Revolutionizing Tesla's Revenue Stream with Mega AI Driving Technology

Tesla's AI driving capabilities are truly mind-blowing and have the potential to revolutionize the company's revenue stream and full self-driving capabilities in the future

Questions to inspire discussion

• What is Tesla's full self-driving software version 123.5?

  —Tesla's full self-driving software version 123.5 is discussed in the video, highlighting its capabilities and potential impact on the company's future revenue stream.

• What is the difference between training and inference in AI?

  —The video explains the difference between training and inference in AI, using the analogy of training and playing matches in sports to illustrate the concept.

• How does Tesla's AI handle navigation and driving decisions?

  —The video demonstrates Tesla's AI driving capabilities and discusses how the AI handles navigation, driving decisions, and potential challenges on the road.

• What is the significance of compute power in Tesla's AI models?

  —The importance of compute power in training and inference for neural network models is highlighted in the video, emphasizing its impact on Tesla's AI capabilities.

• What are the potential future applications of Tesla's AI technology?

  —The video discusses the potential future applications of Tesla's AI technology, including robo-taxis and distributed computing nodes, showcasing the versatility and potential of the AI technology.

Key Insights

• 🤯 "Wow this would be amazing okay holy crap okay that is mind-blowing that is absolutely mind-blowing that is just I'm kind of speechless."
• 🚦 The Tesla AI's ability to anticipate and react to the truck slowing down to the left is really interesting and impressive.
• 🚗 The Tesla's AI driving behavior is so human-like, it's almost eerie.
• 🤑 Tesla's potential to earn money through AI inference while the car is idle could be a game-changer for the company's revenue stream.
• 🚗 The AI's ability to adjust and make decisions in real-time, even in aggressive driving situations, is truly mind-blowing.
• 🗺️ The car's navigation issue recovery shows its capability to figure things out and adapt in real-time.
• 📚 The integration of a large language model with visual information could greatly improve Tesla's full self-driving capabilities in the future.
• 🤯 The mind-blowing capabilities of Tesla's AI that impressed the speaker.
• 🔋 The advantage of utilizing Tesla vehicles for AI purposes is the incredible numbers of vehicles out there, providing way more energy and compute than any server cluster.

