Christian and Griffiths's decision-making benchmarks are the algorithms developed by mathematicians, … Once you’ve assembled a baseline itinerary, you might test some alternatives by making slight perturbations to the city sequence and seeing if that makes an improvement. And for any power-law distribution, Bayes’s Rule indicates that the appropriate prediction strategy is a Multiplicative Rule: multiply the quantity observed so far by some constant factor. (And if that sounds like too much work, you can now download an app that will pick a card for you.) Competitions kills holidays — in Silicon Valley companies started giving unlimited vacations. For example, computer scientists use this approach when trying to decipher codes, since there are lots of ways to begin decrypting a message that look promising at first but end up being dead ends. Protocol is how we get on the same page; in fact, the word is rooted in the Greek protokollon, “first glue,” which referred to the outer page attached to a book or manuscript. Contains mathematical philosophy on decision making on a wide range of topics. More, more, more, SLOW WAY DOWN, ACKS are super important in speed of communication. The Secretary Problem is a form of the Optimum Stopping problem, where you’re not sure when you should stop searching for an optimum form of something. In almost every domain we’ve considered, we have seen how the more real-world factors we include—whether it’s having incomplete information when interviewing job applicants, dealing with a changing world when trying to resolve the explore/exploit dilemma, or having certain tasks depend on others when we’re trying to get things done—the more likely we are to end up in a situation where finding the perfect solution takes unreasonably long. ), a class of problems so truly hellish that computer scientists only talk about it when they’re joking—as we were in imagining shuffling a deck until it’s sorted—or when they really, really wish they were. If you don’t have a clear read on how your work will be evaluated, and by whom, then it’s not worth the extra time to make it perfect with respect to your own (or anyone else’s) idiosyncratic guess at what perfection might be. If you follow this optimal strategy you will also have a 37% chance of finding the best thing. I knew that that would haunt me every day, and so, when I thought about it that way it was an incredibly easy decision.Jeff Bezos. The breakthrough turned out to be increasing the average delay after every successive failure—specifically, doubling the potential delay before trying to transmit again. He is the author, with Tom Griffiths, of Algorithms to Live By, a #1 Audible bestseller, Amazon best science book of the year and MIT Technology Review best book of the year. This is not revolutionary, but it was interesting to read through why, mathematically/theoretically not always looking for the perfect solution is efficient. In their presence, he wrote, “we seem suddenly introduced into a seething caldron of ideas, where everything is fizzling and bobbing about in a state of bewildering activity, where partnerships can be joined or loosened in an instant, treadmill routine is unknown, and the unexpected seems the only law.” (Note here the same “annealing” intuition, rooted in metaphors of temperature, where wild permutation equals heat.). Algorithms to Live By by Brian Christian and Tom Griffiths is an immersive look at the history and development of several algorithms used to solve computer science problems. The optimal strategy for that goal is a simple modification of Shortest Processing Time: divide the weight of each task by how long it will take to finish, and then work in order from the highest resulting importance-per-unit-time (call it “density” if you like, to continue the weight metaphor) to the lowest. It’s a whole other game if you have a metric you’re going by: like typing speed. Finally we’d start going only uphill, and stop when we reached the next local max. In Algorithms to Live By: The Computer Science of Human Decisions, Brian Christian and Tom Griffiths detail how, if you really want to look at problems more rationally, borrowing problem solving techniques or algorithms from computer science can be an enormously productive way to live. Ideally, you have a couple different caches which are organised by category, so you shorten the path of access and don’t have to wade through all information every time. Summary of Algorithms to Live By by Brian Christian and Tom Griffiths | Includes Analysis . These aren’t the concessions we make when we can’t be rational. This elegant approach allows the network to accommodate potentially any number of competing signals. A sobering property of trying new things is that the value of exploration, of finding a new favorite, can only go down over time, as the remaining opportunities to savor it dwindle. Up against hard cases, effective algorithms make assumptions, show of bias toward simpler solutions, trade off the costs of error agains the cost of delay, and take chances. Finding the shortest route under these looser rules produces what’s called the “minimum spanning tree.” (If you prefer, you can also think of the minimum spanning tree as the fewest miles of road needed to connect every town to at least one other town. MIT’s Scott Aaronson says he’s surprised that computer scientists haven’t yet had more influence on philosophy. PRAISE “Compelling and entertaining, Algorithms to Live By is packed with practical advice about how to use time, space, and effort more efficiently. There is wisdom in deliberately thinking less and settling for second best solutions. Discover Algorithms to Live By as it's meant to be heard, narrated by Brian Christian. Many problems that we all deal with as part of life have practical solutions that come from computer science, and this book gives a number of examples. The second, Continuous Relaxation, turns discrete or binary choices into continua: when deciding between iced tea and lemonade, first imagine a 50–50 “Arnold Palmer” blend and then round it up or down. They basically have you select options not based on what’s likely, but by what’s possible. How can it be that the foods that taste best to us are broadly considered to be bad for our health, when the entire function of taste buds, evolutionarily speaking, is to prevent us from eating things that are bad? This is also related to the look-then-leap rule, which is where you spend a certain amount of time looking and not choosing anyone, and then after that point you pick the very first person that’s better than everyone you’ve seen so far. When balancing favorite experiences and new ones, nothing matters as much as the interval over which we plan to enjoy them. For instance, if somebody is younger than the average life span, then simply predict the average; as their age gets close to and then exceeds the average, predict that they’ll live a few years more. Summary of Algorithms to Live By by Brian Christian and Tom Griffiths | Includes Analysis . You can only draw shapes, lines, and boxes. For an uninformative prior, that constant factor happens to be 2, hence the Copernican prediction; in other power-law cases, the multiplier will depend on the exact distribution you’re working with. Scale hurts. It was quite dry and the length didn’t warrant the few interesting insights. Beautiful. “In poker, you never play your hand,” James Bond says in Casino Royale; “you play the man across from you.” In fact, what you really play is a theoretically infinite recursion. One way ML does that is by reducing the weights incrementally until only the strongest signals are considered, also know as Regularization, The Lasso is an algorithm that penalizes algorithms for their total weight, so it pulls the weights so low that most factors end up at zero, and only the strongest remain (at low numbers), Early stopping is an algorithm based on finding the strongest signal, then the next, then the next, instead of just taking all of them at face value to start with. Practically, this means selecting possible adventures based on their potential to be good, not factoring in their potential to be bad. There’s your own hand and the hand you believe your opponent to have; then the hand you believe your opponent believes you have, and the hand you believe your opponent believes you to believe he has … and on it goes. So if you hear a movie has made $6 million so far, you can guess it will make about $8.4 million overall; if it’s made $90 million, guess it will top out at $126 million. This is an algorithm known as Hill Climbing—since the search through a space of solutions, some better and some worse, is commonly thought of in terms of a landscape with hills and valleys, where your goal is to reach the highest peak. Regret Minimisation Framework — when you look back on your life when you’re 80 what will you regret least. This is the first and most fundamental insight of sorting theory. He calls this Computational Kindness. Algorithms to Live By by Brian Christian and Tom Griffiths is an immersive look at the history and development of several algorithms used to solve computer science problems. There’s a general concept of clumps of caches, with smaller faster ones close by, a medium fast one nearby, and then a slow but large one with everything. Algorithms to Live By (2016) is a practical and useful guide that shows how algorithms have much more to do with day-to-day life than you might think.

