A touch-sensitive robotic finger that can be attached to smartphones has been developed by a researcher in France.

If your robotics lab has a quadruped, it's become almost a requirement that you post a video of the robot not falling over when walking across some kind of particularly challenging surface. And quadrupeds are getting quite good at keeping their feet, even while negotiating uneven terrain like steps or rubble. One way to do this is without any visual perception at all, simply reacting to obstacles "blindly" by positioning legs and feet to keep the body of the robot upright and moving in the right direction. This can work for terrain that's continuous, but when you start looking at more dangerous situations like gaps that a robot's leg could get stuck in, being able to use vision to plan a safe path becomes necessary. Vision, though, is a real bag of worms, kettle of fish, bushel of geese, or whatever your own favorite tricky metaphor is.

The Indian government plans to decongest its airports by introducing facial recognition technology next year - a proposal that may once again raise privacy concerns in the South Asian country. India's ministry of civil aviation on Thursday said passengers on domestic flights will be able to choose to use their biometric authentication system and go paperless. "Security will benefit from the ability of the technology to verify the passenger at every checkpoint in a non-intrusive way," ministry secretary Rajiv Nayan Choubey said in a statement. The proposal says passengers would be verified by being photographed at every stage of the check-in process - from entering the airport to proceeding through security and boarding the plane. The India government statement said the biometric technology will be introduced first at Bengaluru and Hyderabad airports by February next year, followed by Kolkata, Varanasi, Pune and Vijayawada by April.

Cruise, the self-driving car arm of General Motors, has an unexpected new ally in its bid to keep its corporate master at the forefront of an industry enduring its greatest period of change in generations: Honda. In a deal announced today, the Japanese automaker will help San Francisco-based Cruise and its Detroit owner develop and mass produce a new sort vehicle for a world in which human drivers are no longer needed. Honda is opening its checkbook too, pledging to spend $2 billion on the project over 12 years, and immediately putting a $750 million equity investment into Cruise. For Honda, the partnership offers entree into a self-driving space where it has thus far spent little time and effort. For Cruise and GM, the newcomer adds engineering know-how, especially with regard to interior design.

Self-driving car company Cruise Automation is rushing to create a new autonomous vehicle with the help of one of the largest names in the automotive industry. Honda said it will invest $2.75 billion into Cruise's autonomous vehicle operations over the next 12 years, an infusion that arrives several months after the Japanese firm SoftBank announced a $2.25 billion investment in the company. Both investments bring the four-year-old company's valuation to a whopping $14.6 billion, General Motors said in a news release Wednesday. Cruise Automation -- which is already building a fleet of autonomous vehicles that could hit American streets as early as next year -- is a subsidiary of GM. The Detroit automaker's stock was up nearly 2 percent Wednesday.

This week, the US Defense Advanced Research Projects Agency announced a challenge to push the limits of robotic design and control. DARPA's Subterranean Challenge will require teams to have robots maneuver objects through three different environments: a series of caves, a bunker-like "urban environment," and a labyrinth of confined tunnels. While the robots will be remote-controlled, they'll need some serious autonomous skills. They will need to rapidly map and explore unfamiliar environments even when communications are spotty and conditions are challenging for sensors. The teams will be allowed to use as many different types of robot as they like, but this will mean dealing with greater complexity in communications and coordination.

A screen shows a demonstration of the cognitive level of a facial recognition software at the Ericsson AB booth at the Mobile World Congress Shanghai in Shanghai, China, on Thursday, June 28, 2018. The global videos surveillance market is expected to post a compound annual growth rate of close to 11% during the period 2018-2022 according to Technavio. The potential benefits of leveraging artificial intelligence (A) in the physical security industry have pros and cons on both sides, but the debate over the ethical ways to leverage AI and surveillance continues as more and more surveillance systems are getting the brains to match what they see. AI startups like Boulder AI, which offers a vision-as-a-service and IC Realtime, which lets you search and analyze your video feeds from CCTV system; are gaining traction. Alongside the Chinese facial recognition startups like Megvii's Face with $600 million in private equity; SenseTime with $62o million from a series C; and, Yitu Technology with $300 million from a series C, the potential uses of facial recognition technology are well funded.

Local authorities have established areas around the airport that are safe to fly for drone operations and qualify for automatic authorization,

Proponents of 5G say it will offer ultra-fast connections, speedier data downloads, and be able to handle millions more connections than 4G mobile networks can cope with today. One use for 5G is self-driving cars, but will they really need it? The telecoms industry envisions autonomous cars equipped with hundreds of sensors collecting and receiving information all at once over a network. It calls this concept "Vehicle-to-everything" (V2X). To achieve this, the car needs to detect blind spots and avoid collisions with people, animals or other vehicles on the road.

Supervised machine-learning models boast remarkable predictive capabilities. But can you trust your model? Will it work in deployment? What else can it tell you about the world? Models should be not only good, but also interpretable, yet the task of interpretation appears underspecified.