Apple harvested almost 40 million worth of gold from recycled gadgets last year, and is now deploying robots to take iPhones apart in a major environmental push. In its latest annual environmental responsibility report, which was published last week, Apple explained that it gathered 2,204 pounds of recycled gold during its fiscal year 2015. The gold, which weighs more than a ton, is worth 39.6 million. Apple recovered more than 63 million pounds of various materials via its "take-back" recycling initiatives in 2015, according to the company's environmental report. The tech giant gathered over 23 million pounds of steel, making it the most recycled material, and more than 13 million pounds of plastics.
Crowdsourcing is an effective tool for scalable data annotation in both research and enterprise contexts. Due to crowdsourcing’s open participation model, quality assurance is critical to the success of any project. Present methods rely on EM-style post-processing or manual annotation of large gold standard sets. In this paper we present an automated quality assurance process that is inexpensive and scalable. Our novel process relies on programmatic gold creation to provide targeted training feedback to workers and to prevent common scamming scenarios. We find that it decreases the amount of manual work required to manage crowdsourced labor while improving the overall quality of the results.
By elaborating on the notion of linear belief functions (Dempster 1990; Liu 1996), we propose an elementary approach to knowledge representation for expert systems using linear belief functions. We show how to use basic matrices to represent market information and financial knowledge, including complete ignorance, statistical observations, subjective speculations, distributional assumptions, linear relations, and empirical asset pricing models. We then appeal to Dempster's rule of combination to integrate the knowledge for assessing an overall belief of portfolio performance, and updating the belief by incorporating additional information. We use an example of three gold stocks to illustrate the approach.
Body odor is a stubborn problem. Sensors and the computing attached to them struggle to perceive armpit odors in the way humans do, because B.O. is really a complex mix of dozens of gaseous chemicals. The UK's PlasticArmPit project is designing the first machine learning–enabled flexible plastic sensor chip. Its target audience: those who think they might stink. The prototype chip will be manufactured and tested in 2019.