Lifeboat Foundation

A review paper by scientists at Zhejiang University summarized the development of continuum robots from the aspects of design, actuation, modeling and control. The new review paper, published on Jul. 26 in the journal Cyborg and Bionic Systems, provided an overview of the classic and advanced technologies of continuum robots, along with some prospects urgently to be solved.

“Some small-scale continuum robots with new actuation methods are being widely investigated in the field of interventional surgical treatment or endoscopy, however, the characterization of mechanical properties of them is still different problem,” explained study author Haojian Lu, a professor at the Zhejiang University.

In order to realize the miniaturization of continuum robots, many cutting-edge materials have been developed and used to realize the actuation of robots, showing unique advantages. The continuum robots embedded with micromagnet or made of ferromagnetic composite material have accurate steering ability under an external controllable magnetic field; Magnetically soft continuum robots, on the other hand, can achieve small diameters, up to the micron scale, which ensures their ability to conduct targeted therapy in bronchi or in cerebral vessels.

Studies say that by combining historical accident data with road maps, satellite imagery, and GPS, a machine learning model is trained to create high-resolution crash maps, we might be getting ever so closer to safer roads. Technology has changed a lot over the years such as GPS systems that eliminated the need to memorize streets orally, sensors and cameras that warn us of objects that are close to our vehicles, and autonomous electric vehicles. However, the precautions we take on the road have largely remained the same. In most places, we still rely on traffic signs, mutual trust, and the hope that we’ll reach our destination safely.

With a view to finding solutions to the uncertainty underlying road accidents, researchers at the MIT Computer Science and Artificial Intelligence Laboratory have been working with the Qatari Center for Artificial Intelligence to develop a deep learning model that can predict high-resolution maps of accident risks. The model calculates the number of accidents predicted for a specific future time frame using past accident data, road maps, simulations and GPS traces. Thus, high-risk zones and future crashes can be identified using the map.

According to reports by homelandsecuritynewswire.com, maps of this type have been captured so far at much lower resolutions, resulting in a loss of vital information. Former attempts have relied mostly on hystorical crash data, whereas the research team has compiled a wide base of critical information, identifying high-risk areas by analyzing GPS signals that provide data on traffic density, speed, and direction, along with satellite imagery that provides data on road structures. They observed that highways, for example, are more hazardous than nearby residential roads, and intersections and exits to highways are even more dangerous than other highways.

The California-based Matternet has been testing its Model M2 drone over the past four years in the US as part of the FAA’s Unmanned Aircraft System (UAS) program. Matternet says getting the green light from the FAA could help streamline the process of “implementing new networks and getting approvals.”

Matternet partnered with UPS in 2019 to deliver medical supplies in North Carolina, and later started delivering prescriptions in Florida. Matternet also expanded its footprint to Switzerland, where it teamed up with the Swiss Post to deliver lab samples and blood tests. The program was briefly suspended in 2019 after its drones suffered two crashes in the country, but Matternet has since announced that it’s taking over the Swiss Post’s drone delivery program starting in 2023.

In a statement, the FAA says Matternet’s Model M2 drone “meets all federal regulations for safe, reliable and controllable operations and provides a level of safety equivalent to existing airworthiness standards applicable to other categories of aircraft.” The four-rotor drone’s been approved to carry four-pound payloads and fly at an altitude of 400 feet or lower with a maximum speed of 45mph.

Or so goes the theory. Most CIM chips running AI algorithms have solely focused on chip design, showcasing their capabilities using simulations of the chip rather than running tasks on full-fledged hardware. The chips also struggle to adjust to multiple different AI tasks—image recognition, voice perception—limiting their integration into smartphones or other everyday devices.

This month, a study in Nature upgraded CIM from the ground up. Rather than focusing solely on the chip’s design, the international team—led by neuromorphic hardware experts Dr. H.S. Philip Wong at Stanford and Dr. Gert Cauwenberghs at UC San Diego—optimized the entire setup, from technology to architecture to algorithms that calibrate the hardware.

