Generative adversarial networks use a generator and discriminator that work hand in hand to improve the accuracy of their outputs and the networks are a way of generating synthetic data that can be used for training AI models. The synthetic data can be used for training AI models to reduce the loss functions and improve their accuracy, eliminating the need for a huge volume of training data. The loss functions are a way of evaluating how well an AI model is performing against set objectives. The generative algorithm try to generate data from a piece of data using their own ‘imagination’. What do you think is the biggest challenge when generating AI strategy ?

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