Motive:
I have been following and hearing about Tesla’s work over the past 6 years or so, but I have been listening to them only from the past couple of years.
I believe, to innovate, we need to stay curious and push the boundaries of our scientific limits. Needless to mention, incorporating an optimistic mindset and culture is pivotal to overcoming the challenges while pursuing these endeavors. Also, Innovation (say, space exploration) fosters a peaceful connection with other nations by finding a common ground. This article is one such example that encompasses my understanding and intuition of Tesla Autopilot AI and its potential that are backed by some facts and ongoing research.
Brief Overview:
PART 1: Andrej Karpathy (Director of AI and Autopilot Vision)
Concepts covered: The Essentials for Neural Networks (Large dataset, Varied dataset, Real dataset), Object Detection, Corner Cases, Data Engine, Fleet Labeling & Learning, Path Prediction, Depth Perception from VISION only.
PART 2: Stuart Bowers (Former VP of Engineering)
Concepts covered: Packaging the Hardware and AI into a viable product at production, Sensor Redundancy, Vector Space, Shadow Mode, Feature life cycle.
PART 3: Pete Bannon (VP of Hardware Engineering)
Concepts covered: A Full Tour of FSD Computer — Image Signal Processor, Neural Network Processor, Video Encoder, GPU, Main Processor, Safety System, Security System, Control Energy, Post Processing, Neural Network Compiler, HW 2.5 vs FSD Results Comparison.
PART 4: Elon Musk
Concepts covered: The Robo-Taxi Fleet & Cost Model, The Future of Self-Driving.
Read the full article on Medium:
https://medium.com/towards-artificial-intelligence/how-tesla-has-been-optimizing-its-software-and-hardware-for-fsd-capabilities-and-a-hyper-efficient-21493079b9bd?source=friends_link&sk=efab60f937d28854911d84aa6b463772
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