I enjoyed the book. The author brings a fresh perspective to computer algorithms, how they apply to real-life situations, and how they can be used to solve everyday challenges.
If you are not well-versed in programming topics it might be a bit hard to understand some points that the author makes but I think it would still be a nice read. ( )

An interesting idea for a book that almost works, to identify knowledge from the domain of computer science that can translate to everyday life.

There are at least a few ideas that are not so well known and provide interesting insight. But there are as many that are relatively well known and not as practical to apply. ( )

NF

Excellent book, provides a lot of insight into various interesting problems. Must go back for a second reading.... ( )

An interesting book about thinking about things from a different perspective and being more optimal. Some sections felt very bloated. 3.25/5 ( )

This was a fun read, my aim with this book was to gain an overview of algorithms being actually applied in the real world, and it absolutely delivered on this!

I also had an impression that it would predominantly be geared towards self-help (with an algorithmic computer science twist) but thankfully, it wasn't so. Rather, it was sprinkled in along the end of every chapter and for the most part, motivating me to continue reading further (again, algorithms being actually applied - yippee!). Most of the contents within every chapter focuses on introducing the specific field, explaining the actual algorithms, and providing insights of their applications in the real world. The authors seem to be quite well read too, I enjoyed them citing different figures and fields.

At times trying to understand the actual algorithms in their abstract nature was a bit frustrating. In fact, I gave up during my first read in the midst of trying to understand the the first chapter. However, what helped me was rereading those excerpts a few times and then it usually clicks. Overall, definitely recommended!



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This book is very much in my wheel house--computer science and interdisciplinary analysis, and I really enjoyed it. I think it makes for a lot of interesting conversation. This may become 5 stars if it sticks with me.

I really liked the ideas about how our seemingly irrational behaviors are often due the fact that the problems are intractable and thus are rational, how the fact that it takes longer to remember things might not be degeneration, but a natural consequence of searching massive amounts of data, that piles are not a disgraceful form of organization, overfitting (I am personally angered by how this happens in our education system), computational kindness, and how exponential backoff "offers a way to have finite patience and infinite mercy."

My one critique was the section about pecking orders and dominance hierarchies, while yes there are observations in the natural world, and that can be related to sorting, making arguments that hierarchies are "scientifically" better or more peaceful is strongly akin to past scientific rationalization that have been used for centuries to justify racism, sexism, etc. It is better to just not go down that road. ( )

One of those books which take concepts from some field and show how they are (or could be) used in daily life (just like [b:Think Like a Rocket Scientist: Simple Strategies You Can Use to Make Giant Leaps in Work and Life|51720411|Think Like a Rocket Scientist Simple Strategies You Can Use to Make Giant Leaps in Work and Life|Ozan Varol|https://i.gr-assets.com/images/S/compressed.photo.goodreads.com/books/1570447685l/51720411._SX50_SY75_.jpg|72907223]).

The narrative is great and entertaining. However, working with computers most of my life, I didn't find it very interesting as the book would try to explain very well known ideas. It can be very different for someone without an IT background though.

What I liked about the book is that I realised that I'm using some of the concepts from computer science as mental models in my daily life and it is such a common sense I didn't really think about it before. ( )

"This is a sunk cost..." ???
Sort vs search trade-off
Explore vs exploit
Protect your priors: turn off the news
Idolatry of data
ACK - acknowledgement of receipt. In speech too! ( )

Started a bit slowly for me and had to return and recheck as well, but wow, what a book! So many useful nuggets and frameworks:
Stack of papers on my desk is great organization! LRU model places most recently used (and most likely next needed) item on top.
Prediction depends on priors. Language distorts priors, especially when magnified by Internet/press. Carefully evaluate priors. (Plane crash deaths since 2000 is small number. Car crash deaths greater than population of Wyoming.) ( )

This is more of an introduction to problems in computer science, than "ten algorithms that will change your life".

I think the thesis of the book is that "there are analog equivalent of computational algorithms and architecture, and they can inform life decision-making". And it opens strongly to support this claim. The first chapter is on optimal stopping, for which the rule of 37% is indeed a powerful solution. It gives its user 37% chance of selecting the best candidate in real-life scenarios such as interviewing candidate and house hunting, where opportunities present itself one-by-one, and there is no chance to backtrack to an earlier rejected one.

However, as one reads on, the analog equivalent of algorithms get less and less well-defined, and the rewards seem less and less concrete and exciting. Partly it is because sometimes our intuitive response to problem is already optimized. For example, the big pile of documents on your desk actually represents a decent implementation of caching (using LRU, or least-recently-used strategy). On the other hand, the book also introduces a whole class of problems for which there is no optimal strategy, or in computational terminology, untraceable. The book becomes meta in this aspect - it goes from "how to solve problem" to "how to attempt a problem if it is unsolvable." For that several strategies were offered, such as sampling, constraint relaxation and simplification. Sometimes knowing when to stop optimization is the most wise thing one can do. It is not the author's fault that the book becomes weaker as it gets to this territory, the problems are simply too specialized to be discussed meaningfully in a popular science book.

