Lifeboat Foundation

Over the last three decades, the digital world that we access through smartphones and computers has grown so rich and detailed that much of our physical world has a corresponding life in this digital reality. Today, the physical and digital realities are on a steady course to merging, as robots, Augmented Reality (AR) and wearable digital devices enter our physical world, and physical items get their digital twin computer representations in the digital world.

These digital twins can be uniquely identified and protected from manipulation thanks to crypto technologies like blockchains. The trust that these technologies provide is extremely powerful, helping to fight counterfeiting, increase supply chain transparency, and enable the circular economy. However, a weak point is that there is no versatile and generally applicable identifier of physical items that is as trustworthy as a blockchain. This breaks the connection between the physical and digital twins and therefore limits the potential of technical solutions.

In a new paper published in Light: Science & Applications, an interdisciplinary team of scientists led by Professors Jan Lagerwall (physics) and Holger Voos (robotics) from the University of Luxembourg, Luxembourg, and Prof. Mathew Schwartz (architecture, construction of the built environment) from the New Jersey Institute of Technology, U.S., propose an innovative solution to this problem where physical items are given unique and unclonable fingerprints realized using cholesteric spherical reflectors, or CSRs for short.

For decades researchers have worked to design robotic hands that mimic the dexterity of human hands in the ways they grasp and manipulate objects. However, these earlier robotic hands have not been able to withstand the physical impacts that can occur in unstructured environments. A research team has now developed a compact robotic finger for dexterous hands, while also being capable of withstanding physical impacts in its working environment.

The team of researchers from Harbin University of Technology (China) published their work in the journal Frontiers of Mechanical Engineering on October 14, 2022.

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Telling artificial intelligence models to “think” step by step when carrying out a task can improve their performance so much that they can outperform humans at jobs AIs usually struggle with.

Researchers have developed a hackable and multi-functional 3D printer for soft materials that is affordable and open design. The technology has the potential to unlock further innovation in diverse fields, such as tissue engineering, soft robotics, food, and eco-friendly material processing—aiding the creation of unprecedented designs.

The findings could help pave the way for greater use of machine learning in materials science, a field that still relies heavily on laboratory experimentation. Also, the technique of using machine learning to make predictions that are then checked in the lab could be adapted for discovery in other fields, such as chemistry and physics, say experts in materials science.

To understand why it’s a significant development, it’s worth looking at the traditional way new compounds are usually created, says Michael Titus, an assistant professor of materials engineering at Purdue University, who was not involved in the research. The process of tinkering in the lab is painstaking and inefficient.

New research from Carnegie Mellon University’s Robotics Institute can help robots feel layers of cloth rather than relying on computer vision tools to only see it. The work could allow robots to assist people with household tasks like folding laundry.

Humans use their senses of sight and touch to grab a glass or pick up a piece of cloth. It is so routine that little thought goes into it. For robots, however, these tasks are extremely difficult. The amount of data gathered through touch is hard to quantify and the sense has been hard to simulate in robotics—until recently.

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Berkeley Lab scientists have developed new machine learning algorithms to accelerate the analysis of data collected decades ago by HERA, the world’s most powerful electron-proton collider that ran at the DESY national research center in Germany from 1992 to 2007.

The stock photography company will incorporate AI-generated content into its website using Open-AI’s DALL-E image generator.

Shutterstock recently announced that it will partner with OpenAI to start selling content created using artificial intelligence software.

Igor Kutyaev/iStock.

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The company that will work with US Space Force has also won some NASA contracts.

It’s official: robots are here to stay in space. Robotics software and engineering company PickNik Robotics announced on Tuesday that it has won a SpaceWERX contract to work on robotics for the US Space Force, according to a press release acquired by IE

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