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0 pointsrerdavies1y ago0 comments

I'm pretty sure that the argument would be that extensions of current LLM and ML techniques could be the solution to the problem of AGI.

And all evidence actually points toward human reason as an incredibly inefficient and horrifyingly error-prone approach, that only got as far as it did because we're running 8.1 billion human minds in parallel.

While evidence suggests that human reasoning uses a fundamentally different approach, it remains to be seen whether human reasoning uses a fundamentally superior approach.

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cornholio1y ago

> human reason as an incredibly inefficient

I have yet to see a single AI system that can learn to produce the word "mama" after doing fully self supervised training, and being fed only the cosine transform of the audio it produces and a few hundred hours of video/audio feed showing a mom saying the word and becoming very happy when the word is finally uttered. Did I mention the output must be produced using an array of mechanical oscillators, resonance chambers and bellows with unknown and highly variable acoustic parameters, that need to be discovered and tuned at runtime?

I have seen this "human intelligence training is wasteful" line and I think it is complete nonsense. The efficiency with which humans can acquire any language with barely any training data is unfathomably better than large scale statistical models.

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0 pointsrerdavies1y ago0 comments

I'm pretty sure that the argument would be that extensions of current LLM and ML techniques could be the solution to the problem of AGI.

While evidence suggests that human reasoning uses a fundamentally different approach, it remains to be seen whether human reasoning uses a fundamentally superior approach.

0 comments

cornholio1y ago

> human reason as an incredibly inefficient

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