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. Leave us a comment Changelog++ members save 3 minutes on this episode because they made the ads disappear. Join today! Sponsors: Changelog News – A podcast+newsletter combo that’s brief, entertaining & always on-point. Subscribe today. Fly.io – The home of Changelog.com — Deploy your apps and databases close to your users. In minutes you can run your Ruby, Go, Node, Deno, Python, or Elixir app (and databases!) all over the world. No ops required. Learn more at fly.io/changelog and check out the speedrun in their docs. Sentry – Launch week! New features and products all week long (so get comfy)! Tune in to Sentry’s YouTube and Discord daily at 9am PT to hear the latest scoop. Too busy? No problem - enter your email address to receive all the announcements (and win swag along the way). Use the code CHANGELOG when you sign up to get $100 OFF the team plan. Typesense – Lightning fast, globally distributed Search-as-a-Service that runs in memory. You literally can’t get any faster! Featuring: Chris Benson – Twitter, GitHub, LinkedIn, WebsiteDaniel Whitenack – Twitter, GitHub, Website Show Notes: YOLOv9: Yolov9: Learning What You Want to Learn Using Programmable Gradient Information Yolov9 Object Detection with Programmable Gradient Information (PGI) and Generalized Efficient Yolov9: A Comprehensive Guide and Custom Dataset Fine-Tuning YOLOv9 SOTA Machine Learning Object Detection Model YOLOv9 Unleashing the Power of YOLOv9 YOLOv9 with NNCF and OpenVINO ArXiv:2402.13616 Parameter efficient LLMs: