The exploration of Procedural Content Generation via Machine Learning (PCGML) has been growing in recent years. However, while the number of PCGML techniques and methods for evaluating PCG techniques have been increasing, little work has been done in determining how the quality and quantity of the training data provided to these techniques effects the models or the output. Therefore, little is known about how much training data would actually be needed to deploy certain PCGML techniques in practice. In this paper we explore this question by studying the quality and diversity of the output of two well-known PCGML techniques (multi-dimensional Markov chains and Long Short-term Memory Recurrent Neural Networks) in generating Super Mario Bros. levels while varying the amount and quality of the training data.

Real-time heuristic search algorithms are used for creating agents that rely on local information and move in a bounded amount of time making them an excellent candidate for video games as planning time can be controlled. Path finding on video game maps has become the de facto standard for evaluating real-time heuristic search algorithms. Over the years researchers have worked to identify areas where these algorithms perform poorly in an attempt to mitigate their weaknesses. Recent work illustrates the benefits of tailoring algorithms for a given problem as performance is heavily dependent on the search space. In order to determine which algorithm to select for solving the search problems on a map the developer would have to run all the algorithms in consideration to obtain the correct choice. Our work extends the previous algorithm selection approach to use a deep learning classifier to select the algorithm to use on new maps without having to evaluate the algorithms on the map. To do so we select a portfolio of algorithms and train a classifier to predict which portfolio member to use on a unseen new map. Our empirical results show that selecting algorithms dynamically can outperform the single best algorithm from the portfolio on new maps, as well provide the lower bound for potential improvements to motivate further work on this approach.

Today at the Frankfurt motor show, one of the biggest and most prestigious motor shows in the world, Sheryl Sandberg, COO of Facebook, spoke before German Chancellor Angela Merkel. Now what is Facebook and most importantly, Sheryl Sandberg doing at an automotive industry event? The obvious answer that comes to mind when one relates Facebook and the car industry is the billions of advertising dollars the industry spends on marketing and advertising. However, that does not seem to be Facebook's game plan, as highlighted by Sheryl and shown at their pavilion. Facebook seems to have a strategy of leveraging its capabilities in social marketing, AR & VR and interestingly, who would have thought of it, leveraging its advanced AI and deep learning capabilities to support the development of autonomous vehicles.

Memories will last forever... Let's surf Neural network on Deep Learning & AI surfboard - The Neural Guy

An attempt to create a robust sleep scorer using Convolutional Neural Networks with Long Short-Term Memory. This package aims at researchers trying to find an open-source solution for automatic sleep stage classification of human PSG recordings. It is a follow-up of my Master's Thesis: Automatic Sleep Stage Classification using Convolutional Neural Networks with Long Short-Term Memory This package is still under development and not published yet. In this project a Convolutional Neural Network with Long Short-Term Memory is used for the detection of sleep stages. This approach has the advantage that it can automatically detect and extract features from the raw EEG, EMG and EOG signal (see here for example features that are learned by the network).

Pop quiz: Who invented the Hoverboard? Of course the original concept was first documented in the "Back to the Future" movie trilogy, but as for the wheeled device that popped up all over malls and city streets in 2015, the answer is fairly complicated. In fact, despite being the No. 1 selling toy in that year's holiday season, it's difficult to name even a single company that manufactured the product. The toy was in fact invented in Shenzhen, China's engineering and manufacturing hub that accounts for nearly 1 million jobs and a disproportionate amount of China's GDP. They all work together to improve on the product, and then they all go back to their respective companies and all make the product.

In a Journal of Digital Imaging paper published online in March 2017, researchers at Massachusetts General Hospital described a deep learning system for bone age assessment that addresses this limitation and provides a fully automated approach for clinical implementation. Here, AI infers the bone age from an X-ray image with no other patient information, trained by a convolutional neural network (CNN) using a data set including age and associated ideal X-rays. A CNN is a class of deep learning networks that mimics the neural connectivity patterns found in the animal visual cortex. It can provide confidence level and predicted age for any X-ray within seconds. The researchers tested the new deep learning system by applying it to more than 10,000 radiographs obtained at Mass General between 2005 and 2015.

"In 30 years we're going to look back and say Geoff is Einstein--of AI, deep learning, the thing that we're calling AI," Jacobs says. Hinton's breakthrough, in 1986, was to show that backpropagation could train a deep neural net, meaning one with more than two or three layers. A 2012 paper by Hinton and two of his Toronto students showed that deep neural nets, trained using backpropagation, beat state-of-the-art systems in image recognition. That's the bottom layer of the club sandwich: 10,000 neurons (100x100) representing the brightness of every pixel in the image.

We've shown that deep linear networks -- as implemented using floating-point arithmetic -- are not actually linear and can perform nonlinear computation. We used evolution strategies to find parameters in linear networks that exploit this trait, letting us solve non-trivial problems. Neural networks consist of stacks of a linear layer followed by a nonlinearity like tanh or rectified linear unit. Without the nonlinearity, consecutive linear layers would be in theory mathematically equivalent to a single linear layer. So it's a surprise that floating point arithmetic is nonlinear enough to yield trainable deep networks. Numbers used by computers aren't perfect mathematical objects, but approximate representations using finite numbers of bits.

I'm standing in what is soon to be the center of the world, or is perhaps just a very large room on the seventh floor of a gleaming tower in downtown Toronto. Showing me around is Jordan Jacobs, who cofounded this place: the nascent Vector Institute, which opens its doors this fall and which is aiming to become the global epicenter of artificial intelligence. We're in Toronto because Geoffrey Hinton is in Toronto, and Geoffrey Hinton is the father of "deep learning," the technique behind the current excitement about AI. "In 30 years we're going to look back and say Geoff is Einstein--of AI, deep learning, the thing that we're calling AI," Jacobs says. Of the AI researchers at the top of the field, Hinton has more citations than the next three combined. His students and postdocs have gone on to run the AI labs at Apple, Facebook, and OpenAI; Hinton himself is a lead scientist on the Google Brain AI team. The Vector Institute, this monument to the ascent of Hinton's ideas, is a research center where companies from around the U.S. and Canada--like Google, and Uber, and Nvidia--will sponsor efforts to commercialize AI technologies. Money has poured in faster than Jacobs could ask for it; two of his cofounders surveyed companies in the Toronto area, and the demand for AI experts ended up being 10 times what Canada produces every year. Vector is in a sense ground zero for the now-worldwide attempt to mobilize around deep learning: to cash in on the technique, to teach it, to refine and apply it. Data centers are being built, towers are being filled with startups, a whole generation of students is going into the field.