This week Amazon debuted a new warehouse robot that has a sense of "touch," but the company also promised its new bot will not replace human warehouse workers. On Monday, at Amazon's Delivering the Future event in Dortmund, Germany, the retail giant introduced the world to Vulcan, a robot designed to sort, pick up, and place objects in storage compartments with the finesse and dexterity of human hands. Instead, the robot's "end of arm tooling" looks like a "ruler stuck onto a hair straightener," as Amazon describes it. The Vulcan warehouse robot is also loaded with cameras and feedback sensors to process when it makes contact with items and how much force to apply to prevent damage. In its warehouses, Amazon's inventory is stored in soft fabric compartments of about one square foot in size.

Amazon said it has made a "fundamental leap forward in robotics" after developing a robot with a sense of touch that will be capable of grabbing about three-quarters of the items in its vast warehouses. Vulcan – which launches at the US firm's "Delivering the Future" event in Dortmund, Germany, on Wednesday and is to be deployed around the world in the next few years – is designed to help humans sort items for storage and then prepare them for delivery as the latest in a suite of robots which have an ever-growing role in the online retailer's extensive operation. Aaron Parness, Amazon's director of robotics, described Vulcan as a "fundamental leap forward in robotics. It's not just seeing the world, it's feeling it, enabling capabilities that were impossible for Amazon robots until now." The robots will be able to identify objects by touch using AI to work out what they can and can't handle and figuring out how best to pick them up.

The CAD design process includes a number of repetitive steps when creating assemblies. This issue is compounded when engineering whole product lines or design families, as steps like inserting parts common to all variations, such as fasteners and product-integral base parts, get repeated numerous times. This makes creating designs time-, and as a result, cost-intensive. While many CAD software packages have APIs, the effort of creating use-case specific plugins to automate creation of assemblies usually outweighs the benefit. We developed a plugin for the CAD software package "Fusion 360" which tackles this issue. The plugin adds several graphical interfaces to Fusion 360 that allow parts to be annotated with types, subtype hierarchies to be managed, and requests to synthesize assembly programs for assemblies to be posed. The plugin is use-case agnostic and is able to generate arbitrary open kinematic chain structures. We envision engineers working with CAD software being able to make designed parts reusable and automate the generation of different design alternatives as well as whole product lines.

Deep Learning has established itself as the de facto standard in wearable, inertial-based Human Activity Recognition (HAR), consistently outperforming classical Machine Learning approaches in recognition performance. A persistent trend in Deep Learning has been the applicability of machine learning concepts such as self-attention [51] to other areas and application scenarios than originally introduced for. With significant progress having been made since the introduction of deep neural architectures such as the DeepConvLSTM [35], researchers have followed this trend and continuously worked on improving the architectural design of networks by incorporating newly introduced techniques (see e.g., [67]). A promising recent approach in video-based Human Activity Recognition (HAR) is Temporal Action Localization (TAL), which aims to locate activity segments, defined by a class label, start, and end point, within an untrimmed video. Even though introduced architectures have almost doubled in performance over the last 5 years on existing datasets like THUMOS-14 [21], results on large corpora such as EPIC-KITCHENS-100 [14] and Ego4D [17] show that the prediction problem is far from being saturated. Recently, Zhang et al. [62] introduced the ActionFormer, an end-to-end, single-stage TAL model utilizing transformer-based layers.

E-Spirit has added an artificial intelligence-powered personalization content engine into its FirstSpirit Digital Experience Hub. The Dortmund, Germany-based web content management provider is calling it the FirstSpirit Intelligent Content Engine. FirstSpirit is the company's web content management system that enables its Digital Experience Hub. E-Spirit wanted to "step it up" when it comes to personalization and has done so, Gerard said, with AI- and machine learning-powered engines that personalize content delivered to prospects and customers. The Intelligent Content Engine includes an advanced customer segmentation engine with AI.

Classical machine learning algorithms are currently used for performing complex computational tasks, such as pattern recognition or classification in large amounts of data, and constitute a crucial part of many modern technologies. The aim of quantum learning algorithms is to bring these features into scenarios where information is in a fully quantum form. The scientists, Alex Monràs at the Autonomous University of Barcelona, Spain; Gael Sentís at the University of the Basque Country, Spain, and the University of Siegen, Germany; and Peter Wittek at ICFO-The Institute of Photonic Science, Spain, and the University of Borås, Sweden, have published a paper on their results in a recent issue of Physical Review Letters. "Our work unveils the structure of a general class of quantum learning algorithms at a very fundamental level," Sentís told Phys.org. "It shows that the potentially very complex operations involved in an optimal quantum setup can be dropped in favor of a much simpler operational scheme, which is analogous to the one used in classical algorithms, and no performance is lost in the process.

Classical machine learning algorithms are currently used for performing complex computational tasks, such as pattern recognition or classification in large amounts of data, and constitute a crucial part of many modern technologies. The aim of quantum learning algorithms is to bring these features into scenarios where information is in a fully quantum form. The scientists, Alex Monràs at the Autonomous University of Barcelona, Spain; Gael Sentís at the University of the Basque Country, Spain, and the University of Siegen, Germany; and Peter Wittek at ICFO-The Institute of Photonic Science, Spain, and the University of Borås, Sweden, have published a paper on their results in a recent issue of Physical Review Letters. "Our work unveils the structure of a general class of quantum learning algorithms at a very fundamental level," Sentís told Phys.org. "It shows that the potentially very complex operations involved in an optimal quantum setup can be dropped in favor of a much simpler operational scheme, which is analogous to the one used in classical algorithms, and no performance is lost in the process.

Lukas Kalemba was walking home with some friends after a night of partying and drinking in Dortmund, Germany, in 2003. While crossing a bridge along the way, he stopped to rest but lost his balance and fell over. In an attempt to break his fall, he instinctively reached out and grabbed a wire that stretched across. It kept him from falling 20 feet to the ground immediately but the wire sent a high-voltage current through the left side of his body, causing irreparable damage to his leg. Kalemba became an above-the-knee amputee when he was 19 years old.