A recently introduced text classifier, called SS3, has obtained state-of-the-art performance on the CLEF's eRisk tasks. SS3 was created to deal with risk detection over text streams and therefore not only supports incremental training and classification but also can visually explain its rationale. However, little attention has been paid to the potential use of SS3 as a general classifier. We believe this could be due to the unavailability of an open-source implementation of SS3. In this work, we introduce PySS3, a package that not only implements SS3 but also comes with visualization tools that allow researchers deploying robust, explainable and trusty machine learning models for text classification.

This paper presents a novel motion and trajectory planning algorithm for nonholonomic mobile robots that uses recent advances in deep reinforcement learning. Starting from a random initial state, i.e., position, velocity and orientation, the robot reaches an arbitrary target state while taking both kinematic and dynamic constraints into account. Our deep reinforcement learning agent not only processes a continuous state space it also executes continuous actions, i.e., the acceleration of wheels and the adaptation of the steering angle. We evaluate our motion and trajectory planning on a mobile robot with a differential drive in a simulation environment.

Recent success in Artificial Intelligence (AI) and Machine Learning (ML) allow problem solving automatically without any human intervention. Autonomous approaches can be very convenient. However, in certain domains, e.g., in the medical domain, it is necessary to enable a domain expert to understand, why an algorithm came up with a certain result. Consequently, the field of Explainable AI (xAI) rapidly gained interest worldwide in various domains, particularly in medicine. Explainable AI studies transparency and traceability of opaque AI/ML and there are already a huge variety of methods. For example with layer-wise relevance propagation relevant parts of inputs to, and representations in, a neural network which caused a result, can be highlighted. This is a first important step to ensure that end users, e.g., medical professionals, assume responsibility for decision making with AI/ML and of interest to professionals and regulators. Interactive ML adds the component of human expertise to AI/ML processes by enabling them to re-enact and retrace AI/ML results, e.g. let them check it for plausibility. This requires new human-AI interfaces for explainable AI. In order to build effective and efficient interactive human-AI interfaces we have to deal with the question of how to evaluate the quality of explanations given by an explainable AI system. In this paper we introduce our System Causability Scale (SCS) to measure the quality of explanations. It is based on our notion of Causability (Holzinger et al., 2019) combined with concepts adapted from a widely accepted usability scale.

BRUSSELS (Reuters) - U.S. chipmaker Nvidia (NVDA.O) is set to win unconditional EU antitrust approval for its $6.8 billion acquisition of Mellanox Technologies (MLNX.O), people familiar with the matter said on Wednesday. Nvidia, known for its powerful gaming graphics chips, is looking to boost its data center and artificial intelligence business via the takeover, its biggest deal, helping it to better compete with rival Intel (INTC.O). The European Commission, which is scheduled to decide on the deal by Dec. 19, declined to comment. Nvidia and Mellanox also declined to comment. U.S. authorities have already cleared the deal without conditions while approval is still pending in China where Mellanox has major customers such as Alibaba (BABA.N) and Baidu (BIDU.O) .

Artificial intelligence (AI) continues to expand its footprint in the enterprise and the economy. That's the word from the AI Index, an annual data update from Stanford University's Human-Centered Artificial Intelligence Institute. The index tracks AI growth across a range of metrics, from papers published to patents granted to employment numbers. In terms of total employment, while AI-related jobs are but a small fraction, the share is rapidly expanding. In the U.S., the share of jobs in AI-related topics increased from 0.26% of total jobs posted in 2010 to 1.32% in October 2019 -- or five-fold growth.

What is the internet of medical things, or "IoMT" as it's sometimes called today? With the explosion of IoT use cases across industries, the medical space is no exception. Given the transformation of US healthcare to evidence-based outcomes with incentives that are beginning to align, metrics and patient feedback have become essential for care providers. Payers are increasingly interested in optimizing costs with treatments that are more effective than others. My personal experience with orthopedic sensors and the analytics possible with these sensors make me feel confident of a couple of things.

Some of the largest employers in the world are increasingly, and in some cases controversially, relying on AI-based technologies to hire new workers. Companies like Tesla, Accenture and LinkedIn are using technology from Pymetrics to better vet qualified candidates and reduce the time and resources required for what has traditionally been a labor-intensive hiring process. The company, which boasts more than 80 global clients, uses a blend of data science and I/O psychology to create its "people recommendation engine." The Pymetrics platform is designed to improve employee retention while also increasing efficiency and diversity throughout the recruiting process. The results are parsed by AI to generate measurements related to candidates' problem-solving skills, ability to multitask and even their levels of altruism.

The world's first'employment agency' offering AI controlled robots to undertake'strenuous work normally endured by humans' has launched in Israel. MusashiAI, a joint venture between SixAI of Israel and Musashi Seimitsu of Japan has a completely autonomous forklift and a visual inspection robot on its books. The company says its fully autonomous robots will be able to integrate seamlessly with human workers in a modern factory environment. They say their model allows factories to hire robot labour by the hour or pay a task-completed rate, rather than buy expensive robot equipment outright. The world's first'employment agency' offering AI controlled robots to factories to undertake'strenuous work normally endured by humans' has launched in Israel.

Robots are ringing in the holidays at Bloomingdale's New York store. Three of the 12 windows at the 59th street location feature robots in an bid to show how the retail company will'enhance the future retail experience'. Customers watch robots work together to create an'Autonomous Christmas Tree Decorating' display, play instruments in a full orchestra and sing'Christmas Carol Karaoke'. Bloomingdale's is known for its stunning and whimsical holiday displays, but this year it has teamed up with ABB robots and robot animator Andy Flessas to create a unique display to showcase how retailers can enhance the future retail experience. Two floor-mounted and two ceiling-mounted IRB 120 robots co-ordinate their movements to pass 20 gold ornaments to each other, placing them on the branches, before stripping the tree and starting the 30-minute process again.

Deep Learning System Market report offers detailed analysis and a five-year forecast for the global Deep Learning System industry. Deep Learning System market report delivers the insights which will shape your strategic planning as you estimate geographic, product or service expansion within the Deep Learning System industry.. The Deep Learning System market accounted for $XX million in 2018, and is expected to reach $XX million by 2024, registering a CAGR of YY% from 2019 to 2024. The global Deep Learning System market is segmented based on product, end user, and region. Region wise, it is analyzed across North America (U.S., Canada, and Mexico), Europe (Germany, UK, Italy, Spain, France, and rest of Europe), Asia-Pacific (Japan, China, Australia, India, South Korea, Taiwan, and, rest of Asia-Pacific) and EMEA (Brazil, South Africa, Saudi Arabia, UAE, rest of EMEA). Ask more details or request custom reports to our experts at https://www.proaxivereports.com/pre-order/12206 Moreover, other factors that contribute toward the growth of the Deep Learning System market include favorable government initiatives related to the use of Deep Learning System.