LessWrong 2.0 Reader

While you nod to 'politics is the mind-killer', I don't think the right lesson is being taken away, or perhaps just not with enough emphasis. 

Whether one is an accelerationist, Pauser, or an advocate of some nuanced middle path, the prospects/goals of everyone are harmed if the discourse-landscape becomes politicized/polarized. All possible movement becomes more difficult. 

"Well we of course don't want that to happen, but X ppl are in power, so it makes sense to ask how X ppl tend to think and cater our arguments to them" 

If your argument is taking advantage of features of {group of ppl X} qua X, then that is almost unavoidably going to run counter to some Y qua Y, (either as a direct consequence of the arguments and/or because Nuance cannot survive public exposure) and if it isn't, then why couldn't the argument have been made completely apolitically to begin with? 

I just continue to think that any mention, literally at all, of ideology or party is courting discourse-disaster for all, again no matter what specific policy one is advocating for. Do we all remember what happened with covid masks? Or what is currently happening with discourse surrounding elon? Nuance just does not survive public exposure, and nobody is going to fix that in the few years we have left. (and this is a public document). The best way forward continues to be apolitical good arguments. Yes those arguments are going to be sent towards those who are in power at any given time, but you can do that without routing through ideology.  

To touch, even in passing reference, ideology/alliance (ex: the c word included in the title of this post) is to risk the poison/mindkill spreading in a way that is basically irreversible, because to correct it (other than comments like this just calling to Stop Referencing Ideology) usually involves Referencing An Ideology. Like a bug stuck in a glue trap, it places yet another limb into the glue in a vain attempt to push itself free. 

Absolutely true. 

But also Altman's actions since are very clearly counter to the spirit of that email. I could imagine a version of this plan, executed with earnestness and attempted cooperativeness, that wasn't nearly as harmful (though still pretty bad, probably). 

Part of the problem is that "we should build it first, before the less trustworthy" is a meme that universalizes terribly. 

Part of the problem is that Sam Altman was not actually sincere in the the execution of that sentiment, regardless of how sincere his original intentions were.

Offering $7,500 total is likely on the high end of what is practical before people start inefficiently gaming the system.

What does that mean? The wikipedia article Child benefit lists several examples of child benefit systems that yield more than $7,500.

Nobody admitted to trying repeated data at scale yet (so we don't know that it doesn't work), which from the tiny experiments can 5x the data with little penalty and 15x the data in a still-useful way. It's not yet relevant for large models, but it might turn out that small models would greatly benefit already.

There are 15-20T tokens in datasets whose size is disclosed for current models (Llama 3, Qwen 2.5), plausibly 50T tokens of tolerable quality can be found (pretraining only needs to create useful features, not relevant behaviors). With 5x 50T tokens, even at 80 tokens/parameter^[1] we can make good use of 5e27-7e27 FLOPs^[2], which even a 1 gigawatt 500K B200s system of early 2026 would need 4-6 months to provide.

The isoFLOP plots (varying tokens per parameter for fixed compute) seem to get loss basins that are quite wide, once they get about 1e20 FLOPs of compute. The basins also get wider for hybrid attention (compare 100% Attention isoFLOPs in the "Perplexity scaling analysis" Figure to the others). So it's likely that using a slightly suboptimal tokens/parameter ratio of say 40 won't hurt performance much at all. In which case we get to use 9e27-2e28 FLOPs by training a larger model on the same 5x 50T tokens dataset. The data wall for text data is very unlikely to be a 2024-2026 issue.

I note you didn't mention the info-sec aspects of the war, I have heard China is better at this than the US, but that doesn't mean much because you would expect to hear that if China was really terrible too.

These emails and others can be found in document 32 here.

Still very plausible as a route to continued capabilities progress. Such things will have very different curves and economics, though, compared to the previous era of scaling.

I think you're mostly right about the world, but I'm going to continue to articulate disagreements based on my sense of dissatisfaction. You should probably mostly read me as speaking from a should-world rather than being very confused about the is-world.

The bitter lesson is insufficient to explain the lack of structure we're seeing. I gave the example of Whisper. I haven't actually used Whisper, so correct me if I'm wrong -- maybe there is a way to get more nuanced probabilistic information out of it? But the bitter lesson is about methods of achieving capabilities, not about the capabilities themselves. Producing a plain text output rather than a more richly annotated text that describes some of the uncertainty is a design choice.

To give another example, LLMs could learn a conditional model that's annotated with metadata like author, date and time, etc. Google Lambda reportedly had something like author-vectors, so that different "characters" could be easily summoned. I would love to play with that, EG, averaging the author-vectors of specific authors I'm interested in to see what it's like to mix their voices. In theory, LLMs could also learn to predict the metadata. You could use partially-labeled-data approaches such as the EM algorithm to train LLMs to predict the metadata labels while also training them to use those labels in text generation. This would give a rich set of useful capabilities. This would be scientifically useful, too: predictions of date, author, and other metadata would be interesting for historians.

In this way, we could actually get closer to a "truth machine". There's a world of difference between the kinds of inferences you can make from ChatGPT's opinion about a text vs inferences you could make by treating these as legit latent variables and seeing what latent-variable-inference algorithms think. 

To give a third example, there's Drexler's Quasilinguistic Neural Representations.

You say "it's a matter of demand". So why does it feel so much like big tech is eager to push the chatbot model on everyone? Everyone is scared to fall behind in the AI race, ever since ChatGPT made it feel plausible that AI would drive significant market share. But did big tech make the correct guess about what there was demand for? Or did everyone over-update on the specific form of ChatGPT, and now there's just not that much else out there to reveal what the demand really is? Little sparkle-emoji buttons decorate everything; press to talk to the modern equivalent of Clippy.

Not cynical enough! They make billions of dollars and for most of the time they've done this there have been little-to-no people with serious political power or prestige in the world who strongly and openly hold the position that it's doomed, so I think it's been pretty easy for people to come up with rationalizations that lets them go ahead and build some of the most incredible and powerful things humanity has ever built without facing much social pressure or incentive to do change course.

You might not think it, but basically none of the leadership of these organizations has even written a public justification for why they're willing to risk the lives of everyone on earth while getting rich, never mind argued with critics. So I don't take them very seriously as ethical/responsible people nor do I think their words about plans to avoid extinction/disempowerment should primarily be modeled as tracking reality rather than as useful rationalizations.

(Sorry dude. But it's good to be on Earth with you before some fools end it.)

If the default path is AI's taking over control from humans, then what is the current plan in leading AI labs? Surely all the work they put in AI safety is done to prevent exactly such scenarios. I would find it quite hard to believe that a large group of people would vigorously do something if they believed that their efforts will go to vain.