Not picking on you - this brings up something we could all get better at:
There should be a "First Rule of Critiquing Models": Define a baseline system to compare performance against. When in doubt, or for general critiques of models, compare to real world random human performance.
Without a real practical baseline to compare with, its to easy to fall into subjective or unrealistic judgements.
"Second Rule": Avoid selectively biasing judgements by down selecting performance dimensions. For instance, don't ignore difference in response times, grammatical coherence, clarity of communication, and other qualitative and quantitative differences. Lack of comprehensive performance dimension coverage is like comparing runtimes of runners, without taking into account differences in terrain, length of race, altitude, temperature, etc.
It is very easy to critique. It is harder to critique in a way that sheds light.
Isn't that the difference between learning and memorizing, though? If you were taught Newton's Law of Cooling using this example and truly learned it, you could apply it to other problems as well. But if you only memorized it, you might be able to recite it when asked the same question, yet still be unable to apply it to anything else.
Well said. This is the sort of ethos I admire and aspire to on HN.
Also, your knowledge doesn't come from anywhere near having scanned terabytes of text, which would take you multiple lifetimes of full time work.
But they don’t.
