after prodding around the playground, I marvel at how nice mech interp for machine intelligence is compared to biological intelligence. it took us 53 years to demonstrate a Jennifer Aniston neuron in human brains, and here we easily (and scalably) did the same in machine minds
In Neural Networks are Already Clean, @nickcammarata argued that neurons were far more legible than previously thought – and that this was great news for interpretability and safety.
Now, we’re releasing the first technical companion to Nick’s essay. It shares our findings from studying thousands of neurons using our novel automated interpretability pipeline, and outlines why we now think of neurons by their computational function, instead of whether or not they are polysemantic. Here’s what we found: 🧵
@swann_bessa@rosinality great work! just played with it. I note that unlike traditional k-means the cluster never reaches equilibrium point and is jittery (e.g. at epoch 15+). I am assuming this is because of the nature of the algo with ema?
@sarahwooders I think the concept of harness needs to be disentangled between workflow / actions (e.g. stream framing) and knowledge (e.g. all skills.md). The latter can be amortized into model weights while the former should be separate from model
on a diff note, long time!
@sheriyuo I've long contended that when LLM does the writeup + reviews, and then eventually some of it drips down into developing better LLMs, it is essentially RSI (with meat proxies in the loop)
@LuluYZeng@geoffreyhinton Yah he was giving a talk the other day! Super cool that i can cycle for 10 minutes and listen to the godfather rant about LLMs being too complex