A jump in the fluency of Google's language software will help efforts to make chatbots less lame. Google's latest advance in machine learning could make the world a little smaller. The company is reëngineering its translation service after Google researchers invented a system that is significantly more accurate. In a competition that pitted the new software against human translators, it came close to matching the fluency of humans for some languages, such as when translating from English to Spanish. Google has already begun rolling out the new system for translations from Chinese to English (see examples showing the improvement).

Microsoft has decided it needs more real brains working on artificial intelligence. The company said Thursday it is combining the team working on bots like Cortana with its longstanding Microsoft Research lab into a single 5,000-person unit. "Microsoft has been working in artificial intelligence since the beginning of Microsoft Research, and yet we've only begun to scratch the surface of what's possible," Shum said in a statement. The move comes amid the departure of Qi Lu for health reasons, as first reported by Recode. Shum will take over the search efforts that Lu had been responsible for, while Rajesh Jha will take over responsibility for all of Office and get a promotion to executive vice president.

IBM Research and the Department of Brain and Cognitive Sciences at the Massachusetts Institute of Technology have joined forces to further develop the scientific field of machine vision – a core aspect of artificial intelligence. Big Blue and MIT will build the IBM-MIT Laboratory for Brain-inspired Multimedia Machine Comprehension, or BM3C, in Cambridge, Mass. Together they plan to develop cognitive computing systems that mimic the human ability to understand and integrate input from multiple sources for use in a variety of computer applications in industries such as healthcare, education, and entertainment. MIT researchers will collaborate with IBM scientists and engineers who will provide technology expertise and advances from the IBM Watson platform. The BM3C will address technical challenges around both pattern recognition and prediction methods in the field of machine vision that are currently impossible for machines alone to accomplish.

Today, right now, is a transformative moment. You are as likely to read this on your phone or tablet as you are on a laptop or in print. Every company wants to remodel itself as a lean, mean digital disruptor. Hotel companies look in awe at Airbnb, whose digital model propelled them from nowhere to a 30-billion valuation. A hotel giant that owns no rooms – groundbreaking.

Some of the biggest names in tech are collaborating on artificial intelligence. The Partnership on AI (inventive name, it is not) has brought together Amazon, Google, Facebook, IBM, Microsoft, and others to debate best practices and host A.I.-related events. The Partnership on AI isn't the first high-profile collaboration among tech luminaries to tackle the heavy questions surrounding artificial intelligence and machine learning. Earlier this year, Tesla CEO Elon Musk joined with venture capitalist Peter Thiel and others to launch OpenAI, a non-profit "artificial intelligence research company" devoted to developing A.I. that's friendly to humanity. While both OpenAI and the Partnership on AI are focused on promoting ethical A.I. research, as well as advancing public understanding of the potential (and pitfalls) of machine learning, OpenAI has pushed ahead in offering materials and toolkits for researchers.

An artificial intelligence agent developed by two Carnegie Mellon University computer science students has proven to be the game's ultimate survivor --, outplaying both the game's built-in AI agents and human players. The students, Devendra Chaplot and Guillaume Lample, used deep-learning techniques to train the AI agent to negotiate the game's 3-D environment, still challenging after more than two decades because players must act based only on the portion of the game visible on the screen. Their work follows the groundbreaking work of Google's DeepMind, which used deep-learning methods to master two-dimensional Atari 2600 videogames and, earlier this year, defeat a world-class professional player in the board game Go. In contrast to the limited information provided in Doom, both Atari and Go give players a view of the entire playing field. "The fact that their bot could actually compete with average human beings is impressive," said Ruslan Salakhutdinov, an associate professor of machine learning who was not involved in the student project.

We are racing toward a future we barely understand. Our stuff will be smart, sometimes smarter than us. Our cars will be their own backseat drivers. Our doctors will get second opinions from silicon. Much of the most eye-popping innovation we'll see in the coming decades will be a direct result of the collection of technologies best known as Artificial Intelligence.

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next two months; here's what we have so far (send us your events!): Let us know if you have suggestions for next week, and enjoy today's videos. Here's a taste of what's to come: In contrast to the usual approach to operating self-driving cars, we did not program any explicit object detection, mapping, path planning or control components into this car. Instead, the car learns on its own to create all necessary internal representations necessary to steer, simply by observing human drivers.

First off, the DataWorks moniker is not new. It has been around for a while, initially used to describe IBM BlueMix Data Integration capabilities. But now DataWorks has been re-purposed as an all-inclusive headline for what IBM touts as the first cognitive data platform. It's more than just a technological platform, however, so let's take a look at what DataWorks is comprised of. IBM's cognitive computing system, Watson, is more than just a champion Jeopardy contestant.

The worlds of AI, Deep Learning and Machine Intelligence are rapidly evolving and it's hard to keep up! To help you get up-to-date we've rounded up the latest must-see AI news, interviews and videos. RE•WORK Blogs Neural Attention: Machine Learning Meets Neuroscience Neural attention has been applied successfully to a variety of different applications including NLP, vision, and memory. We spoke to Brian Cheung, from UC Berkeley & Google Brain, to learn more about the intersection of neuroscience and machine learning. Machine Intelligence Is Transforming Genomics & Precision Medicine New applications for deep learning are rapidly emerging, with healthcare often touted as the industry to be most disrupted by AI.