#072 - Chris Gully and the rise of Small Language Models

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18d ago 48:26

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Summary
Chris Gully discusses his current role in the new Broadcom organization and highlights of his career. He emphasizes the importance of staying relevant in the technology industry and the value of working with cool and smart people. The conversation then shifts to the topic of small language models (SLMs) and their role in the landscape of gen AI applications. Gully explains that SLMs offer a more progressive approach to working with large language models (LLMs) and enable more efficient and scalable deployments. The discussion also touches on the components of gen AI applications, the need for right-sizing models, and the challenges of scalability and efficiency. Gully highlights the importance of data and its role in driving business outcomes through AI. The conversation concludes with a discussion on the benefits and limitations of fine-tuning LLMs and the potential future of SLMs. The conversation explores the concept of SLMs (Small Language Models) and their role in AI development. It discusses the advantages of SLMs over LLMs (Large Language Models) regarding efficiency, optimization, and governance. The conversation also touches on the challenges of infrastructure management and resource allocation in AI deployments. It highlights the importance of right-sizing workloads, distributing workloads across data centers, and maximizing resource utilization. The conversation concludes with a discussion on the future trends in machine learning and AI, including advancements in math and the need for accessible and efficient technology.

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Takeaways
Staying relevant in the technology industry is crucial for career success.

Small language models (SLMs) offer a more efficient and scalable approach to working with large language models (LLMs).
Data is the most important and untapped asset for organizations, and leveraging it through AI can drive business outcomes.
Scalability and efficiency are key challenges in deploying gen AI applications.
Fine-tuning LLMs can enhance their precision and reduce the need for extensive training.
The future of SLMs may involve dynamic training and efficient distribution to support evolving business needs. SLMs offer advantages in terms of efficiency, optimization, and governance compared to LLMs.
Infrastructure management and resource allocation are crucial in AI deployments.
Right-sizing workloads and maximizing resource utilization are key considerations.
Future trends in machine learning and AI include advancements in math and the need for accessible and efficient technology.

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Disclaimer: The thoughts and opinions shared in this podcast are our own/guest(s), and not necessarily those of Broadcom or VMware by Broadcom.

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