The Role of Geometry in Language Learning

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When you're immersed in a conceptual set, you end up tiling the way you experience everything. I think of more working out as regularization where in machine learning, there's a sense that you can simplify a model by constraining its weights to be within a certain regime. And just the generality of geometry is kind of stunning. You see just how many right angles are everywhere in the way that they recurse on themselves? Well, they have a bookshelf and every book on the bookshelf has right angles and the bookshe shelf has right angles. So I think of that, if you do sufficiently deep immersion into some set of ideas, you do just start associating

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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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