The point where I really feel the difference is that Western Zen seems to be about how to train the self to become stronger, whereas actual Seon (Zen) in East Asia is about going with nature, letting go of the self, and allowing things to flow. In the actual practice of Seon, it's about doubting the self, letting go of attachments, and realizing that achievement, comparison, and the desire for control are all just fleeting. There's a famous phrase: 'Banghasak (放下著)' — let it all go.

If anything, I think ancient Roman Stoicism feels more like Zen than Western Zen does

So that's fascinating. When I saw this article, I was expecting it to be about whether we should give up the desire for success, but instead it took a completely different direction, which was surprising

As I studied these dynamics, something occurred to me... Different people need to see signs of success at different frequencies. Because of the nature of our product, measuring the performance of a new/updated model required the model to be live for at least a full calendar month. So, between initial work and final analysis, it was often a 2 month wait or more. For many back end tasks, you can build a quick prototype, run it to see if it works, and be on your way - the signals come all day long. The varying frequency needs of different people went a long way to determining which of them liked working on ML.

This is sort of a manager's version of feature engineering. ;-) The people on that team taught me a lot!

like the author said, so much of 'success' or 'progress' (in research but of course also across disciplines) depends upon temperament. just straight up having a good attitude about things. the skills that make a good researcher could not be more transferable: patience, innate curiosity, and a resilience against failure.

that said, these skills are increasingly rare/at a premium given our culture of minimizing discomfort tolerance via hyperconvenience. people have a harder and harder time waiting or failing.

> on days we do not find insight, we sit.

This reminds me of Ed Witten (greatest living physicist?) in an interview by Brian Green. Green asked Witten what his day-to-day was like at the Institute for Advanced Study ...

Wittens' reply: "I sit at my desk".

I've found this to be the right balance between using your creativity and getting stuck too long

But from what I see, it is the opposite - a lot (if not virtually all) progress in the last decade of deep learning was not because of a fundamental idea, but incremental, experimentally-verified practice. Even though I think there is good intuition for why ReLU is better than sigmoid (tl;dr: last layer is log(sigmoid) ~ ReLU, putting anything different inside kills the gradient), the original paper by Hinton himself was more or less "because it trains 3x faster".

Re-thinking fundamentals might help, but most "let's change the fundamentals" is rarely how it works. Even the most seminal papers, i.e. AlexNet and "Attention Is All You Need", are refinements of existing ideas, and show how they help.

Machine learning is an experimental science. Many mathematically cool ideas do not work. Many engineering ones do.

> I've tweeted before that one of the most important traits in a researcher is healthy paranoia. Be paranoid!

I have seen so many PhDs burned out to cinders; I don't think it is any more a good piece of advice than "depression is good for philosophers". Sure, be a relentless explorer.

> In short, holding on to ideas for too long can actually be counterproductive. Stay open-minded and refuse to let ego cloud your judgement.

Which I think is true.

Sometimes a coworker will be an ML star for a year or two, but then suddenly run out of steam. It's brutal to watch.

I used to think most smart people had similar distributions of good ideas, and it was just that the hardest working tried out all 50 of their ideas to pick out the 2 good ones. But I've seen smart and hardworking people have a hit rate of 0.