Nearly half of the tasks currently undertaken by humans could already be automated, even at current levels of technology. Within the next decade it is likely large sections of society will be looking for new jobs. People are calling it the fourth industrial revolution or "industry 4.0". The first industrial revolution used steam power to mechanise production. The second used electric power to mass produce products while the third introduced computers to automate production. The fourth revolution is happening now, disruptive technologies including the internet of things, virtual reality, robotics and artificial intelligence are changing the way we interact, work and live.

Much attention has been focused on the potential loss of jobs that robotics and artificial intelligence may bring. However, advancements in robotics technology and their application in new areas can make jobs easier, more pleasant and safer. Exploring opportunities to automate the "dull, dirty and dangerous" work that humans are still doing in the digital age, we see the potential to improve the quality of work, enhance capabilities and reduce employee risks. We may be well into the digital age, but there is no shortage of work that still requires human intervention. Some of these jobs are laborious.

Reinforcement learning (RL) combines a control problem with statistical estimation: the system dynamics are not known to the agent, but can be learned through experience. A recent line of research casts `RL as inference' and suggests a particular framework to generalize the RL problem as probabilistic inference. Our paper surfaces a key shortcoming in that approach, and clarifies the sense in which RL can be coherently cast as an inference problem. In particular, an RL agent must consider the effects of its actions upon future rewards and observations: the exploration-exploitation tradeoff. In all but the most simple settings, the resulting inference is computationally intractable so that practical RL algorithms must resort to approximation. We demonstrate that the popular `RL as inference' approximation can perform poorly in even very basic problems. However, we show that with a small modification the framework does yield algorithms that can provably perform well, and we show that the resulting algorithm is equivalent to the recently proposed K-learning, which we further connect with Thompson sampling.

Human teams are able to easily perform collaborative manipulation tasks. However, for a robot and human to simultaneously manipulate an extended object is a difficult task using existing methods from the literature. Our approach in this paper is to use data from human-human dyad experiments to determine motion intent which we use for a physical human-robot co-manipulation task. We first present and analyze data from human-human dyads performing co-manipulation tasks. We show that our human-human dyad data has interesting trends including that interaction forces are non-negligible compared to the force required to accelerate an object and that the beginning of a lateral movement is characterized by distinct torque triggers from the leader of the dyad. We also examine different metrics to quantify performance of different dyads. We also develop a deep neural network based on motion data from human-human trials to predict human intent based on past motion. We then show how force and motion data can be used as a basis for robot control in a human-robot dyad. Finally, we compare the performance of two controllers for human-robot co-manipulation to human-human dyad performance.

Small satellite constellations provide daily global coverage of the earth's landmass, but image enrichment relies on automating key tasks like change detection or feature searches. For example, to extract text annotations from raw pixels requires two dependent machine learning models, one to analyze the overhead image and the other to generate a descriptive caption. We evaluate seven models on the previously largest benchmark for satellite image captions. We extend the labeled image samples five-fold, then augment, correct and prune the vocabulary to approach a rough min-max (minimum word, maximum description). This outcome compares favorably to previous work with large pre-trained image models but offers a hundred-fold reduction in model size without sacrificing overall accuracy (when measured with log entropy loss). These smaller models provide new deployment opportunities, particularly when pushed to edge processors, on-board satellites, or distributed ground stations. To quantify a caption's descriptiveness, we introduce a novel multi-class confusion or error matrix to score both human-labeled test data and never-labeled images that include bounding box detection but lack full sentence captions. This work suggests future captioning strategies, particularly ones that can enrich the class coverage beyond land use applications and that lessen color-centered and adjacency adjectives ("green", "near", "between", etc.). Many modern language transformers present novel and exploitable models with world knowledge gleaned from training from their vast online corpus. One interesting, but easy example might learn the word association between wind and waves, thus enriching a beach scene with more than just color descriptions that otherwise might be accessed from raw pixels without text annotation.

