Neural Net Processing
Articles tagged with Neural Net Processing
New AI tool developed by Stowers Institute and Helmholtz Munich scientists predicts how cells choose their future — helping uncover hidden drivers of development
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.
WVU legal expert finds judges cautiously adopting AI while guarding human authority
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.
New report looks at how AI is impacting software development
The report examines how generative AI tools are transforming software development, offering benefits such as increased productivity but also raising security vulnerabilities and technical debt. Strong software engineering practices are still required to ensure systems are secure, reliable, and maintainable.
Terrence Sejnowski wins inaugural World Digital Technology Academy Award
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.
Beyond silicon: the soft, dissolvable brain chips engineered to learn and vanish
Artificial synapses are built from soft, bio-friendly materials that operate like human brain synapses, merging data storage and computing into a single unit. Laboratory prototypes demonstrate immense capabilities, consuming energy on the scale of femtojoules.
AI models lean on autism stereotypes when giving social advice, new study finds
Artificial intelligence models provide personalized advice, but may perpetuate negative stereotypes about people with autism. Researchers found that up to 70% of the time, AI discourages those with autism from socializing.
AI tracks motor heat in real time – enabling more efficient electric drives without extra sensors
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.
New technique improves accuracy of graph neural networks
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%.
ACM AI Letters journal publishes first issue
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.
AI set to transform personality testing, new research finds
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.
Geneva becomes world’s capital of AI in July for ITU’s AI for Good Global Summit
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.
New approach finds privacy vulnerability and performance are intertwined in AI neural networks
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 pioneer new technique to stop LLMs from giving users unsafe responses
Researchers at North Carolina State University have identified key components in large language models that ensure safe responses to user queries. They've developed a new technique to improve LLM safety while minimizing the alignment tax, which allows AI systems to provide safe responses without affecting performance.
How the brain charts emotion in a map-like way
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.
A comprehensive review charts how psychiatry could finally diagnose what it actually treats
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 ...
AI, monkey brains, and the virtue of small thinking
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.
Robots that can see around corners using radio signals and AI
Researchers at the University of Pennsylvania have developed HoloRadar, a system that enables robots to reconstruct hidden 3D spaces beyond their line of sight using radio waves processed by AI. This capability can improve safety and performance in driverless cars and cluttered indoor settings.
Powering AI from space, at scale
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.
Rice establishes Global Brain Economy Initiative in Davos, aligned with new report on brain health and AI
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.
New UMass Amherst-led study shows that analog hardware may solve internet of things’ speedbumps and bottlenecks
A UMass Amherst-led study suggests old-school analog computing can improve energy efficiency and computing speed in the IoT. Researchers have developed a brain-inspired sensing system that combines touch sensors and smart memory chips to reduce data transmission speeds.
AI-based system for real-time detection of whip sounds in horse racing
Researchers developed an AI-based system that accurately detects whip sounds in horse racing, achieving detection rates of up to 70% in audio data. The system's ability to process audio in real-time and its reliance on high-frequency components make it a promising tool for improving animal welfare and fair competition.
Explore AI innovations transforming finance, cybersecurity, and healthcare with Bentham Science’s newest release
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.
Stowers Institute appoints first AI Fellow to help advance biological research with artificial 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.
Generative AI enters nephrology: Towards an augmented medicine
The conference explores how generative AI is reshaping kidney medicine through AI-driven diagnostics, data integration, and omics analysis. Key findings include the use of LLMs to transform diagnostic precision, patient management, and research design.
Exploring a novel approach for improving generative AI models
Researchers at Institute of Science Tokyo developed a new framework for generative diffusion models by reinterpreting Schrödinger bridge models as variational autoencoders. This approach reduces computational costs and prevents overfitting, enabling more efficient generative AI models with broad applicability.
Physical neural networks, the new frontier for sustainable artificial intelligence
Researchers at Politecnico di Milano developed photonic chips for training physical neural networks, eliminating digitisation requirements. This allows for faster, more robust, and efficient network training using light signals.
