I agree -- abstract algebra is elementary and some aspects of it can (and should), be taught in high-school. AA is a domain of maths which people won't even encounter if not studying a STEM field and such crash course could serve as a good outreach/vulgarisation material.
I really liked the materials in the link and think they are suitable for talented high-schoolers. Cox-Little-O'Shea is a fantastic book I studied as an undergrad and learned a lot from it, I wish someone would expose me to it earlier in life.
There's plenty of good math software for algebra which is not very popular (SageMath comes to mind as the most commonly used) and even more code that is simply inside knowledge. The problem is that people in the academia are not being paid for writing software but for publishing articles, even though the community value of a good package outweighs many papers. For example, good luck finding something that will compute non-commutative Groebner bases :)
Magma should do it (http://magma.maths.usyd.edu.au/magma/handbook/text/970 claims a Alan Steel's non-commutative generalization of Faugère F4 and noncommutative Buchberger algorithm) and US institutions can get it via the Simmons agreement. Not sure what's good for home users, though.
Curious why you find it so fundamental. In my experience abstract algebras primary benefit was drilling rigorous proofs for group theory etc.
Between physics and comp sci I'd rate abstract algebras contribution to understanding as one of the least important contributors ( although AA does provide a rigorous framework underpinning these fields in one way or another)
When I was in school, I felt it was very important because it taught me how to think of things in terms of mappings and structure-preserving mappings. After learning algebra found learning everything else became much easier.
During my maths phd time in France I really enjoyed so-called "colles". It was a 1h long 1 on 1 conversation with the student where (s)he was given a complex problem (way too difficult to simply solve in an hour) and a sequence of hints and smaller, easier tasks, leading to the solution. I would often help the interviewee when stuck/correct computations, let them follow the wrong path to make them realize why it was wrong and generally allow for unsupervised exploration of the task. Even though quite often students were not able to get to the end, or even close, I could get a decent idea about their knowledge and understanding of the matter. When I first did it, I instantly thought that's a good way to do a job interview -- being prepared and interactive, have a conversation instead of one way question-answer traffic.
The caveat: I needed to really think through the problems beforehand, understand other possible solutions, traps and potential dead ends (ie. spend time on it).
When a few years later I was asked to do some recruiting at my company (DS/R&D positions, not SE), first thing I did was to prepare a few sets of interconnected problems, to gauge the person's knowledge and how does (s)he think when encountering a new problem with all necessary tools at hand. The problems were difficult but I never expected anyone to actually solve them.
I did dome technical interviews as a candidate, being asked about random math puzzles/algos one can google in 30 sec and which are already implemented in standard libraries -- even though I usually could solve most of them I sincerely wish such interviewers would ef off and stop wasting my time. We are grown-ups, I already spent my fair share of time solving elementary riddles during high school math competitions and I'd like to be treated like a serious professional. During my years as a ML Engineer/dev I never EVER needed to implement a single tree/graph/whatever algorithm from scratch. Also, many adults have a life, family, other full-time job and grinding leetcode is not something anyone should be expected to do.
To be honest, quite a few times I doubted that the interviewer would know how to solve a similar problem without having the solution checked in company's interview problems database. Also, time pressure and stress are a buzzkiller.
As for multiple interview rounds -- a non-starter. Recently, while exploring the market, a top-tier betting company asked me to do a take home, 4h technical interview and then another long take-home (unpaid, of course). I told them that they are ridiculous and asked to never contact me again. I'm at liberty to do so as I have a job I'm happy with and zero need to actually change it, but if someone has been laid off I can see people get grinded to death -- both mentally and physically -- by such interview processes.
I attended a conference talk of a FB AI-engineer talking about her paper with backprop equations so obviously wrong my eyes hurt, and incorrect definitions of objects. It did not stop her from participating (btw. this is always unclear -- who did what) in state-of-the art research in object detection.
PhD is overrated in the deep learning context. It is more about forging the intellectual resilience and ability to pursue ideas for months/years than learning useful things/tricks/theorems.
Most of them, like most people in general, don't primarily identify as some loaded political identity. Out of the US members, three would likely satisfy the criteria you're thinking of: Jamal Greene is associated with the Federalist Society, Michael McConnell was appointed to a judgeship by Bush, and John Samples is a member of the Cato Institute.
Finished a PhD in mathematics and wanted to 1) leave academia 2) move back to my country of origin.
Picking up some programming was pretty straightforward, writing code is half (or even less) as frustrating as doing abstract research and pays 3x better here. The choice was easy.
I think that's the problem West have. Everyone must be an entrepreneur, which translates to profit and money. Even scientists.
The article mentions Kolmogorov and his work on randomness and how his disciple is more famous for finding useful applications on it. Also another collaborator he only met once at a cybernetics conference in moscow, a 23 year old japanese named Karatsuba, came up with another improvement on his ideas and is responsible for the way our CPUs are able to do fast multiplication to this day. Karatsuba didn't even know his paper was being included in a book by Kolmogorov until he received a pre-print!
