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Language, Graphs, and AI in Industry // Paco Nathan // #201

Jan 5, 2024
Paco Nathan, Managing Partner at Derwen, Inc., talks about key findings from conferences, commonalities among teams with ROI on ML in production, leveraging existing resources and domain expertise in AI, the importance of software engineering and ops in AI, and the need to regulate AGI and implement universal basic income.
01:18:28

Episode guests

Podcast summary created with Snipd AI

Quick takeaways

  • Ensembling smaller specialized models can provide comparable results to large language models while reducing costs and enhancing data privacy.
  • Strong software engineering skills and effective workflow orchestration are essential for successful AI deployment, with ops often being the bottleneck.

Deep dives

Specialized models offer cost savings and better data privacy

In the podcast episode, Paco Dathan discusses the benefits of using smaller specialized models as opposed to large language models. He highlights the work of Wally Caduce and Christopher Win, who advocate for ensembling smaller models to reduce costs and enhance data privacy. They demonstrate that this approach can provide comparable results while reducing the resources required. This is particularly relevant for enterprise teams with limited resources who are looking for practical and cost-effective AI solutions.

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