yurimo

There is something to say about the power of brute force approaches. Right now scaling multi agent is seemingly what is becoming the next important axis, though very strongly constrained by quality of communication protocol and compute. Even if one model is not strong enough individually, more models and more attempts as well as differing opinions may nudge the models into a better solution basins.

Kind of an extension of the effect where you talk to a model, it is super confident (but wrong), you correct it not directly but perhaps with a question and asking it to clarify. This then in turn leads to a better approach overall.

RIP to the legend. Thank you for making my childhood and making me believe in the possibility of the better future.

I have yet to fully finish going trough this but I would not rush to say that superposition is completely wrong. I understand that the message is instead of first learning a new representation (e.g. SAEs/transcoders) and then interpreting those learned features, you try to understand the neurons and circuits that already exist in the model.

This approach is part of a way some researchers in the field approach interp, (me including) because I am personally cautious of developing some new representation model that claims to be the way we should represent the network. It is another layer of abstraction that takes us away from core model components, very often this requires training a separate model that was not on its own a part of original model organism during training (SAE).

I would argue that models will likely be further increasing in complexity, not just in size, when model trains continuously and becomes adaptive yet parameter count does not change, it is not hard to imagine that neurons may reorganize themselves to pack more information.

A thing that I think about is that what you have said kinda compares itself to current math crisis. If we are only focused on the outcome and not the path it took to get there (i.e. understanding mechanisms of how the drug works) we might lose additional paths for discovery that would have bloomed from that process. Maybe an approach that did not yield a usable drug had a mechanism that would aid somewhere else. Yet the failure would be discarded as this does not grant the outcome for a model that attempts to optimize. Same is for math.

As aside I agree with you, but also worth noting that long horizon reasoning is difficult and AI, as good as it is, cannot predict something like 5-10 year drug interaction. Can it heal the patient now but cause a more aggressive cancer to come back and kill them in 5 years? You might say it is cruel to deny a person this treatment but if the doctor and manufacturer don't know the long term outcomes would it not be dishonest to tell the patient they are out of the danger? My point is, when we accelerate, we still should make sure we won't accelerate into a wall.

This is very cool, thank you. I understand the choice of no account, but would have loved if my progress could be linked between devices, maybe icloud sync?

Recent recording from NYU Courant institute.

This is cheap. Plenty of scientists, many of whom are my friends, are working very hard on finding new therapies for cancer, they were doing it before genomic models came along and still doing it now. The amount of times something in the media is lauded as "holy grail" that is never heard from again because it either only works in mice or turns out to be toxic or 100s of different reasons is massive. In my opinion this attitude of putting rose glasses on is detrimental to scientific progress. People outside of cancer research routinely underestimate how hard it is to find a working protocol. I think it is better to have sober attitude because it allows one to see the limitations and challenges that need to be tackled, blindly hoping AI can solve everything and deliver miracle cures is exactly the attitude that lets people sit on their asses and do nothing.

I think it is important to divine what currently AI is good for and what it is not even in such verifiable environments like math. Current hyped announcements about breaking conjectures are notable and are a marker of how much improvement was made. But as I read them, and maybe I am wrong, I see it as a large model+ harness executing a broad brute force search and trying solutions until something sticks. There are lots of problems like that and they should be solved, as often they are perhaps less important or overlooked, or just a slog, any field of research has these, math even more so.

However, this is very different from inventing new mathematical machinery that allows to break old problems, I think it will be a while until AI will be able to do it if at all. For now I think we will be moving to a symbiosis where an AI cracking a problem and giving a solution, inspires a human to invent new techniques.

Or use sticky notes! My friend annotated her favorite book with notes for me and it was awesome.

Personally found newest paperlike to be a lot like glass still, you barely get that paper feel at all and I hated it. They tried to keep clarity of the screen near glass level, but that affects the feeling a lot. Eventually I did some research and found that Japanese screen protectors that give you a feel of Kent paper are excellent for writing, so I wholeheartedly recommend those. You can find best rated ones by looking at top rated ones on jp websites, I have bellemond one.

I was considering getting M5 Max mbp with 128GB, but at that price it is just ridiculous, 64 version costs now roughly what 128 was before and that was a stretch for me. At this point might as well stick to my ol reliable M1 Pro mbp.

Edit: I'll say that now it seems also very hard to justify buying top of the line apple hardware for the enterprise. Getting top laptops for just a team of 10 people now means extra $20k just for RAM on top of already a higher base price.