May 15th, 2018

On grad school, science, academia, and also a problem on Riemann surfaces

Originally published at 狗和留美者不得入内. You can comment here or there.

I like mathematics a ton and I am not bad at it. In fact, I am probably better than many math graduate students at math, though surely, they will have more knowledge than I do in some respects, or maybe even not that, because frankly, the American undergrad math major curriculum is often rather pathetic, well maybe largely because the students kind of suck. In some sense, you have to be pretty clueless to be majoring in just pure math if you’re not a real outlier at it, enough to have a chance at a serious academic career. Of course, math professors won’t say this. So we have now an excess of people who really shouldn’t be in science (because they much lack the technical power or an at least reasonable scientific taste/discernment, or more often both) adding noise to the job market. On this, Katz in his infamous Don’t Become a Scientist piece writes:

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Face recognition in China

Originally published at Inside the Mind of the G Machine. You can comment here or there.

I recently learned that face recognition, led by unicorns SenseTime and Megvii, has reached the level of accuracy and comprehensiveness that it is percolating into retail and banking, and moreover police are using it to detect suspects, or so various media articles say, like this one. Just Google “face recognition china.” I’m both surprised and impressed. Of course, in hindsight, what they did was mostly collect, aggregate, and organize enough data to train the deep learning models to the level that they can be put to production. The Chinese government has, after all, resident identity cards for all Chinese citizens with photos. I was certainly somewhat envious of the people involved in that in China, and I feel like such a failure compared to them, and that my life has been so boring and uneventful in comparison. Of course, whether I’m suited to do deep learning is another matter. After playing a bit with neural nets, including on the canonical MNIST data set, I sure was disappointed, and I understood immediately why this guy, who is doing a machine learning PhD at Stanford, had said to me that deep learning is very engineering heavy. I wish I had the enthusiasm and motivation for stuff like GPUs. As for that, all I’ve done was play with CUDA in a way so minor almost as if I did absolutely nothing. Again I don’t see myself as terribly suited towards engineering (I’m too much a purist at heart), but I might eventually be compelled to become interested in that, and once I do, I don’t think I’ll do badly. This also makes me wonder what I would’ve ended up like had I stayed in China. I’m sure I would’ve been weird there too, though I would also be more like everyone else. I wonder what I would have ended up majoring in there, and what I would’ve ended up doing afterwards. I’d like to think that I would have gotten a much better education and cultural experience there, though of course, the grass is always greener on the other side of the fence. For instance, in America, Asian quotas means you are judged relative to other Asians, but being in China means that automatically, and China, by virtue of having low resources per capita, is, needless to say, a grossly competitive society with fewer second chances, and thereby even harsher on late bloomers, though surely, the gaokao happens at age 18, whereas in America, grades start necessarily mattering at as early as age 14-5, when many are still very immature. I must acknowledge that as much as I dislike various aspects of the American education system, it is extremely generous, from what I see, relatively speaking, in tolerating failure at a young age. In China, you test into a specific department at a university, and once you’re in, it’s very hard to change, which means some land in majors they end up finding themselves unsuitable for. At age 18, it’s really hard to make such a decision, especially when you don’t really know anything about the actual content of the major, which is usually the case when one is a clueless kid. This is why I say that before you commit officially to an area, always try to learn something about it on your own beforehand to increase confidence that you actually have at least reasonable, and preferably high, talent for it.

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Face recognition in China

Originally published at 狗和留美者不得入内. You can comment here or there.