New light-based chip boosts power efficiency of AI tasks 100 fold
Researchers have developed a new light-based chip that cuts power consumption for image recognition tasks by up to 100 times, using lasers and microscopic lenses fabricated onto circuit boards. This breakthrough enables faster performance and potentially strain-free AI systems.
Smart packaging reveals product condition through color changes – precise automated color recognition opens doors to new types of indicators
Researchers at the University of Vaasa developed smart packaging that can detect subtle color changes in printed packages, enabling cost-effective solutions for industries like food and beverage, healthcare, and logistics. This technology provides a human-eye accurate and environmentally friendly alternative to electronic sensors, pavi...
AI reveals unexpected new physics in dusty plasma
Physicists used a machine-learning method to identify surprising new twists on the non-reciprocal forces governing a many-body system. The AI approach provides precise approximations for these forces, correcting common theoretical assumptions with an accuracy of over 99%.
New book highlights real-world applications of artificial intelligence (AI) and machine learning (ML)
The new book highlights the transformative role of artificial intelligence (AI) and machine learning (ML) across various domains, including mechatronics, cybersecurity, digital health, and automation. Readers will gain practical insights into AI-based techniques in power systems, social media management, and healthcare diagnostics.
Accelerating science with AI
Researchers use AI to solve differential equations, such as Schrodinger's equation, for large-scale systems, improving efficiency and accuracy in fields like drug discovery and material design.
Researchers found a better way to teach large language models new skills
A new technique called WeGeFT improves large language model performance in tasks such as commonsense reasoning and code generation. By fine-tuning key parameters, researchers reduce the need for significant computational power, advancing the field of artificial intelligence.
TU Wien makes uncertainty in artificial intelligence quantifiable
An interdisciplinary team at TU Wien has developed a method that allows for the exact calculation of how reliably a neural network operates within a defined input domain. This enables mathematical guarantees for the safe use of AI in sensitive applications.
Explaining AI decisions using a combination of images and language: Leopoldina and Commerzbank Foundation honour Zeynep Akata with the 2025 “ZukunftsWissen” Award
Computer scientist Zeynep Akata has developed innovative methods to combine visual, linguistic, and conceptual elements in AI to increase user trust. Her research on explainable AI aims to make image classification decisions more transparent.
AI shortens the development time of new materials
An AI model developed by Ehsan Ghane at the University of Gothenburg can predict the durability and strength of woven composite materials, reducing development time. The model integrates material laws to make extrapolations outside training data, enabling better understanding of material behavior.
Teaching theory of mind to robots to enhance collaboration
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.
New AI model dramatically improves subgraph matching accuracy by eliminating noise
A new deep learning model, ENDNet, significantly enhances subgraph matching accuracy by identifying and neutralizing extra nodes that interfere with the matching process. This improves performance in pattern recognition tasks across various fields, including drug discovery and natural language processing.
Building trust in artificial intelligence for healthcare: Lessons from clinical oncology
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.
New AI models possible game-changers within protein science and healthcare
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.
inait announces collaboration with Microsoft to deploy novel AI based on digital brains across industries
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.
Neural network deciphers gravitational waves from merging neutron stars in a second
A new machine learning algorithm can fully characterize systems of merging neutron stars in under a second, compared to traditional methods which take around an hour. This allows for rapid localization of the source and pointing of telescopes towards the merging neutron stars.
A new mathematical model improves prediction of human mobility between cities
A new mathematical model developed by URV's SeesLab research group improves prediction of human mobility between cities. The model combines machine learning techniques, statistical physics and Bayesian statistics to efficiently balance complexity and accuracy.
A groundbreaking approach: Researchers at The University of Texas at San Antonio chart the future of neuromorphic computing
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.
Development of a high-performance AI device utilizing ion-controlled spin wave interference in magnetic materials
Researchers at NIMS developed a next-generation AI device leveraging ion-controlled spin wave interference in magnetic materials, outperforming conventional devices by up to 10 times. The technology enables energy-efficient computations with minimal degradation when miniaturized, opening doors for various industrial applications.
Reading signs: New method improves AI translation of sign language
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.
