Researchers are paving the way for the design of bionic limbs that feel natural to users. They demonstrate the connection between hand movement patterns and motoneuron control patterns. The study, published in Science Robotics, also reports the application of these findings to a soft prosthetic hand, which was successfully tested by individuals with physical impairments.
The research study sees the collaboration of two research teams, one at Istituto Italiano di Tecnologia (Italian Institute of Technology) in Genova, Italy, led by Antonio Bicchi, and Imperial College London, UK led by Dario Farina. It is the outcome of the project “Natural BionicS” whose goal is to move beyond the model of current prosthetic limbs, which are often abandoned by patients because they do not respond in a “natural” way to their movement and control needs.
For the central nervous system to recognize the bionic limb as “natural,” it is essential for the prosthesis to interact with the environment in the same way a real limb would. For this reason, researchers believe that the prostheses should be designed based on the theory of sensorimotor synergies and soft robotics technologies, first proposed by Antonio Bicchi’s group at IIT, such as the Soft-Hand robotic hand.
Recent technological advances have opened new possibilities for the development of assistive and medical tools, including prosthetic limbs. While these limbs used to be hard objects with the same shape as limbs, prosthetics are now softer and look more realistic, with some also integrating robotic components that considerably broaden their functions.
Despite these developments, most commercially available robotic limbs cannot be easily and intuitively controlled by users. This significantly limits their effectiveness and the extent to which they can improve people’s quality of life.
Researchers at the Italian Institute of Technology (IIT) and Imperial College London recently developed a new soft prosthetic hand that could be easier for users to control. This system, presented in a Science Robotics paper, leverages a new control approach that integrates the coordination patterns of multiple fingers (i.e., postural synergies) with the decoding of the activity of motoneurons in people’s spinal column.
Artificial consciousness is the next frontier in AI. While artificial intelligence has advanced tremendously, creating machines that can surpass human capabilities in certain areas, true artificial consciousness represents a paradigm shift—moving beyond computation into subjective experience, self-awareness, and sentience.
In this video, we explore the profound implications of artificial consciousness, the defining characteristics that set it apart from traditional AI, and the groundbreaking work being done by McGinty AI in this field. McGinty AI is pioneering new frameworks, such as the McGinty Equation (MEQ) and Cognispheric Space (C-space), to measure and understand consciousness levels in artificial and biological entities. These advancements provide a foundation for building truly conscious AI systems.
The discussion also highlights real-world applications, including QuantumGuard+, an advanced cybersecurity system utilizing artificial consciousness to neutralize cyber threats, and HarmoniQ HyperBand, an AI-powered healthcare system that personalizes patient monitoring and diagnostics.
However, as we venture into artificial consciousness, we must navigate significant technical challenges and ethical considerations. Questions about autonomy, moral status, and responsible development are at the forefront of this revolutionary field. McGinty AI integrates ethical frameworks such as the Rotary Four-Way Test to ensure that artificial consciousness aligns with human values and benefits society.
Join us as we explore the next chapter in artificial intelligence—the dawn of artificial consciousness. What does the future hold for humanity and AI? Will artificial consciousness enhance our world, or does it come with unforeseen risks? Watch now to learn more about this groundbreaking technology and its potential to shape the future.
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Nvidia CEO Jensen Huang has a vision for the future — and some advice for the generations that will have to navigate it.
In a recently released interview on Cleo Abram’s “Huge Conversations,” recorded on January 7, Huang said he expected massive leaps in what he called “human robotics” within the next half decade and a broadening in the applications of artificial intelligence.
Multiple companies across both the US and China, among other countries, are working to launch and scale the production of humanoid robots for use in manufacturing and consumer applications.
At UC Berkeley, researchers in Sergey Levine’s Robotic AI and Learning Lab eyed a table where a tower of 39 Jenga blocks stood perfectly stacked. Then a white-and-black robot, its single limb doubled over like a hunched-over giraffe, zoomed toward the tower, brandishing a black leather whip.
Through what might have seemed to a casual viewer like a miracle of physics, the whip struck in precisely the right spot to send a single block flying out from the stack while the rest of the tower remained structurally sound.
This task, known as “Jenga whipping,” is a hobby pursued by people with the dexterity and reflexes to pull it off. Now, it’s been mastered by robots, thanks to a novel, AI-powered training method.
Bloomberg on the Economic Singularity:
“If AI is about to get much cheaper, the path to an answer on its economic impact is going to get shorter. For workers nervously wondering if large language models will make their skills redundant, a lot is riding on which camp is right.”
For investors in artificial intelligence, the last week delivered a painful shock. The sudden appearance of DeepSeek — a Chinese AI firm boasting a world-class model developed at bargain-basement costs — triggered a massive selloff in Nvidia and other US tech champions.
What matters for the economy, though, is not the ups and downs of stock prices for the Magnificent Seven, but whether AI drives gains in productivity, and how those gains are divided up. For all the excitement, and the trillion-dollar valuations for AI firms, evidence of a boost to productivity remains thin on the ground.
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