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Unlock the future of automotive AI with insights from Ashutosh and Guru Prashad of BOSS India, as they unravel the transformative potential of Small Language Models (SLMs) in vehicles. Discover how these compact powerhouses are reshaping the landscape of vehicular edge systems by offering customization and performance reliability, all while keeping data privacy at the forefront. Dive into the captivating interplay between SLMs and their larger counterparts, Large Language Models (LLMs), and learn how they together address domain-specific tasks and complex computations in the cloud. This episode promises to equip you with a deeper understanding of why SLMs are the secret sauce for advancing automotive AI in this data-sensitive era.

We also spotlight the remarkable optimization journey of the Tiny Llama 1.1 model. Learn about the fine-tuning process that brought about an astounding 700% improvement in throughput and a drastic reduction in model size. Uncover the fascinating world of edge deployment using devices like Pi 4B and Jetson Oren Nano, and explore the audio and chat functionalities that are setting new standards in vehicular AI. Finally, imagine the future of personalized interactions in cars, where generative AI transforms the way we communicate and engage with our vehicles and surroundings. This episode is a treasure trove of forward-thinking solutions and innovative ideas, perfect for anyone eager to explore the cutting edge of automotive AI.

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Learn more about the EDGE AI FOUNDATION - edgeaifoundation.org

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Discover how the EDGE AI FOUNDATION is evolving from its roots in the tinyML Foundation to becoming a central hub for innovation and collaboration. Learn how initiatives like EDGE AI Labs and the EDGE AIP program are bridging the gap between academia and industry, training future AI leaders while tackling the ethical challenges of responsible AI development.

Explore the transformative potential of generative AI on edge devices, from providing vital healthcare diagnostics in remote areas to enabling adaptive robotics in factories. We'll highlight compelling reasons for companies to engage with the Edge AI Foundation, offering unparalleled access to cutting-edge research, top talent, and a voice in shaping the industry's future. As we navigate through real-life scenarios and ethical considerations, you’ll see why the urgency and opportunity surrounding Edge AI is something you don't want to miss.

Join us on this journey to ensure the benefits of AI are shared widely and responsibly by visiting edgeaifoundation.org.

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Unlock the secrets to deploying machine learning models on edge devices with Chen Lai from the PyTorch Edge team at Meta. Discover how XTorch, a brainchild of the PyTorch team, is transforming edge deployment by addressing challenges like memory constraints and hardware diversity. Get an insider's view on the technical collaborations with tech giants like Apple, Arm, Qualcomm, and MediaTek, which are revolutionizing the deployment of advanced language models like LLAMA on platforms such as iOS and Android. With Chen's expert insights, explore the fascinating process of converting PyTorch models into executable programs optimized for performance, stability, and broad hardware compatibility, ensuring seamless integration from server to edge environments.

Immerse yourself in the world of XTorch within the Red Bull Ecosystem, where deploying machine learning models becomes effortless even without extensive hardware knowledge. Learn how key components like Torchexport and Torchio capture compute graphs and support quantization, elevating edge deployment capabilities. Discover how Torchchat facilitates large language model inference on various devices, ensuring compatibility with popular models from Hugging Face. As we wrap up, hear about the community impact of Meta's Executorch initiative, showcasing a commitment to innovation and collaboration. Chen shares his passion and dedication to advancing edge computing, leaving a lasting impression on listeners eager for the next wave of technological breakthroughs.

Learn more about the tinyML Foundation - tinyml.org