~/talk

Keynote: State of Open GenAI

linkedin:zemlin
slides:none on file

Argues that openness enables transparency, trust and attribution in generative AI, warns real risks of bias, hallucinations, privacy and security, and reports a survey where 41% vs 9% prefer open source over proprietary models. Names specific tools and standards being supported like PyTorch, MLflow, ONNX and a unified acceleration foundation, notes practical provenance work via C2PA digital watermarks embedded in cameras, and says EU drafts dropped proposed bans while France released a high‑performance open model.

AI-written summary · about

  1. 0:00Welcome
  2. 1:25Openness defines GenAI
  3. 3:19Openness fears vs reality
  4. 5:49Transparency trust attribution
  5. 8:20Global adoption demand
  6. 11:50Linux Foundation in open AI
  7. 12:54Call to action
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