Summary of Algorithms to Live By by Brian Christian and Tom Griffiths | Includes Analysis


Algorithms to Live By by Brian Christian and Tom Griffiths is an immersive look at the history and development of several algorithms used to solve computer science problems. A third type is Additive, where you just add a constant to the end. Summary of Algorithms to Live By by Brian Christian and Tom Griffiths | Includes Analysis Preview: Algorithms to Live By by Brian Christian and Tom Griffiths is an immersive look at the history and development of several algorithms used to solve computer science problems. A Nash Equilibrium is where both sides should keep doing what they’re doing, assuming both sides keep doing what they’re doing. 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. The third, Lagrangian Relaxation, turns impossibilities into mere penalties, teaching the art of bending the rules (or breaking them and accepting the consequences). Laplace’s Law, and it is easy to apply in any situation where you need to assess the chances of an event based on its history. Thanks for exploring this SuperSummary Plot Summary of “Algorithms To Live By” by Brian Christian. Think long and hard: the complexity and effort are appropriate. For the grosses of movies, for instance, it happens to be about 1.4. Think, for example, of the difference between reading a 400-page book and reading every possible such book, or between writing down a thousand-digit number and counting to that number. Make a mess on occasion. Try it with a few more random pieces of data. If you want to be a good intuitive Bayesian—if you want to naturally make good predictions, without having to think about what kind of prediction rule is appropriate—you need to protect your priors. Once you know about overfitting, you see it everywhere. Sorting is one of the most fundamental problems that computers are solving for us. It also considers potential applications of algorithms in human life including memory storage and network communication. It doesn’t mean you’ve found THE solution, but it does mean that the more you do this the more likely that becomes.

algorithms to live by chapter summary

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