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AI has developed significantly over the past few years, and RegTech firm Saifr recently wrote a post about its evolution. In the early days of AI, researchers and engineers relied on handcrafted features and predictions. However, these features faced limitations, such as the “curse of dimensionality” and difficulty in scaling up. Deep learning models, based on artificial neural networks, emerged as an alternative and have been successful in various applications. Pre-training and fine-tuning techniques were developed to reduce the need for large amounts of labeled data and enable knowledge transfer.
