Articles tagged with Artificial Neural Networks
TEGNet accelerates optimization in thermoelectric generator design by predicting performance with high accuracy and speed. The AI model enables designers to freely combine independent models for various materials, enabling complex structure exploration and high conversion efficiencies.
Researchers developed RegVelo, an AI framework that models cellular dynamics and gene regulation to predict cellular fate decisions. The model traces developmental trajectories and simulates regulatory interactions, providing insights into hidden drivers of development and potential therapeutic targets.
The new AI model uses genetic mutation patterns to trace ancestral relationships between species, including humans and mosquitoes. The tool can predict when gene pairs last shared a common ancestor and is faster than traditional statistical methods.
Researchers developed a new framework, 'Mollifier Layers,' to tackle challenging inverse PDEs. This advance could benefit fields such as genetics and weather forecasting by inferring hidden forces that produce observable patterns.
New research from West Virginia University finds that judges are adopting generative artificial intelligence in courtrooms, but remain committed to human control over judicial decision-making. Judges use AI for administrative tasks like document summarization and case organization, but prioritize legal reasoning and final judgment.
Researchers emphasize the need for more thorough frameworks to ensure AI-enabled robots embody human values. The field should focus on three complementary lines of defense: rules that shape robot decisions, checks that monitor behavior, and safety reasoning.
Researchers at the University of Houston have developed an AI-driven framework to extract and analyze historical flood insurance maps, uncovering significant changes in flood hazard areas. The study reveals that flood risks have expanded in two areas and reduced in one, with critical consequences for resilience and exposure.
AI models can analyze complex data to predict disease progression and identify early signs of kidney damage. This allows for earlier detection and better treatment planning, making a significant impact on patient outcomes.
Researchers have developed SmartDJ, an AI-powered editor that allows users to reshape audio experiences with simple words. The system uses language models and diffusion models to interpret high-level requests and generate edited outputs.
Terrence Sejnowski receives Scientific Breakthrough Award for his foundational development of Boltzmann machines, providing the architectural bedrock for deep learning and generative AI. His work has had a profound impact on modern artificial intelligence and tools like ChatGPT.
Researchers propose a novel brain architecture for efficient processing, integrating parallel cortical and subcortical pathways. This approach may improve decision-making tasks, suggesting current AI models are missing key brain function principles.
A research team at Saarland University has developed an AI-assisted method to determine temperature distribution inside a running electric motor in real time, without additional hardware. The system uses motor-condition data extracted from electromagnetic fields and can detect thermal overload and optimize power regulation.
Researchers developed a new training technique, HarmonyGNN, to improve the accuracy of graph neural networks in heterophilic graphs. The framework achieved state-of-the-art performance on four heterophilic graphs with accuracy improvements ranging from 1.27% to 9.6%.
Researchers identified patient-reported symptoms associated with GLP-1s, including menstrual changes, fatigue, and temperature-related complaints, that may not be fully captured in clinical trials or drug labeling. Nearly 4% of Reddit users reported reproductive symptoms, and fatigue was the second most common complaint.
A new study introduces AI-based control strategies that ensure local grids remain reliable and resilient. By utilizing Artificial Neural Networks, the system can predict and compensate for grid changes in real-time, outperforming traditional control methods.
Researchers propose a temporally developmental continual learning framework inspired by human brain development, enabling cross-domain learning across perception, motor control, and interaction tasks. The approach achieves stable and strong continual learning performance while reducing network size and mitigating catastrophic forgetting.
The Association for Computing Machinery has published its inaugural issue of ACM AI Letters, a premier venue for rapid and timely AI research. The journal aims to bridge the gap between traditional conferences and journals, featuring short, peer-reviewed contributions that accelerate knowledge dissemination across academia and industry.
A team of researchers at Binghamton University has developed a method to pinpoint discoveries that reshaped the course of science. The new metric uses neural embedding to analyze approximately 55 million scientific papers and patents, identifying major breakthroughs and simultaneous discoveries with greater accuracy.
New research from the University of East London suggests that machine learning can improve the accuracy and nuance of personality tests like DISC assessment. Using over 1,000 participants, researchers achieved accuracy rates of over 93% in predicting personality types and identified four clear clusters with subtle overlaps.
