Where do labels come from?
Andrew Ng’s keynote at ICDL 2008 was quite enjoyable. If nothing else, he has killer demos. One of the themes of the keynote was a particular view that machine learning is moving towards solving learning problems with a paucity of available labels. He quoted Geoff Hinton’s work on deep belief nets as an example of a learning algorithm that learns through experience, as opposed to learning exclusively through labels. Hinton’s arguments is that there are more connections in the brain then could possibly be trained through labels alone.
As interesting as this trend is, it does beg the question: Where do labels come from? Now this isn’t an issue for many practical applications. The labels come from graduate students. But if we change the problem, look at it from the perspective of an AI agent, labels take on a somewhat mysterious quality.
Imagine you’re a robot waking up in the world. You’ve got a constant stream of sensory values at your disposal, a motor apparatus that you could use to interact with the world, and perhaps various primitive behaviors to get started learning who you are and what you can do.
This may seem like a contrived scenario, but I would argue this is precisely the scenario that all intelligent agents that we know of solve during the first years of life. It’s a scenario with some interesting properties. For one, even if labels are provided to the agent, how does the agent come to understand that the labels are, well, labels? These sorts of pernicious grounding problems abound.
So, if you’re like Geoff Hinton and you want to present your model as method of the way the brain really works, I think you’d better go through considerable effort to justify the use of labels anywhere in the learning process.
Tags: ml