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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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Learnt 12% of what AI A Guide For Humans offered

Take quotes from interviewees + same old AlphaZero amazements + assumptions that everything is fungible with code/maths + hype narratives about just needing to scale these into generality + Gladwellesque padding like 'It was a chill Chicago day and [insert AI luminary name] was about to discuss logic and neural networks' is the recipe for this book. I found it a waste of time and much recommend Melanie Mitchell's book AI A Guide For Thinking Humans instead. For addressing societal issues Virginia Eubank's Automating Inequality is also great albeit a winding narrative. Or even The Atlas of AI for a more braodswept 'material culture' account.

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history of ML

A nice multidisciplinary history of ML with philosophy and psychology as well as comp sci

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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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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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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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  • Ahmed ELGazzar
  • 04-11-21

One of the best outlook books on AI

It was a pleasure listening to the book. It's cosncie enough to suit experts in the field and also offer a broad general overview if you don't know much about the current state of AI. Particularly enjoyes the chapters on RL

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  • ehan ferguson
  • 16-11-20

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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  • Casey
  • 29-11-21

Must read/listen

The book dives deep into AI and machine learning. Excellent story, excellently written. In some ways it is humanity's most pressing topic.

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  • Mary
  • 10-09-21

About much more than just the alignment problem

I was pleasantly surprised how this book tied together the history of AI, psychology, and more with current developments in machine learning. I was also pleasantly surprised how well the book explains complex topics such as deep reinforcement learning. And the discussion of the alignment problem itself is first-rate.

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  • Amazon Customer
  • 28-08-21

Interesting overall

I liked the subject of the book. I also liked the ideas and stories shared in it.

However, the book ultimately came across as a summarization of research over the last 100 years related to machine learning and artificial intelligence -- instead of a what I expected to be an in depth description of the potential severity of the "alignment problem" in a modern or future context.

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  • Preston B. Miller
  • 25-08-21

I LOVED this book

great stories, explanations, linkages to examples. if you're interested in all the ways AI is changing things and how that's gone and what we're doing about it, listen to this.

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  • Mohammad Mobasher Hossain
  • 15-06-21

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
  • 13-06-21

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
  • 12-06-21

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
  • 03-04-21

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.