A partial digital reconstruction of the brain previously made by Harvard. We've talked a lot about making a computer that works like the mammalian brain. The U.S. government is now betting $28 million dollars that all these projects are wrong. A series of three grants snagged by Harvard University from Intelligence Advanced Research Projects Activity (IARPA) last week has funded a "moonshot" project to throw out all of the previous attempts at understanding the brain and start fresh. But while DARPA focuses on military projects, IARPA focuses on intelligence agency research.)

If the above gif looks familiar it's probably because it looks eerily similar to this: This, of course, is how the T-800 Terminator sees and recognizes objects in the world upon arrival from the future in Terminator 2: Judgement Day. Similar to the movie, researchers at MIT's Computer Science and Artificial Intelligence Laboratory have created an object recognition system that can accurately identify objects using a normal RGB camera (no threatening blood-red color filter required). This system could help future robots interact with objects more efficiently while they navigate our complex world. "Ideally we want robots to be cleaning our dishes at some point in the future. We want recognition systems where it does in fact see the objects that the robot should care about and manipulate them," says Sudeep Pillai, lead author of the study.

Two rhinos at the Kuzikus Nature Reserve in Namibia, photographed by drone. When the U.S. military needed to identify mines in a dangerous valley in Afghanistan, aerial-imagery specialist Tudor Thomas helped build a plane-based system to map it. Back in 2013, similar systems cost the military and its contractors one to five million dollars, Thomas says--and that didn't even include the cost of the plane. "It's hard to comprehend how much was getting spent just to make a simple aerial image," he says. The experience sparked an idea for a business: mapping by drone.

Bonirob is more than 90 percent effective in destroying weeds in carrot cultivation trials. While the world's first fully-robotic farm will operate indoors, traditional outdoor farms aren't immune to the coming robotic revolution. Bonirob, developed by Bosch's Deepfield Robotics, is billed to eliminate some of the most tedious tasks in modern farming, plant breeding, and weeding. The autonomous robot is built to be a mobile plant lab, able to decide which strains of plant are most apt to survive insects and viruses and how much fertilizer they would need, and then smash any weeds with a ramming rod. Bonirob employs a type of machine learning (a stab at artificial intelligence) called decision tree learning. Researchers show Bonirob lots of pictures of healthy leaves that are tagged to be good, and pictures of weeds that are tagged to be bad, and the machine makes a series of choices based on observed in new data to judge whether a plant in the field is good or bad.

If your conversations with digital personal assistants like Apple's Siri, Microsoft's Cortana, or Google Now haven't been useful enough, a new challenger of Biblical proportions is about to arrive. Gabriel, a project by researchers at Carnegie Mellon University, and funded by the National Science Foundation, is a personal cognitive assistant that "whispers" instructions into a user's ear, for things like how to change a tire, perform CPR, or even assemble IKEA furniture. It would be like GPS for everyday actions, but one that knows when to shut up, according to principal investigator Mahadev Satyanarayanan. The name comes from the angel Gabriel, who Biblically served as the messenger of God. CMU's Gabriel is just a software platform, though.

Trails are narrow ribbons of civilization cutting through wilderness. They are as much about what is worth exploring as they are about what's off limits. A hiker loses a trail, and suddenly they're in a deep wilderness, unmoored from the world until they stumble back to that thin filament again. To find missing hikers, it makes sense to look near trails, and to do that, a team at the University of Zurich is training drones to identify and follow trails into the woods. The drone used by the Swiss researchers observes the environment through a pair of small cameras, similar to those used in smartphones.

Denton initially shod the Mantis in modified go-kart tires. "They worked out really well," he notes, "but they weren't very grippy." So he fabricated custom rubber feet, modeling the hexagonal pattern after off-road tires. Now he alternates shoes based on the terrain. In 2007, Matt Denton stopped on the side of the road near his home in Hampshire, England, to watch an excavator dig.

That's one of the questions animating the field of computational creativity, which seeks to design artificial intelligence that can replicate human creativity. We wrote recently about a Google effort to create algorithms that make original music. But what if artificial intelligence could design and make 3D objects you could actually hold in your hand? That was the challenge that Joel Lehman, an assistant professor at the IT University of Copenhagen, set out to tackle. Lehman wondered if he could somehow leverage the remarkable image recognition power of deep neural networks (DNN) to create new artifacts without human input.

You can lob all of these things at a newly developed robotic arm, and it will catch them. The robotic arm was developed by researchers from École Polytechnique Fédérale de Lausanne (EPFL) in Switzerland, who "taught" the device how to catch by throwing objects at it, and then manually guiding the arm toward them. This allowed the robot to develop its own models of objects' trajectories and how to catch them, a type of "programming by demonstration." Both the robotic arm's hardware and learning abilities are new, although they have been in development for the past couple years. It works like this: several cameras allow the robot to see what is coming its way, and based on its "experience" the arm's onboard computer produces a mathematical model to represent the objects course.

As micro-molding gives way to "nano-molding," processors will need creative answers to the problems of handling flyspeck-sized parts. Farms may replace oil wells as the source of new plastics. Biopolymers made from cornstarch or other renewable feedstocks will supple-ment petrochemical-derived polymers in a wide range of applications. What if you could change the color of every part right at the machine? Instant color changes may be part of the coming era of "mass customization." New methods of polymer production will allow custom materials to be "programmed" for individual applications. Say Hello to Nano Molding The new frontier of injection molding is "shrinking," says Carl Schiffer, managing partner at Dr. Boy GmbH in Germany. Miniaturization in electronic and medical parts will help push today's micro-molding toward "nano"-size parts. Machinery will need to evolve to meet the "nano" challenge. Shot sizes must become smaller, and screw diameters are already shrinking from the standard lower limit of 14 mm.