Feynman’s Breakthrough, Disregard Others (2017)
stepsandleaps.wordpress.com
stepsandleaps.wordpress.com
In a lot of ways, getting advice from the outliers on the very edge of a field is like getting advice from lottery winners. That's not to say you shouldn't - but at the same time a lot of effort has to be made to separate the wheat from the chaff.
I feel like the problem is there's a lot of wannabes who skip the steps of actually gaining competence and then disregard the advice of others. That's not going to work. The first step is years of hard work. For instance, crank physicists who don't really understand graduate level physicists are not going to accomplish anything by disregarding the advice of others.
However, if you know what you're doing, don't second guess yourself when embarking down a risky path. It's probably not as dangerous as it may seem.
The way research funding is structured now, researchers are incentivized towards taking small publishable steps without too much risk. This is needed of course, but it isn't going to bring about another Newton.
I think we desperately need a few more Newtons if we're to prosper in the next hundred years.
I often wonder how much this applies today. Academic science is currently heavily driven by funding of popular, low-risk research.
Also, without meaning any disrespect to Feynman or the advice, it's easy enough to say something like "disregard what others are doing" when you've won a Nobel. It's another thing to say this to people in just about any other inequivalent scenario, which is nearly everyone.
This low variance attitude is why we can't have nice things. We have more "scientists" alive now than at any time in human history, and it is boring and useless with virtually no downstream results.
And if you're doing something really mainstream, like writing the next big client-side JavaScript framework, the one that will replace React, watch out! Your chances are a lot worse than that, because there are hundreds of thousands of people who fight with React every day and are frustrated with its shortcomings. Your chances are literally millions to one, unless you work at Microsoft or Google and have management support to beat those Facebook fuckers.
But if you're doing something offbeat, working on a problem† that's mainstream enough to be interesting if you're successful but not mainstream enough that dozens of people are already spending their weekends trying to solve it, you have a much better chance of finding a niche for your project. Maybe it's non-mainstream because people take for granted that it can't be solved (in which case they might be right, as with the reactionless drive — the objective here is to be weird in your project goal, not your epistemology); maybe because they don't understand why it would be important to solve it ("Where's the market?"), and you do; maybe, as with OpenSSL, it's an important problem, but there's no way to get paid for solving it.
A thousand hackers writing a thousand versions of the same library in the same way are only epsilon more productive than one hacker. A thousand hackers writing a thousand different libraries are almost a thousand times as productive.
Winning the lottery? Well, that's pretty much out of your control — but if you do decide to waste your money on the lottery, don't pick a number lots of other people are picking. Then you'll have to split the already-improbable winnings N ways.
But what if you're determined to solve a mainstream problem anyway, one where a thousand people are also trying to solve it? Then you need all the outcome variance you can get! If, of those thousand hackers, 500 are using a very conservative approach that is guaranteed to solve the problem with some quality metric 10 ±1 in 26 weeks ±2 weeks (these being the standard deviations, not some kind of 95% confidence interval), while the other 500 are using all kinds of wild approaches that give them quality metric exp(ln(4)±ln(4)) in time exp(ln(52)±ln(4)) weeks, it's pretty much guaranteed that the "winner" is going to be someone with a totally insane approach that hacked together a library with quality 49 in only 14 weeks. It's not going to be one of the 26-week plodders, because 14 weeks is 12 standard deviations out on their distribution (vs. 0.95 out on the crazy hackers' distribution), and quality 49 is 39 standard deviations out (vs. 1.3 out on the crazy hackers' distribution).
In fact, it's even worthwhile to sacrifice expectation to get higher variance in these situations. If your only hope of winning is to beat everyone else in a single round, you should do whatever will increase your minuscule chance of a home run, regardless of how it affects your chances of striking out.
It's still not socially optimal for people to behave this way — note that here you have 1000 hackers whose aggregate productivity is only about 20× the productivity of an average plodder — but if you've gotten suckered into competing for a mainstream niche, that's the way to play the game.
All of the above is for the simplified situation where you're working on a project by yourself. In a teamwork situation, the relevant actor is your team, not you individually. Do not write your code in Clojure if the rest of the team is working in Ruby. Do not try to solve only problems that nobody else on the team thinks are important.
And this advice definitely does not apply to a situation where doing the same thing someone else already did is valuable. If you're making a sandwich, there's no reason it needs to be different from the sandwich someone else is making across the street. They're two different sandwiches. If someone eats the sandwich across the street, it's gone and it can't feed your customer. They're going to be happy if you make them a sandwich they like, even if it's a little worse than the sandwich across the stret; even if there are many just like it, this sandwich is theirs. This is very different from the situation in software, where one sandwich feeds everybody in the world at once, except people with celiac disease. Nobody is going to be happy that you wrote a web browser from scratch for them instead of just installing Firefox.
† I recognize that a painting is not "solving" a "problem", but many of the same principles apply.
The biggest difficulty I've had with such things is a sort of Gresham's Law of attention: because Twatter and Fecebutt (and IRC) offer the possibility of instant reactions, I have a strong temptation to spend time on them instead of in more thoughtful, slower forms of interaction. The wild success of Fecebutt and, even more so, Zynga, suggests I'm not the only one.
> The Rotisserie implements an innovative approach to online discussion that encourages measured, thoughtful discourse in a way that traditional threaded messaging systems cannot. In contrast to the completely asynchronous, broadcast-to-broadcast mode of existing threaded messaging systems, the Rotisserie adds structure to both the timing and the flow of the discussion.
