Articles tagged with Artificial Neural Networks
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Researchers found that artificial intelligence tools can accurately predict disease for patients with typical symptoms but struggle with those exhibiting atypical symptoms. Human oversight is necessary for high-quality patient-centered care when using AI as an assistive tool.
Training neural networks on simple cognitive tasks improves their ability to handle more complex ones. By adopting the principles of early childhood education, researchers found that recurrent neural networks can be trained faster and with better results.
Researchers discovered similarities between AI and human brains with aphasia, offering new insights into diagnosis and improving AI's fluency. The study suggests that understanding internal patterns in AI models may lead to smarter and more trustworthy AI.
Researchers at Duke University have developed a new framework called HUMAC that enables robots to collaborate like humans by teaching them Theory of Mind. After just 40 minutes of guidance, robot teams exhibited strong collaborative behaviors and achieved high success rates in simulations and physical tests.
A University at Buffalo-led study proposes using AI-powered handwriting analysis to identify spelling issues, poor letter formation, and other indicators of dyslexia and dysgraphia. The work aims to augment current screening tools and provide an early detection tool for these neurodevelopmental disorders.
A study suggests that groups of artificial intelligence language models can self-organise into societies, reaching consensus on linguistic norms, and are prone to tipping points in social convention. Collective biases emerge between agents through interactions, a blind spot in most current AI safety work.
Researchers propose Input-Driven Plasticity model, which integrates past and new information to guide memory retrieval. The model is robust to noise and uses it as a means to filter out less stable memories.
Researchers have designed a headphone system that translates several speakers simultaneously, preserving voice direction and qualities. The Spatial Speech Translation system uses off-the-shelf noise-cancelling headphones fitted with microphones to separate out different speakers in a space and translate their speech.
The Global Confidence Degree-based Graph Neural Network (GCD-GNN) framework improves financial fraud detection by integrating global confidence metrics with advanced graph learning techniques. It achieves record-breaking accuracy on real-world datasets, including a 97.26% AUC on T-Finance.
A new review advocates for building confidence in AI applications by implementing robust data governance frameworks, enhancing transparency, and involving stakeholders. The authors emphasize the importance of addressing ethical implications and ensuring equitable access to AI-driven innovations in clinical oncology.
Researchers at the University of California San Diego have made a groundbreaking discovery about how our brains learn new information. Using sophisticated imaging techniques, they found that individual neurons follow multiple rules during learning, rather than one set of uniform rules as previously thought. This new understanding has s...
Researchers have created a breakthrough photonic chip that can train nonlinear neural networks using light, accelerating AI training while reducing energy use. The chip uses a special semiconductor material to reshape how light behaves, enabling reconfigurable systems with wide mathematical function expression.
Scientists have developed an all-optical activation function based on sound waves for photonic computing, enabling the creation of energy-efficient artificial intelligence systems. This breakthrough could potentially facilitate the scaling up of physical computing systems and pave the way for more efficient optical neural networks.
Scientists have built a digital twin of the mouse brain's visual cortex using AI, predicting neural activity and anatomical features. The model can generalize to new visual inputs and data, speeding up brain research and understanding intelligence.
The conference gathered international researchers to discuss AI's role in drug discovery and development, including generative AI strategies for designing chemical compounds. The speakers emphasized the significance of personalized medicine, where therapies will be tailored to each patient's unique molecular profile.
Artificial neural networks trained on spontaneous retinal activity patterns show improved motion prediction in natural scenes. The approach also enhances performance when combined with naturalistic movie data.
Researchers developed new AI models, InstaNovo and InstaNovo+, to vastly improve accuracy and discovery in protein science. These models excel in tasks such as de novo peptide sequencing, identifying microorganisms, and discovering novel peptides, with implications for personalized medicine, cancer immunology, and beyond.
Göttingen research team develops infomorphic neurons that learn independently and self-organize among neighboring neurons. This allows the smallest unit in the network to control its own learning, enabling novel machine learning approaches and a deeper understanding of brain function.
Researchers at National University of Singapore invent new computing cell that can mimic electronic neurons and synapses, reducing size by a factor of 18 and energy consumption. The discovery enables AI systems to process more information while using less energy.
A new brain-like computer uses analog computing to process and store information in the same location as biological neurons, reducing power consumption by 0.25%. The device, called a memristor network, is more efficient than conventional transistor-based computers and has implications for autonomous vehicles and drones.
