The Importance of Normal Distributions in Research

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There are a lot of things that actually follow normal distributions, right? That's actually really common too. And I think it's really important to notice that the insight is conditional. So you can't actually be a hedgehog about it. You have to check. Am I in the conditions where my model is actually going to work? If those conditions hold, by all means, use your idea. The fox realizes that these conditionals have to happen. And the fox has models for when the condition holds and different models for when it doesn't hold.

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What is "The Index"? What are some benefits of externally compiling and organizing one's knowledge? When is spaced repetition useful? How can we co-opt our visual systems to boost memory? Would we all be more interested in producing an external personal knowledgebase if we could feel on a visceral level how much information is constantly being forgotten? How and when should we move up and down the ladder of abstraction? What sorts of problems can be solved by simulation? What is a generative model (as opposed to a predictive model)? How can constraints improve creativity? How useful are credentials as a guide to how much a person knows and whether or not a person is "allowed" to have an opinion on a topic? What do credentials actually signal about a person? What are "fox" and "hedgehog" thinking? What is deugenesis?

Jeremy is the founder of Consilience, an immersive information retrieval company. Previously he did machine learning research at Google Brain and studied Applied Mathematics at Harvard University. Jeremy works relentlessly towards aggregating knowledge, acquiring knowledge, and creating new knowledge. Find him on Twitter at @jvnixon or email him at jnixon2@gmail.com.

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