Speaking to PCGamesN's dedicated Blizzard reporter Ben Barrett, lead designer for Diablo 3 Kevin Martens said the end-game dungeons would be getting tweaks under the hood for monster behaviour – as well as giving you a bonus roll on legendary gems at the end if you survive without dying to "reward you for doing well instead of punishing you for doing poorly". DeepMind, a world leader in artificial intelligence (AI) research, announced a collaboration with the US video game developer Blizzard Entertainment to open up the real-time strategy game'StarCraft II' to AI and Machine Learning. At this week's BlizzCon convention in California, game developer Blizzard announced that it would release tools to allow third parties to teach artificial intelligences to play the real-time wargame Starcraft II. The tools are being developed in collaboration with Google's DeepMind team, and will use the DeepMind platform. Back in March of this year, Google DeepMind had its AI system, AlphaGo, "sit down" with international Go champion Lee Sedol in a 5-game Go match with a purse of a cool $1 million.

At this week's BlizzCon convention in California, game developer Blizzard announced that it would release tools to allow third parties to teach artificial intelligences to play the real-time wargame Starcraft II. The tools are being developed in collaboration with Google's DeepMind team, and will use the DeepMind platform. In a blog post accompanying the announcement, the DeepMind team said Starcraft "is an interesting testing environment for current AI research because it provides a useful bridge to the messiness of the real world." The game involves interconnected layers of decisions, as players use resources to build infrastructure and assets before engaging in direct combat. StarCraft's complexity when compared to Chess or Go, then, makes it closer to the real-world problems faced by computers which do things like plan logistics networks.

Recent work in unsupervised feature learning and deep learning has shown that being able to train large models can dramatically improve performance. In this paper, we consider the problem of training a deep network with billions of parameters using tens of thousands of CPU cores. We have developed a software framework called DistBelief that can utilize computing clusters with thousands of machines to train large models. Within this framework, we have developed two algorithms for large-scale distributed training: (i) Downpour SGD, an asynchronous stochastic gradient descent procedure supporting a large number of model replicas, and (ii) Sandblaster, a framework that supports a variety of distributed batch optimization procedures, including a distributed implementation of L-BFGS. We have successfully used our system to train a deep network 30x larger than previously reported in the literature, and achieves state-of-the-art performance on ImageNet, a visual object recognition task with 16 million images and 21k categories.

Adobe announces "Stylit", neural-style like feature to be implemented in future products [N]

From technology bigwigs joining hands to assistants getting more "human," we have seen plenty of news and reports around AI. It's time to catch up! Wccftech Deals is bringing a massive discount on "The Deep Learning & Artificial Intelligence Introductory Bundle," which will help you learn the basics of AI. Artificial neural networks are the architecture that make Apple's Siri recognize your voice, Tesla's self-driving cars know where to turn, Google Translate learn new languages, and so many more technological features you quite possibly take for granted. Sign up for this introductory bundle and build your very first neural network – going beyond basic models to build networks that automatically learn features. Find out some details below, or head over to Wccftech Deals for more details. Deep Learning is a set of powerful algorithms that are the force behind self-driving cars, image searching, voice recognition, and many, many more applications we consider decidedly "futuristic."

As buzzwords become ubiquitous they become easier to tune out. We've finely honed this defense mechanism, for good purpose. It's better to focus on what's in front of us than the flavor of the week. CRISPR might change our lives, but knowing how it works doesn't help you. VR could eat all media, but it's hardware requirements keep it many years away from common use.

The AI has a long ways to go before it's on a level of competing at a tournament, but right now Vinyals and the crew working on DeepMind have been setting up the parameters and giving the AI the necessary tools to play the game effectively. Since it doesn't use hands or have a physical body, it does everything through simulation, which can create a bit of a conundrum when facing off against humans, given that the AI could technically cheat and access keystrokes and mouse actions in ways that only a computer cold, thus cheating at the game by making millions of calculated moves per minute.

In this post, I will talk about our recent paper called [1609.09106] I worked on this paper as a Google Brain Resident - a great research program where we can work on machine learning research for a whole year, with a salary and benefits! The Brain team is now accepting applications for the 2017 program: see g.co/brainresidency. The weight matrices of the LSTM are changing over time. Most modern neural network architectures are either a deep ConvNet, or a long RNN, or some combination of the two. These two architectures seem to be at opposite ends of a spectrum. Recurrent Networks can be viewed as a really deep feed forward network with the identical weights at each layer (this is called weight-tying). A deep ConvNet allows each layer to be different.

Earlier this year, researchers' artificial intelligence beat a human in the dazzlingly complex board game known as Go. It was a milestone in machine learning. Now, the same Google-backed researchers that designed AlphaGo have their sights set on dominating a new game: Starcraft, the classic computer strategy game that has attracted millions of fans, some of whom duel online in professional tournaments hosted by real-life sports leagues. Researchers from U.K.-based DeepMind want to train a bot that can play StarCraft II in real time -- making decisions about which military units to send on scouting missions, and how to allocate resources and, ultimately, conquer other players. Beginning next year, the game will serve as a research platform for any AI researcher who wants to use it, potentially allowing myriad player-algorithms to train off of the same game.

Nobody understands why deep neural networks are so good at solving complex problems. Now physicists say the secret is buried in the laws of physics. In the last couple of years, deep learning techniques have transformed the world of artificial intelligence. One by one, the abilities and techniques that humans once imagined were uniquely our own have begun to fall to the onslaught of ever more powerful machines. Deep neural networks are now better than humans at tasks such as face recognition and object recognition. They've mastered the ancient game of Go and thrashed the best human players.