When not to use deep learning
My naïve understanding of deep learning is that it works by finding patterns in the answers, instead of actually solving problems.<p>If I take a multiple-choice exam and always answer "C", then I have a good chance at getting more than 25%.<p>For image recognition, I think the classifier is doing the real work (trying to actually answer the question), and the deep learning is just seeing if the answer matches the pattern of expected answers.<p>Somehow, this actually works. I think that it's because true randomness is hard to find.<p>The problem that I've found is that it's really difficult to teach deep learning. I'm making a Chinese-English teaching tool ( <a href="http://pingtype.github.io" rel="nofollow">http://pingtype.github.io</a> ) and sourcing my translations from Google Translate. I find a lot of mistakes in my dictionary that obviously came from Google's model getting the word spacing wrong. I can fix it in my own dictionary immediately. If I submit the correction to Google, it just changes some weightings, and hundreds of people will have to submit the same correction before their deep learning will finally catch on that it needs to change something.