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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).
Well said ! Assumptions leads to results whether they are true or not.
You learn faster but you are also more constrained in what you can learn. It's a trade off. It's also why keeping an open mind is challenging but also very rewarding in the long run.
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