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Category: robotics/AI – Page 3

Electrofluidic fiber muscles could enable silent robotic systems

Muscles are remarkably effective systems for generating controlled force, and engineers developing hardware for robots or prosthetics have long struggled to create analogs that can approach their unique combination of strength, rapid response, scalability, and control. But now, researchers at the MIT Media Lab and Politecnico di Bari in Italy have developed artificial muscle fibers that come closer to matching many of these qualities.

Like the fibers that bundle together to form biological muscles, these fibers can be arranged in different configurations to meet the demands of a given task. Unlike conventional robotic actuation systems, they are compliant enough to interface comfortably with the human body and operate silently without motors, external pumps, or other bulky supporting hardware.

The new electrofluidic fiber muscles—electrically driven actuators built in fiber format—are described in a recent paper published in Science Robotics. The work is led by Media Lab Ph.D. candidate Ozgun Kilic Afsar; Vito Cacucciolo, a professor at the Politecnico di Bari; and four co-authors.

AI diffusion models tailor drug molecules to custom-fit protein targets, speeding drug development and evaluation

University of Virginia School of Medicine scientists have developed a bold new approach to drug development and discovery that could dramatically accelerate the creation of new medicines. UVA’s Nikolay V. Dokholyan, Ph.D., and colleagues have developed a suite of artificial intelligence-powered tools, called YuelDesign, YuelPocket and YuelBond, that work together to transform how new drugs are created. The centerpiece, YuelDesign, uses a cutting-edge form of AI called diffusion models to design new drug molecules tailored to fit their protein targets exactly, even accounting for the way proteins flex and shift shape during binding.

A companion tool, YuelPocket, identifies exactly where on a protein a drug can attach, while YuelBond ensures the chemical bonds in designed molecules are accurate. Together, the approach is poised to improve both how new drugs are designed and how quickly and efficiently existing drugs can be evaluated for new purposes.

“Think of it this way: Other methods try to design a key for a lock that’s sitting perfectly still, but in your body, that lock is constantly jiggling and changing shape. Our AI designs the key while the lock is moving, so the fit is much more realistic,” said Dokholyan, of UVA’s Department of Neurology. “This could make a real difference for patients with cancer, neurological disorders and many other conditions where we desperately need better drugs targeting these wiggly proteins but keep hitting dead ends.”

AI-designed proteins built from scratch can recognize specific compounds

Professor Gyu Rie Lee of the Department of Biological Sciences successfully designed artificial proteins that selectively recognize specific compounds using AI through joint research with Professor David Baker. The research, published in the journal Nature Communications, is characterized by using AI to design proteins that recognize specific compounds from scratch (de novo) and implementing them as functional biosensors.

While the conventional approach mainly involved searching for natural proteins or modifying some of their functions, this research is highly significant in that it “custom-built” proteins with desired functions through AI-based design and even completed experimental verification.

In particular, the research team successfully designed a protein that selectively recognizes the stress hormone cortisol and implemented an AI-designed biosensor based on it. This is evaluated as a case that extends beyond protein design to actual measurable sensor technology, solving the long-standing challenge of small-molecule recognition in the field of protein design.

AI can design and run thousands of lab experiments without human hands. Humanity isn’t ready for the new risks this brings to biology

Faster protein engineering could mean faster responses to emerging infections and cheaper drugs.

The dual-use problem

Researchers have raised concerns that these same AI tools could be misused, a challenge known as the dual-use problem: Technologies developed for beneficial purposes can also be repurposed to cause harm.

AI uncovers hidden immune defenses inside bacteria

Researchers at the Massachusetts Institute of Technology (MIT) have discovered thousands of new proteins that protect bacteria from virus attacks using an AI system called DefensePredictor. What would usually take months of lab work can now be narrowed down to promising candidates in minutes.

Bacteria are under constant attack from viruses called bacteriophages. One of their most powerful defenses is CRISPR-Cas, a system that cuts up viral DNA to stop an infection and is now a valuable biotechnology tool for precisely editing genes in a lab.

Traditional methods of finding these defenses are long and laborious, equivalent to looking for a needle in a haystack. They involve searching for nearby known defensive genes and manually testing thousands of DNA fragments. But now, AI can take the strain.

AI trained like a Rubik’s Cube solver simplifies particle physics equations

For years, Rutgers physicist David Shih solved Rubik’s Cubes with his children, twisting the colorful squares until the scrambled puzzle returned to order. He didn’t expect the toy to connect to his research, but recently he realized the logic behind the puzzle was exactly what he needed to solve a problem involving particle physics.

That idea led to a new artificial intelligence (AI) method that can simplify some of the extremely complex equations used in particle physics. Shih described the method in a study posted to the arXiv preprint server, a widely used site where scientists share new research.

“In reaching our solutions, we found that an analogy between mathematical simplification and solving Rubik’s Cubes was key,” said Shih, a professor in the Department of Physics and Astronomy at the Rutgers School of Arts and Sciences. “Both can be viewed as scrambling and unscrambling problems.”

The Final Device

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For a century, the human-computer interface has been shrinking, and now, it’s crossing the final physiological barrier: your nervous system. In this episode of Technomics, we expose the terrifying and awe-inspiring evolutionary roadmap of the \.

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