What do you think?
Rate this book
Als dit, dan dat
Algorithms are the hidden methods that computers apply to process information and make decisions. Nowadays, our lives are run by algorithms. They determine what news we see. They influence which products we buy. They suggest our dating partners. They may even be determining the outcome of national elections. They are creating, and destroying, entire industries. Despite mounting concerns, few know what algorithms are, how they work, or who created them.
Poems that Solve Puzzles tells the story of algorithms from their ancient origins to the present day and beyond. The book introduces readers to the inventors and inspirational events behind the genesis of the world's most important algorithms. Professor Chris Bleakley recounts tales of ancient lost inscriptions, Victorian steam-driven contraptions, top secret military projects, penniless academics, hippy dreamers, tech billionaires, superhuman artificial intelligences, cryptocurrencies, and quantum computing. Along the way, the book explains, with the aid of clear examples and illustrations, how the most influential algorithms work.
Compelling and impactful, Poems that Solve Puzzles tells the story of how algorithms came to revolutionise our world.
Poems that Solve Puzzles tells the story of algorithms from their ancient origins to the present day and beyond. The book introduces readers to the inventors and inspirational events behind the genesis of the world's most important algorithms. Professor Chris Bleakley recounts tales of ancient lost inscriptions, Victorian steam-driven contraptions, top secret military projects, penniless academics, hippy dreamers, tech billionaires, superhuman artificial intelligences, cryptocurrencies, and quantum computing. Along the way, the book explains, with the aid of clear examples and illustrations, how the most influential algorithms work.
Compelling and impactful, Poems that Solve Puzzles tells the story of how algorithms came to revolutionise our world.
- GenresScienceTechnology
447 pages, Kindle Edition
Published February 9, 2023
11 people are currently reading
110 people want to read
Ratings & Reviews
Friends & Following
Create a free account to discover what your friends think of this book!
Community Reviews
Displaying 1 - 10 of 10 reviews
April 28, 2023
Een leerrijke ervaring in de wereld van algoritmen. De nadruk ligt wel vooral op het ontstaan ervan. Ik mis wat meer huidige en toekomstige situaties aangaande algoritmes. Toch heeft het een andere manier van denken getoond.
December 13, 2024
Perhaps my low rating is too harsh and simply stems from a lack of alignment between the book's scope and what I had hoped to find.
I was looking for comprehensive information on ancient algorithms, when I stumbled upon the book's first chapter; because of obsessive completionism, I got the whole book and read it within two days.
From its foreword, it was apparent that it was not what I was looking for; this is not a textbook on algorithmic history, but a popularization book, written for the general reader, who isn't programming or studying algorithms but might have become curious about the concept, having heard the term so much (especially in relation to social media).
The book is well-written; each chapter flows nicely into the next one, the last paragraph stirring anticipation for what comes next. Its narrative is compelling and it covers many of the glamorous areas of computer science.
Here I might have some nitpicks about the outline: the modern history of algorithms is of course strongly tied to that of computer technology, but at times the book seems to delve too much into the broader field of "computer history"; a little less background on the ENIAC, for example, would have been acceptable.
It's also strange that the entire section about networking and the Internet does not contain an explicit description of congestion control algorithms.
The presentation of the algorithms themselves is the strong point of the book: the description reveals enough about the core of the concept, without delving into edge cases or technical details. An experienced programmer could probably write an implementation based on the description here; someone for whom algorithms are alien, would be satisfied of having understood the big picture.
However, I believe writing for the general should not come at the expense of correctness. The book contains some problematic, misleading or even wrong statements, casually thrown in without qualifing explanations.
For example: "Nowadays, the mark of a general-purpose computer is that it is Turing complete. In other words, it can mimic the operation of a Turing machine. [...] All modern computers are Turing complete. If they weren't Turing complete, they wouldn't be general-purpose computers." ("Turing completeness" has a formal definition that is here skirted in favor of a vague "mimic" ability; but many computers are fundamentally limited to only access a finite, fixed amount of information, thus their operation is not Turing-complete; the crux of the issue here is that Turing-completeness is not practically relevant and it is not what makes a computer "general-purpose").
In the chapter on Turing machines, the presentation plays on a common anachronism: that Turing proved the halting problem to be undecidable. I agree that you can safely present this sort of thing in a course on computability, as there is a straightforward relation between the modern formulation of the halting problem, together with its undecidability proof and Turing's approach. However, it feels wrong to see this so represented in a historical book.
Chapter 6 is where it really goes wrong: the presentation of P vs. NP is at best misleading and at worst wrong.
It starts promising, with the sentence "How long NP problems take to solve is not defined", immediately undermined by the following sentence: "Some can be solved in P time; others cannot."
This is a strong claim that P and NP are unequal! A statement unproven at the time of writing (both of the book and of this review).
This is not a one-off sloppily expressed statement; a further paragraph claims "Problems that are in NP but not in P are called NP/P problems [...]. These problems are slow to solve but the answers, when they are known, are quick to check. Sudoku is an NP/P problem."
