To be clear I’m not expert. But I know a bit.

The way LLMs (like ChatGPT, GPT-4, etc) work, is that they continuously decide what the next best-sounding word might be, and they print it, over and over and over, until it makes sentences and paragraphs. And the way that next-word decision works under the hood, is with a deep neural net that was initially a theoretical tool designed to imitate the neural circuits that make up our biological nervous system and brain. The actual code for LLMs is rather small, it’s just about storing and managing representations of a neuron, and rearranging the connections between neurons as it learns more; just like the brain does.

I was listening to the first part of this “This American Life” episode this morning that covers it really well: https://podcasts.apple.com/us/podcast/this-american-life/id201671138?i=1000618286089 In it, Microsoft AI experts also express excitement and confusion about how GPT-4 seems to actually reason about things, rather than just bullshitting the next word to make it look like it reasons, like it’s supposed to be designed to do.

And so I was thinking: the reason why it works might be the other way around. It’s not that LLMs are smart enough to reason instead of bullshit, it’s that human’s reasoning actually works out of constantly bullshitting too, one word at a time. Imitate the human brain exactly, and I guess we shouldn’t be surprised that we land with a familiar-looking kind of intelligence - or lack thereof. Right?