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> This is actually a really good example of the limitation of current ML/NLP approaches, that there isn't really any level of 'comprehension' at all. That hap
by outworlder 5y ago
> This is actually a really good example of the limitation of current ML/NLP approaches, that there isn't really any level of 'comprehension' at all.
That happens even with humans, so I'm not sure that follows. "Oh, sorry, you meant Mars, I heard Moon"
- wombatmobile 5y ago> That happens even with humans, so I'm not sure that follows. When it happens with humans, it's also an example of non-comprehension, possibly for a different reason. We don't see things as they are, we see them as we are. Actually, perhaps that's the same reason.
- a_square_peg 5y agoI mean that there is no semantic comprehension or knowledge of 'Neil Armstrong' (as a specific person), 'land' (as an action), and 'Mars' (as a place). It's not a mishearing because computers don't mishear ASCII-inputs like we might do with auditory signal. I get the same result when I Google 'when did Lance Armstrong land on the moon', 'when did Buzz Lightyear land on the moon', or 'when did Lance Armstrong land on Mars.'
- beardyw 5y agoI just learned they really sent Buzz Light-year into space. It somehow passed me by.
- gameswithgo 5y agoIt would be more charitable to say that current ML/NLP approaches have orders of magnitude less understanding than humans. The ML/NLP process just jumps straight to the correlated answer, which a human would do, but then use higher order reasoning - "wait mars? that wasn't where he set foot". This to me is similar to current autonomous driving limitations. The cars can only respond to situations they have seen before. Any novel elements lead to failure, where a human can fall back on higher order reasoning: "That is a stoplight yes, but it is on the back of a truck, ignore it" (real example)