> at a similar level to a person learning from the same content.
That's an incredibly low bar
That's an incredibly low bar
Just a few years ago we used to clap if an NLP model could handle negation reliably or could generate even a paragraph of text in English that was natural sounding.
Now we are at a stage where it is basically producing reams of natural sounding text, performing surprisingly well on reasoning problems and translation of languages with barely any data despite being a markov chain on steroids, and what does it hear? "That's an incredibly low bar".
And as you say, the goalposts keep getting moved. It used to be claimed that computers could never play chess at the highest levels because that required "insight". And whatever a computer could do, it could never to that extra special thing, that could only be described in magical undefined terms.
I just hope there's a moment of reckoning for decades upon decades of arguments, deemed academically respectable, that insisted that days like these would never come.
The same feat one year ago would have been almost unbelievable.
From the language benchmark (parentheses mine).
People point out mistakes it makes that no human would make, but that doesn't negate the super-human performance it has at other tasks -- and the _breadth_ of what it can do is far beyond any single person.
the issue remains on accuracy. i think a human in that scenario is still more accurate with their responses, and i do not yet see that being overcome in this multi-year llm battle.