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Algorithms to Live By

Written by: Brian Christian, Tom Griffiths
Narrated by: Brian Christian
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Publisher's Summary

A fascinating exploration of how computer algorithms can be applied to our everyday lives, helping to solve common decision-making problems and illuminate the workings of the human mind

All our lives are constrained by limited space and time, limits that give rise to a particular set of problems. What should we do, or leave undone, in a day or a lifetime? How much messiness should we accept? What balance of new activities and familiar favorites is the most fulfilling? These may seem like uniquely human quandaries, but they are not: computers, too, face the same constraints, so computer scientists have been grappling with their version of such problems for decades. And the solutions they've found have much to teach us.

In a dazzlingly interdisciplinary work, acclaimed author Brian Christian (who holds degrees in computer science, philosophy, and poetry, and works at the intersection of all three) and Tom Griffiths (a UC Berkeley professor of cognitive science and psychology) show how the simple, precise algorithms used by computers can also untangle very human questions. They explain how to have better hunches and when to leave things to chance, how to deal with overwhelming choices and how best to connect with others. From finding a spouse to finding a parking spot, from organizing one's inbox to understanding the workings of human memory, Algorithms to Live By transforms the wisdom of computer science into strategies for human living.

©2016 Brian Christian and Tom Griffiths (P)2016 Brilliance Audio, all rights reserved.
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What listeners say about Algorithms to Live By

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I feel 10x smarter

The algorithms are applicable to so many daily decisions! It was an interesting twist framing everyday situations in a new light. I feel like I'm faster at deciding things now, ha!

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Interesting CS view of the world

Love it. In the beginning it was bit difficult to follow but it gets more interesting.

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Interesting and pragmatic

This is one of the best books I have read in long time.
The authors explain many beautiful computer science, math, statistics, and machine learning problems and their use cases in real life.
They conclude with suggestions about a more computationally humane society.
I genuinely hope policy makers, researchers, designers, and anyone whose decisions affect a lot of people read this book and take its advice to heart.

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Fantastic description of computer algorithms!

This is a great book that makes these concepts easily accessible to a layperson. Loved it, highly recommended to all audiences.

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Thoroughly Enjoyed

Authors draw parallels between computer science problems and real-world problems, and while not every CS problem can be directly translated/applied to the real world, many insights can be taken away. Authors explain basic computer vernacular as needed as well for the recently interested learner in the field of CS.
Appreciated the cute little pop cultural references that produced a few laughs along the way.

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Fascinating way to view the world

A really interesting book applying some of the solutions we have learned from mathematics and computers to decision making for human beings in real life. Not only is it interesting how the one can apply to the other, but just the process of talking about it gives different insights in how to look at these real life problems.

If you've ever wondered things like:
- is having an empty inbox worth the time and effort it takes to organize your email daily?
- is having a tidy desk/house worth more than having the things you often need close to hand?
- how can I decide if I've found the "best" (house/spouse/job/etc) without predicting the future?

All these puzzles and more are discussed in this book through the lens of what mathematics/computer science theory tells us is the "best" solution, along with why this may or may not also apply to real life, and what you can do about it. No detailed mathematics is necessary, this is more about the theory and even philosophy than any direct numbers or formulas.

Not only is the theory itself fascinating (how do programmers deliberately introduce "randomness" or "mistakes" into a system to avoid computers getting stuck in loops) but so is the applicability to real world problems and why what we think we SHOULD do (such as keep an immaculately clean home) may not actually make logical sense (and now when my desk is a mess I can prove it's the most efficient solution!)

If these are topics that sound interesting to you, then I highly recommend picking up this book.

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  • Overall
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Life 101

My first audiobook experience and I couldn't be happier. Great content and amazing narrative that makes anyone fall back in love with math, logic, philosophy, and neuroscience. As a Neurociencias myself, I find this book wonderful and I hope more people in my field would know about it. 10/10 would recommend. 10/10 will buy and it will occupy a very special in my library, to dive back into the amazing chapters and content useful for science and life.

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A gem of a book! Fascinating.

I loved this book. The authors make things really simple for everyone to understand; NO PRIOR COMPUTER SCIENCE KNOWLEDGE REQUIRED. There are tons of real life examples to make algorithms in the domain of computer science relatable for anyone. Also, this is a fun way to understand the inner workings of computers and the mathematics behinds them.
Some chapters are more demanding than others - I found the one on Bayes/Laplace Probability to be the most complex. All the chapters were AN ABSOLUTE JOY TO LISTEN, my favourites being Optimal Stoping, Randomness and Game theory,
Highly recommended.

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Very Interesting

Really interesting and informative
I like it and I highly recommend it
It’s a good read whether you just want to listen to something or make your life more efficient

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Fantastic

I absolutely loved this book. Brian Christians performance was wonderful. The content is thought provoking and stimulating.
I’ve now finished this book twice within a short while of purchasing it.

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