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.

Every company today is becoming in part a software company – we see this every day at Microsoft. Whether it's auto manufacturers, retailers, health care providers or financial services firms, our customers are not only transforming their own business operations with our software but collaborating with our consultants and engineers to create new digital products and services that run on our platform. As we look to the future, advancements and the adoption of cloud services, data analytics and artificial intelligence will only accelerate this phenomenon. That's why today we are announcing Microsoft's Shared Innovation Initiative. It is based on a set of principles designed to address co-created technology and intellectual property (IP) issues that give customers clarity and confidence regarding their work with Microsoft.

Already, the electronic brains of the most advanced robotic models surpass human intelligence and are able to do things that will make some of us shudder uncomfortably. But what is your reaction going to be after learning about recent advances in robotics and artificial intelligence? Scientists at the University of Texas (Austin) have simulated mental illness for a computer, testing schizophrenia on artificial intelligence units. The test subject is DISCERN – a supercomputer that functions as a biological neural network and operates using the principles of how human brain functions. In their attempt to recreate the mechanism behind schizophrenia, the scientists have applied the concepts described in the theory of hyper-learning, which states that schizophrenic brain processes and stores too much information too thoroughly by memorizing everything, even the unnecessary details.

Voice-enabled personal assistants seem to be headed for ubiquity in the consumer world. Market research firm Gartner predicts consumer demand for voice devices such as Amazon Echo and Google Home will generate $3.5 billion by 2021. But they are not confined to living rooms, telling you the day's weather or reading out news briefings. Intelligent voice assistants are slowly but steadily being adopted in healthcare. Several hospitals, such as Boston Children's Hospital and Beth Israel Deaconess Medical Center, are experimenting with voice assistants and conversational artificial intelligence (AI) technologies to provide relevant information and answer queries of patients and medical staff.

Artificial intelligence and machine learning systems are made up of code and algorithms, and as such, they work as fast as computers can process them. Often this means massive amounts of learning can be accomplished every second without stop 24x7x365. Code doesn't need to take weekends off, holidays, or sick time. It can recognize complex patterns, areas of potential improvement and problems in real-time (aka digital-time). Given these available computing capabilities and speeds, what are executives to do with AI and machine learning, when we live and operate in relatively slow human-time, and work within organizations that work at an even slower pace of organizational-time.

Police said Thursday they recorded 68 illegal drone flight incidents in 2017, almost double the previous year's 36, at a time when delivery services and other businesses are looking to utilize unmanned aircraft. Authorities took action against 77 people, up from 37 the year before, the National Police Agency said, indicating a need to improve public awareness of regulations. "It seems (the regulations) are not widely known," an NPA official said, suggesting the need to improve public awareness. The aviation law bans drone flights in airspace around airports and above densely populated areas. Drones are restricted to flying in daylight hours and need to be monitored at all times.

SENDAI – Municipalities and private firms are hoping robots and drones will be able to help with future disaster recovery efforts -- an initiative that incorporates lessons learned from the 2011 Great East Japan Earthquake -- by sending out warnings, gauging damage and accessing places were people cannot. To that end, the Sendai Municipal Government is testing a speaker-equipped drone for sending evacuation warnings during flight. Drones are quieter than helicopters, meaning messages would be easier for those on the ground to hear, city officials said. In such a system, the drone would automatically take flight after receiving a warning from the country's J-Alert early warning system and would issue evacuation messages to local residents. In the 2011 disaster, two city government workers and three volunteer fire department rescuers were killed in the tsunami while warning local residents to evacuate.

Korea Advanced Institute of Science and Technology demonstrates a robot designed for rescue missions or helping people with disabilities. The institute is facing a boycott from artificial intelligence researchers from nearly 30 countries over concerns that a new lab that has partnered with a leading defence company could lead to'killer robots'

Artificial intelligence researchers from nearly 30 countries are boycotting a South Korean university over concerns a new lab in partnership with a leading defence company could lead to "killer robots". More than 50 leading academics signed the letter calling for a boycott of Korea Advanced Institute of Science and Technology (KAIST) and its partner, defence manufacturer Hanwha Systems. The researchers said they would not collaborate with the university or host visitors from KAIST over fears it sought to "accelerate the arms race to develop" autonomous weapons. "There are plenty of great things you can do with AI that save lives, including in a military context, but to openly declare the goal is to develop autonomous weapons and have a partner like this sparks huge concern," said Toby Walsh, the organiser of the boycott and a professor at the University of New South Wales. "This is a very respected university partnering with a very ethically dubious partner that continues to violate international norms."

This paper addresses the general problem of accurate identification of oil reservoirs. Recent improvements in well or borehole logging technology have resulted in an explosive amount of data available for processing. The traditional methods of analysis of the logs characteristics by experts require significant amount of time and money and is no longer practicable. In this paper, we use the semi-supervised learning to solve the problem of ever-increasing amount of unlabelled data available for interpretation. The experts are needed to label only a small amount of the log data. The neural network classifier is first trained with the initial labelled data. Next, batches of unlabelled data are being classified and the samples with the very high class probabilities are being used in the next training session, bootstrapping the classifier. The process of training, classifying, enhancing the labelled data is repeated iteratively until the stopping criteria are met, that is, no more high probability samples are found. We make an empirical study on the well data from Jianghan oil field and test the performance of the neural network semi-supervised classifier. We compare this method with other classifiers. The comparison results show that our neural network semi-supervised classifier is superior to other classification methods.