Artificial skin stands to have a variety uses, with potential applications in everything from robots to prosthetics. And in recent years, researchers have been able to instill sensory perception, like touch and pressure, into artificial skin. However, while those sorts of senses will be incredibly important in engineered skin, they've so far been rather limited. For example, while current versions can be quite sensitive to light touch, they don't fare so well with high pressures that could cause damage. So researchers at the Huazhong University of Science and Technology in China set out to fix that problem and they drew their inspiration from jellyfish.

Chinese judges and prosecutors are using the latest information technology -- from big data and cloud computing to artificial intelligence -- to improve efficiency and accuracy. Big data services have helped judges search precedents, correct legal documents and analyze cases, said a report submitted by the Supreme People's Court (SPC) to the Standing Committee of the National People's Congress (NPC) on Wednesday at the bi-monthly legislative session. China launched a national database of legal documents and academic papers in March 2016, consisting of more than 20 million documents. Computing has also assisted prosecutors to screen evidence, refer to similar cases, research relevant laws, and draft legal documents, said another report submitted by the Supreme People's Procuratorate (SPP) at the same session. Speech recognition technologies have significantly saved trial time by turning recordings into formal transcripts.

In the simulation that they were connected, Morpheus, who tries to explain what The Matrix is, talks about how human kind celebrated the founding of artificial intelligence in the beginning of 21st century. We all already know how things continued afterwards: Machines that fight with human race and won, people who was turned into batteries and waiting for salvation, ones who keep fighting and the chosen one who helps them. If Elon Musk would be living in the world that The Wachowkis draw, he would probably be The Oracle. Elon Musk is pretty sure about the things that happened in The Matrix will be real. He believes that human race should take security precautions at both national and international levels starting from today.

Abstract--This paper proposes a practical approach for automatic sleep stage classification based on a multilevel feature learning framework and Recurrent Neural Network (RNN) classifier using heart rate and wrist actigraphy derived from a wearable device. The feature learning framework is designed to extract low-and mid-level features. Low-level features capture temporal and frequency domain properties and mid-level features learn compositions and structural information of signals. Since sleep staging is a sequential problem with long-term dependencies, we take advantage of RNNs with Bidirectional Long Short-T erm Memory (BLSTM) architectures for sequence data learning. T o simulate the actual situation of daily sleep, experiments are conducted with a resting group in which sleep is recorded in resting state, and a comprehensive group in which both resting sleep and non-resting sleep are included. We evaluate the algorithm based on an eightfold cross validation to classify five sleep stages (W, N1, N2, N3, and REM). V arious comparison experiments demonstrate the effectiveness of feature learning and BLSTM. We further explore the influence of depth and width of RNNs on performance. Our method is specially proposed for wearable devices and is expected to be applicable for long-term sleep monitoring at home. Without using too much prior domain knowledge, our method has the potential to generalize sleep disorder detection. Index Terms--Heart rate, Long Short-T erm Memory, Recurrent neural networks, Sleep stage classification, Wearable device. Xin Zhang, Weixuan Kou, Y ubo Fan and Y an Xu are with the State Key Laboratory of Software Development Environment and the Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education and Research Institute of Beihang University in Shenzhen and Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing 100191, China (email: xinzhang0376@gmail.com; Eric I-Chao Chang, and Y an Xu are with Microsoft Research, Beijing 100080, China (email:echang@microsoft.com; xuyan04@gmail.com).

Ultrasound diagnosis is routinely used in obstetrics and gynecology for fetal biometry, and owing to its time-consuming process, there has been a great demand for automatic estimation. However, the automated analysis of ultrasound images is complicated because they are patient-specific, operator-dependent, and machine-specific. Among various types of fetal biometry, the accurate estimation of abdominal circumference (AC) is especially difficult to perform automatically because the abdomen has low contrast against surroundings, non-uniform contrast, and irregular shape compared to other parameters.We propose a method for the automatic estimation of the fetal AC from 2D ultrasound data through a specially designed convolutional neural network (CNN), which takes account of doctors' decision process, anatomical structure, and the characteristics of the ultrasound image. The proposed method uses CNN to classify ultrasound images (stomach bubble, amniotic fluid, and umbilical vein) and Hough transformation for measuring AC. We test the proposed method using clinical ultrasound data acquired from 56 pregnant women. Experimental results show that, with relatively small training samples, the proposed CNN provides sufficient classification results for AC estimation through the Hough transformation. The proposed method automatically estimates AC from ultrasound images. The method is quantitatively evaluated, and shows stable performance in most cases and even for ultrasound images deteriorated by shadowing artifacts. As a result of experiments for our acceptance check, the accuracies are 0.809 and 0.771 with the expert 1 and expert 2, respectively, while the accuracy between the two experts is 0.905. However, for cases of oversized fetus, when the amniotic fluid is not observed or the abdominal area is distorted, it could not correctly estimate AC.

Deep reinforcement learning (DRL) methods such as the Deep Q-Network (DQN) have achieved state-of-the-art results in a variety of challenging, high-dimensional domains. This success is mainly attributed to the power of deep neural networks to learn rich domain representations for approximating the value function or policy. Batch reinforcement learning methods with linear representations, on the other hand, are more stable and require less hyper parameter tuning. Yet, substantial feature engineering is necessary to achieve good results. In this work we propose a hybrid approach -- the Least Squares Deep Q-Network (LS-DQN), which combines rich feature representations learned by a DRL algorithm with the stability of a linear least squares method. We do this by periodically re-training the last hidden layer of a DRL network with a batch least squares update. Key to our approach is a Bayesian regularization term for the least squares update, which prevents over-fitting to the more recent data. We tested LS-DQN on five Atari games and demonstrate significant improvement over vanilla DQN and Double-DQN. We also investigated the reasons for the superior performance of our method. Interestingly, we found that the performance improvement can be attributed to the large batch size used by the LS method when optimizing the last layer.

Alphabet chairman Eric Schmidt says the US is at risk of falling behind in the race to develop cutting-edge artificial intelligence. Speaking at a tech summit organized by national security think tank CNAS, Schmidt predicted that America's lead in the field would continue "over the next five years" before China catches up "extremely quickly." "They are going to use this technology for both commercial and military objectives, with all sorts of implications," said Schmidt, referencing a Chinese policy document by outlining the country's ambition to become the global leader in AI by 2030. Schmidt reiterated several familiar talking points in this debate: that the US is failing to invest in basic research, and that a restrictive immigration policy hobbles the country's ability to attract AI talent from overseas. "Some of the very best people are in countries that we won't let into America. Would you rather have them building AI somewhere else, or rather have them here?" said Schmidt.

Sony has resurrected Aibo, the robot dog it stopped selling back in 2006. The electronic, internet-connected pet has been updated with modern components and been given a brand new look, which the company describes as "adorable" and "irresistible". It's autonomous, meaning it can wander around your home on its own, and Sony says it can "develop its own unique personality" and also show "love and affection". It measures 180 293 305mm, weighs approximately 2.2kg and has a battery that lasts for two hours. The dog takes three hours to charge back up again when it runs flat. Sony says it loves the colour pink and can learn new tricks, but dislikes heights and tight spaces.

Microsoft first launched HoloLens in 2015 as a gaming-centric consumer product, but so far, very few folks have so much as picked up a Minecraft block with the $3,000 device. HoloLens has been a big success with businesses, allowing designers to visualize digital changes on real-life objects and helping employees do complex tasks or high-tech sales demos. In fact, it's been so popular with companies that Microsoft is now expanding sales to 29 new European markets, taking the total up to 39 nations. Microsoft says that companies like Ford and Thyssenkrupp have been asking for HoloLens availability in Spain, Sweden and Turkey, where it's currently unavailable. The device has been particularly popular for so-called firstline workers that repair elevators or build cars, for instance.

Sony said Wednesday it will roll out a new robot dog on Jan. 11, 2018, the year of the dog, that will be packed with the latest robotic technologies, including sensors to help it recognize its environment, and artificial intelligence to help it "think," plus the mechanics to act on its thoughts. Sony said the new robot will be able to respond to human calls and will be a loving companion to families. The robot will grow more intelligent over time by collecting, learning from and storing information from its owners. It will have a price tag of 198,000 yen ($1,740) plus tax. Sony has not disclosed a sales target. For another 2,980 yen, masters can give their pet an aibone, a bone-shaped toy accessory.