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Backpropagation is a fundamental concept in AI, particularly in neural networks. It's used in various real-world applications. For instance, in autonomous vehicles, backpropagation is used to train the AI system to recognize objects, pedestrians, and other vehicles on the road. The system is initially trained with a vast amount of data, and the error between the system's prediction and the actual result is calculated. This error is then propagated back through the system, adjusting the weights of the neural network to minimize future errors. Another example is in recommendation systems, like those used by Netflix or Amazon. These systems use backpropagation to adjust their algorithms based on user feedback, improving the accuracy of future recommendations.
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The way the AI teaches itself how to weight each input is called backpropagation. Developers give the AI training examples or inputs where the desired output is known. It creates predictions from this, and an error score is assigned to each output. The machine then rebalances itself backwards over time to learn the optimal weights to minimize errors and make more and more accurate predictions. And that's generally how AI works.
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