Ciro Santilli @cirosantilli 40

Composed by Wang Huiran in 1960.

The Yi people are one of the 55 Chinese ethnic minorities officially recognized by the Chinese government.

MacKenzie Bezos' new husband after she divorced Bezos.

Science teacher at the Lakeside School in Seattle.

www.dailymail.co.uk/femail/article-9338723/Who-billionaire-Mackenzie-Scotts-new-husband-Dan-Jewett.html Who IS billionaire Mackenzie Scott's new husband Dan Jewett?

A charismatic, perfect-English-accent (Received Pronunciation) physicist from University of Cambridge, specializing in quantum field theory.

He has done several "vulgarization" lectures, some of which could be better called undergrad appetizers rather, a notable example being Video "Quantum Fields: The Real Building Blocks of the Universe by David Tong (2017)" for the prestigious Royal Institution, but remains a hardcore researcher: scholar.google.com/citations?hl=en&user=felFiY4AAAAJ&view_op=list_works&sortby=pubdate. Lots of open access publications BTW, so kudos.

The amount of lecture notes on his website looks really impressive: www.damtp.cam.ac.uk/user/tong/teaching.html, he looks like a good educator.

David has also shown some interest in applications of high energy mathematical ideas to condensed matter, e.g. links between the renormalization group and phase transition phenomena. TODO there was a YouTube video about that, find it and link here.

Ciro Santilli wonders if his family is of East Asian, origin and if he can still speak any east asian languages. "Tong" is of course a transcription of several major Chinese surnames and from looks he could be mixed blood, but as mentioned at www.ancestry.co.uk/name-origin?surname=tong it can also be an English "metonymic occupational name for a maker or user of tongs". After staring at his picture for a while Ciro is going with the maker of tongs theory initially.

Debugging sucks. But there's also nothing quite that "oh fuck, that's why it doesn't work" moment, which happens after you have examined and placed everything that is relevant to the problem into your brain. You just can't see it coming. It just happens. You just learn what you generally have to look at so it happens faster.

Example: Figure "caesium-137 decay scheme"

Deep learning is the name artificial neural networks basically converged to in the 2010s/2020s.

It is a bit of an unfortunate as it suggests something like "deep understanding" and even reminds one of AGI, which it almost certainly will not attain on its own. But at least it sounds good.

github.com/deepmind/lab/tree/master/game_scripts/levels/contributed/dmlab30 has some good games with video demos on YouTube, though for some weird reason they are unlisted.

TODO get one of the games running. Instructions: github.com/deepmind/lab/blob/master/docs/users/build.md. This may helpgithub.com/deepmind/lab/issues/242: "Complete installation script for Ubuntu 20.04".

It is interesting how much overlap some of those have with Ciro's 2D reinforcement learning games

The games are 3D, but most of them are purely flat, and the 3D is just a waste of resources.

Gridworld version of DeepMind Lab.

Open sourced in 2020: analyticsindiamag.com/deepmind-just-gave-away-this-ai-environment-simulator-for-free/

A tiny paper: arxiv.org/pdf/2011.07027.pdf

Very similar to gvgai, Julian Togelius actually called them out on that: DeepMind Lab2D vs gvgai.

TODO get running, publish demo videos on YouTube.

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:

A suggested at Physics from Symmetry by Jakob Schwichtenberg (2015) chapter 3.9 "Elementary particles", it appears that in the Standard Model, the behaviour of each particle can be uniquely defined by the following five numbers:

E.g. for the electron we have:

Once you specify these properties, you could in theory just pluck them into the Standard Model Lagrangian and you could simulate what happens.

Setting new random values for those properties would also allow us to create new particles. It appears unknown why we only see the particles that we do, and why they have the values of properties they have.

Given a matrix $A$ with metric signature containing $m$ positive and $n$ negative entries, the indefinite orthogonal group is the set of all matrices that preserve the associated bilinear form, i.e.:Note that if $A=I$, we just have the standard dot product, and that subcase corresponds to the following definition of the orthogonal group: Section "The orthogonal group is the group of all matrices that preserve the dot product".

As shown at all indefinite orthogonal groups of matrices of equal metric signature are isomorphic, due to the Sylvester's law of inertia, only the metric signature of $A$ matters. E.g., if we take two different matrices with the same metric signature such as:and:both produce isomorphic spaces. So it is customary to just always pick the matrix with only +1 and -1 as entries.