LG will reveal an OLED TV that unfurls from the ceiling and another that'hangs like wallpaper' at the Consumer Electronics Show in Las Vegas next week. The 65-inch UHD Roll-Down TV can be stored in the ceiling and pulled down when desired or rolled up when not in use. Also on show will be a 77-inch UHD Film Cinematic Sound & Wallpaper OLED display that can be hung like wallpaper. The larger display has a wafer-thin screen and sound system that's embedded into the display. OLED video walls, made of 55-inch OLED displays installed on the wall of a plane, enable passengers to'feel more openness' in the narrow space of an enclosed cabin The devices point to'the future of home interior design', according to LG Display.

The potential of Artificial Intelligence is already being discovered by many industries, including the Oil and Gas, which is investing majorly in Artificial Intelligence and other data technologies with a goal to secure their future competitiveness in a fast-changing environment. Oil is one of the most precious commodities in the energy sector. With the rise in the oil prices and depleting crude oil levels globally, organizations involved in the oil and gas industry are now turning towards modern technologies, specifically Artificial Intelligence, to maximize and optimize their efficiency and revenues. Companies involved in the oil and gas industry leverage AI to drill and mine raw hydrocarbons and other products required to produce fuel. AI helps these companies by developing algorithms that provide accurate and precise intelligence to guide drills on water and land.

Many real-world control problems involve both discrete decision variables - such as the choice of control modes, gear switching or digital outputs - as well as continuous decision variables - such as velocity setpoints, control gains or analogue outputs. However, when defining the corresponding optimal control or reinforcement learning problem, it is commonly approximated with fully continuous or fully discrete action spaces. These simplifications aim at tailoring the problem to a particular algorithm or solver which may only support one type of action space. Alternatively, expert heuristics are used to remove discrete actions from an otherwise continuous space. In contrast, we propose to treat hybrid problems in their 'native' form by solving them with hybrid reinforcement learning, which optimizes for discrete and continuous actions simultaneously. In our experiments, we first demonstrate that the proposed approach efficiently solves such natively hybrid reinforcement learning problems. We then show, both in simulation and on robotic hardware, the benefits of removing possibly imperfect expert-designed heuristics. Lastly, hybrid reinforcement learning encourages us to rethink problem definitions. We propose reformulating control problems, e.g. by adding meta actions, to improve exploration or reduce mechanical wear and tear.

Delivering useful hydrological forecasts is critical for urban and agricultural water management, hydropower generation, flood protection and management, drought mitigation and alleviation, and river basin planning and management, among others. In this work, we present and appraise a new methodology for hydrological time series forecasting. This methodology is based on simple combinations. The appraisal is made by using a big dataset consisted of 90-year-long mean annual river flow time series from approximately 600 stations. Covering large parts of North America and Europe, these stations represent various climate and catchment characteristics, and thus can collectively support benchmarking. Five individual forecasting methods and 26 variants of the introduced methodology are applied to each time series. The application is made in one-step ahead forecasting mode. The individual methods are the last-observation benchmark, simple exponential smoothing, complex exponential smoothing, automatic autoregressive fractionally integrated moving average (ARFIMA) and Facebook's Prophet, while the 26 variants are defined by all the possible combinations (per two, three, four or five) of the five afore-mentioned methods. The findings have both practical and theoretical implications. The simple methodology of the study is identified as well-performing in the long run. Our large-scale results are additionally exploited for finding an interpretable relationship between predictive performance and temporal dependence in the river flow time series, and for examining one-year ahead river flow predictability.

DOE/Argonne National Laboratory researchers have turned to the power of machine learning and artificial intelligence to dramatically accelerate battery discovery. The press release likens designing new batteries with the best molecular building blocks for battery components to trying to create a recipe for a new kind of cake, when you have billions of potential ingredients. The challenge involves determining which ingredients work best together – or, more simply, produce an edible (or, in the case of batteries, a safe) product. But even with state-of-the-art supercomputers, scientists cannot precisely model the chemical characteristics of every molecule that could prove to be the basis of a next-generation battery material. As described in two new papers, the Argonne researchers first created a highly accurate database of roughly 133,000 small organic molecules that could form the basis of battery electrolytes.