3 ms·
It's all about incentives. $1 million is a lot of money. The vast majority of hard problems don't have much brainpower dedicated to them, because the bang/buck
by p-e-w 3y ago
It's all about incentives. $1 million is a lot of money. The vast majority of hard problems don't have much brainpower dedicated to them, because the bang/buck ratio doesn't work out. Machine learning, math, and adjacent fields already have many careers that pay very well, so getting top-notch experts to dedicate their attention to what might be a futile endeavor is difficult.
And this isn't only about the monetary value itself, but also the fact that a large cash prize attached to a challenge boosts the prestige of finding a solution. Nobel Prizes come with about a million bucks on top of them, after all.
I'm quite confident that if someone offered $100 million for deciphering the Voynich manuscript or Linear A, we'd have a solution within 3 years.
- CityOfThrowaway 3y agoI'm 90% sure the people that did this project did it because they got nerd sniped by it and got to hang out with nat while earning a reasonable salary
- BenFranklin100 3y agoNot to be argumentative, but $1M isn’t very much money, certainly not for a project of this scope. It’s a testament to the creativity, competence, and dedication of those involved they’ve gotten this far with such little funding. Hopefully their early success will attract more resources to this very worthy project.
- fragmede 3y agoit's $700k divided three ways, too. $234k is well within FAANG compensation range, but you get to work on such an awesome project.
- jobs_throwaway 3y agoFAANG compensation range but no benefits and 100x the risk Make it $8m or $12m and FAANG employees can actually start to justify working on it seriously from a money perspective
- varjag 3y agoMost of them are not good enough to make a dent in this task.
- bluGill 3y agoMost of the developers are. They might not have the right background and so it might take an extra year or two to get up to speed with the difficult areas of this project. However most developers I know have the "smarts" to switch to a different complex area and figure it out. Sure most of them are just doing standard CRUD apps that move a bit of data - but that is because that is what we need a lot of not because they can't do something else.
- jobs_throwaway 3y agoCertainly. But you only need a few that are good enough to make a huge difference
- varjag 3y agoProblem is determining which candidates who were preselected for cramming Leetcode for comp are actually any good. Can't see how going for handsomely paid tech folks is any better than enthusiasts from academia.
- jobs_throwaway 3y agoThat's what the contest is for. There's no screening other than by results
- p-e-w 3y agoWhat do you mean by "of this scope"? The winning solution was produced by students and interns who coordinated over the Internet, in less than a year. The problem isn't scope, the problem is attracting lots of bright individuals to work on such a task (for free). And offering a substantial monetary incentive to the winner is probably the best way to do that. And yes, $1 million is very substantial for an individual. And the cool thing about offering it as a prize (from the point of view of the organizers, that is) is they only have to pay one person or team, although potentially thousands ultimately contribute to the solution, directly or indirectly.
- xipho 3y agoThat's not at all what they did. They explicitly made endpoints to doll out the prizes, to ensentivize collaboration. That tactical aspect of the the whole project and how they set it up is worth highlighting on its own.
- namaria 3y agoI think most of all this is a testament to just how much raw talent and intellectual potential is locked up in the winner-takes-all dynamics and shortsightedness of the stock market. Imagine the exploits and results in a world where everyone had the baseline resources and opportunities for extra funding for pursuing niche interests.
- chx 3y agoYou discount passion as motivator. But it's not clear what current tech could help with. Machine learning can't be applied to something you don't have training data for.