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The Alignment Problem: Machine Learning and Human Values by

• The Alignment Problem: Machine Learning and Human Values
• Page: 496
• Format: pdf, ePub, mobi, fb2
• ISBN: 9780393868333
• Publisher: Norton, W. W. & Company, Inc.

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A jaw-dropping exploration of everything that goes wrong when we build AI systems and the movement to fix them. Today’s “machine-learning” systems, trained by data, are so effective that we’ve invited them to see and hear for us—and to make decisions on our behalf. But alarm bells are ringing. Recent years have seen an eruption of concern as the field of machine learning advances. When the systems we attempt to teach will not, in the end, do what we want or what we expect, ethical and potentially existential risks emerge. Researchers call this the alignment problem. Systems cull résumés until, years later, we discover that they have inherent gender biases. Algorithms decide bail and parole—and appear to assess Black and White defendants differently. We can no longer assume that our mortgage application, or even our medical tests, will be seen by human eyes. And as autonomous vehicles share our streets, we are increasingly putting our lives in their hands. The mathematical and computational models driving these changes range in complexity from something that can fit on a spreadsheet to a complex system that might credibly be called “artificial intelligence.” They are steadily replacing both human judgment and explicitly programmed software. In best-selling author Brian Christian’s riveting account, we meet the alignment problem’s “first-responders,” and learn their ambitious plan to solve it before our hands are completely off the wheel. In a masterful blend of history and on-the ground reporting, Christian traces the explosive growth in the field of machine learning and surveys its current, sprawling frontier. Readers encounter a discipline finding its legs amid exhilarating and sometimes terrifying progress. Whether they—and we—succeed or fail in solving the alignment problem will be a defining human story. The Alignment Problem offers an unflinching reckoning with humanity’s biases and blind spots, our own unstated assumptions and often contradictory goals. A dazzlingly interdisciplinary work, it takes a hard look not only at our technology but at our culture—and finds a story by turns harrowing and hopeful.

The Alignment Problem: Machine - Politics and Prose
The Alignment Problem: Machine Learning and Human Values (Hardcover). By Brian Christian. $28.95. Backordered.
The Alignment Problem - Machine Learning and Human Values
The Alignment Problem offers an unflinching reckoning with humanity's biases and blind spots, our own unstated assumptions and often contradictory goals. A Dimensions: 16.51 x 4.32 x 24.38 cmPrint length: 356 pages Rating: 4.6 · ‎105 reviews
The Alignment Problem: Machine - Pass Christian Books
A jaw-dropping exploration of everything that goes wrong when we build AI systems and the movement to fix them. Today's “machine-learning” 
The Alignment Problem (Machine Learning and Human Values)
This book title, The Alignment Problem (Machine Learning and Human Values), ISBN: 9780393635829, by Brian Christian, published by W. W. Norton & Company 
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The Alignment Problem: How Can Machines Learn Human Values? - Kindle edition by Christian, Brian. Download it once and read it on your Kindle device, PC, 
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Home » The Alignment Problem: Machine Learning and Human Values (Paperback) Today's “machine-learning” systems, trained by data, 
The Alignment Problem: Machine Learning and Human Values
AI and Machine Learning made huge advances in the last decade and billions of dollars flowed into R&D. This book is about the collision of new AI technologies 
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The Alignment Problem : Machine Learning and Human
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The Alignment Problem: Machine Learning and Human Values
The Alignment Problem: Machine Learning and Human Values is written by Brian Christian and published by W. W. Norton & Company. The Digital and eTextbook 
The Alignment Problem: Machine Learning and Human Values
A jaw-dropping exploration of everything that goes wrong when we build AI systems and the movement to fix them. Today's “machine-learning” 

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