Simon working on his proof of the Fundamental Theorem of Arithmetic (he got stuck and then searched for existing proofs online).
Simon had his first public performance in front of a large audience last Saturday (February 9, 2019): he spoke about his Times Tables Visualization project at the Processing Community Day in Amsterdam!
Simon writes: You can access the code of the poster and the animation (and the logo for my upcoming company!) and download the presentation in PowerPoint, on GitHub at https://github.com/simon-tiger/times_tables
If you’d like to buy a printed copy of the poster, please contact me and I’ll send you one. Status: 3 LEFT.
How many bits will computer operation memory have and how many do we need to have to link every single particle in the Universe to the internet? And how useful are quantum computers?
Simon was watching Daniel Shiffman’s live coding lesson on Wednesday, and when fluid dynamics and Navier-Stokes equations came up (describing the motion of fluid in substances and used to model currents and flow), Simon remarked in the live chat that the Navier–Stokes equations are actually one of the seven most important unsolved math problems and one can get a million dollar prize for solving them, awarded by the Clay Mathematics Institute.
(I looked this up on Wikipedia and saw that it has not yet been proven whether solutions always exist in 3D and, if they do exist, whether they are “smooth” or infinitely differentiable at all points in the domain).
We had read an in-depth history of the Navier–Stokes equations in Ian Stewart’s book several weeks ago, but I must confess I didn’t remember much of what we’d read anymore. “Is it that chapter where Stewart describes how Fourier’s paper got rejected by the French Academy of Sciences because his proof wasn’t rigid enough?” I asked Simon. – “No, Mom, don’t you remember? That was Chapter 9 about Fourier Transform! And the Navier-Stokes equations was Chapter 10!” – “Oh, and the Fourier Transform was also the one where there was a lot about the violin string, right?” – “No!”, – Simon really laughs at me by now, – “That was in Chapter 8, about the Wave Function! You keep being one chapter behind in everything you say!” Simon honestly finds it hilarious how I can’t seem to retain the information about all of these equations after reading it once. I love his laugh, even when he’s laughing at me.
Today though, he was weeping inconsolably and there was nothing I could do. Daniel Shiffman had to cancel the live session about CFD, computer fluid dynamics. Simon had been waiting impatiently for this stream. My guess, because it’s his favourite teacher talking about something interesting from a purely mathematical view, a cocktail of all things he enjoys most. And because he never seems to be able to postpone the joy of learning. He had explained to me once that if he has this drive inside of him to conduct a certain experiment or watch a certain tutorial now, he simply can’t wait, because later he doesn’t seem to get the same kick out of it anymore.
I’m baking Simon’s favourite apple pie to pep him up. Here are a couple more screen shots of him taking part in the Wednesday lesson:
Simon has been fascinated about the Opponent-process theory (suggesting that color perception is controlled by the activity of three opponent systems, three independent receptor types which all have opposing pairs: white and black, blue and yellow, and red and green). He has been complaining that all the papers on Opponent-process Theory he has managed to find online were too superficial.
And some more winter physics: trying to powders snow and ice:
We were reading a bedtime story, from the book “17 Equations that Changed the World” and Simon told me about how a computer disproved Euler’s Conjecture. I was surprised at how he could give me this micro- lecture on the spot and asked him to repeat the essence of it the following morning:
The video below is part of Daniel Shiffman’s livestream hosted by GROW Le Tank in Paris on 6 January 2019 about KNN, machine learning, transfer learning and image recognition. Daniel kindly allowed Simon to take the stage for a few minutes to make a point about image compression (the algorithm that Daniel used was sort of a compression algorithm):
Here is a different recording (in two parts) of the same moment from a different angle: