I have always maintained that real mathematics starts when you address the infinite. I don't see how you can get anything interesting (like analysis, differential geometry, topology) without the assumption that the infinite exists.
> uutils now runs the upstream GNU coreutils test suite against itself in CI. That’s the right scale of defense for this class of bug.
That's the minimum, it is absurd that they did not start from that!
The article is badly written. This has nothing to do with fractals, they are talking about the assumption that there is an UV fixed point for all physical laws, which can even be true and personally like. It means there are no ultraviolet divergences and that at some point quantum field theory becomes finite. Over a certain large mass scale (which still can be much smaller than the Plank scale) all the coupling constants freeze, there are no radiative corrections and all is simple and well behaved. The problem is, you need some extra fields to do that, with some symmetry cancelling the divergences over the mass scale (it does not have to be supersymmetry) and we lack a theory for that. Moreover, the low energy physics will be nearly independent from the high energy modifications, so the predictive power is low. Yes, they predicted the Higgs mass, but it could very well be a coincidence. If they could predict something really new, though, then it would become quite interesting.
As a former Theoretical Physicist, this result is remarkable. I myself I tried to use AI for calculations in Perturbative Quantum Field Theory and I was impressed. I agree with the authors: it looks like the future of Theoretical Physics would be more in verification and consistency checking of AI-assisted results rather than in manual calculations.
Nowadays I am on the other part of the fence, I am the interviewer. We are not a FAANG, so we just use a SANE interview process. Single interview, we ask the candidate about his CV and what his expectations are, what are his competences and we ask him to show us some code he has written. That's all. The process is fast and extremely effective. You can discriminate week candidates in minutes.
I am scientific Python programmer. 99% to 100% of my programs require parallelism, but it is ALWAYS embarrassingly trivial parallelism, nothing is ever mutated and I never need locks. Right now I am forced to use multiprocessing to get the performance, with all problems of multiprocessing, the major one being that I need to use more memory. For me using multithreading could mean the difference between running out of memory and not running out memory. The GIL removal matters for people like me, the proponents of the GIL removal comes from the scientific community.
I remember when I took my course of "Theoretical Physics" (so called, it was an Introduction to Quantum Field Theory in actuality) and I realized that particles do not exist. It was quite an enlightenment. Particles are a convenient description of Quantum Fields in the perturbative regime, but what Quantum Fields are is not known, not even at the mathematical level. When you live at frontier of knowledge, as a Theoretical Physicists, reality becomes fuzzy. But that's life.
I was in your situation 20 years ago, as a postdoc in Theoretical Physics. My advice is to contribute to some known Open Source project, so that you have some provable programming skills when entering the job market. In my case I contributed to Python, not by coding, but by writing various articles on advanced features of the language and an essay on the Method Resolution Order that at the time was new and undocumented. Guido van Rossum in person put that essay on the official Python site. Having something like that in your CV helps when looking for an IT job. Nowadays I would probably contribute to Julia, you need something that shows promises but it is not mainstream yet to make a good impression.
However, I do not think there is any standard API to follow, I used "info cfg" but many alternatives were possible. So sysadmins have to explore to find out which is the introspection command to give. Not ideal, but I don't think there is a solution.
Pity that I never met antirez in all this time. I first heard of him from David Welton who was a coworker of mine in 2004, well before the existence of Redis. But hope is not lost. For the moment, I think I will buy his book ;-)
>In my experience teaching programming, we spend very little time helping students actually write code.
>Instead, the mentors mainly deal with problems of tooling – “it says ‘EADDRINUSE’ and crashes”; “git is giving an error”; “npm just says segmentation fault”.
He is doing things wrong. If he wants the students to use modern tools he first need to give a course on the tools and later another one on programming.
Otherwise he must not use modern tools (no git, no react) and use some simplified teaching environment (not ideal, but at least he will not be distracted by the tooling issues).
In my experience (I have to work a lot with Ph.D. students in science with very little IT expertise) the tools are always the issue, while the algorithmic thinking is rarely a problem, so probably a course on the tools is more useful than one on programming.
He must focus on tools that are not the fashion of the moment, so the terminal is okay, git is okay, React is not okay.
I use numba a lot nowadays. Works perfectly well on all platforms (linux, windows, mac, even the M1) and gives speedups as expected (few percent for already well vectorized numpy code, and extra-large speedups for loopy code). I strongly recommend it for the performance critical part of your code. Many things are not supported yet, so it has to be used with care. I remember I needed a missing scipy special function and I the end I implemented it myself by vectorizing math.erf: it was surprisingly easy to do and a big success in terms of performance.
Having been on both sides of the peer review process in my field (Theoretical Physics), I say we should completely abolish it. Just publish the papers on the preprint archives. Some will be bad, some will be good, and you will know the impact of a paper after 10 years, nothing different from how it is now. Currently the very big majority of the effort in the reviews (on both sides of the process) is wasted time.
Speaking as a physicist, I have always hated this nomenclature. There is no vacuum at all. The universe is filled with quantum fields, they are the source of the Casimir effect. Terms as "vacuum energy" are simply a bad naming, the energy comes from the quantum field, which is a very physical object even if not intuitive, as anything quantum.
I work as a research engineer in Italy. My path was academia (Physics)->industry (Finance)->mix. The "mix" where I am now is a strange case: it is located inside an university, nearly everybody has a PhD and we also have PhD students, but we are not academia, it is a private no profit company doing applied research. I am getting the same money I was getting in the industry but also 7+ weeks of vacation per year, more autonomy and less stress. Plus, I am not working to make rich people richer.
Most of my job is about optimizing numeric code, lots of numpy, pandas, numba but also thinking of new algorithms. I get respect and they put my name in the research papers they publish, even if I did not write a word in the paper and have no knowledge at all of the underlying science ;-)
Such companies are rare but they do exist. Much better than academia because there is no bureaucracy. I got in because of a friend of a friend...