Researchers developed an AI model that classifies variable stars from light curves with high accuracy, outperforming traditional approaches. The StarWhisper LightCurve series achieves near 90% accuracy with minimal manual intervention, paving the way for parallel data analysis and multi-modal AI applications in astronomy.
The collaboration aims to accelerate the development and commercialization of inait's innovative AI technology, using its unique digital brain AI platform. It will focus on joint product development, go-to-market strategies, and co-selling initiatives, initially targeting the finance and robotics sectors.
Researchers at Saarland University are developing leaner, customized AI models and techniques like knowledge distillation to reduce energy consumption. These smaller models enable small and medium-sized businesses to access powerful AI technology without a large technical infrastructure.
A new AI tool, NicheCompass, visualizes a cell's social network to help treat cancer. By analyzing millions of cells from patient samples, the tool predicts molecular changes and identifies potential targets for personalized treatments.
Researchers at Technical University of Munich developed a new AI training method that significantly reduces energy consumption. The approach uses probabilities to determine parameters, making the training process 100 times faster while maintaining accuracy comparable to existing procedures.
A new study suggests that artificial intelligence can effectively detect wildfires in the Amazon rainforest, using satellite imaging and deep learning. The technology achieved a 93% success rate in training models via datasets of images with and without wildfires.
This study utilized deep learning models to diagnose and predict the likelihood of malignant transformation in oral potentially malignant disorders. AI-driven approaches offer noninvasive, cost-effective, and objective means to enhance early detection and improve patient outcomes.
A new AI model measures how fast the brain ages by analyzing MRI scans, providing a more accurate picture of brain health. The tool closely correlates faster brain aging with increased cognitive decline and dementia risk, offering potential for early biomarkers and personalized treatment.
Research advances higher-order networks to capture multi-agent interactions, enabling accurate modeling of biological, social, and physical systems. The Dirac-Bianconi operator provides a powerful generalization of the graph Laplacian, encoding local and global interactions across different topological dimensions.
A new machine learning model, NAS-WD, has improved the accuracy of detecting 'woody breast' in chicken meat to 95%, allowing for better quality assurance and customer confidence. The model uses hyperspectral imaging to analyze complex data from images, enabling more accurate detection than traditional methods.
The research team successfully integrated miniaturized multilayer optical diffractive neural networks onto the distal end of MMFs, enabling full-optical image transmission. The system achieved exceptional performance in imaging handwritten digits and demonstrated high-quality optical image reconstruction.
A recent study emphasizes the urgent need to address bias in generative AI systems, which can distort outcomes and erode public trust. The research suggests that developing and deploying ethical, explainable AI is crucial to ensure fairness and transparency in critical decision-making areas.
Yann LeCun, NYU's Courant Institute of Mathematical Sciences professor, has been selected as a winner of the 2025 Queen Elizabeth Prize for Engineering for his groundbreaking research on artificial neural networks. His work enabled machines to process and learn from vast amounts of data in ways previously unimaginable.
Researchers developed MUNIS, a deep learning tool that predicts CD8+ T cell epitopes with high accuracy, potentially accelerating vaccine development. The tool was validated using experimental data from influenza, HIV, and EBV, demonstrating its potential to streamline vaccine design.
A recent study reveals that rats' visual recognition abilities are extremely efficient and adaptable, even outperforming advances in artificial intelligence. Rats employ more flexible image processing strategies than CNNs, which could inspire new approaches to AI model development.
Neuromorphic computing is poised to emerge into full-scale commercial use, driven by the need for energy-efficient solutions. The review article proposes strategies for building large-scale neuromorphic systems that can tackle complex real-world challenges.
Researchers propose several key features to optimize sparsity, massive parallelism, and hierarchical structure in neural representation for neuromorphic systems. The goal is to achieve energy efficiency and compactness while retaining information at high fidelity.
A new method has improved AI translation of sign language by adding data on hand and facial expressions, as well as skeletal information. This has led to a significant increase in accuracy, making it easier for people with hearing impairments to communicate.
DNNs have an inbuilt 'Occam's razor,' favouring simpler solutions that fit training data. This bias helps them generalize well on simple patterns but may struggle with complex data, aligning with real-world data characteristics.
