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Modernizing bp’s application landscape with AI

CIO Business Intelligence

The scope of its efforts so far is demonstrated by its shift into lower-carbon businesses, power trading, and convenience stores, which represented just 3% of its investment in 2019 but 23% in 2023. This change in business focus is accompanied by an ongoing digital transformation.

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Generative AI in the Real World: Danielle Belgrave on Generative AI in Pharma and Medicine

O'Reilly on Data

We want to identify targets more quickly for experimentation by ranking probability of success. 15:30 : Weve had a responsible AI team since 2019. Weve built agents for genetic data or lab experimental data. Can we perturb the cells? Can we create embeddings that will give us representations of patient response?

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AI adoption in the enterprise 2020

O'Reilly on Data

The new survey, which ran for a few weeks in December 2019, generated an enthusiastic 1,388 responses. This year, about 15% of respondent organizations are not doing anything with AI, down ~20% from our 2019 survey. It seems as if the experimental AI projects of 2019 have borne fruit. But what kind?

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How To “Ultralearn” Data Science: deep understanding and experimentation, Part 4

KDnuggets

In this fourth and final part of the ultralearning data science series, it's time to take the final steps toward developing a deep understanding of the fundamentals and learning how to experiment -- the two aspects that are the ultimate keys to ultralearning.

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Driving Discovery and Experimentation in your Organization

Speaker: Teresa Torres, Product Discovery Coach, Product Talk, David Bland, Founder and CEO, Precoil, and Hope Gurion, Product Coach and Advisor, Fearless Product LLC

This is where continuous discovery and experimentation come in. Join Teresa Torres (Product Discovery Coach, Product Talk), David Bland (Founder, Precoil), and Hope Gurion (Product Coach and Advisor, Fearless Product) in a panel discussion as they cover how - and why - to build a culture of discovery and experimentation in your organization.

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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. Spring 2019 Full Stack Deep Learning Bootcamp (Berkeley).

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Interview with: Sankar Narayanan, Chief Practice Officer at Fractal Analytics

Corinium

It is also important to have a strong test and learn culture to encourage rapid experimentation. What are you most looking forward to about CDAOI Insurance 2019? A properly set framework will ensure quality, timeliness, scalability, consistency, and industrialization in measuring and driving the return on investment.