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I worked at Tesla starting from 2017 thru 2021. I spent most of my time in the distribution and supply chain organizations in leadership positions.
Before Tesla, I was a Director of Business Intelligence and Pricing at the largest Pet Food & Supply distributor in the US, Phillips Pet Food & Supplies. My wife and I also owned a small business in Bethlehem, PA between 2016 and 2019.
I have been a shareholder of Tesla since 2012 and currently own Tesla stock. Nothing I say constitutes as investment or financial advice.
My thoughts are my own and are not representative of everyone who currently works, or has worked at Tesla, Phillips, or any other company or organization I discuss on this channel.
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Exploring the Evolution and Future of Tesla FSD with James Dowell
Join us as we dive deep into the world of Tesla’s Full Self-Driving (FSD) technology with special guest James Dowell. In this engaging discussion, James shares his extensive experience with Tesla’s FSD version 13, highlighting its significant improvements and real-world performance across long road trips. We discuss the advancements in Tesla’s AI, including its rollout of FSD in China without using local data, and the implications of this breakthrough for global expansion. The conversation also explores the potential future of unsupervised FSD, the challenges ahead, and the possible regulatory and market impacts. We touch upon Tesla’s AI training techniques, the development of the Optimus robot, and the crucial role of computing power in advancing these technologies. Whether you’re a Tesla enthusiast or tech aficionado, this episode offers valuable insights into the cutting-edge developments shaping the future of autonomous driving and robotics.
00:00 Introduction and Initial Impressions
00:38 Discussion with James Dowell
01:08 Version 13 Insights and Experiences
03:32 Hardware Comparisons and Road Trips
10:32 Sponsor Message and FSD Performance
18:10 Regulatory and Market Considerations
20:40 Cyber Cab and Fleet Management
26:44 FSD Rollout in China
29:55 Tuning Neural Networks with Synthetic Data
30:45 Challenges of Simulated vs. Real Data
31:27 Real-World Applications and Performance
32:14 Unique Driving Conditions Across Regions
36:37 Human Mimicking in Robotics
48:24 Training Robots for Various Tasks
52:47 The Importance of Compute Power
59:26 Conclusion and Final Thoughts
