Open-source deep-learning framework for building, training, and fine-tuning deep learning models using state-of-the-art Physics-ML methods — NVIDIA/physicsnemo
Category: robotics/AI – Page 190
AI-designed waveguides pave the way for next-generation photonic devices
A team of researchers at the University of California, Los Angeles (UCLA) has introduced a novel framework for designing and creating universal diffractive waveguides that can control the flow of light in highly specific and complex ways.
This new technology uses artificial intelligence (AI), specifically deep learning, to design a series of structured surfaces that guide light with high efficiency and can perform a wide range of functions that are challenging for conventional waveguides.
The work is published in the journal Nature Communications.
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A comprehensive overview of prompt engineering
Quantum machine learning: Small-scale photonic quantum processor can already outperform classical counterparts
One of the current hot research topics is the combination of two of the most recent technological breakthroughs: machine learning and quantum computing.
An experimental study shows that already small-scale quantum computers can boost the performance of machine learning algorithms.
This was demonstrated on a photonic quantum processor by an international team of researchers at the University of Vienna. The work, published in Nature Photonics, shows promising new applications for optical quantum computers.
Tumor diagnostics: AI model detects more than 170 types of cancer
The MRI shows a brain tumor in an inauspicious location, and a brain biopsy will entail high risks for a patient who had consulted doctors due to double vision. Situations such as this case prompted researchers at Charité—Universitätsmedizin Berlin to look for new diagnostic procedures. The result is an AI model.
The model makes use of specific characteristics in the genetic material of tumors—their epigenetic fingerprint, obtained for example from cerebrospinal fluid, among other things. As the team shows in the journal Nature Cancer, the new model classifies tumors quickly and very reliably.
Today, far more types of tumors are known than the organs from which they arise. Each tumor has its own characteristics: certain tissue features, growth rates and metabolic peculiarities. Nevertheless, tumor types with similar molecular characteristics can be grouped together. The treatment of the individual disease depends decisively on the type of tumor.
