6 ms·

>rightfully so How the hell can people be so confident about this? You describe two smart people reasonably disagreeing about a complicated topic

• jumploops 3y ago
  The LLMs of today are just multidimensional mirrors that contain humanity's knowledge. They don't advance that knowledge, they just regurgitate it, remix it, and expose patterns. We train them. They are very convincing, and show that the Turing test may be flawed. Given that AGI means reaching "any intellectual task that human beings can perform", we need a system that can go beyond lexical reasoning and actually contribute (on it's own) to advance our total knowledge. Anything less isn't AGI. Ilya may be right that a super-scaled transformer model (with additional mechanics beyond today's LLMs) will achieve AGI, or he may be wrong. Therefore something more than an LLM is needed to reach AGI, what that is, we don't yet know!
  • dboreham 3y ago
    Prediction: there isn't a difference. The apparent difference is a manifestation of human brain delusion about how human brains work. The Turing test is a beautiful proof of this phenomenon: so and so thing is impossibility hard only achievable via magic capabilities of human brains...oops no actually it's easily achievable now so we better re-define our test. This cycle Will continue until the singularly. Disclosure: I've been long term skeptical about AI but that writing is up on the wall now.
    • mlyle 3y ago
      Clearly there's a difference, because the architectures we have don't know how to persist information or further train. Without persistence outside of the context window, they can't even maintain a dynamic, stable higher level goal. Whether you can bolt something small to these architectures for persistence and do some small things and get AGI is an open question, but what we have is clearly insufficient by design. I expect it's something in-between: our current approaches are a fertile ground for improving towards AGI, but it's also not a trivial further step to get there.
      • razodactyl 3y ago
        From my experience there's definitely context beyond the current set of LLM state. It's how they're able to regurgitate facts or speak at all.
        • mlyle 3y ago
          > regurgitate facts or speak at all. Most of that is encoded into weights during training, though external function call interfaces and RAG are broadening this.
      • darkerside 3y ago
        > Without persistence outside of the context window, they can't even maintain a dynamic, stable higher level goal. I mean, can't you say the same for people? We are easily confused and manipulated, for the most part.
        • mlyle 3y ago
          I can remember to do something tomorrow after doing many things in-between. I can reason about something and then combine it with something I reasoned about at a different time. I can learn new tasks. I can pick a goal of my own choosing and then still be working towards it intermittently weeks later. The examples we have now of GPT LLM cannot do these things. Doing those things may be a small change, or may not be tractable for these architectures to do at all... but it's probably in-between: hard but can be "tacked on."
          • blackoil 3y ago
            That just proves we real-time fine tuning of the neuron weights. It is computationally intensive but not fundamentally different. A million token context would look close to long short-term memory and frequent fine-tuning will be akin to long-term memory. I most probably am anthropomorphizing completely wrong. But point is humans may not be any more creative than an LLM, just that we have better computation and inputs. Maybe creativity is akin to LLMs hallucinations.
  • FeepingCreature 3y ago
    If LLMs can copy the symbolic behaviors that let humans generate new knowledge, it'll be there.
  • satvikpendem 3y ago
    > , they just regurgitate it, remix it, and expose patterns Who cares? Sometimes the remixation of such patterns is what leads to new insights in us humans. It is dumb to think that remixing has no material benefit, especially when it clearly does.
  • bitcharmer 3y ago
    > They are very convincing, and show that the Turing test may be flawed The only think flawed here is this statement. Are you even familiar with the premise of Turing test?
  • mrangle 3y ago
    I agree with your premise. You're right: I haven't seen evidence of LLM novel pattern output that is basically creative. It can find and remix patterns where there are pre-existing rules and maps that detail where they are and how to use them (ie: grammar, phonics, or an index). But it can't, whatsoever, expose new patterns. At least public facing LLM's can't. They can't abstract. I think that this is an important distinction when speaking of AI pattern finding, as the language tends to imply AGI behavior. But abstraction (as perhaps the actual marker of AGI) is so different from what they can do now that it essentially seems to be futurism whose footpath hasn't yet been found let alone traversed. When they can find novel patterns across prior seemingly unconnected concepts, then they will be onto something. When "AI" begins to see the hidden mirrors so to speak.
• smilekzs 3y ago
  Maybe "rightfully so" meant "it is totally within Sam's right to claim that LLMs aren't sufficient for AGI"?