Artificial intelligence (AI) is no longer the stuff of science fiction. The technology is already disrupting multiple industries, many of which impact you on a daily basis. Own an iPhone X? Its facial recognition system is powered by AI. Ever been redirected by Google Maps because of an accident or construction ahead? And those are just a couple of small examples.

Real-world project scheduling often requires flexibility in terms of the selection and the exact length of alternative production activities. Moreover, the simultaneous scheduling of multiple lots is mandatory in many production planning applications. To meet these requirements, a new flexible resource-constrained multi-project scheduling problem is introduced where both decisions (activity selection flexibility and time flexibility) are integrated. Besides the minimization of makespan, two alternative objectives inspired by a steel industry application case are presented: maximization of balanced length of selected activities (time balance) and maximization of balanced resource utilization (resource balance). New mixed integer and constraint programming (CP) models are proposed for the developed integrated flexible project scheduling problem. The real-world applicability of the suggested CP models is shown by solving large steel industry instances with the CP Optimizer of IBM ILOG CPLEX. Furthermore, benchmark instances on flexible resource-constrained project scheduling problems (RCPSP) are solved to optimality.

Like early mammals scuttering between the legs of tyrannosaurs, a lot of little companies are trying to weave around--and maybe even outlast--the big boys of self-driving technology. One such example is Perrone Robotics, a small Virginia company that has developed a self-driving package that it says can be quickly adapted to any vehicle. This Swiss Army knife of an AI can give smarts to an existing car, shuttle bus, or truck--even the gargantuan trucks used in mining. Tiny shuttles and behemoth trucks sell in small numbers, and equipping them to drive themselves is beneath the dignity of major players, like Alphabet's Waymo and General Motors' Cruise Automation. "What we're doing, certainly Waymo and GM Cruise could do, but they are focused on their own agenda. This is our niche, and we are going where we can add real value," says David Hofert, the chief marketing officer at Perrone Robotics.

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