81–90 of 227 posts
Graduate Student Descent -- there's also the other saying: Every moment you spend writing, publishing, or working on your dissertation is a moment that you are getting behind on doing unique and cutting edge research.
Some good points in the article, but I disagree with the tone and the conclusion. > we’ve rewarded and lauded incremental researchers as innovators, increased their budgets so they can do even more incremental research There isn't a scientific field where every single paper is groundbreaking. It's a Brownian motion of small incremental innovations, until eventually we stumble upon something big (like deep learning).…
The author cites no compelling trend that ML is stagnating, esp relative to other disciplines.
I’d also add: is it BAD if universities are churning out highly skilled workers when there is high demand for them?
This seems to be more a rant of ML becoming more mainstream and accessible to a wider variety of students than anything.
They admit that some of the most “innovative and challenging” problems are still around, but many do not focus on that. Ok, so what, there are maybe more tiers and splitting of work into subdomains, some more “vocational” and some more “research-y” in nature. Is this a BAD thing?
I still feel like much of AI is a plot to dumb down the modern economy. We want our business people to be just as effective as our quants; we want nothing to require real intellectual labor. The idea that you traditionally have these programmers who spout mumbo-jumo all day, cost a lot of money, and seem to always be planning stuff behind your back is threatening, and all the more so because you are utterly dependent…
That reminds me how SQL was advertised - "Now you can program computers in plain English and fire all your programmers!". Fast forward, and today good luck getting a job as a Microsoft SQL programmer if you only know Oracle SQL.
I blame less SQL false advertising, than the insane tunability and lack of effort into migrations tooling for making the relational database world so much more kafkaesque. Or really between the nature of Oracle, Microsoft, and their customers, it might have been an inevitable insanity along the lines of Conways law and too profitable -> too many cooks in the kitchen -> too complex.
Earlier quoted context omitted.
Trying to use ML to get rid of programmers will just replace them with ML experts who also have to be programmers to implement the models and munge all the data. These people will in turn have to be paid more than the original programmers were.
Won't they be much fewer in number, though?
I still feel like much of AI is a plot to dumb down the modern economy. We want our business people to be just as effective as our quants; we want nothing to require real intellectual labor. The idea that you traditionally have these programmers who spout mumbo-jumo all day, cost a lot of money, and seem to always be planning stuff behind your back is threatening, and all the more so because you are utterly dependent…
This seems fair to me. The executive view of ML is "can you do me a magic?" And as this article's "Graduate Student Descent" bit makes clear, the worker response is often to semi-randomly perturb code, show some graphs, and say, "Is this a magic?" For me, most software development is about finding something boring and laborious. We get a computer to do the work so humans can level up and work on something requiring a…
I still feel like much of AI is a plot to dumb down the modern economy. We want our business people to be just as effective as our quants; we want nothing to require real intellectual labor. The idea that you traditionally have these programmers who spout mumbo-jumo all day, cost a lot of money, and seem to always be planning stuff behind your back is threatening, and all the more so because you are utterly dependent…
It's just another tool. I have this saying: all machine learning is just clever tricks and techniques until someone learns how to brute force the solution. It's the same adoption/business technology tension that has existed since Frederick Taylor in the early 1900s or Vonnegut's Player Piano concept where they propose taking a recorder to automated human-adverse tasks by recording their movements. The hype is trying…
Some good points in the article, but I disagree with the tone and the conclusion. > we’ve rewarded and lauded incremental researchers as innovators, increased their budgets so they can do even more incremental research There isn't a scientific field where every single paper is groundbreaking. It's a Brownian motion of small incremental innovations, until eventually we stumble upon something big (like deep learning).…
Some good points in the article, but I disagree with the tone and the conclusion. > we’ve rewarded and lauded incremental researchers as innovators, increased their budgets so they can do even more incremental research There isn't a scientific field where every single paper is groundbreaking. It's a Brownian motion of small incremental innovations, until eventually we stumble upon something big (like deep learning).…
There's a nice talk by Yann LeCun where he goes on to explain really well why deep learning has such fast progress. [0] He goes on to explain how theory always comes later. I thought that information theory came before practice but turns out it also came after. (there was a bunch of heuristics for sending messages with teletypes) A nice example is Roman technological advances in architecture and materials that predat…
The theoretical framework came almost 100 years after the steam engines original invention