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Archive for the ‘robotics/AI’ category: Page 6

Jul 12, 2022

Smaller Than a Flea — The Smallest Remote-Controlled Walking Robot Ever

Posted by in categories: biotech/medical, robotics/AI

The tiny crabs, which are about half a millimeter wide, can bend, twist, crawl, walk, turn, and even leap. Additionally, the scientists created millimeter-sized robots that resemble inchworms, crickets, and beetles. The study is experimental at this time, but the researchers think their technique might move the field closer to developing tiny robots that can carry out useful tasks in small, cramped areas.

The study was recently published in the journal Science Robotics. The same team also unveiled a winged microprocessor in September of last year; it was the tiniest flying object ever created by humans (published on the cover of Nature).

“Robotics is an exciting field of research, and the development of microscale robots is a fun topic for academic exploration,” said John A. Rogers, who led the experimental work. “You might imagine micro-robots as agents to repair or assemble small structures or machines in industry or as surgical assistants to clear clogged arteries, to stop internal bleeding or to eliminate cancerous tumors — all in minimally invasive procedures.”

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Jul 12, 2022

AI Improves Robotic Performance in DARPA’s Machine Common Sense Program

Posted by in category: robotics/AI

What do #robots and babies have in common? Both learn from experience & their surroundings. Our Machine Common Sense program applies lessons from child cognition to make robotic systems more robust, like walking in new/tough terrain as shown.

Jul 12, 2022

Use That Everyday A.I. in Your Pocket

Posted by in category: robotics/AI

Artificial intelligence powers more apps on your mobile devices than you may realize. Here’s how to take advantage of the technology — or turn it off.

Jul 12, 2022

AI made these stunning images. Here’s why experts are worried

Posted by in category: robotics/AI

A million bears walking on the streets of Hong Kong. A strawberry frog. A cat made out of spaghetti and meatballs.

These are just a few of the text descriptions that people have fed to cutting-edge artificial intelligence systems in recent weeks, which these systems — notably OpenAI’s DALL-E 2 and Google Research’s Imagen — can use to produce incredibly detailed, realistic-looking images.

Jul 12, 2022

Chinese spacecraft acquires images of entire planet of Mars

Posted by in categories: robotics/AI, space

BEIJING, June 29 (Reuters) — An uncrewed Chinese spacecraft has acquired imagery data covering all of Mars, including visuals of its south pole, after circling the planet more than 1,300 times since early last year, state media reported on Wednesday.

China’s Tianwen-1 successfully reached the Red Planet in February 2021 on the country’s inaugural mission there. A robotic rover has since been deployed on the surface as an orbiter surveyed the planet from space.

Among the images taken from space were China’s first photographs of the Martian south pole, where almost all of the planet’s water resources are locked.

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Jul 11, 2022

AI researchers tackle longstanding ‘data heterogeneity’ problem for federated learning

Posted by in categories: biotech/medical, robotics/AI

Researchers from North Carolina State University have developed a new approach to federated learning that allows them to develop accurate artificial intelligence (AI) models more quickly and accurately. The work focuses on a longstanding problem in federated learning that occurs when there is significant heterogeneity in the various datasets being used to train the AI.

Federated learning is an AI training technique that allows AI systems to improve their performance by drawing on multiple sets of data without compromising the privacy of that data. For example, federated learning could be used to draw on privileged patient data from multiple hospitals in order to improve diagnostic AI tools, without the hospitals having access to data on each other’s patients.

Federated learning is a form of machine learning involving multiple devices, called clients. The clients and a centralized server all start with a basic model designed to solve a specific problem. From that starting point, each of the clients then trains its local model using its own data, modifying the model to improve its performance. The clients then send these “updates” to the centralized server. The centralized server draws on these updates to create a , with the goal of having the hybrid model perform better than any of the clients on their own. The central server then sends this hybrid model back to each of the clients. This process is repeated until the system’s performance has been optimized or reaches an agreed-upon level of accuracy.

