The Importance of Generative Modeling in Deep Learning

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David Haus: In the face of deep learning, so many ML modalities have been forgotten. So I guess great and boosted decision trees. But there's another formal definition of generative modeling which focuses on a sort of as an version of Bayes rule. And if you can just find a ton of inputs like images or text in trying to recreate all of those images or texts, there's a huge amount that can be learned about the patterns in text or pictures.

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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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