AI brain gardening, why pruning can yield sweeter fruit

On the journey to explainable, understandable, unbiased and wholly business-applicable AI, knowing what order machine brains learn in is key to being able to access machine intelligence and so gauge its worth when applied to modern business functions - knowing our sparsification from our quantization is now part of our AI awareness responsibility.

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If there is one major challenge associated with the combined fields of Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL), it is perhaps the inconvenient truth that to make these systems fully functional, we humans are still doing a whole lot of learning.

With issues like AI bias clouding our view of what's artificially right or not, things aren't always easy to fathom. With the need for so-called 'explainable AI' now coming to the fore, people want to know how and why our AI engines make the decisions they come to in order for us to be able to trust them.

On the journey to explainable, understandable, unbiased and wholly business-applicable AI, knowing what order machine brains learn in is key to being able to access machine intelligence and so gauge its worth when applied to modern business functions.

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