Introduction to Tesla’s neural network

July 15, 2020

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Tesla, led by visionary Elon Musk, has garnered an almost fanatical fan base that eagerly promotes the company’s products. Thousands of podcasts and YouTube videos scrutinize Tesla cars from every angle, marveling at their design, interface, sustainability, technology, and performance.

Introduction

The adoration bestowed upon Tesla is well-deserved, as their vehicles successfully bridge the gap between sustainability and sex appeal. A significant part of their value lies in their advanced deep learning neural networks. These cars are constantly learning, updating, and improving, with autonomous learning relying heavily on convolutional neural networks (CNN). These CNNs allow Tesla’s self-driving vehicles to understand lane markings, drivable space, traffic lights, pedestrians, signs, and other drivers.

Tesla cars

Tesla vehicles are equipped with eight surround cameras that provide 360-degree visibility around the car for up to 250 meters of range. The onboard computer employs unsupervised learning algorithms to detect objects, distances, and labels data in photos and videos without human intervention, saving both time and money.

In addition to cameras, Tesla cars utilize forward-facing radar technology, which sends out an electromagnetic signal and measures the distance of objects from the car. The resulting data creates a 3D scan of the surrounding area, including road layout, infrastructure, and physical objects. By combining data from cameras and radar sensors, the car gains a comprehensive understanding of its environment.

Tesla’s fleet of a million cars provides a significant advantage over competitors like Waymo. The company’s engineers continuously train the cars in “shadow mode” using real-world data sent back by the vehicles. Anytime a Tesla makes an incorrect prediction, the data snapshot is saved and added to Tesla’s training set. This iterative process refines the neural network’s capabilities, allowing it to understand shadows, reflections, and various road conditions throughout the day.

Deep learning

Tesla’s investment in advanced deep learning CNNs serves as an inspiration to all industries. While they were not the first to develop self-driving technology, they questioned the use of LIDAR and devised a more efficient, cost-effective, and future-proof approach. As the construction industry ventures into automation, it should learn from Tesla’s precedent.

For instance, Built Robotics’ excavation robot operates autonomously but uses LIDAR instead of radar, which may be appropriate for its confined environment. Neural networks may not be necessary for this application, as the robot does not encounter variables like pedestrians and cyclists, focusing instead on tasks such as digging.

In the construction field, convolutional neural networks can also be employed to monitor worker behavior and identify unsafe habits. Such data can inform future training priorities and potentially prevent accidents in real-time, addressing the critical concern of workplace safety. Additionally, on-site monitoring data can improve overall efficiency and productivity.

Moreover, Japan’s Advanced Industrial Science and Technology Institute has developed a humanoid robot capable of handling various construction tasks, particularly in situations with labor shortages or hazardous conditions. Although it is not confirmed if this robot uses neural networks, it possesses environment and object detection sensors, enabling it to navigate and install materials, such as drywall. With Japan facing a declining birth rate and an aging population, the construction industry must explore solutions like deep learning robots to supplement the workforce and address housing shortages.

Conclusion

In conclusion, Tesla’s pioneering work in neural networks has elevated self-driving technology and inspired progress in various industries. From autonomous vehicles to construction site monitoring and humanoid robots, the adoption of deep learning offers significant potential for innovation, efficiency, and safety.