I feel that the Zen used in the West and the Zen in East Asia are quite different. I think the Western Zen is probably the one from the 1970s book Zen and the Art of Motorcycle Maintenance. It usually carries a sense of equanimity and beginner's mind. But in East Asia, Zen actually emphasizes aimlessness or non‑purposefulness.
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
Perhaps I've been deep in my own issues for too long, but it seems to me that the author is trying to say "don't trust the current evaluation suites too much"; scores only reflect a small part of the problem. What's interesting is discovering a new, stable evaluation metric, doing something new based on it, and having that new thing yield some unexpected intelligent results
I have some coworkers that are similar in everything--education, work ethic, and intelligence--but some of the tick out ML ideas that work like clockwork, while others get hits rarely if ever. I cannot tell what makes it work for some and not others. Their ideas both sound equally good.
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.
In spirituality it is believed that ideas and inspirations aren't our own. That our mind is like an LLM that gets prompted by higher beings. In research everyone has high param count minds, trained for many years by studying. But just like LLMs by themselves are useless at creating new original work, no matter the compute you have available, so the mind can not create anything new without "inspiration"
That's the nature of research. You try every idea that may be a good avenue and only a handful work out, if at all. That's why quantifying research credibility via publication and citation counts inherently lead to toxic work cultures. The best ideas must be given time to be discovered, not forced out and contorted to fit the requirements of a journal.
this is part of why I think most researchers get less productive over time... Someone gets some big result during grad school or early career, get some big job from it, and then struggle to get new results of similar quality :shrug:
With ML in particular, there's also the sheer volume of people basically all looking at (essentially) the same problems... so it's kind of like monkeys with type writers spamming ideas until some work.
I feel that the Zen used in the West and the Zen in East Asia are quite different. I think the Western Zen is probably the one from the 1970s book Zen and the Art of Motorcycle Maintenance. It usually carries a sense of equanimity and beginner's mind. But in East Asia, Zen actually emphasizes aimlessness or non‑purposefulness.
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
Perhaps I've been deep in my own issues for too long, but it seems to me that the author is trying to say "don't trust the current evaluation suites too much"; scores only reflect a small part of the problem. What's interesting is discovering a new, stable evaluation metric, doing something new based on it, and having that new thing yield some unexpected intelligent results
I have some coworkers that are similar in everything--education, work ethic, and intelligence--but some of the tick out ML ideas that work like clockwork, while others get hits rarely if ever. I cannot tell what makes it work for some and not others. Their ideas both sound equally good.
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.
In spirituality it is believed that ideas and inspirations aren't our own. That our mind is like an LLM that gets prompted by higher beings. In research everyone has high param count minds, trained for many years by studying. But just like LLMs by themselves are useless at creating new original work, no matter the compute you have available, so the mind can not create anything new without "inspiration"
That's the nature of research. You try every idea that may be a good avenue and only a handful work out, if at all. That's why quantifying research credibility via publication and citation counts inherently lead to toxic work cultures. The best ideas must be given time to be discovered, not forced out and contorted to fit the requirements of a journal.
this is part of why I think most researchers get less productive over time... Someone gets some big result during grad school or early career, get some big job from it, and then struggle to get new results of similar quality :shrug:
With ML in particular, there's also the sheer volume of people basically all looking at (essentially) the same problems... so it's kind of like monkeys with type writers spamming ideas until some work.