LessWrong 2.0 Reader

Counter point: We would likely guess that the graph of rent to income would look similar. 

This is actually corrected on the Epoch website but not here (https://epochai.org/blog/the-longest-training-run)

The reason why EY&co were relatively optimistic (p(doom) ~ 50%) before AlphaGo was their assumption "to build intelligence, you need some kind of insight in theory of intelligence". They didn't expect that you can just take sufficiently large approximator, pour data inside, get intelligent behavior and have no idea about why you get intelligent behavior.

UPDATE: we've corrected equations 9 and 10 in the paper (screenshot of the draft below) and also added a footnote that hopefully helps clarify the derivation. I've also attached a revised figure 6, showing that this doesn't change the overall story (for the mathematical reasons I mentioned in my previous comment). These will go up on arXiv, along with some other minor changes (like remembering to mention SAEs' widths), likely some point next week. Thanks again Sam for pointing this out!

Updated equations (draft):

Updated figure 6 (shrinkage comparison for GELU-1L):

In some of his books on evolution, Dawkins also said very similar things when commenting on Darwin vs Wallace, basically saying that there's no comparison, Darwin had a better grasp of things, justified it better and more extensively, didn't have muddled thinking about mechanisms, etc.

List sorting does not play well with few-shot mostly doesn't replicate with davinci-002.

When using length-10 lists (it crushes length-5 no matter the prompt), I get:

• 32-shot, no fancy prompt: ~25%
• 0-shot, fancy python prompt: ~60% 
• 0-shot, no fancy prompt: ~60%

So few-shot hurts, but the fancy prompt does not seem to help. Code here.

I'm interested if anyone knows another case where a fancy prompt increases performance more than few-shot prompting, where a fancy prompt is a prompt that does not contain information that a human would use to solve the task. This is because I'm looking for counterexamples to the following conjecture: "fine-tuning on k examples beats fancy prompting, even when fancy prompting beats k-shot prompting" (for a reasonable value of k, e.g. the number of examples it would take a human to understand what is going on).

We could also combine this with the rate of growth of investments. In that case we would end up with a total rate of growth of effective compute equal to $g_H+g_I+g_S\approx 0.28+3.84+0.54=4.66$. This results in an optimal training run length of $L=1/(g_H+g_I+g_S)\approx 0.21$ years, ie $2.52$ months.

Why is g_I here 3.84, while above it is 1.03?

I'm against intuitive terminology [epistemic status: 60%] because it creates the illusion of transparency; opaque terms make it clear you're missing something, but if you already have an intuitive definition that differs from the author's it's easy to substitute yours in without realizing you've misunderstood.

I'm not alexithymic; I directly experience my emotions and have, additionally, introspective access to my preferences. However, some things manifest directly as preferences which I have been shocked to realize in my old age, were in fact emotions all along. (In rare cases these are stronger than the ones directly-felt even, despite reliably seeming on initial inspection to be simply neutral metadata).

That's right. We initially thought it might be important so that the LLM "understood" the task better, but it didn't matter much in the end. The main hyperparameters for our experiments are in train_ray.py, where you can see that we use a "token_loss_weight" of 0.

(Feel free to ask more questions!)