On a side note, I find that this flow state has it's addiction of it's own. I find myself doing whatever I can to find time for it. I feel like the reason mathematicians, physicists and artists of the past produced such great results is, they found the flow state so addictive, more addictive than balancing your health or family life, and thus dedicated almost entirety of their lives on it. Just have to be careful on that one. After all, our purpose is (I think) is not just working.
It is addictive, because it feels like living life at its fullest. It feels like life should always feel.
> After all, our purpose is (I think) is not just working.
Agreed, but I think that our purpose is also not just experiencing, nor is it just eating, pairing up, multiplying, and dying (like all life on Earth does, +/- the pairing up stuff).
I also feel that "working" != "working", specifically working for money usually stands in opposition to the kind of work you'd find fulfilling and that benefits from the state of flow.
We associate things and people with the experiences where we encountered them.
Once I made the same observation that GP did, I reflected back on conflicts over code. The most vitriolic arguments I’ve gotten into about design decisions at work have all, almost to a man, boiled down to the person who authored it having done so in flow state and how fucking dare you question the beauty of the output of that effort. They make it personal because the experience was deeply personal.
Flow state cannot make nuanced ethical decisions. It’s right in the characteristics. And both DevEx and maintainability come down to thinking about the people who have to deal with your code for the next four years.
The only way I’ve been able to avoid this trap myself is to spent more effort on refactoring, taking notes, taking breaks, and saving up the Deep Work for special occasions where I have choreographed much of it ahead of time. So I know exactly what to do and why. Exploratory dev in flow state leads to all of these sins. Because you get the bear to dance and then you stop.
>>On a side note, I find that this flow state has it's addiction of it's own.
For this reason, there are societies and cultures where Chess is treated more on the lines of a dangerous addiction one must stay away from. Co-incidentally I have seen similar damage in people with video game addiction. Years wasted online, or on board games. Often when people are young, they could be attending college, or starting a trade or learning new skills. Or just working and earning money.
People are instead playing video games, or chess, where you are not only addicted to gaming, but you also get a illusion that you are doing productive work, where all of your mental faculties are engaged, and you are thinking and executing. Its easy to fall into this simulated productivity trap. Given levels to these games, its easy to create a flow like situation for years.
>>I feel like the reason mathematicians, physicists and artists of the past produced such great results is, they found the flow state so addictive, more addictive than balancing your health or family life, and thus dedicated almost entirety of their lives on it.
Trust me, most people who warn against going into academia are saying precisely this. If you are not too good at Math or Physics, its possible to get addicted to intermediate or beginner intermediate levels for all life and never really go out and make a living.
The sheer amount of failed musicians, mathematicians, physicists who went to in to the field because of curiosity, but got addicted to 'flow' and could never really bail and go on to earn or have a good living is quite large, and in many ways this is a bigger let down than even gaming addiction.
I know quite a few musicians and even graduate level math professors, who have totally broken families and finances because they can't explain anyone why they like doing it, and families don't get why they must remain broke.
I'm a physicist and musician, though I'm certainly not one of the greats in either area. Musicians have their reputation for dealing with addictions, though I've avoided them myself.
But I've read that the stereotype of the musician needing the junk in order to be creative, is a myth. When people study the actual timelines, they've found that the great artists did their best work when they were relatively clean, and that the addictions detracted from their work.
I, for one, can do heavy bodily harm to myself if I attempt to live in this state for long periods. It turns out my mind is much stronger than my body. Perhaps the flow state removes the regular messages the body sends when it's had enough.
Either way, life without play is dangerous for me.
One of my tricks is keeping a glass of water at my desk. Filling the glass is a break. So is emptying my bladder. The break lets you reflect, and decide if you’re chasing your tail or doing something questionable.
Drinking is also a work appropriate fidget. Sip of water instead of tapping a pen or bouncing your leg. And easier on your kidneys than overdosing on caffeine all day.
Thanks. Yeah there are papers that I am interested on, in fact, I am interested on too many things at once which doesn't help. Also, I think I am always thinking of solving bigger problems where it's hard to make progress on.
