A Quantum Diaries Survivor

What is multithreading? It is the use of multiple processors to perform tasks in parallel by a single computer program. I have known this simple fact for over thirty years, but funnily enough I never explored it in practice. The reason is fundamentally that I am a physicist, not a computer scientist, and as a physicist I tend to stick with a known skillset to solve my problems, and to invest time in more physics knowledge than software wizardry. You might well say I am not a good programmer altogether, although that would secretly cause me pain. I would answer that while it is certainly true that my programs are ugly and hard to read, they do what they are supposed to do, as proven by a certain record of scientific publications. 

Wait a minute - why is an article about automatic differentiation labeled under the "Physics" category? Well, I will explain that in a minute. First of all, let me explain what automatic differentiation is. Computing derivatives of functions is a rather error-prone job. Maybe it is me, but if you give me a complex function where the dependence on a variable is distributed in several sub-functions, I am very likely to find N different results if I do it N times. Yes, I am 57 years old, and I should be handling other things and leave these calculations to younger lads, I agree. 

Although researchers in fundamental science have a tendency to "stick to what works" and avoid disruptive innovations until they are shown to be well-tested and robust, the recent advances in computer science leading to the diffusion of deep neural networks, ultimately stemming from the large increases in performance of computers of the past few decades (Moore's law), cannot be ignored. And they haven't - the 2012 discovery of the Higgs boson, for instance, heavily used machine learning techniques to improve the sensitivity of the acquired particle signals in the ATLAS and CMS detectors.

Summer is supposedly a period where people take it a tad easier, spend some vacation time away from anything that is work-related, and "tune out" of the deadlines and rythms of daily work activities that dominate their existence at other times of the year. 

One of the things that keeps me busy these days is the organization of a collective publication by a number of experts in artificial intelligence and top researchers in all areas of scientific investigation. I will tell you more of that project at another time, but today I wish to share with you the first draft of a short introduction I wrote for it. I am confident that it will withstand a number of revisions and additions, so by the time we will eventually publish our work, the text will be no doubt quite different from what you get to read here, which makes me comfortable about pre-publishing it.

Blitz games on the internet are a lot of fun, if you love chess as I do. Perhaps a good share of the fun is due to the complete chaos that may arise, when you have few seconds left on the clock and decisions have to be taken instantly. But sometimes you may happen to play correct chess, too. It is exceedingly rare, and when it happens it is a good indication that you have been able to hold on to clear strategic ideas leading your play into the correct decisions.

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).