Evolution Sets the Stage for More Powerful Spiking Neural Networks

Spiking neural networks (SNNs) closely replicate the structure of the human brain, making them an important step on the road to developing artificial intelligence. Researchers recently advanced a key technique for training SNNs using an evolutionary approach. This approach involves recognizing and making use of the different strengths of individual elements of the SNN.

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New Machine Learning-Based Model More Accurately Predicts Liver Transplant Waitlist Mortality

Data from a new study presented this week at The Liver Meeting Digital Experience® – held by the American Association for the Study of Liver Diseases – found that using neural networks, a type of machine learning algorithm, is a more accurate model for predicting waitlist mortality in liver transplantation, outperforming the older model for end-stage liver disease (MELD) scoring. This advancement could lead to the development of more equitable organ allocation systems and even reduce liver transplant waitlist death rates for patients.

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Recipe for Neuromorphic Processing Systems?

The field of “brain-mimicking” neuromorphic electronics shows great potential for basic research and commercial applications, and researchers in Germany and Switzerland recently explored the possibility of reproducing the physics of real neural circuits by using the physics of silicon. In Applied Physics Letters, they present their work to understand neural processing systems, as well as a recipe to reproduce these computing principles in mixed signal analog/digital electronics and novel materials.

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Applying Deep Learning to Automate UAV‐Based Detection of Scatterable Landmines

Recent advances in unmanned‐aerial‐vehicle‐ (UAV‐) based remote sensing utilizing lightweight multispectral and thermal infrared sensors allow for rapid wide‐area landmine

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ORNL researchers develop ‘multitasking’ AI tool to extract cancer data in record time

To better leverage cancer data for research, scientists at ORNL are developing an artificial intelligence (AI)-based natural language processing tool to improve information extraction from textual pathology reports. In a first for cancer pathology reports, the team developed a multitask convolutional neural network (CNN)—a deep learning model that learns to perform tasks, such as identifying key words in a body of text, by processing language as a two-dimensional numerical dataset.

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Researchers from TU Delft discover real Van Gogh using artificial intelligence

What did Vincent van Gogh actually paint and draw? Paintings and drawings fade, so researchers from TU Delft are using deep learning to digitally reconstruct works of art and discover what they really looked like. ‘What we see today is not the painting or drawing as it originally was,’ says researcher Jan van der Lubbe.

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