The current technological boom, and the increasing consumerism is doing that every day appear more machines that are playing an essential role in our lives and have already become a vital necessity in every activity that we, or actually they, carry out. Either at home, in the car, at work or anyplace, we will soon have a dependence of these machines. In this post I wanted to reflect the positive aspect of machines in our lives and I have considered that the machines will take the right decisions that will make be better people but without controlling us, but the risks to become too dependent on machines exist and we cannot forget.

The infrastructure and tools necessary for large-scale data analytics, formerly the exclusive purview of experts, are increasingly available. Whereas a knowledgeable data-miner or domain expert can rightly be expected to exercise caution when required (for example, around fallacious conclusions supposedly supported by the data), the nonexpert may benefit from some judicious assistance. This article describes an end-to-end learning framework that allows a novice to create models from data easily by helping structure the model building process and capturing extended aspects of domain knowledge. By treating the whole modeling process interactively and exploiting high-level knowledge in the form of an ontology, the framework is able to aid the user in a number of ways, including in helping to avoid pitfalls such as data dredging. Prudence must be exercised to avoid these hazards as certain conclusions may only be supported if, for example, there is extra knowledge which gives reason to trust a narrower set of hypotheses. This article adopts the solution of using higher-level knowledge to allow this sort of domain knowledge to be used automatically, selecting relevant input attributes, and thence constraining the hypothesis space. We describe how the framework automatically exploits structured knowledge in an ontology to identify relevant concepts, and how a data extraction component can make use of online data sources to find measurements of those concepts so that their relevance can be evaluated. To validate our approach, models of four different problem domains were built using our implementation of the framework. Prediction error on unseen examples of these models show that our framework, making use of the ontology, helps to improve model generalization.

After fifty years of soaring crop yields thanks to fertilizers, pest control, and irrigation, that growth is bottoming out. We solved the food shortfall in the 20th century, but we need to do it again in this century. The UN says crop production must rise 70% by 2050 to meet demand. Startups see cheap sensors and artificial intelligence as the solution. Clever algorithms are processing a deluge of high-resolution data enabling real-time monitoring of crops and their environment for the first time.

Dr. Ratner has written a unique book that distinguishes between statistical and machine-learning data mining. The book includes 14 statistical data mining and 17 machine-learning data mining techniques. All techniques are quite practical, making this volume a handbook for every statistician, data miner, and machine-learner. Let me describe a few chapters that present approaches and techniques that I really favored. Chapter 3 introduces a new data mining method: a smoother scatterplot based on CHAID.

If you have ever wondered what space smells like, a new fragrance may be your best chance yet to find out. Perfumers have created a scent to mimic the smell of comet 67P/Churyumov-Gerasimenko – the rubber duck-shaped comet which was the target of the Rosetta mission. It was commissioned by scientists on the Rosetta team to interpret the variety of smelly chemical compounds the mission found in the comet's micro-atmosphere, with hints of cat wee, rotten eggs and bitter almonds. The Rosetta Orbiter Sensor for Ion and Neutral Analysis got its first taste of 67P in 2014, when its sensors passed through the comet's trailing atmosphere. Rosetta got its first taste of 67P in 2014, when its sensors passed through the comet's trailing atmosphere.

This image, from a 1962 internal General Electric document, conveyed the idea of random access storage using a set of "pigeon holes" in which data could be placed. Fifty-three years ago a small team working to automate the business processes of the General Electric Company built the first database management system. The Integrated Data Store--IDS--was designed by Charles W. Bachman, who won the ACM's 1973 A.M. Turing Award for the accomplishment. Before General Electric, he had spent 10 years working in engineering, finance, production, and data processing for the Dow Chemical Company. He was the first ACM A.M. Turing Award winner without a Ph.D., the first with a background in engineering rather than science, and the first to spend his entire career in industry rather than academia.

Process mining techniques aim to extract insights in processes from event logs. One of the challenges in process mining is identifying interesting and meaningful event labels that contribute to a better understanding of the process. Our application area is mining data from smart homes for elderly, where the ultimate goal is to signal deviations from usual behavior and provide timely recommendations in order to extend the period of independent living. Extracting individual process models showing user behavior is an important instrument in achieving this goal. However, the interpretation of sensor data at an appropriate abstraction level is not straightforward. For example, a motion sensor in a bedroom can be triggered by tossing and turning in bed or by getting up. We try to derive the actual activity depending on the context (time, previous events, etc.). In this paper we introduce the notion of label refinements, which links more abstract event descriptions with their more refined counterparts. We present a statistical evaluation method to determine the usefulness of a label refinement for a given event log from a process perspective. Based on data from smart homes, we show how our statistical evaluation method for label refinements can be used in practice. Our method was able to select two label refinements out of a set of candidate label refinements that both had a positive effect on model precision.

Recovering the planet's natural resources is hard. It's difficult, dangerous, and can be environmentally damaging. Cue an IT revolution, with smart communications, 'extreme Wi-Fi' covering vast deserts, autonomous vehicles that extract vital rocks and minerals, and geofenced employees who receive warnings if they get close to a mine's famously colossal big machinery. There's even a'smart bolt' that creates an underground support structure which is classic Internet of Things. The final goal is the autonomous mine, where humans are completely removed from the mining process.

It's a strong contender for the geekiest video ever made: a close-up of a smartphone with line upon line of numbers and symbols scrolling down the screen. But when visitors stop by Nicola Marzari's office, which overlooks Lake Geneva, he can hardly wait to show it off. "It's from 2010," he says, "and this is my cellphone calculating the electronic structure of silicon in real time!" Even back then, explains Marzari, a physicist at the Swiss Federal Institute of Technology in Lausanne (EPFL), Switzerland, his now-ancient handset took just 40 seconds to carry out quantum-mechanical calculations that once took many hours on a supercomputer -- a feat that not only shows how far such computational methods have come in the past decade or so, but also demonstrates their potential for transforming the way materials science is done in the future. Instead of continuing to develop new materials the old-fashioned way -- stumbling across them by luck, then painstakingly measuring their properties in the laboratory -- Marzari and like-minded researchers are using computer modelling and machine-learning techniques to generate libraries of candidate materials by the tens of thousands.

A self-driving John Deere tractor rumbles through Ian Pigott's 2,000-acre farm every week or so to spray fertilizer, guided by satellite imagery and each plot's harvesting history. The 11-ton behemoth, loaded with so many screens it looks like an airplane cockpit, relays the nutrient information to the farmer's computer system. With weather forecasts and data on pesticide use, soil readings, and plant tissue tests pulled by various pieces of software, Pigott can keep tabs on the farm down to the square meter in real time without ever leaving his carpeted office. "This is becoming more standard," says Pigott, who grows a rotation of wheat, oilseed, oats, and barley on his farm in the rolling Hertfordshire countryside an hour north of London. German chemical company Bayer cited the growth in such digitally assisted farming as a key reason for its 62 billion bid for Monsanto, which has become a leading provider of analytics used by growers.