The resulting NeuRRAM chip is a powerful neuromorphic computing behemoth with 48 parallel cores and 3 million memory cells. Extremely versatile, the chip tackled multiple AI standard tasks—such as reading hand-written numbers, identifying cars and other objects in images, and decoding voice recordings—with over 84 percent accuracy.

Understanding how the brain organizes and accesses spatial information — where we are, what’s around the corner, how to get there — remains an exquisite challenge. The process involves recalling an entire network of memories and stored spatial data from tens of billions of neurons, each connected to thousands of others. Neuroscientists have identified key elements such as grid cells, neurons that map locations. But going deeper will prove tricky: It’s not as though researchers can remove and study slices of human gray matter to watch how location-based memories of images, sounds and smells flow through and connect to each other.

Artificial intelligence offers another way in. For years, neuroscientists have harnessed many types of neural networks — the engines that power most deep learning applications — to model the firing of neurons in the brain. In recent work, researchers have shown that the hippocampus, a structure of the brain critical to memory, is basically a special kind of neural net, known as a transformer, in disguise. Their new model tracks spatial information in a way that parallels the inner workings of the brain. They’ve seen remarkable success.

“The fact that we know these models of the brain are equivalent to the transformer means that our models perform much better and are easier to train,” said James Whittington, a cognitive neuroscientist who splits his time between Stanford University and the lab of Tim Behrens at the University of Oxford.

https://www.youtube.com/watch?v=9UM8kpnLSNw

In this age of innovation and technology, Humanoid robots working closely.
with actual humans, are used for research and space exploration, personal.
assistance and caregiving, education and entertainment, search and.
rescue, manufacturing and maintenance, public relations, and healthcare.

This is not a dream or the distant future but current reality!

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Bionic technology is removing physical barriers faced by disabled people while raising profound questions of what it is to be human. From DIY prosthetics realised through 3D printing technology to customised AI-driven limbs, science is at the forefront of many life-enhancing innovations.

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Researchers have created a way for artificial neuronal networks to communicate with biological neuronal networks. The new system converts artificial electrical spiking signals to a visual pattern than is then used to entrain the real neurons via optogenetic stimulation of the network. This advance will be important for future neuroprosthetic devices that replace damages neurons with artificial neuronal circuitry.

A prosthesis is an artificial device that replaces an injured or missing part of the body. You can easily imagine a stereotypical pirate with a wooden leg or Luke Skywalker’s famous robotic hand. Less dramatically, think of old-school prosthetics like glasses and contact lenses that replace the natural lenses in our eyes. Now try to imagine a prosthesis that replaces part of a damaged brain. What could artificial brain matter be like? How would it even work?

Creating neuroprosthetic technology is the goal of an international team led by by the Ikerbasque Researcher Paolo Bonifazi from Biocruces Health Research Institute (Bilbao, Spain), and Timothée Levi from Institute of Industrial Science, The University of Tokyo and from IMS lab, University of Bordeaux. Although several types of artificial neurons have been developed, none have been truly practical for neuroprostheses. One of the biggest problems is that neurons in the brain communicate very precisely, but electrical output from the typical electrical neural network is unable to target specific neurons. To overcome this problem, the team converted the electrical signals to light. As Levi explains, “advances in optogenetic technology allowed us to precisely target neurons in a very small area of our biological neuronal network.”

The most powerful Exascale Supercomputer is going to release in 2021 and will feature a total of 64 Exaflops. More than 6 times as much, as the Leonardo Supercomputer that’s also set to release this year.
This is accomplished with the help of a new type of processor technology from Tachyum that’s called “Prodigy” and is described as the first Universal Processor.

This new processor is set to enable General Artificial Intelligence at the speed of the human brain in real-time. It’s many times faster than the fastest intel xeon, nvidia graphics card or apple silicon. This new super-computer will enable previously-thought impossible simulations of the brain, medicine and more.
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Continue reading “Most Powerful Supercomputer — SURPASSES The HUMAN BRAIN (64 EXAFLOPS)” »