If they come bearing hot French fries and gooey pizza, I, for one, will welcome our new robot overlords with open arms. The artificial intelligence revolution is one step closer to that reality in Europe, where a food delivery service, a package delivery company, and a retail chain are testing out autonomous robot couriers. Food delivery companies Just Eat and Pronto will be trying out the self-driving robots in London, while Germain retail chain Metro AG and German parcel delivery company Hermes will test them out in Dusseldorf, Germany, and Bern, Switzlerland, as well as another undisclosed German city, says the robot's maker, Starship Technologies. Starship Technologies, a London-based company started by two Skype co-founders, has been testing the so-called "ground drones" in Europe over the last nine months, but these trials will mark the first time businesses will be using the technology to deliver real orders to paying customers, Allan Martinson, Starship's chief operating officer tells Bloomberg. Each company's trial will involve anywhere between five and 10 robots in one or two areas of each city.
Update: Machine Learning is Fun! Part 3 is now available! In Part 1, we said that Machine Learning is using generic algorithms to tell you something interesting about your data without writing any code specific to the problem you are solving. This time, we are going to see one of these generic algorithms do something really cool -- create video game levels that look like they were made by humans. We'll build a neural network, feed it existing Super Mario levels and watch new ones pop out!
Machine learning is a branch of artificial intelligence concerned with the design of data-driven programs which autonomously demonstrate intelligent behavior in a variety of domains. Machine learning systems are all around us. When you deposit a check, scan your fingerprint, or post a picture on social media, autonomous algorithms are deployed on the spot to sift through and make sense of your constant interactions with our technology. Machine learning silently underpins the fabric of our digital infrastructure, discriminating spam e-mail and banking fraud, making light-speed transactions in the global financial market, recommending music and films for customers to buy, deciding what search results are relevant to your queries, and countless more of the daily interactions with electronic media that we take for granted. Machine learning is the backbone that powers self-driving cars, content recommendation in social media, face identification in digital forensics, and countless other high-level tasks.
Given access to your Google calendar, a personal assistant named Amy will happily schedule all your appointments. Manoush Zomorodi and Alex Goldmark of the WNYC podcast New Tech City put Amy's skills to the test, and they're here to talk about what worked, what didn't, as well as the inevitable awkwardness of life with a bot.
Between the Terminator movies, Ava from'Ex Machina', Google's AlphaGo beating the world's best human Go players, machines mimicking Rembrandt's style to paint portraits, debates about morality and privacy, and Stephen Hawking's warnings about the consequences of intelligent machines manned by idiot humans; it's no wonder that Artificial Intelligence (AI) has already made people a little uncomfortable. In the marketing world, it's caused the customary outbreak of confusion. Thus resulting in a series of obtuse declarations like "AI will change everything. EVERYTHING!" that are typical of the industry. Any time anything new threatens to upset business as usual, comrades in the marketing and advertising industry especially lunge headlong into an existential crisis.
As computers around the world get smarter and more powerful, internet heavyweight Google is determined to stay ahead of the game. The company said on Wednesday that it is buying a French artificial intelligence company called Moodstocks that specializes in image recognition. Moodstocks focuses on building image-recognition capabilities into phones, and first introduced software that could do this back in 2012. Over the past two and a half years it has extended its reach into physical object recognition, the company says on its website. Joining forces with Google, Moodstocks said, will allow the company "to deploy our work at scale" and "build great image recognition tools within Google."
Tesla Motors's statement last week disclosing the first fatal crash involving its Autopilot automated driving feature opened not with condolences but with statistics. Autopilot's first fatality came after the system had driven people over 130 million miles, the company said, more than the 94 million miles on average between fatalities on U.S. roads as a whole. Soon after, Tesla's CEO and cofounder Elon Musk threw out more figures intended to prove Autopilot's worth in a tetchy e-mail to Fortune (first disclosed yesterday). "If anyone bothered to do the math (obviously, you did not) they would realize that of the over 1M auto deaths per year worldwide, approximately half a million people would have been saved if the Tesla autopilot was universally available," he wrote. Tesla and Musk's message is clear: the data proves Autopilot is much safer than human drivers.