Ciro Santilli @cirosantilli  40

What makes lasers so special: Lasers vs other light sources.

Bibliography:

The opposite of elementary particle.

Standard from 2011: abcnotation.com/wiki/abc:standard:v2.1

A decent way to write diatonic music as plaintext!

Conversion to MIDI with abcmidi.

No bend/vibratto/slides :-(

Multitrack volatile: abcnotation.com/wiki/abc:standard:v2.1#multiple_voices

Higher level than Csound: describes the notes only, not the exact waveforms it seems.

Therefore also a bit harder to convert to actual sound: stackoverflow.com/questions/33775336/convert-musicxml-to-wav but possibly easier to convert to LilyPond.

Now they need to create a "MusicCSS" that gives the waveforms! :-)

The usual "let's make a standard without a reference implementation" W3C approach.

Lots of demos.

A computer is a highly layered system, and so you have to decide which layers you are the most interested in studying.

Although the layer are somewhat independent, they also sometimes interact, and when that happens it usually hurts your brain. E.g., if compilers were perfect, no one optimizing software would have to know anything about microarchitecture. But if you want to go hardcore enough, you might have to learn some lower layer.

It must also be said that like in any industry, certain layers are hidden in commercial secrecy mysteries making it harder to actually learn them. In computing, the lower level you go, the more closed source things tend to become.

But as you climb down into the abyss of low level hardcoreness, don't forget that making usefulness is more important than being hardcore: Figure 1. "xkcd 378: Real Programmers".

First, the most important thing you should know about this subject: cirosantilli.com/linux-kernel-module-cheat/should-you-waste-your-life-with-systems-programming

Here's a summary from low-level to high-level:

Some of the earlier computers of the 20th centure were analog computers, not digital.

At some point analog died however, and "computer" basically by default started meaning just "digital computer".

As of the 2010's and forward, with the limit of Moore's law and the rise of machine learning, people have started looking again into analog computing as a possile way forward. A key insight is that huge floating point precision is not that crucial in many deep learning applications, e.g. many new digital designs have tried 16-bit floating point as opposed to the more traditional 32-bit minium. Some papers are even looking into 8-bit: dl.acm.org/doi/10.5555/3327757.3327866

As an example, the Lightmatter company was trying to implement silicon photonics-based matrix multiplication.

A general intuition behind this type of development is that the human brain, the holy grail of machine learning, is itself an analog computer.

This section is about companies that were primarily started as computer makers.

For companies that make integrated circuits, see also: Section "Semiconductor company".

A branch of mathematics that attempts to prove stuff about computers.

Unfortunately, all software engineers already know the answer to the useful theorems though (except perhaps notably for cryptography), e.g. all programmers obviously know that iehter P != NP or that this is unprovable or some other "for all practical purposes practice P != NP", even though they don't have proof.

And 99% of their time, software engineers are not dealing with mathematically formulatable problems anyways, which is sad.

The only useful "computer science" subset every programmer ever needs to know is:

Funnily, due to the formalization of mathematics, mathematics can be seen as a branch of computer science, just like computer science can be seen as a branch of Mathematics!

Evil. Just use Python instead for software, and a well known data file format for data, please.

The main reason Ciro Santilli never touched it is that it feels that every public data set has already been fully mined or has already had the most interesting algorithms developed for it, so you can't do much outside of big companies.

This is why Ciro started Ciro's 2D reinforcement learning games to generate synthetic data and thus reduce the cost of data.

The other reason is that it is ugly.