At twitter.com/togelius/status/1328404390114435072 called out on DeepMind Lab2D for not giving them credit on prior work!As seen from web.archive.org/web/20220331022932/http://gvgai.net/ though, DeepMind sponsored them at some point.
This very much looks like like GVGAI which was first released in 2014, been used in dozens (maybe hundreds) of papers, and for which one of the original developers was Tom Schaul at DeepMind...
This is notably what the United States emerged to be after World War II. But it was likely what Nazi Germany also was, and many other superpowers.
Ciro Santilli feels that much more relevant would be to also include academia as in "military-industrial-academic" complex, the Wikipedia page actually mentions precedents to this idea.
The addition of congress/politicians is also relevant.
It is basically in this context that American science and technology flourished after World War II, including notably the development of quantum electrodynamics, Richard Feynman being a prototypical example, having previously worked on the Manhattan Project.
Ciro Santilli would like to fully understand the statements and motivations of each the problems!
Easy to understand the motivation:
- Navier-Stokes existence and smoothness is basically the only problem that is really easy to understand the statement and motivation :-)
- p versus NP problem
Hard to understand the motivation!
- Riemann hypothesis: a bunch of results on prime numbers, and therefore possible applications to cryptographyOf course, everything of interest has already been proved conditionally on it, and the likely "true" result will in itself not have any immediate applications.As is often the case, the only usefulness would be possible new ideas from the proof technique, and people being more willing to prove stuff based on it without the risk of the hypothesis being false.
- Yang-Mills existence and mass gap: this one has to do with finding/proving the existence of a more decent formalization of quantum field theory that does not resort to tricks like perturbation theory and effective field theory with a random cutoff valueThis is important because the best theory of light and electrons (and therefore chemistry and material science) that we have today, quantum electrodynamics, is a quantum field theory.
Tested on Ubuntu 23.10;
git clone https://github.com/google-deepmind/mujoco
cd mujoco
git checkout 5d46c39529819d1b31249e249ca399f306a108ac
mkdir -p build
cd build
cmake ..
make -jNow let's play. Minimal interactive UI simulation of a simple MJCF scene with one falling cube:Test soure code: github.com/google-deepmind/mujoco/blob/5d46c39529819d1b31249e249ca399f306a108ac/sample/basic.cc. The only thing you can do is rotate the scene with the computer mouse it seems. Mentioned at: mujoco.readthedocs.io/en/2.2.2/programming.html#sabasic
bin/basic ../doc/_static/hello.xmlSome more interesting models can be found under the
model/ directory: github.com/google-deepmind/mujoco/tree/5d46c39529819d1b31249e249ca399f306a108ac/model E.g. the imaginary humanoid robot DeepMind used in many demos can be seen with:bin/basic ../model/humanoid/humanoid.xmlA more advanced UI with a few controls:Test soure code: github.com/google-deepmind/mujoco/tree/5d46c39529819d1b31249e249ca399f306a108ac/simulate. Mentioned at: mujoco.readthedocs.io/en/2.2.2/programming.html#sasimulate
bin/simulate ../doc/_static/hello.xmlA very cool thing about that UI is that you can manually control joints. There are no joints in the hello.xml, but e.g. with the humanoid model:under "Control" you move each joint of the robot separately which is quite cool.
bin/simulate ../model/humanoid/humanoid.xmlThere's also a Mentioned at: mujoco.readthedocs.io/en/2.2.2/programming.html#sarecord but TODO that produced a broken video, related issues:
bin/record test executable that presumably renders the simulation directly to a file:bin/record ../doc/_static/hello.xml 5 60 rgb.out
ffmpeg -f rawvideo -pixel_format rgb24 -video_size 800x800 -framerate 60 -i rgb.out -vf "vflip" video.mp4This is the one that hit Ciro Santilli the hardest, coming in at the point in which he started to discern between games and the real world a little better. His parents bought it for him during a trip to Disney World in Florida in 1996 (?), since electronics were much cheaper in the USA.
So as Ciro became older, and turned into a software engineer, he started to become more and more morbidly curious about "N64 internals": tool-assisted speedrun, how the devkit looks like, how games were developed for it, hardware leaks, etc.
Luckily Ciro's mind is not interested enough by that useless shit for Ciro to seriously study it himself. But that's what YouTube is for, right? Why do useless stuff when other more useless people can do it for you?
