[ainch]

Deep Blue's strength was leveraging massive compute to execute a task-specific, human-written algorithm. The problems which LLMs are tackling don't elude mathematicians because they require too much number-crunching, but because they demand creative problem solving. The latter seems more profound, even if it doesn't imply sentience.

[endymion-light]

Not to get too reductive - but LLMs are essentially performing massive number-crunching at a textual representation level. If you've ever played around with early NLP models, they can give you some astounding results for aspects like sentiment analysis that would seem like creative problem solving to someone in 1950. You could say the same for using a neural network to perform things like object recognition.

That's not removing the amazing ability of LLMs and the scale of this accomplishment, don't get me wrong - it's incredibly impressive that specialised models are now able to walk down pathways for creative problem solving - but I don't think that suggests a sentience or profoundness - rather it's just number-crunching at a astronomically larger scale.