Sanctuary AI advances robotics with 21-DOF hands using hydraulic actuation, enabling intricate tasks and boosting humanoid robot potential.
“We’ve achieved peak data and there’ll be no more,” OpenAI’s former chief scientist told a crowd of AI researchers.
From smart textiles to self-driving cars: Empa researchers are developing new types of detectors for infrared radiation that are more sustainable, flexible and cost-effective than conventional technologies. The key to success is not (only) the composition of the material, but also its size.
What do we think?
The latest AI News. Learn about LLMs, Gen AI and get ready for the rollout of AGI. Wes Roth covers the latest happenings in the world of OpenAI, Google, Anthropic, NVIDIA and Open Source AI.
Southeast Asia’s emerging economies are vying to become a top AI hub — a race that has them both coming together and, quietly, battling among themselves.
The Association of Southeast Asian Nations (ASEAN), made up of 10 countries with a combined population of 672 million people, already has some advantages when compared to Europe or the U.S.
With over 200 million people aged 15 to 34, the region’s youthful and largely tech savvy populations make the region adaptable to future technological advances. That, combined with government support for accelerating AI in the region, could deliver substantial rewards for local workers.
Author(s): Jesus Rodriguez Originally published on Towards AI. Created Using IdeogramI recently started an AI-focused educational newsletter, that already has over 170,000 subscribers. TheSequence is a no-BS (meaning no hype, no news, etc) ML-oriented newsletter that takes 5 minutes to read. The goal is to keep you up to date with machine learning projects, research papers, and concepts. Please give it a try by subscribing below:
RAVEN (Robotic Avian-inspired Vehicle for multiple ENvironments) (Image: © Alain Herzog CC BY SA) EPFL researchers have built a drone that can walk, hop, and jump into flight with the aid of birdlike legs, greatly expanding the range of potential environments accessible to unmanned aerial vehicles.
For human researchers, it takes many years of work to discover new super-resolution microscopy techniques. The number of possible optical configurations of a microscope—for example, where to place mirrors or lenses—is enormous.
Researchers at the Max Planck Institute for the Science of Light (MPL) have developed an artificial intelligence (AI) framework which autonomously discovers new experimental designs in microscopy. The framework, called XLuminA, performs optimizations 10,000 times faster than well-established methods.
The researchers’ work is published in Nature Communications.
In a world’s first, surgeons from NYU Langone Health performed a successful fully roboitc double lung transplant.
Quantum calculations of molecular systems often require extraordinary amounts of computing power; these calculations are typically performed on the world’s largest supercomputers to better understand real-world products such as batteries and semiconductors.
Now, UC Berkeley and Lawrence Berkeley National Laboratory (Berkeley Lab) researchers have developed a new machine learning method that significantly speeds up atomistic simulations by improving model scalability. This approach reduces the computing memory required for simulations by more than fivefold compared to existing models and delivers results over ten times faster.
Their research has been accepted at Neural Information Processing Systems (NeurIPS) 2024, a conference and publication venue in artificial intelligence and machine learning. They will present their work at the conference on December 13, and a version of their paper is available on the arXiv preprint server.
