Interesting…
Slime that can be controlled by a magnetic field can navigate tight spaces and grasp objects, making it ideal for possible uses inside the body.
Category: robotics/AI – Page 1331
It’s like “Venom” or “Flubber” come to life.
Scientists have created a bizarre magnetic slime that can be remotely controlled via a magnetic field, New Scientist reports. They’re hoping the odd entity could be used to navigate narrow passages inside the human body, likely to collect objects that were mistakenly swallowed.
Of course, that’s if you’re willing to swallow this thing and have it poking around inside of you — video footage of the thing certainly doesn’t make it look appetizing. In fact, people online are already cracking potty humor jokes about it.
Robot Maid: The child-sized robot can mop, pick up stuff off the floor, put dishes away, and even move furniture. It can even make and bring you coffee.
Remember the Jetsons? As kids, we hoped someday we’d have flying cars or those jetpacks Elroy used to zip around with. As we become older, the thing we really want most from the Jetsons is their lovable maid Rosie. Because let’s be honest, we all despise cleaning. Whether it’s vacuuming the living room, mopping up the kitchen or picking up our kid’s toys, nobody cleans with a smile on their face. Wouldn’t it be great if we had a robot maid like Rosie to clean up while we focused on other stuff?
Well having a “Rosie” might be closer than you think thanks to a company called Aeolus Robotics. They unveiled their as of yet unnamed “maid” robot earlier this year. The child-sized robot can mop, pick up stuff off the floor, put dishes away, and even move furniture.
The robot isn’t just about cleaning, it can even make and bring you coffee if you so shall desire. It can recognize both voice and text commands so you can simply say “Mop the floor then bring me my coffee” and walla! The robot integrates with Alexa, Google Home, and other smart devices. The robot’s owner can use an app to interact with and monitor it’s activities, even being able to see the world exactly as the robot does.
Artificial intelligences are promising in future societies, and neural networks are typical technologies with the advantages such as self-organization, self-learning, parallel distributed computing, and fault tolerance, but their size and power consumption are large. Neuromorphic systems are biomimetic systems from the hardware level, with the same advantages as living brains, especially compact size, low power, and robust operation, but some well-known ones are non-optimized systems, so the above benefits are only partially gained, for example, machine learning is processed elsewhere to download fixed parameters. To solve these problems, we are researching neuromorphic systems from various viewpoints. In this study, a neuromorphic chip integrated with a large-scale integration circuit (LSI) and amorphous-metal-oxide semiconductor (AOS) thin-film synapse devices has been developed.
The study found that if such a system were put in place nationwide, 94 percent of human operator hours may be affected, which could account for as many as 500,000 jobs.
In a situation where automation is restricted only to Sun Belt states, as rough weather poses a challenge to automation, about 10 percent of worker operator hours will be affected. If automation is deployed nationwide only during the spring and summer months, about half the nation’s trucking hours could go driverless.
“I think the most surprising thing there was that everyone we spoke to basically said ‘yeah, this can be done,’” Vaishnav said.
NVIDIA says their 3D scene rendering AI runs up to 1,000 times faster than older models without sacrificing image quality.
Training datasets are very important for experimenting with varied data to train new AI models. However, many commonly used public data sets contain labeling errors. This makes it challenging to train robust models, particularly for novel tasks. Many researchers use techniques such as employing a variety of data quality control procedures to overcome these shortcomings. However, there is no centralized repository consisting of examples of using these strategies.
Meta AI researchers have recently released Mephisto. It is a new platform to collect, share, and iterate on the most promising approaches to collecting training datasets for AI models. Researchers can exchange unique collecting strategies with Mephisto in a reusable and iterable format. It also allows them to change out components and quickly locate the exact annotations required, minimizing the barrier to custom task creation.
The team uncovers many common pathways for driving a complex annotation activity from concept to data collection in Mephisto. In addition to improving the quality of datasets, Mephisto also enhances the experience of the researchers and annotators who created the data set.
A new approach to in-memory computing proposes a new set up to create an artificial synapse that can both store and process data.
In this blossoming era of AI, efficient computational approaches to processing and storing large amounts of data are required. However, current computer designs have inherent performance limitations.
In recent years, research has been focused on the development of alternative computing architectures that mimic the brain. These devices, called neuromorphic computers, circumvent many of the issues associated with the traditional von Neumann architecture, which has been around since 1945 and is composed of processing and memory units.
So-called energy-based models, which borrow concepts from statistical physics, may lead the way to ‘abstract prediction,’ says Yann LeCun, allowing for a ‘unified world model’ for AI capable of planning.