This is an integrative review that address the question, "What makes for a good explanation?" with reference to AI systems. Pertinent literatures are vast. Thus, this review is necessarily selective. That said, most of the key concepts and issues are expressed in this Report. The Report encapsulates the history of computer science efforts to create systems that explain and instruct (intelligent tutoring systems and expert systems). The Report expresses the explainability issues and challenges in modern AI, and presents capsule views of the leading psychological theories of explanation. Certain articles stand out by virtue of their particular relevance to XAI, and their methods, results, and key points are highlighted. It is recommended that AI/XAI researchers be encouraged to include in their research reports fuller details on their empirical or experimental methods, in the fashion of experimental psychology research reports: details on Participants, Instructions, Procedures, Tasks, Dependent Variables (operational definitions of the measures and metrics), Independent Variables (conditions), and Control Conditions.

At some point last year, Google's constant requests to prove I'm human began to feel increasingly aggressive. More and more, the simple, slightly too-cute button saying "I'm not a robot" was followed by demands to prove it -- by selecting all the traffic lights, crosswalks, and storefronts in an image grid. Soon the traffic lights were buried in distant foliage, the crosswalks warped and half around a corner, the storefront signage blurry and in Korean. There's something uniquely dispiriting about being asked to identify a fire hydrant and struggling at it. These tests are called CAPTCHA, an acronym for Completely Automated Public Turing test to tell Computers and Humans Apart, and they've reached this sort of inscrutability plateau before. In the early 2000s, simple images of text were enough to stump most spambots.

Despite recent setbacks, solar remains a bright spot in the often wobbly renewable energy sector. In the U.S., the solar market is projected to top $22.90 billion by 2025, driven by falling materials costs and growing interest in offsite and rooftop installations. Moreover, in China -- the world's leading installer of solar panels and the largest producer of photovoltaic power -- 1.84 percent of the total electricity generated in the country two years ago came from solar. There's clearly growth -- which San Francisco startup Aurora Solar seeks to capitalize on with a novel solution combining lidar data, computer-assisted design, and computer vision. The company, which develops a suite of software that streamlines the solar panel installation process, today announced it has secured $20 million in a Series A round of financing from Energize Ventures, with contributions from S28 Capital and existing investor Pear.

Staff scheduling is a universal problem that can be encountered in many organizations, such as call centers, educational institution, industry, hospital, and any other public services. It is one of the most important aspects of workforce management strategy and the one that is most prone to errors or issues as there are many entities should be considered, such as the staff turnover, employee availability, time between rotations, unusual periods of activity, and even the last-minute shift changes. The nurse scheduling problem is a variant of staff scheduling problems which appoints nurses to shifts as well as rooms per day taking both hard constraints, i.e., hospital requirements, and soft constraints, i.e., nurse preferences, into account. Most algorithms used for scheduling problems fall short when it comes to the number of inputs they can handle. In this paper, constraint programming was developed to solve the nurse scheduling problem. The developed constraint programming model was then implemented using python programming language.

Digital Adherence Technologies (DATs) are an increasingly popular method for verifying patient adherence to many medications. We analyze data from one city served by 99DOTS, a phone-call-based DAT deployed for Tuberculosis (TB) treatment in India where nearly 3 million people are afflicted with the disease each year. The data contains nearly 17,000 patients and 2.1M phone calls. We lay the groundwork for learning from this real-world data, including a method for avoiding the effects of unobserved interventions in training data used for machine learning. We then construct a deep learning model, demonstrate its interpretability, and show how it can be adapted and trained in three different clinical scenarios to better target and improve patient care. In the real-time risk prediction setting our model could be used to proactively intervene with 21% more patients and before 76% more missed doses than current heuristic baselines. For outcome prediction, our model performs 40% better than baseline methods, allowing cities to target more resources to clinics with a heavier burden of patients at risk of failure. Finally, we present a case study demonstrating how our model can be trained in an end-to-end decision focused learning setting to achieve 15% better solution quality in an example decision problem faced by health workers.

The idea of'Automata' originates from the mythologies of many cultures across the globe. Early inventors and engineers from ancient civilisation such as Greek, Chinese or Ptolemaic Egyptian attempted to develop a self-operating or automated machine resembling humans and animals. The term'Robot' comes from the Czech word "Robota" refers to "Forced Work or Labor" which was first used to refer the word'Artificial Automata' in a 1920 play R.U.R (Rossum's Universal Robots) by the Czech interwar writer'Karl Capek.' In 1928, one of the first'Humanoid Robots' invented by W.H.Richards, delivered a speech in the annual event of the'Model Engineers Society' in London. The brief history shows that'Robots' are not a new innovation but is a'Thinking Machine' which is programmed by a computer and is capable of doing complex series' of actions automatically.

In the present paper, a method of defining the industrial process parameters for a new product using machine learning algorithms will be presented. The study will describe how to go from the product characteristics till the prediction of the suitable machine parameters to produce a good quality of this product, and this is based on an historical training dataset of similar products with their respective process parameters. In the first part of our study, we will focus on the ultrasonic welding process definition, welding parameters and on how it operate. While in second part, we present the design and implementation of the prediction models such multiple linear regression, support vector regression, and we compare them to an artificial neural networks algorithm. In the following part, we present a new application of Convolutional Neural Networks (CNN) to the industrial process parameters prediction. In addition, we will propose the generalization approach of our CNN to any prediction problem of industrial process parameters. Finally the results of the four methods will be interpreted and discussed.

We propose the Relational Tucker3 (RT) decomposition for multi-relational link prediction in knowledge graphs. We show that many existing knowledge graph embedding models are special cases of the RT decomposition with certain predefined sparsity patterns in its components. In contrast to these prior models, RT decouples the sizes of entity and relation embeddings, allows parameter sharing across relations, and does not make use of a predefined sparsity pattern. We use the RT decomposition as a tool to explore whether it is possible and beneficial to automatically learn sparsity patterns, and whether dense models can outperform sparse models (using the same number of parameters). Our experiments indicate that---depending on the dataset--both questions can be answered affirmatively.

The overall equipment effectiveness (OEE) is a performance measurement metric widely used. Its calculation provides to the managers the possibility to identify the main losses that reduce the machine effectiveness and then take the necessary decisions in order to improve the situation. However, this calculation is done a-posterior which is often too late. In the present research, we implemented different Machine Learning algorithms namely; Support vector machine, Optimized Support vector Machine (using Genetic Algorithm), Random Forest, XGBoost and Deep Learning to predict the estimate OEE value. The data used to train our models was provided by an automotive cable production industry. The results show that the Deep Learning and Random Forest are more accurate and present better performance for the prediction of the overall equipment effectiveness in our case study.

An algorithm highlights obvious animals in blue and possible animals in yellow. After a promising first run in Namibia, a Swiss project could aid savanna conservation using drones and automatic image analysis. To get a sense of how many animals live in a given area, game counts are typically done in real time by sharp-eyed people in vehicles. A project funded by the Swiss National Science Foundation (SNSF) uses drones and artificial intelligence (AI) to count wild animals more efficiently. "Human eyes are very good at detecting animals, but not at screening countless images. Computers can process a lot more data," explains Swiss geo-information specialist Devis Tuia, who received a personal grant from SNSF to form a lab to improve wildlife monitoring methods in places like Namibia.