euleriancon

The 'Exploring Image Models' section addresses that specifically, particularly the 'What Neurons Do Under Pressure' subsection.

'If it switched to superposition, we should see overlapping mixtures on top of each other, ... But that’s not what we see. The neurons stay interpretable and generalized, but coarser'

I think it is clear he is referring to zero shot classifiers with an LLM backbone. That tech has existed for a long time.

I've been waiting for symbolic music generators for ages. This is so nice for musicians. Being able to intelligently translate parts, generate additional parts, generate sheet music from audio, generate variations on pieces, making pieces easier or harder.

So many possibilities.

Spent some time playing on it just now on Linux. I couldn't easily get the automatic ingestion or gardening working, but everything else is working okay. For my workflows I think I actually prefer wiki updates being manual and deliberate.

This isn't as easy as it sounds. Every ML model is struggling to balance between generalization and test performance.

Taking a good model like GLM5.2 and just fine tuning it on coding can decrease real world performance due to mechanics like catastrophic forgetting. There is also other interesting behaviors were training on a broad training set can improve coding performance because there is positive transfer.

There is 100% an effort to make solid coding focused models, but it is very hard to do that without including capabilities across a broad set of adjacent tasks.

Help me understand this viewpoint that AGI being possible in the near-ish future is a myth, I see it repeated quite a lot.

I've been in NLP since the LSTM days and it's hard for me to look at LLMs and not just think they are incredible. It's truly a different level of expressiveness. So much of capabilities research is pointing to LLMs effectively learning a world model.

RLVR is also proving really effective. It is hard for me to imagine a world in the future where LLMs aren't at human level performance across a wide variety of tasks.

I fully acknowledge that current LLM labs have a financial interest in people believing AGI is very near, but from what I'm reading in the literature and seeing myself experimenting with the SOTA models it doesn't seem totally unreasonable.

What evidence are you seeing that makes you confident that AGI in the soon-ish future is a complete myth?

There doesn't really seem to be anything of substance in the actual executive order.

Section 1 doesn't say anything

Section 2 seems to boil down to: "improve cyber security and maybe use AI if we can find funding for it"

Section 3 proposes building a benchmark for evaluating cyber security performance of models that developers can choose to benchmark against. This seems like a good idea, I know Jack Clark has been a huge advocate for government's getting in with benchmarking.

Section 4 says to prioritize prosecuting cyber crimes. Not sure why they wouldn't already be prosecuted.

Section 5 doesn't say anything

Are we looking at the same data? On that site I see that opus 4.7's and gpt 5.5's g scores are within each others confidence intervals, and both significantly ahead of the number 3 model.

Your comment makes it sound like they are miles apart, which the benchmark doesn't seem to support.

Edit: I looked at the data more and the two models are only basically equal when looking at the mean of all the tests. Gpt 5.5 significantly outperforms opus 4.7 in coding, while opus 4.7 significantly outperforms in "decision making." I'm not seeing details on what decision making explicitly means.

An alternative explanation is that cold places with long winters are depressing, and because they are depressing fewer people want to live there.

Alaskan winters are hard regardless of how many friends you have.

I had a similar experience recently, where I logged in to Facebook after not using it for years and was shocked by how much garbage was there. My spouse does use Facebook somewhat regularly so I looked at her feed and it was much more reasonable.

I wonder if for those of us that haven't used Facebook in years the recommendation algorithm is essentially default. Which much like the default youtube algorithm, is completely garbage. But if we did use it (which I have no intention of doing), it would start being more reasonable.

In practice you can use 2d generation on spheres with simple UV mapping techniques. Your pixel height becomes distance from the sphere origin.

I worked on something very similar for my master's degree.

The problem I could never solve was the speed, and from reading the paper it doesn't seem like they managed to solve that either.

In the end, for my work, and I expect for this work, it is only usable for pre generated terrains and in that case you are up against very mature ecosystems with a lot of tooling to manipulate and control terrain generation.

It'll be interesting to see of the authors follow up this paper with research into even stronger ability to condition and control terrain outputs.

The main issues I see are immich identifying things like statues or paintings as people, and not dealing with people (especially kids) aging.

Google photos isn't perfect either but I never saw these kind of issues when I was still using it.

There are still some features that a miss from Google photos. There isn't any way (that I know of) to auto add pictures to an album based on the face. I used to have dedicated albums for family members, and it was nice to have the auto updated.

Face recognition in general just isn't as good as Google Photos.

It's still an amazing piece of software and I'd never go back, but it isn't perfect yet.