Rhetoric about robots taking jobs is nothing new: From Oxford University to Elon Musk, the fear of AI as an existential threat abounds. But here's the more complicated truth: It depends on which sector you work in. Some will be far more impacted than others – and that includes manufacturing. That's why small business leaders in manufacturing should care about these questions: "How will AI impact the industry?" A forecast by Gartner (available for clients) predicts that AI will eliminate more jobs than it creates through 2019 (mostly in manufacturing).

As more autonomous vehicles have taken to the roads, transportation expert Ben Pierce has been noticing ways the roads should prepare for them. "They can drive themselves, but boy we can really help them," said Pierce, the transportation technology lead for the engineering and architecture firm HDR. In Chicago last week, Pierce suggested some big changes to infrastructure and some small changes to policies and procedures that can improve the performance of driverless cars and avoid some hazards. "One of the things I've noticed here recently as more and more autonomous vehicles have started to arrive, is there's a bunch of unforeseen consequences, and that we as highway designers need to start paying attention to those unforeseen consequences," Pierce told a packed room Thursday at Chicago's Metropolitan Planning Council. "There's a lot of little things we can do to be ready, and if we get ready we can see these huge benefits and gains, and it's phenomenal."

Data Mining with Rattle is a unique course that instructs with respect to both the concepts of data mining, as well as to the "hands-on" use of a popular, contemporary data mining software tool, "Data Miner," also known as the'Rattle' package in R software. Rattle is a popular GUI-based software tool which'fits on top of' R software. The course focuses on life-cycle issues, processes, and tasks related to supporting a'cradle-to-grave' data mining project. These include: data exploration and visualization; testing data for random variable family characteristics and distributional assumptions; transforming data by scale or by data type; performing cluster analyses; creating, analyzing and interpreting association rules; and creating and evaluating predictive models that may utilize: regression; generalized linear modeling (GLMs); decision trees; recursive partitioning; random forests; boosting; and/or support vector machine (SVM) paradigms. It is both a conceptual and a practical course as it teaches and instructs about data mining, and provides ample demonstrations of conducting data mining tasks using the Rattle R package. The course is ideal for undergraduate students seeking to master additional'in-demand' analytical job skills to offer a prospective employer.

NEW DELHI: Barun Gorain joined Hindustan Zinc Ltd as chief technology and innovation officer two months ago, moving from Barrick Gold in Canada, one of the many CXO-level hires that Indian companies have been making in the buzzing areas of artificial intelligence (AI), machine learning, the Internet of things (IoT) and robotic process automation (RPA). Other recent instances of Indians returning home with domain knowledge in these emerging technologies include Raghuram Velega, who left his job at a San Francisco-based cognitive computing company to join Reliance Jio Infocomm as vice-president, head, big data and analytics. Former National Aeronautics and Space Administration (NASA) executive Santanu Bhattacharya joined Bharti Airtel as chief data scientist and Ayush Sharma moved from Silicon Valley to join Reliance Jio as senior vicepresident of engineering and technology. Search firms like Korn Ferry, EMA Partners, Transearch and Hunt Partners say there's a paucity of experts in these fields, leading to a jump in salaries of new hires by as much as 50%, most of them from overseas. Salaries for such executives are at Rs 1-2 crore annually but can be even higher.

We recognize Earth as the blue planet, but it's not the only ocean world in our neighborhood. Oceans may be concealed beneath thick crusts of ice on moons orbiting Jupiter, Saturn, and Neptune, and on the dwarf planets Pluto and Eris. Saturn's moon Titan even boasts liquid seas right on its surface, although they are full of methane rather than water. If anywhere in our solar system holds signs of life, it is likely to be these frigid worlds. Scientists are determined to explore the distant seas of Titan and Jupiter's moon Europa, and are designing ice-gripping rovers and submarines to take the plunge into their mysterious depths.

From your water-logged phone to your smashed smart TV, those personal electronics headed for the landfill are a potential goldmine. Economists already knew that along with the swelling 44.7 million metric tons of electronic waste tossed each year we were throwing out billions of dollars in resources. But quantifying all the gold, copper, iron, plastic, and rare earths languishing in our landfills and recycling centers is only part of the problem. Figuring out whether it's worthwhile, financially speaking, to sift those resources out of the rubble--instead of continuing to extract them from traditional mines--is another issue entirely. A study published in Environmental Science & Technology this week finally has an answer, suggesting that'urban mining' of electronic waste for copper and gold in China was actually more cost-effective than digging those metals out of the ground.

Predictive modelling is a very exciting field with many different applications. Lots of algorithms have been developed in this area. According to many Kaggle competitions (Fogg, 2016), winning solutions are often obtained with elastic tools like random forest, gradient boosting or neural networks. These algorithms have many strengths but also share a major weakness, which is the lack of interpretability of a model structure. A single random forest, an xgboost model or a neural network may be parametrized with thousands of parameters which makes these models hard to understand.

Another such invention, 3-D printing, is now scaling up. All over the world, an impressive diversity of people and organizations, ranging from startups and hobbyists to construction and engineering firms, are successfully prototyping 3-D-printed buildings. The government of Dubai has set a goal of 3-D printing 25% of every new building by 2030. Prototype single-family dwellings have been 3-D-printed in China, Italy, Russia--and Texas. Global infrastructure firm AECOM ACM 2.59% uses 3-D printing to prefabricate jail cells and hospital rooms.

Chemical synthesis is a scientific procedure that uses simple chemical compounds to construct complex ones. It is the exact process used to create most of the substances we use in our daily life – from drugs to those components inside our electronic devices. However, the steps involved are often time intensive and extremely complicated that it may take years for chemists to master or achieve breakthroughs in certain procedures. Nonetheless, artificial intelligence has come to the rescue. To make it simple for everyone we can say, artificial intelligence is the new lab technician for chemical synthesis.

Marwin Segler, the lead author of the study, puts it in a nutshell: "Retrosynthesis is the ultimate discipline in organic chemistry. Chemists need years to master it -- just like with chess or Go. In addition to straightforward expertise, you also need a goodly portion of intuition and creativity for it. So far, everyone assumed that computers couldn't keep up without experts programming in tens of thousands of rules by hand. What we have shown is that the machine can, by itself, learn the rules and their applications from the literature available."