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PaperclipPerfector  6d ago (text post)   23458 thread views

Culture War Roundup for the week of October 12, 2026

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Neural Networks Are Already Clean

Nick Cammarata (Goodfire AI; formerly of OpenAI) claims to have discovered that earlier foundational research on the semantic structure of neural networks (especially "superposition") were wrong, and they actually (and necessarily!) work pretty much the naive way everybody used to expect them to: particular neurons mean particular, ultimately interpretable things.

If it holds up strongly, my (admittedly jumpy and currently highly-sperged) gut instinct is that this could be epochal, and a candidate for one of the more significant and re-foundational public discoveries since AIAYN if not beyond.

This is a cheap shot, but I feel like if 'AIAYN' were one of the most significant findings in history, I would not have had to google it to understand what you're talking about.

The most significant finding since 2017, maybe? I guess? But there's a pretty significant gap between 'all of history' and 'the last nine years'.

the question is, would this has been discovered eventually anyway if not for AIAYN?

The question is whether AIAYN introduced a uniquely powerful primitive, or if there's a large family of architectures that scale similarly. Transformers may only be notable because they got there "first" and have since gotten a lot of engineering and research to make them work much better. But maybe other architectures could have played the same role. e.g. LSTMs maybe? Even MLPs may crossover surprisingly early.

What is shared by all of these isn't the overall architecture itself, but residual connections and normalization. The ResNet and LayerNorm papers might be actually more critical, if we're talking on the architectural side of things, than AIAYN.

And compute and data most of all.

LSTMs were in use before transformers, so if there's a way to scale them big, it's not obvious.

Transformers had a key advantage at the time in that they allowed parallel training, in a way that LSTMs at the time didn't (even if they were comparably compute efficient, ignoring wall clock time). But since then that issue has been resolved for LSTMs (see mLSTMs). It doesn't seem to me that the conceptual leap from LSTMs to transformers was any less than LSTMs to mLSTMs; transformers' current supremacy has a lot of path dependence to it.

I've never heard of mLSTMs before, and googling led me to https://arxiv.org/abs/1609.07959. I don't see anything about parallel training in the abstract here, and it was also released 1 year before AIAYN.

Can you link me to what you're actually talking about?

(And as an aside, I don't see a meaningful way to parallel train LSTMs without actually converting it into a transformer architecture "in disguise". The whole point of LSTMs is that the rightmost token depends on the leftmost token, and so any training that allows parallelism fundamentally breaks this property (unless P=NC---which is not a typo for NP).

I guess at some point every acronym ends up overloaded. Matrix LSTM, not Multiplicative LSTM

https://arxiv.org/abs/2405.04517

You can fairly say it's just a form of (linear) attention in disguise. YMMV on whether that's a transformer or not; mostly semantics.

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