How Tesla Uses Deep Learning For It's Self Driving Cars

Smitha Kolan - Machine Learning Engineer

10 Oct 201905:03

Summary

TLDRIn this video, Smith explores Tesla's use of deep learning in self-driving technology, highlighting a recent software update featuring Smart Summon. He discusses the evolution of Tesla's goal from human-like driving to full autonomy, emphasizing the importance of computer vision for identifying various objects on the road. Smith explains how deep learning enables cars to learn and recognize patterns in data, such as images and text. He also details Tesla's advanced AI chips, which enhance processing power and redundancy. The video concludes by expressing optimism about the future of fully autonomous vehicles.

Takeaways

🚗 Tesla's new software update features 'Smart Summon,' allowing users to summon their car.
🌐 Experts predict that future generations may never learn to drive due to the rise of self-driving technology.
👨‍💼 Elon Musk's initial goal was for Tesla to create cars that drive like humans, evolving to fully autonomous vehicles.
💡 Self-driving cars are expected to save time, money, resources, and lives.
🧠 Deep learning is a crucial subfield of machine learning, enabling the analysis of large volumes of unstructured data.
🔍 Tesla cars must excel in computer vision, identifying various objects like cars, pedestrians, and traffic signals.
📸 Tesla trains its models using deep learning to recognize specific features unique to cars.
⚙️ Real-time processing is a challenge for Tesla, requiring significant computing power.
🔧 Tesla's self-driving chips are 21 times more efficient than their previous Nvidia chips, optimizing performance.
🤖 Each Tesla vehicle uses dual AI chips for redundancy, ensuring decisions are verified before actions are taken.

Q & A

What recent feature did Tesla introduce in their software update?
-Tesla introduced a feature called Smart Summon, which allows users to summon their car to their location.
What is the main goal of Tesla regarding self-driving cars?
-Tesla's goal has evolved from creating cars that drive as well as humans to developing fully autonomous cars.
How does deep learning contribute to Tesla's self-driving technology?
-Deep learning helps Tesla process large amounts of unstructured data to identify patterns and features necessary for autonomous driving.
What challenges does Tesla face in real-time computer vision tasks?
-The main challenge is the immense amount of computing power required to perform computer vision tasks in real time.
What innovation did Tesla unveil to enhance its self-driving capabilities?
-Tesla unveiled its own AI chips that are 21 times more efficient than previous Nvidia chips used in their vehicles.
How does Tesla ensure redundancy in its AI chip system?
-Each Tesla vehicle is equipped with two AI chips, allowing for a backup in case one fails.
What process does the AI chip use to make decisions for the car?
-Each chip makes independent decisions, and the system compares both; if they agree, the car proceeds with that action.
What is the processing capability of Tesla's AI chips?
-Tesla's AI chips perform 36 trillion operations per second.
What advancements are still needed for fully autonomous cars?
-Further improvements in computing power and algorithm efficiency are necessary to achieve fully autonomous driving.
What impact do self-driving cars potentially have on society?
-Self-driving cars are expected to save time, money, resources, and even lives by reducing human error on the roads.