The lesson is it is always worthwhile to figure out how hard the problem is before attempting a solution:

- First, try to figure out whether an optimal strategy already exists. If yes, what is the big O notation of this solution? Does it scale well?
- If there is no, try to solve a variation of it. Can the problem constraint be reduced or baked into scoring system? Can the rules change? Is there a three-way trade-off between space, time and accuracy?
- Walk away if marginal return for further optimization is low.

Also some rules to live by:

- Constantly provide feedback in communication so people know their last message went through
- Be clear and honest, because guessing another person's mind is computationally untraceable
- Prefer simplicity because extra parameters make models brittle
- The goal optimization is *less* computation

Overall a fruitful read and I would recommend to anyone with an interest in computer science and mathematics.

Bonus: There are quite a lot of intellectual heroes in this book. I think I will check out Von Neumann (mentioned in multiple chapters; this guy is a genius) and William Vickery (who is also the hero of [b:Radical Markets|36515770|Radical Markets Uprooting Capitalism and Democracy for a Just Society|Eric A. Posner|https://i.gr-assets.com/images/S/compressed.photo.goodreads.com/books/1519379027l/36515770._SY75_.jpg|58236821]) ( )

دليل عملي يبيّن كيف لعلم الإدراك الحاسوبي والخوارزميات الرياضية مساعدتنا في إنجاز المهام واتخاذ القرارات في حياتنا اليومية.
نظرية الألعاب والتوزع الاحتمالي الطبيعي وما بينها من مفاهيم رياضية مثيرة للاهتمام جعلت قراءة الكتاب رياضة ذهنية ممتعة وخفيفة لمحبي الإحصاء والاحتمال الرياضي. ( )

Very good book. I'm a bit skeptical on the applicability of some of the things mentioned here to our lives, but I would still recommend this book. ( )

Decently curated anecdotes and real-world examples of how algorithmic thinking / approaches can optimize decision-making. The historical background on the origin of certain problems or concepts was presented well. It was interesting to see the intersection between computer science and the behavioral science work I do. Would perhaps have been better as a slower chapter-a-day read rather than something to burn through in two. ( )

I knew about most of these algorithms and theorems already but the book goes beyond that and shows how they apply outside abstract problems. Thought stimulating and well written to boot, in a friendly and informal manner but without cramming in forced humour (my pet peeve in pop-science books). ( )

Excellent book, provides a lot of insight into various interesting problems. Must go back for a second reading.... ( )

Very cool book. ( )

A decent overview of some important algorithms and CS concepts. Great as an introduction to topics you might not know so much about but chapters on topics you know will seem a little underwhelming. ( )

Regardless of what Feynman said (it wouldn't matter if even Gödel said it), Computer Science is not engineering. I add some caveats to that, Computer Science is not so easily categorized, so by Computer Science I refer to the formal science aspect. Now, referring to "Nature", two things, some results of Computer Science if they were different we would expect the universe to be fundamentally different. A predictable argument from me, but result still to come is P vs NP - if we have P = NP then I think we would all agree that the universe is not quite as we thought it was. Computer Science does say something about Nature.

Some of Computer Science that is. This is where the murkiness comes in because it also investigates many other things. Some Computer Scientists work on logic, and as yet whether logic is normative or descriptive is one of those big questions that is currently left to philosophy. What about Programming Language design? Well, formal work in this area does involve proving theorems, but about human artefacts. We can further cloud the picture with inter-disciplinary work. Computer Science used to discover how fish coordinate their behavior, for example.

But, removing the mud, we have left theoretical Computer Science and that is really the easiest thing to defend as Science. By which I mean, decidability, complexity, etc. I have already pointed out this says something about the universe, I think the main attack would be "how can saying something about computers be saying anything about nature when they are human-made artefacts?". The thing is, we are really talking about computation and not computers, computation is of course a human concept but it conceptualizes something real in nature. We can make other conceptualisations of course, but so long as there is a morphism between them and nature, anything we say about them is also said about the universe around us. ( )

There was a lot I learned, and some great review. ( )

I quite liked Algorithms to Life By. It gave me lots of “food for thought” about computers and life.

When listening to the book I thought quite a few times: “How interesting! I want to read this again in detail!”

Here the main topics from the table of contents:

1. Optimal Stopping: When to Stop Looking
2. Explore/Exploit: The Latest vs. the Greatest
3. Sorting: Making Order
4. Caching: Forget About It
5. Scheduling: First Things First
6. Bayes’s Rule: Predicting the Future
7. Overfitting: When to Think Less
8. Relaxation: Let It Slide
9. Randomness: When to Leave It to Chance
10. Networking: How We Connect
11. Game Theory: The Minds of Others

Maybe the most interesting chapter for me was the last one on Game Theory. It discussed implications from the Nash equilibrium:The Nash Equilibrium for a game can be a bad outcome for all players, therefore we might - through Mechanism design - need to “change the rules to change the game.”

