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micromacrofoottoday at 4:50 PM1 replyview on HN

To reach a more brain-like behavior LLMs need to integrate your inputs into their model dynamically, essentially retraining real-time based on the most salient input. Human brains do this selectively all the time and it's part of our plasticity.

Biologically humans do similar compression, so introducing a similar concept to an LLM also feels reasonable. Hardware isn't fast/cheap enough to do this on an ongoing basis, similar to how it's too expensive for our brains to do this while we're moving through the world.

All we have now most of the time in LLMs is "working memory" we're missing a lot of the functionality that allows for episodic memory and selective plasticity.

The more you read about how human brains work, the more you realize that we may have figured out a piece with LLMs, but it's certainly nothing approaching AGI. People insisting so are blowing smoke for investor hype or don't understand a big piece of the concepts involved.


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logicchainstoday at 5:31 PM

>To reach a more brain-like behavior LLMs need to integrate your inputs into their model dynamically, essentially retraining real-time based on the most salient input.

That's already possible with LLMs. The challenge is that 1. it would allow permanently jail-breaking models and 2. there'd be no way for them to efficiently transfer what they'd learned to a new model generation.

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