The Shapes Constraint Language (SHACL) has been recently introduced as a W3C recommendation to define constraints that can be validated against RDF graphs. Interactions of SHACL with other Semantic Web technologies, such as ontologies or reasoners, is a matter of ongoing research. In this paper we study the interaction of a subset of SHACL with inference rules expressed in datalog. On the one hand, SHACL constraints can be used to define a "schema" for graph datasets. On the other hand, inference rules can lead to the discovery of new facts that do not match the original schema. Given a set of SHACL constraints and a set of datalog rules, we present a method to detect which constraints could be violated by the application of the inference rules on some graph instance of the schema, and update the original schema, i.e, the set of SHACL constraints, in order to capture the new facts that can be inferred. We provide theoretical and experimental results of the various components of our approach.

When we hear technology we think of electronic gadgets and a hundred types of software. But the problems of the future are going to be more basic. Food, water, and shelter are important to talk about. They're essential to sustain human life and limited in availability. Moreover, the increasing population and concentration of population in major cities will possibly lead to scarcity unless we take due action.

AI in healthcare is having a tremendous impact for the benefit of patients, providers, and payers. The opportunities to deploy AI in health care are increasing exponentially as we become better at capturing and integrating vast amounts of data from multiple sources, making sense of this data in a clinically relevant way, and understand methodologies that explain its use. In some cases, AI will replicate human intelligence, in others it will it will augment what we can do to improve health and lower cost. Pros and cons of various approaches and use cases will be discussed in this exciting session. Recon Strategy is a boutique strategy consulting firm founded in 2010 by alumni of the Boston Consulting Group.

Today, the recycling market is changing rapidly due to global changes where the quality requirements of the incoming and outgoing material are increased. As a result, systems that are separating waste material from the target material need to be improved continuously to cope with this change. Stora Enso's Langerbrugge Mill in northwest Belgium, which is one of the largest paper mills in Europe, required a more effective paper-cardboard sorting solution and technology that can easily be retrained for anomaly detection. This technology was developed by Robovision, a company specializing in deep learning-based machine vision and robot programming, and Imec, which is the world-leading R&D and innovation hub in nanoelectronics and digital technologies within the framework of the AUTOWARE project. A big challenge in paper recycling is the separation of cardboard and waste materials from paper.

Proteins, the fundamental nanomachines of life, have provided scientists like me with many lessons in our own efforts to create nanomachinery. Proteins are large molecules containing hundreds to thousands of atoms and are typically a few nanometers (billionths of a meter) to tens of nanometers across. Our bodies contain at least 20,000 different proteins that, among other things, cause our muscles to contract, digest our food, build our bones, sense our environment and tirelessly recycle hundreds of small molecules within our cells. As a chemistry undergraduate in 1986, I dreamed of the possibility of designing and synthesizing macromolecules (molecules containing more than 100 atoms) that could do the amazing things that proteins do and more. I have programmed computers since the first TRS-80s came out in the late 1970s, and I thought it would be wonderful if I could build complex molecular machines as easily as I could write software. I wanted to create a programming language for matter--a combination of software and chemistry that would enable people to describe a nanomachines shape and would then determine the series of chemical processes that a chemist or a robot should carry out to build the nanodevice. Unfortunately, the idea of inventing nanomachines by designing new proteins runs into a severe obstacle.

On a meetup that I attended a couple of months ago in Sydney, I was introduced to an online machine learning course by fast.ai. I never paid any attention to it then. This week, while working on a Kaggle competition, and looking for ways to improve my score, I came across this course again. I decided to give it a try. Here is what I learned from the first lecture, which is a 1 hour 17 minutes video on INTRODUCTION TO RANDOM FOREST.

A lot of nat gas analysts would at times reference EIA's Nuclear Capacity Outage (NCO henceforth), yet I haven't seen anyone do a detailed explanation of how they apply it toward an objective bias in implied Nat Gas demand, i.e. Fair Value bias going forward expected by traders paying attention to NCO. So I got curious, and first look at NG prices vs. YOY change in NCOs: So it looks like there is likely somewhat of a rough relationship, that some traders are paying attention to it. Then the next step would be an attempt toward precision via Time Series Analysis. So, what I'd do here is a 2 Step Machine Learning process of 1) Forecast expected NCO for the rest of 2019, then apply that to estimate Natural Gas futures fair value bias going forward.

Cologne – LANXESS is broadening its use of artificial intelligence (AI) in product development. The specialty chemicals company has launched a project aimed at expanding its range of prepolymers. The goal is to offer customers tailor-made polyurethane systems with even shorter lead times, including for entirely new applications with different requirements. The Urethane Systems business unit is using the potential of AI and has brought materials AI company Citrine Informatics on board as a project partner. LANXESS data specialists and process experts used the Citrine Platform for artificial intelligence to add further data points to the company's formulation database.

Brazil's mining major Vale is set to start applying machine learning to identify new drilling targets at its Coleman nickel mine. Coleman Mine, which is the flagship asset of Vale in Ontario, Canada, is part of the company's base metals operations. Vale has selected technology company GoldSpot Discoveries to examine and analyse the vast amount of data acquired by it over decades of mining at Coleman. GoldSpot Discoveries' team of geologists and data scientists will also discover previously unrecognised data trends, which may point to unknown areas of in-depth mineralisation. By using its geoscience and machine science expertise, GoldSpot Discoveries' team will clean, unify and analyse exploration data from Vale's Coleman Mine.

In the workplace of the not-too-distant future, employees will need to go beyond being tech-savvy to being able to comfortably work alongside digital colleagues. Artificial intelligence (AI), machine learning and intelligent bots will be automatically making decisions to streamline business processes and empower efficient automation. The widespread adoption of machines to do much of the "heavy lifting" will change some jobs from the inside out, making individual workers far more productive and less bogged down with repetitive tasks. Smart chatbots can already handle first- and even second-level customer service calls, and AI is powering everything from manufacturing lines to automated vehicles. For example, BHP is rolling out automated trucks at its iron ore and coal mines across Australia over the next 5 years, following the success of its Jimblebar mine trial program, which saw a 90 per cent reduction in the number of dangerous incidents.