[sperandeo]

I don't like to say one way or the other on things. Especially LLM's. However, if ive learned anything about LLMs and real life problems, is to break it down to the foundation like already mention with weights being compared to neurons and map the parallels.

[advisedwang]

The neural network in LLMs are not really that similar to brains. Here are a couple of the biggest differences:

1. Brains are plastic, making connections, breaking connections and changing "weights" on the fly. LLM have static weights. The best they have is writing to MEMORY.md or data getting used in the training run for the next model.

2. LLMs neural networks do not have loops. The best they have is that their output is available as future input, but that is not the same.

[sperandeo]

great perspective. makes you wonder how we will innovate onto this.

[stringfood]

well the article definitely made that parallel - reminds me of how when we cut into brains we can see the neurons, we can see the axons, the grey and white matter, the language centers - but no closer to explaining why we do what we do