In the interview with Dr. Laibson, I found the discussion about Occam's Razor to be the most interesting. I had to do some Google searching outside the video to read more about the principle. However, it made sense that this principle is still being applied today and adds complexity to the oversimplification of ideas like Aristotle’s. I suppose what caught me off guard was the notion that machine learning, which is the opposite of a simple theory, would be the way to escape relying solely on these principles/approaches like Occam’s Razor. Then, as Dr. Laibson mentioned, "We're going to work together with some simple theories, machine learning which is the opposite of a simple theory, and we'll hopefully hybridize all of this." So, while machine learning can help us move beyond these methods, we won't be able to escape them entirely, and instead, we'll need to find a way to combine them effectively.
So a question that I would have asked is: Do you have any specific ideas or examples in mind for how we could combine these two seemingly opposite approaches?