Scientists teamed up to use artificial intelligence to discover new alternatives to steel in record time. As a result, they discovered three new blends to form metallic glass and did this 200 times faster than it has ever been done before. Fang Ren, who developed algorithms to analyze data on the fly while a postdoctoral scholar at SLAC, at a Stanford Synchrotron Radiation Lightsource beamline where the system has been put to use. Metallic glass is essentially an alloy of the future. Normally, a few metals can be mixed together so that the ideal properties of each metal are'added' together to make a'super-metal'.

Officials say using the RadPiper robot is safer, tremendously faster and more accurate than the current method of workers taking external measurements. They also say it could save tens of millions of public dollars on cleanups of that site and one near Paducah, Kentucky.

Cunjiang Yu, Bill D. Cook Assistant Professor of mechanical engineering, said potential applications range from surgery and rehabilitation to search and rescue in natural disasters or on the battlefield. Because the robot body changes shape in response to its surroundings, it can slip through narrow crevices to search for survivors in the rubble left by an earthquake or bombing, he said. "They sense the change in environment and adapt to slip through," he said. These soft robots, made of soft artificial muscle and ultrathin deformable sensors and actuators, have significant advantages over the traditional rigid robots used for automation and other physical tasks. The researchers said their work, published in the journal Advanced Materials, took its inspiration from nature.

The paper and packaging industry is in the midst of major change. Print circulation for magazines and newspapers has dropped dramatically in the last decade. Craigslist is replacing the classified section. Google and social media are replacing direct mail advertising. Cloud storage is replacing the office filing cabinet.

Apple has announced the creation of Daisy, a robot specifically designed to quickly disassemble several different iPhone models and recycle parts that can be used again, the company detailed in a Thursday press release. Daisy is actually a bit of a composite itself--the robot is made up of parts from another recycling robot, Liam, that was created in 2016, the release said. Daisy will be used first in the US and Europe and then expand worldwide. According to Apple's release, Daisy will be able take apart nearly 200 iPhones per hour, pushing the company closer to its goal of ending its reliance on mining for vital smartphone materials like cobalt. From every 100,000 iPhones Daisy disassembles, the release said, Apple will be able to harness about 1 kg of gold, 7.5kg of silver, almost two tons of aluminum, and 11kg worth of certain rare-earth elements and minerals like cobalt, palladium, tungsten, tantalum, and tin.

Mining is all about profitability. But tracking that can prove tricky, particularly when you're relying on people on the ground to report on activities. It all comes down to big data: the more data you can collect, the more information you have at your disposal, and the more insight you have into the business. IOT technology is paving the way for mine optimisation initiatives, says Johan Pietersen, MD of Virtualscape Technologies. "Mines are looking for the ability to gather data for various purposes, including improvements in efficiency and safety. They need to be able to monitor activities, assets and people and make better decisions based on real-time data. AI applied to such data is able to provide a realistic view of the mining operation's performance on an hourly basis, allowing mine managers to make pro-active judgment calls on operational execution to their financial benefit."

We live in amazing times. In fact, this entire "thing" of robotics and artificial intelligence (AI) is now being called the next industrial revolution. And it does not come without a lot of fear about a large workforce losing its jobs and means of making a living. When the first industrial revolution hit, factories and mass production drew workers to the cities in droves. Manufacturing put individual craftsmen out of business. Consumers could get products cheaper and faster and that was a good thing.

Inside a facility in Austin, Texas, there's a huge box filled with hundreds of dead iPhones. It's a salad of smashed screens and bent cases that spans nine different models--from iPhone 5 to the iPhone 8 plus, minus the plastic 5C (sorry, little guy). These devices are waiting for their chance to travel through Daisy, Apple's new robotic disassembly system. Daisy salvages raw materials, so they can be recycled, rather than join the more than 35 million tons of e-waste that hit landfills in 2016. Daisy addresses many needs for Apple--from sustainability to secrecy--but its explicit purpose is to separate the phone into discrete parts, which the company can send out for processing by its partners.

Data mining because of many reasons is really promising. The process helps in getting concealed and valuable information after scrutinizing information from different databases. Some of the data mining techniques used are AI (Artificial intelligence), machine learning and statistical. The process, in fact, helps various industries for intensifying their business efficacy. Among different processes, one of the most reliable and user-friendly is the CRISP-DM technique.

I've been able to liberate my best employees from busy work, enabling them to think critically. Instead of saddling my marketing team with a time-consuming data-mining project, I simply ask my diligent AI software to crunch the numbers. After assigning my chatbot the automated grunt work, I give the resulting data to my team to interpret strategically. And once my creative crew has concocted a plan based on the numbers, I can ask my chatbot how this plan aligns with other data points. It's an ongoing symbiotic conversation that encourages my workers to lean on the chatbot and use it as a valuable resource in their daily routine.