Related to #10, I’ve found that building up understanding of complex topics (e.g., physics, mathematics, machine learning, etc.) is unusually enhanced by following the history of their development. Especially in mathematical topics, where the drive for elegant proofs leads to presentations that strip away the messy history of all the cognitive efforts that went into solving the problem in the first place.
I suppose this is really just an unconventional application of the general principle of learning from history.
I think there’s a lot of the intuitions and thought processes that let you come up with new discoveries in mathematics and machine learning that aren’t generally taught in classes or covered in textbooks. People are also quite bad at conveying their intuitions behind topics directly when asked to in Q&As and speeches. I think that at least in machine learning, hanging out with good ML researchers teaches me a lot about how to think about problems, in a way that I haven’t been able to get even after reading their course notes and listening to their presentations. Similarly, I suspect that autobiographies may help convey the experience of solving problems in a way that actually lets you learn the intuitions or thought processes used by the author.
Related to #10, I’ve found that building up understanding of complex topics (e.g., physics, mathematics, machine learning, etc.) is unusually enhanced by following the history of their development. Especially in mathematical topics, where the drive for elegant proofs leads to presentations that strip away the messy history of all the cognitive efforts that went into solving the problem in the first place.
I suppose this is really just an unconventional application of the general principle of learning from history.
I think there’s a lot of the intuitions and thought processes that let you come up with new discoveries in mathematics and machine learning that aren’t generally taught in classes or covered in textbooks. People are also quite bad at conveying their intuitions behind topics directly when asked to in Q&As and speeches. I think that at least in machine learning, hanging out with good ML researchers teaches me a lot about how to think about problems, in a way that I haven’t been able to get even after reading their course notes and listening to their presentations. Similarly, I suspect that autobiographies may help convey the experience of solving problems in a way that actually lets you learn the intuitions or thought processes used by the author.