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Category: robotics/AI – Page 1,673

Decoding brain signals to control a robotic arm

Researchers have developed a mind-reading system for decoding neural signals from the brain during arm movement. The method, described in the journal Applied Soft Computing, can be used by a person to control a robotic arm through a brain-machine interface (BMI).

A BMI is a device that translates into commands to control a machine, such as a computer or a robotic limb. There are two main techniques for monitoring neural signals in BMIs: electroencephalography (EEG) and electrocorticography (ECoG).

The EEG exhibits signals from on the surface of the scalp and is widely employed because it is non-invasive, relatively cheap, safe and easy to use. However, the EEG has low spatial resolution and detects irrelevant neural signals, which makes it difficult to interpret the intentions of individuals from the EEG.

NexStem Announces the General Availability of Its Award-winning BCI Headsets

NexStem, a MedTech and robotics startup that creates non-invasive robotic solutions controlled exclusively by a user’s thoughts, today announced the finalization of its latest round of funding and the general availability of its NexStem Headsets and Wisdom-SDK (software development kit). This pioneer in the development of advanced end-to-end Brain-Computer Interfaces (BCIs) devices and applications, has cracked the code on improving the quality of the electroencephalography (EEG) signals harnessed by BCIs — a critical next step in inserting the human into the metaverse.

“Active Matter” Breakthrough Enables Shape-Shifting Next-Generation Robots

Physicists have discovered a new way to coat soft robots in materials that allow them to move and function in a more purposeful way. The research, led by the University of Bath, is described in a paper published on March 11, 2022, in Science Advances.

Authors of the study believe their breakthrough modeling on ‘active matter’ could mark a turning point in the design of robots. With further development of the concept, it may be possible to determine the shape, movement, and behavior of a soft solid not by its natural elasticity but by human-controlled activity on its surface.

The Rise of Artificial Intelligence | Wondrium Perspectives

For almost a century, we’ve been intrigued and sometimes terrified by the big questions of artificial intelligence. Will computers ever become truly intelligent? Will the time come when machines can operate without human intervention? What would happen if a machine developed a conscience?

In this episode of Perspectives, six experts in the fields of robotics, sci-fi, and philosophy discuss breakthroughs in the development of AI that are both good, as well as a bit worrisome.

Clips in this video are from the following series on Wondrium:

Mind-Body Philosophy, presented by Patrick Grim.
https://www.wondrium.com/mind-body-philosophy.

Introduction to Machine Learning, presented by Michael L. Litman.
https://www.wondrium.com/introduction-to-machine-learning.

Redefining Reality, presented by Steven Gimbel.

Continue reading “The Rise of Artificial Intelligence | Wondrium Perspectives” | >

Mathematical paradoxes demonstrate the limits of AI

Humans are usually pretty good at recognizing when they get things wrong, but artificial intelligence systems are not. According to a new study, AI generally suffers from inherent limitations due to a century-old mathematical paradox.

Like some people, AI systems often have a degree of confidence that far exceeds their actual abilities. And like an overconfident person, many AI systems don’t know when they’re making mistakes. Sometimes it’s even more difficult for an AI system to realize when it’s making a mistake than to produce a correct result.

Researchers from the University of Cambridge and the University of Oslo say that instability is the Achilles’ heel of modern AI and that a mathematical paradox shows AI’s limitations. Neural networks, the state of the art tool in AI, roughly mimic the links between neurons in the brain. The researchers show that there are problems where stable and accurate exist, yet no algorithm can produce such a . Only in specific cases can algorithms compute stable and accurate neural networks.

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