Google announced in a blog post on Thursday that it has set up a new AI research group in Europe to focus on machine learning (ML). Machine learning is a field of computer science that gives computers the ability to learn without being explicitly programmed. Google Research, Europe -- as the group is known -- is based out of Google's office in Zurich, Switzerland, which is home to Google's largest engineering office outside the US. Google said the group, which is expected to grow to over 100 people in the coming years, will focus on three key areas: machine intelligence, natural language processing and understanding, and machine perception. Companies like Amazon, Facebook, and Microsoft are all investing heavily in these areas as they look to make their platforms and services more intelligent.

What do you get when you cross self-driving artificial intelligence, 3-D printing, and public transportation? Local Motors, a manufacturer known for its focus on open-source vehicle designs, unveiled Olli on Thursday at its new facility in National Harbor, Md., a development just outside Washington D.C. To test Olli, Local Motors plans to offer free rides to the public around the development in what is believed to be the first public trial of a completely self-driving vehicle in the United States, reported The Washington Post. In February, the Netherlands launched a fleet of WEpod driverless buses, which can carry six passengers, on the campus of Wageningen University in a central Dutch agricultural town. Miami-Dade County has bought two Olli shuttles, and Las Vegas has bought one.

Image recognition, determining all the objects within a photo, is something Facebook's AI does with relative ease. The company's approach to machine learning is called deep learning, a popular route to AI also followed by Google and others. Deep learning employs algorithms to recognize patterns, learn from those patterns and complete sophisticated tasks. For Facebook, it could be tagging friends. For Google, it may be creating a program that plays the game Go well enough to beat human champions.

Companies like Clearpath, Fetch, and Locus Robotics are doing some amazing work in order fulfillment and other warehouse tasks by developing mobile platforms that can autonomously and intelligently ferry items between locations. We don't want to minimize how much of a challenge this is, but at the same time, it's only half of the order fulfillment problem (and not the most difficult half). The hard part is getting those robots to pick items from shelves, and apparently it's really hard: Amazon (whose warehouse robots are capable of tranporting items but not picking them) is holding its second Picking Challenge at RoboCup this year, and even with teams of researchers all collaborating on picking tasks with very expensive robots, results have been good but not inspiring. German startup Magazino is another company trying to solve both problems. It has begun deploying a mobile warehouse robot called Toru designed to not only transport items but also pick them (some of them, anyway) directly off of shelves. They haven't completely solved the problem of humans, but it's a step in the right direction.

Google is diving deeper into artificial intelligence, with the company opening a dedicated machine learning research center in its Zurich office, the search company announced on Thursday, June 16. The Google Research Europe center will focus on three areas: Machine intelligence, natural language processing, and understanding and machine perception. The research center aims to deliver machine learning that can be put into practical use, to improve the machine learning infrastructure, and to assist the research community overall. "Google's ongoing research in machine intelligence is what powers many of the products being used by hundreds of millions of people a day -- from Translate to Photo Search to Smart Reply for Inbox," Emmanuel Mogenet, head of Google Research Europe, wrote in the blog post announcing the center. Mogenet noted machine learning software engineers and researchers will be able to develop products and conduct research at the Zurich center, which also holds the largest Google engineering office outside of the US.

High-Performance and Tunable Stereo Reconstruction (ICRA '16) In this work, we propose a high-performance and tunable stereo disparity estimation method, with a peak frame-rate of 120Hz (VGA resolution, on a single CPU-thread), that can potentially enable robots to quickly reconstruct their immediate surroundings and maneuver at high-speeds. Published in ICRA '16, Stockholm, Sweden.

The traditional definition of artificial intelligence is the ability of machines to execute tasks and solve problems in ways normally attributed to humans. Some tasks that we consider simple--recognizing an object in a photo, driving a car--are incredibly complex for AI. Machines can surpass us when it comes to things like playing chess, but those machines are limited by the manual nature of their programming; a 30 gadget can beat us at a board game, but it can't do--or learn to do--anything else. This is where machine learning comes in. Show millions of cat photos to a machine, and it will hone its algorithms to improve at recognizing pictures of cats.

I visited my local health-insurance office a few months ago. After entering the building, I was welcomed into a long and dark corridor, full of nervous people carrying bloated folders. The atmosphere was gloomy, and it was obvious that no one wanted to be there. After about 30 minutes I realized why: During that time, the line had barely moved, and it took me the better part of the day to reach a clerk. As a result, I was late for two other errands I had planned.

Following on from my recent article, Artificial Intelligence and Real Estate: Can We Automate the Industry?, I've been talking to Angela Meadows, Senior VP of Client Development at Humley. They are one of the fastest-growing tech companies in the UK, concerned with harnessing cognitive computing to empower companies to access and implement an artificial intelligence platform within their business, whether this is to transform their customer service tools or business processes, or simply to enable the end-user to find and action solutions for themselves without the need or cost of human intervention. Historically, Humley began by creating experiences for network operators and handset manufacturers that allowed them to have on-device, ongoing, long term relationships with their customers via real-time communication personalised to the customer's context. The experience would help the customer easily set up their device (reducing customer service calls, thus reducing costs for the brand), as well as discover new exciting features about their device and/or network. They have now combined their contextual targeting with elements of artificial intelligence (powered by IBM Watson), that enables the end-user to ask questions in natural language and receive relevant answers, based on their individual context.

When MIT Computer Science and Artificial Intelligence Lab researchers showed videos of a drumstick hitting and brushing through various objects, subjects were fooled into believing that the sounds they heard actually came from the objects and materials on screen. Instead, a computer programmed to analyze the video and apply the correct sounds from its own library of samples chose the audio clips for all the videos. And the subjects were none the wiser. The team's work is described in a new paper released Monday and being presented next week at the Computer Vision and Pattern Recognition conference in Las Vegas. To be clear, there really isn't any such thing as an Auditory Turing test.