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5 tips for building foundation models for AI

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Many businesses are just beginning to grapple with the impact of artificial intelligence, but some have been using machine learning (ML) and other emerging technologies for over a decade.

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For Manish Jethwa, CTO at Ordnance Survey (OS), the UK's national mapping service, the priority is to combine his organization's AI and ML experiences with recent advances in generative AI to refine, distribute, and apply its treasure troves of data.

Jethwa explained to ZDNET how language models (LLMs) are helping OS users find and query geospatial data. One of the key elements here is the organization's foundation models for AI, which serve as a base for building more specialized applications.

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While tech analysts like Gartner suggest there is much conjecture about whether business leaders should buy or build AI models, Jethwa and his team at OS combine foundation models with commercially available tools to exploit and distribute geospatial data.

Here are five key lessons that business leaders can learn from Jethwa's deployment of foundation models for AI.

1. Develop a strong use case

Jethwa said OS is developing foundation models to extract environmental features for analysis in a copyright-sensitive manner.

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