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There's nothing wrong with this paper and it's important to understand exactly what LLMs (and related technologies) are capable of and what they aren't, but I d

There's nothing wrong with this paper and it's important to understand exactly what LLMs (and related technologies) are capable of and what they aren't, but I do have a problem with the general argument that <ai technology> can't do <some task> and so that means it's useless and won't lead to AGI. I feel like a lot of people think that if a single neural network architecture can't achieve something on it's own that building a system that supplements the AI with other tools is somehow _cheating_. General AI if it ever happens is going to very likely be a system of specialized components working together, and there's not going to be a single piece of it that you can point to and say "that is where the intelligence lives" -- it's sort of liking digging through the brain looking for the homunculus that's piloting the ship. If an LLM can't track state, hook it up to something that can. If it doesn't have long term memory and retrieval, hook it up to something that does.

andoando 2y ago
I think a way to connect these different systems as a homogenous system is the difficult part.
szvsw 2y ago
Very well said. It absolutely drives me crazy - the focus on the model architecture for autoregressive text generation as the “key” (or blocker) to AGI seems so misguided as a debate. It seems clear to me (and maybe I’m wrong!) that a much larger system of modules interacting is essential (actuators, sensors, short term and long term memory, working memory, etc), and I completely fail to understand why it feels like the debate revolves around neural network architectures as opposed to the larger system architecture for agency. The language and decision making component is obviously extremely important and necessary, but it feels to me like it will always just be a necessary but not sufficient component for AGI. The fact that we have already obviously developed a technology which has an excellent grasp of language, can select between tools, perform knowledge retrieval from external databases, perform some form of simplistic reasoning and planning… it really feels (again, only intuitively!) like everything outside the perimeter of LLMs is where the actual frontier of AGI will be. It feels like with enough sensorimotor affordances and mechanisms for saving and accessing memory, all that is left is the (obviously complex) framework for connecting all of these together, with potentially many instances of each module working together… especially when you consider that one such system can potentially use human actors as tools (eg “hello fellow human, please complete this captcha for me, as I am low-vision”). I guess more succinctly - I do not believe that GPT-4 et al are remotely close to AGI on their own, but I do feel (again, this is just “vibes,” and obviously people smarter than me disagree!) that GPT-4 could be a component combined with lots of other tech we already have to essentially achieve AGI already. Perhaps what I am describing would just be a pale imitation of what others mean by AGI and be reductively called another mechanical Turk. I am probably the misguided one, but nonetheless, I can’t escape the feeling that “the one model to rule them all” is a red herring vis-a-vis a complex network (ha) of modules interacting together to achieve meaningful agency. Maybe it’s just that I am more interested in “meaningful agency” as a guiding principle over “AGI.”
- genevra 2y ago
  Exactly, we're already adding basic memory functions to GPT models Text generation can only get so much better but the other modules required to simulate intelligence have tons of room for improvement
logicchains 2y ago
>the general argument that <ai technology> can't do <some task> and so that means it's useless and won't lead to AGI The paper isn't making that argument. The point it makes is that parallelisable SSMs are theoretically no more powerful than transformers, contrary to some people's assumption that they'd be theoretically equivalent to RNNs and hence more efficient at certain kinds of problems.

andoando 2y ago

I think a way to connect these different systems as a homogenous system is the difficult part.

szvsw 2y ago

Very well said. It absolutely drives me crazy - the focus on the model architecture for autoregressive text generation as the “key” (or blocker) to AGI seems so misguided as a debate. It seems clear to me (and maybe I’m wrong!) that a much larger system of modules interacting is essential (actuators, sensors, short term and long term memory, working memory, etc), and I completely fail to understand why it feels like the debate revolves around neural network architectures as opposed to the larger system architecture for agency. The language and decision making component is obviously extremely important and necessary, but it feels to me like it will always just be a necessary but not sufficient component for AGI. The fact that we have already obviously developed a technology which has an excellent grasp of language, can select between tools, perform knowledge retrieval from external databases, perform some form of simplistic reasoning and planning… it really feels (again, only intuitively!) like everything outside the perimeter of LLMs is where the actual frontier of AGI will be. It feels like with enough sensorimotor affordances and mechanisms for saving and accessing memory, all that is left is the (obviously complex) framework for connecting all of these together, with potentially many instances of each module working together… especially when you consider that one such system can potentially use human actors as tools (eg “hello fellow human, please complete this captcha for me, as I am low-vision”). I guess more succinctly - I do not believe that GPT-4 et al are remotely close to AGI on their own, but I do feel (again, this is just “vibes,” and obviously people smarter than me disagree!) that GPT-4 could be a component combined with lots of other tech we already have to essentially achieve AGI already. Perhaps what I am describing would just be a pale imitation of what others mean by AGI and be reductively called another mechanical Turk. I am probably the misguided one, but nonetheless, I can’t escape the feeling that “the one model to rule them all” is a red herring vis-a-vis a complex network (ha) of modules interacting together to achieve meaningful agency. Maybe it’s just that I am more interested in “meaningful agency” as a guiding principle over “AGI.”

genevra 2y ago
Exactly, we're already adding basic memory functions to GPT models Text generation can only get so much better but the other modules required to simulate intelligence have tons of room for improvement

logicchains 2y ago

>the general argument that <ai technology> can't do <some task> and so that means it's useless and won't lead to AGI The paper isn't making that argument. The point it makes is that parallelisable SSMs are theoretically no more powerful than transformers, contrary to some people's assumption that they'd be theoretically equivalent to RNNs and hence more efficient at certain kinds of problems.