Lifeboat Foundation

How can mobile robots perceive and understand the environment correctly, even if parts of the environment are occluded by other objects? This is a key question that must be solved for self-driving vehicles to safely navigate in large crowded cities. While humans can imagine complete physical structures of objects even when they are partially occluded, existing artificial intelligence (AI) algorithms that enable robots and self-driving vehicles to perceive their environment do not have this capability.

Robots with AI can already find their way around and navigate on their own once they have learned what their environment looks like. However, perceiving the entire structure of objects when they are partially hidden, such as people in crowds or vehicles in traffic jams, has been a significant challenge. A major step towards solving this problem has now been taken by Freiburg robotics researchers Prof. Dr. Abhinav Valada and Ph.D. student Rohit Mohan from the Robot Learning Lab at the University of Freiburg, which they have presented in two joint publications.

The two Freiburg scientists have developed the amodal panoptic segmentation task and demonstrated its feasibility using novel AI approaches. Until now, self-driving vehicles have used panoptic segmentation to understand their surroundings.

AN ARTIFICIAL intelligence text-to-image model has forecasted a disturbing end to mankind’s existence.

The popular Craiyon AI, formerly DALL-E mini AI image generator, designed some barren landscapes and scorched plains when prompted to predict the end of humans.

The AI has been trained to create its masterpieces using unfiltered data from the internet.

ROBOTS could one day overthrow humans in an ‘apocalyptic’ takeover, a tech expert has predicted.

Aidan Meller, the creator of the Ai-Da robot, believes that within three years artificial intelligence (AI) could overtake humanity, per The Daily Star.

He also backs Elon Musk’s belief that advances in AI could impact mankind more than nuclear war.

Being able to decode brainwaves could help patients who have lost the ability to speak to communicate again, and could ultimately provide novel ways for humans to interact with computers. Now Meta researchers have shown they can tell what words someone is hearing using recordings from non-invasive brain scans.

Our ability to probe human brain activity has improved significantly in recent decades as scientists have developed a variety of brain-computer interface (BCI) technologies that can provide a window into our thoughts and intentions.

The most impressive results have come from invasive recording devices, which implant electrodes directly into the brain’s gray matter, combined with AI that can learn to interpret brain signals. In recent years, this has made it possible to decode complete sentences from someone’s neural activity with 97 percent accuracy, and translate attempted handwriting movements directly into text at speeds comparable to texting.

Superintelligent AI is “likely” to cause an existential catastrophe for humanity, according to a new paper, but we don’t have to wait to rein in algorithms.

THE artificial intelligence revolution has only just begun, but there have already been numerous unsettling developments.

AI programs can be used to act on humans’ worst instincts or achieve humans’ more wicked goals, like creating weapons or terrifying its creators with a lack of morality.

Artificial intelligence is a catch-all phrase for a computer program designed to simulate, mimic or copy human thinking processes.

A team of researchers at University College London, working with a colleague from Nylers Ltd. and another from XPCI Technology Ltd., has developed a new way to X-ray luggage to detect small amounts of explosives. In their paper published in the journal Nature Communications, the group describes modifying a traditional X-ray device and applying a deep-learning application to better detect explosive materials in luggage.

Prior research has shown that when X-rays strike materials, they produce tiny bends that vary depending on the type of material. They sought to take advantage of these bends to create a precision X-ray machine.

The researchers first added a small change to an existing X-ray machine—a box containing masks, which are sheets of metal with tiny holes in them. The masks serve to split the X-ray beam into multiple smaller beams. The researchers then used the device to scan a variety of objects containing embedded explosive materials and fed the results to a deep-learning AI application. The idea was to teach the machine what the tiny bends in such materials looked like. Once the machine was trained, they used it to scan other objects with embedded explosives to see if it could identify them. The researchers found their machine to be 100% accurate under lab settings.

In the Providentia++ project, researchers at the Technical University of Munich (TUM) have worked with industry partners to develop a technology to complement the vehicle perspective based on onboard sensor input with a bird’s-eye view of traffic conditions. This improves road safety, including for autonomous driving.

The expectations for autonomous driving are clear: “Cars have to travel safely not only at low speeds, but also in fast-moving traffic,” says Jörg Schrepfer, the head of Driving Advanced Research Germany at Valeo. For example, when objects fall off a truck, the “egocentric” perspective of a car will often be unable to detect the hazardous debris in time. “In these cases, it will be difficult to execute smooth evasive action,” says Schrepfer.

Researchers in the Providentia++ project have developed a system to transmit an additional view of the traffic situation into vehicles. “Using sensors on overhead sign bridges and masts, we have created a reliable, real-time digital twin of the traffic situation on our test route that functions around the clock,” says Prof. Alois Knoll, project lead manager TUM. “With this system, we can now complement the vehicle’s view with an external perspective—a bird’s-eye view—and incorporate the behavior of other road users into decisions.”

University of Portsmouth joins leading AI researchers at DeepMind to help engineer faster acting enzymes for recycling some of the worlds most polluting single use plastics.

The University’s Centre for Enzyme Innovation (CEI) has used DeepMind’s ground-breaking AI system to make strides in their research on circular recycling.