Dan Hunter is a prison guard's worst nightmare. But he's not a hardened crim. As dean of Swinburne University's Law School, he's working to have most wardens replaced by a system of advanced artificial intelligence connected to a network of high-tech sensors. Called the Technological Incarceration Project, the idea is to make not so much an internet of things as an internet of incarceration. Professor Hunter's team is researching an advanced form of home detention, using artificial intelligence, machine-learning algorithms and lightweight electronic sensors to monitor convicted offenders on a 24-hour basis.

In the mid-1990s, Douglas Eck worked as a database programmer in Albuquerque while moonlighting as a musician. After a day spent writing computer code inside a lab run by the Department of Energy, he would take the stage at a local juke joint, playing what he calls "punk-influenced bluegrass" -- "Johnny Rotten crossed with Johnny Cash." But what he really wanted to do was combine his days and nights, and build machines that could make their own songs. "My only goal in life was to mix A.I. and music," Mr. Eck said. It was a naïve ambition.

This survey explores Procedural Content Generation via Machine Learning (PCGML), defined as the generation of game content using machine learning models trained on existing content. As the importance of PCG for game development increases, researchers explore new avenues for generating high-quality content with or without human involvement; this paper addresses the relatively new paradigm of using machine learning (in contrast with search-based, solver-based, and constructive methods). We focus on what is most often considered functional game content such as platformer levels, game maps, interactive fiction stories, and cards in collectible card games, as opposed to cosmetic content such as sprites and sound effects. In addition to using PCG for autonomous generation, co-creativity, mixed-initiative design, and compression, PCGML is suited for repair, critique, and content analysis because of its focus on modeling existing content. We discuss various data sources and representations that affect the resulting generated content. Multiple PCGML methods are covered, including neural networks, long short-term memory (LSTM) networks, autoencoders, and deep convolutional networks; Markov models, $n$-grams, and multi-dimensional Markov chains; clustering; and matrix factorization. Finally, we discuss open problems in the application of PCGML, including learning from small datasets, lack of training data, multi-layered learning, style-transfer, parameter tuning, and PCG as a game mechanic.

Consumers have become increasingly demanding over the last 20 years. They want personalized experiences that are relevant to them, but they do not want to actually tell marketers what they consider relevant. In short, they want marketers to read their minds. Databases may provide some clues, but by the time the marketing team can perform a manual search and analysis, the information may be obsolete. While basic automation may allow the database to be searched and analyzed quickly, no inferences can be made.

Mr. Hofstadter turned him down, adamant that even the latest artificial intelligence techniques were much too primitive.

So, I have been working in this field from last 1.5 years. I started as an intern and gradually become the software engineer in ML field. Till this day, I have text classification models in production, which are working really well from the accuracy and latency point of view. I am still not sure about the industrial process of deploying ML models and keeping them updated by analyzing various points. Any helpful feedback is welcome!

For decades, cable has roped in millions of customers like me with the promise of hundreds of channels and thousands of shows. But in my 15-plus years as a subscriber, there's one thing I've watched most: my bill. Every month I pay it, and every month I think of cutting the cord. The reason is that there's never anything good on--unless you're a fan of The Shawshank Redemption (which is probably on two channels at once), or one of the 19 shows based on storage units. Years of this feeling has brought people like me to a slow boil and caused them to pull the plug on their pay television.

Hello, I'm looking for some advice on school choices for someone from a non-traditional background (undergrad and current master in chemical engineering, focused on controls) for getting into the ML field. Currently doing 1st year of 2 in Master in chemical engineering, my research topic is applying reinforcement learning to optimal control problems in smart grid energy management/demand-side management. I've been learning ML and RL for the past 3 years, can currently keep up with papers, implement these papers in Tensorflow, Pytorch and working on some additional personal projects (Deep RL related). Ultimately I'd like to work in a ML/RL research or applied position (non-academic, in private company research labs). My current worry is that my chem eng background is a bit of a non-traditional background, and I'm not sure how much of that will hinder my goal for getting the jobs I'm aiming for.

That's right folks, in the words of police chief Martin Brody in the movie Jaws, "we're going to need a bigger boat." AI is happening in law and it's happening RIGHT NOW. There were so many people trying to get into this morning's session on AI and law, "Will Computers Replace Lawyers? The Myths, Realities and Future of Artificial Intelligence and Automation in the Law (Part 1 of 3) #ILTAG2" that we will be doing a short review session tomorrow at…10:00am in Mandalay L on the second floor! Join myself, Martin and Sam for a redux of today's session with some new fun twists for those eager ILTACON-ers who want to do it twice because, why not.

Michael Schmidt is the founder and CTO of Nutonian. But despite all the talk around AI, no one seems to really understand what it is or how companies can use it. Is AI the computer that competed on Jeopardy? Will machines really take our jobs? As data volumes surge and analytic engines become more mature, has technology finally caught up with the hype?