Today we’re joined by Jilei Hou, a VP of Engineering at Qualcomm Technologies. In our conversation with Jilei, we focus on the emergence of generative AI, and how they've worked towards providing these models for use on edge devices. We explore how the distribution of models on devices can help amortize large models' costs while improving reliability and performance and the challenges of running machine learning workloads on devices, including model size and inference latency. Finally, Jilei we explore how these emerging technologies fit into the existing AI Model Efficiency Toolkit (AIMET) framework.
The complete show notes for this episode can be found at twimlai.com/go/633
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Inverse Reinforcement Learning Without RL with Gokul Swamy - #643
Explainable AI for Biology and Medicine with Su-In Lee - #642
Transformers On Large-Scale Graphs with Bayan Bruss - #641
The Enterprise LLM Landscape with Atul Deo - #640
BloombergGPT - an LLM for Finance with David Rosenberg - #639
Are LLMs Good at Causal Reasoning? with Robert Osazuwa Ness - #638
Privacy vs Fairness in Computer Vision with Alice Xiang - #637
Unifying Vision and Language Models with Mohit Bansal - #636
Data Augmentation and Optimized Architectures for Computer Vision with Fatih Porikli - #635
Mojo: A Supercharged Python for AI with Chris Lattner - #634
Modeling Human Behavior with Generative Agents with Joon Sung Park - #632
Towards Improved Transfer Learning with Hugo Larochelle - #631
Language Modeling With State Space Models with Dan Fu - #630
Building Maps and Spatial Awareness in Blind AI Agents with Dhruv Batra - #629
AI Agents and Data Integration with GPT and LLaMa with Jerry Liu - #628
Hyperparameter Optimization through Neural Network Partitioning with Christos Louizos - #627
Are LLMs Overhyped or Underappreciated? with Marti Hearst - #626
Are Large Language Models a Path to AGI? with Ben Goertzel - #625
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