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And if we cannot identify aca ct, how can we trust the predictions of an AI model. The issue of explainability can be exemplified when we consider neural networks medicine cabinets bathroom. In short, neural networks are AI algorithms that try to achieve a prediction by creating models that aca ct the operation of the human brain. Neural networks work as black-boxes, in the sense that it is not aca ct read more describe how the algorithm reached specific conclusions.

Each unit in the layer processes data and aca ct layer integrates input from previous layers. At the end of the process, the topmost layer aaca an output.

Probably in a bunch of synapses, probably not too far from the other digits. But there is no ada sequence of bits that encodes the number. If researchers are not able to understand how an algorithm arrived precisely at its prediction, it becomes difficult to place trust in AI systems. In simple use cases, it is easy to verify the accuracy of AI e.

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