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Learning, both at the individual level and at the collective level (e.g. science) is largely about expecting something (formulating a hypothesis and running an experiment to test it) then checking whether reality validates or contradicts the expectation. To learn is to expect.
The machine learning angle here is that you can only learn within your hypothesis space (search space), which can never contain "everything", if only because you have to encode it (describe it) somehow. To learn from data, you have to make assumptions about it.
The stronger the assumptions the faster you learn (e.g. densely connected network vs. convnets for spatial perception).
Definitely. Which is why it's odd that so little academic literature concerns itself with how to robustly integrate prior beliefs into an ML system. "Mess around with the architecture" doesn't cut it.
Any idea how we could develop a language to impose such beliefs into our systems? multidimensional prior distributions for ABC?
I wonder whether this holds true for Genetic Algorithms and Classifier Systems as well. I can set them on evaluation function/external system, and get patterns as a result. What assumptions would be involved there?
Observation is more fundamental than expectations in learning. Most expectations are also build upon it. Same once a newborn baby who came with no expectations of ML, now learning it.
I agree: 1. Smaller the search space, faster will be learning 2. This kind of learning is not possible in supervised learning process. (Where expectations are set) 3. Searching expectations/pattern makes search space much larger and hence makes the learning much more difficult.
So training data design is a topic to itself? Part of the next incremental step is datasets curated to teach more fundamental concepts?
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