3,710 karma · joined January 2, 2011
I'd wager that non-trivial (non-tutorial?) usage of scikit-learn is significantly higher.
- Content production and editing
- Improving the design and branding of open textbook purveyors
- Advocating for the adoption of open source textbooks in your local K-12 system and community colleges (especially the more cash-strapped ones)
I know of no organization that attempts to organize community efforts in these directions, though. If there is one, I'd love to know about it and get involved. If not, maybe it's an idea whose time has come.
I enjoy seeing updates like this to learn more about how the engine works, but: these changes are exactly the reason you _don't_ want to design your code around specific micro-optimizations, particularly when it comes to JS. They're at parity now and it is quite possible that 6 or 12 or 24 weeks from now, the cleaner destructuring approach will actually be faster than the tmp-var approach. Remember this the next time someone suggests in code review that you should design your code to meet the whims of the JS engine.
Is there any platform with higher barriers to entry than the App Store? iOS development requires a proprietary toolchain only available on Apple hardware, programming languages hardly used elsewhere, not to mention the $99/year developer fee and the litany of vague and arbitrarily enforced rules and content guidelines for App Store release.
After reading your response and digging into this a bit further, I retract my previous comment and no longer put any stock in Brad DeLong's intellectual honestly. I apologize for spreading it further.
I've always wanted to like David Graeber. Superficially, he seems like a smart, rigorous, heterodox left-wing thinker. But his work is intellectually bankrupt more often than not. Brad DeLong has been doing the hard work of breaking down Graeber's facile arguments and incorrect information for years. Here's one blog post to look at if you're curious. http://delong.typepad.com/sdj/2014/11/monday-smackdown-in-th...
In some organizations, "data scientist" is the modern name for a business analyst and the responsibilities largely involve basic data sanitization and analysis, streamlining processes that were manual and tedious in previous eras. In other organizations, people with advanced mathematics skills find ways to apply mathematics and machine learning to gain a marginal advantage over competitors or to deliver something novel to the market. This latter case does indeed require a deep understanding of mathematics.
Using off-the-shelf implementations of ML algos might seem like it obviates the math skills, but problem definition, algorithm selection and feature engineering are difficult for laymen to do well, and can insidiously poison the validity or efficacy of the results if done poorly.
It's clear that salaries and awards and grants are driving the increase in cost. Maybe this is damning evidence of a decadent culture, which the author of this op-ed clearly presume, but I doubt it. I would expect that Wikipedia's employees have been working very hard for a long time to keep the site running and they've cultivated expertise in governing the site in a way that avoids controversy and maintains credibility. I'd rather Wikipedia spend to retain long-tenured experts who have paid their dues than be an underpaid-college-graduate-mill like so many non-profits are. It seems that they've done that, and they've waited until the organization was financially stable to do so.
A family watches the local news while awaiting their grandfather's arrival at Sunday dinner. The lead story is about a person causing chaos driving the wrong way on the highway. They quickly realize that the person is on the same highway that their grandfather usually takes to their house and call their grandfather to warn him.
"Grandpa, there's a person going the wrong way on the highway! Be careful!"
"_A_ person?" the grandfather replies angrily. "There's hundreds of 'em!"