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.
Frank D. Smith (Tony Smith for his friends) has been following this blog since the beginning. He is an independent researcher who is very interested in phenomena connected with the top quark and the Higgs boson. He has a theory of his own and he has been trying to check whether LHC data is compatible or not with it. His ideas are reported here as a guest post, as a tribute to his faithfulness to this site. Of course the views expressed below are his own, as I retain a healthy dose of scepticism to any bit of new physics apparent in today's data... Also, I will comment in the thread below to inform the reader of what my ideas are on his interpretation of public LHC results.
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.
These days the use of machine learning is exploding, as problems which can be solved more effectively with it are ubiquitous, and the construction of deep neural networks or similar advanced tools is at reach of sixth graders. So it is not surprising to see theoretical physicists joining the fun. If you think that the work of a particle theorist is too abstract to benefit from ML applications, you better think again.