Posted in robotics/AI
Ishikawa Komuro Lab’s high-speed robot hand performing impressive acts of dexterity and skillful manipulation. For more information, see Hizook.com — https://youtu.be/redirect?q=http%3A%2F%2Fwww.hizook.com%2Fbl…cxMjg2MTI5
With the Skydio 2 Dock, a drone-in-a-box solution, the California startup wants to let companies rely on its obstacle-dodging, self-flying drone for automated mapping and surveillance — no humans needed.
Dr. Qingsong Zhu, the COO of Insilico Medicine, discussed the use of deep learning in creating biomarkers for aging. Initially discussing existing clocks and the problems with animal translation, he went on to discuss what sorts of markers are ideal for age-related research and the details of training and testing a model that works with these markers, showing that a deep model compares favorably to other models.
He also used his model to show that smoking does, in fact, cause accelerated aging.
Researchers at TU Delft have developed a new supercompressible but strong material without conducting any experimental tests at all, using only artificial intelligence (AI). “AI gives you a treasure map, and the scientist needs to find the treasure,” says Miguel Bessa, first author of a publication on this subject in Advanced Materials on 14 October.
Foldable bicycle
Miguel Bessa, assistant professor in materials science and engineering at TU Delft, got the inspiration for this research project during his time at the California Institute of Technology. At a corner of the Space Structures Lab, he noticed a satellite structure that could open long solar sails from a very small package.
The team observed an abstract “pull” that lures neural networks into an optimal functional state, so they never stray far from their dedicated “set points.”
By and Targeting Metabesity to examine the links between metabesity, Longevity and the USA’s current health shortfalls, including low health-adjusted life expectancy (“HALE”) and the large gap between HALE and life expectancy, despite its extremely high per-capita healthcare expenditures, and to chart policy recommendations to neutralize this vast health vs wealth deficit.
Metabesity and Longevity: USA Special Case Study is an 85-page open-access analytical report produced jointly by Aging Analytics Agency and Targeting Metabesity to examine the links between metabesity, Longevity and the USA s current health shortfalls, including low health-adjusted life expectancy ( HALE ) and the large gap between HALE and life expectancy, despite its extremely high per-capita healthcare expenditures, and to chart policy recommendations to neutralize this vast health vs wealth deficit.
Link to Special Case Study: https://aginganalytics.com/longevity-usa/
As the issue of aging population intensifies, sick care will become increasingly expensive and ineffective. America needs to rapidly deploy a government-led shift from treatment to prevention, and from prevention to precision health, using deep diagnostics and prognostics in combination with biomarkers of aging, metabesity, health and intervention-effectiveness, to delay the onset of disease with as minimal intervention as possible, as early as possible. Seeking synergies between Longevity research, P4 (preventive, personalized, precision and participatory) medicine and Artificial Intelligence has the potential to enable rapid and widespread policy and infrastructural reforms for USA healthcare to quickly boost National Healthy Longevity, but only with sufficient government commitment.
Brain-machine interface enthusiasts often gush about “closing the loop.” It’s for good reason. On the implant level, it means engineering smarter probes that only activate when they detect faulty electrical signals in brain circuits. Elon Musk’s Neuralink—among other players—are readily pursuing these bi-directional implants that both measure and zap the brain.
But to scientists laboring to restore functionality to paralyzed patients or amputees, “closing the loop” has broader connotations. Building smart mind-controlled robotic limbs isn’t enough; the next frontier is restoring sensation in offline body parts. To truly meld biology with machine, the robotic appendage has to “feel one” with the body.
This month, two studies from Science Robotics describe complementary ways forward. In one, scientists from the University of Utah paired a state-of-the-art robotic arm—the DEKA LUKE—with electrically stimulating remaining nerves above the attachment point. Using artificial zaps to mimic the skin’s natural response patterns to touch, the team dramatically increased the patient’s ability to identify objects. Without much training, he could easily discriminate between the small and large and the soft and hard while blindfolded and wearing headphones.
Researchers at OpenAI have developed a new method for transferring complex manipulation skills from simulated to physical environments.
In work that combines a deep understanding of the biology of soft-bodied animals such as earthworms with advances in materials and electronic technologies, researchers from the United States and China have developed a robotic device containing a stretchable transistor that allows neurological function.
Cunjiang Yu, Bill D. Cook Associate Professor of Mechanical Engineering at the University of Houston, said the work represents a significant step toward the development of prosthetics that could directly connect with the peripheral nerves in biological tissues, offering neurological function to artificial limbs, as well as toward advances in soft neurorobots capable of thinking and making judgments. Yu is corresponding author for a paper describing the work, published in Science Advances.
He is also a principal investigator with the Texas Center for Superconductivity at the University of Houston.
Artificial intelligence research organization OpenAI has achieved a new milestone in its quest to build general purpose, self-learning robots. The group’s robotics division says Dactyl, its humanoid robotic hand first developed last year, has learned to solve a Rubik’s cube one-handed. OpenAI sees the feat as a leap forward both for the dexterity of robotic appendages and its own AI software, which allows Dactyl to learn new tasks using virtual simulations before it is presented with a real, physical challenge to overcome.
In a demonstration video showcasing Dactyl’s new talent, we can see the robotic hand fumble its way toward a complete cube solve with clumsy yet accurate maneuvers. It takes many minutes, but Dactyl is eventually able to solve the puzzle. It’s somewhat unsettling to see in action, if only because the movements look noticeably less fluid than human ones and especially disjointed when compared to the blinding speed and raw dexterity on display when a human speedcuber solves the cube in a matter of seconds.
But for OpenAI, Dactyl’s achievement brings it one step closer to a much sought-after goal for the broader AI and robotics industries: a robot that can learn to perform a variety of real-world tasks, without having to train for months to years of real-world time and without needing to be specifically programmed.
