I'm curious on how/if goal coherence over long term plans is explained by your "planning as reward shaping" model? If planning amounts to an escalation of more and more "real thoughts" (i.e. I'm idly thinking about prinsesstårta -> A fork-full of prinsesstårta is heading towards my mouth), because these correspond to stronger activations in a valenced latent in my world model, and my thought generator is biased towards producing higher valence thoughts, it's unclear to me why we wouldn't just default to the production of untopical thoughts (i.e. I'm idly thinking about prinsesstårta -> I'm thinking about underneath a weighted blanket) and never get anything done in the world.
One reply would be to bite the bullet and say yup, humans due in fact have deficits in their long term planning strategies and this accounts for them but this feels unsatisfying; if the story given in my comment above was the only mechanism I'd expect us to be much worse. One possible reply is that "non-real thoughts" don't reliably lead towards rewards from the steering subsystem and the thought assessors down weight the valence associated w/ these thoughts thus leading to them being generated w/ lower frequency; consequently then, the only thought sequences which remain are ones which terminate in "real thoughts" and stimulate accurate predictions of the steering subsystem. This seems plausibly sufficient, but it still doesn't answer the question of why people don't arbitrarily switch into "equally real but non-topical" thought sequences at higher frequencies.
FWIW, links to the references point back to localhost
Sure, but I think that misses the point that I was trying to convey. If we end up in a world similar to the ones forecasted in ai-2027, the fraction of compute which labs allocate towards speeding up their own research threads will be larger than the amount of compute which labs will sell for public consumption.
My view is that even in worlds with significant speed ups in R&D, we still ultimately care about the relative speed of progress on scalable alignment (in the Christiano sense) compared to capabilities & prosaic safety; doesn't matter if we finish quicker if catastrophic ai is finished quickest. Thus, an effective TOC for speeding up long horizon research would still route through convincing lab leadership of the pursuitworthiness of research streams.
Labs do have a moat around compute. In the worlds where automated R&D gets unlocked I would expect compute allocation to substantially pivot, making non-industrial automated research efforts non-competitive.
As far as I am concerned, AGI should be able to do any intellectual task that a human can do. I think that inventing important new ideas tends to take at least a month, but possibly the length of a PhD thesis. So it seems to be a reasonable interpretation that we might see human level AI around mid-2030 to 2040, which happens to be about my personal median.
There is an argument to be made that at the larger scales of length, cognitive tasks become cleanly factored, or in other words it's more accurate to model completing something like a PhD as different instantiations of yourself coordinating across time over low bandwidth channels, as opposed to you doing very high dimensional inference for a very long time. If that's the case, then one would expect to roughly match human performance in indefinite time horizon tasks once that scale has been reached.
I don't think I fully buy this, but I don't outright reject it.
I believe intelligence is pretty sophisticated while others seem to think it's mostly brute force. This tangent would however require a longer discussion on the proper interpretation of Sutton's bitter lesson.
I'd be interested in seeing this point fleshed out, as it's a personal crux of mine (and I expect many others). The bullish argument which I'm compelled by goes something along the lines of:
- Bitter Lesson: SGD is a much
betterscalable optimizer than you, and we're bringing it to pretty stupendous scales - Lots of Free Energy in Research Engineering: My model of R&D in frontier AI is that it is often blocked by a lot of tedious and laborious engineering. It doesn't take a stroke of genius to think of RL on CoT; it took (comparatively) quite a while to get it to work.
- Low Threshold in Iterating Engineering Paradigms: Take a technology, scale it, find it's limits, pivot, repeat. There were many legitimate arguments floating around last year around the parallelism tradeoff and shortcut generalization which seemed to suggest limits of scaling pretraining. I take these to basically be correct, it just wasn't that hard to pivot towards a nearby paradigm which didn't face similar limits. I expect similar arguments to crop up around the limits of model-free RL, or OOD generalization of training on verifiable domains, or training on lossy representations of the real world (language), or inference on fixed weight recurrence, or... I expect (many) of them to basically be correct, I just don't expect the pivot towards a scalable solution to these to be that hard. Or in other words, I expect that much of the effort that comes from unlocking these new engineering paradigms to be made up of engineering hours which we expect to be largely automated.
Interesting. Curious to know what your construction ended up looking like and I'm looking forward to reading the resulting proof!
I see it now
Nit: The title give the impression of a demonstrated result as opposed to a working hypothesis and proposed experiment.