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The Alignment Problem: How Can Machines…
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The Alignment Problem: How Can Machines Learn Human Values? (original 2020; edition 2021)

by Brian Christian (Author)

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652340,892 (4.45)1
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.… (more)
Member:fwilhelm
Title:The Alignment Problem: How Can Machines Learn Human Values?
Authors:Brian Christian (Author)
Info:Atlantic Books (2021), Edition: Main
Collections:Your library
Rating:****
Tags:artificial intelligence, ai

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The Alignment Problem: Machine Learning and Human Values by Brian Christian (2020)

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An impressive, conversation-based analysis of how AI systems developed through processes of machine learning (ML) might be constrained to be both safe and ethical. I had little idea of how rich and massive the research on this has been. In nine chapters with carefully chosen one-word headings (Representation, Fairness, Transparency, Reinforcement, Shaping, Curiosity, Imitation, Inference, and Uncertainty), the author describes a sequence of diverse and increasingly sophisticated ML concepts, culminating in what is called Cooperative Inverse Reinforcement Learning (CIRL). Whether AI will ever stop being part of what I regard as the wrongness of modern technology, I don't know, but at least there are people in the field who have their hearts in the right place.
  fpagan | Mar 21, 2022 |
There is a great book trapped inside this good book, waiting for a skillful editor to carve it out. The author did vast research in multiple domains and it seems like he could neither build a cohesive narration that could connect all of it nor leave anything out.

This book is probably the best intro to machine learning space for a non-engineer I've read. It presents its history, challenges, what can be done, and what can't be done (yet). It's both accessible and substantive, presenting complex ideas in a digestible form without dumbing them down. If you want to spark the ML interest in anyone who hasn't been paying attention to this field, give them this book. It provides a wide background connecting ML to neuroscience, cognitive science, psychology, ethics, and behavioral economics that will blow their mind.

It's also very detailed, screaming at the reader "I did the research, I went where no one else dared to go!". It will not only present you with an intriguing ML concept but also: trace its roots to XIX century farming problem or biology breakthrough, present all the scientist contributing to this research, explain how they met and got along, cite author's interviews with some of them, and present their life after they published their masterpiece, including completely unrelated information about their substance abuse and dark circumstances of their premature death. It's written quite well, so there might be an audience who enjoys this, but sadly I'm not a part of it.

If this book was structured to touch directly the subject of the alignment problem it would be at least 3 times shorter. It doesn't mean that 2/3 are bad - most of it is informative, some of it is entertaining, a lot seems like ML things that the author found interesting and just added to the book without any specific connection to its premise. I really liked the first few chapters where machine learning algorithms are presented as the first viable benchmark to the human thinking process and mental models that we build. Spoiler alert: it very clearly shows our flaws, biases, and lies that we tell ourselves (that are further embedded in ML models that we create and technology that uses them).

Overall, I enjoyed most of this book. I just feel a bit cheated by its title and premise, which advertise a different kind of book. This is the Machine Learning omnibus, presenting the most interesting scientific concepts of this field and the scientists behind them. If this is what you expect and need, you won't be disappointed! ( )
  sperzdechly | Mar 18, 2021 |
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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.

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