Ciro Santilli @cirosantilli 40

Allows us to draw with JavaScript pixel by pixel! Great way to create computational physics demos!

Here is an animation demo with some useful controls:

new class extends OurbigbookCanvasDemo {
  init() {
    super.init('hello');
    this.pixel_size_input = this.addInputAfterEnable(
      'Pixel size',
      {
        'min': 1,
        'type': 'number',
        'value': 1,
      }
    );
  }
  draw() {
    var pixel_size = parseInt(this.pixel_size_input.value);
    for (var x = 0; x < this.width; x += pixel_size) {
      for (var y = 0; y < this.height; y += pixel_size) {
        var b = ((1.0 + Math.sin(this.time * Math.PI / 16)) / 2.0);
        this.ctx.fillStyle =
          'rgba(' +
          (x / this.width) * 255 + ',' +
          (y / this.height) * 255 + ',' +
          b * 255 +
          ',255)'
        ;
        this.ctx.fillRect(x, y, pixel_size, pixel_size);
      }
    }
  }
}

Bibliography: developer.mozilla.org/en-US/docs/Web/API/Canvas_API/Tutorial/Basic_animations">https://developer.mozilla.org/en-US/docs/Web/API/Canvas_API/Tutorial/Basic_animations

Ah, the Holy Grail of both biology and computer science!

Ciro Santilli feels it is not for his generation though, and that is one of the philosophical things that saddens him the most in this world.

On the other hand, Ciro's playing with the Linux kernel and other complex software which no single human can every fully understand cheer him up a bit. But still, the high level view, that we can have...

For now, Ciro's 2D reinforcement learning games.

By cranks:

www.nature.com/articles/d41586-023-02600-xThey overreached it seems.

Small splits present in all levels due to interaction between the electron spin and the nuclear spin if it is present, i.e. the nucleus has an even number of nucleons.

As the name suggests, this energy split is very small, since the influence of the nucleus spin on the electron spin is relatively small compared to other fine structure.

TODO confirm: does it need quantum electrodynamics or is the Dirac equation enough?

The most important examples:

The Klein-Gordon equation can be written in terms of the d'Alembert operator as:so we can expand the d'Alembert operator in Einstein notation to:

The key takeaway is that setting an explicit value function to an AGI entity is a good way to destroy the world due to poor AI alignment. We are more likely to not destroy by creating an AI whose goals is to "do want humans what it to do", but in a way that it does not know before hand what it is that humans want, and it has to learn from them. This approach appears to be known as reward modeling.

Some other cool ideas:

As mentioned by Craig Venter in 100 Greatest Discoveries by the Discovery Channel (2004-2005), the main outcomes of the project were:

Important predecessors:

As of 2020's and earlier, humans were far far behind. As of 2020s and earlier, even an average personal computers without a GPU, the hallmark of deep learning beats every human.

Chess is just too easy!

Equation "Hydrogen spectral series mnemonic" gives for example from principal quantum number 1 to 2 a difference:which with Planck-Einstein relation gives about 121.6 nm ($2.47\times 10^{1}5$ Hz), which is a reasonable match with the value of 121.567... from the NIST Atomic Spectra Database.

This was the pre-Internet precursor of wikis. This program was likely venerable, shame it predates Ciro Santilli's era.

But the thing was much more bloated it seems, and also included visual programming elements, and WYSISYG UI creation.

A parameter that you choose which determines how the algorithm will perform.

In the case of machine learning in particular, it is not part of the training data set.

Hyperparameters can also be considered in domains outside of machine learning however, e.g. the step size in partial differential equation solver is entirely independent from the problem itself and could be considered a hyperparamter. One difference from machine learning however is that step size hyperparameters in numerical analysis are clearly better if smaller at a higher computational cost. In machine learning however, there is often an optimum somewhere, beyond which overfitting becomes excessive.

Created by Dr. Rod Nave from Georgia State University, where he worked from 1968 after his post-doc in North Wales on molecular spectroscopy.

While there is value to that website, it always feels like it falls a bit too short as too "encyclopedic" and too little "tutorial-like". Most notably, it has very little on the history of physics/experiments.

Ciro Santilli likes this Rod, he really practices some good braindumping, just look at how he documented his life in the pre-social media Internet dark ages: hyperphysics.phy-astr.gsu.edu/Nave-html/nave.html

The website evolved from a HyperCard stack, as suggested by the website name, mentioned at: hyperphysics.phy-astr.gsu.edu/hbase/index.html.

Shame he was too old for CC BY-SA, see "Please respect the Copyright" at hyperphysics.phy-astr.gsu.edu/hbase/index.html.

exhibits.library.gsu.edu/kell/exhibits/show/nave-kell-hall/capturing-a-career has some good photo selection focused on showing the department, and has an interview.

Kell hall is a building of GSU that was demolished in 2019: atlanta.curbed.com/2020/1/31/21115980/gsu-georgia-state-atlanta-kell-hall-demolition-park-library-north