Apart from a clickbait title this article offers zero evidence (or even plausible arguments) why the presented responses were "the best".
3k deaths in Germany is already a lot and, as far as I know (correct me if I'm wrong), they classify COVID-19 deaths with pre-existing medical conditions as not covid-related. Also, Germany is a very rich country which clearly helps.
Norway and Finland are sparsely populated (with low total population) and New Zealand / Iceland are isolated islands (also with low population totals). I do find these explanations more plausible and consistent with other countries.
Taiwan is a different ballgame as they had rough time with the previous SARS epidemic. Instant lockdown was the winning play here, correctly implemented. I see zero connection with woman being the leader here.
For men stress response is more or less always fight or flight. And that instinct increases quick as unknowns multiply as with covid and pressure builds on leaders.
[2] Its a Hyperconnected world these days. Tend and befriend keeps as many of those connections intact while fight or flight breaks connections. While dealing with unknowns/hard problems (like covid) producing good outcomes really hinge on having access to the largest widest distribution of skills and knowledge, unlike when dealing with known issues.
Women leaders will enable different outcomes in this hyperconnected world as they take advantage of that tend and befriend instinct in a hyperconnected setting.
How much better or worse these outcomes will be we will see in the coming years...
Having experienced the decisions of our fearlful german woman leader, it's clear that she didn't act by reason, only by fear. Why did no politican look at the numbers by themselves, and rather trust "interesting" advisers? Esp. when knowingly throwing the world into a global crisis, which could be worse than in the 20ies. Which brought us WW2. The numbers clearly tell that the death threat is no death threat, that flattening the curve will cause at least two times more dead than letting it running its spike for 4 weeks. Hundreds of thousands of dead? Give me a break. A flu season has 550.000, how much have we got now for COVID-19? The normal flu still caused many more dead than COVID-19.
Every medium flu season is more deadly than this one. By far. Keeping people locked in at home and keeping the schools closed will only help to keep the virus alive which should be already dead by now, if people would go out into the sun. There is no chance for a vaccine, there is no chance to keep the spread under control with lockdowns with an R0 of over 5. There is no danger with an IFR of 0.3 - 0.6% (0.4% looking the most reliable number for now). Italy always has an IFR of 1% for every such flu outbreak. But they went bonkers when they saw the military vans on TV. Well done.
The women leaders I see were all hysterical.
While the men around her (the neighboring countries) did respond in a more rational and less hysterical fashion.
4 weeks lockdown ok, why not. Does not do much harm. But the whole summer? Closing all summer events? Pure insanity. That way they will never get it under control and reach herd immunity before fall. They already found out about their massive mistake, now they are only doing damage control by controlling the message, censorship, fearing a political turmoil in the next elections.
I appreciate your reply and explanation. But the title was not a clickbait. It was the title of the article and the point of the article. If you disagree with the message of the article, that's fine. But the title WAS NOT a click bait.
Nvidia GPU drivers and CUDA installations on Linux. Got better recently but I've spent many nights fixing these installs, wrestling with nouveau etc.
Compilation of deep learning libraries from source on Windows -- don't do it.
Tikz -- LaTeX drawing "language". The images produced can be stuninngly beatiful, but the manual has 1200+ pages and it takes forever to become productive. If only journals accepted hand-drawn images...
Also audio libraries on linux. ALSA vs whatever. Had a lot of issues with my particular hardware & Arch.
Also way back when, X itself. Back when it was likely to not work on many systems (~2001). I spent so many hours going back and forth to use my friend's computer & internet to Altavista some help.
Absolutely felt your pain too. So many nights trying to build the latest tensorflow package only to find out I couldn't run it on my current Cuda version, so I need to uninstall it, reinstall the new one only to find out I installed 10.0, then 10.1 but I needed 9. It was just so silly I ended up just never updating once it worked.
Their proprietary drivers suck and I much prefer using the default Linux ones... but the performance just isn't the same and I always end up relenting and installing their terrible software
This, this, this. I needed to run some code that was only compatible with an older version of tensorflow. Figuring out how to get the proper Nvidia drivers, CUDA version, etc. was an absolute and utter nightmare.
I really liked the materials in the link and think they are suitable for talented high-schoolers. Cox-Little-O'Shea is a fantastic book I studied as an undergrad and learned a lot from it, I wish someone would expose me to it earlier in life.
There's plenty of good math software for algebra which is not very popular (SageMath comes to mind as the most commonly used) and even more code that is simply inside knowledge. The problem is that people in the academia are not being paid for writing software but for publishing articles, even though the community value of a good package outweighs many papers. For example, good luck finding something that will compute non-commutative Groebner bases :)