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    Summary of Algorithms to Live By: by Brian Christian and Tom Griffiths | Includes Analysis
    Summary of Algorithms to Live By: by Brian Christian and Tom Griffiths | Includes Analysis
    Summary of Algorithms to Live By: by Brian Christian and Tom Griffiths | Includes Analysis
    Ebook36 pages33 minutes

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

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

     

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    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 com

    LanguageEnglish
    PublisherInstaread, Inc
    Release dateFeb 23, 2019
    ISBN9781683784791
    Summary of Algorithms to Live By: by Brian Christian and Tom Griffiths | Includes Analysis

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      Summary of Algorithms to Live By - Instaread Summaries

      Overview

      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. It also considers potential applications of algorithms in human life including memory storage and network communication.

      One such computer science problem is the optimal stopping problem, the mathematical puzzle for determining how long to review options and gather data before settling on the best choice available. The algorithm, based on statistical analysis, shows that there is an optimal place or time to stop researching options or solutions to a problem and instead commit to the next option that’s just as good as those already considered. Similarly, the mathematical way to decide whether to try something new or stick with the familiar choice is expressed by the Gittins Index score of any given alternative. It values a complete unknown more highly than a choice that has proven to be disappointing once or twice in the past depending on how many times that choice had a positive outcome.

      When planning the schedule for a single machine or person, the order of tasks should depend on whether the goal is to achieve deadlines or to minimize the time a task remains undone. However, once tasks that rely on each other to be completed are introduced, the algorithm becomes too complex for a computer to

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