Jacob Andreas is an Assistant Professor at MIT, where he leads the language and intelligence group, focusing on language as a communicative and computational tool.
His PhD thesis is titled "Learning from Language" which he completed in 2018 at UC Berkeley. We discuss compositionality and neural module networks, the intersection of RL and language, and translating a neural communication channel called 'neuralese', and how this can lead to more interpretable machine learning models.
Episode notes: https://cs.nyu.edu/~welleck/episode11.html
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