Ciro Santilli @cirosantilli 40

By writing in English you reach more people.

Writing in any other language is a waste of time.

The reason is simple: English speakers control a huge proportion of the world's GDP.

English is the de-facto Lingua Franca of the second half of the 20th Century, it is the new lingua franca, the new Latin, and there is no escaping it.

Students who don't know English will never do anything truly useful in science and technology. So it is pointless to teach them anything (besides English itself).

See also: having more than one natural language is bad for the world.

Whenever you make a change to your material, people should still be able to access the previous version.

Maybe there was something in the previous version that they needed, and you just removed.

Git + GitHub is the perfect way to do versioning.

en.wikipedia.org/wiki/Help:Footnotes#Footnotes:_using_a_source_more_than_once gives the following method:

Definition, anywhere on article, likely ideally as the first usage:

And then you can use it later on as:which automatically expands the exact same thing, or using the shortcut:

To cite multiple pages of a book: en.wikipedia.org/wiki/Wikipedia:Citing_sources#Citing_multiple_pages_of_the_same_source, the best method is to define and use the reference without adding the p or location in cite as:Do not set the page in cite, otherwise it shows up on the references. Instead we use the {{rp}} template. And then use the reference with the {{r}} template as:or for multiple pages:

TODO:

Ciro Santilli often sees all those genius who are much smarter than him making shitty forum/mailing list posts, they need to learn this:

Examples:

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!