maaaaattttt

Would be cool to re-launch a kind of SETI@Home where locally running models (and OAI/anthropic accounts) could participate with their agents (acting as sub-agents) to do research during the night. I think there is something already about renting local GPU time out, but for profit. Lots of security issues etc with this. But conceptually it would be nice to be able to contribute to space research that way.

What's happening in the math field at the moment represents on a smaller scale the issue we (probably) will face when AI becomes smarter than us in general. Do we slow the AI down so we can understand what it does and if it's correct (and align with our values for bonus points); do we, humans, adjust the way we work to the new speed or do we give in and let the AI advance while we're not completely sure of what it does and the correctness of it. The math field is in the position of showing us the way.

Have you thought about making the whole thing "self-similar"? Every time I hear about MoE I think (and I know it's way easier thought than done) "why stay shallow"? I mean by that: would it be possible to extend/adapt the architecture so that an expert can be a previously trained Mini-AGI model? And recurse like this? Inuitively I would think some form of generalization could happen, as higher level experts (in the recurrence stack) would become sort of the "intuition" layer.

This assumes we don't create other bugs/vulnerabilities while fixing the existing ones.

In my experience you can “stop reading the code” if you adhere to prompting the AI aiming for changes that would be reasonable in a PR to review, if you’re using rather opinionated framework(s) to base your work on and explicitly ask for tests (models tend to add them on their own if there are some). I personally still read the output and start by checking if existing tests were modified (kind of a red flag when this unexpectedly happens imo).

When I read posts about AI generating garbage nowadays it’s either because of a small prompt/big ask combination or a lack of an underlying framework.

Very naive approach but wouldn't training a model on a few specific buckets like "someone is naked", "image is explicit" and "there is a child" in the picture, would do the job without having to train explicitely on CSAM? If the model returns both a high probability of "there is a child" and a high probability of any other bucket than this image is classified as CSAM. It'd be high recall,low precision but one would be on a rather safe side.

If I had true A(G|S)I, I would release patents, not an API.

If more and more of the web's content is AI generated, AI companies are bound to train on each other's data.

Or, what if I generate content with Claude/ChatGPT/Gemini, warp it in HTML using an open model, put this on my website conveniently dedicated to "Best practices in prompt and AI answers" for example, then train my own model that only scraps my website?

The only ban that should be put in place is on models' general and specific capabilities. If they have open or closed weigths is irrelevant. With the current state of capabilities there is already plenty we can do (in terms of creation and destruction unfortunately) before we as a society need more powerful capabilities. Once the limit is set, any non- governmental/military entity providing/hosting/using a more capabable model can be prosecuted.

And I didn't realize "audio" had only one consonant!

This was harder than I thought it would be. It's pretty fun to play! May I suggest displaying the final result and/or ongoing progress as 18 circles/shapes that fill up depending on how far you made it?

This guide is also made from me (or some of the me from a couple years back). I haven't read the whole thing yet and it's probably clearly stated at some point (though one can deduce it with the beginning already) but the surprise for me was that this field is highly statistical. Before starting I had the (very) naive view that it was possible to read the genome as one reads a file and look at what's going on. But the sequencing technics (and accompanying algorithms) only allow to statistically read the genome. So variants/mutations found are only found with a given statistical certainty. If the sample wasn't well prepared for example it could be that this certainty is ultimately not high enough to do a proper analysis/diagnostic. It's a fascinating field (try to watch a video on sequencing by expansion, to feel how sci-fi this field actually is) that is very hard to approach with only high-school biology level and this guide is really well done to sort of bridge this first gap.

Could it be that Anthropic is using the Chinese characters trick to consume less tokens behind the scenes?

I would be curious to know if the WGS was done using short read or long read. There is a lot of hope that long read is "just" what is needed to catch these type of cases nowadays (not sure about this particular one).

The contrast in my opinion comes more from the fact that 50 years prior to the space age people rode horses as a standard mean of transportation (more or less). It’s underwhelming to not see the line going from horse to rocket continue on the same path 50 years later.