Also, since you mentioned scaling systems and equations, are you by any chance working on numerical linear algebra stuffs like iterative solvers etc.? MPI/HPC etc? If so, I am in HPC as well.
> in fact, I am interested on too many things at once which doesn't help.
Be careful of "surface level work". Your Ph.D. is not going to come from surface level work. It will come from picking something and developing a deep understanding of that topic ... deeper than most of the people in the audience, anyway.
Also, since you mention HPC, be aware of what areas are "well-explored" and stay away from them. Your advisor should be able to help with this. You want an area that has not been dug up by many brilliant minds before you, leaving only small nuggets of semi-precious metal for you to find.
My advisor was into that stuff, and some of my original efforts were to tie those together
The field I researched in was Symbolic Regression / Genetic Programming. My research was able to recover differential equations and systems of equations from data. We worked with GLEON to help scientists better understand lake dynamics through such systems
> numerical linear algebra stuffs like iterative solvers
Been there; done some of that:
Once worked on numerical linear algebra, e.g., Gauss-Seidel. Then ran into the M. Newmann numerically exact technique based on (i) multiply by a suitable power of 10 to have only whole numbers, (ii) for a list of prime numbers, solve the system in the integers modulo each prime, (ii) construct the multi-precision rational results using the Chinese remainder theorem.
From a course, a rule: "For numerical calculation, multiply and divide freely, add OK, but avoid subtraction, especially avoid subtracting two numbers whose difference is small, i.e., nearly equal."
One day, talking with Richard Bartels, mentioned that once I wanted a random unitary matrix so generated some random vectors and applied the Gram-Schmidt process, and right away Bartels responded that Gram-Schmidt is "numerically unstable" to which I replied "Wondered about that so applied Gram-Schmidt twice".
In general, Bartels has done a lot in numerical methods.
In summary, in my experience, for many decades, a LOT has been done on numerical linear algebra, including iterative methods, for the simplex algorithm, etc. E.g., at one time, used Linpack -- it seemed terrific; on a computer with a 1.8 GHz clock, called Linpack 11,000 times a second.
As I recall, in numerical linear algebra there are some fundamental issues having to do with the eigenvectors of the polar decomposition. E.g., can argue that for these issues, sometimes Gauss-Seidel cannot work well.
Might look at some of the Golub LU decomposition work, e.g., in linear programming.
If you can find some problems where can get new, correct, significant results, okay. Maybe can get some problems from some of the current AI work.
Examples: (1) Took an industrial problem, did some math and computing, and got an engineering style solution. Some other students in the department did much the same but for different practical problems. (2) Had a course in optimization, in the summer went over the notes word by word and rewrote the notes, got deep into the subject, found an unanswered question, got a solution, along the way found a surprising, general result, wrote a paper, got it accepted right away at Mathematical Programming, published later elsewhere. (3) Working in some AI to monitor systems, wanted some results with meager assumptions so used the general result tightness. So, each of these is an example of how to find a problem and get some results.
But broadly for some decades, "numerical linear algebra", including iterative approaches, is a 'well plowed field'.
Considering EU is so far behind on AI and tech as a whole, I hope they see this an opportunity to develop something of their own with better data privacy or whatever suits their boat(I don't know how though). I am really rooting for EU and their economy to bounce back. The world desperately needs a balance from all sides.
If anything, this revealed they aren’t actually that much behind. It’s just that the US’ lead is completely inflated and that the business models they dreamt of were finicky at best.
I am doing my PhD and occasionally my supervisor and I come across papers that don't have any meaningful results/statistics but you gotta publish somehow. Like, they conclude with "We observed that the latency can be reduced by 2x-300x." I feel like the group that did this research spent a decade, and their supervisor was like, well, we gotta publish something. Choose an age interval that fits the whole data. 30-90.
Exactly what I thought put into words. There is not even a comparison to the obvious first question. Do women have the same effects or is it limited to men?
My father-in-law's stage 4 brain tumor was discovered when he was 50. He's in good health now. They operated on him and removed the tumors and did chemotherapy. They continuously monitored for regrowth but it didn't happen. He did yoga religiously for most of his adult life. Even his hair has grown back.
