Video as a Universal Interface for AI Reasoning with Sherry Yang - #676
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Integrating Data-Driven Learning and Physics Prior Knowledge
The simulation of external inputs into systems often requires a combination of data-driven learning approaches and physics-based simulation. While visual information is crucial in scientific domains, there is a gap between simulation and reality due to the limitations of human knowledge. By integrating data sets with diverse information and leveraging both graphics and machine learning expertise, it becomes possible to bridge the sim-to-real gap effectively. Graphics experts excel in precise simulation control, while machine learning practitioners have access to vast unlabelled data. Collaborating and combining these approaches promises a more comprehensive solution for simulating the world.
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