YVERDON-LES-BAINS, Switzerland/CHICAGO: In a field of sugar beet in Switzerland, a solar-powered robot that looks like a table on wheels scans the rows of crops with its camera, identifies weeds and zaps them with jets of blue liquid from its mechanical tentacles. Undergoing final tests before the liquid is replaced with weedkiller, the Swiss robot is one of new breed of AI weeders that investors say could disrupt the US$100 billion pesticides and seeds industry by reducing the need for universal herbicides and the genetically modified (GM) crops that tolerate them. Dominated by companies such as Bayer, DowDuPont, BASF and Syngenta, the industry is bracing for the impact of digital agricultural technology and some firms are already adapting their business models. Herbicide sales are worth US$26 billion a year and account for 46 percent of pesticides revenue overall while 90 percent of GM seeds have some herbicide tolerance built in, according to market researcher Phillips McDougall. "Some of the profit pools that are now in the hands of the big agrochemical companies will shift, partly to the farmer and partly to the equipment manufacturers," said Cedric Lecamp, who runs the US$1 billion Pictet-Nutrition fund that invests in companies along the food supply chain.

Even by the standards of Bitcoin, things are crazy in China. As the boom in cryptocurrencies has become the biggest speculative bubble in recorded history, a single company in Beijing's Haidian District has been selling the chips that generate as much as 80 percent of the world's cryptocoins. "We feel lucky," says Jihan Wu, the co-chief executive of Bitmain Technologies Ltd., which was more or less unknown two years ago and, according to Wu, booked revenue of $3.5 billion in 2017. Cryptocurrency networks run on number-crunching, electricity-hogging "mining" technology, and to play in that game with any seriousness, you pretty much need Bitmain's chips. And because it's China, the whole thing could fall apart at any minute.

We devise an anomaly detection system that detects stolen phones. In this system, we use a mining algorithm to extract sequential patterns from a user’s past behavior to construct a personalized model. We then put forward scoring functions and threshold setting strategies to detect stealing events. We evaluate our approach with a data set from the MIT Reality Mining project. Experimental results indicate that our approach can detect 87% of simulated stealing events with an average false positive rate of 0.9%.

Description This easy video tutorial is the fastest way to master modern data science best practices and use them to promote timely, evidence-based decision-making! Applied Data Mining LiveLessons demystifies current best practices, showing how to uncover hidden patterns and leverage them to improve all aspects of business performance. Drawing on extensive experience as a researcher, practitioner, and instructor, Dr. Dursun Delen shows you exactly how analytics and data mining work, why they've become so important, and how to apply them to your problems. Delen reviews key concepts, applications, and challenges; introduces advanced tools and technologies, including IBM Watson; and discusses privacy concerns associated with modern data mining. You'll watch him demonstrate prediction, classification, decision trees, and cluster analysis...key algorithms such as nearest neighbor...artificial neural networks...regression and time-series forecasting...text analytics and sentiment analysis...big data techniques, technologies, and more.

From high-tech unicorns to specialty chemicals, the country's economy is moving swiftly beyond its lower-margin roots. The Chinese are now the world's most avid online purchasers of goods and services, which they are likely to pay for with a mobile device. The deepening digital ethos reflects a broader consumerization of the Chinese economy. These trends are creating fertile grounds for digital start-ups while also transforming traditional industries such as specialty chemicals as they supply materials for advanced industries and higher-margin consumer goods. Global companies in China should ensure that they're not competing for yesterday's markets.

There isn't a software company out there worth its salt that doesn't have some kind of artificial intelligence initiative in progress right now. These organizations understand that AI is going to be a game-changer, even if they might not have a full understanding of how that's going to work just yet. In March at the Adobe Summit, I sat down with Adobe executive vice president and CTO Abhay Parasnis, and talked about a range of subjects with him including the company's goal to build a cloud platform for the next decade -- and how AI is a big part of that. Parasnis told me that he has a broad set of responsibilities starting with the typical CTO role of setting the tone for the company's technology strategy, but it doesn't stop there by any means. He also is in charge of operational execution for the core cloud platform and all the engineering building out the platform -- including AI and Sensei.

From targeted online advertising to more precise recommendation engines, consumer markets are bursting with innovation around machine learning and advanced analytics. While there's less buzz around business-to-business markets, these innovations are changing the game in B2B as well, even in old-line industries selling what might be considered commodity products. A growing number of B2B companies are using data and analytics to add services that bring new elements of value to customers, and in some cases new sources of revenue. These elements are fundamental attributes of a company's offering in their most essential and discrete forms – things like product quality, flexibility, and associated expertise; they lift value propositions above commodity status and benefit customers in particular ways. Consider recent moves by Australia-based Orica, which provides packaged explosives materials to mining companies worldwide.

Predictive business process monitoring methods exploit historical process execution logs to generate predictions about running instances (called cases) of a business process, such as the prediction of the outcome, next activity or remaining cycle time of a given process case. These insights could be used to support operational managers in taking remedial actions as business processes unfold, e.g. shifting resources from one case onto another to ensure this latter is completed on time. A number of methods to tackle the remaining cycle time prediction problem have been proposed in the literature. However, due to differences in their experimental setup, choice of datasets, evaluation measures and baselines, the relative merits of each method remain unclear. This article presents a systematic literature review and taxonomy of methods for remaining time prediction in the context of business processes, as well as a cross-benchmark comparison of 16 such methods based on 16 real-life datasets originating from different industry domains.

After the successful completion of the production of the material-optimised concrete façade mullions, Fabio Scotto and Ena Lloret-Frischti of the Gramazio Kohler Research Group at ETH Zurich and the Chair for Physical Chemistry of Building Materials, ETH Zurich take a look back at the experiments and prototypes which were necessary in the development of a final robotic fabrication process. The integration of Smart Dynamic Casting (SDC) for the production of the façade mullions for the first floor of DFAB HOUSE has led us to the development of an adaptive robotic setup which allows us to produce custom-made reinforced concrete structures. Until the final development of a robust robotic process, we had to overcome several challenges during the experimental and prototypical phase. Scaling down the production system and minimizing the friction forces Our first main task was to scale down the production system to realise structures with a minimal cross section of 100 70 mm. This had a direct impact on the formwork system we were working with previously.

During the first industrial revolution, workers flocked in the cities in masses because of the factories that were coming up. Individual craftsmen were kicked out of business because of manufacturing, and this allowed consumers to access cheaper products much faster. Although some workers were replaced, new jobs were created and as time passed by, employment levels rose up to all-time highs. It is with certainty that technology will eliminate very many jobs, from a few million to more than a billion. According to Andrew Charlton, "This huge variance is due to the fact that we are not yet sure about the number of jobs that robots will take over in the next couple of years; we are not sure where technology will be headed."