This chapter delves into the challenges of selecting content for the podcast amidst rapid technological advancements, focusing on parameter efficient models like Qualcomm AI models and YOLO for on-device computing. It explores the evolving trends in software systems and their impact on AI model development, emphasizing the importance of understanding use cases for selecting appropriate model types. Additionally, the chapter discusses the integration of MLOps with software architecture and the usage of APIs for AI services, highlighting the significance of adapting to the changing landscape of AI development.
While everyone is super hyped about generative AI, computer vision researchers have been working in the background on significant advancements in deep learning architectures. YOLOv9 was just released with some noteworthy advancements relevant to parameter efficient models. In this episode, Chris and Daniel dig into the details and also discuss advancements in parameter efficient LLMs, such as Microsofts 1-Bit LLMs and Qualcomm’s new AI Hub.
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YOLOv9:
Parameter efficient LLMs:
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