Articles tagged with Adaptive Systems
Vasile's research aims to map and model an agent's capabilities, particularly in motion, manipulation, and perception, to reliably predict their behavior. The goal is to use this understanding to plan effectively for large teams of agents.
Researchers successfully transferred a gift-giving courtship behavior from Drosophila subobscura to Drosophila melanogaster by manipulating a single gene in insulin-producing neurons. This study represents the first example of transferring behavior between species through genetic manipulation.
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.
A new robotic slip-prevention method has been developed to improve robots' grip and handling of fragile or slippery objects. This bio-inspired approach allows robots to predict when an object might slip and adapt their movements in real-time, outperforming traditional strategies.
Researchers developed an AI system that enables a four-legged robot to adapt its gait to different terrain, just like animals. The robot learned to switch gaits on the fly and navigate uneven surfaces without any alterations to the system itself, overcoming previous limitations around adaptability.
Cassie, a digital-human assistant developed by Texas A&M University, is transforming the way patients interact with healthcare providers. With facial recognition and emotional intelligence, Cassie offers a two-way interaction that feels like a conversation.
The Digits framework uses compressed air to produce shape changes, vibrations, and haptic feedback, offering a versatile platform for virtual reality and physical therapy. The device's modular design and pneumatic actuation enable adaptable and scalable control methods.
Researchers used proactive and transfer learning strategies to mitigate data shifts in AI models for hospital applications. They found that models trained on one hospital type performed better than those trained on all hospitals using transfer learning.
Aerial robots are limited to manipulating rigid objects, but Lehigh University researcher David Saldaña aims to expand their capabilities with an adaptive controller and reinforcement learning. His research has potential applications in construction, disaster response, and industrial automation.
Researchers from Empa developed machine learning algorithms to optimize laser-based manufacturing techniques, reducing preliminary experiments by two-thirds. They also implemented real-time optimization using field-programmable gate arrays (FPGAs) for improved welding processes.
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.
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 Dartmouth team's AI model recognized Navajo with near-perfect accuracy, identifying related languages such as Apache and Native Alaskan languages. The study suggests that this technology could be a bridge to including smaller languages in online translation services.
Researchers developed a novel vote-based model for accurate hand-held object pose estimation, addressing issues with existing approaches. The new framework achieves significant improvements in accuracy and robustness, enabling robots to handle complex objects and advancing AR technologies.
The Digital Evolution Index reveals a crucial inflection point in the global digital landscape, with slowing growth and plateauing digital inclusion metrics. Emerging post-pandemic challenges include the 'winner-takes-most' scenario driven by AI, highlighting the need for resilient digital economies.
Researchers found that obesity causes a disruption in the liver's ability to adapt to starvation, specifically in the temporal coordination of molecules. This suggests that obesity makes the body more vulnerable to the negative effects of starvation, despite no significant structural disruptions in the molecular network.
Researchers develop active metamaterials that can autonomously roll, crawl, and wiggle over unpredictable terrain, including uphill and obstacles. These 'odd' objects achieve motion through unusual interactions between motorized building blocks, demonstrating decentralized and robust locomotion.
A new study forecasts Australia's road traffic fatalities to rise to 998 by 2030 and 715 by 2050, with older drivers and male motorcyclists at the greatest risk
Researchers developed a machine learning model that predicts the presence of animal feeding operations with high accuracy, filling a data gap crucial for managing their environmental impacts. The model uses predictors such as surface temperature and phosphorus levels to identify locations without relying on aerial images.
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.
A new study warns that human civilization is poised for a significant transformation as industrial civilization declines, giving rise to a postmaterialist, clean energy-based system. Rising authoritarianism poses a threat to this transition.
The Virginia Tech Transportation Institute received nearly $1 million in grants to develop and enhance tractor-trailer educational programs. The programs focus on advanced driver assistance systems (ADAS) and safer driving outreach, aiming to save lives by educating drivers on the benefits of these technologies.
