A paper by B. Fornal and B. Grinstein published last week in Physical Review Letters is drawing a lot of interest to one of the most well-known pieces of subnuclear physics since the days of Enrico Fermi: beta decay.

Visual observation of the planets of our solar system has always been an appealing pastime for amateur astronomers, but the digital era has taken away a little bit of glamour to this activity. Until 30 years ago you could spot with your eye more detail than was at reach of normal photography even for large telescopes, so amateur astronomers could contribute to planetary science by producing detailed drawings of the surface of Jupiter, Saturn, Venus, and Mars. 

Playing chess games flawlessly is a super-human endeavour, which even machines are still having a hard time achieving. However, the occasional flawless game does arise in human practice, albeit rarely. Usually it is a grandmaster who pulls it off. The absence of sub-optimal moves can be ascertained by extensive computer analysis these days, so the quality of the moves is not in question. 

Academics direly need objective, meaningful metrics to judge the impact their publications have on their field of expertise. Nowadays any regular Joe will be able to show many authored papers in their CV, and it will be impossible to objectively assess the relative merits of each and every one of them, if you are trying to rank Joe in a list of candidates for tenure, or just a research job at a University. 

CERN has equipped itself with an inter-experimental working group on Machine Learning since a couple of years. Besides organizing monthly meetings and other activities fostering the dissemination of knowledge and active research on the topic, the group holds a yearly meeting at CERN where along with interesting presentations on advances and summaries, there are tutorials to teach participants the use of the fast-growing arsenal of tools that any machine-learning enthusiast these days should master.