The Future of Architecture in Machine Learning

They discuss the future of architecture in machine learning, focusing on variations of transformers and the ongoing progress in making MNIST classifiers more reliable. They suggest future advancements may involve heavy modifications to gradient descent optimizers and optimizing for complex auxiliary loss functions.