Monster algorithms: Ed Finn
by Athena Aktipis and Dave Lundberg-Kenrick, Zombified Podcast
What Sci-Fi Futures Can (and Can’t) Teach Us About AI Policy (in Washington, DC)
RSVP Join us in Washington, DC for a half-day mini-conference on the intersection of science fiction, futurism, and real-world AI policy! Our anxieties about what we can do with AI
Phoenix will no longer be Phoenix if Waymo’s driverless-car experiment succeeds
By Ed Finn MIT Technology Review
Art by Algorithm
The Serendipity of Semiautonomous Systems
The MIT Press Podcast
What Algorithms Want – Future Out Loud Podcast
Future Out Loud Podcast
What Algorithms Want
In this book, Ed Finn considers how the algorithm—in practical terms, “a method for solving a problem”—has its roots not only in mathematical logic but also in cybernetics, philosophy, and magical thinking.
Facebook Trending story: The Wizard of Oz algorithm
Algorithms Are Like Kirk, Not Spock
When technologists describe their hotshot new system for trading stocks or driving cars, the algorithm at its heart always seems to emerge from a magical realm of Spock-like rationality and mathematical perfection. Algorithms can save lives or make money, the argument goes, because they are built on the foundations of mathematics: logical rigor, conceptual clarity, and utter consistency. Math is perfect, right? And algorithms are made out of math.
What Algorithms Want
We spend an awful lot of time now thinking about what algorithms know about us: the ads we see online, the deep archive of our search history, the automated photo-tagging of our families. We don’t spend as much time asking what algorithms want.
What if Computers Know You Better Than You Know Yourself?
I recently read about the launches of both an “ultrasecure” mobile phone for protecting privacy and a clip-on camera that takes a picture of everything you do at 30-second intervals. Our cultural relationship with data is more complicated and contradictory than it has ever been, and our debates on the subject almost always center on privacy. But privacy, the notion that only you should be able to control information about yourself, cloaks a deeper tension between information and meaning, between databases and insights.