ARTIFICIAL INTELLIGENCE | NEWS
Tesla AI Day 2021 Review — Part 2: Training Data. How Does a Car Learn?
The challenge of getting high-quality real-world data.
This article is the second part of a 4-part series:
1. The Promise of Fully Self-Driving Cars
2. Training Data. How Does a Car Learn?
3. Project Dojo. Tesla’s New Supercomputer
4. Why Tesla Won’t Have an Autonomous Humanoid Robot in 2022
On the 19th of August Tesla hosted one of the most important AI events of 2021; the Tesla AI day (you can watch the entire thing here ).
The leading researchers and engineers of the company presented the latest developments in hardware, software, AI, robotics, computing, and self-driving cars. The event was focused on attracting the attention of potential candidates to work in current and future projects.
The talk was divided into four big sections. I’ll use the same outline to separate the articles of this series:
- Tesla Autopilot. How to make the car fully autonomous solving vision, planning, and control.
- Training data generation. How to create the large datasets needed to train the networks: Manual labeling, auto labeling, and simulations.
- Project Dojo and D1 chip. The next generation of AI training computers.
- Tesla bot. The promise of an autonomous humanoid robot that would carry out “dangerous, repetitive, boring tasks,” Musk said. “In the future physical work will be a choice.”
Let’s go for the second part: Training data. How does a car learn?
Disclaimer: Due to the impossibility to contact Tesla, I’ll link to the relevant visuals directly on the YouTube presentation at the corresponding time frame. I recommend clicking the links as your read to get a better grasp of the explanations.
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