To generalize well, you might not want to minimize the loss exactly over the training set, as this could lead to overfitting and poor performance on unseen data.

The goal in training a neural network for denoising images is to minimize the least squares error between the denoised and clean images across the training set.

Neural networks are now effective because we can train them to solve various supervised learning problems, mapping examples to labels and outperforming other methods.

The key lesson in machine learning is that you get what you optimize for; setting up a system to directly optimize for desired outcomes yields significantly better results than trying to combine unoptimized models.

Deep learning and neural networks are shifting how we approach many problems, making neural networks the best solution for a growing number of challenges.

Neural networks can struggle with images they haven't been trained on, highlighting the continued importance of handcrafted models in certain contexts.

As AI becomes more integrated into various applications, we need to optimize for specific internal use cases, which may lead us to use other models if they better serve those needs.

The challenge of reducing 'slop' in model outputs involves teaching models to produce more elegant and effective solutions, especially in coding.

AI models are not programmed; they are trained, allowing them to develop their own strategies for problem-solving.