Lifeboat Foundation

Is this artificial intelligence or a time machine?

Bas Uterwijk, an Amsterdam-based artist, is using AI to create extremely lifelike photographs of historical figures and monuments such as the Statue of Liberty, artist Vincent van Gogh, George Washington and Queen Elizabeth I.

Using a program called Artbreeder, which is described as “deep learning software,” Uterwijk builds his photographs based on a compilation of portraits, reports the Daily Mail. The program pinpoints common facial features and photograph qualities to produce an image.

Last fall, NASA unveiled the new suits that Artemis astronauts will wear when they take humanity’s first steps on the lunar surface for the first time since way back in 1972. The look of the A7LB pressure suit variants that accompanied those earlier astronauts to the Moon, and later to Skylab, has since gone on to signify for many the definitive, iconic symbol of humanity’s most ambitiously-realized space dreams.

With Artemis’ 2024 launch target approaching, NASA’s original Moon suit could soon be supplanted in the minds of a new generation of space dreamers with the xEMU, the first ground-up suit made for exploring the lunar landscape since Apollo 17’s Eugene Cernan and Harrison Schmitt took humanity’s last Moon walk (to date). Unlike those suits, the xEMU’s design is getting an assist from a source of “brain” power that simply wasn’t available back then: artificial intelligence.

Researchers from the University of Warwick, Imperial College London, EPFL (Lausanne) and Sciteb Ltd have found a mathematical means of helping regulators and business manage and police Artificial Intelligence systems’ biases towards making unethical, and potentially very costly and damaging commercial choices—an ethical eye on AI.

In today’s video I want to show you symbiosis of bicycle and walking robotic creature Strandbeest!

If you like this video don’t forget to sucscribe smile

Continue reading “Epic Cycling | Truly Unique Bicycle that Walks” »

U.S. researchers have designed a new robotic system that kills the vast majority of surface microorganisms, using UV light.

Tesla provided the first clues that radar could be trained to do more than just detect objects straight ahead. After the death of Joshua Brown on a Florida highway in 2016, Tesla tore up the Autopilot software created by MobilEye and pivoted from a camera-based to a radar-based system. In the process, it learned how to bounce radar signals under the car directly ahead to “see” what the next car in line was doing. That way, if a truck or SUV is blocking the view of the road ahead, a Tesla with the updated system could still detect if a car further up the road slowed or braked unexpectedly and take appropriate action.

Hyundai unveiled the first look of a vehicle with robotic legs that will let it walk or crawl over treacherous terrain.

Hyundai Elevate can roll along on extended legs or retract them to be driven like a car. Extended legs could also be used to climb or crawl while keeping the passenger compartment level.

Continue reading “Hyundai introduces a car that can climb walls” »

With every droplet that we can’t see, touch, or feel dispersed into the air, the threat of spreading COVID-19 persists. It’s become increasingly critical to keep these heavy droplets from lingering—especially on surfaces, which are welcoming and generous hosts.

Thankfully, our chemical cleaning products are effective, but using them to disinfect larger settings can be expensive, dangerous, and time-consuming. Across the globe there are thousands of warehouses, grocery stores, schools, and other spaces where cleaning workers are at risk.

Continue reading “MIT robot disinfects Greater Boston Food Bank” »

Over the past decade or so, researchers have been trying to develop techniques that could enable effective collaborative strategies among teams of robots. One of the tasks that teams of robots could complete better than individual robots is simultaneously searching for several targets or objects in their surrounding environment.

The ability of a team of robots to collectively seek and identify numerous targets at once could be useful for a wide range of applications. For instance, it could aid surveillance applications and help to better track individuals or vehicles.

Researchers at Tongji University and University of Stuttgart have recently devised a systematic framework for enabling more effective multiple target search in swarm robots. This framework, presented in a paper published in IEEE Access, is based on the use of a mechanical particle swarm optimization method and artificial potential fields.