Jul 11, 2022

A new approach to enhance multi-fingered robot hand manipulation

Posted by in category: robotics/AI

In recent years, roboticists have developed increasingly advanced robotic systems, many of which have artificial hands or robot hands with multiple fingers. To complete everyday tasks in both homes and public settings, robots should be able to use their “hands” to efficiently grasp and manipulate objects.

Enabling dexterous manipulation involving multiple fingers in robots, however, has so far proved challenging. This is primarily because it is an advanced skill that entails an adaptation to the shape, weight, and configuration of objects.

Researchers at Universität Hamburg have recently introduced a new approach to teach robots to grasp and manipulate objects using a multi-fingered robotic hand. This approach, introduced in IEEE Transactions on Neural Networks and Learning Systems, allows a robotic hand to learn from humans through teleoperation and adapt its manipulation strategies based on human hand postures and the data gathered when interacting with the environment.

Jul 11, 2022

A new programming language for hardware accelerators

Posted by in categories: bioengineering, robotics/AI

Moore’s Law needs a hug. The days of stuffing transistors on little silicon computer chips are numbered, and their life rafts—hardware accelerators—come with a price.

When programming an accelerator—a process where applications offload certain tasks to system especially to accelerate that task—you have to build a whole new software support. Hardware accelerators can run certain tasks orders of magnitude faster than CPUs, but they cannot be used out of the box. Software needs to efficiently use accelerators’ instructions to make it compatible with the entire application system. This translates to a lot of engineering work that then would have to be maintained for a new chip that you’re compiling code to, with any programming language.

Now, scientists from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) created a new called “Exo” for writing high-performance code on hardware accelerators. Exo helps low-level performance engineers transform very simple programs that specify what they want to compute, into very complex programs that do the same thing as the specification, but much, much faster by using these special accelerator chips. Engineers, for example, can use Exo to turn a simple matrix multiplication into a more complex program, which runs orders of magnitude faster by using these special accelerators.

Jul 11, 2022

First wireless earbuds that clear up calls using deep learning

Posted by in categories: biotech/medical, mobile phones, robotics/AI

As meetings shifted online during the COVID-19 lockdown, many people found that chattering roommates, garbage trucks and other loud sounds disrupted important conversations.

This experience inspired three University of Washington researchers, who were roommates during the pandemic, to develop better earbuds. To enhance the speaker’s voice and reduce , “ClearBuds” use a novel microphone system and one of the first machine-learning systems to operate in real time and run on a smartphone.

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Jul 11, 2022

AI as a ‘wise counsel’ for synthetic biology

Posted by in categories: bioengineering, biotech/medical, information science, robotics/AI

Machine learning is transforming all areas of biological science and industry, but is typically limited to a few users and scenarios. A team of researchers at the Max Planck Institute for Terrestrial Microbiology led by Tobias Erb has developed METIS, a modular software system for optimizing biological systems. The research team demonstrates its usability and versatility with a variety of biological examples.

Though engineering of biological systems is truly indispensable in biotechnology and , today machine learning has become useful in all fields of biology. However, it is obvious that application and improvement of algorithms, computational procedures made of lists of instructions, is not easily accessible. Not only are they limited by programming skills but often also insufficient experimentally-labeled data. At the intersection of computational and experimental works, there is a need for efficient approaches to bridge the gap between machine learning algorithms and their applications for biological systems.

Now a team at the Max Planck Institute for Terrestrial Microbiology led by Tobias Erb has succeeded in democratizing machine learning. In their recent publication in Nature Communications, the team presented together with collaboration partners from the INRAe Institute in Paris, their tool METIS. The application is built in such a versatile and modular architecture that it does not require computational skills and can be applied on different biological systems and with different lab equipment. METIS is short from Machine-learning guided Experimental Trials for Improvement of Systems and also named after the ancient goddess of wisdom and crafts Μῆτις, or “wise counsel.”

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