After listening to early adopters, Boston Dynamics gave its robot dog a hardware boost and extended WiFi capabilities. It can be controlled remotely using the company's new web browser-based interface, Scout. It's the first Boston Dynamics device equipped with self-charging capabilities and a dock, which means it can be deployed for longer-term missions "with little to no human interaction," Boston Dynamics said. The previous version of Spot had around 90 minutes of battery life before requiring a manual charge.

CHIP's highest-level goals were programmed C and runs on board. The RAP system is designed to deal with achieving goals in a dynamic environment. Each RAP task description encodes a set of methods for carrying out the task in different situations, a success check to tell when the task has accomplished its purpose, and notations that describe when things are not going as expected. At run time, a RAP task examines its methods and selects one that is appropriate in the current situation. By doing method selection at run time, RAPs are more likely to select the best method, even if the world is changing or contains details that cannot be predicted in advance.

Robots that can be physically reconfigured to do lots of different things are, in theory, a great way to maximize versatility while saving time and effort. Okay, yeah, that may not sound super exciting, but it means you can teach a dodecapod robot to transition into a septapod robot that can carry stuff with two arms while using a third to point a camera. Programmed in advance, that is, which is fine, except that as robots get more modular and easier to physically reconfigure, it becomes more and more useful to have a generalized system that can dynamically generate gaits (and transitions between gaits) on the fly no matter what the leg configuration of your robot happens to be. The researchers are planning on extending their method to include dynamic gaits, which means things like (we hope) running and jumping, and they're also going to generalize to other morphologies like bipeds and tripeds.

ARM has unveiled a set of new processors to provide the brainpower for our mobile devices to cope with advanced artificial intelligence (AI), virtual reality (VR), and mixed reality (MR) technologies. On Monday, the British semiconductor giant said the new Cortex-A75 and Cortex-A55 processors, alongside the new Mali-G72 graphics processor, have been designed to "address the changing nature of computers driven by AI and other more human-like experiences." "Distributed intelligence" is at the heart of this trend, which includes connecting AI and the cloud, on-device learning, enhanced security and privacy, and the use of 4K, HDR, and 5G for more "human-like" interfaces. ARM says that by providing low-power, efficient and powerful processors, device vendors will be able to explore the possibilities of distributed intelligence, and the new Cortex-A architecture enables system-on-a-chip (SoC) architecture designers to scale up to eight cores in a single cluster. The Cortex-A75 and Cortex-A55 have been designed with this concept in mind.

When ARM showed up at Computex last year, it brought a bundle of smartphone processors that pushed for better mobile VR. As you might've noticed, though, AI is one of the big new trends in mobile this year -- is it any surprise that the ARM's pushing that angle with its latest batch of silicon? First up is the Cortex-A75 CPU core, which the company says can deliver laptop-level performance without burning through any more power than existing mobile processors. ARM is promising a 50 percent boost in performance compared to the older A73 core, which should lend itself well to machine learning processes that run right on your devices. Remember: we're starting to see more smartphones optimize their performance on the fly based on behaviors sussed out by these kinds of algorithms.

For many, the Switch represents the Nintendo's return to form. It's the console that sheds both the name and the gimmicky motion controls that defined the Wii era of gaming. With traditional games like The Legend of Zelda: Breath of the Wild and Mario Kart 8 Deluxe leading the way, motion controls seem to be all but a thing of the past. Or they did, until Nintendo announced Arms -- a gesture-based boxing game for the Switch that seems to lean heavily on Wii Sports' legacy. It seems like a bizarre step backward, but don't worry: It turns out that Arms isn't repeating the mistakes of the Wii; it's showing how Nintendo has learned from them.

ARM has unveiled its next generation of processor designs, a new microarchitecture named Dynamiq. Chips built using Dynamiq will be easier to configure, says ARM, allowing manufacturers to connect together a wider variety of CPUs. This should allow for more powerful systems-on-chip, but also processors that better serve computing tasks of the future from artificial intelligence to self-driving cars. "It's a step change in how we build CPUs and the way we stitch CPUs together," ARM product marketing head John Ronco told The Verge. "It'll be in smartphones and tablets, for sure, but also automotive networking and a whole range of other embedded devices. Anywhere a Cortex processor is used today, Dynamiq is going to be the next step forward."

Cars are turning into computers with a unique set of requirements. One of the more important components is a camera, which is a secondary feature in PCs. Cameras are aiding mirrors in allowing cars to self park, and they will serve as the eyes for autonomous cars, helping capture and analyze images. The number of cameras on cars will only grow as drivers seek a better view of the vehicle's interiors and exteriors. For car makers, the next big goal is to bring context and understanding to those images.

It is notable In real world applications, data are often with multiple that strong modal features can lead to a better performance, modalities. Previous works assumed that each nevertheless, are more expensive, therefore a group of serialized modality contains sufficient information for target feature extraction methods were proposed. These methods and can be treated with equal importance. However, extract weak modal features firstly, and then extract more it is often that different modalities are of various strong modal features gradually to improve the performance importance in real tasks, e.g., the facial feature and reduce the overall cost as well. Marcialis et al.[2010] proposed is weak modality and the fingerprint feature is a serial fusion technique for multiple biometric modal strong modality in ID recognition. In this paper, we features through extracting gaits information and face information point out that different modalities should be treated step by step; Zhang et al.[2014] addressed the serialized with different strategies and propose the Auxiliary multi-modal learning techniques in a semi-supervised information Regularized Machine (ARM), which learning scenario. These methods handle strong and weak works by extracting the most discriminative feature modalities independently while leaving the fact of unsatisfied subspace of weak modality while regularizing the performance on weak modality unexplained.

Bayesian mixture models are widely applied for unsupervised learning and exploratory data analysis. Markov chain Monte Carlo based on Gibbs sampling and split-merge moves are widely used for inference in these models. However, both methods are restricted to limited types of transitions and suffer from torpid mixing and low accept rates even for problems of modest size. We propose a method that considers a broader range of transitions that are close to equilibrium by exploiting multiple chains in parallel and using the past states adaptively to inform the proposal distribution. The method significantly improves on Gibbs and split-merge sampling as quantified using convergence diagnostics and acceptance rates. Adaptive MCMC methods which use past states to inform the proposal distribution has given rise to many ingenious sampling schemes for continuous problems and the present work can be seen as an important first step in bringing these benefits to partition-based problems