr: sleeping beauty is an edge case where different reward structures are intuitively possible and so people imagine different game payout structures behind the definition of “probability”. Once the payout structure is fixed, the confusion is gone. With a fixed payout structure&preference framework rewarding the number you output as “probability”, people don’t have a disagreement about what is the best number to output. Sleeping beauty is about definitions.)

Your posts argue that if a tree falls on a deaf Sleeping Beauty, in a forest with no one to ... (read more)

1simon3h
Yeah, that was sloppy language, though I do like to think more in terms of bets than you do. One of my ways of thinking about these sorts of issues is in terms of "fair bets" - each person thinks a bet with payoffs that align with their assumptions about utility is "fair", and a bet with payoffs that align with different assumptions about utility is "unfair". OK, I was also being sloppy in the parts you are responding to. Scenario 1: bet about a coin toss, nothing depending on the outcome (so payoff equal per coin toss outcome) * 1:1 Scenario 2: bet about a Sleeping Beauty coin toss, payoff equal per awakening * 2:1  Scenario 3: bet about a Sleeping Beauty coin toss, payoff equal per coin toss outcome  * 1:1 It doesn't matter if it's agreed to before or after the experiment, as long as the payoffs work out that way. Betting within the experiment is one way for the payoffs to more naturally line up on a per-awakening basis, but it's only relevant (to bet choices) to the extent that it affects the payoffs. Now, the conventional Thirder position (as I understand it) consistently applies equal utilities per awakening when considered from a position within the experiment. I don't actually know what the Thirder position is supposed to be from a standpoint from before the experiment, but I see no contradiction in assigning equal utilities per awakening from the before-experiment perspective as well.  As I see it, Thirders will only regret a bet (in the sense of considering it a bad choice to enter into ex ante given their current utilities) if you do some kind of bait and switch where you don't make it clear what the payoffs were going to be up front. Speculation; have you actually asked Thirders and Halfers to solve the problem? (while making clear the reward structure? - note that if you don't make clear what the reward structure is, Thirders are more likely to misunderstand the question asked if, as in this case, the reward structure is "fair" from the Ha
1Ape in the coat11h
Yes, if the bet is about whether the room takes the color Red in this experiment. Which is what event "Red" means in Technicolor Sleeping Beauty according to the correct model. The fact that you do not observe event Red in this awakening doesn't mean that you don't observe it in the experiment as a whole. The situation is somewhat resembling learning that today is Monday and still being ready to bet at 1:1 that Tuesday awakening will happen in this experiment. Though, with colors there is actually an update from 3/4 to 1/2. What you, probably, tried to ask, is whether you should agree to bet at 1:1 odds that the room is Red in this particular awakening after you wake up and saw that the room is Blue. And the answer is no, you shouldn't. But probability space for Technicolor Sleeping beauty is not talking about probabilities of events happening in this awakening, because most of them are illdefined for reasons explained in the previous post.
1Signer10h
So probability theory can't possibly answer whether I should take free money, got it. And even if "Blue" is "Blue happens during experiment", you wouldn't accept worse odds than 1:1 for Blue, even when you see Blue?

This is my personal opinion, and in particular, does not represent anything like a MIRI consensus; I've gotten push-back from almost everyone I've spoken with about this, although in most cases I believe I eventually convinced them of the narrow terminological point I'm making.

In the AI x-risk community, I think there is a tendency to ask people to estimate "time to AGI" when what is meant is really something more like "time to doom" (or, better, point-of-no-return). For about a year, I've been answering this question "zero" when asked.

This strikes some people as absurd or at best misleading. I disagree.

The term "Artificial General Intelligence" (AGI) was coined in the early 00s, to contrast with the prevalent paradigm of Narrow AI. I was getting my undergraduate computer science...

Yes, I agree. Whenever I think of things like this I focus on how what matters in the sense of "when will agi be transformational" is the idea of criticality.

I have written on it earlier but the simple idea is that our human world changes rapidly when AI capabilities in some way lead to more AI capabilities at a fast rate.

