You might think that you have the best CRM product on the market and best overall customer engagement, at least the best-maintained CRM as it relates to quality of data, right? However, what if you could apply machine learning to that CRM copper mine and turn it into a gold mine? Essentially, you could predict outcomes and close times, discover opportunity insights, automate certain customer service tasks, possibly suggest next steps in the sales process. While the list could go on, these are just a few areas where machine learning could be leveraged to capture more opportunities and help prevent service recovery situations. We've already mentioned a few ways that machine learning can improve your sales predictions and improve customer engagement, but harnessing its true power you can do so much more.

In this paper we present a loss-based approach to change point analysis. In particular, we look at the problem from two perspectives. The first focuses on the definition of a prior when the number of change points is known a priori. The second contribution aims to estimate the number of change points by using a loss-based approach recently introduced in the literature. The latter considers change point estimation as a model selection exercise. We show the performance of the proposed approach on simulated data and real data sets.

Zeiss Zen Intellesis, a new machine learning capability that enables researchers to perform advanced analysis of their imaging samples across multiple microscopy methods has been luanched. The first algorithmic solution introduced by the platform makes integrated, easy to use, powerful segmentation for 2D and 3D datasets available to the routine microscopy user. The software is available for the company's full range of optical, confocal, X-ray, electron and ion microscopes.

Editor: Jerome Feldman's "Essay Concerning Robotic Understanding" (AI Magazine, Fall 1990) shows a remarkable naivete about humans. Although he admits to some limitations on human understanding (understanding/h): "We actually use understanding/h loosely, normally excluding infants, idiots and so on. We acknowledge that there are strong limitations on the extent to which we can convey understanding/h across barriers of gender, race and culture." If we are using understanding/h in Locke's sense, to mean reason, with all its eighteenth century freight, including the exclusion of women and blacks from the category of reasoning beings, there are, strangely enough, no barriers to this category for machines. After all, Boole later invented his logic to help mechanize the process of jurisprudence.

Moreover, the system was designed from the beginning to be maintained on an ongoing basis without the involvement of senior knowledge engineers. In the manufacture of paper, wood is first pulped to separate its fibers. One of the predominant pulp processes is done in a kraft pulp mill and consists of cooking wood chips at elevated temperature and pressure in the presence of certain chemicals (alkali and sulfide), washing the resultant brown pulp, bleaching to make the pulp white, and drying the pulp for shipment to a paper mill. Pitch, or wood resin, is the material in wood that is insoluble in water but soluble in organic solvents. It usually makes up 14 percent of the weight of wood after the bark is removed and is often a sticky material.

Soil characteristics are extremely important when determining yield potential. Fertilization and liming are commonly used to adapt soils to the nutritional requirements of the crops to be cultivated. Planting the crop that will best fit the soil characteristics is an interesting alternative to minimize the need for soil treatment, reducing costs and potential environmental damages. In addition, farmers usually look for investments that offer the greatest potential earnings with the least possible risks. Regarding the objectives to be considered, the crop-selection problem may be difficult to solve using traditional tools.

Implementation of the case-based reasoner in rules and objects using a commercial knowledge-based system shell is described. Although some refinements remain, the performance of the case-based reasoner has met its design goals. The chemical industry is heavily regulated. Every hazardous chemical product must have a set of shipping descriptions that conform to strict regulations issued by the Department of Transportation (DOT), the International Maritime Organization (IMO), and the International Air Transport Association (IATA). Shipping descriptions provide a concise characterization of the hazards a chemical can present during transportation (figure 1). Failing to comply with transport regulations can result in penalties ranging from delayed shipments to heavy fines or even incarceration of corporate officials. In addition, each chemical product has a material safety data sheet (MSDS) that conforms to Occupational Safety and Health (OSHA) and American National Standards Institute (ANSI) standards. Unlike shipping descriptions, MSDSs are lengthy documents of 8 to 10 pages that provide a detailed description of the health hazards a product can pose in the workplace (figure 2). They also contain information on procedures for storing, handling, and disposing of a chemical. Inadequately prepared MSDSs can lead to substantial product-liability lawsuits against the company if the product is involved in an industrial accident. The ultimate goal of these regulations is to ensure proper communication of health and safety information for the protection of the public. Air Products is committed to the initiative of the Chemical Manufacturers Association (CMA) known as Responsible Care. This initiative focuses on the safe manufacturing, distribution, use, recycling, and disposal of chemicals. Proper communication through accurate shipping descriptions and full disclosure of hazard information in the MSDS plays a key role in fulfilling obligations under Responsible Care. Maintaining shipping descriptions and MSDSs requires a major effort. Most corporate systems are intensely manual.

CLAVIER's central purpose is to find the most The use of composite materials, especially in aerospace applications, is on the increase because of their unique weight and strength qualities. Depending on the orientation of the graphite fibers, a part can be extremely flexible in one direction but rigid in another. In addition, a part made from composite material is both lighter and stronger than aluminum. The increased use of graphite parts, as well as the high cost of a spoiled part (as much as $50,000 for a single part), has put greater reliability and efficiency demands on a relatively new and complex manufacturing process. Composite part fabrication requires two major steps: layup and curing.

A Norwegian container ship called the Yara Birkeland will be the world's first electric, autonomous, zero-emissions ship. With a capacity of up to 150 shipping containers, the battery-powered ship will be small compared to modern standards (the biggest container ship in the world holds 19,000 containers, and an average-size ship holds 3,500), but its launch will mark the beginning of a transformation of the global shipping industry. This transformation could heavily impact global trade as well as the environment. The Yara Birkeland is being jointly developed by two Norwegian companies: agricultural firm Yara International, and agricultural firm, and Kongsberg Gruppen, which builds guidance systems for both civilian and military use. The ship will be equipped with a GPS and various types of sensors, including lidar, radar, and cameras--much like self-driving cars.

Deep learning has the potential to revolutionize quantum chemistry as it is ideally suited to learn representations for structured data and speed up the exploration of chemical space. While convolutional neural networks have proven to be the first choice for images, audio and video data, the atoms in molecules are not restricted to a grid. Instead, their precise locations contain essential physical information, that would get lost if discretized. Thus, we propose to use continuous-filter convolutional layers to be able to model local correlations without requiring the data to lie on a grid. We apply those layers in SchNet: a novel deep learning architecture modeling quantum interactions in molecules. We obtain a joint model for the total energy and interatomic forces that follows fundamental quantum-chemical principles. Our architecture achieves state-of-the-art performance for benchmarks of equilibrium molecules and molecular dynamics trajectories. Finally, we introduce a more challenging benchmark with chemical and structural variations that suggests the path for further work.