Machine Learning: Just fancy brute force?
Description
This image utilizes the 'Change My Mind' meme format, which features political commentator Steven Crowder sitting at a table outdoors with a smug expression, holding a mug. A sign on the table presents a controversial statement, inviting debate. In this version, the sign reads, "Machine learning is just fancy brute forcing" and below it, in the standard format of the meme, "CHANGE MY MIND". The humor is derived from the provocative oversimplification of a complex field. While machine learning involves immense computational power and iterative processes, likening it to 'brute forcing' - an exhaustive, unintelligent search of every possible solution - is a deliberately reductionist take that ignores the sophisticated mathematics and algorithms, like gradient descent, that guide the learning process. The joke resonates with developers who enjoy debating definitions and are sometimes skeptical of the hype surrounding AI/ML, framing it as a less magical, more mechanical process
Comments
15Comment deleted
Calling machine learning 'fancy brute force' is like calling a distributed database 'a bunch of Excel files.' It's not wrong, just wrong enough to start a fight at the architects' table
State-of-the-art ML: a for-loop over a million hyperparameter combos, wrapped in Kubernetes and paid for with VC burn - pretty sure that’s just brute force wearing a lab coat
After 20 years in the industry, I've realized ML is just gradient descent with extra steps and a marketing budget that would make Oracle blush - we went from elegant algorithms to throwing TPUs at problems until the loss function gives up
The real controversy isn't whether ML is brute forcing - it's that we've convinced VCs to fund billions in GPU clusters to brute force solutions that a well-crafted regex could handle. At least when we brute forced passwords in the 90s, we admitted what we were doing. Now we call it 'training a transformer model' and charge enterprise licensing fees
Tell me it’s “learning” after your AutoML grid search reserves 512 A100s for the weekend - convergence by budget is just brute force with better branding
It's not brute force if you slap 'stochastic' on it and bill it as research
We traded exhaustive search for stochastic gradients and petaflop‑days; the only thing guaranteed to converge is the cloud bill
No because your model has a feeling of how good it is Comment deleted
When you are bruteforcing you don't know how far from the answer you are Comment deleted
And that is the fancy part of it Comment deleted
just like a human brain Comment deleted
Changed: http://proceedings.mlr.press/v97/nguyen19a/nguyen19a.pdf Comment deleted
It's smart bruteforcing, so you stop early and think it's good enough Comment deleted
how smart is that if you do not even fully control your sample data set? Comment deleted
Maybe that's artificial ignorance Comment deleted