Andrej Karpathy

Founder · Eureka Labs

I am founder at Eureka Labs. I recently elaborated on its vision on the Dwarkesh podcast. While work on Eureka continues, I create educational videos on AI on my YouTube channel. There are two tracks.

General audience track:

1. Deep Dive into LLMs like ChatGPT — on under-the-hood fundamentals of LLMs.
2. How I use LLMs — a more practical guide to examples of use in my own life.
3. Intro to Large Language Models — a third, parallel, video from a longer time ago.

Technical track: Follow the Zero to Hero playlist.

For all the latest, I spend most of my time on 𝕏/Twitter or GitHub.

Returned to OpenAI

I came back to OpenAI where I built a new team working on midtraining and synthetic data generation.

Director of AI · Tesla

I was the Director of AI at Tesla, where I led the computer vision team of Tesla Autopilot and (very briefly) Tesla Optimus. My team handled all in-house data labeling, neural network training and deployment on Tesla's custom inference chip. Today, the Autopilot increases the safety and convenience of driving, but the team's goal is to make Full Self-Driving a reality at scale. See Aug 2021 Tesla AI Day for more.

Founding member · OpenAI

I was a research scientist and a founding member at OpenAI.

PhD · Stanford

My PhD was focused on convolutional/recurrent neural networks and their applications in computer vision, natural language processing and their intersection. My adviser was Fei-Fei Li at the Stanford Vision Lab and I also had the pleasure to work with Daphne Koller, Andrew Ng, Sebastian Thrun and Vladlen Koltun along the way during the first year rotation program.

I designed and was the primary instructor for the first deep learning class at Stanford — CS 231n: Convolutional Neural Networks for Visual Recognition. The class became one of the largest at Stanford and has grown from 150 enrolled in 2015 to 330 students in 2016, and 750 students in 2017.

Along the way I squeezed in 3 internships at (baby) Google Brain in 2011 working on learning-scale unsupervised learning from videos, then again in Google Research in 2013 working on large-scale supervised learning on YouTube videos, and finally at DeepMind in 2015 working on the deep reinforcement learning team with Koray Kavukcuoglu and Vlad Mnih.

MSc · UBC

MSc at the University of British Columbia where I worked with Michiel van de Panne on learning controllers for physically-simulated figures (i.e., machine-learning for agile robotics but in a physical simulation).

BSc · University of Toronto

BSc at the University of Toronto with a double major in computer science and physics and a minor in math. This is where I first got into deep learning, attending Geoff Hinton's class and reading groups.