Two independent groups have experimentally demonstrated surface-code quantum error correction—an approach for remedying errors in quantum computations.
The small robotic crab can walk, bend, twist, turn and jump The smallest-ever remote-controlled walking robot has been created by Northwestern University engineers, and it takes the shape of a tiny, cute peekytoe crab. The tiny crabs, which are about half a millimeter wide, can bend, twist, craw.
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.”
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.
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.
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 hybrid model, 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.
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.
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 hardware 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 programming language 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.
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 background noise, “ClearBuds” use a novel microphone system and one of the first machine-learning systems to operate in real time and run on a smartphone.
The researchers presented this project June 30 at the ACM International Conference on Mobile Systems, Applications, and Services.