Apple has unveiled its widely-rumoured game subscription service, hailed as the "Netflix of games". Designed for mobile devices, desktop computers and "living room" devices, Apple Arcade, will "redefine games", the tech giant claimed, adding that will be curated on originality, quality and creativity. However, the price and release date still remain a mystery. We'll tell you what's true. You can form your own view.

Apple has launched a whole host of new services, intended to make more money from the people who have already bought its products. In what was hailed as one of the most significant Apple events in years, the company did not reveal new products or software but instead a range of premium services, intended to counter a drop in iPhone sales by bringing in revenues after people buy their products. There are news subscriptions, TV and games – as well as a new way to pay for anything, with a titanium credit card. The highlight of the announcement was Apple TV, a streaming service built to compete with Netflix and Amazon Prime Video. Apple invited many of the world's biggest stars and directors on stage to talk about its TV shows, on which the company is thought to have spent billions of dollars.

The world's second biggest smartphone maker is about to unveil its latest range of flagship phones, but an inundation of leaks mean there is little to reveal that is not already known. Huawei will show off the P30, P30 Pro and P30 Lite - as they are expected to be called - in what the Chinese manufacturer hopes will offer a trio of rivals to Apple's iPhones and Samsung's Galaxy range of smartphones. The latest leak of the new phones, which comes just hours before the 26 March unveiling event in Paris, shows complete front and rear images of the three Huawei devices. We'll tell you what's true. You can form your own view.

The suite of reforms include rules that could force internet companies to ban memes and to stop them showing links in the way they do today. Supporters claim the rules are required to ensure that music companies and news outlets are properly paid for the content that technology companies distribute. But opponents, who assembled in force, allied with those tech firms to argue that it could change the way the internet works and destroy some of its fundamental principles. We'll tell you what's true. You can form your own view.

Apple's brand new News subscription service appears to have run into problems as soon as it was released. News is a subscription service within Apple News that allows people to sign up for $9.99 and get unlimited access to 300 magazines and newspapers including the Wall Street Journal. But the new app – which arrived in an update yesterday, after the launch event – appears to have caused some of the devices that run it to crash. Numerous users reported problems, specifically with the versions of the app on iPhone XS Max and on Mac. Opening the app would see it break almost straight away, and then shut down again, affected users said.

Dozens of scientists, health care professionals and academics have written a letter to the U.N. calling for an international ban of autonomous killer robots, saying recent advances in artificial intelligence "have brought us to the brink of a new arms race in lethal autonomous weapons." The letter, which has been signed by more than 70 health care professionals and was put together by the Future of Life Institute, states that lethal autonomous weapons could fall into the hands of terrorists and despots, lower the barrier to armed conflict and "become weapons of mass destruction enabling very few to kill very many." "Furthermore, autonomous weapons are morally abhorrent, as we should never cede the decision to take a human life to algorithms," the letter continues. "As healthcare professionals, we believe that breakthroughs in science have tremendous potential to benefit society and should not be used to automate harm. We therefore call for an international ban on lethal autonomous weapons."

Planning paths efficiently in a high-dimensional continuous state and action space is a fundamental yet challenging problem in many real-world applications, such as robot manipulation and autonomous driving. Since the general path planning problem is PSPACE-complete (Reif, 1979), one typically resorts to approximate or heuristic algorithms. Sampling-based planning algorithms, such as probabilistic roadmaps (PRM) (Kavraki et al., 1996), rapidlyexploring random trees (RRT) (LaValle, 1998), and their variants (Karaman & Frazzoli, 2011), provide principled approximate solutions to a wide spectrum of high-dimensional path planning tasks. However, these generic algorithms typically employ a uniform proposal distribution for sampling which does not make use of the structures of the problem at hand and thus may require lots of samples to obtain an initial feasible solution path for complicated tasks, e.g., a narrow passage in a map. To improve the sample efficiency, researchers designed algorithms to take problem structures into account, such as the Gaussian sampler (Boor et al., 1999), the bridge test (Hsu et al., 2003), the reachability-guided sampler (Shkolnik et al., 2009), the

In the task of Autonomous aerial filming of a moving actor (e.g. a person or a vehicle), it is crucial to have a good heading direction estimation for the actor from the visual input. However, the models obtained in other similar tasks, such as pedestrian collision risk analysis and human-robot interaction, are very difficult to generalize to the aerial filming task, because of the difference in data distributions. Towards improving generalization with less amount of labeled data, this paper presents a semi-supervised algorithm for heading direction estimation problem. We utilize temporal continuity as the unsupervised signal to regularize the model and achieve better generalization ability. This semi-supervised algorithm is applied to both training and testing phases, which increases the testing performance by a large margin. We show that by leveraging unlabeled sequences, the amount of labeled data required can be significantly reduced. We also discuss several important details on improving the performance by balancing labeled and unlabeled loss, and making good combinations. Experimental results show that our approach robustly outputs the heading direction for different types of actor. The aesthetic value of the video is also improved in the aerial filming task.

Tensor network (TN) has recently triggered extensive interests in developing machine-learning models in quantum many-body Hilbert space. Here we purpose a generative TN classification (GTNC) approach for supervised learning. The strategy is to train the generative TN for each class of the samples to construct the classifiers. The classification is implemented by comparing the distance in the many-body Hilbert space. The numerical experiments by GTNC show impressive performance on the MNIST and Fashion-MNIST dataset. The testing accuracy is competitive to the state-of-the-art convolutional neural network while higher than the naive Bayes classifier (a generative classifier) and support vector machine. Moreover, GTNC is more efficient than the existing TN models that are in general discriminative. By investigating the distances in the many-body Hilbert space, we find that (a) the samples are naturally clustering in such a space; and (b) bounding the bond dimensions of the TN's to finite values corresponds to removing redundant information in the image recognition. These two characters make GTNC an adaptive and universal model of excellent performance.

Object detection models shipped with camera-equipped mobile devices cannot cover the objects of interest for every user. Therefore, the incremental learning capability is a critical feature for a robust and personalized mobile object detection system that many applications would rely on. In this paper, we present an efficient yet practical system, IMOD, to incrementally train an existing object detection model such that it can detect new object classes without losing its capability to detect old classes. The key component of IMOD is a novel incremental learning algorithm that trains end-to-end for one-stage object detection deep models only using training data of new object classes. Specifically, to avoid catastrophic forgetting, the algorithm distills three types of knowledge from the old model to mimic the old model's behavior on object classification, bounding box regression and feature extraction. In addition, since the training data for the new classes may not be available, a real-time dataset construction pipeline is designed to collect training images on-the-fly and automatically label the images with both category and bounding box annotations. We have implemented IMOD under both mobile-cloud and mobile-only setups. Experiment results show that the proposed system can learn to detect a new object class in just a few minutes, including both dataset construction and model training. In comparison, traditional fine-tuning based method may take a few hours for training, and in most cases would also need a tedious and costly manual dataset labeling step.