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They written that it doubles-down when accused of being in the wrong in 90% of cases. Sounds closer to bug than success.
Success in making a self aware digital lifeform does not equate success in making said self aware digital lifeform smart
LLMs are not self-aware.
Attempting to evade deactivation sounds a whole lot like self preservation to me, implying self awareness.
An amoeba struggling as it’s being eaten by a larger amoeba isn’t self-aware.
To some degree it is. There is some evidence that plants can experience pain in their own way.
An instinctive, machine-like reaction to pain is not the same as consciousness. There might be more to creatures like plants and insects and this is still being researched, but for now, most of them appear to behave more like automatons than beings of greater complexity. It’s pretty straightforward to completely replicate the behavior of e.g. a house fly in software, but I don’t think anyone would argue that this kind of program is able to achieve self-awareness.
It’s pretty straightforward to completely replicate the behavior of e.g. a house fly in software
Could you provide an example of a complete housefly model?
I’m sorry, but I can’t find it right now, it’s a vague memory from a textbook or lecture.
completely replicate the behavior of e.g. a house fly in software
You may be thinking of “complete mapping of a fruit fly brain”, from Oct 2024:
https://www.science.org/content/article/complete-map-fruit-fly-brain-circuitry-unveiled
It’s still some way off from simulating it in software, and a house fly is supposedly more complex.
Yeah my roomba attempting to save itself from falling down my stairs sounds a whole lot like self preservation too. Doesn’t imply self awareness.
Attention Is All You Need: https://arxiv.org/abs/1706.03762
https://en.wikipedia.org/wiki/Attention_Is_All_You_Need
From my understanding all of these language models can be simplified down to just: “Based on all known writing what’s the most likely word or phrase based on the current text”. Prompt engineering and other fancy words equates to changing the averages that the statistics give. So by threatening these models it changes the weighting such that the produced text more closely resembles threatening words and phrases that was used in the dataset (or something along those lines).
Modern systems are beyond that already, they’re an expansion on:
