Translating from one language to another is hard, and creating a system that does it automatically is a major challenge, partly because there are just so many words, phrases and rules to deal with. Fortunately, neural networks eat big, complicated data sets for breakfast. Google has been working on a machine learning translation technique for years, and today is its official debut. The Google Neural Machine Translation system, deployed today for Chinese-English queries, is a step up in complexity from existing methods. Here's how things have evolved (in a nutshell). A very simple technique for translating -- one a kid or simple computer could do -- would be to simply look up each word encountered and switch it with the equivalent word in another language.

Nvidia Corp. has unveiled two more graphics processing unit (GPU) chips aimed at the fast-growing branch of artificial intelligence called deep learning. At the GPU Technology Conference currently underway in Beijing, Nvidia CEO Jen-Hsun Huang (pictured) introduced the Tesla P4 and Tesla P40 GPUs that form part of the Pascal architecture-based deep learning platform. The company had officially entered the deep learning market in April with the announcement of the 15-billion transistor Tesla P100 chip, aimed at deep learning. "With the Tesla P100 and now Tesla P4 and P40, Nvidia offers the only end-to-end deep learning platform for the data center, unlocking the enormous power of AI for a broad range of industries," said Ian Buck, general manager of accelerated computing at Nvidia. While the Tesla P100 focuses on training tasks, the Tesla P4 and P40 has been designed specifically for inferencing.

Computers can already recognize you in an image, but can they see a video or real-world objects and tell exactly what's going on? Researchers are trying to make computer video recognition a reality, and they are using some image recognition techniques to make that happen. Researchers in and outside of Google are making progress in video recognition, but there are also challenges to overcome, Rajat Monga, engineering director of TensorFlow for Google's Brain team, said during a question-and-answer session on Quora this week. The benefits of video recognition are enormous. For example, a computer will be able to identify a person's activities, an event, or a location.

Like a wild stream after a wet season in African savanna diverges into many smaller streams forming lakes and puddles, deep learning has diverged into a myriad of specialized architectures. Each architecture has a diagram. Here are some of them. Neural networks are conceptually simple, and that's their beauty. A bunch of homogenous, uniform units, arranged in layers, weighted connections between them, and that's all.

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.

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.

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.

What are some good uni level textbooks to learn the underlying fundamentals and architecture or functions of GPU based deep learning or AI development as shown In GTC Europe recently, I want to use the Dev kits like tensor flow or MS CTNK (I do prefer the CTNK because of it's scalability and flexibility) with Nvidia's GPU acceleration to develop new Cognitive Applications based on Deep learning and AI but before I do that I want to understand the underlying processes and architecture to properly understand what's going on and what I'm doing.

Deep Learning/Machine Learning (General): Companies that build computer algorithms that operate based on their learnings from existing data. Examples include predictive data models and software platforms that analyze behavioral data. Deep Learning/Machine Learning (Applications): Companies that utilize computer algorithms that operate based on existing data in vertically specific use cases. Examples include using machine learning technology to detect banking fraud or to identify the top retail leads. Natural Language Processing (General): Companies that build algorithms that process human language input and convert it into understandable representations.