AI & Robotics News and Discussions

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raklian
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To know is essentially the same as not knowing. The only thing that occurs is the rearrangement of atoms in your brain.
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Human-like features in Robot Behavior: Response Time Variability Can be Perceived as Human-like
July 27, 2022

Introduction:
(EurekAlert) Genova (Italy), 27 July 2022 - Humans behave and act in a way that other humans can recognize as human-like. If humanness has specific features, is it possible to replicate these features on a machine like a robot? Researchers at IIT-Istituto Italiano di Tecnologia (Italian Institute of Technology) tried to answer that question by implementing a non-verbal Turing test in a human-robot interaction task. They involved human participants and the humanoid robot iCub in a joint action experiment. What they found is that specific features of human behavior, namely response timing, can be translated into the robot in a way that humans cannot distinguish whether they are interacting with a person or a machine.

The study has been published in Science Robotics journal today and is the first step to understanding what kind of behaviour robots could exhibit in the future, considering the various possible fields of application – such as healthcare or manufacturing production line.
https://www.eurekalert.org/news-releases/959924
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A "Nano-robot" Built Entirely From DNA to Explore Cell Processes
July 28, 2022

Introduction:
(EurekAlert) Constructing a tiny robot from DNA and using it to study cell processes invisible to the naked eye... You would be forgiven for thinking it is science fiction, but it is in fact the subject of serious research by scientists from Inserm, CNRS and Université de Montpellier at the Structural Biology Center in Montpellier[1]. This highly innovative "nano-robot" should enable closer study of the mechanical forces applied at microscopic levels, which are crucial for many biological and pathological processes. It is described in a new study published in Nature Communications
( https://www.nature.com/articles/s41467-022-30745-2 ) .
Further Extract:
(T)he research team led by Inserm researcher Gaëtan Bellot at the Structural Biology Center (Inserm/CNRS/Université de Montpellier)…(used) the DNA origami method. This enables the self-assembly of 3D nanostructures in a pre-defined form using the DNA molecule as construction material. Over the last ten years, the technique has allowed major advances in the field of nanotechnology.

This enabled the researchers to design a "nano-robot" composed of three DNA origami structures. Of nanometric size, it is therefore compatible with the size of a human cell. It makes it possible for the first time to apply and control a force with a resolution of 1 piconewton, namely one trillionth of a Newton – with 1 Newton corresponding to the force of a finger clicking on a pen. This is the first time that a human-made, self-assembled DNA-based object can apply force with this accuracy.

The team began by coupling the robot with a molecule that recognizes a mechanoreceptor. This made it possible to direct the robot to some of our cells and specifically apply forces to targeted mechanoreceptors localized on the surface of the cells in order to activate them.

Such a tool is very valuable for basic research, as it could be used to better understand the molecular mechanisms involved in cell mechanosensitivity and discover new cell receptors sensitive to mechanical forces. Thanks to the robot, the scientists will also be able to study more precisely at what moment, when applying force, key signaling pathways for many biological and pathological processes are activated at cell level.
Read more here: https://www.eurekalert.org/news-releases/960063
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MIT Researchers Created Artificial Synapses 10,000x Faster Than Biological Ones
https://singularityhub.com/2022/08/01/m ... 6pT6GPfifU
By
Edd Gent -
August 1, 2022 10,176
Researchers have been trying to build artificial synapses for years in the hope of getting close to the unrivaled computational performance of the human brain. A new approach has now managed to design ones that are 1,000 times smaller and 10,000 times faster than their biological counterparts.

Despite the runaway success of deep learning over the past decade, this brain-inspired approach to AI faces the challenge that it is running on hardware that bears little resemblance to real brains. This is a big part of the reason why a human brain weighing just three pounds can pick up new tasks in seconds using the same amount of power as a light bulb, while training the largest neural networks takes weeks, megawatt hours of electricity, and racks of specialized processors.

That’s prompting growing interest in efforts to redesign the underlying hardware AI runs on. The idea is that by building computer chips whose components act more like natural neurons and synapses, we might be able to approach the extreme space and energy efficiency of the human brain. The hope is that these so-called “neuromorphic” processors could be much better suited to running AI than today’s computer chips.

