366 karma · joined April 29, 2017
Also, thanks for including the source so they can be seen in the browser dev tools, don't know if that gets done enough with interactive visualizations like this.
Could the construction of these blocks, as well as the composition step, benefit from training an RL agent on the state space of circuit configurations/the action space of connecting two blocks (or other operations)? Does any facet of the problem make the idea of using reinforcement learning intractable/otherwise a bad idea?
I think "feeling behind" means different things for different people, depending on the experiences in their formative years they have associated with the thought of being behind. The ideal response to the feeling depends on what the feeling is.
I personally haven't listened to it in very great detail as if I were taking the course, but I often skip around to listen to the dissections of various aspects of human behavior.
[1]: https://www.youtube.com/playlist?list=PLD7E21BF91F3F9683
* It's really self-important. Not only is it selling you on a particular strategy for attaining success, it also tries to sell success in things like public speaking and doing architectural work as an absolutely important part of one's life, and that implicitly a person's existence is invalidated if they aren't constantly trying to achieve this kind of success. It doesn't do it explicitly but the very notion of "real" and "fake" and other words like "wasted" complete with the trite diagrams showing that "hey, all your efforts are going into this small circle" give a very strong implicit value-judgement of the reader.
* There's no proof. I don't know if I'm on the mark with this one, but I think that the act of omitting any sort of data about measuring the outcome of success when taking different approaches seems to imply to the reader that the argument should just "make sense" i.e. it's a truth that the reader already knows, they should just find it within their own observations in order to understand it. Here, have a handful of anecdotes to top it all off in case you weren't convinced. Overall this just feels like it's made to make the reader feel a certain way (motivated) rather than actually teach them any solid information.
* What even is real and fake? The readers are given a bunch of examples and then we're left on our own to figure out what falls into which category. Someone commented on the article saying that if someone wanted to watch and understand anime in Japanese, they could just do that and that'd be the real thing, with the fake thing being taking the time to learn Japanese. This is obviously not going to be successful, so at this point the author's prescription has failed as a framework for achieving success.
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This kind of fiery motivational content could be harmful as much as it is useful. It'd be fine if an article, devoid of substance as it may be, was only meant to make readers feel motivated, but the problem is that this kind of fiery motivational content does different things for different readers. A person in a bad, self-loathing emotional state could be rendered feeling even worse, thinking that everything that they're doing at present is fake while everything that their peers are doing are more real, even when that's blatantly untrue. The devil's in the details and personally, I'm not going to let myself get affected by this personal philosophy if the case for it is this weak.
On another note. I don't think technical proficiency (or whatever your elitism-metric is) is very correlated with using Windows as the primary platform. You can Google stuff related to a Windows problem and through that learn how to work with the registry, and yes, some of the things you need to do require you to RTFM. So this elitism also suffers from being off the mark.
I'm wondering now, is the goal of theoretical physics sort of like the goal of topological data analysis[0]? Reading about things like how Max Planck came up with quantum theory to explain the measurements of black body radiation reminded me a lot of the ML task trying to fit a model to data.
My bias is definitely showing here but is this a useful framework to be able to think about these things?
[0]: https://en.wikipedia.org/wiki/Topological_data_analysis
Really concise introduction to programming in Python; easy enough for someone to absolutely devour and learn incredibly quickly.