Nathan is a robot learning researcher, writer, non-professional athlete, and a mental-health advocate.

There really is too much noise.

I do my best to only contribute high signal content on machine learning, human optimization, and the nature of life.

Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.

Blogos

Job Hunt as a PhD in AI / ML / RL: How it Actually Happens

The full breakdown of what a job search in AI with a new Ph.D. looks like.

Read more...

The last reliable (available) path into AI

A confluence of trends leaves AI+something, rather than pure AI, as the last great path into machine learning research.

Read more...

ML/RL & Microrobotics

A memo I wrote to my research group on the open questions when applying machine learning to another research area: novel microrobotics.

Read more...
More musings →

Papers

Measuring Data

Learn more.
When you "measure data", you quantify its characteristics to support dataset comparison & curation. You also begin to know what systems will learn. Many ML systems don't reason with this, we posit you should.

Synergy of Prediction and Control in Model-based Reinforcement Learning

My thesis on model-based RL. Let's make models work with tasks!

Reward Reports for Reinforcement Learning

We propose a new type of documentation for dynamic machine learning (and reinforcement learning) systems!
More papers →