Even Scott's fantasy dream scenario for what prediction markets could be like and what questions they could answer feels... ...deliberately naive? ...like libertarian brainrot? ...disconnected from reality?

Ask yourself: what are the big future-prediction questions that important disagreements pivot around? When I try this exercise, I get things like:

Will the AI bubble pop? Will scaling get us all the way to AGI? Will AI be misaligned?

Huge amounts of money are being dumped into a bubble based on hype, so to hope a predict market would or could make better predictions than the existing business-idiot VCs funding this bubble feels hopelessly naive in a libertarian kind of way. There is already a method of aggregating the wisdom of the crowd and it is failing to incredibly lazy hype and PR.

Will Trump turn America into a dictatorship? Make it great again? Somewhere in between?

Again, there is already a mechanism for aggregating wisdom of the crowds, its called an election, and its also failed to get a answer predicated on reality or truth, so again, it seems incredibly naive to expect prediction markets to do better!

Will YIMBY policies lower rents? How much?

I mean, the councils and communities making these decision already ignore or overlook longer-term broader predictions of economic impact in favor of immediate home-owner value, I don't see why Scott would expect prediction markets to help decision making go better here.

Overall, it feels like Scott is overlooking the way decision making often already ignores science and experts. Society doesn't have a problem making decent predictions compared to the problems it has communicating expert opinions to the public effectively and crafting policy aligned with the public interest.

The prediction markets seem to have all the basic problems that sneerclubbers: problems with resolution mechanisms, all sorts of insider trading and gaming the market, people using it for gambling...

Various prediction markets have made various half-assed attempts at solutions, but so far nothing seems to actually work well enough to make prediction markets nearly as useful as rationalists expected.

Some of the change probably involves the discovery of a natural bat coronavirus with a furin cleavage site last October, but I’m surprised by the extent of the decline.

That actually seems like the prediction market sort of did its job in this case? I mean, 27% yes is still too high, but actually changing in response to real evidence is much better than my low low expectations for prediction markets. It seems like he should take his own advice and actually take the prediction market seriously in this case.

Yeah that was a good article. I think that is one of the fundamental issues with rationalists, they are basically a group formed around neat sci-fi ideas and not actually getting anything done, and their strong libertarian biases prevent them from actually pursuing the strategies that would be most effective for many of their nominal goals.

Their proposed sort of solution (controlled miscalibration) even amounts to forcing the model to generalize less by memorizing more, which used to be the opposite of why you would choose to use this type of topography.

Yeah, it does seem to be running into the basic issue that what boosters want LLMs to be (all knowing oracle) is in sharp contrast to what LLMs actually are (churn out statistically plausible content).

You've described the problem with generalization yes. Well, you could maybe sort of train it not to generate "all men are cats", but then that might also prevent it from making the more correct generalization "all cats are mortal" or even completely valid generalizations like combing "all men are mortal" and "Socrates is man" to get "Socrates is mortal".

The problem with monofacts is a bit more subtle. Let's say the fact that “John Smith was born in Seattle in 1982, earned his PhD from Stanford in 2008, and now leads AI research at Tech Corp,” appears only once in the training data set. Some of the other words the model will have seen multiple times and be able to generate tokens in the right way for. Like Seattle as a location in the US, Stanford as a college, 2008 as a date, etc. But the combination describing a fact about John Smith appearing uniquely trains the model to try to generate facts that are unique combinations of data. So the model might try to make up a fact like "Jane Doe was born in Omaha in 1984, earned her master from Caltech in 2006, and is now CEO of Tech Corp" because it fits the pattern of a unique fact that was in its training data set.

For the chain of thought instruction following model gpt-oss-20b, I've noticed its reasoning content often includes it talking about stuff it is supposed to avoid in the final output and it double checking that it doesn't have that forbidden output. So it would waste tokens talking about pink elephants in its reasoning content, but then do okayish at avoiding pink elephants in its final output.

Theoretically if the people responsible for that training and reinforcement did their jobs well then those patterns should only include true statements but if it was that easy then you wouldn’t have [insert the entire intellectual history of the human species].

I'm chiming in to agree with Architeuthis and mention a citation explaining more. LLMs have a hard minimum rate of hallucinations based on the rate of "monofacts" in their training data (https://arxiv.org/html/2502.08666v1). Basically, facts that appear independently and only once in the training data cause the LLM to "learn" that you can have a certain rate of disconnected "facts" that appear nowhere else, and cause it to in turn generate output similar to that, which in practice is basically random and thus basically guaranteed to be false.

And as Architeuthis says, the ability of LLMs to "generalize" basically means they compose true information together in ways that is sometimes false. So to the extent you want your LLM to ever "generalize", you also get an unavoidable minimum of hallucinations that way.

So yeah, even given an even more absurdly big training data source that was also magically perfectly curated you wouldn't be able to iron out the intrinsic flaws of LLMs.

-3 upvotes and 0 karma, but the article is absolutely right (they hate this post because it tells the truth). If Eliezer wants to influence public discourse and policy on an international level, he absolutely does need a respectable image (with maybe a touch of eccentricity in an allowable way). But apparently (what he thinks is) the literal end of the world isn't enough to make him actually try for normie public image. Or maybe he has some galaxy brain plan about how looking like a weirdo actually helps his cause? If he does, I strongly suspect it is a rationalization.

Wonder of the goblin stuff is the start of some model collapse.

That is exactly it. Their official explanation avoids the phrase model collapse, but that is exactly what they describe: using the output of one model as training data for another amplified the occurrence of the word goblin (and other creatures), which apparently initially occurred because of their system prompt which was aimed at maximizing the Eliza effect (again they avoid an honest framing, but that is totally what they are doing and it is pretty gross considering all the cases of AI psychosis that have been occuring) by telling the model "You are an unapologetically nerdy, playful and wise AI mentor to a human. "

Widespread financial fraud which was legitimized and in some cases directly backed by EAs! Surely there are no parallels!

Zitron’s analogy is excellent because the bubble is multifactorial and the analogies that we can make are factor-to-factor. Here’s some things that caused the dot-com bubble; people were overly optimistic about:

Ed has also been clear there are a few factors that make this bubble worse (for the economy and the general public) than the dotcom bubble. For one, Ed is strongly convinced that GPU lifecycles are much shorter and worse than fiber optic life cycles. You build fiber optic infrastructure and it will last for decades. Meanwhile, GPUs used constantly at max load have life cycles of 3-5 years. The end result of the internet is also much more useful and less of a double-edged sword than the slop generators which churn out propaganda and spam.