Mimicking the brain can improve AI performance

According to a study published in Neurocomputing, mimicking the brain's neural wiring can significantly improve the ...

Large reasoning models almost certainly can think

Recently, there has been a lot of hullabaloo about the idea that large reasoning models (LRM) are unable to think. This is ...
GEN - Genetic Engineering and Biotechnology News

Beyond Process Development: AI Reshaping Use of Digital Twins

In industrial engineering, digital twins—computer models of systems or processes—let scientists try out ideas before finalizing designs. In addition, on the factory floor, twins can model processes in ...
PsyPost on MSN

The secret to sustainable AI may have been in our brains all along

Researchers have developed a new method for training artificial intelligence that dramatically improves its speed and energy ...

Former OpenAI researchers launch Applied Compute with $80M in funding

Applied Compute announced on Wednesday that it has raised $80 million in funding. The capital was provided by Benchmark, ...
American Association for the Advancement of Science (AAAS)

Prize winner’s research reveals how complex neural circuits are correctly wired during brain development

Using single-cell RNA sequencing and genetic screening, the researchers identified key surface proteins that mediate ...

MicroCloud Hologram Inc. Releases Hybrid Quantum-Classical Convolutional Neural Network, Achieving New Breakthrough in MNIST Multi-Class Classification

MicroCloud Hologram Inc. (NASDAQ: HOLO), (“HOLO” or the "Company"), a technology service provider, proposed a Quantum Convolutional Neural Network (QCNN) based on hybrid quantum-classical learning and ...
IEEE

Fast Adversarial Training For Graph Neural Network Based Resource Allocation

Abstract: In this letter, we propose a meta-learning-based fast adversarial training method to address the vulnerability of graph neural network (GNN) based resource allocation method to adversarial ...
IEEE

Multiattention Spatiotemporal Fusion Graph Neural Network for Chemical Process Fault Diagnosis

Abstract: Fault diagnosis of complex industrial processes becomes challenging due to temporal and spatial dependencies in process data. This means that the emergence and evolution of faults are ...