Riga, Latvia – February 21, 2019 – The new automatic Facade Scan tool of UgCS for drone inspection mission planning is a time and cost saver for construction, engineering and mining industries. Various tools for surveying horizontal surfaces, even the uneven ones, have been developed to a high standard and are widely available on the market. Inspecting vertical surfaces is a completely different story -- previously it required a lot of manual work and so was a burden for professional drone users. But now, with the automatic Facade Scan tool from UgCS, this has changed. Making accurate digital models of buildings or cultural heritage objects, and finding heat leaks or damage to walls: these are some of the applications of the new Facade Scan tool for construction and architecture.

As part of the Kingspan research team, my passions lie with the development of structural mechanics and how we can further enhance the technological development of the built environment. As a part of my masters thesis I was working on the applications of Artificial Intelligence (AI) and Machine Learning (ML) in the AEC industry. My research looked at how AI and ML are shaping the way we work, how projects are managed and delivered and most importantly, the question of whether the industry is ready to embrace this level of digital ingenuity. It's no secret that public attention on AI has rapidly increased recently, despite the fact that the technology has been slowly developing for the past 70 years. If we consider that structural mechanics has been developing accurate theoretical models for predicting strain and stresses for the past few decades and that these theoretical models require a fixed set of input parameters such as material properties, boundary conditions etc. to produce results such as deflection, stresses etc. – it comes as no surprise that this is a pretty complex and time-consuming process. Therefore, because of these complexities, experienced engineers are often needed to interpret the results for other parties.

In complex simulation environments, certain parameter space regions may result in non-convergent or unphysical outcomes. All parameters can therefore be labeled with a binary class describing whether or not they lead to valid results. In general, it can be very difficult to determine feasible parameter regions, especially without previous knowledge. We propose a novel algorithm to explore such an unknown parameter space and improve its feasibility classification in an iterative way. Moreover, we include an additional optimization target in the algorithm to guide the exploration towards regions of interest and to improve the classification therein. In our method we make use of well-established concepts from the field of machine learning like kernel support vector machines and kernel ridge regression. From a comparison with a Kriging-based exploration approach based on recently published results we can show the advantages of our algorithm in a binary feasibility classification scenario with a discrete feasibility constraint violation. In this context, we also propose an improvement of the Kriging-based exploration approach. We apply our novel method to a fully realistic, industrially relevant chemical process simulation to demonstrate its practical usability and find a comparably good approximation of the data space topology from relatively few data points.

Underscoring the importance of local participation in forest improvement, the analysis shows that if grazing lands are snatched away from farming communities for afforestation, forest protection is unlikely. Land stewardship is the key. There is context to this, as Rana explained, in case of JFM where the state is in control, parcels of land where trees are planted are fenced in, restricting access to grazing grounds for cattle. JFM (a Forest Development Agency program funded by the national government) was the flagship community participatory initiative for forests in India starting in the 1990s. In the early 2000s, JFM interventions were begun in several of the study FMRs to involve communities in forest regeneration and protection.

The technology coupled with hands-on work and measurements is used to create a forest management plan. The Duncannon Borough Watershed is a 1,600-acre property key to generating money in the local community. "In 300 spots, we measured every tree for a tenth of an acre," said Josh Parrish, the director of the Working Woodlands program at the Nature Conservancy. Understanding what you have is important in moving forward. So, the Nature Conservancy is doing just that by working with a company that uses artificial intelligence.

Artificial intelligence, or AI, is largely an experimental science—at least as much progress has been made by building and analyzing programs as by examining theoretical questions. MYCIN is one of several well-known programs that embody some intelligence and provide data on the extent to which intelligent behavior can be programmed. As with other AI programs, its development was slow and not always in a forward direction. But we feel we learned some useful lessons in the course of nearly a decade of work on MYCIN and related programs. In this book we share the results of many experiments performed in that time, and we try to paint a coherent picture of the work. The book is intended to be a critical analysis of several pieces of related research, performed by a large number of scientists. We believe that the whole field of AI will benefit from such attempts to take a detailed retrospective look at experiments, for in this way the scientific foundations of the field will gradually be defined. It is for all these reasons that we have prepared this analysis of the MYCIN experiments.

Edward H. Shortliffe Chapter 6 Details of the Revised Therapy Algorithm

Real-life mobile phone data may contain noisy instances, which is a fundamental issue for building a prediction model with many potential negative consequences. The complexity of the inferred model may increase, may arise overfitting problem, and thereby the overall prediction accuracy of the model may decrease. In this paper, we address these issues and present a robust prediction model for real-life mobile phone data of individual users, in order to improve the prediction accuracy of the model. In our robust model, we first effectively identify and eliminate the noisy instances from the training dataset by determining a dynamic noise threshold using naive Bayes classifier and laplace estimator, which may differ from user-to-user according to their unique behavioral patterns. After that, we employ the most popular rule-based machine learning classification technique, i.e., decision tree, on the noise-free quality dataset to build the prediction model. Experimental results on the real-life mobile phone datasets (e.g., phone call log) of individual mobile phone users, show the effectiveness of our robust model in terms of precision, recall and f-measure.

"Graduates at IBM are coming into roles where you provide services to a range of different industries as opposed to just working in one," she says. "From a career perspective, they can move across different industries but they also move across different functions. You retain your core expertise but you also get to do different jobs because of the diversity of our clients." IBM works with dozens of Australia's biggest companies using Watson to drive machine learning and data analysis within business, and has the advantage of being able to supply whole teams of experts as challenges arise. In contrast, even big industry employers often have only a few specialists in key areas.

The artificial intelligence (AI) in health care market is set to top $34 billion by 2025, according to some estimates -- and it's no real wonder why. One startup that's successfully maintained pole position is Health Catalyst, a Salt Lake City, Utah-based health care big data company founded in 2009 by Steven Barlow and Thomas Burton. It aims to drive clinical and operational performance improvements in state and regional health plan providers, physician groups, and extended care facilities through its suite of analytics apps. And it's raising capital to help further progress toward that goal. Health Catalyst today announced that it has secured $100 million in series F equity and debt financing led by health care investment firm OrbiMed, with participation from existing partners Sequoia Capital, Norwest Venture Partners, Sands Capital Ventures, UPMC Enterprises, and Kaiser Permanente Ventures.