Industry 4.0 systems have a high demand for optimization in their tasks, whether to minimize cost, maximize production, or even synchronize their actuators to finish or speed up the manufacture of a product. Those challenges make industrial environments a suitable scenario to apply all modern reinforcement learning (RL) concepts. The main difficulty, however, is the lack of that industrial environments. In this way, this work presents the concept and the implementation of a tool that allows us to convert any dynamic system modeled as an FSM to the open-source Gym wrapper. After that, it is possible to employ any RL methods to optimize any desired task. In the first tests of the proposed tool, we show traditional Q-learning and Deep Q-learning methods running over two simple environments.

Formalisms inspired by Quantum theory have been used in Cognitive Science for decades. Indeed, Quantum-Like (QL) approaches provide descriptive features that are inherently suitable for perception, cognition, and decision processing. A preliminary study on the feasibility of a QL robot perception model has been carried out for a robot with limited sensing capabilities. In this paper, we generalize such a model for multi-sensory inputs, creating a multidimensional world representation directly based on sensor readings. Given a 3-dimensional case study, we highlight how this model provides a compact and elegant representation, embodying features that are extremely useful for modeling uncertainty and decision. Moreover, the model enables to naturally define query operators to inspect any world state, which answers quantifies the robot's degree of belief on that state.

The COVID-19 pandemic has created unprecedented challenges worldwide. Strained healthcare providers make difficult decisions on patient triage, treatment and care management on a daily basis. Policy makers have imposed social distancing measures to slow the disease, at a steep economic price. We design analytical tools to support these decisions and combat the pandemic. Specifically, we propose a comprehensive data-driven approach to understand the clinical characteristics of COVID-19, predict its mortality, forecast its evolution, and ultimately alleviate its impact. By leveraging cohort-level clinical data, patient-level hospital data, and census-level epidemiological data, we develop an integrated four-step approach, combining descriptive, predictive and prescriptive analytics. First, we aggregate hundreds of clinical studies into the most comprehensive database on COVID-19 to paint a new macroscopic picture of the disease. Second, we build personalized calculators to predict the risk of infection and mortality as a function of demographics, symptoms, comorbidities, and lab values. Third, we develop a novel epidemiological model to project the pandemic's spread and inform social distancing policies. Fourth, we propose an optimization model to re-allocate ventilators and alleviate shortages. Our results have been used at the clinical level by several hospitals to triage patients, guide care management, plan ICU capacity, and re-distribute ventilators. At the policy level, they are currently supporting safe back-to-work policies at a major institution and equitable vaccine distribution planning at a major pharmaceutical company, and have been integrated into the US Center for Disease Control's pandemic forecast.

An artificial intelligence (AI) system analyzing data from the Gaia space telescope has identified more than 2,000 large protostars, young stars that are still forming and could hold clues to the origin of the stars in our Milky Way. Scientists had previously cataloged only a 100 of these stars and investigating them has generated much of the knowledge underpinning star formation studies. The project was led by Miguel Vioque, a Ph.D. researcher at the University of Leeds, and the findings--New catalog of Herbig AE/BE and classical Be stars: A machine learning approach to Gaia DR2--have been published in the journal Astronomy and Astrophysics. He believes studying these newly identified stars has the potential to change scientists' understanding of massive star formation and their approach to studying the galaxy. Mr Vioque and his colleagues were interested in what are known as Herbig Ae/Be stars, stars that are still forming and have a mass that is at least twice that of the Sun. They are also involved in the birth of other stars.

One kind of robot has endured for the last half-century: the hulking one-armed Goliaths that dominate industrial assembly lines. These industrial robots have been task-specific -- built to spot weld, say, or add threads to the end of a pipe. They aren't sexy, but in the latter half of the 20th century they transformed industrial manufacturing and, with it, the low- and medium-skilled labor landscape in much of the US, Asia, and Europe. You've probably been hearing a lot more about robots and robotics over the last couple years. That's because, for the first time since the 1961 debut of GM's Unimate, regarded as the first industrial robot, the field is once again transforming world economies. Only this time the impact is going to be broader. That's particularly true in light of the COVID-19 pandemic, which has helped advance automation adoption across a variety of industries as manufacturers, fulfillment centers, retail, and restaurants seek to create durable, hygienic operations that can withstand evolving disruptions and regulations.

Various classes of Graph Neural Networks (GNN) have been proposed and shown to be successful in a wide range of applications with graph structured data. In this paper, we propose a theoretical framework able to compare the expressive power of these GNN architectures. The current universality theorems only apply to intractable classes of GNNs. Here, we prove the first approximation guarantees for practical GNNs, paving the way for a better understanding of their generalization. Our theoretical results are proved for invariant GNNs computing a graph embedding (permutation of the nodes of the input graph does not affect the output) and equivariant GNNs computing an embedding of the nodes (permutation of the input permutes the output). We show that Folklore Graph Neural Networks (FGNN), which are tensor based GNNs augmented with matrix multiplication are the most expressive architectures proposed so far for a given tensor order. We illustrate our results on the Quadratic Assignment Problem (a NP-Hard combinatorial problem) by showing that FGNNs are able to learn how to solve the problem, leading to much better average performances than existing algorithms (based on spectral, SDP or other GNNs architectures). On a practical side, we also implement masked tensors to handle batches of graphs of varying sizes.

The AI in Enterprise Accounting Market has witnessed continuous growth in the past few years and is projected to grow even further during the forecast period (2020-2025). The assessment provides a 360 view and insights, outlining the key outcomes of the industry. These insights help the business decision-makers to formulate better business plans and make informed decisions for improved profitability. In addition, the study helps venture or private players in understanding the companies more precisely to make better-informed decisions. The AI in Enterprise Accounting Market study covers current status, % share, future patterns, development rate, SWOT examination, sales channels, to anticipate growth scenarios for years 2020-2025.

The evolution of technology is taking artificial intelligence (AI) to the fore in nearly every industry. As AI gradually becomes mature, it is being applied in the energy management sector. A number of Internet of Things (IoT) companies are using AI to help businesses reduce energy consumption and expenses. U.S.-based BuildingIQ is one of these companies that aim to improve energy efficiency in large, complex building structures. BuildingIQ's Predictive Energy Optimization (PEO) service uses cloud-based software to calculate heating, ventilation and air conditioning (HVAC) related utility expenses.

As lawmakers in Brasilia debated a controversial pension overhaul for months, a robot more than 5,000 miles away in London kept a close eye on all 513 of them. The algorithm, designed by technology startup Arkera Inc., tracked their comments in Brazilian newspapers and government web pages each day to predict the likelihood the bill would pass. Weeks before the legislation cleared its biggest obstacle in July, the machine's data crunching allowed Arkera analysts to predict the result almost to the letter, giving hedge fund clients in New York and London the insight to buy the Brazilian real near eight-month lows in May. It's since rallied more than 8%. This is the kind of edge that a new generation of researchers are betting will upend the research marketplace.

Learn Computer Vision. Introduction course to Computer Vision with Python. Make Computer Vision Apps? Learn Computer Vision theory? Build a strong portfolio with Computer Vision & Image Processing Projects? Looking to add Computer Vision algorithms in your current software project ? Whatever be your motivation to learn Computer Vision, I can assure you that you’ve come to the right course. You get. Complete course with 1 hour of video tutorials, Source code for all examples in the course. What you'll learn. Use basic Computer Vision techniques. Do image processing. Build: Image Similarity app, Face Detection app and Object Detection app! Master Computer Vision! .