I recently learned that face recognition, led by unicorns SenseTime and Megvii, has reached the level of accuracy and comprehensiveness that it is percolating into retail and banking, and moreover police are using it to detect suspects, or so various media articles say, like this one. Just Google “face recognition china.” I’m both surprised and impressed. Of course, in hindsight, what they did was mostly collect, aggregate, and organize enough data to train the deep learning models to the level that they can be put to production. The Chinese government has, after all, resident identity cards for all Chinese citizens with photos. I was certainly somewhat envious of the people involved in that in China, and I feel like such a failure compared to them, and that my life has been so boring and uneventful in comparison. Of course, whether I’m suited to do deep learning is another matter. After playing a bit with neural nets, including on the canonical MNIST data set, I sure was disappointed, and I understood immediately why this guy, who is doing a machine learning PhD at Stanford, had said to me that deep learning is very engineering heavy. I wish I had the enthusiasm and motivation for stuff like GPUs. As for that, all I’ve done was play with CUDA in a way so minor almost as if I did absolutely nothing. Again I don’t see myself as terribly suited towards engineering (I’m too much a purist at heart), but I might eventually be compelled to become interested in that, and once I do, I don’t think I’ll do badly. This also makes me wonder what I would’ve ended up like had I stayed in China. I’m sure I would’ve been weird there too, though I would also be more like everyone else. I wonder what I would have ended up majoring in there, and what I would’ve ended up doing afterwards. I’d like to think that I would have gotten a much better education and cultural experience there, though of course, the grass is always greener on the other side of the fence. For instance, in America, Asian quotas means you are judged relative to other Asians, but being in China means that automatically, and China, by virtue of having low resources per capita, is, needless to say, a grossly competitive society with fewer second chances, and thereby even harsher on late bloomers, though surely, the gaokao happens at age 18, whereas in America, grades start necessarily mattering at as early as age 14-5, when many are still very immature. I must acknowledge that as much as I dislike various aspects of the American education system, it is extremely generous, from what I see, relatively speaking, in tolerating failure at a young age. In China, you test into a specific department at a university, and once you’re in, it’s very hard to change, which means some land in majors they end up finding themselves unsuitable for. At age 18, it’s really hard to make such a decision, especially when you don’t really know anything about the actual content of the major, which is usually the case when one is a clueless kid. This is why I say that before you commit officially to an area, always try to learn something about it on your own beforehand to increase confidence that you actually have at least reasonable, and preferably high, talent for it.

Collapse )

Face recognition in China

Originally published at 狗和留美者不得入内. You can comment here or there.

I recently learned that face recognition, led by unicorns SenseTime and Megvii, has reached the level of accuracy and comprehensiveness that it is percolating into retail and banking, and moreover police are using it to detect suspects, or so various media articles say, like this one. Just Google “face recognition china.” I’m both surprised and impressed. Of course, in hindsight, what they did was mostly collect, aggregate, and organize enough data to train the deep learning models to the level that they can be put to production. The Chinese government has, after all, resident identity cards for all Chinese citizens with photos. I was certainly somewhat envious of the people involved in that in China, and I feel like such a failure compared to them, and that my life has been so boring and uneventful in comparison. Of course, whether I’m suited to do deep learning is another matter. After playing a bit with neural nets, including on the canonical MNIST data set, I sure was disappointed, and I understood immediately why this guy, who is doing a machine learning PhD at Stanford, had said to me that deep learning is very engineering heavy. I wish I had the enthusiasm and motivation for stuff like GPUs. As for that, all I’ve done was play with CUDA in a way so minor almost as if I did absolutely nothing. Again I don’t see myself as terribly suited towards engineering (I’m too much a purist at heart), but I might eventually be compelled to become interested in that, and once I do, I don’t think I’ll do badly. This also makes me wonder what I would’ve ended up like had I stayed in China. I’m sure I would’ve been weird there too, though I would also be more like everyone else. I wonder what I would have ended up majoring in there, and what I would’ve ended up doing afterwards. I’d like to think that I would have gotten a much better education and cultural experience there, though of course, the grass is always greener on the other side of the fence. For instance, in America, Asian quotas means you are judged relative to other Asians, but being in China means that automatically, and China, by virtue of having low resources per capita, is, needless to say, a grossly competitive society with fewer second chances, and thereby even harsher on late bloomers, though surely, the gaokao happens at age 18, whereas in America, grades start necessarily mattering at as early as age 14-5, when many are still very immature. I must acknowledge that as much as I dislike various aspects of the American education system, it is extremely generous, from what I see, relatively speaking, in tolerating failure at a young age. In China, you test into a specific department at a university, and once you’re in, it’s very hard to change, which means some land in majors they end up finding themselves unsuitable for. At age 18, it’s really hard to make such a decision, especially when you don’t really know anything about the actual content of the major, which is usually the case when one is a clueless kid. This is why I say that before you commit officially to an area, always try to learn something about it on your own beforehand to increase confidence that you actually have at least reasonable, and preferably high, talent for it.

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