The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence) cover image

The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

AI at the Edge: Qualcomm AI Research at NeurIPS 2024 with Arash Behboodi - #711

Dec 3, 2024
Arash Behboodi, Director of Engineering at Qualcomm AI Research, discusses what's on the agenda for this year's NeurIPS conference. He highlights the challenges of differentiable simulation, particularly in wireless systems, and dives into how uncertainty quantification can enhance machine learning models through conformal prediction and entropy. Behboodi also previews innovative demos like on-device video editing and 3D content generation, showcasing Qualcomm's commitment to making cutting-edge AI accessible.
54:47

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Quick takeaways

  • Differentiable simulation techniques can optimize the design of wireless systems by bridging statistical models with physical simulations.
  • Recent advancements in low-rank adaptation enable effective generative AI deployment on edge devices, enhancing on-device personalization without high computational costs.

Deep dives

Advancements in AI Simulators

The podcast discusses the complexities of forward and inverse problems in wireless system design, particularly the use of simulators. Forward problems involve using a model to project real-world behavior, while inverse problems require extracting a model from existing data. For example, optimizing antenna placement in a home for optimal coverage exemplifies an inverse problem, requiring iterative testing through various simulators. The discussion highlights the importance of differentiable models, which can streamline these processes and potentially close the gap between statistical and physical simulators.

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