Adam Shai's Shortform

Some personal reflections on the last year, and some thoughts for next:
 

• 1 year ago I quit my career as an academic experimental neuroscientist and began doing AI technical safety research full time. This was emotionally difficult! For more than a decade I had been committed to becoming a neuroscience professor, and had spent a lot of my 20s and 30s pursuing that end. So the move, which had its natural uncertainties (can I succeed in a totally different field? will I be able to support my family financially?) was made more difficult by an ingrained identity as a neuroscientist. In retrospect I wish I had made the move earlier (as Neel Nanda had suggested to me!), oh well, live and learn!
• I was extremely lucky to have the support of PIBBSS as I transitioned (special thanks to Nora and Lucas). The main work that came out of my time there is a dream come true. I had read about computational mechanics ~1 decade ago after reading a Melanie Mitchell book, and had tried multiple times to apply it to neuroscience data. I completely failed each time, but would come back to it every now and then. Meeting Paul Riechers was game changing - both his deep knowledge and, even more importantly, his supportive and positive attitude have been a true blessing.
• I also want to mention Alexander Oldenziel, who has been and continues to be supportive, and is an inspirational model of tenaciousness and agency. He was the first person in the AI safety community who heard me rant about comp mech, and who suggested that comp mech might be able to do some work there.
• Paul and I started Simplex this year! It kind of feels like starting an academic lab, except not in academia, and with someone else. Definitely an exciting journey! One thing that feels different than I imagine staying in academia would feel is the sense of purpose - I really do believe our point of view and work will be important for AI safety.
• Speaking just for myself, I underestimated how difficult it would be to raise money, and how much time it would take. Getting better at this skill is going to be a focus of the next year.
• I watched my daughter grow from 1 to 2 years old. Everything about this fact is hard to put into words. I won't try.
• While people have definitely shown in interest in our initial work at Simplex, I think for the most part people are unaware of the larger picture of how we think about comp mech and its relation to AI safety. This is mostly because we really haven't spoken about it in public very much! That will change in the coming year. Comp mech is much deeper and broader than the belief state geometry story presented.
• For the most part though, we've chosen to take a show rather than tell approach. We want the quality of our work to be very high, we want to overdeliver. If someone doesn't understand our point of view we would rather show them its utility by example rather than by argument or philosophy. I'm happy with that, though it has probably meant a slower public facing start. We have a lot more public facing things in store for 2025.
• I can't seem to update my beliefs appropriately when new AI capabilities come out. I am shocked. Every. Single. Time. This still feels like magic to me. Scary magic. Beautiful magic Weird magic. Where are we going?

Happy New Year everyone! 

I've been trying to get my head around how to theoretically think about scaling test time compute, CoT, reasoning, etc. One frame that keeps on popping into my head is that these methods are a type of un-amortization. 

In a more standard inference amortization setup one would e.g. train directly on question/answer pairs without the explicit reasoning path between the question and answer. In that way we pay an up-front cost during training to learn a "shortcut" between question and answers, and then we can use that pre-paid shortcut during inference. And we call that amortized inference.

In the current techniques for using test time compute we do the opposite - we pay costs during inference in order to explicitly capture the path between question and answer. 

Uncertainties and things I would like to see:

• I'm far from an expert in amortization and don't know if this is a reasonable use of the concept
• Can we use this framing to make a toy model of using test time compute? I'd really like for the theoretically minded style of interp I do to keep up with current techniques.
• If we had a toy model I could see getting theoertical clarity on the following:
  • What's the relation between explicit reasoning vs. internal reasoning
  • What does it mean to have CoT be "faithful" to the internals
  • What features and geometric structures underlie reasoning
  • Why is explicit reasoning such a strong mechanism for out of distribution generalization?

A neglected problem in AI safety technical research is teasing apart the mechanisms of dangerous capabilities exhibited by current LLMs. In particular, I am thinking that for any model organism ( see Model Organisms of Misalignment: The Case for a New Pillar of Alignment Research [LW · GW]) of dangerous capabilities (e.g. sleeper agents paper), we don't know how much of the phenomenon depends on the particular semantics of terms like "goal" and "deception" and "lie" (insofar as they are used in the scratchpad or in prompts or in finetuning data) or if the same phenomenon could be had by subbing in more or less any word. One approach to this is to make small toy models of these type of phenomenon where we can more easily control data distributions and yet still get analogous behavior. In this way we can really control for any particular aspect of the data and figure out, scientifically, the nature of these dangers. By small toy model I'm thinking of highly artificial datasets (perhaps made of binary digits with specific correlation structure, or whatever the minimum needed to get the phenomenon at hand).