The whole premise of this article hinges on the idea that LLMs have fundamental limitations that they clearly don’t have if you’ve looked at lots of gpt4 examples. For example, it can do scientific thinking if you specifically ask it to, and it can reason about totally new situations outside of the training data based on generalizable models of reality it creates to predict training data. If you are certain these limitations must exist based on how LLMs are made, rather than observed capability, it is critical to reevaluate those assumptions.

Moreover, what she defines as scientific thinking is an outdated notion that is no longer widely adopted by scientific researchers, in favor of a more holistic Bayesian process: use intuition to think of something new try, try it, and then update your beliefs with the new data. This is actually more similar to how human brains and LLMs operated before the concept of a scientific method.