Why do you need to do all of this on current models? I can see arguments for this, for instance, perhaps certain behaviors emerge in large models that aren’t present in smaller ones.
I think that Anthropic’s current work on RL from AI Feedback (RLAIF) and Constitutional AI is based on large models exhibiting behaviors that don’t work in smaller models? (But it’d be neat if someone more knowledgeable than me wanted to chime in on this!)
My current best understanding is that running state of the art models is expensive in terms of infrastructure and compute, the next generation models will get even more expensive to train and run, and Anthropic doesn’t have (and doesn’t expect to realistically be able to get) enough philanthropic funding to work on the current best models let alone future ones – so they need investment and revenue streams,
There’s also a consideration that Anthropic wants to have influence in AI governance/policy spaces, where it helps to have a reputation/credibility as one of the major stakeholders in AI work.
Your summary seems fine!
I think that Anthropic’s current work on RL from AI Feedback (RLAIF) and Constitutional AI is based on large models exhibiting behaviors that don’t work in smaller models? (But it’d be neat if someone more knowledgeable than me wanted to chime in on this!)
My current best understanding is that running state of the art models is expensive in terms of infrastructure and compute, the next generation models will get even more expensive to train and run, and Anthropic doesn’t have (and doesn’t expect to realistically be able to get) enough philanthropic funding to work on the current best models let alone future ones – so they need investment and revenue streams,
There’s also a consideration that Anthropic wants to have influence in AI governance/policy spaces, where it helps to have a reputation/credibility as one of the major stakeholders in AI work.