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Make gen AI work: The landscape, SLMs vs. LLMs, cost and more

VentureBeat/Ideogram
VentureBeat/Ideogram

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Are you fascinated by the ever-evolving world of machine learning (ML), AI and data analytics? Tech entrepreneur Bruno Aziza takes a ride through that “MAD landscape” in this week’s carcast.

In this week’s edition, Aziza discusses the fast growing ML, AI and data world with Matt Turck, a partner at FirstMark. Turck’s 2024 MAD landscape report documents 2,000 companies in infrastructure, analytics and applications. Ten years ago, there were just 139 companies in the landscape — so, in just 10 years, the space has grown by 14X.

The two delve into the mind-boggling growth of the MAD landscape, with Turck arguing that the focus is shifting from structured data to the vast, uncharted territory of unstructured data, which demands a whole new set of technological tools.

Aziza and Turck also dive deep into the world of generative AI and unpack the key differences between small language models (SLMs) and large language models (LLMs). Think of SLMs as the focused athletes excelling in specific tasks, while LLMs are the all-around champions with a broader range of capabilities. The future of enterprise AI is likely to be shaped by a powerful hybrid architecture that leverages the strengths of both.


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This carcast also tackles questions including:

  • Where are we in the AI hype cycle?
  • Is traditional AI dead?
  • Will 2024 be the year of AI in the enterprise?
  • How much does AI actually cost?
  • What explains the emergence of SLMs vs. LLMs?

Bruno Aziza is a technology entrepreneur.