maxaravind

The blog discusses a potential path to making LLMs self-referential and self-directed continual learners.

Solving continual learning is key to AGI - and we maybe close to solving it.

Didn't get this error before. Try now, it should be fixed.

I'd say the way to think about it is in terms of the questions you ask being in-distribution or out of distribution w.r.t the model training dataset.

Consider this, if something fundamental has changed in the world after the model was released(ie after the knowledge cut off date), then it would be very difficult for the model to reason about it. One concrete example is the the following: If you ask Opus or any decent coding model to do effort estimation on a coding task, then it would come up with multi week timelines - the models themselves doesn't know that because "they exist", these timelines have now been slashed to a few hours - you can try saying this in the prompt, however, they don't seem to internalise this.

Nope. Even if context can theoretically encode arbitrary computation under fixed weights, this requires the weights to implement a usable interpreter. Random weights almost surely do not. Training is what constructs that interpreter. Without it context has no meaningful computational semantics.

It's kind of like asking if I make a random circuit with logic gates, does that become a universal computer that can run programs.

lol nice analogy. LLMs are frozen diamonds forged in compute. We need then to be malleable in production and change with experience.

I think both the views have their merits. In my mind the hardware vs software analogy for weights vs context holds better because in most modern computing systems, the hardware is fixed and the software changes. What the system can do efficiently, in practice, is a function of both the limitations/capabilities of the hardware and the software their respective capability ceilings.

The brain theory also kind of says the same thing, but it's hard to say what stays fixed vs changes with experience in the brain ig.