Andrej Karpathy

Andrej Karpathy is a renowned Slovak-Canadian computer scientist specializing in deep learning and computer vision.

Born on October 23, 1986, in Bratislava, Czechoslovakia (now Slovakia), he moved to Toronto with his family at the age of 15. He completed his bachelor's degrees in Computer Science and Physics at the University of Toronto in 2009 and his master's degree at the University of British Columbia in 2011. He received his PhD from Stanford University in 2016, where he worked on Convolutional/Recurrent Neural Network architectures and their applications in Computer Vision and Natural Language Processing.

Karpathy is known for his significant contributions to the field of artificial intelligence (AI). He was a founding member of the AI research group OpenAI, where he worked as a research scientist from 2015 to 2017. During his time at OpenAI, he worked on deep reinforcement learning and deep learning for generative models.

In 2017, he joined Tesla as the director of artificial intelligence, where he led the computer vision team that designed Tesla Autopilot. His responsibilities at Tesla included overseeing in-house data labeling, neural network training, and deployment in production running on Tesla's custom inference chip. He was named Senior Director of AI at Tesla, where he led the team responsible for all neural networks on the Autopilot.

Karpathy's work at Tesla was instrumental in increasing the safety and convenience of driving, with the ultimate goal of developing and deploying Full Self-Driving. He was also involved in the development of the "Optimus" humanoid robot, which incorporated features and sensors from Tesla's Autopilot system.

In addition to his work in AI research and development, Karpathy has made significant contributions to education. He designed and was the primary instructor for Stanford's first deep learning class, CS 231n: Convolutional Neural Networks for Visual Recognition. The class quickly became one of the largest at Stanford and has grown significantly since its inception.

After a sabbatical from Tesla, Karpathy announced in July 2022 that he was leaving the company. He rejoined OpenAI in 2023, where he continues to work on deep learning and computer vision. His return to OpenAI was inspired by the impact of the company's work and his personal benefits from it.

Karpathy's expertise and contributions have earned him recognition in the AI community. He was named one of MIT Technology Review's Innovators Under 35 for 2020. He also received the WTF Innovators Award for his contributions to deep neural networks and computer vision, and continued research into making AI more effective for humanity.

What are some of Andrej Karpathy's educational resources?

Andrej Karpathy has contributed to several educational resources in the field of deep learning and artificial intelligence:

1. CS231n: Convolutional Neural Networks for Visual Recognition

   • Karpathy was the primary instructor for this Stanford course, which became one of the largest at the university. The course materials, including class notes, lecture slides, and a subreddit for discussion (r/cs231n), are available online.

2. Neural Networks Tutorial

   • Karpathy authored a tutorial on neural networks from a hacker's perspective, focusing on code and physical intuitions rather than mathematical derivations. However, he now recommends better materials such as the CS231n course lectures, slides, and notes, or the Deep Learning book.

3. Online Courses and Mini-Courses

   • Class Central lists several online courses taught by Karpathy, covering topics such as neural network optimization, multilayer perceptron character-level language models, and building generative models like GPT. These courses are designed to be concise, ranging from 1 to 3 hours in length.

4. Neural Networks: Zero to Hero

   • This is a course by Karpathy on building neural networks from scratch in code. It starts with the basics of backpropagation and builds up to modern deep neural networks like GPT. The course is designed for those with solid programming skills in Python and introductory-level math knowledge.

These resources reflect Karpathy's approach to teaching deep learning, emphasizing practical implementation and understanding through coding.

Who is Andrej Karpathy?

Andrej Karpathy is a leading expert in the field of artificial intelligence and deep learning. He is currently the Director of Artificial Intelligence and Autopilot Vision at Tesla, Inc. Prior to Tesla, Karpathy completed his PhD in machine learning and computer vision at Stanford University. His research work has been influential in the field of AI, particularly in the use of convolutional neural networks in visual recognition tasks. Andrej Karpathy has also co-authored the popular deep learning library, ConvNetJS, and has made significant contributions to the open-source machine learning community.

