I share this intuition that the solution as stated is underwhelming. But from my perspective that’s just because that key central piece is missing, and this wasn’t adequately communicated in the available public resources about PreDCA (even if it was stressed that it’s a work in progress). I guess this situation doesn’t look as worrisome to Vanessa simply because she has a clearer picture of that central piece, or good motives to believe it will be achievable, which she hasn’t yet made public. Of course, while this is the case we should treat optimism with suspicion.
Also, let me note that my a priori understanding of the situation is not
let’s suppose amazing theory will solve imperfect search, and then tackle the other inner misalignment directly stemming from our protocol
but more like
given our protocol, we have good mathematical reasons to believe it will be very hard for an inner optimizer to arise without manipulating hypothesis update. We will use amazing theory to find a concrete learning setup and prove/conjecture that said manipulation is not possible (or that the probability is low). We then hope the remaining inner optimization problems are rare/few/weak enough as for other more straightforward methods to render them highly unlikely (like having the core computing unit explicitly reason about the risk of inner optimization).
I share this intuition that the solution as stated is underwhelming. But from my perspective that’s just because that key central piece is missing, and this wasn’t adequately communicated in the available public resources about PreDCA (even if it was stressed that it’s a work in progress). I guess this situation doesn’t look as worrisome to Vanessa simply because she has a clearer picture of that central piece, or good motives to believe it will be achievable, which she hasn’t yet made public. Of course, while this is the case we should treat optimism with suspicion.
Also, let me note that my a priori understanding of the situation is not
but more like