Chen predicts that the growth trajectory of robotic foundation models is accelerating rapidly, with real-world applications already being implemented. He suggests that by 2024, there will be an exponential number of commercially viable robotic applications deployed at scale. However, he also notes the challenges, such as the need for AI to adapt to different hardware applications and the complexity of building a large, high-quality dataset for training AI in robotics.
Key takeaways:
- The next significant advancement in AI is expected to be in robotics, with AI-powered robots learning to interact with the physical world, enhancing efficiency in various sectors.
- Building the "GPT for robotics" involves a foundation model approach, training on a large, proprietary, and high-quality dataset, and the use of reinforcement learning.
- While similar to GPT, achieving human-level autonomy in the physical world presents unique challenges, including adapting to different hardware applications and managing complex physical requirements in various real-world settings.
- The growth trajectory of robotic foundation models is accelerating rapidly, with an expectation of an exponential number of commercially viable robotic applications deployed at scale in 2024.