Introduction to Representational Learning


Just as there are odors that dogs can smell and we cannot, as well as sounds that dogs can hear and we cannot, so too there are wavelengths of light we cannot see and flavors we cannot taste.

Why then, given our brains wired the way they are, would the remark “Perhaps there are thoughts we cannot think,” surprise you?

Evolution, so far, may possibly have blocked us from being able to think in some directions; there could be unthinkable thoughts.

These sounds that we can’t hear, this light that we can’t see, how do we even know about these things in the first place? Well, we built tools. We built tools that adapt these things that are outside of our senses, to our human bodies, our human senses.

We can’t hear ultrasonic sound, but you hook a microphone up to an oscilloscope and there it is. You’re seeing that sound with your plain old monkey eyes. We can’t see cells and we can’t see galaxies, but we build microscopes and telescopes and these tools adapt the world to our human bodies, to our human senses.

We will explore how representational learning helps us think unthinkable thoughts particularly applied to NLP.


Nathaniel earned his AB/SM in Computer Science from Harvard. He previously worked as a Quant and Trader at Jane Street and Goldman Sachs before transitioning into the pure tech industry. Nathaniel worked as a Data Scientist at Facebook, a Product Manager at Microsoft and a Software Engineer at Google before joining Vicarious. He is an avid reader and learner. He teaches part time at General Assembly and is developing open source teaching material for data science, machine learning, and web development.

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