https://buff.ly/3yezAQJ #UAV #Defence #OSINT
Technicians plan to conduct deck-handling testing of the MQ-25 Stingray on the aircraft carrier USS George HW Bush (CVN 77) while the ship is underway in December. (Michael Fabey)
Technicians aboard the aircraft carrier USS George HW Bush (CVN 77) were preparing equipment for at-sea deck-handling testing of the Boeing MQ-25 Stingray unmanned aerial vehicle (UAV) on 7 December, according to a media briefing provided that day in the ship’s hangar bay.
Alphabet’s AI research company DeepMind has released the next generation of its language model, and it says that it has close to the reading comprehension of a high schooler — a startling claim.
It says the language model, called Gopher, was able to significantly improve its reading comprehension by ingesting massive repositories of texts online.
DeepMind boasts that its algorithm, an “ultra-large language model,” has 280 billion parameters, which are a measure of size and complexity. That means it falls somewhere between OpenAI’s GPT-3 (175 billion parameters) and Microsoft and NVIDIA’s Megatron, which features 530 billion parameters, The Verge points out.
These longstanding challenges are both related to how functionals behave when presented with a system that exhibits “fractional electron character.” By using a neural network to represent the functional and tailoring our training dataset to capture the fractional electron behaviour expected for the exact functional, we found that we could solve the problems of delocalization and spin symmetry-breaking. Our functional also showed itself to be highly accurate on broad, large-scale benchmarks, suggesting that this data-driven approach can capture aspects of the exact functional that have thus far been elusive.
For years, computer simulations have played a central role in modern engineering, making it possible to provide reliable answers to questions like “will this bridge stay up?” to “will this rocket make it into space?” As technology increasingly turns to the quantum scale to explore questions about materials, medicines, and catalysts, including those we’ve never seen or even imagined, deep learning shows promise to accurately simulate matter at this quantum mechanical level.
An artificial intelligence system developed in John Dabiri’s lab could one day swim and navigate autonomously underwater even more effectively than living creat… See more.
New AI uses reinforcement learning to efficiently navigate oceans.
Gene therapy is a powerful developing technology that has the potential to address myriad diseases. For example, Huntington’s disease, a neurodegenerative disorder, is caused by a mutation in a single gene, and if researchers could go into specific cells and correct that defect, theoretically those cells could regain normal function.
A major challenge, however, has been creating the right “delivery vehicles” that can carry genes and molecules into the cells that need treatment, while avoiding the cells that do not.
Now, a team led by Caltech researchers has developed a gene-delivery system that can specifically target brain cells while avoiding the liver. This is important because a gene therapy intended to treat a disorder in the brain, for example, could also have the side effect of creating a toxic immune response in the liver, hence the desire to find delivery vehicles that only go to their intended target. The findings were shown in both mouse and marmoset models, an important step towards translating the technology into humans.
😃
In a paper published today in the scientific journal Science, DeepMind demonstrates how neural networks can be used to describe electron interactions in chemical systems more accurately than existing methods.
Density Functional Theory, established in the 1960s, describes the mapping between electron density and interaction energy. For more than 50 years, the exact nature of mapping between electron density and interaction energy—the so-called density functional—has remained unknown. In a significant advancement for the field, DeepMind has shown that neural networks can be used to build a more accurate map of the density and interaction between electrons than was previously attainable.
By expressing the functional as a neural network and incorporating exact properties into the training data, DeepMind was able to train the model to learn functionals free from two important systematic errors—the delocalisation error and spin symmetry breaking—resulting in a better description of a broad class of chemical reactions.