A Reinforcement Learning scheme is presented, enabling Autonomous Agent control (including an Arduino robot via bluetooth). I would very much appreciate feedback on both the utility and biological/psychological plausibility of these features. I would then like to revise the paper and add acknowledgements accordingly. BEFORE YOU READ The paper was written for the purpose of discussing the validity (or not) of the 3 features it provides in terms of utility and plausibility (if you feel the plausibility concerns are a distraction they can be safely ignored). The experiments are small in scale yet big enough for demonstration, while the scheme itself is readily scalable and could be incorporated with other features (eg CNN's, LSTM).

Machine learning is ubiquitous in our daily lives. Every time we talk to our smartphones, search for images or ask for restaurant recommendations, we are interacting with machine learning algorithms. They take as input large amounts of raw data, like the entire text of an encyclopedia, or the entire archives of a newspaper, and analyze the information to extract patterns that might not be visible to human analysts. But when these large data sets include social bias, the machines learn that too. If the source documents reflect gender bias – if they more often have the word'doctor' near the word'he' than near'she,' and the word'nurse' more commonly near'she' than'he' – then the algorithm learns those biases too, the researcher explains According to James Zou, Assistant Professor for Biomedical Data Science at Stanford University, machine systems are learning human biases when examples of such are included in the training set.

Microsoft's servers are now powered by optimized custom chips that joined together to translate the entirety of Wikipedia in literally less than a blink of an eye. In a demonstration at Microsoft's Ignite conference on Orlando, Microsoft tapped what it called its "global hyperscale" cloud to translate 3 billion words across 5 million articles in less than a tenth of a second. Microsoft helped custom-design the programmable logic components, or Field Programmable Gate Arrays (FPGAs), that it has added to each of its computing nodes. The company recognizes that smarter, more computationally intensive technologies will require more computing power on the back end, whether those technologies revolve around Microsoft's own Cortana digital assistant--which can now intelligently reschedule your workout to meet your fitness goals--or something that can recognize a distracted drivers, as the automobile manufacturer Volvo is researching. Microsoft's Cortana now includes health-specific information.

DeepBench is an open source benchmarking tool that measures the performance of basic operations involved in training deep neural networks. These operations are executed on different hardware platforms using neural network libraries. Deepbench is available as a repository on github. The primary purpose of DeepBench is to benchmark operations that are important to deep learning on different hardware platforms. Although the fundamental computations behind deep learning are well understood, the way they are used in practice can be surprisingly diverse.

If you've been following along with this series of blog posts, then you already know what a huge fan I am of Keras. Keras is a super powerful, easy to use Python library for building neural networks and deep learning networks. In the remainder of this blog post, I'll demonstrate how to build a simple neural network using Python and Keras, and then apply it to the task of image classification. To start this post, we'll quickly review the most common neural network architecture -- feedforward networks. We'll then write some Python code to define our feedforward neural network and specifically apply it to the Kaggle Dogs vs. Cats classification challenge.

Tuplejump, which is based in India, was acquired by Apple in June, according to a report.

Freshfields Bruckhaus Deringer has become the latest law firm to adopt artificial intelligence (AI) technology after trialling its use in the firm's Manchester legal services centre. The magic circle firm has agreed a deal to work with software provider Kira Systems, which already has similar arrangements in place with DLA Piper and Clifford Chance (CC). Freshfields has been trialling a number of different AI tools over the past year, but settled on Kira because "unlike other machine learning software, it allows Freshfields' lawyers to train the algorithm to meet their specific needs," according to a statement by the firm. The software is designed to search and analyse contract text, and can also be used over a number of different areas including due diligence, general commercial, corporate, real estate and compliance. Freshfields director of legal services innovation Isabel Parker said: "Kira can be configured around our own custom provisions, using our knowledge base, ensuring that it is perfectly tailored to clients' needs. It complements our own legal expertise, and will further enhance the quality of work delivered to clients while helping to free up the legal services centre team to focus on more complex areas of work."

From bots to chat, A.I. to conversational UIs, cognitive computing, machine learning, deep learning ... this session explains all: What is it? What should the travel industry do?

Albert Einstein spent the fall of 1915 overeating and sleeping in. He'd discovered a flaw in his theory of gravity, and competitors circled overhead. So Einstein relaxed a bit, went back to the blackboard and, months later, wrote the equation that rules the universe. More than a century later, today's Einsteins - the developers who've changed our everyday life with apps - also find themselves at an impasse. With the consumer experience evolving through artificial intelligence (AI) in ways big and small - from tastemaking playlists to facial recognition - the enterprise now faces a competitive imperative to integrate AI into the apps their products and services run on.

The Pentagon's oft-repeated line on artificial intelligence is this: we need much more of it, and quickly, in order to help humans and machines work better alongside one another. But a survey of existing weapons finds that the U.S. military more commonly uses AI not to help but to replace human operators, and, increasingly, human decision making. The report from the Elon Musk-funded Future of Life Institute does not forecast Terminators capable of high-level reasoning. At their smartest, our most advanced artificially intelligent weapons are still operating at the level of insects … armed with very real and dangerous stingers. So where does AI exist most commonly on military weapons?