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I think the analogy actually works better the other way. LLMs “think” the way humans speak. This is closer to having a machine that worked by flapping its wings

• Dylan16807 1y ago
  At least the roundabout flappy machine gets off the ground in your analogy. The other options we have are big logic chains that don't work and neural simulations that would require all the computers in the world.
  • catlifeonmars 1y ago
    Yeah definitely. We have to start somewhere and what we have works. I just think that language based models are a v0
• lostmsu 1y ago
  LLMs don't think the way humans speak. LLMs process sequences of high-dimensional vectors.
  • catlifeonmars 1y ago
    Yeah that was very hand wavy on my part. What I meant to say is that LLMs encode the relationships between words. The idea being that the relationship between words is a good enough representation of the relationship between the things that the words represent. I am conjecturing 1. that solely relying on written artifacts by produced by humans has some upper bound on the amount of knowledge that can be represented. 2. that language is an inefficient representation of human knowledge. It’s redundant and contains inaccuracies. Using written artifacts is not the shortest path to learning. For example, take mathematics. It’s not sufficient to read a ton of math literature to effectively learn math. There’s a component of discovery that comes from e.g attempting to write a proof that can’t be replaced by reading all of the proofs that already exist. Anyway I would take all this with a giant grain of salt.