The last 30 or so years in scholarly communications have been marked by a steady trend towards digital content creation and delivery; particularly for journals, the industry has now moved from print to online. However, online delivery may mask underlying print-based attitudes, as is evidenced from the continuing preference academics have for reading PDF rather than any other format. In this regard, the advent of machine learning, because it involves a semantic engagement with the content, may in the end be more far-reaching, in fact genuinely disruptive, as it looks to challenge existing business processes. Here, for the first time, is a tool that can identify the meaning contained within articles. Machine learning will have an impact throughout the publishing lifecycle, from discovery (the most obvious quick win) to authoring, classification and presentation.

The problem is that robots that are good at one mode of transportation are usually bad at another. Airborne drones are fast and agile, but generally have too limited of a battery life to travel for long distances. Ground vehicles, on the other hand, are more energy efficient, but slower and less mobile. Researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) are aiming to develop robots that can both maneuver around on land and take to the skies. In a new paper, the team presented a system of eight quadcopter drones that can fly and drive through a city-like setting with parking spots, no-fly zones, and landing pads.

Being able to both walk and take flight is typical in nature – many birds, insects and other animals can do both. If we could program robots with similar versatility, it would open up many possibilities: picture machines that could fly into construction areas or disaster zones that aren't near roads, and then be able to squeeze through tight spaces to transport objects or rescue people. The problem is that usually robots that are good at one mode of transportation are, by necessity, bad at another. Drones are fast and agile, but generally have too limited of a battery life to travel for long distances. Ground vehicles, meanwhile, are more energy efficient, but also slower and less mobile.

Gripping, moving, controlling for energy efficiency or functionality – nature shows us how to optimize and adapt to its surrounding environment. Those inherent learnings can be transferred to engineering designs in the disclpline of biomimicry. The suctioned feet of a tree frog trigger a tiny robot designed to stick to tissue once inside your body; domestic house cat's running movements inspire a running platform for robots and fly's ears are the blueprint for a tiny microphone; and, now the elephant in the room, is actually the elephant. Dubbed the Bionic Handling Assistant, Festo, a Germany automation technology with a heavy emphasis on bionic learning, has created a robotic arm based on the flexibility and functionality of the trunk of an elephant. The new Bionic Handling Assistant has basic elements for spatial movement: a hand axis and a gripper with adaptive fingers.

I've been writing marathon-related blog posts for about 2 years now, describing a range of studies on different aspects of marathon running, such as the influence of age, gender, and experience on performance and pacing, and focusing on race-records from a wide range of big-city marathons around the world. To date these studies have focused on analysing marathon data with a view to gaining insights into what has happened in the past; something that is often referred to as descriptive analytics in the world of data science. Recently I have turned my attention to the future, to use this marathon data to gain insights into what might happen in the future -- predictive analytics -- and, in particular to make predictions about the potential of runners to achieve new personal best (PB) finish-times. In fact, what began as a bit of data-fun in my spare-time, has now started to leak into my day-job, and this week I will present a scientific paper based on this prediction work. This is not so unusual. As a Professor in the area of artificial intelligence, machine learning, and recommender systems, a major part of my job involves publishing and presenting research ideas.

Soon robots could learn from each other and work together to complete tasks, say researchers. Artificial intelligence experts at Oxford University have suggested they can teach bots to assess their surroundings and then learn to act on their own. Supported by the Microsoft Research PhD Scholarship Program, the research was showcased at the AI Summit in London recently. The researchers hope their AI technology could make it easier for drones to find and rescue people in remote areas ridden with network connectivity issues. The technology could be used in self-driving cars, as the researchers believe the vehicles could talk to each other about roads and traffic.

Volkswagen is working with Nvidia to expand its usage of its artificial intelligence and deep learning technologies beyond autonomous vehicles and into other areas of business, the two companies revealed today. VW set up its Munich-based data lab in 2014. Last year it pushed on with the hiring of Prof. Patrick van der Smagt to lead a dedicated AI team that is tasked with taking the technology into areas such as'robotic enterprise,' or use of the technology in enterprise settings. VW wants to use AI and deep learning to power new opportunities within its corporate business functions and, more widely, "in the field of mobility services." As an example, the German car-maker said it is working on procedures to help optimize traffic flow in cities and urban areas, while it sees the potential for intelligent human-robot collaboration, too.

With virtually every major car manufacturer dumping R&D money into the field, it looks like autonomous vehicles are going to be a thing whether we want them to or not. Early Tuesday morning NVIDIA and Volvo announced that they are redoubling their self-driving system efforts by teaming with a number of other companies to develop and distribute a proprietary autonomous AI platform by the start of the next decade. First off, Volvo has gone in halfsies with Autoliv to create a new software development subsidiary called Zenuity. Volvo and NVIDIA have announced that they're teaming up with Zenuity to develop the next generation of self-driving vehicle systems which will be built on NVIDIA's Drive PX AI module. This is the same module that Tesla already uses and which both Audi and Toyota have begun developing on.

D. Fox Harrell (fox@csail.mit.edu) is Professor of Digital Media in both the Comparative Media Studies Program and the Computer Science and Artificial Intelligence Laboratory at the Massachusetts Institute of Technology, Cambridge MA, and the founder and director of the Imagination, Computation, and Expression Laboratory. Chong-U Lim (culim@csail.mit.edu) recently completed his Ph.D. in electrical engineering and computer science from the Computer Science and Artificial Intelligence Laboratory at the Massachusetts Institute of Technology, Cambridge MA, where he was a member of the Imagination, Computation, and Expression Laboratory.

How I Built an AI to Sort 2 Tons of Lego Pieces For many years as a child, I did nothing but play with Lego. Eventually I had children of my own, who had a nice Lego collection themselves, but nothing you'd need machinery to sort. That changed after a trip to Legoland in Denmark. I noticed adults at the park buying Lego in vast quantities, despite its high price. Even second-hand Lego isn't cheap, sold as it is by the part on specialized websites, or by the boxed set and in bulk on eBay.