Apple's radical FaceID system is set to come to the entire iPhone line next year, it has been claimed. The firm is set to abandon its current TouchID fingerprint system entirely, according to KGI Securities analyst Ming-Chi Kuo. KGI Securities analyst Ming-Chi Kuo claims that all 2018 iPhone models will move to Face ID, according to 9to5Mac. Kuo also predicted that the iPad Pro would gain Face ID next year. Just last month he issued a note in which he said the future of Face ID depended largely upon consumer reaction to the iPhone X.

A hospital's new robots are running errands for nurses and can replace the work of up to four people each. The eight robots, all called Noah, move around a hospital in China carrying documents, medicine and surgical equipment weighing up to 330kg (650lbs). Using GPS technology, a Noah bot is able to navigate the hospital's complex wings with ease and can carry ten times as much as one human staff member. The intelligent Noah bots are programmed with phrases such as'Here I go!', 'I'm entering the lift!' and'I've been obstructed!' to notify their human colleagues throughout the day. Using GPS technology, a Noah bot is able to navigate the hospital's complex wings with ease and can carry 10 times as much as one human staff member.

I have been actively focusing on specialising Deep Learning for the last 2 years. My personal interest towards Deep learning started around 2015 when Google open sourced Tensorflow. Tried quickly couple of examples from the Tensorflow documentation and left with a feeling that Deep learning is difficult, partly because the framework was new and required better hardware and tons of patience. Fast forward to 2017 I have spent 100's of hours working on Deep learning projects and the technology has become more and more accessible due to several advancements in software (ease of usage -- Keras, PyTorch), hardware(GPU becoming commercially viable for someone like me sitting in India - Not still cheap), availability of data, good books and MOOCs. After completing the 3 most popular MOOCS in deep learning from Fast.ai, deeplearning.ai/Coursera

SoftBank and Huawei jointly demonstrated various use cases for their forthcoming 5G network. With 5G technology, getting and staying connected will get easier. You'll still need a robust network provider but your devices will learn to do things like sync or pair automatically. When 5G comes online, around 50 billion "things" will be connected and that number will be growing exponentially. Think of self-driving cars that have capabilities to communicate with traffic lights, smart city sensor systems, savvy home appliances, industrial automation systems, connected health innovations, personal drones, robots and more.

An aquatic robot called Avexis is being tested in Japan ahead of being deployed into the damaged core of the Fukushima Daiichi Nuclear Power Plant. A link has been sent to your friend's email address. A link has been posted to your Facebook feed. An aquatic robot called Avexis is being tested in Japan ahead of being deployed into the damaged core of the Fukushima Daiichi Nuclear Power Plant.

Consider what your first reaction would be if, during a protest turned violent, you came face-to-face with a riot cop barking at you from behind his clear shield. Not the words that come out of your mouth nor the moves you could make to escape. Before any of that, your emotions would already be fired up and broadcasting to the policeman what you might do next. In RIOT 2, an interactive film by Karen Palmer, controlling these emotions is the key to your escape. Inspired by protests in Baton Rouge and Ferguson to Turkey and Venezuela, the goal of RIOT is essentially to keep calm.

Besides being exactly a year before India conducted its second nuclear tests, it is the day that IBM's Deep Blue computer made Gary Kasparov, the human world champion, concede defeat in less than 20 moves in the 6th Game of Chess that they played together. Kasparov reflect today, 20 years later, in his book "Deep Thinking – where machine intelligence ends and human creativity begins", even if he would have won, it was just a matter of time when computers would have started winning. The supporters of Artificial Intelligence – called the hard AI – were delighted then – proclaiming a day, not in too far in future ahead, when machines will be able to replicate the human decision-making process. In contrast, soft AI proponents believe that intelligence cannot be created artificially. It can at best be simulated at an appropriate level of detail to create solutions for some of human decision-making problems.

Chinese messaging app WeChat has reportedly apologised after an AI error resulted in it translating a neutral Chinese phrase into the n-word. The WeChat error was reported by Shanghai-based theatre producer and actor Ann James, a black American. In a post on the service's Twitter-like Moments feature, she wrote that it had translated hei laowai – a neutral phrase which literally means "black foreigner" – as the n-word. "We're very sorry for the inappropriate translation," a WeChat spokesperson told Chinese news site Sixth Tone. "After receiving users' feedback, we immediately fixed the problem."

Apple's radical FaceID system is still causing major production issues for the firm just weeks before its launch, it has been claimed. The firm's suppliers are still struggling to perfect manufacturing of the iPhone X's TrueDepth camera and 3D facial recognition system, according to Japan's Nikkei Asian Review. Jeff Pu, an analyst with Taipei-based Yuanta Investment Consulting, believes the problems could mean Apple will face even bigger shortages of its flagship handset than previously thought. The latest forecast from Ming-Chi Kuo of KGI says Apple still cannot make enough of the handsets due to technical problems with the front facing TrueDepth camera it uses to scan people's faces. Multiple reports have claimed it has taken more time to assemble the TrueDepth system's so-called'Romeo' module than the'Juliet' module.

In this paper, we study the Nystr{\"o}m type subsampling for large scale kernel methods to reduce the computational complexities of big data. We discuss the multi-penalty regularization scheme based on Nystr{\"o}m type subsampling which is motivated from well-studied manifold regularization schemes. We develop a theoretical analysis of multi-penalty least-square regularization scheme under the general source condition in vector-valued function setting, therefore the results can also be applied to multi-task learning problems. We achieve the optimal minimax convergence rates of multi-penalty regularization using the concept of effective dimension for the appropriate subsampling size. We discuss an aggregation approach based on linear function strategy to combine various Nystr{\"o}m approximants. Finally, we demonstrate the performance of multi-penalty regularization based on Nystr{\"o}m type subsampling on Caltech-101 data set for multi-class image classification and NSL-KDD benchmark data set for intrusion detection problem.