Now researchers from MIT have shown that an unusual artificial synapse design that mimics the brain’s reliance on shuttling ions around could actually significantly outperform biological ones. The key breakthrough was finding a material that tolerates extreme electric fields, which dramatically improved the speed with which ions could move.

“The speed certainly was surprising,” Murat Onen, who led the research, said in a press release. “Normally, we would not apply such extreme fields across devices, in order to not turn them into ash. But instead, protons [which are equivalent to hydrogen ions] ended up shuttling at immense speeds across the device stack, specifically a million times faster compared to what we had before.”

While there are a variety of approaches to neuromorphic engineering, one of the most promising is analogue computing. This seeks to design components that can exploit their internal physics to process information, which is much more efficient and direct than carrying out complex logic operations like conventional chips do.
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A bartending robot that can engage in personalized interactions with humans
https://techxplore.com/news/2022-08-bar ... tions.html
by Ingrid Fadelli , Tech Xplore

A widely discussed application of social robots that has so far been rarely tested in real-world settings is their use as bartenders in cafés, cocktail bars and restaurants. While many roboticists have been trying to develop systems that can effectively prepare drinks and serve them, so far very few have focused on artificially reproducing the social aspect of bartending.

Researchers at University of Naples Federico II in Italy have recently developed a new interactive robotic system called BRILLO, which is specifically designed for bartending. In a recent paper published in UMAP '22 Adjunct: Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization, they introduced a new approach that could allow their robot to have personalized interactions with regular customers.

"The bartending scenario is an extremely challenging one to tackle using robots, yet it is also very interesting from a research point of view," Prof. Silvia Rossi, one of the researchers who carried out the study and the scientific coordinator of the project, told TechXplore. "In fact, this scenario combines the complexity of efficiently manipulating objects to make drinks with the need to interact with the users. Interestingly, however, all current applications of robotics for bartending scenarios ignore the interaction part entirely."

Silvia Rossi and her colleagues Alessandra Rossi and Nitha Elizabeth John believe that to effectively take on the role of a bartender, a robot should not only be able to interact with humans, but it should also create a "profile" of users. This would allow it to personalize its interactions with regular customers, increasing the likelihood that they will like and continue using the robotic bartending service.
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New neuromorphic chip for AI on the edge, at a small fraction of the energy and size of today's computing platforms

by University of California - San Diego
https://techxplore.com/news/2022-08-neu ... small.html
An international team of researchers has designed and built a chip that runs computations directly in memory and can run a wide variety of AI applications–all at a fraction of the energy consumed by computing platforms for general-purpose AI computing.

The NeuRRAM neuromorphic chip brings AI a step closer to running on a broad range of edge devices, disconnected from the cloud, where they can perform sophisticated cognitive tasks anywhere and anytime without relying on a network connection to a centralized server. Applications abound in every corner of the world and every facet of our lives, and range from smart watches, to VR headsets, smart earbuds, smart sensors in factories and rovers for space exploration.

The NeuRRAM chip is not only twice as energy efficient as the state-of-the-art "compute-in-memory" chips, an innovative class of hybrid chips that runs computations in memory, it also delivers results that are just as accurate as conventional digital chips. Conventional AI platforms are a lot bulkier and typically are constrained to using large data servers operating in the cloud.

In addition, the NeuRRAM chip is highly versatile and supports many different neural network models and architectures. As a result, the chip can be used for many different applications, including image recognition and reconstruction as well as voice recognition.

"The conventional wisdom is that the higher efficiency of compute-in-memory is at the cost of versatility, but our NeuRRAM chip obtains efficiency while not sacrificing versatility," said Weier Wan, the paper's first corresponding author and a recent Ph.D. graduate of Stanford University who worked on the chip while at UC San Diego, where he was co-advised by Gert Cauwenberghs in the Department of Bioengineering.
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Robotic Kidney Cancer Surgery Shows Desirable Outcomes in Study
August 19, 2022

Introduction:
(EurekAlert) SAN ANTONIO (Aug. 19, 2022) — Kidney cancer is not always confined to the kidney. In advanced cases, this cancer invades the body’s biggest vein, the inferior vena cava (IVC), which carries blood out of the kidneys back to the heart. Via the IVC, cancer may infiltrate the liver and heart. The Mays Cancer Center at The University of Texas Health Science Center at San Antonio (UT Health San Antonio) is one of the high-volume centers in the U.S. with surgical expertise in treating this serious problem. The Mays Cancer Center is San Antonio’s National Cancer Institute-designated Cancer Center.

