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Publisher's Summary

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. Listeners 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. 

©2020 Brian Christian (P)2020 Brilliance Publishing, Inc., all rights reserved.

What listeners say about The Alignment Problem

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  • ehan ferguson
  • 16-11-2020

Required reading for any AI course

Brian Christian’s holistic approach is approachable to wide audience both technical and otherwise. I read this book while also taking an AI course in college. This book alone easily surpassed what I learned in that course. That’s not even mentioning the methodological and philosophical knowledge picked up from this read. Brian Christian is easily the Malcom Gladwell of computational philosophy.

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  • Mohammad Mobasher Hossain
  • 15-06-2021

I am a huge fan of Brian Christian.

I am a huge fan of Brian Christian. So all my reviews will be biased. :)

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  • Anonymous User
  • 13-06-2021

Great book even for non-computer scientists

I learned a lot about the state of AI and AI safety. Brian Christian is a great author!

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  • Amazon Customer
  • 12-06-2021

Required reading

This should be required reading for developers and researchers working in this field. To hear the numerous stories of where ML has gone wrong is sobering. Also, to learn of the efforts done to remove bias and mistakes is encouraging.

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  • TS
  • 03-04-2021

Brilliant exposition of a complex issue

This is a defining text on the alignment problem. It explores a wide spectrum of issues that we will all confront together as artificial intelligence and machine learning become more ingrained in our daily lives. The book is technically rather complex (at least to my layperson's ind), but it describes the issues with clarity.

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  • B. Herbert
  • 06-03-2021

explores relationships between past and present innovations, ideas, and hard lessons

Impressive combination of technical, ethical, and psychological perspectives with ML/AI. i have a degree in Psychology but my career has been in telecommunications, so i like to see the current synergy between these fields, both passions of mine!

Thought-provoking examples from a wide range of applied research throughout this book. The author had insightful conversations with leading people in industries from criminal justice to computer vision to self-driving vehicles, and seems to nail the crux of the issue with each subject he covers.

the coverage of shaping behavior (whether human or machine) via rewards and incentives, spontaneity and exploration, and/or values and objectives captures the complexity, challenges and risks in endowing a machine with sometimes ambiguous or contradictory motivations. the assumed expertise and certainty of decisions for human actors when training a machine has led to some of the greatest failures, as author describes. He Raises the question, ‘do we expect these models to be perfect, do we need them to be perfect, can we even define perfection?!

His coverage of Bayesian probability models could have gone deeper imo- throughout the scientific world results are distorted by neglecting chance, probability and confidence intervals, its huge not just for AI. The author describes a machine learning model which creates and pools multiple training sets, randomly turning off features in each, and applying randomized Bayesian weights. i could see how such a system might provide an effective way to catch unwanted bias or saliency effects such as with labeled image data. i may not have described that very well- next, im actually going to research the model that he explained as im very interested in probabilistic models.
great book, i highly recommend!

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  • Matthew Rimol
  • 15-02-2021

Interesting, understandable, important

Interesting subject matter throughout and very well written for any non technical person to understand but simultaneously interesting for a technical person if you are one. Great narration by the author as well.

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  • Hila
  • 11-12-2020

A must read to every data scientist👩🏼‍💻

This book is most definitely the best creation by Brian Christian. I’m speechless, and listened to the whole book in 6 days. This is a brilliant niche in AI, up to date, and fascinating.
I cannot emphasize in words how much I enjoyed it (perhaps in a word2vec it’s the infinity vector?) And I can’t wait for Brian’s next audiobook.

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  • Jessica Lindquist
  • 28-11-2020

Relevant and Engaging

This book provides background on critical issues facing our society. It is great to read a book that focuses on educating the audience, sharing ethical concerns and raising thought provoking yet unsolved challenges. Brian is a talented author that has yet again engaged me to learn more about the interaction between humans and machines!

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  • Philip Van Stockum
  • 15-11-2020

Best overview of the topic I’ve read

This is an excellent overview of the field of machine learning - its history, the problems it faces as it is applied in the real world, and the potential routes for its (and our) future. I’ve read many layperson books on the topic in the past, and I learned a ton from The Alignment Problem. I now have a more complete understanding of the structure of the field and the technical framework it’s built on, as well as the what the technology can and so far cannot do for us. Equally of interest are the questions about human society that the book raises. Can we succeed at aligning AI’s goals with our own if we don’t really understand what our own goals are? Christian describes the current ideas for how this may be possible, as well as the ways in which AI has so far been highlighting moral questions that we didn’t even realize we were confused about.

The amount of research that went into this book is astounding - Christian draws from interviews with the top figures in every aspect of the field. It’s written clearly and succinctly, in a way that should be accessible to any reader, regardless of their prior knowledge of the topic. It’s also narrated by the author in a clear and incisive voice. An overall fantastic experience.

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  • David Mears
  • 08-06-2021

5 stars because I want to relisten

some earlier chapters were skippable. when I relisten ill see which chapters I marked for relistening

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  • Anonymous User
  • 13-04-2021

Absolutely Fantastic!

I've learned a lot about machine learning from this book. The level of detail is excellent whilst remaining accessible and engaging throughout. Well written and well read. Definitely the most interesting book I've listened to in a long time.

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  • C Vernon
  • 17-10-2020

Don't think machine learning's important? Read on.

Great overview of the current machine learning landscape and importantly how we got here. Useful sections on uncertainty and safety with strong concluding remarks on models in general. The map is not the territory.

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