The last sentence is even more disturbing, as "Sudoku" by itself is a constant-size puzzle; you might give the author the benefit of the doubt that what is meant here is generalized Sudoku. But this should be explicitly stated in a book for the general public: it's a perfect way to bring up what a "problem" actually is and how we're interested in how the solution time scales with input size. Alas, the book goes on to define Sudoku as being "a Japanese number puzzle played on a 9x9 grid".
These problematic sentences continue: "Beyond this, are greater than NP problems (>NP). These are problems that cannot be solved or verified in polynomial time. Their solvers and checkers require superpolynomial time. The Travelling Salesman Problem is one such problem: solviing it requires factorial time."
Surely, a text that introduces "P vs. NP" to the general public should clarify the unsolved status of the problem.
But perhaps the author adheres to a bizarre mathematical philosophy, in which the truth of statements varies with time and there is always a default state. This section's conclusion contains the strange summary: "Most researchers now think that P is not equal to NP, and never will be." I'm sure that there are adherents to such a philosophy that could defend it thoroughly and diasrm me of any counterargument, but I still find it uncomfortable to be simply thrown in with no explicit support.
I was looking for comprehensive information on ancient algorithms, when I stumbled upon the book's first chapter; because of obsessive completionism, I got the whole book and read it within two days.
From its foreword, it was apparent that it was not what I was looking for; this is not a textbook on algorithmic history, but a popularization book, written for the general reader, who isn't programming or studying algorithms but might have become curious about the concept, having heard the term so much (especially in relation to social media).
The book is well-written; each chapter flows nicely into the next one, the last paragraph stirring anticipation for what comes next. Its narrative is compelling and it covers many of the glamorous areas of computer science.
Here I might have some nitpicks about the outline: the modern history of algorithms is of course strongly tied to that of computer technology, but at times the book seems to delve too much into the broader field of "computer history"; a little less background on the ENIAC, for example, would have been acceptable.
It's also strange that the entire section about networking and the Internet does not contain an explicit description of congestion control algorithms.
The presentation of the algorithms themselves is the strong point of the book: the description reveals enough about the core of the concept, without delving into edge cases or technical details. An experienced programmer could probably write an implementation based on the description here; someone for whom algorithms are alien, would be satisfied of having understood the big picture.
However, I believe writing for the general should not come at the expense of correctness. The book contains some problematic, misleading or even wrong statements, casually thrown in without qualifing explanations.
For example: "Nowadays, the mark of a general-purpose computer is that it is Turing complete. In other words, it can mimic the operation of a Turing machine. [...] All modern computers are Turing complete. If they weren't Turing complete, they wouldn't be general-purpose computers." ("Turing completeness" has a formal definition that is here skirted in favor of a vague "mimic" ability; but many computers are fundamentally limited to only access a finite, fixed amount of information, thus their operation is not Turing-complete; the crux of the issue here is that Turing-completeness is not practically relevant and it is not what makes a computer "general-purpose").
In the chapter on Turing machines, the presentation plays on a common anachronism: that Turing proved the halting problem to be undecidable. I agree that you can safely present this sort of thing in a course on computability, as there is a straightforward relation between the modern formulation of the halting problem, together with its undecidability proof and Turing's approach. However, it feels wrong to see this so represented in a historical book.
Chapter 6 is where it really goes wrong: the presentation of P vs. NP is at best misleading and at worst wrong.
It starts promising, with the sentence "How long NP problems take to solve is not defined", immediately undermined by the following sentence: "Some can be solved in P time; others cannot."
This is a strong claim that P and NP are unequal! A statement unproven at the time of writing (both of the book and of this review).
This is not a one-off sloppily expressed statement; a further paragraph claims "Problems that are in NP but not in P are called NP/P problems [...]. These problems are slow to solve but the answers, when they are known, are quick to check. Sudoku is an NP/P problem."
The last sentence is even more disturbing, as "Sudoku" by itself is a constant-size puzzle; you might give the author the benefit of the doubt that what is meant here is generalized Sudoku. But this should be explicitly stated in a book for the general public: it's a perfect way to bring up what a "problem" actually is and how we're interested in how the solution time scales with input size. Alas, the book goes on to define Sudoku as being "a Japanese number puzzle played on a 9x9 grid".
These problematic sentences continue: "Beyond this, are greater than NP problems (>NP). These are problems that cannot be solved or verified in polynomial time. Their solvers and checkers require superpolynomial time. The Travelling Salesman Problem is one such problem: solviing it requires factorial time."
Surely, a text that introduces "P vs. NP" to the general public should clarify the unsolved status of the problem.
But perhaps the author adheres to a bizarre mathematical philosophy, in which the truth of statements varies with time and there is always a default state. This section's conclusion contains the strange summary: "Most researchers now think that P is not equal to NP, and never will be." I'm sure that there are adherents to such a philosophy that could defend it thoroughly and diasrm me of any counterargument, but I still find it uncomfortable to be simply thrown in with no explicit support.
December 29, 2024
The writing and presentation of most topics were great. In particular, the background and history of the development of the algorithms were a nice addition, but it read at times like a Wikipedia article. There were multiple times where I would be interested in looking up some more information about somebody mentioned in the book and it felt like the book basically paraphrased the “Personal Life” section of the article.