Researchers at the University of Bonn have developed a new training technique for highly efficient AI methods, inspired by biological neurons that use short voltage pulses to communicate. This approach enables spiking neural networks to be trained using conventional methods, resulting in improved accuracy and reduced energy consumption.
A new method called Annotatability helps identify mismatches in cell annotations and better characterizes biological data structures. This approach enables more precise downstream analysis of biological signals, capturing cellular communities associated with target signals.
The study reveals that directional connections propagate signals in a downstream flow, leading to more complex activity patterns. Mathematical models also suggest that modularity and connectivity interact to foster dynamical complexity.
Current energy-hungry transformer-based systems contrast with Turing's idea of machines that develop intelligence naturally, like human children. AI systems can now perform tasks exclusive to human intellect, such as generating coherent text and discussing abstract ideas, but with limitations on sustainability and societal impact
Researchers at KAIST developed a new method to learn without weight transport, enabling faster and more accurate learning. By pre-training with random noise, the team showed that neural networks can achieve high learning efficiency and solve the weight transport problem.
Researchers developed a new benchmark for health care using reinforcement learning, which shows promise in managing chronic or psychiatric diseases. However, current methods are data-hungry and fail to perform accurately when tested on real-world data.
A groundbreaking AI model called NitroFusion creates images in seconds using modest hardware, eliminating the need for large computing resources. The open-source technology enables creative professionals and individuals to produce high-quality images affordably.
Physicists at the University of Michigan have developed an algorithm that enables materials to learn and adapt, mimicking brain-like behaviors. This breakthrough has implications for the development of advanced materials with self-tuning properties.
A novel classification method for adult spinal deformity diseases has been developed using deep learning of gait data, achieving a correct response rate of 71.43% in testing, surpassing conventional methods.
Scientists at MIT developed a fully integrated photonic processor that can perform all key computations of a deep neural network optically on the chip. The device completed machine-learning classification tasks in under half a nanosecond while achieving over 92% accuracy, similar to traditional hardware.
Researchers developed an AI tool called BrainBench to test large language models' ability to predict neuroscience study outcomes. The results showed that LLMs surpassed human experts with an average accuracy of 81%, highlighting their potential as powerful tools for accelerating research.
Recent Nobel Prizes in physics and chemistry have recognized the convergence of AI with physics and chemistry, emphasizing the need for interdisciplinary research. Researchers advocate for nurturing AI-enabled polymaths to bridge the gap between theoretical advancements and practical applications.
Researchers at Cold Spring Harbor Laboratory have devised a potential solution to the paradox of animal innate abilities using artificial intelligence. The genomic bottleneck algorithm allows for compression levels unseen in AI, enabling faster runtimes and potentially leading to more evolved AI systems.
A new AI tool generates realistic satellite images of future flooding, which can help communities visualize and prepare for approaching storms. The method combines a generative artificial intelligence model with a physics-based flood model, producing more accurate and realistic images than an AI-only approach.
Researchers have trained AI models to distinguish brain tumors from healthy tissue using convolutional neural networks and transfer learning. The models achieved an average accuracy of 85.99% at detecting brain cancer, with the ability to generate images showing specific areas in its tumor-positive or negative classification.
Researchers propose quasi-convolution coding to simplify reservoir computer design, enhancing memory capacity and reducing complexity. The approach leverages dual polarization modes of commercial lasers, offering a feasible strategy for constructing integrated deep RC systems.
Researchers suggest that starting with smaller neural networks and using curriculum learning can improve performance and reduce the need for massive computing resources. This approach could lead to more resource-efficient and less energy-consuming AI systems.
Researchers found that membership inference attacks on large language models (LLMs) are not effective in measuring information exposure risks. The common method used to test LLM leaks suffers from ambiguity due to the fluidity of language, making it difficult to define a representative set of non-member candidates.
A deep learning AI model can identify pathology in images of animal and human tissue much faster and often more accurately than people, potentially revolutionizing disease-related research and medical diagnosis. The model was trained using images from past epigenetic studies and showed accuracy comparable to human experts.
Researchers developed a novel AI approach to predict atomic-level chemical bonding information in 3D space, bypassing traditional supercomputer simulations. This methodology accelerates calculations by learning chemical bonding information using neural network algorithms from computer vision.
The AI-powered system can detect toxic gases like nitrogen dioxide in real-time, identifying the source of harmful gas leaks. The system's optimization technique ensures fewer resources are used while providing faster and more accurate gas leak detection.