New data analysis competitions

How-to

  • I don't remember if I talked about this before, but ggguitar is an R library that uses ggplot2 to display guitar chords as tablatures.

  • Machine Learning Bookshelf.

  • Bowtie, a library for writing dashboards in Python.

Tech

Baidu is demonstrating some of its most recent tech advancements in novel ways, including a partnership with KFC China (yes, the fried chicken KFC). The search giant sometimes referred to as the 'Google of China' partnered with KFC to open a new "smart restaurant" in Beijing, which employs facial recognition to make recommendations about what customers might order, based on factors like their age, gender and facial expression.

Concerns have been growing about AI's so-called "white guy problem2 and now scientists have devised a way to test whether an algorithm is introducing gender or racial biases into decision-making.

Beijing and other Chinese cities are choking under a blanket of smog. It's so thick in Tianjin that planes can't land. Authorities have issued the first "red alert" of 2016, and 1,200 Beijing-area factories were ordered to shut down or to reduce production, according press reports.

This winter, officials will be equipped with forecasting tools from IBM and Microsoft that they tested last year. IBM's tool, used by the city government, is designed to incorporate data from traditional sources, such as the 35 official multipollutant air-quality monitoring stations in Beijing, and lower-cost but more widespread sources, such as environmental monitoring stations, traffic systems, weather satellites, topographic maps, economic data, and even social media. Microsoft's system incorporates data from over 3,000 stations around the country. Both IBM's and Microsoft's tools blend traditional physical models of atmospheric chemistry with data-hungry statistical tools such as machine learning to try to make better forecasts in less time.

Speaking at a recent AI conference in Barcelona, Spain, Ian Goodfellow, a research scientist at OpenAI who has done pioneering work on deceiving machine-learning systems, said attacking the systems is easy. "Almost anything bad you can think of doing to a machine-learning model can be done right now," he said. "And defending it is really, really hard."