Remove 2019 Remove Experimentation Remove Measurement Remove ROI
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Interview with: Sankar Narayanan, Chief Practice Officer at Fractal Analytics

Corinium

A properly set framework will ensure quality, timeliness, scalability, consistency, and industrialization in measuring and driving the return on investment. It is also important to have a strong test and learn culture to encourage rapid experimentation. What do you recommend to organizations to harness this but also show a solid ROI?

Insurance 250
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What you need to know about product management for AI

O'Reilly on Data

Because it’s so different from traditional software development, where the risks are more or less well-known and predictable, AI rewards people and companies that are willing to take intelligent risks, and that have (or can develop) an experimental culture. Measurement, tracking, and logging is less of a priority in enterprise software.

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Themes and Conferences per Pacoid, Episode 6

Domino Data Lab

Spoiler alert: a research field called curiosity-driven learning is emerging at the nexis of experimental cognitive psychology and industry use cases for machine learning, particularly in gaming AI. The ability to measure results (risk-reducing evidence). Ensure a culture that supports a steady process of learning and experimentation.

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Interview with Dominic Sartorio, Senior Vice President for Products & Development, Protegrity

Corinium

For example, P&C insurance strives to understand its customers and households better through data, to provide better customer service and anticipate insurance needs, as well as accurately measure risks. What do you recommend to organizations to harness this but also show a solid ROI?

Insurance 150
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Themes and Conferences per Pacoid, Episode 9

Domino Data Lab

Visualizations are vital in data science work, with the caveat that the information that they convey may be 4-5 layers of abstraction away from the actual business process being measured. measure the subjects’ ability to trust the models’ results. Information can get quite distorted after being abstracted that many times.