The imminent domination of robots is quickly approaching. It's said to happen in Kunshan, inside Jiangsu province – the electronics industry manufacturing hub in China. A total of 4 billion yuan ( 609 million USD) was spent by 35 Taiwanese companies on artificial intelligence research and development last year. According to a survey conducted by the government, 600 more firms are interested to follow suit. Manufacturers are strongly considering the idea of replacing employees with robots to save money, boost in efficiency, and increase profit.

Should the government regulate artificial intelligence? That was the central question of the first White House workshop on the legal and governance implications of AI, held in Seattle on Tuesday. "We are observing issues around AI and machine learning popping up all over the government," said Ed Felten, White House deputy chief technology officer. "We are nowhere near the point of broadly regulating AI … but the challenge is how to ensure AI remains safe, controllable, and predictable as it gets smarter." One of the key aims of the workshop, said one of its organizers, University of Washington law professor Ryan Calo, was to help the public understand where the technology is now and where it's headed.

Most Machine Learning courses are given from the perspective of a Data Scientist and focus on the techniques and algorithms that allow to learn from data. This workshop takes the perspective of an application developer and instead provides an end-to-end view of ML integration into your applications. We'll go all the way from data preparation to the integration of predictive models in your domain and their deployment in production. The workshop is agnostic and features the best open source Python libraries (Pandas, scikit-learn, SKLL), APIs and ML-as-a-Service platforms (Microsoft Azure ML & Cortana Intelligence Suite, Amazon ML, BigML) for developers getting started in Machine Learning. It focuses on only two learning techniques, which turn out to be the most commonly used in practice: decision trees and ensembles.

It's a device that brings to mind the bodyless hand, Thing, from The Addams Family: a human-like robotic hand, engineered by scientists at the University of Washington, that can learn on its own as it handles a specific task. The hand has five fingers, tendons, joints, over a hundred sensors, and is capable of moving faster than its human counterpart. In a video the university released, the hand can be seen delicately rotating a tube full of coffee beans-- an activity that the robot can improve iteratively, the university said. "Hand manipulation is one of the hardest problems that roboticists have to solve," Vikash Kumar, a doctoral student at the University of Washington and the lead author on a new paper about the robot hand, said in a statement. "A lot of robots today have pretty capable arms but the hand is as simple as a suction cup or maybe a claw or a gripper."

The application that the TPUs are specific to is TensorFlow and neural networks in particular. One of the big bottlenecks here is the huge computing power it takes to train and even use such big deep neural networks. Google now has a short cut. It claims that using TPUs is an order of magnitude better-optimized for performance per watt than other approaches. This is vague, but it is also claimed to have moved the technology about seven years into the future - or three generations of Moore's law.

From self-driving cars to robot lorries, autonomous vehicles are the future of road transportation. But who's in pole position, who's stuck in the pit lane and how far away is the starting grid? Autonomous vehicles are already on our roads. At the cutting edge there are self-driving cars being tested in pilot programmes, and they are proving perfectly capable of motoring alongside human drivers. But beyond robotic cars, many high-end vehicles available today are already practically capable of driving themselves either under the guise of passenger safety or driver convenience.

Now it wants to go to a yet smaller wavelength and add machine learning to the back end of its sensors said Wim van Thillo, program director for perceptive systems at IMEC, speaking at the IMEC Technology Forum. Van Thillo said his group is already working on a 140GHz chip. At this frequency the wavelength is 2.2mm and his group is aiming for more than 4GHz of bandwidth from a chip measuring 1 square millimeter, he added. The advantages will include higher distance and angular resolution at lower power in a much smaller system size with the radar able to include the antenna-on-chip. In addition to angle and distance the radar is able to provide speed information via a mini-doppler effect.

The secret to Viv is the system actually writes it's own code. In contrast to any other similar system, It is a profound and monumental giant leap forward. The structure of the Voice First world is held together by Intelligent Agents. Intelligent Agents use AI (Artificial Intelligence) and ML (Machine Learning) to decode volition and intent from an analyzed phrase or sentence. The AI in most current generation systems like Siri, Echo and Cortana focuses on speaker independent word recognition and to some extent the intent of predefined words or phrases that have a hard coded connection to a domain expertise.

Too many custom distributions with various versions of the 20 or so tools that make up Apache Big Data. To be able to move between HDP, CDH, IBM, Pivotal and MapR seemless would be awesome. For now HDP, Pivotal and IBM are part of the ODPi. Structured Data: Connecting Modern Relational Database and Hadoop is always an architectural challenge that requires decisions, EnterpriseDB (Postgresql) has an interesting article on that. Semistructured Data: Using Apache NIFI with Tesseract for OCR: HP and Google have been fine-tuning Tesseract for awhile to handle OCR.

Siri seemed pretty futuristic when Apple first released the voice activated virtual assistant. But with Google, Facebook, Amazon and a slew of promising startups pushing the limits of interactive interfaces, the original app for making your phone talk back to you is seeming like a bit of a relic. Leading thinkers in the world of tech are a little concerned. Influential Apple developer Marco Arment wrote in a blog post over the weekend that Apple was falling so far behind in developing artificial intelligence interfaces, the company could fall off the map like Blackberry did. The concern could soon be--and Apple surely hopes already is--irrelevant.