- How to Design Applied Filters — The most frequently observed issue during usability testing were filtering values changing placement when the user applied them – either to another position in the list of filtering values (typically the top) or to an “Applied filters” summary overview. During testing, the subjects were often confounded as they noticed that the filtering value they just clicked was suddenly “no longer there.”
- Twitter Heron — a real-time analytics platform that is fully API-compatible with Storm […] At Twitter, Heron is used as our primary streaming system, running hundreds of development and production topologies. Since Heron is efficient in terms of resource usage, after migrating all Twitter’s topologies to it we’ve seen an overall 3x reduction in hardware, causing a significant improvement in our infrastructure efficiency.
- ntm — an implementation of neural Turing machines. (via @fastml_extra)
- Bayesian Truth Serum — a scoring system for eliciting and evaluating subjective opinions from a group of respondents, in situations where the user of the method has no independent means of evaluating respondents’ honesty or their ability. It leverages respondents’ predictions about how other respondents will answer the same questions. Through these predictions, respondents reveal their meta-knowledge, which is knowledge of what other people know.
"filters" entries
Four short links: 3 June 2015
Filter Design, Real-Time Analytics, Neural Turing Machines, and Evaluating Subjective Opinions
Four short links: 27 January 2014
Real Time Exploratory Analytics, Algorithmic Agendas, Disassembly Engine, and Future of Employment
- Druid — open source clustered data store (not key-value store) for real-time exploratory analytics on large datasets.
- It’s Time to Engineer Some Filter Failure (Jon Udell) — Our filters have become so successful that we fail to notice: We don’t control them, They have agendas, and They distort our connections to people and ideas. That idea that algorithms have agendas is worth emphasising. Reality doesn’t have an agenda, but the deployer of a similarity metric has decided what features to look for, what metric they’re optimising, and what to do with the similarity data. These are all choices with an agenda.
- Capstone — open source multi-architecture disassembly engine.
- The Future of Employment (PDF) — We note that this prediction implies a truncation in the current trend towards labour market polarization, with growing employment in high and low-wage occupations, accompanied by a hollowing-out of middle-income jobs. Rather than reducing the demand for middle-income occupations, which has been the pattern over the past decades, our model predicts that computerisation will mainly substitute for low-skill and low-wage jobs in the near future. By contrast, high-skill and high-wage occupations are the least susceptible to computer capital. (via The Atlantic)
Four short links: 6 January 2014
Tiny Emulator, iBeacon iPwn, Filter Principles, and Steadicam
- 4043-byte 8086 Emulator — manages to implement most of the hardware in a 1980’s era IBM-PC using a few hundred fewer bits than the total number of transistors used to implement the original 8086 CPU. Entry in the obfuscated C contest.
- Hacking the CES Scavenger Hunt — At which point—now you have your own iBeacon hardware—you can just go ahead and set the UUID, Major and Minor numbers of your beacon to each of the CES scavenger hunt beacon identities in turn, and then bring your beacon into range of your cell phone running which should be running the CES mobile app. Once you’ve shown the app all of the beacons, you’ll have “finished” the scavenger hunt and can claim your prize. Of course doing that isn’t legal. It’s called fraud and will probably land you in serious trouble. iBeacons have great possibilities, but with great possibilities come easy hacks when they’re misused.
- Filtering: Seven Principles — JP Rangaswami laying down some basic principles on which filters should be built. 1. Filters should be built such that they are selectable by subscriber, not publisher. I think the basic is: 0: Customers should be able to run their own filters across the information you’re showing them.
- Tremor-Correcting Steadicam — brilliant use of technology. Sensors + microcontrollers + actuators = a genuinely better life. Beats figuring out better algorithms to pimp eyeballs to Brands You Love. (via BoingBoing)
Subscription vs catchment
As sources become less important, filters are the natural target for those who want to sway opinion.
When people are trawling so many content sources, it no longer pays to concentrate on sources at all. It makes much more sense to study how the trawlers work and become part of the filtering infrastructure.
Four short links: 2 July 2010
Street Demographics, Hack for Africa, Opportunity Spotting, News or Filters?
- Brien Lane, Melbourne — an alleyway painted with statistics about the area. Urban spaces as screens. Check out the other photos. (via Pete Warden)
- Apps 4 Africa — from US State Department, The challenge is to build the best digital tools to address community challenges in areas ranging from healthcare to education and government transparency to election monitoring. (via Clay Johnson)
- Hopeful Monsters and the Trough of Disillusionment (Berg London) — this was a great Foo talk, lovely to see the ideas written up and circulated widely.
- Tyranny of the Daily 10 Percent (Julie Starr) — do we have a production quality problem, or do we have a filter problem? Intersection of two trends we’ve seen: “news reinvention” and “information overload”. I find myself wanting to spend more time quantifying what we’ve already got that’s good and being clearer about what we think is missing, before thinking about what to replace it with and how to foot the bill.