Lifeboat Foundation

Soft robotics have several key advantages over rigid counterparts, including their inherent safety features—soft materials with motions powered by inflating and deflating air chambers can safely be used in fragile environments or in proximity with humans—as well as their flexibility that enables them to fit into tight spaces. Textiles have become a choice material for constructing many types of soft robots, especially wearables, but the traditional “cut and sew” methods of manufacturing have left much to be desired.

Now, researchers at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) have established a new approach for additively manufacturing soft robotics, using a 3D knitting method that can holistically “print” entire soft robots. Their work is reported in Advanced Functional Materials.

Continue reading “Additively manufacturing soft robots could reduce waste, increase performance” »

Time travelers might want to keep this one handy.

The adoption of AI in clinical settings has increased exponentially over the past decade, but AI models still haven’t achieved the level of ubiquity that they could within the sector.

A few years ago, a group of Mayo Clinic researchers recognized this major problem. The health system was producing a huge amount of research on AI in clinical contexts, but it was still having a hard time actually deploying those AI models at scale.

Continue reading “Mayo Clinic-born Startup Scores $7.7M for Its Clinical AI Deployment Platform” »

An official launch date for the new accessibility features is yet to be announced but Apple has confirmed that it will be by the end of 2023.

The eyeglasses, called EchoSpeech, use acoustic-sensing and artificial intelligence to continuously recognize up to 31 unvocalized commands, based on lip and mouth movements.

35-year-old Weibao Wang was charged with stealing Apple’s trade secrets for self-driving cars and fleeing to China. Officials say Wang is still at large and if convicted faces ten years in prison for each trade secret violation. NBC News’ Dana Griffin shares the latest.

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Continue reading “DOJ charges former Apple employee with theft of autonomous car tech for China” »

The threat that technology will replace workers is something more people are grappling with due to the introduction of new tools powered by generative artificial intelligence. Creative workers like artists, writers, and filmmakers are among those raising the loudest alarm. But is their concern warranted? And what impact could AI have on the future workforce?

Join us for the third episode of our series “Artificially Minded” with host Zoe Thomas.

Continue reading “How Generative AI Could Replace Artists in Creative Industries | Tech News Briefing | WSJ” »

AI tools based on artificial neural networks (ANNs) are being introduced in a growing number of settings, helping humans to tackle many problems faster and more efficiently. While most of these algorithms run on conventional digital devices and computers, electronic engineers have been exploring the potential of running them on alternative platforms, such as diffractive optical devices.

A research team led by Prof. Tie Jun Cui at Southeast University in China has recently developed a new programmable neural network based on a so-called spoof surface plasmon polariton (SSPP), which is a surface electromagnetic wave that propagates along planar interfaces. This newly proposed surface plasmonic neural network (SPNN) architecture, introduced in a paper in Nature Electronics, can detect and process microwaves, which could be useful for wireless communication and other technological applications.

“In digital hardware research for the implementation of artificial neural networks, optical neural networks and diffractive deep neural networks recently emerged as promising solutions,” Qian Ma, one of the researchers who carried out the study, told Tech Xplore. “Previous research focusing on optical neural networks showed that simultaneous high-level programmability and nonlinear computing can be difficult to achieve. Therefore, these ONN devices usually have been limited to specific tasks without programmability, or only applied for simple recognition tasks (i.e., linear problems).”

In a paper published in the journal Nature on May 11, researchers at Google Quantum AI announced that they had used one of their superconducting quantum processors to observe the peculiar behavior of non-Abelian anyons for the first time ever. They also demonstrated how this phenomenon could be used to perform quantum computations. Earlier this week the quantum computing company Quantinuum released another study on the topic, complementing Google’s initial discovery. These new results open a new path toward topological quantum computation, in which operations are achieved by winding non-Abelian anyons around each other like strings in a braid.

Google Quantum AI team member and first author of the manuscript, Trond I. Andersen says, “Observing the bizarre behavior of non-Abelian anyons for the first time really highlights the type of exciting phenomena we can now access with quantum computers.”

Imagine you’re shown two identical objects and then asked to close your eyes. Open them again, and you see the same two objects. How can you determine if they have been swapped? Intuition says that if the objects are truly identical, there is no way to tell.