- Machine Learning Done Wrong — [M]ost practitioners pick the modeling algorithm they are most familiar with rather than pick the one which best suits the data. In this post, I would like to share some common mistakes (the don’t-s).
- Bandits for Recommendations — A common problem for internet-based companies is: which piece of content should we display? Google has this problem (which ad to show), Facebook has this problem (which friend’s post to show), and RichRelevance has this problem (which product recommendation to show). Many of the promising solutions come from the study of the multi-armed bandit problem.
- Droplets — the Droplet is almost spherical, can self-right after being poured out of a bucket, and has the hardware capabilities to organize into complex shapes with its neighbors due to accurate range and bearing. Droplets are available open-source and use cheap vibration motors and a 3D printed shell. (via Robohub)
- Apple’s App Store Approval Guidelines — some of the plainest English I’ve seen, especially the Introduction. I can only aspire to that clarity. If your App looks like it was cobbled together in a few days, or you’re trying to get your first practice App into the store to impress your friends, please brace yourself for rejection. We have lots of serious developers who don’t want their quality Apps to be surrounded by amateur hour.
"open data" entries
An exploration of themes in Joel Gurin's book Open Data Now.
As governments and businesses — and increasingly, all of us who are Internet-connected — release data out in the open, we come closer to resolving the tiresomely famous and perplexing quote from Stewart Brand: “Information wants to be free. Information also wants to be expensive.” Open data brings home to us how much free information is available and how productive it is in its free state, but one subterranean thread I found in Joel Gurin’s book Open Data Now highlights an important point: information is very expensive.
In this article, I’ll explore a few themes that piqued my interest in Gurin’s book: the value of open data, the expense it entails, the questions of how much we can use and trust it, and the role the general public and the private sector play in bringing us data’s benefits. This is not meant to be a summary or a review of Gurin’s book; it is an exploration of themes that interest me, inspired by my reading of Gurin.
Open, trustworthy, and useful
“Open data” occupies hierarchies of usefulness. One way of describing its usefulness is the structure of its presentation, as Gurin and others such as Tim Berners-Lee have pointed out. Much data is still fairly unstructured, like the reviews and social media status postings that people generate by the millions and that are funneled into eager consumption by marketing analysts. Some data is more structured, existing as tables. And finally, a tiny fragment can be reached through the RESTful APIs supported by libraries in every modern programming language. Read more…