Happenings from our public calendar
Forum Eco-villages, Les Diablerets, August 25
Smart villages, energy autonomy, sustainability.
Lab212 opening at MuDA, Zürich, August 27
A Paris based interdisciplinary art collective, cofounded in 2008 by some friends. By their sensible, poetic and tangible approach, they try to empower people and cast a different light on the technology surrounding our daily life. Lab212’s lines of enquiry include appropriation and narration.
Founding the Open Network Infrastructure Association, Zürich, August 29
- Promoting, installing and operating one or many open networks, freely accessible by anyone.
- Organizing events and meetups for both the open network infrastructure community and for the broader public.
- Offering educational material and workshops in the area of open infrastructure.
Mad Scientist Festival, Bern, September 9
200 years after Frankenstein: artificial beings become reality. This year, the festival revolves entirely around Artificial Intelligence and the man-machine relationship. Monster or best friends? Siri and Co. invite to play, meet research robots to digital art.
2016.aalhackathon.eu, St. Gallen, September 25
Propose a challenge or workshop, share data, sign up for the upcoming Hack for Ageing Well.
Opinions worthy of discourse
In image analysis, years of tedious visual examination become a few hours via an automated pipeline. Algorithms can also tease out subtleties that would elude manual analysis. Computational work is particularly attractive if you like a team approach to science, and enjoy playing an indispensible role as part of a larger effort. Seeing a biologist overjoyed at the results of computational analysis is a great job perk. -Anne Carpenter, Broad Institute
Marc Lee creates network-oriented interactive art installations, new media art and online artworks that often uses social media as medium
a new campaign calling for more open and accessible data on local elections across the UK
Just across the bridge from Facebook HQ, a radical education experiment is underway.
The media is ruining science (Washington Post)
The best way to challenge scientific findings is simply to find the time and read the original study. Evaluate the methodology for yourself. Are there legitimate limitations to the research? Does the sample size seem large enough? If at any point the answers to these questions seem way over your head and the long gobbledygook of equations looks like another language, try Googling it. Check out other articles on the topic, or simply start with the basics.
The lines are blurring, in some cases dramatically, between what it means to be a media company and what it means to be a technology firm.
A new crop of startups is taking aim at bait-and-switch listings and sky-high broker fees.
Wikipedia Is Not Therapy! (Backchannel)
How the online encyclopedia manages mental illness and suicide threats in its volunteer community.
The perspective of distributed Bayesian inference therefore reveals how collective rationality emerges from the boundedly rational decision mechanisms people use.
Learnings to ponder and share
In this article I am going to show you how I was able to extract and process some information from Wikipedia only using a combination of common bash utilities like curl and grep.
This post is part of a series covering the exercises from Andrew Ng’s machine learning class on Coursera. The original code, exercise text, and data files for this post are available here.
Public Web Portal for NASA Research (openNASA)
For all you fellow space buffs! “Citizens now have easy access to NASA-funded research data, including peer-reviewed scholarly journals and papers in juried conference proceedings.”
For Beginners in R, here is a 15 page example based tutorial that covers the basics.
This demo does handwritten digit recognition by evaluating a Convolutional Neural Network on the GPU with WebGL. The network was trained in TensorFlow by this script, and the network was then reimplemented on the GPU by hand with WebGL.
If you passed high school math and can hack around in Python, I want to teach you Deep Learning.
The very simple model of fire used for the effect contains four parts: a heat source (generally something that is burning, i.e the top of a torch), convection (the fact that hot stuff tends to rise and cool stuff tends to sink), diffusion (heat will gradually spread out), and cooling. The basic idea is to start with a grid of pixels…
[This] is the central repository for how we run the company. As part of our dedication to being as open and transparent as possible, the handbook is open to the world, and we welcome feedback.