I once was an active chessplayer, but work duties have long taken tournaments off my plate - I simply do not have the time to sit through long hours of chess battles. So I play blitz online on chess.com (my handle is "tommasodorigo", in case you wondered).
No.... Ok, ok, I will elaborate. But first I feel the need to explain what we are talking about here, to anybody who does not have a Ph.D. in particle physics and is still reading this column.Background: The Tevatron, CDF, and the W boson
Ever since experimental physics was a thing, the worth of scientists could be appraised by how carefully they designed their experiments, making sure that their devices could answer as precisely as possible the questions that crowded their mind. Indeed, the success of their research depended on making the right choices on what apparatus to build, with what materials, what precise geometry, and how to operate it for best results.(Above: Ramsay and Pierre Curie in their lab)
The late Martin Gardner held for many years a fantastic feature in the popular Scientific American magazine. It was called "Mathematical Games", and it was worth the whole magazine by itself, although SciAm always featured many interesting articles about scientific advancements. Upon picking the magazine up at a newsstand, "Mathematical Games" was the first article I would read as a teenager eager to learn about the endless tricks Gardner taught there, in his wonderful tale-telling style.
Like the vast majority of readers of this column, I very strongly condemn the Russian invasion of Ukraine and the ensuing atrocities. War is never an answer to international controversies. And I would like to add: I am in favor of all sanctions that financially hit the aggressor, including cutting Russia from use of international banking circuits and similar impactful actions.That said, I will say here what I think about this ongoing rush to find ways to hurt a country whose citizens are largely innocent of their leader's crimes. I think most of these creative initiatives are counter-productive, reaching the nonsensical, the irrational, and the plain nuts.
Over the course of the past two decades we have witnessed the rise of deep learning as a paradigm-changing technology. Deep learning allows algorithms to dramatically improve their performance on multivariate analysis tasks. Deep neural networks, in particular, are very flexible models capable of effective generalization of available data, with unbeatable results in their predictions. Indeed, from the outside, nowadays it looks as if the game changer in predictive analysis was the construction of large neural network architectures. But it was not.
The behaviour of matter at quantum level includes a number of surprising effects, which we are lucky enough to be able to study and observe in different physical systems. Some of these effects are due to the radically different properties of particles endowed with integer amounts of spin (which we call bosons), and particles endowed with half-integer amounts of spin (which we call fermions).
Creativity is one of the things that really makes us human - in fact, a number of human activities which we identify as specific of our nature, and which we believe could hardly be mimicked by artificial intelligence, rely on our inventiveness and capability of creating new objects, images, concepts, methods, or finding new purpose in old tools. Art, among all of these activities, is the quintessential result of our willful act of creating beauty - or even ugliness, if that is considered a worthy pursuit by the artist.
Although unconventional, the ideas of Gregory Ryskin on vacuum energy sound interesting to me, so I invited him to share them with you in this guest post. Ryskin's physics journey began with fluid dynamics, first in Russia, then in the US, at Caltech. Later, the flow of complex fluids, such as polymer solutions or liquid crystals. Then Brownian motion and Markov processes. In 2000, he became interested in geology and geophysics, particularly in the causes of mass extinctions and the origin of the Earth’s magnetic field. His current research is focused on cosmology. His academic home is Northwestern University, Department of Chemical and Biological Engineering.The text below is Gregory's.---
If you are a follower of Science20, you probably know that I have always been very liberal in this column about what deserves to be mentioned as a possible new idea in Physics. I even invited some "non-conventional", independent scientists to write about their own ideas and pet theories here, in many occasions. I do not think this collides with the main purpose of this blog, which is to discuss real science and do some proper outreach and dissemination. In fact, I find it instructive and enlightening on what really Science is.
The title of this post is no news for particle physicists - particle detectors are complex instruments and they work by interpreting the result of stochastic phenomena taking place when radiation interacts with the matter of which detectors are built, and it looks only natural that deep learning algorithms can help improve our measurements in such a complex environment.However, in this post I will give an example of something qualitatively different to providing an improvement of a measurement: one where a deep convolutional network model may extract information that we were simply incapable of making sense of. This means that the algorithm allows us to employ our detector in a new way.
The neutron, discovered in 1932 by Chadwick, is a fascinating particle whose existence allows for the stability of heavy nuclei and a wealth of atoms of different properties. Without neutrons, Hydrogen would be the only stable element: protons cannot be brought together and bound in a stable system, so e.g. Helium-2 (an atom made of two protons with two electrons) is very short-lived, as are atoms with more protons and no neutrons. So our Universe would be a very dull place.
