- Where did we receive our wisdom on which models to build?
- How does this get data scientists “trapped” in local optima?
- Why do we avoid time dimensions in our search for value?
- What principles will lead us to better choices in what to build? (Hint: we must first recognise the true competition for AI)
Shaun McGirr – AI Evangelist | DataikuShaun McGirr is a data leader with experience across official statistics, academia, consulting, and data science in a large automotive services company. He recently achieved minor stardom in a documentary Data Science Pioneers, coining the phrase “things that happen 35% of the time, happen ALL the time” to explain why quite likely outcomes are often dismissed out of hand. Shaun believes the toughest part of doing data well is finding the right questions and ensuring the answers will actually push a lever to change the world, a theme developed further in his podcast Half Stack Data Science. At Dataiku, he helps customers and colleagues identify and articulate the value of putting data science in the hands of everyone.
October 14 @ 14:00
14:00 — 14:20 (20′)
Day 1 | M1 | Applied Innovation & AI Transformation Stage
Shaun McGirr – AI Evangelist | Dataiku