4 ms·

This is almost a year old, thoughts on it today?

• fsndz 2y ago
  LLMs still do not reason or plan. And nothing in their architecture, training, post-training points toward real reasoning as scaling continues. Thinking does not happen one token at a time.
  • isaacfung 2y ago
    I don't get why some people seem to think the only way to use a LLM is for next token prediction or AGI has to be bult using LLM alone. You want planning, you can do monte carlo tree search and use LLM to evaluate which node to explore next. You want verifiable reasoning, you can ask it to generate code(an approach used by recent AI olympiad winner and many previous papers). What is even "planning", finding desirable/optimal solutions to some constrained satisfaction problems? Is the llm based minecraft bot voyager not doing some kind of planning? LLMs have their limitations. Then augment them with external data sources, code interpreters, give it ways to interact with real world/simulation environment.
    • threeseed 2y ago
      The problem is that every time you ask the LLM to evaluate what to do next it will return a wrong answer X% of the time. Multiple that X across the number of steps and you have a system that is effectively useless. X today is ~5%. I do think LLMs could be used to assist in building a world model that could be a foundation for an AGI/agent system. But it won't be the major part.
      • fsndz 2y ago
        absolutely
  • jhanschoo 2y ago
    As the other reply has said, the article points to limitations of LLMs, but that doesn't preclude synthesizing a system of multiple components that uses LLMs. To the extent that I'm bearish on AI capabilities, I'll note that program synthesis / compression / general inductive reasoning which we expect intelligent agents to do is a computationally very hard problem.