Like this whole "is this AGI" thing is totally irrelevant, all that matters is criticality. You can imagine subhuman systems using AGI reaching criticality, and superhuman systems being needed. (Note ordinary humans do have criticality... (read more)

2abramdemski3h
I haven't watched the LeCun interview you reference (it is several hours long, so relevant time-stamps to look at would be appreciated), but this still does not make sense to me -- backprop already seems like a way to constantly predict future experience and update, particularly as it is employed in LLMs. Generating predictions first and then updating based on error is how backprop works. Some form of closeness measure is required, just like you emphasize.
1cubefox2h
Well, backpropagation alone wasn't even enough to make efficient LLMs feasible. It took decades, till the invention of transformers, to make them work. Similarly, knowing how to make LLMs is not yet sufficient to implement predictive coding. LeCun talks about the problem in a short section here from 10:55 to 14:19.
2abramdemski4h
Yeah, I didn't do a very good job in this respect. I am not intending to talk about a transformer by itself. I am intending to talk about transformers with the sorts of bells and whistles that they are currently being wrapped with. So not just transformers, but also not some totally speculative wrapper.

previously: https://www.lesswrong.com/posts/h6kChrecznGD4ikqv/increasing-iq-is-trivial

I don't know to what degree this will wind up being a constraint. But given that many of the things that help in this domain have independent lines of evidence for benefit it seems worth collecting.

Food

dark chocolate, beets, blueberries, fish, eggs. I've had good effects with strong hibiscus and mint tea (both vasodilators).

Exercise

Regular cardio, stretching/yoga, going for daily walks.

Learning

Meditation, math, music, enjoyable hobbies with a learning component.

Light therapy

Unknown effect size, but increasingly cheap to test over the last few years. I was able to get Too Many lumens for under $50. Sun exposure has a larger effect size here, so exercising outside is helpful.

Cold exposure

this might mostly just be exercise for the circulation system, but cold showers might also have some unique effects.

Chewing on things

Increasing blood...

4Gunnar_Zarncke16h
Please provide more details on sources or how you measured the results.

Sources are a shallow dive of google and reading a few abstracts, this is intended as trailheads for people, not firm recommendations. If I wanted them to be reccs I would want to estimate effect sizes and estimates of the quality of the related research.

2Adam Zerner1h
The subtext here seems to be that such references are required. I disagree that it should be. It is frequently helpful but also often a pain to dig up, so there are tradeoffs at play. For this post, I think it was fine to omit references. I don't think the references would add much value for most readers and I suspect Romeo wouldn't have found it worthwhile to post if he had to dig up all of the references before being able to post.

Lots of people already know about Scott Alexander/ACX/SSC, but I think that crossposting to LW is unusually valuable in this particular case, since lots of people were waiting for a big schelling-point overview of the 15-hour Rootclaim Lab Leak debate, and unlike LW, ACX's comment section is a massive vote-less swamp that lags the entire page and gives everyone equal status. 

It remains unclear whether commenting there is worth your time if you think you have something worth saying, since there's no sorting, only sifting, implying that it attracts small numbers of sifters instead of large numbers of people who expect sorting.

Here are the first 11 paragraphs:

Saar Wilf is an ex-Israeli entrepreneur. Since 2016, he’s been developing a new form of reasoning, meant to transcend normal human bias.

His

...
4gwern2h
My current initial impression is that this debate format was not fit for purpose: https://www.astralcodexten.com/p/practically-a-book-review-rootclaim/comment/52659890
9Steven Byrnes3h
Way back in 2020 there was an article A Proposed Origin For SARS-COV-2 and the COVID-19 Pandemic, which I read after George Church tweeted it (!) (without comment or explanation). Their proposal (they call it "Mojiang Miner Passage" theory) in brief was that it WAS a lab leak but NOT gain-of-function. Rather, in April 2012, six workers in a "Mojiang mine fell ill from a mystery illness while removing bat faeces. Three of the six subsequently died." Their symptoms were a perfect match to COVID, and two were very sick for more than four months. The proposal is that the virus spent those four months adapting to life in human lungs, including (presumably) evolving the furin cleavage site. And then (this is also well-documented) samples from these miners were sent to WIV. The proposed theory is that those samples sat in a freezer at WIV for a few years while WIV was constructing some new lab facilities, and then in 2019 researchers pulled out those samples for study and infected themselves. I like that theory! I’ve like it ever since 2020! It seems to explain many of the contradictions brought up by both sides of this debate—it’s compatible with Saar’s claim that the furin cleavage site is very different from what’s in nature and seems specifically adapted to humans, but it’s also compatible with Peter’s claim that the furin cleavage site looks weird and evolved. It’s compatible with Saar’s claim that WIV is suspiciously close to the source of the outbreak, but it’s also compatible with Peter’s claim that WIV might not have been set up to do serious GoF experiments. It’s compatible with the data comparing COVID to other previously-known viruses (supposedly). Etc. Old as this theory is, the authors are still pushing it and they claim that it’s consistent with all the evidence that’s come out since then (see author’s blog). But I’m sure not remotely an expert, and would be interested if anyone has opinions about this. I’m still confused why it’s never been much discusse

