Lifeboat Foundation

Tesla’s progress with artificial intelligence and neural nets has propelled its Autopilot and Full Self Driving solutions to the front of the pack. This is the result of the brilliant work of a large team of Autopilot directors and staff, including Tesla’s Senior Director of AI, Andrej Karpathy. Karpathy presented Tesla’s methods for training its AI at the Scaled ML Conference in February. Along the way, he shared specific insights into Tesla’s methods for achieving the accuracy of traditional laser-based lidar with just a handful of cameras.

The secret sauce in Tesla’s ever-evolving solution is not the cameras themselves, but rather the advanced processing and neural nets they have built to make sense of the wide range and quality of inputs. One new technique Tesla’s AI team has built is called pseudo-lidar. It blends the lines between traditional computer vision and the powerful point map world of lidar.

Traditional lidar-based systems rely on an array of lidar hardware to provide an unparalleled view of the world around the vehicle. These systems leverage invisible lasers or similar tech to send a massive number of pings out into the world to detect surrounding objects.

Robots could soon assist humans in a variety of fields, including in manufacturing and industrial settings. A robotic system that can automatically assemble customized products may be particularly desirable for manufacturers, as it could significantly decrease the time and effort necessary to produce a variety of products.

To work most effectively, such a robot should integrate an assembly planner, a component that plans the sequence of movements and actions that a robot should perform to manufacture a specific product. Developing an assembly planner that can rapidly plan the sequences of movements necessary to produce different customized products, however, has so far proved to be highly challenging.

Continue reading “An algorithm to enhance the robotic assembly of customized products” »

Google announced in late 2010 that for the past year, Californian drivers had been sharing the road with a secret self-driving car project. Many years and one death at the hands of rival Uber later, we can finally see 2009’s testing footage.

Social distancing requires rethinking the layout of workplaces. A new breed of robots can help keep factories and warehouses running.

WASHINGTON — The Defense Advanced Research Projects Agency awarded Lockheed Martin a $5.8 million contract for satellite integration work for the Blackjack program, the company announced April 24.

Blackjack is a project to deploy a constellation of 20 satellites in low Earth orbit by 2022 and demonstrate that a LEO system can provide global high-speed communications.

Lockheed Martin will define and manage interfaces between Blackjack’s satellite buses, payloads and the so-called Pit Boss autonomous data processor. The work will be performed at the company’s satellite manufacturing plant in Sunnyvale, California.

— China revealed the name and logo for its first mission aimed at landing on the planet Mars to mark the 50th anniversary of its first satellite launch.

Celebrating the country’s Space Day on Friday (April 24), the China National Space Administration (CNSA) announced that its upcoming robotic mission to the Red Planet will be named “Tianwen-1.” The name, borrowed from an ancient Chinese verse by poet Qu Yuan, translates to “questions about the heavens.”

“In ‘Tianwen,’ Qu Yuan raised a series of questions in verse involving the sky, stars, natural phenomena, myths and the real world, showing his doubts about some traditional concepts and the spirit of seeking the truth,” reported the state-run Chinese news service Xinhua.

The future looks stealthy and unmanned.

In every hospital labor and delivery department, a resource nurse decides which patients go to which room, which nurses care for which patients, and much more. “It’s a lot to keep in your head,” says Kristen Jerrier, a resource nurse in the labor and delivery department at Beth Israel Deaconess Medical Center in Boston.

The era of telecommunications systems designed solely by humans is coming to an end. From here on, artificial intelligence will play a pivotal role in the design and operation of these systems. The reason is simple: rapidly escalating complexity.

Each new generation of communications system strives to improve coverage areas, bit rates, number of users, and power consumption. But at the same time, the engineering challenges grow more difficult. To keep innovating, engineers have to navigate an increasingly tangled web of technological trade-offs made during previous generations.

In telecommunications, a major source of complexity comes from what we’ll call impairments. Impairments include anything that deteriorates or otherwise interferes with a communications system’s ability to deliver information from point A to point B. Radio hardware itself, for example, impairs signals when it sends or receives them by adding noise. The paths, or channels, that signals travel over to reach their destinations also impair signals. This is true for a wired channel, where a nearby electrical line can cause nasty interference. It’s equally true for wireless channels, where, for example, signals bouncing off and around buildings in an urban area create a noisy, distortive environment.