Book Review — Algorithms to Live By by Brian Christian & Tom Griffiths
This afternoon I finished “Algorithms to Live By: The Computer Science of Human Decisions”. The book is written by Brian Christian and Tom Griffins. Brian is a computer science and philosophy graduate whose best-selling work has already been translated into eleven languages, while Tom is a professor of psychology and cognitive science at Princeton University. The book was published in 2016.
Algorithms to Live By is an informative exploration of how computer algorithms can be applied to our everyday lives to enable us solve problems.
To the uninitiated mind, algorithm can bring up the image of something undiscernible that only the computer nerd knows about, but in actual fact, an algorithm is simply a series of steps that help solve a problem. Without recognizing it, this is how humans solve problems all the time.
In fact, long before algorithms were ever used by machines, they were used by people. It’s just a set of instructions designed to perform a specific task. When you cook Jollof Rice following recipe you got from cheflolaskitchen.com, you’re following an algorithm. When you knit a sweater from a pattern, you’re following an algorithm.
#DoYouKnow The word “algorithm” comes from the name of Persian mathematician al-Khwārizmī, author of a ninth-century book of techniques for doing mathematics by hand.
In this book, the authors offer us eleven (11) situations or problems where computer science offers strategies that we can employ in our human minds to solve problems.
As we go about our days and our lives, we have limited space and time to do all we have to do. We obviously cannot do all that we need to do. So what things should we do and leave undone? How much perfection and messiness should we allow? As unique as these questions seem to us, it is in fact what computer scientists think about all the time as they design computers for our use.
Imagine the following scenario: you have to hire a secretary from a pool of fixed applicants. You have to interview the candidates one by one and make a hire/no-hire decision right after each interview. If you pass on someone, you cannot come back to them. If you hire someone, the process stops and they are your new secretary. How do you maximize your chances to find the best secretary in the group? This is the famous Secretary Problem, and it forms the basis for the discussion in the first chapter.
The Secretary problem has remarkable applications in vast areas of real life too — from dating to parking your car to buying a house. What is the best use of your time? When should you stop looking knowing that you have done your optimal best in finding a school for your child or selling that furniture before you move to Canada?
The big picture problems tackled in this book are tremendous. These are problems you encounter in everyday life such as if you should try out new restaurants or just stick to good old ones you know. What is the fastest way you can sort out your books? Should you even try sorting out my shelves? How can you best schedule your tasks for maximum productivity?
Remarkably, these problems have a mathematical and computer science basis to which the scientific community has found optimum or near to optimum answers. We rarely take time to think about them but questions and dilemmas like the ones I have highlighted have been asked as they work on making available the best possible computer machines for you.
How should a processor (on your phone) allocate its “attention” to perform all that you ask of it, with the minimum overhead and in the least amount of time? When should it switch between different tasks, and how many tasks should it take on in the first place? What is the best way for it to use its limited memory resources? Should it collect more data, or take an action based on the data it already has?
The answers to these problems in the digital world have proven valuable to humans’ everyday life. For instance, if you’ve ever had a moment where you wanted to stop doing everything just to have the chance to write down everything you were supposed to be doing, but couldn’t spare the time, you’ve thrashed. And the cause is much the same for people as for computers: each task is a draw on our limited cognitive resources. Have you been so busy that you suddenly feel like you are in a state of panic, that is, paralysis by way of hyperactivity? I have! Then it’s called thrashing, and computers know it well. How does the computer deal with it? How can you scientifically deal with it? Read the book.
#NewWordAlert ineluctable
Often times we poke fun at the old for not remembering as many things as the young. But research has suggested that what we call “cognitive decline” may not be about memory deteriorating, but (at least partly) an unavoidable consequence of the amount of information we have to navigate getting bigger and bigger. Just like old computers take longer times searching your files because you have stuffed them with a lot of information, the old remember less because they have many more experiences and memories stored in their brains to the point that it just takes them more time for their brains to search through those files in their heads. According to the authors, “the old can mock the young for their speed: “It’s because you don’t know anything yet!””
I found chapter 7 on Overfitting absorbing that I found myself discussing it with a friend who is into machine learning. He loved it too. It’s about reducing the complexity of decision making by reducing the amount of factors under consideration or using the weight of them in our favour. It’s about holding back some of your data to test later. If you don’t do that you run the risk of overfitting.
To understand, a dramatic case was mentioned in the book. A police officer instinctively grabbed the gun out of the hands of an assailant and then instinctively handed it right back — just as he had done time and time again with his trainers in practice. He’s been over-trained. In production, a factory focusing on overfitting production metrics will lead to supervisors neglecting maintenance and repairs, setting up future catastrophe. Such problems can’t simply be dismissed as a failure to achieve management goals. Rather, they are the opposite: the ruthless and clever optimization of the wrong thing. Same thing for website that are over-optimized for ads. The website might gain a little more money in the short term, but ad-crammed articles, slow-loading multi-page slide shows, and sensationalist clickbait headlines will drive away readers in the long run.
#DoYouKnow The world’s most difficult word to translate has been identified as “ilunga,” from the Tshiluba language spoken in south-eastern DR Congo.… Ilunga means “a person who is ready to forgive any abuse for the first time, to tolerate it a second time, but never a third time.”
Even in the world of computer science where the most brilliant people are increasingly drawn to, there are certain problems about computers that are insolvable — we have no way to compute a perfect solution in any reasonable amount of time. The most famous example of this is the Travelling Salesman Problem. What is the impossible Travelling Salesman Problem and why is it so useful in your everyday life? Read the book.
Imagine you wanted to throw a party for all your friends and acquaintances, but didn’t want to pay for all the envelopes and stamps that so many invitations would entail. What is a simple solution to use? The mathematical solution is so useful that political campaign managers and corporate marketers use the solution to spread their messages most effectively. It’s also so useful that I realize that it’s what epidemiologists are using today in getting the minimum number of people in a population — and the kind of people — to vaccinate to protect our society from communicable diseases, and in trying to curtail the coronavirus.
For all of the goodness of this book, it occurred to me that the title of this book — Algorithms to Live By — sounds so mentally taxing that many people put off reading it because of its title. And that is a lot of disservice. The last chapter for example which is about Game Theory deals with the classic Prisoner’s Dilemma; understanding the paradox of how one person will act in accordance with self-interest with incomplete information of how the other participant(s) in the transaction will act.
But I must warn you, if you are not already fluent in the terminology discussed in this book, it’s pretty easy to get lost. A friend picked up the book the moment I shared my first excerpt and while flipping through, the first lines he chanced upon were,
“If you have two polynomial expressions, such as 2x3 + 13x2 + 22x + 8 and (2x + 1) × (x + 2) × (x + 4), working out whether those expressions are in fact the same function — by doing all the multiplication…”
For someone who has a phobia for mathematics, he sent me the statement with the words, “Thanks, but no thanks.” 😂😂😂
I’m not surprised that several people found the explanations vague and sparse. There is a fair amount of computer science terminology and the authors spend a good amount of time talking about the history of different aspects of computer science. If you are that kind of person who has no background and interest in the physical sciences at all you may find the book academic and complain the book could not keep your attention.
But as in many things in life, you would not know if you don’t open the first page.
For me, it was thought-provoking and educational.