I have a separate DEVONthink document from my Audible notes created: Notes about "Algorithms to Live By". ( )

At first I was a bit disappointed: the first ~5 chapters were reasonably entertaining, but shallower than I'd expected, given how enthusiastic the 80,000 Hours guys were & the general vibe I'd picked up that this was a bit more substantial than average for pop science. The self-helpy bits were a bit forced, and mostly pretty silly. The examples tended to be unrealistically simplified in ways that made their conclusions useless (optimal stopping problems where the only consideration is % chance of choosing the single best option), trivially obvious (don't put off 'unimportant' tasks that are prerequisites for important tasks), or just plain trivial (advice on whether and how to alphabetise your bookshelves).

I enjoyed the second half more. That was partly because I'd lowered my expectations, but also because some of the technical stuff was a little more detailed and novel, and some of the real-world parallels either seemed like less of a stretch (e.g. in the chapter on overfitting) or at least were allowed to be kind of suggestive and metaphorical. The book still has the basic problem, though, that it's a fairly shallow pop-CS book with some life advice forcibly bolted on to give it a unique hook.

(Possible Gell-Mann amnesia note: the authors' claim that John Rawls "deliberately sidesteps [critiques of various possible ethical positions] by leaving open the question of what to do with the information we get from behind the veil [of ignorance]" seems clearly false, unless I'm missing some subtle interpretive point. On the other hand, Rawls is super verbose and tedious, so going to the source would have been a gigantic waste of time, and 'x believed y' is the most boring and pointless approach to philosophy (either engage with the arguments or don't bother!), so I don't really mind if they skimped on their secondary research. And it's barely more than a throwaway line, so it probably doesn't say much about the accuracy of the core material.) ( )

Algorithms To Live By takes as its starting assumption the view that we are computers just as our laptop counterparts are, and that there are sometimes common algorithms between us, or perhaps even there are lessons we can learn and apply from computer science.
It starts with a chapter that perfectly illustrates these points: when should you stop searching for a house - or wife - and just pick the best so far? It turns out there is a clear computer science answer, 37% of the pool. In theory this can be useful for us in terms of practical strategies, though obviously much in life is far more complex than this picture. The second chapter has a similar topic of when should we explore new opportunities, and when should we exploit what we've learnt and stick with tried and trusted favourites or strategies. This is clearly a strategy ripe for computer science, but is also a fundamental question in cognition, and one that we see evolving over each of our lifetimes, as we start far more on the explore side of the spectrum and end far more on the exploit. Part of this is simply because we have more knowledge to take advantage of - we know what our favourite local restaurants are and so on. But it is also a matter of genetic programming - we are built to be less new-knowledge seeking as we get older, and we are also less good at assessing it (if it's complex) anyway. These subtleties are not really addressed in the book, and many times I felt that the computer science descriptions simplified the complexities both of our decisions to make, and the way we make decisions.
Other topics include sorting, caching, scheduling, making predictions, avoiding overfitting (making a model that's too well suited to the current data, and not generalisable), adding randomness to help otherwise intractable problems, networking and making connections, and finally game theory.
Some of these topics fit the initial assumption above perfectly, otherwise not so much. A few chapters towards the end especially feel more of a stretch. The networks chapter, for instance, was actually quite fascinating about detailing the technical guts of the internet, but its extension to human thought was thin.
This book is generally a fascinating read, though, and it does make the case very well that we are ourselves computers on multiple levels, and that sometimes embracing that fact, and adopting computer science algorithms more may help us. At times, though, the book was a strain. Some complex computer science algorithmic points were imprecisely and poorly explained, which was frustrating. At other times the links were more of an afterthought. I would have preferred these chapters to be replaced by more fundamental background information about how the human brain really is a computer, and how some computer algorithms (e.g. deep neural nets) work well precisely because they neural arthitectures.
Nevertheless, the book is readable, unusual, at times extremely interesting, did indeed make me question how I approach certain tasks, and may even have provided some invaluable advice for how I could possibly improve the way I think. ( )

Ingenious book about how many human decision problems are in fact algorithmic, i.e. the kind of problems researched in computer science. The authors structure the book around classes of algorithms (eg. sorting, optimal stop, regularization, network flow, bayesian stats or game theory) and find interesting anecdotes or every day life examples of each algorithm.
You won't learn any math, programming or even pseudo-coding from "Algorithms to live by", but it will give most readers a broad range of examples of how computer science is not just about computers, but the study of complexity and how to do decision making using rules (which may be automated).

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A very interesting primer on the sort of "real-life problems" that have parallels with computer science problems, explained elegantly without numbers and formulae, opting for the better approach of explaining the rationale behind the problems, their assumptions and how they lead to a desired output. Highly recommended for everyone looking to improve in practical matters, this book throws in a good number of questions to ponder about in our lives, expectations and how we go about reaching them ( )