Researchers develop energy-efficient supercomputing with neural networks and charge density waves.
Researchers are creating efficient systems using neural networks and charge density waves to reduce supercomputing’s massive energy use.
Category: robotics/AI – Page 530
Kalmogorov-Arnold Neural Networks Shake Up How AI Is Done
Artificial neural networks—algorithms inspired by biological brains—are at the center of modern artificial intelligence, behind both chatbots and image generators. But with their many neurons, they can be black boxes, their inner workings uninterpretable to users.
Researchers have now created a fundamentally new way to make neural networks that in some ways surpasses traditional systems. These new networks are more interpretable and also more accurate, proponents say, even when they’re smaller. Their developers say the way they learn to represent physics data concisely could help scientists uncover new laws of nature.
China develops robot with human-like, highly expressive facial features
Now, scientists in China have developed robots that give human-like realistic expressions.
The humanoid robot with highly expressive facial features is developed by Liu Xiaofeng, a professor at Hohai University in east China’s Jiangsu Province, and his research team.
For the development of this robot, the research team developed a new algorithm for generating facial expressions on humanoid robots.
The human mind and AI are now closer than ever — and will soon surpass us in nearly every way
He writes that AI is now exceeding the human brain at several cognitive tasks and that it will eventually do all things far better than even the most expert humans.
These new machines can learn, reason, plan and act with intention, and they are becoming far smarter far faster than most people, save Kurzweil, could have predicted.
Soon, he forecasts, they will be indistinguishable from human brains, before accelerating past them in nearly every way.
New substrate material for flexible electronics could help combat e-waste
Electronic waste, or e-waste, is a rapidly growing global problem, and it’s expected to worsen with the production of new kinds of flexible electronics for robotics, wearable devices, health monitors, and other new applications, including single-use devices.
A new kind of flexible substrate material developed at MIT, the University of Utah, and Meta has the potential to enable not only the recycling of materials and components at the end of a device’s useful life, but also the scalable manufacture of more complex multilayered circuits than existing substrates provide.
The development of this new material is described in the journal RSC Applied Polymers (“Photopatternable, Degradable, and Performant Polyimide Network Substrates for E-Waste Mitigation”), in a paper by MIT Professor Thomas J. Wallin, University of Utah Professor Chen Wang, and seven others.
Meet Figure 02, the ‘Most Advanced Humanoid Robot on the Market’ Backed By Jeff Bezos, OpenAI
Figure 2 can pick up objects as heavy as 55 pounds and put parts in place accurately, down to the millimeter. It was created by Figure AI, a startup backed by OpenAI, Jeff Bezos, Nvidia, and Microsoft.
Autofac by Philip K Dick | SCIENCE FICTION | SHORT STORY
Written in 1955, this short story by Philip K Dick revolves around a group of post-apocalyptic survivors resistingthe domination of an automated factory system.
AI Helps Decode the Language of DNA
DNA contains foundational information needed to sustain life. Understanding how this information is stored and organized has been one of the greatest scientific challenges of the last century.
With GROVER, a new large language model trained on human DNA, researchers could now attempt to decode the complex information hidden in our genome.
Language Model Can Listen While Speaking
https://huggingface.co/papers/2408.
How can AI truly converse like a human if it cannot listen and respond simultaneously, or handle interruptions when things go awry?
Enter the world of full duplex modeling (FDM) and the innovative listening-while…
Join the discussion on this paper page.