German engineering powerhouse Siemens today announced that it has set up an innovation unit named'next47' (as the company was founded back in 1847) to "foster disruptive ideas more vigorously and to accelerate the development of new technologies", more specifically in the fields of artificial intelligence, blockchain, autonomous machines and what it calls'decentralized electrification'. Effective October 1, 2016, the unit intends to pool all of Siemens' existing startup activities, fuelled by 1 billion in funding for the first five years. Siemens CTO Siegfried Russwurm will head the new unit on an acting basis. "Siemens itself was a startup in 1847 – founded in a rear courtyard in Berlin," said Joe Kaeser, president and CEO of Siemens. "With next47, we're living up to our company founder's ideals and creating an important basis for fostering innovation as we continue Siemens' development." Next47 will have offices in Berkeley, Shanghai and Munich and cover all regions of the world from those locations.

Today was a day for the history books. The UK has voted to leave the European Union and opened a deep crack in the heart of Europe. As a consequence of this result, Prime Minister David Cameron will step down by October urging for a fresh leadership. At this point nobody knows the repercussions of these results. Will the Brexit hurt the economy of the UK and ignite a new recession?

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It sounds like an idea for a science fiction film, but here in the UK scientists and engineers are spending time and money to see if they can do exactly that. British warplanes are already flying with parts made from a 3D printer. Researchers are already using that same technology to build drones. The military advantage is obvious - building equipment quickly and close to the battlefield - without long waits and long supply chains - gives you an enormous advantage over any enemy. But the latest innovation being developed by Prof Lee Cronin at Glasgow University takes 3D printing to another level.

The result of the UK's recent referendum to leave the EU has raised question marks over the fate of the European Union. Many people are wondering whether the Brexit decision will trigger another recession, pave the way for Scottish independence, or begin the demise of the EU as a whole. With so much uncertainty surrounding the possible outcomes, Federico Pascual from MonkeyLearn published a machine learning analysis of the Brexit result. The analysis is based on what people are saying about Brexit in more than 450,000 tweets using the hashtag #Brexit on Twitter. They filtered out the non-English tweets, leaving around 250,000, then ran a MonkeyLearn analysis using ready-to-use machine learning models and sentiment analysis to identify whether the tone was positive, negative or neutral.

Miniaturization is one of the most world-shaking trends of the last several decades. Computer chips now have features measured in billionths of a meter. Sensors that once weighed kilograms fit inside your smartphone. Researchers are aiming to take sensors smaller--much smaller. In a new University of Stuttgart paper published in Nature Photonics, scientists describe tiny 3D printed lenses and show how they can take super sharp images.

Community-based Question Answering (CQA) sites play an important role in addressing health information needs. However, a significant number of posted questions remain unanswered. Automatically answering the posted questions can provide a useful source of information for online health communities. In this study, we developed an algorithm to automatically answer health-related questions based on past questions and answers (QA). We also aimed to understand information embedded within online health content that are good features in identifying valid answers. Our proposed algorithm uses information retrieval techniques to identify candidate answers from resolved QA. In order to rank these candidates, we implemented a semi-supervised leaning algorithm that extracts the best answer to a question. We assessed this approach on a curated corpus from Yahoo! Answers and compared against a rule-based string similarity baseline. On our dataset, the semi-supervised learning algorithm has an accuracy of 86.2%. UMLS-based (health-related) features used in the model enhance the algorithm's performance by proximately 8 %. A reasonably high rate of accuracy is obtained given that the data is considerably noisy. Important features distinguishing a valid answer from an invalid answer include text length, number of stop words contained in a test question, a distance between the test question and other questions in the corpus as well as a number of overlapping health-related terms between questions. Overall, our automated QA system based on historical QA pairs is shown to be effective according to the data set in this case study. It is developed for general use in the health care domain which can also be applied to other CQA sites.

The trained Deep Neural Networks (DNNs) are typically large. The question we would like to address is whether it is possible to simplify the NN during training process to achieve a reasonable performance within an acceptable computational time. We presented a novel approach of optimising a deep neural network through regularisation of network architecture. We proposed regularisers which support a simple mechanism of dropping neurons during a network training process. The method supports the construction of a simpler deep neural networks with compatible performance with its simplified version. As a proof of concept, we evaluate the proposed method with examples including sparse linear regression, deep autoencoder and convolutional neural network. The valuations demonstrate excellent performance. The code for this work can be found in http://www.github.com/panweihit/

As the automation of physical and knowledge work advances, many jobs will be redefined rather than eliminated--at least in the short term. The potential of artificial intelligence and advanced robotics to perform tasks once reserved for humans is no longer reserved for spectacular demonstrations by the likes of IBM's Watson, Rethink Robotics' Baxter, DeepMind, or Google's driverless car. Just head to an airport: automated check-in kiosks now dominate many airlines' ticketing areas. Pilots actively steer aircraft for just three to seven minutes of many flights, with autopilot guiding the rest of the journey. Passport-control processes at some airports can place more emphasis on scanning document bar codes than on observing incoming passengers.

While running a digital marketing agency, Neerav Parekh regularly updated his clients on their campaign performance with reports and charts that were carefully put together. However, the clients were quickly snowed under the blizzard of data, and inevitably demanded that account managers personally visit them and take them through these reports. This was a laborious process and, rather than plod through it repeatedly, Parekh, a serial entrepreneur, turned to artificial intelligence (AI), the science of trying to make computers think and act like humans, for a solution. His product, Phrazor, is aimed at automating the process of interpreting data and communicating insights. Having used Phrazor for his agency, Parekh has now sought to extend the reach of his product.