Data mining isn't a new invention that came with the digital age. The concept has been around for over a century, but came into greater public focus in the 1930s. According to Hacker Bits, one of the first modern moments of data mining occurred in 1936, when Alan Turing introduced the idea of a universal machine that could perform computations similar to those of modern-day computers. Forbes also reported on Turing's development of the "Turing Test" in 1950 to determine if a computer has real intelligence or not. To pass his test, a computer needed to fool a human into believing it was also human.

A new drone designed by Russian researchers is the hulk of the quadcopter world - and can carry a 400-pound (181-kg) payload and fly for up to eight hours. The multi-rotor, autonomous drone, called SKYF, was designed with logistics and agribusinesses companies in mind to create a air freight platform to help business carry out tasks. The vertical take-off and landing drone has applications in areas such as the aerial application of pesticides and fertilizers, seed planting for forest restoration and emergency situations for food and medicine delivery. The drone, designed by Russian company ARDN technology, has a maximum flight speed of 70 kilometers per hour (43.5 miles per hour) at a maximum height of 3,000 meters (9,843 feet) and has a positional accuracy of 30 centimeters (11.8 inches) The drone, designed by Russian company ARDN technology, has a maximum flight speed of 70 kilometers per hour (43.5 miles per hour) and is 5.2 meters (17 feet) by 2.2 meters (7.2 feet). It can fly at a maximum height of 3,000 meters (9,843 feet) and has a positional accuracy of 30 centimeters (11.8 inches).

Farmers in China have caught up with the country's booming drone trend and started using unmanned aircraft to spray pesticide onto the fields. Not only that, a team of villagers in central China recently bought 30 of these bug-zapping vehicles in hope of turning it into a new business. Zhu Xiwang and his neighbours said they hoped their squad of agri-drones to could help them start a pest-killing service, according to Huanqiu.com, an affiliation to People's Daily Online. This £24.8K flat pack folding home takes just SIX HOURS to build Pictures show the 30 drones lining up on a field, ready to take off. The unmanned aircraft, known by its model name MG-1S, is produced by Shenzhen-based Da Jiang Innovation, one of the largest drone manufacturers in China.

Do you believe that artificial intelligence is poised to significantly improve our societies, or do you imagine extreme dangers resulting from this technology in the future? Tech moguls Elon Musk and Mark Zuckerberg have been publicly debating this issue recently, with Musk claiming that Zuckerberg's knowledge about AI is "limited". The Tesla CEO and outspoken innovator has been pushing for the proactive regulation of artificial intelligence based on his belief that the technology is a "fundamental existential risk for human civilization." On the other side, Zuckerberg has denounced Musk's warnings, calling his statements "pretty irresponsible." While many academics, such as Pedro Domingos, a professor who works on machine learning at the University of Michigan, believe that Musk's nightmare scenarios could eventually happen, but his perspective is entirely wrong.

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If finding information in the workplace is a manual hunt-and-peck exercise, and you keep adding more digital information to the mix, your employees are getting frustrated, and even worse, disengaged. After all, they are used to easy, intuitive search experiences in their personal lives with tools like Google, Alexa, and Siri. Yet when it comes to the workplace, the systems don't deliver that ease of use. Get the eBook How AI-driven Search Empowers the Digital Workplace to learn how AI technologies and cognitive search deliver a personalized, highly relevant experience for information access in the enterprise, and help you engage a modern workforce.

There is an intuitive analogy of an organic chemist's understanding of a compound and a language speaker's understanding of a word. Consequently, it is possible to introduce the basic concepts and analyze potential impacts of linguistic analysis to the world of organic chemistry. In this work, we cast the reaction prediction task as a translation problem by introducing a template-free sequence-to-sequence model, trained end-to-end and fully data-driven. We propose a novel way of tokenization, which is arbitrarily extensible with reaction information. With this approach, we demonstrate results superior to the state-of-the-art solution by a significant margin on the top-1 accuracy. Specifically, our approach achieves an accuracy of 80.1% without relying on auxiliary knowledge such as reaction templates. Also, 66.4% accuracy is reached on a larger and noisier dataset.

Our sense of smell is gloriously specific. The mellow aroma of butter and flour rising from warm pie crust, the synthetic bite of fresh paint, the familiar odor of a new car--when we get a whiff of something, we know immediately what it is. But this natural delicacy of perception far exceeds our ability to tell how a given molecule, drawn on a blackboard and considered as an abstraction, will strike our noses. Two substances with completely different chemical shapes might smell almost identical, while two others with similar shapes might smell nothing alike. That's in direct contrast to, say, color vision; by examining the wavelengths of light bouncing off a rose or a child's hat, a scientist can say that a human will see them as red or blue (unless the human happens to be color-blind--though, even then, the shade is predictable).

Data mining isn't a new invention that came with the digital age. The concept has been around for over a century, but came into greater public focus in the 1930s. According to Hacker Bits, one of the first modern moments of data mining occurred in 1936, when Alan Turing introduced the idea of a universal machine that could perform computations similar to those of modern-day computers. Forbes also reported on Turing's development of the "Turing Test" in 1950 to determine if a computer has real intelligence or not. To pass his test, a computer needed to fool a human into believing it was also human.

To produce high-quality food and feed a growing world population with the given amount of arable land in a sustainable manner, we must develop new methods of sustainable farming that increase yield while minimizing chemical inputs such as fertilizers, herbicides, and pesticides. I and my colleagues are working on a robotics-centered approaches to address this grand challenge. My name is Andres Milioto, and I am a research assistant and Ph.D. student in robotics at the Photogrammetry and Robotics Lab (http://www.ipb.uni-bonn.de) Together with Philipp Lottes, Nived Chebrolu, and our supervisor Prof. Dr. Cyrill Stachniss we are developing an adaptable ground and aerial robots for smart farming in the context of the EC-funded project "Flourish" (http://flourish-project.eu/), where we collaborate with several other Universities and industry partners across Europe. The Flourish consortium is committed to develop new robotic methods for sustainable farming that aim at minimizing chemical inputs such as fertilizers, herbicides, and pesticides in order to reduce the side-effects on our environment.