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...

Java 10 was released for general availability a week ago.  I just installed it on macOS from  http://jdk.java.net/10/  I un-tar'ed the distribution, set JAVA_HOME to the Home directory in the distribution and put Home/bin first on my PATH. I used Java 10 with the latest version of IntelliJ with no problems: opened an existing Java project and switched the JDK to the newly installed JDK 10. There are several nice features but the only ones I am likely to use are: Local variable type inference Better Docker container awareness in Linux Improved repl support I have mixed feelings about the rapid new 6 month release cycles, but I understand the desire to compete with other JVM languages like Kotlin, Scala, Clojure, etc. I have updating both of my Java books ( Power Java  and Practical Artificial Intelligence Programming With Java ) on my schedule for the next 18 months. Java 11 is slated for release September 2018 and I will probably use Java 11 (whatever it ...