So we know that implementation doesn't matter as long as it is somehow making predictions (and predictions also on different things)?

Most living things are prediction engines in a way, why compare to the brain then to start with and not, e.g., bacteria?

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danielbln a day ago | parent | next [-]

Implementation does matter, my point is that different implementations can lead to the same result.

And why compare to the brain? Mostly because of complexity. I can't interface with a bacteria in any meaningful way, but I can interface with an LLM to a significant degree.

	▲	lukan a day ago \| parent \| next [-]
		"I can't interface with a bacteria in any meaningful way" But you do. There are more bacterias in and on the body, than body cells. We are bacterias forming lasting bonds and we still interact with the free floating ones in various ways. Mainly in the gut and that has many effects, also on the brain, but also in various other ways we are beginning to understand. https://en.wikipedia.org/wiki/Human_microbiome So no idea about a microbiome consciousness - but who am I to know.
	▲	RandomLensman a day ago \| parent \| prev [-]
		But we don't know - just saying it could isn't enough. Bacteria are quite complex, btw.

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imtringued 5 hours ago | parent | prev [-]

This is getting incredibly stupid. The implementation defines the compute budget and the ability to learn continuously and consequently the ability to retain knowledge.

According to you, a model that can simply predict the entire future and then pre-record the answers would be considered intelligence simply because you're obsessed with the hypothetical power of prediction.

The truth is that the intelligence doesn't sit inside the model parameters, the model parameters are just the current state of the intelligence. The training process itself is the intelligence and the model parameters are just an artifact that can be copied around.