Having been at two LH parties, one with music and one without, I definitely ended up in the "large conversation with 2 people talking and 5 people listening"-situation much more in the party without music.

That said, I did find it much easier to meet new people at the party without music, as this also makes it much easier to join conversations that sound interesting when you walk past (being able to actually overhear them).

This might be one of the reasons why people tend to progressively increase the volume of the music during parties. First give people a chance to meet interesting people and easily join conversations. Then increase the volume to facilitate smaller conversations.

I just finished reading "Zen and the Art of Motorcycle Maintenance" yesterday, which you might enjoy reading as it explores the topic of Quality (what you call excellence). From the book:

“Care and Quality are internal and external aspects of the same thing. A person who sees Quality and feels it as he works is a person who cares. A person who cares about what he sees and does is a person who’s bound to have some characteristic of quality.”

Interesting, we find that all features in a smaller SAE have a feature in a larger SAE with cosine similarity > 0.7, but not all features in a larger SAE have a close relative in a smaller SAE (but about ~65% do have a close equavalent at 2x scale up). 

Yes! This is indeed a direction that we're also very interested in and currently working on. 

As a sneak preview regarding the days of the week, we indeed find that one weekday feature in the 768-feature SAE, splits into the individual days of the week in the 49152-feature sae, for example Monday, Tuesday..

The weekday feature seems close to mean of the individual day features.
 

Interesting! I actually did a small experiment with this a while ago, but never really followed up on it.

I would be interested to hear about your theoretical work in this space, so sent you a DM :)

Thanks!

Yeah, I think that's fair and don't necessarily think that stitching multiple SAEs is a great way to move the pareto frontier of MSE/L0 (although some tentative experiments showed they might serve as a good initialization if retrained completely). 

However, I don't think that low L0 should be a goal in itself when training SAEs as L0 mainly serves as a proxy for the interpretability of the features, by lack of good other feature quality metrics. As stitching features doesn't change the interpretability of the features, I'm not sure how useful/important the L0 metric still is in this context.

According to this Nature paper, the Atlantic Meridional Overturning Circulation (AMOC), the "global conveyor belt", is likely to collapse this century (mean 2050, 95% confidence interval is 2025-2095). 

Another recent study finds that it is "on tipping course" and predicts that after collapse average February temperatures in London will decrease by 1.5 °C per decade (15 °C over 100 years). Bergen (Norway) February temperatures will decrease by 35 °C. This is a temperature change about an order of magnitude faster than normal global warming (0.2 °C per decade) but in the other direction!

This seems like a big deal? Anyone with more expertise in climate sciences want to weigh in?

 

I expect the 0.05 peak might be the minimum cosine similarity if you want to distribute 8192 vectors over a 512-dimensional space uniformly? I used a bit of a weird regularizer where I penalized: 

mean cosine similarity + mean max cosine similarity  + max max cosine similarity

I will check later whether the 0.3 peak all have the same neighbour.

A quick and dirty first experiment with adding an orthogonality regularizer indicates that this can work without too much penalty on the reconstruction loss. I trained an SAE on the MLP output of a 1-layer model with dictionary size 8192 (16 times the MLP output size).

I trained this without the regularizer and got a reconstruction score of 0.846 at an L0 of ~17. 
With the regularizer, I got a reconstruction score of 0.828 at an L0 of ~18.

Looking at the cosine similarities between neurons:

Interesting peaks around a cosine similarity of 0.3 and 0.05 there! Maybe (very speculative) that tells us something about the way the model encodes features in superposition?

Thanks for the suggestion! @BeyondTheBorg suggested something similar with his Transcendent AI. After some thought, I've added the following:

Transcendent AI: AGI uncovers and engages with previously unknown physics, using a different physical reality beyond human comprehension. Its objectives use resources and dimensions that do not compete with human needs, allowing it to operate in a realm unfathomable to us. Humanity remains largely unaffected, as AGI progresses into the depths of these new dimensions, detached from human concerns.