  Kind of hijacking, ...

I'm glad you did! Of all the procrastination techniques I have mastered, engaging smart people on HN about artificial cognition is probably one of the more useful ;) Apologies in advance for the diatribe(s) -- I think about this stuff a lot.

  ...would you say that LLM's have solved the frame problem? To me, the frame problem is: Can you function in an open vs closed world, and to me the answer is yes, LLM's can definitely function in an open world where the rules are fuzzy, changing, undefined, etc.

First, a nit: I would describe your definition as a valid transformation (isomorphism?) of the original phrasings, which were about technical context and epistemological belief[1]. I mention this because A) (semi-)symbolically providing context to LLM calls is the challenge at the core of harnesses, routers, pipelines, 'orbs', and a long list of other marketing terms that must amount to an ∞-B\$/y industry by now, and B) it shows how arbitrary the phrasing was, at the end of the day. (I also prefer this to the wiki article btw, for the curious: https://plato.stanford.edu/entries/frame-problem/)

My actual answer here is a resounding "yes" and "no" at once, in the exact same way that the Turing test is both so obviously surmounted in 2023 (post-RLHF) to anyone applying 20c standards, while also somehow being so far away that we're not sure it'll ever be possible. The key is to 'dissolve the binary' for both, if you'll excuse the phil-ism: Turing's 1950 paper Computing Machinery & Intelligence was never intended to prescribe some yes/no evaluation procedure, and the people frustrated by the Frame Problem were not worried about a single yes/no "Frame Test", either.

Instead, Turing settled on behavioral comparison on an intuitive, human level as the best shared dimension to test, but only after calling Ed Zitron "absurd" and leaving room open for ESP & ghosts to end up proving souls (one of those is literally true, the other only figuratively).

By this metric, current LLM-backed agents are clearly able to behave like a reasonable-ish human over a long-ish timeframe -- that's just objectively an incredible achievement IMO, even from 2015 standards. The promised inversion is the retort that invites, namely: the '-ish' makes all the difference! An artificial mind that behaves in completely alien ways randomly is a much less useful tool even if those events are rare; ditto for an artifical mind that loses coherence across """mere""" days.

I'm cutting this as much as possible, but hopefully it's clear why all the above applies to the Frame Problem, too -- just replace 'behavioral' with 'epistemic'.

  The issue is now grounding - It can "function", but what would it take to "ground" them? A personality, maybe? Actual consequences? Making them interact only with constrained tools that are formally verified?

I think your use of 'ground' is nice and understandable, but is conflating too many things to work as a summary of the remaining work. All of the things you mentioned are absolutely being explored --both by scientists and by highschoolers collectively speedrunning 76 years of science live on HuggingFace to generate the best uncensored model for their polycule's DnD campaign-- but they hinge on distinct metrics.

For example, the last one deals with reliability, which is closely related to the "randomly alien" stuff I mentioned earlier.

"Actual consequences", OTOH, most directly relates to the camp(s) focused on "embodiment", which is basically the idea that truly human intuition is too spatial to reasonably emulate without the ability to experimentally interact with the world -- AKA the "AI needs robots" camp.

And finally, the "personality" bit... I personally think we have to rediscover the subfield of Affective Computing, but the closest lane so far is "Constitutional AI", an approach popularized by Anthropic that (wisely) just moves the whole problem over to the world of prose and optimizes it from there.

All three are important steps indeed, but I think deserve finer delineation than ~'does it connect the agent to the real world more/better/stronger/truer'.

  Right now it's a combination of harness engineering, and ml philosophers arguing about compression leading to the "objectively correct intelligence", whatever that means.

Ha, totally agree on the distaste for intelligence as a single dimension. I will also say that 'harness engineering' is gonna end up being an outdated term for 'the rest of AI' over time, MMW. A more (in)famous voice beating this same drum is Gary Marcus (I know!) under the term 'neurosymbolic' (?).

  they are still missing some oomph factors that need to be clarified IMO. Maybe it's something as "mundane" as just having actual persistent memory, or maybe it's some deep philosophical thing like qualia. Who knows.

Again, you have great intuitions here... I think it might help to consider how the human capacity for memory is simultaneously mundane and profound, at different levels of analysis. I think this situation is similar: we're not gonna need to invent Memory 2.0 (and can't, probably?), but there's a long list of human-specific heuristics, control planes, and other neural machines of some vague character that must exist, only a teeny tiny portion of which have been explored by "harness" engineering as of yet (for the best, probably...)

TL;DR: We're not through the Kuhnian paradigm shift just yet -- the new episteme has far from penetrated all the subfields of cognitive science, IMHO. Predicting the landing point feels a little pointless, for that both that reason and an even bigger one: if RSI ends up being realistic (which it very likely is for our 2026 human computers, to some significant extent), this is all just the anteshock anyway. As "the singularity" implies, that kind of exponential shift could really take us anywhere (or nowhere, forever).

P.S. Never done this before, but fuck it: I'm currently seeking exciting remote work ASAP -- if you found this interesting, please consider this my cover letter. Sorry mods if against the rules, but, y'know... one-time exceptions for the singularity?

I trained a small transformer in 1.5hrs and it beats many LLMs