LessWrong 2.0 Reader

So the usual refrain from Zvi and others is that the specter of China beating us to the punch with AGI is not not real because limits on compute, etc. I think Zvi has tempered his position on this in light of Meta's promise to release the weights of its 400B+ model. Now there is word that SenseTime just released a model that beats GPT-4 Turbo on various metrics. Of course, maybe Meta chooses not to release its big model, and maybe SenseTime is bluffing--I would point out though that Alibaba's Qwen model seems to do pretty okay in the arena...anyway, my point is that I don't think the "what if China" argument can be dismissed as quickly as some people on here seem to be ready to do.

It's actually worse than what you say -- the first two datasets studied here have privileged basis 45 degrees off from the standard one, which is why the SAEs seem to continue learning the same 45 degree off features. It turns out that both datasets have principle components 45 degrees off from the basis the authors present as natural, and so as SAE in a sense are trying to capture the principle directions of variation in the activation space, they will also naturally use features 45 degrees off from the "natural" basis. 

Consider the first example - by construction, since x_1 and x_2 are anticorrelated perfectly, as are y_1 and y_2,, the data is 2 dimensional and can be represented as x = x_1 - x_2 and y = y_1 - y_2. Indeed, this this is exactly what their diagram is assuming. But here, x and y have the same absolute magnitude by construction, and so the dataset lies entirely on the diagonals of the unit square,  and the principal components are obviously the diagonals. 

Now, why does the SAE want to learn the principle components? This is because it allows the SAE to have smaller activations on average for a given weight norm.

Consider the representation that is axis aligned, in that the SAE neurons are x_1, x_2, y_1, y_2 -- since there's weight decay, the encoding and decoding weights want to be of the same magnitude. Let's suppose that the encoding and decoding weights are of size s. Now, if the features are axis aligned, the total size of the activations will be 2A/s^2. But if you instead use the neurons x_1 + y_1, x_1 + y_2, x_2 + y_1, x_2 + y_2, the activations only need to be of size \sqrt 2 A/s^2. This means that a non-axis aligned representation will have lower loss. 

The story for the second dataset is pretty similar as the first -- when the data is uniformly distributed over a unit square, the principle directions are the diagonals of the square, not the standard basis.  

Also astronomers: anything heavier than helium is a "metal". 

That makes sense. I think I may have misjudged your post, as I expected that you would classify that kind of approach as a "duct tape" approach.

In the general case I agree it's not necessarily trivial; e.g. if your program uses the whole range of decimal places to a meaningful degree, or performs calculations that can compound floating point errors up to higher decimal places. (Though I'd argue that in both of those cases pure floating point is probably not the best system to use.) In my case I knew that the intended precision of the input would never be precise enough to overlap with floating point errors, so I could just round anything past the 15th decimal place down to 0.

The sum of two numbers should have a precision no higher than the operand with the highest precision. For example, adding 0.1 + 0.2 should yield 0.3, not 0.30000000000000004.

I would argue that the precision should be capped at the lowest precision of the operands. In physics, if you add to lengths, 0.123m+0.123456m should be rounded to 0.246m.

Also, IEEE754 fundamentally does not contain information about the precision of a number. If you want to track that information correctly, you can use two floating point numbers and do interval arithmetic. There is even an IEEE standard for that nowadays. 

Of course, this comes at a cost. While monotonic functions can be converted for interval arithmetic, the general problem of finding the extremal values of a function in some high-dimensional domain is a hard problem. Of course, if you know how the function is composed out of simpler operations, you can at least find some bounds. 

Or you could do what physicists do (at least when they are taking lab courses) and track physical quantities with a value and a precision, and do uncertainty propagation. (This might not be 100% kosher in cases where you first calculate multiple intermediate quantities from the same measurement (whose error will thus not be independent) and continue to treat them as if they were. But that might just give you bigger errors.) Also, this relies on your function being sufficiently well-described in the region of interest by the partial derivatives at the central point. If you calculate the uncertainty of $f(x,y)=xy$ for $x=0.1\pm 1$, $y=0.1\pm 1$ using the partial derivatives you will not have fun.

Hm, maybe.

Sometimes it can be a win-win situation. For example, if the call leads to you identifying a problem they're having and solving it in a mutually beneficial way.

But often times that isn't the case. From their perspective, the chances are low enough where, yeah, maybe the cold call just feels spammy and annoying.

I think that cold calls can be worthwhile from behind a veil of ignorance though. That's the barometer I like to use. If I were behind a veil of ignorance, would I endorse the cold call? Some cold calls are well targeted and genuine, in which case I would endorse them from behind a veil of ignorance. Others are spammy and thoughtless, in which case I wouldn't endorse them.

(Surely cryonics doesn't matter given a realistic action space? Usage of cryonics is extremely rare and I don't think there are plausible (cheap) mechanisms to increase uptake to >1% of population. I agree that simulation arguments and similar considerations maybe imply that "helping current humans" is either incoherant or unimportant.)

If step 5 is indeed grounded in the spatial attention being on other people, this should be testable! For example, people who pay less spatial attention to other people should feel less intense social emotions - because the steering system circuit gets activated less often and weaker. And I think that is the case. At least ChatGPT has some confirming evidence, though it's not super clear and I haven't yet looked deeper into it.  

Whoops! You're right! Will do.