Ciro Santilli @cirosantilli 37

Ciro Santilli is a fan of this late 2010's buzzword.

It basically came about because of the endless stream of useless software startups made since the 2000's by one or two people with no investments with the continued increase in computers and Internet speeds until the great wall was reached.

Deep tech means not one of those. More specifically, it means technologies that require significant investment in expensive materials and laboratory equipment to progress, such as molecular biology technologies and quantum computing.

And it basically comes down to technologies that wrestle with the fundamental laws of physics rather than software data wrangling.

Computers are of course limited by the laws of physics, but those are much hidden by several layers of indirection.

Full visibility, and full control, make computer tasks be tasks that eventually always work out more or less as expected.

The same does not hold true when real Physics is involved.

Physics is brutal.

To start with, you can't even see your system very clearly, and often doing so requires altering its behaviour.

For example, in molecular biology, most great discoveries are made after some new technique is made to be able to observe smaller things.

But you often have to kill your cells to make those observations, which makes it very hard to understand how they work dynamically.

What we would really want would be to track every single protein as it goes about inside the cell. But that is likely an impossible dream.

The same for the brain. If we had observations of every neuron, how long would it take to understand it? Not long, people are really good at reverse engineering things when there is enough information available to do so, see also science is the reverse engineering of nature.

Then, even when you start to see the system, you might have a very hard time controlling it, because it is so fragile. This is basically the case of quantum computing in 2020.

It is for those reasons that deep tech is so exciting.

The next big things will come from deep tech. Failure is always a possibility, and you can't know before you try.

But that's also why its so fun to dare.

Stuff that Ciro Santilli considers "deep tech" as of 2020:

In leanpub you write your book in a markdown variant they call Markua, marketed as "markdown for books".

TODO is there a reference implementation that runs locally for HTML output? Or the only reference implementation is closed under leanpub?

Spec: markua.com/

A squeezed coherent state of light.

Oh, and if it weren't enough, mathematicians have a separate name for the damned nabla symbol : "del" instead of "nabla".

TODO why is it called "Del"? Is is because it is an inverted uppercase delta?

The first Bitcoin exchange. Coded as a hack, and they didn't manage to fix the hacks as the site evolved in a major way, which led to massive hacks.

Their creation is clearly visible on the archive history of bitcoin.org: web.archive.org/web/20100701000000*/bitcoin.org which started having massively more archives since Mt. Gox opened.

The one parameter subgroup of a Lie group for a given element $M$ of its Lie algebra is a subgroup of $G$ given by:

Intuitively, $M$ is a direction, and $t$ is how far we move along a given direction. This intuition is especially vivid in for example in the case of the Lie algebra of $SO(3)$, the rotation group.

One parameter subgroups can be seen as the continuous analogue to the cycle of an element of a group.

LaTeX subset that output nicely to HTML.

Too insane though due to LaTeX roots, better just move to newer HTML-first markups like OurBigBook Markup or markdown.

A good definition is that the sparse matrix has non-zero entries proportional the number of rows. Therefore this is Big O notation less than something that has $N^2$ non zero entries. Of course, this only makes sense when generalizing to larger and larger matrices, otherwise we could take the constant of proportionality very high for one specific matrix.

Of course, this only makes sense when generalizing to larger and larger matrices, otherwise we could take the constant of proportionality very high for one specific matrix.