Lifeboat Foundation: Safeguarding Humanity
Toggle light / dark theme

Blog

Category: robotics/AI

First published in 2016, predictors of chronological and biological age developed using deep learning (DL) are rapidly gaining popularity in the aging research community.

These deep aging clocks can be used in a broad range of applications in the pharmaceutical industry, spanning target identification, drug discovery, data economics, and synthetic patient data generation. We provide here a brief overview of recent advances in this important subset, or perhaps superset, of aging clocks that have been developed using artificial intelligence (AI).

Read more | >

DARPA’s Ground X-Vehicle Technologies (GXV-T) program aims to improve mobility, survivability, safety, and effectiveness of future combat vehicles without piling on armor. The demonstrations featured here show progress on technologies for traveling quickly over varied terrain and improving situational awareness and ease of operation.

These demonstrations feature technologies developed for DARPA by:

1) carnegie mellon university, national robotics engineering center. 2) honeywell international 3) pratt & miller 4) qinetiq 5) raytheon BBN technologies

Read more | >

Well, each to his own taste. Kittens making friends with balls of yarn are absolute magnets for video surfers but a rival army of video clicksters can never max out staring at humanoids navigating where they want to go.

The latest video showcasing robots on the move is impressing viewers with the deft and successful way they are navigating a cinder block maze.

The video on the latter is IHMC, the Institute for Human and Machine Cognition (IHMC).

Read more | >

NASA has awarded Carnegie Mellon University (CMU) and Astrobotic a US$5.6 million contract to build a new suitcase-sized robotic lunar rover that could land on the Moon as soon as 2021. One of 12 proposals selected as part of the agency’s Lunar Surface and Instrumentation and Technology Payload (LSITP) program, the 24-lb (11-kg) MoonRanger rover is designed to operate autonomously on week-long missions within 0.6 mi (1 km) of its lander.

Read more | >

The Defense Advanced Research Projects Agency made headlines last fall when it announced that it was pledging $2 billion for a multi-year effort to develop new artificial intelligence technology.

Months later, DARPA’s “AI Next” program is already bearing fruit, said Peter Highnam, the agency’s deputy director.

DARPA — which has for decades fostered some of the Pentagon’s most cutting-edge capabilities — breaks down AI technology development into three distinct waves, he said during a meeting with reporters in Washington, D.C.

Read more | >

Seemingly “intelligent” devices like self-driving trucks aren’t actually all that intelligent. In order to avoid plowing into other cars or making illegal lane changes, they need a lot of help.

In China, that help is increasingly coming from rooms full of college students.

Li Zhenwei is a data labeler. His job, which didn’t even exist a few years ago, involves sitting at a computer, clicking frame-by-frame through endless hours of dashcam footage, and drawing lines over each photo to help the computer recognize lane markers.

“Every good-looking field has people working behind the scenes,” says Li. “I’d prefer to be an anonymous hero.”

Li, and many of his classmates at a local vocational school, are benefiting from the Chinese government’s push to move away from an economy based on heavy industry, and toward one focused on high tech.

Li doesn’t have a degree in computer science. So for him, this is a new opportunity to get a foot in the door of a booming tech industry.

Read more | >

It’s difficult to simulate quantum physics, as the computing demand grows exponentially the more complex the quantum system gets — even a supercomputer might not be enough. AI might come to the rescue, though. Researchers have developed a computational method that uses neural networks to simulate quantum systems of “considerable” size, no matter what the geometry. To put it relatively simply, the team combines familiar methods of studying quantum systems (such as Monte Carlo random sampling) with a neural network that can simultaneously represent many quantum states.

Read more | >