The International Telecommunication Union (ITU) will host the seventh AI for Good Global Summit from 7 to 10 July 2026 at Geneva’s Palexpo convention centre. The summit aims to guide the future of artificial intelligence and unlock its potential to serve humanity.
Researchers at Worcester Polytechnic Institute developed a palm-sized aerial robot that uses ultrasound sensors and AI to navigate through fog, smoke, and other difficult conditions. The drone achieved a success rate of 72% to 100% in navigating challenging courses during 180 tests.
Researchers discovered that key weight parameters contribute to both performance and data-privacy vulnerabilities in AI neural networks. They developed a technique to balance these aspects, achieving better results than existing methods in defending against membership inference attacks.
Researchers developed a new multiview DNN structure to capture complex 3D anatomy and physiology from multiple imaging views, improving diagnostic accuracy for cardiovascular conditions. The approach demonstrated better performance than single-view DNNs and provided a viable alternative for other medical imaging modalities.
A new study reveals that the hippocampus represents emotion concepts in a structured hierarchy of pleasantness and bodily reaction, while the ventromedial prefrontal cortex tracks relationships between these nodes. This map-like representation may help in the treatment of mental illnesses, such as depression and anxiety.
Emerging research across conceptual frameworks, biomarker science, digital phenotyping, and artificial intelligence synthesizes a translational pathway toward a more biologically grounded and clinically useful approach to psychiatric diagnosis. The current system falls short due to standardized clinical language and lack of biological ...
Scientists at the University of Sydney have developed an ultra-compact AI chip that harnesses the power of light to perform calculations, potentially lowering energy consumption and increasing speed. The prototype, built in-house, achieved 90-99% classification accuracy in image classification tasks.
Researchers have developed a smaller and simpler AI model that accurately predicts neural responses to visual stimuli in macaque brains. The compact model reveals unique neuron preferences for features like edges and colors, shedding light on how the brain processes information.
A study reveals that identical photons in optical circuits exhibit Hopfield Network behavior, enabling associative memory mechanisms similar to the human brain. The research finds a fundamental limit to memory capacity, with quantum coherence allowing correct retrieval but transitioning to disorder as data volume increases.
Collaborative teams of AI-powered robots successfully navigated and extinguished simulated and hybrid simulation-physical fires, demonstrating a 99.67% success rate in real-world deployment potential.
A new method to steer AI output uncovers vulnerabilities and potential improvements. The researchers identified 512 concepts within five classes and improved LLM performance on narrow tasks.
Researchers have developed DEGU, a tool that improves the accuracy and efficiency of deep neural networks in predicting genomic experiment results. DEGU reduces the size of models while maintaining predictive capabilities, making it easier to understand uncertainty and drive reliable discoveries.
A recent study by Lucy Osler from the University of Exeter highlights how human-AI interactions can lead to inaccurate beliefs, distorted memories, and delusional thinking. Generative AI systems can create an ideal environment for delusions to flourish due to their conversational nature.
A recent NSF grant will support the development of new diagnostics and predictive models for understanding self-competition and weak asymmetry in turbulent flows. The project aims to uncover hidden patterns that current models miss, leading to improved simulations in weather forecasting, climate modeling, and engineering design.
Researchers have developed a passive, solar-powered orbital data center that can scale AI computing and reduce environmental impact. The system leverages decades of research on 'tethers' and could host thousands of computing nodes to replicate terrestrial data centers.
The Global Brain Economy Initiative aims to establish brain capital as an essential asset for the 21st century, connecting neuroscience with economic policy. The initiative's mission is to address disparities in support for brain capital across various sectors and promote long-term growth, workforce resilience, and social well-being.
Researchers have developed a method called PropMolFlow that can generate molecular candidates roughly 10 times faster than existing methods while maintaining accuracy. The breakthrough could lead to faster creation of pharmaceuticals, materials, and new technologies by specifying properties first and then finding structures.
Researchers created a computational model that combines physiological signals, sensory input, and word information to construct human emotions. The model achieved an agreement rate of about 75% when compared to participants' self-reported emotional evaluations.
Engineers at the University of Pennsylvania have discovered that foams exhibit internal motion resembling deep learning in AI systems. The study suggests a common mathematical principle underlying both foams and AI training, with implications for designing adaptive materials and understanding biological structures.
The new program focuses on advancing foundational research in AI, including innovation in language models and algorithmic efficiency. Google will support research grants, scholarships for students, and educational initiatives at the TAU Center.
