How large language models view our world

"DAN SHIPPER: I get to talk to people all the time about how they use AI in their work and in their lives, and also how it has changed them as people. There's many different ways of knowing things, and many different ways of understanding things. Computers, science, what both of those ways of seeing the world are trying to do is reduce the world into a set of really clean universal laws that apply in any situation."

"And what language models see instead is a dense web of causal relationships between different parts of the world that all come together in unique, very context specific ways to produce what comes next. And what's really interesting about neural networks is the way that they think or the way that they operate is a lot like human intuition. Human intuition is also trained by thousands, and thousands, and thousands of hours of direct experience."

Rationalism asserts that explicit, universal rules constitute true knowledge and underlies technologies like computers, vaccines, and weather prediction. That approach reduces phenomena to if-then relationships that aim to apply across situations. Language models capture a dense web of causal connections that combine in highly context-specific ways to generate outcomes. Neural networks operate similarly to human intuition by synthesizing vast amounts of experience rather than applying distilled rules. Intuitive thought develops through extensive direct experience and offers valuable, complementary insight alongside formal, rule-based reasoning in decision-making and creativity.

Read at Big Think