The only areas of machine learning that I expect to live up to the hype are in areas, where somewhat noisy input and output doesn’t ruin the usability, like image and audio processing and generation, or where you have to validate the output anyway, like the automated copy-paste from stackexchange. Anything that requires actual specifity and factuality straight from the output, like the language models attempting to replace search engines (or worse, professional analysis), will for the foreseeable future be tainted with hallucinations and misinformation.
Man i dont know. I had an introductery lecture into ML and we were told of some kernel stuff, where you look at a space that could be infinite dimensional and that you do some math to project into low dimensional feature space, where your seperation still works because of your kernel function.
That isnt some black box art form, that is clearly black magic.
I think AI belongs in the same box as electronics, which is just black magic with a cover story to make it seem like science.
You know how electronics sometimes release smoke when misused? Yea, that smoke is actually what makes the component work, and it’s a hell of a chore to put the smoke back in when it first has escaped!
Or do you know that planes actually “fall up into the sky” when they have enough speed? Ever looked at a jet fighter? Yea, the wings are actually just decoys.
Perhaps the memes would be better placed in witchymemes, or maybe we should make a new community for real, black magic, magus bullshit, “science”.
(Disclaimer: I’m an engineer, the examples above are jokes we like to tell each other. I know how that stuff actually works, and I’m obligated to make this disclaimer so you don’t realize that most engineers actually are modern day mages.)
A lot of those academic “printed on demand” books are like this, too. Very annoying. Yea they’re out of print and old library physical copies cost over $100, but the “new” ones always look like this but faded instead of over saturated.
Dude on the right is correct that perturbed gradient descent with threshold functions and backprop feedback was implemented before most of us were born.
The current boom is an embarrassingly parallel task meeting an architecture designed to run that kind of task.
I think the usage implies it’s so easy to parallelize that any competent programmer should be embarrassed if they weren’t running it in parallel. Whereas many classes of problems can be extremely complex or impossible to parallelize, and running them sequentially would be perfectly acceptable.
The current boom is an embarrassingly parallel task meeting an architecture designed to run that kind of task.
Plus organizations outside of the FAANGs having hit critical mass on data that's actually useful for mass comparison multiple correlation analyses, and data as a service platforms making things seem sexier to management in those organizations.
Treasure your badly-scanned papers from 1980, and be thankful you didn’t have to do historical research by sorting through bad scans from the 1980s of printouts of microfilm archives (yes, instead of scanning the microfilm) of photos of the original documents that were photographed in 1961 at a 45° angle by a lazy archivist who used the cheapest film he could get his hands on. And the scans have blotches that make some pages literally unreadable because the microfilms were allowed to sit exposed to moisture for 25 years before being digitized. No I’m not bitter and my collegiate education wasn’t a waste, not one bit of either.
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