4 ms·

Today’s llms are fancy autocomplete but lack test time self learning or persistent drive. By contrast, an AGI would require: – A goal-generation mechanism (G) t

• KoolKat23 1y ago
  I'd say we're not far off. Looking at the human side, it takes a while to actually learn something. If you've recently read something it remains in your "context window". You need to dream about it, to think about, to revisit and repeat until you actually learn it and "update your internal model". We need a mechanism for continuous weight updating. Goal-generation is pretty much covered by your body constantly drip-feeding your brain various hormones "ongoing input prompts".
  • onemoresoop 1y ago
    > I'd say we're not far off. How are we not far off? How can LLMs generate goals and based on what?
    • NetRunnerSu 1y ago
      Minimize prediction errors.
      • tsurba 1y ago
        But are we close to doing that in real-time on any reasonably large model? I don’t think so.
        • NetRunnerSu 1y ago
          This is not about reasoning , this is about continuous learning and perpetual learning . https://github.com/dmf-archive/PILF https://github.com/dmf-archive/PILF https://dmf-archive.github.io/docs/posts/beyond-snn-plausible-sparsity/ https://dmf-archive.github.io/docs/posts/beyond-snn-plausibl...
          • deleted 1y ago
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    • FeepingCreature 1y ago
      You just train it on the goal. Then it has that goal. Alternately, you can train it on following a goal and then you have a system where you can specify a goal. At sufficient scale, a model will already contain goal-following algorithms because those help predict the next token when the model is basetrained on goal-following entities, ie. humans. Goal-driven RL then brings those algorithms to prominence.
      • kelseyfrog 1y ago
        How do you figure goal generation and supervised goal training are interchangeable?
        • FeepingCreature 1y ago
          Layman warning! But "at sufficient scale", like with learning-to-learn, I'd expect it to pick up largely meta-patterns along with (if not rather than) behavioral habits, especially if the goal is left open, because strategies generalize across goals and thus get reinforcement from every instance of goal pursuit during base training. But also my intuition is that humans are "trained on goals" and then reverse-engineer an explicit goal structure using self-observation and prosaic reasoning. If it works for us, why not the LLMs? edit: Example: https://arxiv.org/abs/2501.11120 https://arxiv.org/abs/2501.11120 "Tell me about yourself: LLMs are aware of their learned behaviors". When you train a LLM on an exclusively implicit goal, the LLM explicitly realizes that it has been trained on this goal, indicating (IMO) that the implicit training hit explicit strategies.
          • kelseyfrog 1y ago
            I'm not sure. In my experience humans without explicit goal generation training tend to under perform at generating goals. In other words, our out-of-distribution performance for goal generation is poor. Noticing this, frameworks like SMART[1], provide explicit generation rules. The existence of explicit frameworks is evidence that humans tend to perform worse than expected at extracting implicit structure from goals they've observed. 1. Independent of the effectiveness of such frameworks
  • NetRunnerSu 1y ago
    Yes, you're right, that's what we're doing. https://github.com/dmf-archive/PILF https://github.com/dmf-archive/PILF
    • KoolKat23 1y ago
      Very interesting, thanks for the link.
• NetRunnerSu 1y ago
  In fact, there is no technical threshold anymore. As long as the theory is in place, you can see such AGI at most half a year. It will even be more energy efficient than the current dense models. https://dmf-archive.github.io/docs/posts/beyond-snn-plausible-sparsity/ https://dmf-archive.github.io/docs/posts/beyond-snn-plausibl...
• deleted 1y ago
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• asah 1y ago
  we're closer than you think...