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Andrej Karpathy

Senior Director of Artifical Intelligence at Tesla

Andrej Karpathy is a Slovak-Canadian computer scientist who has made significant contributions to the field of artificial intelligence, particularly in deep learning and computer vision.1 He was the director of AI and Autopilot Vision at Tesla.1 Karpathy is also a co-founder and former member of OpenAI.1 As of July 16, 2024, Karpathy announced that he started a new AI+Education company called Eureka Labs.1

Early Life and Education::

  • Karpathy was born in Bratislava, Czechoslovakia (now Slovakia), on October 23, 1986.1
  • At age 15, he moved with his family to Toronto, Canada.1
  • He obtained his bachelor's degrees in Computer Science and Physics from the University of Toronto in 2009.1
  • In 2011, he earned a master's degree from the University of British Columbia, where he researched physically simulated figures with advisor Michiel van de Panne.1
  • He completed his PhD at Stanford University in 2015 under Fei-Fei Li, specializing in natural language processing, computer vision, and deep learning models.1

Career and Research::

  • Stanford: Karpathy authored and instructed Stanford's first deep learning course, CS 231n: Convolutional Neural Networks for Visual Recognition, which grew to be one of the largest classes at Stanford.1
  • OpenAI: He was a founding member and research scientist at OpenAI from 2015 to 2017.1
  • Tesla: In June 2017, Karpathy became Tesla's Director of Artificial Intelligence, reporting to Elon Musk.1 He led the Autopilot computer vision team.23 After a sabbatical, he left Tesla in July 2022.12
  • Return to OpenAI: It was reported that Karpathy returned to OpenAI in February 2023 but left a year later.1
  • Eureka Labs: Karpathy started a new AI+Education company called Eureka Labs on July 16, 2024.1 According to Eureka Labs, their first product will be the AI course, LLM101n.1

Karpathy was named one of MIT Technology Review's Innovators Under 35 for 2020.1 He likes to train deep neural nets on large datasets.3

Highlights

Apr 9 · twitter

Judging by my tl there is a growing gap in understanding of AI capability.

The first issue I think is around recency and tier of use. I think a lot of people tried the free tier of ChatGPT somewhere last year and allowed it to inform their views on AI a little too much. This is a group of reactions laughing at various quirks of the models, hallucinations, etc. Yes I also saw the viral videos of OpenAI's Advanced Voice mode fumbling simple queries like "should I drive or walk to the carwash". The thing is that these free and old/deprecated models don't reflect the capability in the latest round of state of the art agentic models of this year, especially OpenAI Codex and Claude Code.

But that brings me to the second issue. Even if people paid $200/month to use the state of the art models, a lot of the capabilities are relatively "peaky" in highly technical areas. Typical queries around search, writing, advice, etc. are not the domain that has made the most noticeable and dramatic strides in capability. Partly, this is due to the technical details of reinforcement learning and its use of verifiable rewards. But partly, it's also because these use cases are not sufficiently prioritized by the companies in their hillclimbing because they don't lead to as much $$$ value. The goldmines are elsewhere, and the focus comes along.

So that brings me to the second group of people, who both 1) pay for and use the state of the art frontier agentic models (OpenAI Codex / Claude Code) and 2) do so professionally in technical domains like programming, math and research. This group of people is subject to the highest amount of "AI Psychosis" because the recent improvements in these domains as of this year have been nothing short of staggering. When you hand a computer terminal to one of these models, you can now watch them melt programming problems that you'd normally expect to take days/weeks of work. It's this second group of people that assigns a much greater gravity to the capabilities, their slope, and various cyber-related repercussions.

TLDR the people in these two groups are speaking past each other. It really is simultaneously the case that OpenAI's free and I think slightly orphaned (?) "Advanced Voice Mode" will fumble the dumbest questions in your Instagram's reels and at the same time, OpenAI's highest-tier and paid Codex model will go off for 1 hour to coherently restructure an entire code base, or find and exploit vulnerabilities in computer systems. This part really works and has made dramatic strides because 2 properties: 1) these domains offer explicit reward functions that are verifiable meaning they are easily amenable to reinforcement learning training (e.g. unit tests passed yes or no, in contrast to writing, which is much harder to explicitly judge), but also 2) they are a lot more valuable in b2b settings, meaning that the biggest fraction of the team is focused on improving them. So here we are.

Apr 4 · twitter

Farzapedia, personal wikipedia of Farza, good example following my Wiki LLM tweet.

I really like this approach to personalization in a number of ways, compared to "status quo" of an AI that allegedly gets better the more you use it or something:

  1. Explicit. The memory artifact is explicit and navigable (the wiki), you can see exactly what the AI does and does not know and you can inspect and manage this artifact, even if you don't do the direct text writing (the LLM does). The knowledge of you is not implicit and unknown, it's explicit and viewable.
  2. Yours. Your data is yours, on your local computer, it's not in some particular AI provider's system without the ability to extract it. You're in control of your information.
  3. File over app. The memory here is a simple collection of files in universal formats (images, markdown). This means the data is interoperable: you can use a very large collection of tools/CLIs or whatever you want over this information because it's just files. The agents can apply the entire Unix toolkit over them. They can natively read and understand them. Any kind of data can be imported into files as input, and any kind of interface can be used to view them as the output. E.g. you can use Obsidian to view them or vibe code something of your own. Search "File over app" for an article on this philosophy.
  4. BYOAI. You can use whatever AI you want to "plug into" this information - Claude, Codex, OpenCode, whatever. You can even think about taking an open source AI and finetuning it on your wiki - in principle, this AI could "know" you in its weights, not just attend over your data.

So this approach to personalization puts you in full control. The data is yours. In Universal formats. Explicit and inspectable. Use whatever AI you want over it, keep the AI companies on their toes! :)

Certainly this is not the simplest way to get an AI to know you - it does require you to manage file directories and so on, but agents also make it quite simple and they can help you a lot. I imagine a number of products might come out to make this all easier, but imo "agent proficiency" is a CORE SKILL of the 21st century. These are extremely powerful tools - they speak English and they do all the computer stuff for you. Try this opportunity to play with one.

Feb 14 · youtube.com
Andrej Karpathy LEAVES OpenAI! Returning to Tesla? - YouTube
Andrej Karpathy LEAVES OpenAI! Returning to Tesla? - YouTube
Oct 29 · youtube.com
Andrej Karpathy: Tesla AI, Self-Driving, Optimus, Aliens, and AGI
Andrej Karpathy: Tesla AI, Self-Driving, Optimus, Aliens, and AGI
Andrej Karpathy departs from Tesla after taking sabbatical [Update]
Sep 21 · covariant.ai
The Robot Brains Podcast: Andrej Karpathy - Covariant
Jun 21 · karpathy.ai
Andrej Karpathy
Andrej Karpathy
Apr 20 · youtube.com
Andrej Karpathy - AI for Full-Self Driving at Tesla - YouTube
Andrej Karpathy - AI for Full-Self Driving at Tesla - YouTube
Aug 10 · en.wikipedia.org
Andrej Karpathy - Wikipedia
Andrej Karpathy - Wikipedia

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Andrej Karpathy
Andrej Karpathy, photo 1
Andrej Karpathy, photo 2
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