> 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".
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
I think the Western sentiment, and why it is attached to strength, comes from a combination of the West's allure to Eastern martial arts and the reality of plateauing during training. Once you've been doing something for 2 years, you are no longer seeing the massive learning gains you saw as a true novice. However in that journey toward "mastery" (a term I hate) you have to keep a positive outlook that the practice takes time.
I now use this phrase from my instructor: "Practice makes permanent". There's no such thing as "perfect practice", but whatever you practice is what will stick.
The Greeks?
From the viewpoint of '不立文字 (Bù lì wén zì): truth is not confined to language; language is merely the finger pointing at the truth' — this is closer to Taoism than to Zen. In fact, the Chinese worldview runs deep throughout her worldbuilding. Le Guin's take on 'magic' reflects a profound understanding of Eastern philosophy. The reason Ged doesn't use magic lightly is precisely a matter of balance, and (without giving away spoilers) the final confrontation between Ged and the Shadow is essentially about embracing one's own dark side — which shows a deep grasp of Taoist thought.
Personally, I also love the Earthsea series. The philosophy underlying that world is exactly the kind that resonates especially well with East Asian readers
And I agree, it's more than excellent. The judicious magic, the way she manages to naturally - without it becoming a sermon - describe acts of kindness as the biggest miracles, is great.
Highly recommended.
The visual metaphor from Taoists is being like 'uncarved wood'. Western Zen has been bastardised and commercialised, whereas one can look into Taoism to find many of the same concepts that, by virtue of their own simplicity, have remained timeless. The "problem", so to speak, with Zen is being associated with Buddhism, which has a long and intricate history and body of works attached to it, yet moves towards the same line of simplicity and spontaneity of Taoism.
In the words of Alan Watts, it all starts with the eternal Tao; all other religions are for people that need the same ideas overcomplicated with too many words.
And I am a big fan of Ron Hogan's "Getting Right with Tao" translation/modern interpretation of the Tao Te Ching.
Some things don't transfer well.
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!
I had a team of data engineers that wanted to do more data science, and 2 data scientists that both wanted to be data engineers(one of them argued that everyone wants to be DS and so it was too crowded, saying that they could make more money as a DE).
I also remember a specific instance where, one day, my friend ranted about how he needs to step away from pure front end and that it's a dead end career (he was quite good at it too!) and then the next day at lunch a colleague started complaining about how front end developers get all the credit and he's considering moving.
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.
I've found this to be the right balance between using your creativity and getting stuck too long
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.
We like to see hard-working, God-fearing people minting raw knowledge from Mount Olympus itself, whereby each shard of crystalline insight is carved meticulously by the Apprentice over the course of a productive and morally pure career.
The reality is it's some skill plus the occasional drive-by of an unknown force of nature, hitting you on the head with a shattered fragment of insight whose provenance you'll remain completely ignorant of. I'd say we just revert back to invoking the muses. It was a fine explanation.
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.
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.