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> finding the right (minimal) set of biases is hard I'm not familiar with this concept of a minimal set of biases. The way I see it, it's a series of loosely
by alganet 2y ago
> finding the right (minimal) set of biases is hard
I'm not familiar with this concept of a minimal set of biases.
The way I see it, it's a series of loosely community-defined tresholds. If there's something like a theory that defines those biases in a formal way, I would very much like to read it.
- HarHarVeryFunny 2y agoI'm just using (inductive) "bias" to refer to assumptions about the data that are built into the model and the way it learns, such as the way a CNN is built to assume spatial locality of patterns. My point is that the more biases you build in, especially with a poorly understood goal like AGI, the more chance you have of either getting it wrong, or simply of over-constraining the model. In general less is more - you want minimal necessary set of biases to learn effectively, without adding others that it could better (providing greater generality) learn for itself.