Thanks for that too. That's exactly the communication platform I made/am making. For me there's also the "human" element as described in Time, Work-Discipline, and Industrial Capitalism [0]. Like how "human time" is not "modern life time," but we're still humans so wouldn't it be nice to have a space where we communicate in human time. But yes Gresham's Law... hmmm...
[0] https://www.sv.uio.no/sai/english/research/projects/anthropo...
The other thing is that there are distributions like the Cauchy distribution that are so heavy-tailed that they don't even have a mean†, or even a variance. The Law of Large Numbers doesn't apply to them at all! And even for more ordinary heavy-tailed distributions that do have means, like the lognormal distribution (relevant here since it's empirically the distribution of how much we misestimate tasks by) the Law of Large Numbers takes a lot more than "a few times" to start making the distribution of the sum look normal. Try it! Pop open Jupyter and convolve the lognormal distribution with itself a few times! How long does it take before it even starts to look Gaussian? How long does it take before it still looks Gaussian in a log-linear plot?
†In your comment you said "mean and expectation", but those are the same thing. Possibly you don't know anything about statistics yet? If so, welcome! You're one of today's lucky ten thousand!
The story is at the point where he is already honored, known and has "outstanding popular touch". That is when he read a book and calls the wife in the middle of the night to say: "I think I’ve figured it out. Now I’ll be able to work again!"
This does not mean that people should disregard others all the time routinely. It means that there are situations where you have to disregard others expectations, advice and what not.
And that’s the tricky part. In order to understand, which situation to disregard and which not.
Well, at least sometimes. I am getting better at it.
For a master of his domain like Feynman that affect would surely have been amplified. He can back himself to go on a tangent, against the backdrop of the backdrop of the fundamentals of his field.
Because common wisdom may be common to your small team, and not at all to a more skilled set of engineers.
Obviously when the same approach went someone to a spectacular ruin, that someone is an obvious crackpot who insisted on a wrong thing despite everyone's opinion. It's not worth a story, so nobody mentions these losers.
So, it makes sense to apply it in circumstances where worse outcomes cost you the same as just bad ones, but best outcomes bring in much more than ordinary good ones.
That is, he did not dismiss people. Or problems brought to him. He just did not worry about reinventing wheels. Odds are, other people are working on something and have made really smart contributions. Don't worry about that. Take advantage of it when you can, but largely don't worry about it.
That is what annoys the fuck out of me with the software community. We actually reinvent constantly but everyone praises that we never should do it. It feels like a secret master plan of a hidden lobby that want to claim novelty and creativity for themselves.
Every repackaged fad in software was pumped and sold by high paid consultants looking to sell the next buzz word to some big company who doesn’t understand software.
A bunch of them even did it to “lean startups” here in Toronto and attempted to apply it to absolutely everything where it didn’t fit or was just lightly repackaged agile. They use it to get gov ‘innovation’ credit here too which is another cancerous industry full of buzzword consultants.
Only occasionally by luck or in small hard fought increments do things actually get better via these hype trains.
Introducing a dependency on an NPM module for so little returned value is a lot different from a physicist or mathematician relying on a peer reviewed result from another academic.
Software is build relative to other software and used relative to other software. If you reinvent something already in use that is slightly incompatible with what already exists, you must also recreate all the other stuff from scratch.
Constant reinventing in software that starts from scratch with simple and clean software, then decade later it's finally useful but full of cruft just like the previous innovation with little or no benefit over it.
Another problem with software is the amount of uneducated innovators. Starting from 'scratch the itch' and not doing research is not a good idea.
Creating his own understanding of underlying truth was his true talent.
Still I suspect the experience must have ingrained in him an understanding of the effectiveness of not caring what other people think, even if you don’t have to be an asshole about it.
In general, caring about what other people think is a good thing. The alternative is to be a sociopath.
“Of note is the story of his first wife, Arline, who was diagnosed with tuberculosis. She died while Feynman was working on the Manhattan Project; the book's title is taken from a question she often put to him when he seemed preoccupied with his colleagues' opinions about his work, which echoed his earlier words to her.”
He makes a breakthrough in his thinking, when he realized that he's not responsible for other people's expectations. That's their mistake not his.
1. young people worry about what others think of them
2. middle age people stop caring what others think of them
3. old people realize that nobody is thinking of them
Really, how much you care about what other people think depends on what phase of life you're in. In your adolescence, your social survival and who you get to date largely depends on the opinions of others.
When you hit your 30s, usually you have your own family and your own tribe, and other people just don't matter so much. You're in a different phase of life with different goals.
I guess it's possible to find a way to not care what people think in your 20s, too, but for those who DO care a lot, telling them not to care isn't helpful.
He ignores the third option: very dishonest. Which is what applied in this case.
My fellow biology grad students when I was in grad school starting in 2005 pretty universally regarded Watson as a waste of oxygen.
On forgotten people what about Raymond Gosling (Franklin's PhD student) who took the famous photo:
https://en.m.wikipedia.org/wiki/Photo_51
Watson was shown Gosling's photo by Gosling new supervisor after transferring from under Franklin's supervision.
Referencing your fellow students' ideology driven groupthink doesn't qualify as such.
Crick also wrote some scathing letters to Watson about how he was being disingenuous in this particular book [0].
In his own book, Watson admitted he had seen her work before publishing his result.
The "follow your own path" mantra is particularly important at the edge of human knowledge where established theories break down. As a physicist, I was personally surprised how much people blindly trust theoretical tools well outside their domain of original application. Even when the data is yelling at you "no that's wrong", people commonly persist because they read it in a book or heard it from a more senior professor. I believe this is the group think that Feynman was getting at.
Rosalind Franklin would probably disagree.
Crick's mathematical breakthrough to calculate the helix twist and playing around with molecular models was also necessary.