Wild Intelligence by Yael Rozencwajg

Wild Intelligence by Yael Rozencwajg

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Wild Intelligence by Yael Rozencwajg
Wild Intelligence by Yael Rozencwajg
๐Ÿ“ฎ Maildrop 04.03.25: LLMs, 2025 trends: Efficiency and scalability, part 1/3
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๐Ÿ“ฎ Maildrop 04.03.25: LLMs, 2025 trends: Efficiency and scalability, part 1/3

The things to know about AI and cyber threats | Tools for the next generation of enterprises in the AI era

Yael Rozencwajg's avatar
Yael Rozencwajg
Mar 04, 2025
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Wild Intelligence by Yael Rozencwajg
Wild Intelligence by Yael Rozencwajg
๐Ÿ“ฎ Maildrop 04.03.25: LLMs, 2025 trends: Efficiency and scalability, part 1/3
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๐Ÿ“ฎ Maildrop 05.03.25: LLMs, 2025 trends: Efficiency and scalability, part 1/3
๐Ÿ“ฎ Maildrop 04.03.25: LLMs, 2025 trends: Efficiency and scalability, part 1/3

LLM trends in 2025: A 3-part series for corporate leaders

A navigational guide for executives, decision leaders, and founders

As we navigate the rapidly evolving landscape of LLMs in 2025, it's crucial for corporate executives, decision leaders, and founders to stay ahead of the curve.

This three-part series explores the key trends shaping the future of LLMs, offering insights, real-world examples, and thought-provoking questions to guide strategic decision-making.

Why this series?

In today's dynamic business environment, staying informed about the latest advancements in AI is no longer a luxury but a necessity.

LLMs are transforming industries, automating tasks, and creating new opportunities for innovation.

This series aims to equip you with the knowledge and insights you need to effectively and responsibly use the power of LLMs.

"The future of AI lies in creating models that are not only powerful but also sustainable and accessible to all. Efficiency and scalability are key to unlocking the full potential of LLMs and ensuring that their benefits can be shared by everyone."โ€” Andrew Ng.

Greener, leaner, and more accessible: optimizing LLMs for the future

LLMs' increasing size and complexity have led to concerns about their environmental impact and accessibility.

Training and deploying these massive models require significant computational resources and energy consumption, raising questions about their sustainability and affordability.

However, recent advancements in model compression and optimization techniques pave the way for more efficient and scalable LLMs.

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