14 million images with more than 20k categories, typically denoting prominent objects in the image, either common daily objects, or a wild range of animals. About 1 million of them also have bounding boxes for the objects. The images have different sizes, they are not all standardized to a single size like MNIST[ref].
Each image appears to have a single label associated to it. Care must have been taken somehow with categories, since some images contain severl possible objects, e.g. a person and some object.
Official project page: www.image-net.org/
The data license is restrictive and forbids commercial usage: www.image-net.org/download.php. Also as a result you have to login to download the dataset. Super annoying.
How to visualize: datascience.stackexchange.com/questions/111756/where-can-i-view-the-imagenet-classes-as-a-hierarchy-on-wordnet
Subset generators:
- github.com/mf1024/ImageNet-datasets-downloader generates on download, very good. As per github.com/mf1024/ImageNet-Datasets-Downloader/issues/14 counts go over the limit due to bad multithreading. Also unfortunately it does not start with a subset of 1k.
- github.com/BenediktAlkin/ImageNetSubsetGenerator
Unfortunately, since ImageNet is a closed standard no one can upload such pre-made subsets, forcing everybody to download the full dataset, in ImageNet1k, which is huge!
Elements of a Lie algebra can (should!) be seen a continuous analogue to the generating set of a group in finite groups.
For continuous groups however, we can't have a finite generating set in the strict sense, as a finite set won't ever cover every possible point.
But the generator of a Lie algebra can be finite.
And just like in finite groups, where you can specify the full group by specifying only the relationships between generating elements, in the Lie algebra you can almost specify the full group by specifying the relationships between the elements of a generator of the Lie algebra.
The reason why the algebra works out well for continuous stuff is that by definition an algebra over a field is a vector space with some extra structure, and we know very well how to make infinitesimal elements in a vector space: just multiply its vectors by a constant that cana be arbitrarily small.
TODO concrete example, please...
Insanely active poster on Stack Overflow 4chan post (2023-07-03) Updated 2025-07-01 +Created 1970-01-01
archive.ph/Dd3aC web.archive.org/web/20230709141533/https://desuarchive.org/g/thread/94445084/#94448535 desuarchive.org/g/thread/94445084/#94448535
Most of the thread went into pro/anti gay trashtalk due to Ciro using Gay Putin at the time on his Stack Overflow profile as a useless way to protest the Russian invasion of Ukraine.
Some comments:
How does this guy manage to be so active on Stack Overflow? I feel like this disgusting avatar is on at least a quarter of all the active posts.
The answers are always pretty good though.
Obviously severe autism. Also racism homophobia Looks like everything is ok if it's Russia/Chinese...
The only new information:
Reminds me of Xah Lee.
Tested on Ubuntu 20.04:Add to your and then to use it on a shell e.g. with Python 3.9 create the environment with:and then use it with:Now you can use
mkdir -p ~/miniconda3
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda3/miniconda.sh
bash ~/miniconda3/miniconda.sh -b -u -p ~/miniconda3
rm -rf ~/miniconda3/miniconda.sh.bashrc:PATH="$PATH:$HOME/miniconda3/bin"conda create -y -n mytest3.9 python=3.9eval "$(command conda 'shell.bash' 'hook' 2> /dev/null)"
conda activate mytest3.9python and pip normally from inside that mytest3.9 environment.At that time, the exact installer under
latest appears to have been: repo.anaconda.com/miniconda/Miniconda3-py311_23.11.0-2-Linux-x86_64.shIt 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.
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:
- brain-computer interface
- fusion power. The question there is, when is "deep", "too deep"?
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 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.
Currently a redirect page on Wikipedia: en.wikipedia.org/?title=Department_of_Statistics,_University_of_Oxford&redirect=no Newbies!
Why is this not part of the Mathematical Institute of the University of Oxford? Who knows!
This is an important metric, because it takes some time for the quantum operations to propagate, and so the depth of a circuit gives you an idea of how long the coherence time a hardware needs to support a given circuit.
Bibliography:
The Dirac equation can be derived basically "directly" from the Representation theory of the Lorentz group for the spin half representation, this is shown for example at Physics from Symmetry by Jakob Schwichtenberg (2015) 6.3 "Dirac Equation".
The Diract equation is the spacetime symmetry part of the quantum electrodynamics Lagrangian, i.e. is describes how spin half particles behave without interactions. The full quantum electrodynamics Lagrangian can then be reached by adding the internal symmetry.
As mentioned at spin comes naturally when adding relativity to quantum mechanics, this same method allows us to analogously derive the equations for other spin numbers.
Bibliography:
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