https://thecaretaker.bandcamp.com/album/everywhere-at-the-en...
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https://thecaretaker.bandcamp.com/album/everywhere-at-the-en...
However, I have found that it is not too hard to learn advanced math without the help of an instructor. A couple math-inclined co-workers and I have been reading Topology by Munkres. Since it is such a popular textbook, there are plenty of solutions to exercises online. Working with other people makes it easier to ask questions if you are confused, and also helps me stay motivated.
[1] http://www.theawl.com/2015/09/a-disruption-cautionary-tale
“We discovered the tile using using a computer to exhaustively search through a large but finite set of possibilities,” said Casey. “We were of course very excited and a bit surprised to find the new type of pentagon.
I would expect it to take longer than seconds, since there are many ways that these shapes can fit together, and there are many possible edge lengths.
[1] http://www.gwern.net/The%20Existential%20Risk%20of%20Mathema...
http://medievalbooks.nl/2014/12/05/medieval-spam-the-oldest-...
[1] https://orionmagazine.org/article/forget-shorter-showers/
>Is your data scientist producing analytics for machines or humans?
This is a really interesting question that I had not considered before. I think it is an important one not only for those hiring, but also for applicants to ask during an interview in order to determine if the position is a good fit.
> We can probably add software to that list: early software engineering work found that, dismayingly, bug rates seem to be simply a function of lines of code, and one would expect diseconomies of scale. So one would expect that in going from the ~4,000 lines of code of the Microsoft DOS operating system kernel to the ~50,000,000 lines of code in Windows Server 2003 (with full systems of applications and libraries being even larger: the comprehensive Debian repository in 2007 contained ~323,551,126 lines of code) that the number of active bugs at any time would be… fairly large. This lead to predictions of doom and spurred much research into automated proof-checking, static analysis, and functional languages
Of course, you need to be pretty confident in your proof-checker. If we aren't totally sure about machine-proof methods, then we comback to the "Doomsday hypothesis" at the end of the article, where many results are invalidated at once.