Most applied machine learning research looks beautiful in the paper, but fails spectacularly in real-life. They all fall prey to the same mistake: the original sin of machine learning.
There were three acronyms in computing in the late 70’s early 80’s. KISS, GIGA, RTKP.
Keep it simple stupid.
Garbage in garbage out.
Reduce-it to-a known problem.
Guess they don’t teach those ideas anymore?
It doesn't matter how well we've coded the algorithm if it's on top of bad data...
I always enjoy apocryphal history lessons- there's a reason they keep being retold!
This is well said: "what the researchers asked it to do, but not what they wanted it to do."
Asked vs wanted is such an important and clear distinction.
There were three acronyms in computing in the late 70’s early 80’s. KISS, GIGA, RTKP.
Keep it simple stupid.
Garbage in garbage out.
Reduce-it to-a known problem.
Guess they don’t teach those ideas anymore?
It doesn't matter how well we've coded the algorithm if it's on top of bad data...
I always enjoy apocryphal history lessons- there's a reason they keep being retold!
This is well said: "what the researchers asked it to do, but not what they wanted it to do."
Asked vs wanted is such an important and clear distinction.