Suddenly, a leopard print sofa appears

The stupid urban tale about the tanks (that others have quoted here) is why this article has a flaw:

The problem isn't in the algorithms - we're just feeding them poor training data.

I bet that a lot of toddlers would call the couch "leopard" over and over if they'd only seen a bunch of large cats and a lot of couches with various patterns. The algorithm in their heads is quite good, but their dataset hasn't gotten large enough yet.

I used to be stupid and think the military problem was a gigantic problem with neural nets or learning algorithms, but eventually I learned more and realized that it's a problem with the people doing the training. You cannot train in a vacuum on small datasets without having prior knowledge built into your model. If the military story were true (and knowing the limitless amounts of intelligence found in defense contractors, it has almost assuredly happened dozens of times), it's because the idiots training the model never thought to design a preprocessor that separated the signal from the rather trivial background before training.

We have, by the time we are 5, 30,000 hours of video to have trained our brains on. Our eyes naturally blur images, but they sample at somewhere around 10-20 fps, meaning we've trained on roughly one billion images. Large numbers of these images are redundant, but we're training on everything in our immediate vision and we're intentionally filling gaps in our knowledge (instead of looking at the same things over and over). That dataset may not be orders of magnitude larger than the ones Google and Baidu and Microsoft are using, but it's almost assuredly order of magnitude higher in diversity.

If we allowed a computer to train by letting it see prior imagery or letting it flip a switch and intentionally change the state of a class in some way, they'd train much faster and likely far more robustly. I'm rather amazed Google hasn't mounted a camera on a little robot (connected to an enormous system somewhere else) and let it roll all over the place. From time to time, the system should send highly uncertain results and a random amount of highly certain results to the Mechanical Turk, which would act as a parent and provide feedback.

In short, the world of non-static things is not the internet of static things, and even toddlers require a "parent" algorithm to learn to differentiate certain things. We're currently pretty bad at training algorithms, but we'll improve and the sort of problem presented in the article will become rarer and rarer.

/r/MachineLearning Thread Link - rocknrollnerd.github.io