Archive for Bayes for Good

Nature tea[dbits]

Posted in Books, pictures, University life, Wines with tags , , , , , , , , , , , , , , , , on February 28, 2019 by xi'an

A very special issue of Nature (7 February 2019, vol. 556, no. 7742). With an outlook section on tea, plus a few research papers (and ads) on my principal beverage. News about the REF, Elsevier’s and Huawei’s woes with the University of California, the dangerous weakening of Title IX by the Trump administration, and a long report on the statistical analysis of Hurricane Maria deaths, involving mostly epidemiologists, but also Patrick Ball who took part in our Bayes for Good workshop at CIRM. Plus China’s food crisis and ways to reduce cropland losses and food waste. Concerning the tea part(y), a philogenetic study of different samples led to the theory that tea was domesticated thrice, twice in Yunnan (China) and once in Assam (India), with a divergence estimated at more than twenty thousand years ago. Another article on Pu-Ehr, with the potential impacts of climate change on this very unique tea. With a further remark that higher altitudes increase the anti-oxydant level of tea… And a fascinating description of agro-forestry where tea and vegetables are grown in a forest that regulates sun exposure, moisture evaporation, and soil nutrients.

crowdsourcing, data science & machine learning to measure violence & abuse against women on twitter

Posted in Books, Statistics, University life with tags , , , , , , , , , on January 3, 2019 by xi'an

Amnesty International just released on December 18 a study on abuse and harassment on twitter account of female politicians and journalists in the US and the UK. Realised through the collaboration of thousands of crowdsourced volunteers labeling  tweets from the database and the machine-learning expertise of the London branch of ElementAI, branch driven by my friend Julien Cornebise with the main purpose of producing AI for good (as he explained at the recent Bayes for good workshop). Including the development of an ML tool to detect abusive tweets, called Troll Patrol [which pun side is clear in French!]. The amount of abuse exposed by this study and the possibility to train AIs to spot [some of the] abuse on line are both arguments that support Amnesty International call for the accountability of social media companies like twitter on abuse and violence propagated through their platform. (Methodology is also made available there.)

off to Luminy for a second Bayesian week

Posted in Statistics with tags , , , , , , , , , on November 23, 2018 by xi'an