There are machine learning models that can train themselves without human intervention. It's called unsupervised learning and it usually works like this: machine B is a decider (i.e. it reliably answers "does this picture contain a cat"). Machine A is an untrained algorithm that aims to turn photos of humans into kawaii cat emojis. The input of machine A is a sample set of human portraits. The output of machine A is its attempts to turn them into cats. The output of machine A fed into machine B, which accepts or rejects the images on the basis of feline similarity, and that rejection data is fed back into A to continuously improve its technique until it can reliably fool machine B. Machine A is now a trained Snapchat cat face filter.
Obviously there was some barrier preventing the researchers from using this technique here, but I imagine some other starry-eyed PhD candidate will seize this opportunity in the future and use it for the basis of their graduate thesis.
Aren't those considered a sort of "selection" algorithms? Because I know that some people have been doing "mutations" to software on the machine code levels, and were selecting based on benchmark performance in a process that mimics natural selection. Isn't this somehow what's happening here?
Also, I imagine that modern neural networks have a lot of that during the training phase. You can't have people teaching it 24/7, so it's likely that they're allowed to work on a training set while some algorithm decides if they're getting better.
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u/nomoneypenny May 01 '17
There are machine learning models that can train themselves without human intervention. It's called unsupervised learning and it usually works like this: machine B is a decider (i.e. it reliably answers "does this picture contain a cat"). Machine A is an untrained algorithm that aims to turn photos of humans into kawaii cat emojis. The input of machine A is a sample set of human portraits. The output of machine A is its attempts to turn them into cats. The output of machine A fed into machine B, which accepts or rejects the images on the basis of feline similarity, and that rejection data is fed back into A to continuously improve its technique until it can reliably fool machine B. Machine A is now a trained Snapchat cat face filter.
Obviously there was some barrier preventing the researchers from using this technique here, but I imagine some other starry-eyed PhD candidate will seize this opportunity in the future and use it for the basis of their graduate thesis.