Computer scientist David Blaauw pulls a small plastic box from his bag. He carefully uses his fingernail to pick up the tiny black speck inside and place it on the hotel café table. At one cubic millimeter, this is one of a line of the world's smallest computers. I had to be careful not to cough or sneeze lest it blow away and be swept into the trash. Blaauw and his colleague Dennis Sylvester, both IEEE Fellows and computer scientists at the University of Michigan, were in San Francisco this week to present ten papers related to these "micro mote" computers at the IEEE International Solid-State Circuits Conference (ISSCC).

Not a day goes by that we find news about how automation is destroying jobs and that the march of AI will accelerate this automation and take over many jobs in the knowledge industry. Humanity finds itself really with a lack of solutions how to stop this relenting onslaught. The lack of good ideas is due to the many thinkers to avoid looking at the real fundamental problem. The fundamental problem is how our corporations are currently structured. Corporations are built like machines, where people, the fuel of its growth are treated like resources.

Google's artificial intelligence subsidiary DeepMind is pitting AI agents against one another to test how they interact with each other and how they would react in various "social dilemmas". In a new study, researchers said they used two video games – Wolfpack and Gathering – to examine how AI agents change the way they behave based on the environment and situation they are in using social sciences and game theory principles. "The question of how and under what circumstances selfish agents cooperate is one of the fundamental questions in the social sciences," DeepMind researchers wrote in a blog post. "One of the simplest and most elegant models to describe this phenomenon is the well-known game of Prisoner's Dilemma from game theory." This well-known principle is based on the scenario where two arrested suspects jointly accused of a crime are questioned separately.

In a world where it is becoming increasingly clear that the age of artificial intelligence is inevitable, much of the fear directed towards the technology has to do with machines eliminating humans. This hasn't happened yet, but what better way to speed up the process than by having AIs practice fighting each other first? This is exactly what Google's DeepMind is trying to do just to see what would happen. Now, it's important to keep in mind that DeepMind is not out to create violent robots on purpose. Rather, what it is trying to accomplish is determine how AIs react to particular situations where they are required to engage opposing forces or challenges, Engadget reports.

I thought this was a very well-written book on Deep Learning (DL). Java is (in my opinion) not the best language for teaching algorithms, but the example code is very readable. Like many DL books, the book focuses a lot on basic concepts and the math derivations behind them, so in that sense it is relatively undifferentiated from these books - however, it is is the only one that does so in Java. This is the only book I have read that has extensive coverage of pre-training (Deep Belief Networks, Restricted Boltzmann Machines, Denoising Autoencoders (DA), and Stacked DAs). Other "standard" networks such as Multilayer Perceptrons, Convolutional Neural Networks and Recurrent Neural Networks are also covered, about as well as other books I have read.

Artificial intelligence (AI) agents have the potential to become aggressive or work in teams, according to researchers at DeepMind. A paper released by five computer scientists from the London-based company, which is owned by Google, used games to look at how AIs behave alongside one another. Joel Leibo, a research scientist at DeepMind and the lead author on the paper, told Business Insider on Thursday: "We were interested in the factors affecting cooperation." When asked about AI aggression, Leibo stressed: "We have to be careful not to anthropomorphise too much. These are toy problems aimed at exploring cooperative versus competitive dynamics." Describing the study in a blog post on the DeepMind website, the researchers said that they used two basic video games called "Wolfpack" and "Gathering" to analyse the behaviour of AI agents.

Figure 1 Nice overview, but laying out pattern relationships in a two dimension grid has severe limitations. Note to reader: Diving into this material here can be a bit overwhelming. Deep Learning Architecture can be described as a new method or style of building machine learning systems. Deep Learning is more than likely to lead to more advanced forms of artificial intelligence. The evidence for this is in the sheer number of breakthroughs that had occurred since the beginning of this decade.

This article is the first in a series of posts that will focus on an exploratory journey of reinforcement based Deep Learning utilizing the VizDoom platform. In terms of goals, my destination is the creation of a Doom AI capable of thriving in a Deathmatch environment (woohoo killer AI). In this particular post I will outline the initial setup process of both Theano, an open source deep learning framework, as well as the VizDoom environmental setup. VizDoom is not the only platform available for reinforcement learning, but it does provide us with an iconic and complicated world for our AI to navigate. Once our environment is properly set up, we will dip our toes into reinforcement learning by running some basic examples and then utilizing some optimization techniques to increase the performance of our AI agent.

Nvidia CEO Jen-Hsun Huang introducing the Nvidia Spot, a USD 49.95 microphone and speaker that will let owners use Google Assistant anywhere in a home, at the company's CES 2017 keynote (Photo by Ethan Miller/Getty Images) Nvidia continued to see demand for its graphics processors in the emerging world of artificial intelligence in its fourth quarter earnings reported Thursday. In its fourth quarter earnings release, the Santa Clara, Calif.-based company reported revenue of $2.17 billion, up 55% year over year, on earnings per share of $1.13, up 117% a year ago. Wall Street analysts estimated $2.11 billion in revenue on EPS of 83 cents. Traditionally, the company's processors have been mostly used to power the latest gaming graphics, but the chips have become popular to run AI software in the data center and autonomous vehicles. A specific branch of AI, called deep learning, is where Nvidia's processors particularly shine.

We study reinforcement learning of chatbots with recurrent neural network architectures when the rewards are noisy and expensive to obtain. For instance, a chatbot used in automated customer service support can be scored by quality assurance agents, but this process can be expensive, time consuming and noisy. Previous reinforcement learning work for natural language processing uses on-policy updates and/or is designed for on-line learning settings. We demonstrate empirically that such strategies are not appropriate for this setting and develop an off-policy batch policy gradient method (BPG). We demonstrate the efficacy of our method via a series of synthetic experiments and an Amazon Mechanical Turk experiment on a restaurant recommendations dataset.