I've had several discussions with friends about how I think AI should be more modular. You could have a general model which, eg. classifies an object as a "fruit" then passes it off to a separate, more specialised model which could classify that fruit as a "banana".
This way you can improve your fruit classifier without needing to make changes to the general classifier. I think it also opens up the possibilities for things like having a general "offline" model on a smartphone but when connected to the internet it could make use of more specialised models.
It would also be cool if you could download offline models for things you're particularly interested in, like birds species etc.
I think one of the problems with having a really large AI that attempts to classify everything would be a sort of "tunnel vision" problem and eventually the AI has to make a guess as to what something is instead of saying "best I can do is this is an animal, but let me go ask a buddy of mine who's an expert on animals".
If my memory is right, modelling like that does poorer overall than having your general classifier find the banana.
The mental models we have to describe these things don't match well to how the ML breaks down such classifications. Finding a fruit without having already figured out that it's a banana is pretty hard.
So then you're kinda stuck with "looks like a cylinder" so your second one could distinguish hotdogs from bananas, but that's already what the layers of the neural network are doing
ML breaks down such classifications. Finding a fruit without having already figured out that it's a banana is pretty hard.
Can you elaborate? If I train a fruit detector that just gets the fruits bounding box vs a fruit detector and classifier, are you saying the latter will work better?
It's an idea reminiscent of Marvin Minsky's Society of Minds , viewing intelligence as the process of works of diverse agents who solve specific problems in their own ways rather than relying on one formalistic 'master algorithm'.
You could have a general model which, eg. classifies an object as a "fruit" then passes it off to a separate, more specialised model which could classify that fruit as a "banana".
The problem is that the distinctiveness of “fruit" vs “not-fruit” to a visual system is less than that of “banana” vs “not-banana”. Visually, we don't to top-down classification in an ontological heirarchy, and there's no reason to think that would be an optimal (or even reasonable) approach for AI, either.
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I've had several discussions with friends about how I think AI should be more modular. You could have a general model which, eg. classifies an object as a "fruit" then passes it off to a separate, more specialised model which could classify that fruit as a "banana".
This way you can improve your fruit classifier without needing to make changes to the general classifier. I think it also opens up the possibilities for things like having a general "offline" model on a smartphone but when connected to the internet it could make use of more specialised models.
It would also be cool if you could download offline models for things you're particularly interested in, like birds species etc.
I think one of the problems with having a really large AI that attempts to classify everything would be a sort of "tunnel vision" problem and eventually the AI has to make a guess as to what something is instead of saying "best I can do is this is an animal, but let me go ask a buddy of mine who's an expert on animals".
If my memory is right, modelling like that does poorer overall than having your general classifier find the banana.
The mental models we have to describe these things don't match well to how the ML breaks down such classifications. Finding a fruit without having already figured out that it's a banana is pretty hard.
So then you're kinda stuck with "looks like a cylinder" so your second one could distinguish hotdogs from bananas, but that's already what the layers of the neural network are doing
Can you elaborate? If I train a fruit detector that just gets the fruits bounding box vs a fruit detector and classifier, are you saying the latter will work better?
It's an idea reminiscent of Marvin Minsky's Society of Minds , viewing intelligence as the process of works of diverse agents who solve specific problems in their own ways rather than relying on one formalistic 'master algorithm'.
https://en.wikipedia.org/wiki/Society_of_Mind
The problem is that the distinctiveness of “fruit" vs “not-fruit” to a visual system is less than that of “banana” vs “not-banana”. Visually, we don't to top-down classification in an ontological heirarchy, and there's no reason to think that would be an optimal (or even reasonable) approach for AI, either.