Andrej Karpathy is a Slovak-Canadian computer scientist known for his work in the field of artificial intelligence, particularly deep learning and computer vision. He was born on October 23, 1986, in Bratislava, Czechoslovakia (now Slovakia).

Karpathy moved to Toronto with his family when he was 15. He completed his bachelor's degrees in Computer Science and Physics at the University of Toronto in 2009, and his master's degree at the University of British Columbia in 2011. His PhD, completed at Stanford University in 2016, focused on connecting images and natural language, and his doctoral advisor was Fei-Fei Li.

Karpathy's professional career includes significant roles at major tech companies. He was a research scientist at OpenAI, where he worked on deep learning in computer vision, generative modeling, and reinforcement learning. He also interned at Google, where he worked on large-scale feature learning over YouTube videos, and at DeepMind on the Deep Reinforcement Learning team.

From 2017 to 2022, Karpathy served as the Senior Director of AI at Tesla, where he led the computer vision team of Tesla Autopilot. This role involved overseeing in-house data labeling, neural network training, and deployment in production running on Tesla's custom inference chip.

As of 2023, Karpathy is back at OpenAI working on the Jarvis personal assitant program. He is also known for his educational contributions, having designed and instructed a Stanford class on Convolutional Neural Networks for Visual Recognition (CS231n). He continues to teach Artificial Intelligence by releasing lectures on YouTube.

2023: Karpathy rejoins OpenAI

It is widely believed that Karpathy is working on breakthrough AI personal assistant technology at OpenAI.

Andrej Karpathy, a lead AI researcher and founding member of OpenAI, has played a significant role in the development of Generative Pretrained Transformer (GPT) models. He has explained the training process of Large Language Models (LLMs) like GPT, which involves four stages: pre-training, supervised fine-tuning, reward modeling/reinforcement learning, and prompt engineering.

During pre-training, large amounts of data are gathered and tokenized, a process that is computationally intensive and requires thousands of GPUs and months of training. The models are then fine-tuned for downstream tasks using a small but high-quality dataset collected through human contractors. This is followed by reinforcement learning from human feedback, which involves creating "completions" and ranking them based on a reward model. The final stage, prompt engineering, compensates for cognitive differences between human and GPT architectures, as transformers lack internal dialogue and cognitive reflection, and reasoning must be spread out across multiple tokens for successful GPT performance.

Karpathy has also given insights into the science of "Prompt Engineering" and the "nature" of GPT-4, including the subtle references to the art of interaction with AI. He has discussed the significance of pre-training, supervised fine-tuning, and reinforcement learning, and brought attention to the importance of human oversight in using LLMs, particularly in low-stakes applications. He has underscored the significance of prompt engineering experimentation and a few shot examples in optimizing performance.

What is Andrej Karpathy known for?

Andrej Karpathy is known for his work in the field of artificial intelligence, particularly in deep learning and neural networks. His key contributions include:

1. Research on Convolutional Neural Networks and Recurrent Neural Networks — Karpathy's PhD research at Stanford University focused on these two types of neural networks, which are key components of many modern AI systems.

2. Leadership at Tesla — As the Director of AI and Autopilot Vision at Tesla, Karpathy leads the development of AI capabilities for autonomous driving. His work is helping to advance the state of the art in self-driving technology.

3. Educational Contributions — Karpathy has made significant contributions to AI education, including developing a popular Stanford course on convolutional neural networks and authoring widely-read blog posts on deep learning topics.

4. Work at OpenAI — Before joining Tesla, Karpathy worked at OpenAI, where he conducted research on reinforcement learning and generative models.

What is Andrej Karpathy's impact on AI?

Andrej Karpathy's work has had a significant impact on the field of AI. His research on deep learning techniques has been widely cited, and his leadership at Tesla is helping to push the boundaries of autonomous driving technology.

In addition to his research contributions, Karpathy's educational efforts have had a broad impact on the AI community. His Stanford course and blog posts have helped to demystify complex deep learning concepts and have inspired many students and researchers in the field.

Karpathy's work continues to influence the development of AI, and his contributions will likely continue to shape the field for years to come.