New book explores promise and perils of AI for scientific community
The book examines AI's current advances, hurdles, and potential, emphasizing the need for science to maintain core norms and values. Experts advocate for human accountability and responsibility when using AI in research, highlighting the importance of transparent disclosure and attribution.
NeuroMechFly v2: Simulating how fruit flies see, smell, and navigate
NeuroMechFly v2 simulates how a fruit fly navigates through its environment while reacting to sights, smells, and obstacles. The model can track moving objects visually or navigate towards an odor source, while avoiding obstacles in its path, enabling researchers to study brain-body coordination and animal intelligence.
Smarter clot busting: WPI researcher to develop real-time imaging technology for stroke treatment
Yihao Zheng and his team are developing a fiber-optic probe that analyzes artery blockages in the brain and guides procedures for blockage removal. The technology uses light and advanced calculations to determine the properties of blood clots, enabling doctors to make informed decisions about how to remove them.
AI helps distinguish dark matter from cosmic noise
A deep-learning algorithm developed by astronomer David Harvey can untangle the complex signals of self-interacting dark matter and AGN feedback in galaxy cluster images. The Inception model achieved an accuracy of 80% under ideal conditions, showcasing its potential for analyzing vast amounts of space data.
An entire brain-machine interface on a chip: Converting brain activity to text on one extremely small integrated system
Researchers at EPFL developed a next-generation miniaturized brain-machine interface capable of direct brain-to-text communication on tiny silicon chips. The MiBMI system can decode neural signals generated when a person imagines writing letters or words with high accuracy and low power consumption.
UCF launches inaugural mentorship, scholarship initiative for students in AI
UCF's STRONG-AI initiative aims to uplift bright, low-income undergraduate students in pursuing well-rounded AI education through faculty and peer mentorship and scholarship. The program has received over 150 applications and will select 10-15 students annually based on financial aid eligibility and academic success.
AI can tell if a patient battling cancer needs mental health support
A new AI model developed by researchers at the University of British Columbia can accurately predict if a patient receiving cancer care will require mental health services. The AI analyzes oncologist's notes and identifies subtle clues that suggest a patient may benefit from early psychiatric or counselling interventions.
The hidden geometry of learning: neural networks think alike
Researchers found that neural networks use a similar path to chart their way from ignorance to truth when presented with images, despite varying network designs and training recipes. This commonality holds the potential for developing more efficient image classification algorithms, reducing the computational power required by AI systems.
Researchers reveal roadmap for AI innovation in brain and language learning
A new study highlights the importance of differentiating between formal and functional competence in language learning models. Researchers argue that leveraging human neuroscience insights can help develop more powerful AIs that mimic the brain's modularity, leading to improved performance and natural user interaction.
Innovations in depth from focus/defocus pave the way to more capable computer vision systems
A new depth from focus/defocus approach, DDFS, combines model-based and learning-based strategies to achieve notable improvements in performance and applicability. The proposed method outperformed state-of-the-art methods in various metrics for several image datasets.
Brainstorming with a bot
A researcher has developed a chatbot with expertise in nanomaterials, leveraging document-retrieval method to provide accurate context. The bot uses embedding to categorize and link information quickly, generating factual responses sourced from trusted documents.
Can AI push the boundaries of privacy and reach the subconscious mind?
The European Union's AI act could enable AI to access our subconscious minds, potentially leading to manipulation. According to Ignasi Beltran de Heredia, only 5% of brain activity is conscious, and the remaining 95% operates subconsciously, making it difficult for us to control or even be aware of.
AI should be better understood and managed – new research warns
A Lancaster University academic argues that AI and algorithms contribute to polarization, radicalism, and political violence, posing a threat to national security. The paper examines how AI has been securitized throughout its history, highlighting the need for better understanding and management of its risks.
UTSA researchers develop energy-efficient AI with $2 million NSF grant
Researchers are combining biology, physics, computer science, and engineering to design electric circuits that mimic the brain's adaptive behavior. The goal is to create a more efficient AI application that can learn from history and adapt without significant energy consumption.