#Tesla #FSD

XMentions: @Tesla @DrKnowItAll16 @HabitatsDigital 

Clips

• 00:00 🚗 Tesla's FSD 12.3.5 software focuses on inference and ignoring stop signs for better driving performance, with training for AI models requiring significant time and effort.
  • Dr. knowall discusses Tesla's full self-driving software version 123.5 and the concept of inference, while demonstrating a drive to the UPS Store.
  • Understanding physics from first principles is important, and the difference between training and inference is like the difference between training and playing matches in sports.
  • Tesla's AI needs to ignore stop signs and focus on the lines for better driving performance.
  • Training for AI models requires a significant amount of time and effort, with the actual inference process being much quicker and more efficient.
• 03:59 🚗 Tesla's AI reacted to a slowing truck and a green light, highlighting the importance of compute power in training neural network models, and the challenges of visibility on the road while driving.
  • The Tesla AI reacted to a slowing truck and a green light, with anticipation for the upcoming left turn.
  • The speaker discusses the importance of compute power in training and inference for neural network models, and highlights a cool moment when the car changed its mind while slowing down.
  • The speaker discusses the challenges of visibility on the road while driving and mentions a Dollar General store.
  • The Tesla car continued driving past its intended destination, which was unusual and unexpected.
  • The speaker discusses the experience of using Tesla's autopilot feature in a challenging driving situation.
• 08:19 🚗 Tesla's FSD 12.3.5 can navigate new road markings and drive safely in traffic, with training likened to copying Serena Williams' skills into a million other versions of her.
  • The speaker discusses the new features of Tesla's FSD 12.3.5, including its ability to navigate new road markings and drive safely in traffic.
  • Tesla's training for Full Self-Driving is like copying and pasting Serena Williams' skills into a million other versions of her, making the competition unhappy.
  • The highway has traffic lights and is not like an Interstate style.
• 11:13 🚗 Tesla plans to invest $10 billion in AI compute for its vehicles, which can perform inference compute and the speaker tests the FSD 12.3.5 software update.
  • The speaker discusses the difference between training and inference in AI, highlighting the potential for continuous competition and improvement without the need to train new individuals from scratch.
  • Elon Musk plans to spend $10 billion on AI compute, with a significant portion going towards hardware and software in Tesla vehicles.
  • Tesla's cars can perform inference as part of their compute budget, and the speaker observes the car's behavior in real-time while driving.
  • The car experienced a navigation issue, but it resolved itself, and the vehicle is currently doing inference compute, not training.
  • The speaker tests Tesla's Full Self-Driving (FSD) 12.3.5 software update and discusses its navigation and parking capabilities.
• 16:38 🚗 Tesla's FSD 12.3.5 drive showed impressive human-like behavior, but also made a questionable decision, while the AI chips could be used for other inference tasks and potentially earn money for the car owner.
  • Tesla's FSD 12.3.5 drive showed impressive human-like behavior at a yield sign and a protected left turn, but it made a questionable decision to follow a slow Prius in the right lane.
  • The full self-driving feature in Tesla cars requires a lot of computing power to make driving decisions, including predicting the behavior of other vehicles on the road.
  • The speaker discusses a navigation error caused by Google Maps that has not been updated for at least 6 months, leading to a potentially interesting drive on a different route.
  • Tesla's chip can be used for other inference tasks like language model inference, and the car is able to anticipate and react to other vehicles on the road.
  • Tesla's AI chips could be used to run neural networks for low bandwidth tasks, potentially earning money for the car owner through inference and robo taxi services.
• 20:53 🚗 Tesla's AI shows human-like decision-making abilities, handling navigation issues and error conditions in a natural way, but still has issues with multiple parking lot entrances.
  • The speaker discusses the incorrect navigation information and the car's attempt to make a U-turn instead of a left turn, highlighting the issues with version 12 of the software.
  • Tesla's AI made a left-hand turn without following the navigation, showing human-like decision-making abilities.
  • The car is able to recover from navigation issues and handle error conditions in a natural human way, which is mind-blowing to witness.
  • Neural networks are a big deal for Tesla as they can handle complex situations that traditional hard code cannot, making it almost like Voodoo.
  • Tesla's neural network can handle bad navigation information and make decisions like a human, which is mind-blowing and impressive.
  • Tesla's full self-driving still has issues with navigation and can get confused when encountering multiple parking lot entrances, showing that it is not yet perfect.
• 27:50 🚗 Tesla integrating a large language model to improve full self-driving capability, with potential for distributed computing nodes in cars to enhance AI inference.
  • Improving Tesla's full self-driving capability by integrating a large language model to enable the car to read and understand visual information like road signs and signage.
  • Tesla's addition of a large language model will improve its ability to understand and navigate new places, and the current capabilities of its AI are impressive.
  • The Tesla AI wiggled a bit while making decisions, but ultimately came to the right conclusion, showing potential for future inference capabilities.
  • Distributed compute in Tesla vehicles may not be as efficient as server clusters, but the sheer number of vehicles provides more energy and compute power, making it fault tolerant and able to handle AI purposes in the future.
  • Cars can be used as distributed computing nodes to improve the speed and robustness of AI inference for Tesla's Full Self-Driving system.
• 31:48 🚗 Tesla's inference technology can be used for robo-taxis, Bitcoin's energy is valuable, and FSD 12.3.5 is impressive, with excitement for the future of full self-driving.
  • Tesla's inference technology can be used for robo-taxis, allowing users to summon the car like Uber and control music and temperature through the app.
  • Utilize the compute power in your car for various opportunities, and Bitcoin's energy is essentially its value as it prevents manipulation of the blockchain.
  • The speaker will discuss Bitcoin and the lightning Network with Andrew Perkins and another person in the Bitcoin space.
  • Electricity generated from solar panels will become valuable as it can be used efficiently for compute, making it the new form of money.
  • Impressed with the feel of FSD 12.3.5, excited for the Cybertruck event in Detroit, and looking forward to the future of full self-driving.

-------------------------------------

Duration: 0:36:16

Publication Date: 2024-04-30T23:33:06Z