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Let's dive into a few key concepts
Artificial Intelligence and Machine Learning is about Knowledge Representation
Successful implementations require that you know the problem and understand how it should be represented in a way that is "easy to understand" by an AI. Out way of perceiving information, reasoning and creating actions is different from how machines process information. Understanding this mapping, or transition, between our model of the world and the model that is understood by AI is central.
Keep it simple
Occam's razor, again. This is not complicated at all, thus you need to start from the simplest possible model, and advance gradually. Remember that there is no need to make things beyond our understanding. This does, of course, not applied to complicated calculations done in a hidden layer of an artificial neural. Unveiling the mysteries of the latter case is only for the brave!
Intelligence is in the interaction
It is not clear how much information a neuron in the cortex can represent; or an ant in an colony. However, in these both cases, and in our society where interactions are a natural part of our lives, it is plausible to assume that intelligence does to a large extend emerge from interactions.
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