Wow, Tesla Full Self-Driving (FSD), the most incredible real-world software system humanity has ever seen, doesn’t rely on explicit code. It’s an end-to-end neural network solution. The new paradigm is: there is no explicit coding; the code is … neural net weights trained on massive data! 🤯
In a recent live stream, Elon Musk and Ashok Elluswami took viewers on a journey through Palo Alto, showcasing the impressive capabilities of Tesla’s Version 12 software as it maneuvered through complex parking lots, city traffic, and traffic lights — they even attempted to locate Mark Zuckerberg’s house. Throughout the drive, the duo discussed various aspects of the next-generation Tesla software, including its machine-learning capabilities, data collection process, and global testing efforts.
Viewers were given mind-boggling insights into the importance of high-quality data for the software. The discussion also shed light on the transition from explicit code to entirely neural networks, self-learning features of Version 12.
Tesla FSD takes the driving experience to the next level and will ensure safety, efficiency, and convenience for Tesla users worldwide, given that currently, millions of people die in traffic each year. Self-driving cars will save millions of lives!
Key Takeaways
- Tesla’s Version 12 software relies on end-to-end machine learning and neural networks for enhanced driving experience
- High-quality data collection and global testing are crucial factors for the software’s development and efficiency
- The elimination of explicit code facilitates greater adaptability, self-learning abilities, and performance improvements in the latest Tesla software version
Discussion on Live Stream
Elon Musk conducted a live stream with Ashok Elluswamy as they drove around Palo Alto in a Tesla Model S. The duo reportedly attempted to locate Mark Zuckerberg’s house, adding some humor to the event. The live stream provided valuable insights into self-driving technology by revealing pertinent information.
During the stream, it was disclosed that the car (Tesla Model S) being driven was operating on Version 12 Alpha of the Full Self-Driving (FSD) software. The entire system now relies solely on neural networks with no explicit code, a significant change from Version 11, which required around 300,000 lines of C++ code. Musk’s Model S was equipped with Hardware 3, despite the recent availability of Hardware 4.
The live stream also disclosed that the FSD software was being tested globally, with testers from countries like New Zealand. Elon and Ashok emphasized the importance of high-quality data in training their AI models. They identified that each time a driver has to intervene in the FSD system, the intervention moment is uploaded to Tesla for analysis and improvement.
Adopting an entirely neural network-based system and focusing on high-quality data, the Tesla FSD system learns directly from the Tesla fleet. Data is the new oil!
With AI codes itself via data and training, here’s a concept you need to understand:
One of the most important concepts for computer scientists to understand is called “mechanistic interpretability”, i.e., reverse engineering algorithms from neural net weights. See this article for more: 👇
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Software Version and Hardware Details
The system is currently running on version 12 Alpha, which uses an entirely neural network-based approach, discarding the previous 300 thousand lines of C++ and C code with only about 3 thousand lines in the current version. 🤯
Let me repeat: replacing 300,000 with only (!) 3,000 lines of code for a fully autonomous robot that can maneuver the real world, save millions of lives each year, and disrupt millions of jobs in the logistics sector.
The Model S is equipped with Hardware 3, the same hardware found in most Tesla models, including the Model Y and Model 3. Some Model S vehicles now come with Hardware 4, but it appears that the FSD version 12 Alpha has not yet been implemented on these newer hardware versions. This suggests that FSD beta might not be available on Hardware 4 vehicles for some time.
Musk mentioned that FSD version 12 is being trained and tested globally, with testers around the world contributing high-quality data. New Zealand was specifically mentioned as a testing location, likely due to its current winter season offering unique and challenging driving conditions.
The current Tesla FSD system has transitioned to a fully neural network-based architecture in its version 12 Alpha, running on Hardware 3 components. Users can expect further improvements as Tesla collects high-quality data from testers worldwide to refine the system.
Driving Environment and Data Collection
Tesla has gathered high-quality data from various locations, including winter environments like New Zealand, to ensure the system is exposed to different driving conditions, weather patterns, and traffic situations. Data collection is more important than coding — the importance of high-quality data over a large amount of mediocre data, as this helps improve the system’s learning process.
💡 Info: Interactions between the driver and the FSD system during testing, such as interventions, are automatically uploaded to Tesla for further analysis and improvements. This active approach to data collection complements the neural network implementation of version 12, as it ensures high-quality information is used for refining the system.
When users of the Full Self-Driving beta have to intervene while the system is operating, the intervention data is automatically uploaded to Tesla for analysis, helping to further train and improve the system. This creates a rapid virtuous cycle, allowing the system to continuously learn from real-world experiences.
Small Amounts of Superior-Quality Data Beats Large Amounts of Average Data
The eminence of high-quality data plays a crucial role in systems that require machine learning and artificial intelligence.
🧑💻 Data collection is the new coding!
The reason behind this is the need for accurate information during the training process of these systems.
- Acquiring massive amounts of average-quality data can often be detrimental and lead to poor learning outcomes.
- On the other hand, smaller amounts of superior-quality data provide a solid foundation for learning.
In other words: garbage in, garbage out.
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