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The Data Exchange with Ben Lorica
Bridging the Hardware-Software Divide in AI
Aug 29, 2024
Jay Dawani, CEO of Lemurian Labs, dives into the challenges of bridging hardware and software in AI development. He discusses how model size influences performance and hurdles in achieving artificial general intelligence. The conversation highlights the critical need for seamless integration between training and inference, as well as the complexities of AI deployment. Dawani also explores the future of supercomputing in AI and the importance of optimizing data representation, showcasing innovative strategies to enhance computational capabilities.
48:27
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Quick takeaways
- Jay Dawani highlights the urgent need for a better software stack in AI, as the growing computational demands create a critical hardware-software divide.
- The concept of scaling loss indicates that increased model capacity and computational resources can lead to unexpected outcomes in AI advancements.
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Foundational Insights into AI's Evolution
The discussion highlights Jay Diwani's decade-long experience in AI, particularly with foundational models that began around 2018. His work with a 2 billion parameter network raised concerns about the future trajectory, particularly regarding the sheer computational power required for advancement in AI. The ambitious notion of needing 400,000 GPUs exemplifies the alarming gap between software and hardware, indicating a crucial disconnect in the AI ecosystem. Ultimately, this led to a shift in focus from hardware to software, emphasizing the need for better architectures and software stacks tailored to AI workloads.
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Discover
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