What I do:

1. Robot learning research at UC Berkeley (PhD expected fall 2021).
2. Write high-signal content on the internet on AI, robotics, and my interests (See below).
3. Nerd out on training, longevity, and what it means to be alive.

Support me financially with this link: https://natolambert.medium.com/membership

Where you will find me:

1. High signal writing: my Substack on AI, robotics, and automation https://robotic.substack.com/
2. Twitter for the knowledge graph @natolambert.
3. Medium is where some of my ideas from the first two end up. It works, but the material will be slightly delayed.

Some of my popular articles:

1. Crisp python plots based on visualization theory — an introduction to making your plots tell a fuller story.
2. How top CS students problem solve — reflections on how the best students I have taught AI to think.
3. Three introductions to reinforcement learning (RL) — 3 skills to master, how to start learning (code & math), and gists of deep RL algorithms.
4. A series on the fundamentals of reinforcement learning (1, 2, 3, 4).

My messages are always open on twitter, or comment below!

Other articles:

AI Startups & Companies:

• “AI” Startups are Data Startups
• Deepmind: the existence proof for RL at scale

Careers:

Machine Learning Engineer versus Software Engineer

Conferences:

Reflections on NeurIPs 2019

Tutorials:

• Design Optimization with Ax in Python
• Train a neural network in python to predict robot dynamics

Deep RL Case Studies:

• Deep RL Case Study: Model-based Planning
• Deep RL Case Study: Chaotic Gradients

Misc.:

1. Automating Code with Machine Intelligence,
2. Baselines in Reinforcement Learning Research,
3. Bias-Variance Tradeoff — Fundamentals of Machine Learning,
4. Recommender systems: value-alignment, reinforcement learning, and ethics,
5. AI & Arbitration of Truth.