Although this is somewhat harsh, I did enjoy it overall. I think the author chose interesting algorithms to talk about, such as Facebook’s EdgeRank and Netflix’s Cinematch, but I wish he talked about more basic stuff first before jumping into this. He talked about Insertion Sort and Quicksort, jumped into ancient algorithms in the second chapter, and then went all over the place with topics for the rest of the book.
On a separate note, I’ve been taught the Euclidean Algorithm in two separate math classes (Abstract Algebra and Number Theory). In both, it followed the same orderly steps to get the answer. I think the version the author presents it here really made it click for me on WHY the algorithm gives the GCD. We know that given two numbers x and y, the GCD will be the minimum of the set of all positive linear combinations of x and y. In this version, it is very clear that each output we get is a linear combination of our original two numbers and the goal is to keep simplifying until we get to the least positive number, which is our GCD.
I think the book would be really great for somebody interested in computer science that has taken a class on algorithms and wants to learn some more, but probably not what you’re looking for if you want to take the information from the book and apply it to something.
Although this is somewhat harsh, I did enjoy it overall. I think the author chose interesting algorithms to talk about, such as Facebook’s EdgeRank and Netflix’s Cinematch, but I wish he talked about more basic stuff first before jumping into this. He talked about Insertion Sort and Quicksort, jumped into ancient algorithms in the second chapter, and then went all over the place with topics for the rest of the book.
On a separate note, I’ve been taught the Euclidean Algorithm in two separate math classes (Abstract Algebra and Number Theory). In both, it followed the same orderly steps to get the answer. I think the version the author presents it here really made it click for me on WHY the algorithm gives the GCD. We know that given two numbers x and y, the GCD will be the minimum of the set of all positive linear combinations of x and y. In this version, it is very clear that each output we get is a linear combination of our original two numbers and the goal is to keep simplifying until we get to the least positive number, which is our GCD.
I think the book would be really great for somebody interested in computer science that has taken a class on algorithms and wants to learn some more, but probably not what you’re looking for if you want to take the information from the book and apply it to something.
April 17, 2022
Hoewel sommige stukken voor mij wat (te) moeilijk om volgen en begrijpen waren , vond ik het een leesbaar boek , dat me een hoop vragen opbracht , Bv wie of wat is verantwoordelijk bij algoritme? Bij het deep learning? Bij de gewonnen ( en verloren) go spelen , is het zoals bij huis of wilde dieren een soort eigen onvoorspelbare wil ?
En wat als er Bv binnen x aantal jaar een zonnestorm plaatsvind , .. zouden de gebruikers dan de huisdieren blijken te zijn , ?
Het stuk over quantum computers vond ik ook interessant, naar ik het begrijp lijkt het over super snel , super veel gokwerk te gaan om de uitkomst , .... lijkt op het leven zelf ,..... gaan al die gokken dezelfde kant op ? Dan lijkt het toch weer deterministisch ,
En wat als er Bv binnen x aantal jaar een zonnestorm plaatsvind , .. zouden de gebruikers dan de huisdieren blijken te zijn , ?
Het stuk over quantum computers vond ik ook interessant, naar ik het begrijp lijkt het over super snel , super veel gokwerk te gaan om de uitkomst , .... lijkt op het leven zelf ,..... gaan al die gokken dezelfde kant op ? Dan lijkt het toch weer deterministisch ,
January 11, 2021
This book is brilliant!
This book explores the history of algorithms and how they evolved from use in ancient civilizations to use in everyday life in the modern world. I would highly recommend this book to anyone who enjoys reading popular science books. It's approach of explaining the history and science of algorithms to the layperson reminds me of Bryson's "A short history of nearly everything" and Harari's "Sapiens: A Brief History of Humankind".
This book explores the history of algorithms and how they evolved from use in ancient civilizations to use in everyday life in the modern world. I would highly recommend this book to anyone who enjoys reading popular science books. It's approach of explaining the history and science of algorithms to the layperson reminds me of Bryson's "A short history of nearly everything" and Harari's "Sapiens: A Brief History of Humankind".
January 4, 2021
A fine read for those interested in history and algorithms. The book tilts more towards history than I would have guessed, but not inappropriately and always in support of the broader storyline in each chapter. Good job throughout in taking the time to define foundational terms. My primary complaint: needs another editing round (on average I encountered perhaps one typo per chapter).
May 13, 2023
This book like covered all my superheroes and all my favorite algorithms ;-). That being said even though the advocates it as targeted to a larger audience, I think a large portion of the large audience will need some encouragement.
December 22, 2021
Good book, pretty easy and fast to read. I liked the initial chapters the most, when the focus steers towards ML/AI I think it loses a little of its charm.
December 11, 2022
We’ve come so far so fast maybe too fast. I’m a negative Nellie so I always dread tomorrow and there always is one so I’m terrible at prophecy. But one day I’ll be correct. Good read.
April 26, 2021
Excellent book can't recommend it enough. It explains the history of algorithms very well and in a really interesting way.
Displaying 1 - 10 of 10 reviews