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).
A colloquium with a former thesis intern yesterday brought me to ponder again over the important question of determining whether a student should choose an experimental or rather a theoretical curriculum of studies. It is a problem that arises at a variable point of the student's trajectory depending on the way the university courses offer is structured, but the issue is universal as it revolves around the skills of the students rather than anything else.
The University of Padova has extended until tomorrow, June 8 at 1PM CEST the deadline to submit applications to be enrolled in a Ph.D. course in Physics at the University of Padova. Below I summarize a previous post that describes the opportunity.
With the development of the new generation of large language models (ChatGPT, Bard, etcetera) we are seeing the first hints of an accelerating pace in the progress of artificial intelligence. These innovations may turn out to be good for humanity (with all problems we have already identified, bias, misinformation, work displacement, etc.), but their possibly exponential (and more-than-exponential) rate of change is something we cannot fathom, and is in and of itself the source of large risks.
The world of elementary particles has something in common with our own: there are large inequalities in the properties of particles, as in the properties of human beings. The heaviest particle, the top quark, with its estimated mass of 172 GeV is five orders of magnitude heavier than the most common matter constituents, the up and down quarks and the electron. Along with the top quark there are three other heavyweight particles that we currently call elementary -perhaps only a label we have to use because we haven't been able to split them further into smaller bits: the Higgs boson (125 GeV), the Z boson (91 GeV), and the W boson (80 GeV).
The recent developments in artificial intelligence, most notably the demonstration of the weird power of GPT4 and other large language models, have brought the scientific community to ponder on some very foundational questions - What is conscience? What is intelligence? Can machines really think? Can machines really become conscient? (Below, the answer of ChatGPT to my silly question on self-awareness.)
The call for applications to Ph.D. positions at the University of Padova opened yesterday, and it will remain active for less than one month (deadline is June 7th at 1PM CEST).
The USERN organization (Universal Scientific Education and Research Network) will soon issue its next bulletin, to which I contribute with an opening message in the function of the president of the organization. I thought the contents of the message would be of some interest to some of my readers here, so I decided to attach below my original text. Before we go there, though, I would invite all of you to learn about USERN by visiting its web site, and consider becoming a member (it is free!) - or even better, if you share our views, support us!A message from the USERN President
A revolution is taking place, but we seem to not yet realize it. Paradigm shifting technologies often produce an abrupt transition when they get adopted. However, that transition is not easy to recognize early on: the effects of an exponential trend appear linear at the begninning, so the explosive force of the transition that occurs a little later takes many by surprise.
Today I am traveling to Banff, a pleasant mountain resort in western Canada, to attend a workshop on systematic uncertainties. Yes, you heard that right - a bunch of physicists and statisticians will be gathering in a secluded location for a whole week, with the sole purpose of debating on that exotic topic. How weird is that? I bet most of you don't think much of systematic uncertainties. What are they, anyway?Known unknowns
The Swedish Institute for Space Physics (IRF) is located in Kiruna, a minerary town close to the northern tip of Sweden, above the arctic polar circle. The Space Campus of Kiruna is one of three different centres - the IRF space lab, the EISCAT center, and the Lulea University of Technology department of Space Technology and Atmospheric Science. Close to the complex is the Esrange space center, a multipurpose launching base benefiting from a large unpopulated impact and recovery area.Above: the IRF from the visitor parking
In the last couple of days I have been busy writing a project to explore the potential of artificial intelligence to extract more information from particle detectors. In fact, while the development of these instruments in the course of the past 80 years has closely followed, and sometimes been a driver, of technological developments, I can see the issue of a progressive mis-alignment of detector design with the ultimate potential and final goals of large experiments. That mis-alignment is due to the rapid evolution of deep learning tools. They have now become the real elephant in the room in this area of studies: an entity that is present, but that risks being ignored despite its enormous impact.
I am exploiting my column today to advertise a workshop that the collaboration I lead, MODE, is organizing at Princeton University this coming July. The workshop, the third of its series, aims to bring together physicists and computer scientists to join forces in the solution of complex optimization problems in experiment design.
