Algorithms 101: A cheat sheet to the terminology, the ethical debates, and more.
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The Biggest Questions About Algorithms

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The Biggest Questions About Algorithms

The citizen’s guide to the future.
Feb. 2 2016 10:28 AM
FROM SLATE, NEW AMERICA, AND ASU

Your Algorithms Cheat Sheet 

Who are the leading thinkers in algorithms? What are the big ethical debates? And what’s procedural generation?

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The Social Network, directed by David Fincher: The Social Network offers a fictionalized depiction of the origins of Facebook, which is now home to one of the most notable and controversial algorithms: the news feed.

The Foundation series, by Isaac Asimov: Asimov’s Foundation stories revolve around the practice of psychohistory, a fictional science that holds that while the actions of people are erratic, the movements of populations are mathematically predictable.

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No Man’s Sky by Hello Games: A game of interplanetary exploration coming out in June 2016, No Man’s Sky isn’t about algorithms, but it is driven by them in a unique way. It has attracted attention from the gaming press for its use of procedural processes to generate a universe of unlimited planets.

Ex Machina, directed by Alex Garland: In Ex Machina, a rogue tech billionaire uses data produced from search algorithms to created something that may or may not be a true artificial intelligence.

One Million Million Poems, by Raymond Queneau: A collection of sonnets designed to be recombined into an almost infinite array of possible texts, One Hundred Million Poems helped initiate the Oulipo movement, which fuses literature and mathematics.

Galatea 2.2, by Richard Powers: In Galatea 2.2, a fictional stand-in for Powers himself helps bring an algorithm to life by teaching leading it through a series of great literary works.

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Nicholas Carr: In popular books and articles, information technology writer Carr has worried over the ways that algorithms like those employed by Google are reshaping the ways we think.

Samir Chopra: A philosophy professor at the City University of New York, Chopra has conducted research into the legal status of autonomous artificial intelligences—and the algorithms from which they might arise.

Chris Cox: As one of the original architects of Facebook’s news feed, Cox has stressed the ongoing importance of human input into algorithms like those employed by the social network.

Tom Gruber: Gruber contributed to one of the best-known personified algorithms: Siri. He was a co-founder of Siri Inc., which was later purchased by Apple.

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Latanya Sweeney: Harvard University government professor Sweeney conducted research to try to determine why Google’s AdSense tended to show advertisements about criminal backgrounds in response to searches from names typically identified with black people.

Stephen Wolfram: As chief designer of Wolfram Alpha—a powerful “answer engine”—Wolfram has helped push forward knowledge-discovery algorithms capable of processing plain language queries.

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The Stupidity of Computers,” by David Auerbach: For all their seeming omnipotence, computers are still direction-following machines. According to Auerbach, that means they will never truly understand us.

Changing the Subject, by Sven Birkerts: An unapologetic luddite, Birkerts argues that our reliance on algorithms is diminishing our capacity for attention, undermining our experience of the unknown, and transforming the way we relate.

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The Master Algorithm, by Pedro Domingos: In clear language, Domingos explains the capabilities and potential of machine learning, offering one of the most thoroughgoing and accessible introductions to the topic in the process.

Who Controls Your Facebook Feed,” by Will Oremus: Granted unusual access to Facebook’s inner workings, Oremus delves into the history of the news feed—and the persistent human influence on its seemingly mechanized operations.

The Magic That Makes Spotify’s Discover Weekly Playlists So Good,” by Adam Pasick: In this impressively researched and illustrated article, Pasick breaks down the operations behind Spotify’s personally calibrated recommendations.

The Black Box Society, by Frank Pasquale: Skeptical of algorithmic opacity, Pasquale questions our increasingly reliance on systems that we neither understand nor have access to.

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Decline of individuality: Though some celebrate the ability of algorithms to connect us to one another and inspire collective action, others worry that they’re promoting hivelike ways of living. Will algorithms destroy our ability to be ourselves?

Excessive automation: Algorithms increasingly take over tedious tasks such as route planning that we once performed by hand. Some critics argue that as they do so they’re draining our experience of the world, leaving us familiar with only the proscribed routes and routines. Are algorithms assuming too many of our responsibilities?

Loss of the unknown: Because they automate the experience of discovery—introducing us to new music, possible romantic partners, and everything in between—algorithms strip some of the pleasure of uncertainty. The better they get at discovering our tastes, the less time we’ll spend wandering. Will we lose our ability to imagine new possibilities in the process?

Opacity: Much as we rely on them, it’s not always clear how algorithms work or why they offer the results that they provide. Even when they’re not pure black boxes, machine-learning algorithms can sometimes become complex enough that even their creators don’t fully understand them. Are we creating a world governed by forces we can’t understand?

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Privacy: Because they often work by creating connections between distinct data points, algorithms can create frighteningly accurate pictures of individuals. In their own turn, those pictures can give information to advertisers, government agencies, and others that we never knowingly offered up. Do algorithms reveal too much?

Shallowness: Good as they are at giving us what we want, algorithms may make it harder for us to look deeper. Will they keep us from delving beyond superficial needs?

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Algorithm: As used in computer science contexts, an algorithm is a set of instructions telling a computer what to do and how to do it. Just that. Really.

Black box: Generally applied to algorithms derogatively, “black box” describes a system whose internal operations remain obscure.

Machine learning: Unlike conventional computer programs, machine-learning algorithms modify themselves to better perform their assigned tasks.

Machine vision: A key goal of many of the most widely recognized algorithms, machine vision is the autonomous classification and analysis of images by computers.

Procedural generation: A way of creating data through algorithmic systems rather than direct human input, procedural generation is sometimes used in video games to randomize gameplay.

This article is part of the algorithm installment of Futurography, a series in which Future Tense introduces readers to the technologies that will define tomorrow. Each month from January through June 2016, we’ll choose a new technology and break it down. Read more from Futurography on algorithms:

Future Tense is a collaboration among Arizona State University, New America, and Slate. To get the latest from Futurography in your inbox, sign up for the weekly Future Tense newsletter.

Jacob Brogan writes for Slate about technology and culture. Follow him on Twitter.