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Quantum field theory (QFT) was a crucial step in our understanding of the fundamental nature of the Universe. In its current form, however, it is poorly suited for describing composite particles, made up of multiple interacting elementary particles. Today, QFT for hadrons has been largely replaced with quantum chromodynamics, but this new framework still leaves many gaps in our understanding, particularly surrounding the nature of strong nuclear force and the origins of dark matter and dark energy. Through a new algebraic formulation of QFT, Dr Abdulaziz Alhaidari at the Saudi Center for Theoretical Physics hopes that these issues could finally be addressed.

The emergence of quantum field theory (QFT) was one of the most important developments in modern physics. By combining the theories of special relativity, quantum mechanics, and the interaction of matter via classical field equations, it provides robust explanations for many fundamental phenomena, including interactions between charged particles via the exchange of photons.

Still, QFT in its current form is far from flawless. Among its limitations is its inability to produce a precise description of composite particles such as hadrons, which are made up of multiple interacting elementary particles that are confined (cannot be observed in isolation). Since these particles possess an internal structure, the nature of these interactions becomes far more difficult to define mathematically, stretching the descriptive abilities of QFT beyond its limits.

Constructing spatial maps from sensory inputs is challenging in both neuroscience and artificial intelligence. Gornet and Thomson show that as an agent navigates an environment, a self-attention neural network using predictive coding can recover the environment’s map in its latent space.

Can black hole jets suffer from cosmic hypertension?

A brain-computer interface from the startup Inbrain could be used to treat Parkinson’s disease.

Kawasaki performed the world’s first public demonstration of its hydrogen-fueled prototype motorcycle this past weekend in Japan.

Dr John Marshall is a leading neuroscientist and a pioneer in the signalling and synaptic trafficking fields, and has made major contributions to understanding brain injury and neurodevelopmental disorders. He received his MSc from the University of Toronto and completed his PhD training in Neurobiology at the MRC at Cambridge University, England. He worked with Professor Len Kaczmarek at Yale University. Marshall assumed his position at Brown University in 1995 and has continued to produce cutting-edge research. His lab focus is on memory and behaviour in rodent models of Angelman syndrome.

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The “neural processing unit” is being pushed as the next big thing for “AI PCs” and “AI smartphones,” but they won’t eliminate the need for cloud-based AI.

Nvidia, the world’s largest company by value, is reportedly developing a new artificial intelligence (AI) chip based on its flagship product B200 for the China market.

The mass production of the new chip, which may be called B20, will commence later this year while shipments will start in the second quarter of next year, Reuters reported, citing sources familiar with the matter.

The report said Nvidia will work with Inspur, one of its distributors in mainland China. However, Inspur said it has not started any business and cooperation related to B20 as of now. It said the Reuters report is not true.

Here, the authors demonstrate a combined atom array-nanophotonic chip platform for quantum networking and distributed quantum computing, enabled by a high-fidelity background-free imaging technique, a semi-open photonic chip geometry, and free-space coupling to the nanophotonic cavities.