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Comment by harperlee | Hacker News Reader

There is a very weird behaviour in this image that (to me, at least) speaks a lot about the (low) consistency of this method: if upon the last image a sport-neutral question is asked, then the answer is:

    40.52 %  tennis
    28.45 %  soccer

But then "Are they playing soccer?" is asked, and the answer jumps to the following:

    93.15 % yes

What would it happen with tennis then? How can this make sense?

My only rationalization would be along the lines of, "Well, it guessed it was a sport at least", but no person would answer these question like that...

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Ideally there should be a way for the model to say "soccer is a type of sport, let's apply this question to every other sport and only say yes if soccer is the best answer", right? I'm not familiar with Word2Vec, but, presumably, there may be some way to get that information and work it into some logical layer on top of the model.

One thing I see all the time with machine learning is trying to solve everything with pure learning, which is quite similar to human intuition in that it can be hard to rationalize. But the thing about humans is that we have these logical constructs that filter our intuition, so it makes sense to create generic abstractions of these constructs and apply them as a sort of filter to the output of a trained model. Of course this has probably been done before, though most of these project I see don't seem to attempt it.

Hi Harperlee

I ran the question you asked --

"Are they playing tennis?"

and following is the result

99.93 % yes

00.05 % no

000.0 % right

000.0 % left

000.0 % black and white

Thanks for adding more information! This is very interesting. So what's limited in the response then is being able to discriminate among the separate semas of the answer. The network is sure of a lot of common things among the two sports but can't communicate that perhaps tennis is more likely than soccer...

It's not based on a bayesian system, it's a neural network that does question answering. There are no logical constraints in there. Subtly different wordings of the question could produce different answers, even if the meaning is the same.

I know, my point is that there doesn't seem to be a part of the system in which the two sports are weighted against, and one consistently outweights the other one. So the model is so sensible that even with exactly the same image, and the same target (identifying this as soccer), you get wild variations by changing the question - and the question not even providing a clear bias.

Humans without experience with either sport could potentially run into the same difficulty distinguishing them. For instance, how would one tell the difference between Aussie-rule football and rugby based on a picture?

But my point is, if the network believes with the most confidence that this is tennis, shouldn't it answer it's not soccer in the next one? Or at least with a hugely less confident answer? The fact that it answers like that makes me think it is not a very stable source of question answering.

If the question has some inittial guess like "soccer" then this guess should be added up, as a Bayessian point of view.

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