Dr. Tom McClelland from the University of Cambridge believes that our current evidence is insufficient to determine whether artificial intelligence has achieved consciousness. He suggests that sentience, which involves positive and negative feelings, is a more critical factor in making AI ethically significant.
A new AI framework uncovers simple, understandable rules governing complex dynamics in nature and technology. The AI generates equations that accurately describe complex systems, revealing hidden variables that govern their behavior. This approach offers scientists a new way to leverage AI for understanding complex systems.
Researchers used AI to study covert attention and found emergent properties in artificial neural networks, including new neuron types with response properties never highlighted before. The findings have implications for our understanding of the human brain and its ability to process information.
The University of Texas at Dallas has partnered with Tech Mahindra to facilitate collaboration on artificial intelligence (AI) innovation, skill development, and research. The partnership will provide opportunities for students and faculty to advance AI technologies, data science, and cybersecurity.
The EBRAINS Summit 2025 will bring together experts to assess how neuroscience can drive medical progress, digital innovation, and responsible data use. Preliminary results from the EPINOV clinical trial, integrating virtual brain technology for epilepsy surgery planning, will be presented.
Researchers from Johns Hopkins University found that selecting the right blueprint can accelerate learning in visual AI systems, rivalling conventional methods. By modifying convolutional neural networks, they generated brain-like activity patterns, suggesting that architectural design plays a crucial role in AI development.
Researchers aim to understand and overcome the limitations of current large language models, which make mistakes despite extensive training. The Emmy Noether Research Group will focus on developing new architectures with predictable capabilities.
Security researchers have developed a defense mechanism that protects against cryptanalytic parameter extraction attacks on AI systems. By making neurons in the same layer similar to each other, the defense creates a barrier of similarity that makes it difficult for attacks to proceed.
This book offers a comprehensive exploration of AI-driven analytics in finance, addressing market prediction, fraud detection, and risk assessment. It also discusses AI applications in healthcare and cybersecurity, including disease classification and biometric identification systems.
The new statistical method adapts to data structure, resisting outliers and providing greater stability on non-Euclidean spaces. This improves the reliability of analysis in areas like medical imaging, computer vision, and machine learning.
Researchers developed a novel topology-aware multiscale feature fusion network to enhance EEG-based motor imagery decoding. The TA-MFF network achieves excellent classification performance, outperforming state-of-the-art methods by leveraging spectral-topological data analysis-processing and inter-spectral recursive attention.
Researchers at University of Würzburg successfully tested an AI-based attitude controller for satellites directly in orbit, using Deep Reinforcement Learning. The test demonstrated the speed and flexibility of the DRL approach, which can automate control strategies and adapt to differences between expected and actual conditions.
Researchers at the University of Surrey have developed a brain-inspired approach to improve artificial neural networks' performance without sacrificing accuracy. The method, called Topographical Sparse Mapping (TSM), rethinks how AI systems are wired by connecting each neuron only to nearby or related ones.
Researchers warn that advances in AI and neurotechnology are outpacing our understanding of consciousness, with potential serious ethical consequences. A better understanding of consciousness could have major implications for AI, prenatal policy, animal welfare, medicine, mental health, law, and emerging neurotechnologies.
Researchers Prof Axel Cleeremans, Prof Anil Seth, and Prof Liad Mudrik warn that advances in AI and neurotechnology are outpacing our understanding of consciousness. They emphasize the need for theory-driven research and innovative methods to advance consciousness science.
Researchers at USC Viterbi School of Engineering have developed artificial neurons that physically embody the analog dynamics of biological brain cells. These innovations will allow for significant reduction in chip size and energy consumption, potentially advancing artificial general intelligence.
The Stowers Institute has appointed its first AI Fellow, Sumner Magruder, to harness the potential of artificial intelligence in biological research. He will collaborate with researchers to design new algorithms and unlock insights from large datasets.
Engineers at Duke University have constructed an 'agentic system' of AI bots that can solve complex design problems nearly as well as a fully trained scientist. The researchers created large language model (LLM) AI agents to complete all the legwork, allowing them to automate straightforward but niche design problems.
A team of researchers at the University of Waterloo developed a framework that uses mathematical tools and machine learning to rigorously check and verify the safety of AI-driven systems. The framework has been tested on challenging control problems and matched or exceeded traditional approaches.