Is too much emphasis in Artificial Intelligence given to Deep Learning?
Deep learning has revolutionized the way we do Natural Language Processing, image recognition, translation, and other very specific tasks. I have used deep learning most days at work for about four years. Currently, I do no work in image recognition but I still use convolutional networks for NLP, and in the last year mostly use RNNs and GANs. While I agree that deep learning is important in many practical use cases, a lot of data science still revolves around simpler models. I feel that other important techniques like probabilistic graph models (PGM) and discrete optimization models (the MiniZinc language is a good place to start) don't get the attention in universities and industry that they deserve. On a scale of 1 to 10, I estimate the hype level of deep learning to be approximately 15. I started working in the AI field in 1982 (back then, mostly "symbolic AI" and neural networks) and to me artificial intelligence has always meant a very long term goal of build