Andrej Karpathy Personality Type, MBTI
What is the personality type of Andrej Karpathy? Which MBTI personality type best fits? Personality type for Andrej Karpathy from Computer Science and what is the personality traits.
INTP (5w6)
Andrej Karpathy personality type is INTP, and he is the creator of the RNN model.
Karpathy is a young and highly talented computer scientist, and his work caught the attention of many.
He is famous for developing the RNN model for deep learning, specifically for text processing. He has also revolutionized the way of creating AI assistants and robots.
But, in this article, I will tell you about the key features of the INTP personality, and how it affects your career.
INTP Personality: A Brief Overview
According to the Myers-Briggs Type Indicator, INTPs are introverted, intuitive, thinking and perceiving people.
They are known as deep thinkers and extremely smart. Their decision-making process can be quite complex and they may take a long time to process information.
They prefer working alone and can be very focused on their goals. They are usually not great at social interactions, but they can be quite happy with their own company.
INTP Personality: Career-Focused
INTPs are usually very goal-oriented people. They hate being stuck in a rut, and they always need a goal in front of them.
I am the Sr. Director of AI at Tesla, where I lead the team responsible for all neural networks on the Autopilot. Previously, I was a Research Scientist at OpenAI working on Deep Learning in Computer Vision, Generative Modeling and Reinforcement Learning. I received my PhD from Stanford, where I worked with Fei-Fei Li on Convolutional/Recurrent Neural Network architectures and their applications in Computer Vision, Natural Language Processing and their intersection. Over the course of my PhD I squeezed in two internships at Google where I worked on large-scale feature learning over YouTube videos, and in 2015 I interned at DeepMind on the Deep Reinforcement Learning team. Together with Fei-Fei, I designed and was the primary instructor for a new Stanford class on Convolutional Neural Networks for Visual Recognition (CS231n). The class was the first Deep Learning course offering at Stanford and has grown from 150 enrolled in 2015 to 330 students in 2016, and 750 students in 2017.
