Adam's Blogroll: click through to the author's blog
Ben Schmidt uses deep scatterplots to visualize millions of data points. It’s a combination of algorithm-based display and hiding of points as you zoom in and out like you might an interactive map. Schmidt describes the process and made the code available on GitHub.
Tags: algorithm
Posted by in algorithm, Statistical Visualization
Genetic algorithms are inspired by natural selection, where the system is given a set of inputs and the “best” iteration is chosen until there’s some kind of convergence to a solution. Joel Simon applied this process to floor plan design.
The creative goal is to approach floor plan design solely from the perspective of optimization and without regard for convention, constructability, etc. The research goal is to see how a combination of explicit, implicit and emergent methods allow floor plans of high complexity to evolve. The floorplan is ‘grown’ from its genetic encoding using indirect methods such as graph contraction and emergent ones such as growing hallways using an ant-colony inspired algorithm.
The results were biological in appearance, intriguing in character and wildly irrational in practice. It was a fun learning experience and I plan to re-use methods in other projects.
[via kottke]
Tags: algorithm, floor plan
Posted by in algorithm, floor plan, statistics
Learning algorithm steps can be a challenge when viewed only through code or words. So Sándor P. Fekete, Sebastian Morr, and Sebastian Stiller put together IDEA. The collection of illustrations describes common programming algorithms, such as Quicksort, in the style of IKEA furniture assembly instructions. Allen wrench not required. [via kottke]
Posted by in algorithm, Humor, IKEA, Infographics
Dan Hurley, reporting for The New York Times, describes the use of statistical software to assist call screeners:
[T]he decision to screen out or in was not Byrne’s alone. In August 2016, Allegheny County became the first jurisdiction in the United States, or anywhere else, to let a predictive-analytics algorithm — the same kind of sophisticated pattern analysis used in credit reports, the automated buying and selling of stocks and the hiring, firing and fielding of baseball players on World Series-winning teams — offer up a second opinion on every incoming call, in hopes of doing a better job of identifying the families most in need of intervention. And so Byrne’s final step in assessing the call was to click on the icon of the Allegheny Family Screening Tool.
I’m glad Hurley highlights the challenges of the inherent biases in the data and the algorithms later in the article. It’s one thing to use data to estimate player value in sports. It’s another thing to use data to decide whether or not to send help to someone calling the police. [Thanks, Jennifer]
Posted by in algorithm, police, statistics
Hearing about machine learning and algorithms a lot recently and not sure what that means? CGP Grey explains:
Tags: algorithm, explainer, machine learning
Posted by in algorithm, explainer, machine learning, statistics
Alec Wilkinson, reporting for The New Yorker, profiled Thomas Hargrove, who is deep into finding serial killers algorithmically and through public data:
Thomas Hargrove is a homicide archivist. For the past seven years, he has been collecting municipal records of murders, and he now has the largest catalogue of killings in the country—751,785 murders carried out since 1976, which is roughly twenty-seven thousand more than appear in F.B.I. files. States are supposed to report murders to the Department of Justice, but some report inaccurately, or fail to report altogether, and Hargrove has sued some of these states to obtain their records. Using computer code he wrote, he searches his archive for statistical anomalies among the more ordinary murders resulting from lovers’ triangles, gang fights, robberies, or brawls. Each year, about five thousand people kill someone and don’t get caught, and a percentage of these men and women have undoubtedly killed more than once. Hargrove intends to find them with his code, which he sometimes calls a serial-killer detector.
Find out more and download data from Hargrove’s nonprofit the Murder Accountability Project.
Tags: algorithm, murder, New Yorker
Posted by in algorithm, Data Sources, murder, New Yorker
Posted by in algorithm, statistics, xkcd
Last week, Facebook announced that it was making the Trending Topics section more automated. More algorithm-based. Less person-based. On Monday, the section showed a fake news story at the top of the list for several hours.
Nick Statt for the Verge on the human element:
The changes instituted on Friday didn’t throw all of that away; Facebook has been slowly stripping away the human element of Trending Topics for months now. Rather, it marked the moment Facebook decided its algorithmic approach was more favorable, or perhaps more cost-effective and less damaging. But in shifting the reins to engineers, the company has minimized the kind of news judgment typically exercised by journalists and editors. Now, just a few days later, we’re realizing just how important that human element was.
Data. Always open for interpretation.
Posted by in algorithm, Facebook, Mistaken Data
I knew I had seen another automated video game thing before. Tom Murphy published work a couple of years ago on creating a computer program that learns how to play classic Nintendo games.
It's a different approach than the project from earlier this week. The program is generalized, so it can be applied to games besides Mario Bros., and the computer can eventually beat more than one level. Plus, the program finds and takes advantage of glitches in games.
Watch Murphy's video explanation below, and geek out about Lexicographic Orderings. Oh my.
https://www.youtube.com/watch?v=xOCurBYI_gY
Here's the paper (pdf) if you want more details. The best part is the title: "The First Level of Super Mario Bros. is Easy with Lexicographic Orderings and Time Travel...after that it gets a little tricky."
Tags: algorithm, Mario, Nintendo, video games
Posted by in algorithm, Mario, Nintendo, statistics, video games
Seth Bling made a bot — MarI/O — that automatically learns how to play Super Mario World. It's based on research by Kenneth O. Stanley and Risto Miikkulainen from 2002 that uses neural networks that evolve with a genetic algorithm. MarI/O starts out really dumb, just standing in place, but after enough simulations it get smart enough to navigate the world.
Code available here and the paper from Stanley and Mikkulainen is here.
See also: the genetic algorithm walkers.
Tags: algorithm, machine learning, Mario, neural networks
Posted by in algorithm, machine learning, Mario, neural networks, statistics
