Sixteen real decisions behind Volume II, told from the published record.
Every chapter of Volume II carries an In Practice case: a real organization that took a decision of the chapter's kind, told from its published record. Each case sets out the decision, the method, the result and what it took to make it work, then what Meridian, the book's case company, takes from it and four questions for a board.
The cards give a preview. The full two-page case is in the chapter, after its Worked Example and before its AXIOM Lab.
About 30 percent cooling savings under direct control, by Google's account
Google had already cut its data centres' energy overhead sharply when DeepMind's neural networks, trained on sensor records, began recommending settings for a data centre's cooling plant; by Google's account, that plant used 40 percent less energy for cooling. Then Google handed the controls to the agent, inside limits set by its operators, behind a confidence filter, two layers of verification and a human override. Under direct control, Google reported, savings grew from 12 percent at launch to about 30 percent on average. How far should a learning agent be trusted to act on its own?
Company and organization names identify the organizations whose published decisions the cases discuss. They are trademarks of their respective owners; no affiliation with or endorsement by them is implied.
