Forget ruby-encrusted swords or diamond-tipped chainsaws. The scanning probe microscope is, quite literally, the sharpest object ever made. Hidden under its bulky silver exterior is a thin metal wire, as fine as a human hair. Scientists wield the wire not as a weapon, but as an intricate paintbrush--using its needlelike tip to position single atoms on a tiny semiconductor canvas. Ever since scientists at IBM invented the scanning probe microscope some 35 years ago, researchers have used it to create designs both goofy and groundbreaking.

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 $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 $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. The company said it is close to signing a financing round with investors and is due to go on the market by early 2019.

Forget ruby-encrusted swords or diamond-tipped chainsaws. The scanning probe microscope is, quite literally, the sharpest object ever made. Hidden under its bulky silver exterior is a thin metal wire, as fine as a human hair. Scientists wield the wire not as a weapon, but as an intricate paintbrush--using its needlelike tip to position single atoms on a tiny semiconductor canvas. Ever since scientists at IBM invented the scanning probe microscope some 35 years ago, researchers have used it to create designs both goofy and groundbreaking.

IBM's crypto anchor verifier shown performing multiple test on objects ranging from olive oil to paper. If eyes really are windows to the soul, IBM's latest product could lead to blockchain enlightenment. Called a crypto anchor verifier, the technology, which is part artificial intelligence software, part an insanely sophisticated, internally developed lens can see the cells of animals, and distinguish between them. Powered by a lens capable of perceiving objects as small as a single micron, the verifier is also designed to search out -- and understand -- the difference between a fake drug and the real deal, a cheap bottle of wine and an expensive one, and imperfections within diamonds undetectable to the naked eye. Perhaps even more remarkable though, is the verifier is designed to do all this by downloading software developed by IBM Watson to any smartphone.

YVERDON-LES-BAINS, Switzerland/CHICAGO (Reuters) - 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 $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 $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 $1 billion Pictet-Nutrition fund that invests in companies along the food supply chain.

AI-powered weed hunters could soon reduce the need for herbicides and genetically modified crops. How it's done now: Current farming methods involve spraying large amounts of indiscriminate weed killer over fields full of crops that have been genetically tweaked (usually by the same company that makes the weed killer) to resist the chemicals. The pesticide and seed industry is enormous, worth $100 billion globally. Of that, herbicide sales alone account for $26 billion. The future: Robots like the one created by ecoRobotix (shown above) will be able to roll through fields, using computer vision to target and spray individual weeds as they go.

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 $100billion 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 $26billion 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 $1billion Pictet-Nutrition fund that invests in companies along the food supply chain.

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%.