I agree, I think the most likely version of the lab leak scenario does not involve an engineered virus. Personally I would say 60% chance zoonotic, 40% chance lab leak.

2Gerald Monroe6h
One thing that occurs to me is that each analysis, such as the Putin one, can be thought of as a function hypothesis. It takes as inputs the variables: Russian demographics healthy lifestyle family history facial swelling hair present And is outputting the probability 86%, where the function is P = F(demographics, lifestyle, history, swelling, hair) and then each term is being looked up in some source, which has a data quality, and the actual equation seems to be a mix of Bayes and simple probability calculations. There are other variables not considered, and other valid reasoning tracks.  You could take into account the presence of oncologists in putin's personal staff.  Intercepted communication possibly discussing it.  Etc.  I'm not here to discuss the true odds of putin developing cancer, but note that if the above is "function A", and another function that takes into account different information is "function B", you should be aggregating all valid functions, forming a "probability forest".   Perhaps you weight each one by the likelihood of the underlying evidence being true.  For example each of the above facts is effectively 100% true except for the hair present (putin could have received a hair transplant) and family history (some relative causes of death could be unknown or suspicious that it was cancer) This implies a function "A'n", where we assume and weight in the probability that each combination of the underlying variables has the opposite value.  For example, if pHair_Present = 0.9, A' has one permutation where the hair is not present due to a transplant. This hints at why a panel of superforecasters is presently the best we can do.  Many of them do simple reasoning like this and we see it in the comment section on Manifold.  But each individual human doesn't have the time to think of 100 valid hypotheses and to calculate the resulting probability, many manifold bettors seem to usually consider 1 and bet their mana. An AI system (LLM bas

He was 90 years old.

His death was confirmed by his stepdaughter Deborah Treisman, the fiction editor for the New Yorker. She did not say where or how he died.

The obituary also describes an episode from his life that I had not previously heard (but others may have):

Daniel Kahneman was born in Tel Aviv on March 5, 1934, while his mother was visiting relatives in what was then the British mandate of Palestine. The Kahnemans made their home in France, and young Daniel was raised in Paris, where his mother was a homemaker and his father was the chief of research for a cosmetics firm.

During World War II, he was forced to wear a Star of David after Nazi German forces occupied the city in 1940. One night

...

(I assume you mean the story with him and the SS soldier; I think a couple of people got confused and thought you were referring to the fact Kahneman had died)

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This is a linkpost for https://arxiv.org/abs/2403.07949

In January, I defended my PhD thesis, which I called Algorithmic Bayesian Epistemology. From the preface:

For me as for most students, college was a time of exploration. I took many classes, read many academic and non-academic works, and tried my hand at a few research projects. Early in graduate school, I noticed a strong commonality among the questions that I had found particularly fascinating: most of them involved reasoning about knowledge, information, or uncertainty under constraints. I decided that this cluster of problems would be my primary academic focus. I settled on calling the cluster algorithmic Bayesian epistemology: all of the questions I was thinking about involved applying the "algorithmic lens" of theoretical computer science to problems of Bayesian epistemology.

Although my interest in mathematical reasoning about uncertainty...

