Find of the Day: 23 July 2016

What do you do when you graduate from Ikea? Where should you shop when you’ve outgrown the DIY-attitude of easy assembly and flat-packed transport, when your tastes (and budget) begin to expand, and your eye now better-trained to spot quality and timeless design over what is the obvious, ubiquitous choice?


Inside a secretive AI nonprofit backed by Elon Musk and other Silicon Valley figures, a handful of robots designed to help out in warehouses are gradually learning how to do useful household chores.

OpenAI, which was created to do basic AI research, is reprogramming robots developed by Fetch Robotics, a company that supplies warehouse automation hardware. Researchers at OpenAI are equipping the robots with software that lets them train themselves through trial and error.

The effort reflects a bet that innovations in software and machine learning, rather than breakthroughs in hardware, are the way to give robotics remarkable new capabilities. Fetch makes a range of robots for warehouses, including systems that follow workers around a building, carrying items dropped into a basket. OpenAI is using a system that features a mobile base but also 3-D depth sensors, a 2-D laser scanner, and a robotic arm with seven degrees of freedom.


The 3.5mm headphone jack as we’ve known it for the last 50 years is on the chopping block. Apple is widely expected to ditch the established audio port on this year’s new iPhones, paving way for Lightning port-compatible earbuds and headphones.

In the Android camp, phones like Lenovo’s Moto Z and Moto Z Force and China’s LeEco have already scrapped the 3.5mm headphone jack; to listen to music on the company’s three latest phones, users need to plug in USB Type-C headphones, go wireless, or use a dongle.

Oh, great! Another damn dongle to buy, attach and detach and eventually lose.


How much (and what kind of) control should we relinquish to driverless cars, artificial diagnosticians, or cyber guardians? How should we design appropriate human control into sophisticated AI that requires us to give up some of that very control? Is there some AI that we should just not develop if it means any loss of human control?

How much of a say should corporations, governments, experts or citizens have in these matters? These important questions, and many others like them, have emerged in response, but remain unanswered. They require human, not human-like, solutions.

Answers to these questions will also require input from the right mix of humans and AI researchers alone can only hope to contribute partial solutions. As we’ve learned throughout history, scientific and technical solutions don’t necessarily translate into moral victories.

Alpha go

Three months ago, 25-year-old Alexey Moiseenkov was working at Russian internet giant Mail.Ru when he had the idea to create an app that could stylise any photo at the tap of a button. Unlike other photo-editing tools that manipulate the image itself, Moiseenkov’s idea was to use deep learning to create totally new images out of people’s photos.

A piece of open source code sparked the idea, which Moiseenkov and his colleague adapted to produce works of art in seconds. The result was Prisma, an app that uses machine learning to transform pictures into works of art.



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