The idea is to bring internet tracking cookies to the real world. The bins record a unique identification number, known as a MAC address, for any nearby phones and other devices that have Wi-Fi turned on. That allows Renew to identify if the person walking by is the same one from yesterday, even her specific route down the street and how fast she is walking.
“We’re trying to approach travel guides from an algorithmic, Google-like perspective,” Triposo co-founder and COO Richard Osinga tells Wired. Triposo uses what it calls an “opinion mining” algorithm. The company analyzes the natural language used in online reviews to determine whether people who have posted about a particular place liked it, and what exactly they liked about it. This helps the app suggest places for very specific qualities — like a restaurant that has spectacular Bolognese, or a hotel that is especially clean.
It also uses the time of day, your GPS location, and local weather to suggest things to see and do while you’re traveling. That, paired with analysis of the behaviors and opinions of other users, lets Triposo figure out what activities people are most likely to be interested in at a certain time — you’re probably not looking for a history museum at 2am in in Paris — and how far they are willing to travel to do that. This means you can nix all the planning you’d normally stress about before a vacation, and be confident that you’ll find unique, interesting attractions no matter what part of town you’re wandering around.
This is the disturbing thought that, sluggish business cycles aside, America’s current employment woes stem from a precipitous and permanent change caused by not too little technological progress, but too much. The evidence is irrefutable that computerised automation, networks and artificial intelligence (AI)—including machine-learning, language-translation, and speech- and pattern-recognition software—are beginning to render many jobs simply obsolete.
This is unlike the job destruction and creation that has taken place continuously since the beginning of the Industrial Revolution, as machines gradually replaced the muscle-power of human labourers and horses. Today, automation is having an impact not just on routine work, but on cognitive and even creative tasks as well. A tipping point seems to have been reached, at which AI-based automation threatens to supplant the brain-power of large swathes of middle-income employees.
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Radiologists, who can earn over $300,000 a year in America, after 13 years of college education and internship, are among the first to feel the heat. It is not just that the task of scanning tumour slides and X-ray pictures is being outsourced to Indian laboratories, where the job is done for a tenth of the cost. The real threat is that the latest automated pattern-recognition software can do much of the work for less than a hundredth of it.
Researchers Michael Anderson from the University of Hartford and Susan Leigh Anderson from the University of Connecticut have developed an approach to computing ethics that entails the discovery of ethical principles through machine learning and the incorporation of these principles into a system’s decision procedure. They’ve programmed their system into the robot NAO, manufactured by Aldebaran Roboticstarget blank image.. It is the first robot to have been programmed with an ethical principle.