3Stephen Bennett15h
Congratulations! I wish we could have collaborated while I was in school, but I don't think we were researching at the same time. I haven't read your actual papers, so feel free to answer "you should check out the paper" to my comments. For chapter 4: From the high level summary here it sounds like you're offloading the task of aggregation to the forecasters themselves. It's odd to me that you're describing this as arbitrage. Also, I have frequently seen the scoring rule be used with some intermediary function to determine monetary rewards. For example, when I worked with IARPA on geopolitical forecasting, our forecasters would get financial rewards depending on what percentile they were in relative to other forecasters. One would imagine that this would eliminate the incentive to report the aggregate as your own answer, but there's a reason we (the researcher/platform/website) aggregate individual forecasts! It's actually just more accurate under typical conditions. In theory an individual forecaster could improve that aggregate by forming their own independent forecast before seeing the work of others, and then aggregating, but in practice the impact of an individual forecast is quite small. I'll have to read about QA pooling, it's surprising to me that you could disincentivize forecasters from reporting the aggregate as their individual forecast. For chapter 7: It seems to me that under sufficiently pessimistic conditions, there would be no good way to aggregate those two forecasts. For example, if Alice and Bob are forecasting "Will AI cause human extinction in the next 100 years?", they both might individually forecast ~0% for different reasons. Alice believes it is impossible for AI to get powerful enough to cause human extinction, but if it were capable of acting it would kill us all. Bob believes any agent smart enough to be that powerful would necessarily be morally upstanding and believes it's extremely likely that it will be built. Any reasonable aggreg

Thanks! Here are some brief responses:

From the high level summary here it sounds like you're offloading the task of aggregation to the forecasters themselves. It's odd to me that you're describing this as arbitrage.

Here's what I say about this anticipated objection in the thesis:

For many reasons, the expert may wish to make arbitrage impossible. First, the principal may wish to know whether the experts are in agreement: if they are not, for instance, the principal may want to elicit opinions from more experts. If the experts collude to report an aggregate

... (read more)

The following is an example of how if one assumes that an AI (in this case autoregressive LLM) has "feelings", "qualia", "emotions", whatever, it can be unclear whether it is experiencing something more like pain or something more like pleasure in some settings, even quite simple settings which already happen a lot with existing LLMs. This dilemma is part of the reason why I think AI suffering/happiness philosophy is very hard and we most probably won't be able to solve it.

Consider the two following scenarios:

Scenario A: An LLM is asked a complicated question and answers it eagerly.

Scenario B: A user insults an LLM and it responds.

For the sake of simplicity, let's say that the LLM is an autoregressive transformer with no RLHF (I personally think that the...

I quality-downvoted it for being silly, but agree-upvoted it because AFAICT that string does indeed contain all the (lowercase) letters of the English alphabet.

4gwern1h
I agree. The problem with AI-generated images is that any image you can generate with a prompt like "robot looking at chessboard" is going to contain, almost by definition, no more information than that prompt did, but it takes a lot longer than reading the prompt to look at the image and ascertain that it contains no information and is just AI-generated imagery added 'to look nice'. This is particularly jarring on a site like LW2 where, for better or worse, images are rarely present and usually highly-informative and dense with information when present. Worse, they usually don't 'look nice' either. Most of the time, people who use AI images can't even be bothered to sample one without blatant artifacts, or to do some inpainting to fix up the worst anomalies, or figure out an appropriate style. The samples look bad to begin with, and a year later, they're going to look even worse and more horribly dated, and make the post look much worse, like a spammer wrote it. (Almost all images from DALL-E 2 are already hopelessly nasty looking, and stuff from Midjourney-v1--3 and SD1.x likewise, and SD2/SD-XL/Midjourneyv4/5 are ailing.) It would be better if the authors of such posts could just insert text like [imagine 'a robot looking at a chessboard' here] if they are unable to suppress their addiction to SEO images; I can imagine that better than they can generate it, it seems. So my advice would be that if you want some writing to still be read in a year and it to look good, then you should learn how to use the tools and spend at least an hour per image; and if you can't do that, then don't spend time on generating images at all (unless you're writing about image generation, I suppose). Quickies are fine for funny tweets or groupchats, but serious readers deserve better. Meaningless images don't need to be included, and the image generators will be much better in a year or two anyway and you can go back and add them if you really feel the need. For Gwern.net, I'm satisf

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