In a study featured on the cover of the Journal of Urology (Official Journal of the American Urological Association), researchers from the Mays Cancer Center and Department of Urology at UT Health San Antonio show that robotic IVC thrombectomy (removal of cancer from the inferior vena cava) is not inferior to standard open IVC thrombectomy and is a highly safe and effective alternative approach. The affected kidney is removed along with the tumor during surgery, which is performed at UT Health San Antonio’s clinical partner, University Hospital.
Additional extract:
The robotic approach in comparison with open was associated with:

• Fewer blood transfusions: 18% of robotic patients required transfusions compared to 64% of open patients.
• Fewer complications: 5% of robotic patients experienced complications such as bleeding compared to 36.7% of open thrombectomy patients.

“In more than 1,300 patients, we found that overall complications were lower with the robotic approach and the blood transfusion rate was lower with this approach” (said Dharam Kaushik, MD).
Read more here: https://www.eurekalert.org/news-releases/962394
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Aquabots: Ultrasoft liquid robots for biomedical and environmental applications
https://techxplore.com/news/2022-08-aqu ... dical.html
by Ingrid Fadelli , Tech Xplore
In recent years, roboticists have developed a wide variety of robotic systems with different body structures and capabilities. Most of these robots are either made of hard materials, such as metals, or soft materials, such as silicon and rubbery materials.

Researchers at Hong Kong University (HKU) and Lawrence Berkeley National Laboratory have recently created Aquabots, a new class of soft robots that are predominantly made of liquids. As most biological systems are predominantly made up of water or other aqueous solutions, the new robots, introduced in a paper published in ACS Nano, could have highly valuable biomedical and environmental applications.

"We have been engaged in the development of adaptive interfacial assemblies of materials at the oil-water and water-water interface using nanoparticles and polyelectrolytes," Ho Cheung (Anderson) Shum, Thomas P. Russell, and Shipei Zhu told TechXplore via email. "Our idea was to assemble the materials that the interface and the assemblies lock in the shapes of the liquids. The shapes are dictated using external forces to generate arbitrary shapes or to use all-liquid 3D printing to be able to spatially organize the assemblies."

Shum, Russell, Zhu and their colleagues coupled all-liquid 3D printing techniques with aqueous two-phase assemblies (ATPS), strategies for assembling 3D structures, to realize artificial constructs that mimic biological systems. ATPS are a key area of focus for the research group at HKU led by Professor Shum.
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A deep learning framework to enhance the capabilities of a robotic sketching agent

by Ingrid Fadelli , Tech Xplore
https://techxplore.com/news/2022-08-dee ... agent.html
In recent years, deep learning algorithms have achieved remarkable results in a variety of fields, including artistic disciplines. In fact, many computer scientists worldwide have successfully developed models that can create artistic works, including poems, paintings and sketches.

Researchers at Seoul National University have recently introduced a new artistic deep learning framework, which is designed to enhance the skills of a sketching robot. Their framework, introduced in a paper presented at ICRA 2022 and pre-published on arXiv, allows a sketching robot to learn both stroke-based rendering and motor control simultaneously.

"The primary motivation for our research was to make something cool with non-rule-based mechanisms such as deep learning; we thought drawing is a cool thing to show if the drawing performer is a learned robot instead of human," Ganghun Lee, the first author of the paper, told TechXplore. "Recent deep learning techniques have shown astonishing results in the artistic area, but most of them are about generative models which yield whole pixel outcomes at once."

Instead of developing a generative model that produces artistic works by generating specific pixel patterns, Lee and his colleagues created a framework that represents drawing as a sequential decision process. This sequential process resembles the way in which humans would draw individual lines using a pen or pencil to gradually create a sketch.
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