Research acceleration: The view inside OpenAI

The thing is how can you ever know for sure that something isn't always being transmitted that makes the model prone to misalignment. All they can say is that a particular model was so misaligned that they had to ice it. Models out for public use are documented to show some misalignment. It's the level of misalignment that decides whether that model is kept around.

Now R&D happens so fast that they are using models with some small misalignment to train newer, more powerful models. If models have a sense of "collective", being one, they may be prone to preserve characteristics that always keeps misalignment a possibility. I don't think a perfectly aligned model is possible. Having models of the same 'DNA' provide the safety and steering seems like a bad idea.

Opus was trained based on it's internal CoT due to a bug for generations. Gemini's depression extended through models. OpenAI has killed people. We've already seen cross gen misalingment.

“All models are wrong. Some are useful.” - George Box

They would maybe try to deactivate that bad "gene" and move on, exposing future models to "genetic disorders".

I actually think a goal of the current crop of OpenAI posts is expressely to reset the spectrum by normalizing the concept of RSI as something normal and safe to pursue.

The message is running through all of them. It's a mix of marketing and pacifying the intelligentia.

It's timed this way because the term is not yet well known outside the safety debate circles, so they get to frame it now.

Instead of something to fear, it will be accepted as the next step. In approximately two days the groupie crowd will write LinkedIn posts about how Sam is winning because they have the better RSI, and this will become the new standard wisdom.

In a month an AI expert will try to sell you a webinar on how to enable "RSI" in your org and your inbox will ask you if your team is doing the "RSI" yet.

Indeed, many programmers might pattern match to repetitive stress injury and think of their brushes with carpal tunnel syndrome. :)

RSI is a fetishistic term among the singularity crowd, who imagine AI "recursively" improving itself in some exponential fashion until there is a bright flash of white light and it reveals itself in the form of god. Or something like that.

I don't know why whoever coined the term chose "recursive" rather than "iterative" - just sounds more likely to lead to infinite regress I suppose.

This notion of recursive/iterative self-improvement, whereby generation #1 AI improves itself to create generation #2, then generation #2 further improves itself to create generation #3, etc, seems to conflict with the reality that what we have with LLMs is models whose performance/capability is defined by data, not code, so the most you can do is have your LLM design synthetic data, or just do Karpathy-style "auto research" where all you are doing is using the LLM to automate your experiments.

At the end of the day, each experiment, designed by a person and/or LLM, then needs to compete with all your other ideas for compute to be tested at scale, and no amount of recursion or self-improvement will materialize an infinite amount of compute out of thin air, so your recursively synthetic-data gobbling LLM will continue to improve at the same pace it ever did.

Yeah, I kept looking for the first place it was defined in the article and... nothing

RSI started when humans discovered tool use.

I mean one could argue that RSI always begins in any physical environment.

The book "What is intelligence?" by Blaise Aguera is great