What's changing and what hasn't in AI
summary
Company history anchors the claims: automation started in 2012 and the platform now reports DataRobot (founded 2012) and $1B+ funding, 700+ data scientists, 1,000+ clients. Enduring prescriptions are concrete: pick use cases that change decisions, start small and scale capacity, and test for ML-specific risks like target leakage and proxy discrimination. Market shifts are specific: experimentation costs fall, algorithmic edge stops being a moat, and regulation moves from principles to laws such as Bill C-11 and EU AI Act, while trends emphasize no/low‑code, foundational models and governance tooling.
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chapters (5)
- 0:00Introduction and agenda
- 1:03Three enduring truths in AI
- 7:23What has changed in AI
- 11:37Future trends in AI and ML
- 17:19Key takeaways and closing
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