Unlocking Gen AI: Understanding the Landscape of SLMs vs. LLMs, Costs, and Key Insights

Home AI News Unlocking Gen AI: Understanding the Landscape of SLMs vs. LLMs, Costs, and Key Insights

Updated on October 27 2024

Are you intrigued by the ever-evolving fields of machine learning (ML), artificial intelligence (AI), and data analytics? This week’s carcast features tech entrepreneur Bruno Aziza exploring the rapidly growing “MAD landscape.”

In this episode, Aziza engages with Matt Turck, a partner at FirstMark, who presents his 2024 MAD landscape report. This comprehensive report highlights over 2,000 companies involved in infrastructure, analytics, and applications—an impressive increase from just 139 companies a decade ago, reflecting a 14-fold expansion in just ten years.

0:02/14:43 Are you ready for AI agents? Aziza and Turck delve into the astounding growth of the MAD landscape, discussing the shift from structured data to the expansive realm of unstructured data, which requires innovative technological solutions.

The conversation also covers generative AI, highlighting the crucial differences between small language models (SLMs) and large language models (LLMs). SLMs can be likened to specialized athletes excelling in specific tasks, while LLMs represent versatile champions with a wide array of capabilities. The future of enterprise AI appears poised for a transformative hybrid model that combines the strengths of both.

Key questions addressed in this carcast include:

- Where are we in the AI hype cycle?

- Is traditional AI obsolete?

- Will 2024 mark the rise of AI in enterprises?

- What are the costs associated with AI implementation?

- What factors contribute to the emergence of SLMs versus LLMs?

Bruno Aziza is a prominent figure in technology entrepreneurship, and his discussions aim to provide keen insights into the future of AI and data technology.

Join us at DataDecisionMakers, where data professionals share insights and innovations. Discover cutting-edge ideas, best practices, and the future of data technology, or consider contributing an article of your own!

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