A study from the University of Arkansas System Division of Agriculture has improved food quality computer predictions by using human perception data. The researchers trained a computer model to mimic human adaptation to environmental conditions, resulting in more consistent predictions under different lighting conditions.
Researchers developed a novel adaptive optics approach to correct dynamical aberrations in optical microscopy, enabling accurate three-dimensional flow measurements. The system reduces measurement uncertainty, paving the way to better understanding water droplet formation and detachment mechanisms for fuel cells.
A first-of-its-kind adaptive 3D printing system developed by the University of Minnesota Twin Cities researchers can identify organism positions and safely move them to specific locations for assembly. This technology saves time and money in bioimaging, cybernetics, and cryopreservation.
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.
Scientists have developed an AI that can navigate new environments, seek rewards, map landmarks and overcome obstacles using a novel approach inspired by the brain circuits of sea slugs and octopuses. The new AI, called CyberOctopus, has the ability to explore and gather information while learning on the job.
A new study on learning has provided insights into the balance between habitual and goal-directed behaviors, with implications for AI development. The research suggests that a balance between these two types of behavior is necessary for efficient and adaptable decision-making in AI systems.
Researchers developed a novel deep learning method to study crystal structure and molecular interactions of perchlorate salts. The analysis revealed that the explosives' nature is linked to chemical bonding and intermolecular interactions.
The TRAILS AI Institute has awarded eight seed grants totaling $1.5 million to advance AI design, development and governance. The funded projects include developing AI chatbots for smoking cessation and designing animal-like robots for autism support.
Researchers developed an AI system that analyzed video recordings from therapy sessions with patients with borderline personality pathology. The system accurately detected the emotional states of patients, including fleeting micro-expressions. It also identified a predictor for therapy success: 'social' smiling at the start of a session.
A new deep learning-based detection system has been developed by INU researchers to improve the detection capabilities of autonomous vehicles. The system, aided by IoT technology, generates bounding boxes and confidence scores for visible obstacles using point cloud data and RGB images as input.
Neurons in the ventrolateral prefrontal cortex (VLPFC) work together to process social interactions by combining facial and vocal information. The study found that individual neurons did not exhibit strong responses to expressions or identities, but population-level activity could be decoded to reveal the identity and expression in vid...
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.
Researchers at Rice University are developing a machine learning framework to improve decision-making processes in military communication networks. The goal is to enable rapid, adaptive action across a broad range of scenarios by combining local data in the most effective manner.
Researchers adapted a COVID-19 collaboration to monitor near-real-time trends in substance use-related hospital and emergency department visits. Detailed data shows large increases in methamphetamine- and opioid-involved hospital and ED visits among Native American, Black, and multiple-race populations.
Yu Yang's NSF-funded research aims to reduce vehicle emissions and promote the use of electric bikes and scooters by developing socially informed traffic signal control systems. The project involves a three-pronged method that uses low-cost mobile air-quality sensing, spatial-temporal graph diffusion learning, and reinforcement learnin...
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.
Post-Acute Sequelae of COVID-19 research aims to track long-term health symptoms in survivors. A $3.7 million grant will support the development of self-supervised deep learning technologies to recognize post-COVID lung progression phenotypes.
Researchers developed Precious1GPT, a multimodal transformer-based approach for aging clock development and feature importance analysis. The model utilizes methylation and transcriptomic data to predict biological age and identify disease-related genes, providing a pathway for therapeutic drug discovery.
Researchers discovered parallels between the brain architecture of sea slugs and more complex segmented creatures with jointed skeletons and appendages. The study suggests that simpler organisms like sea slugs adapted a network of neurons to govern locomotion and posture, which was later inherited by more complex animals.
Researchers propose a 'state space' approach to reframe farming planning questions, enabling analytics and machine learning to explore optimal crop combinations and simulate different scenarios. This framework allows farmers to design diverse agricultural landscapes based on natural ecosystems, increasing crop yield and sustainability.
Researchers develop unsupervised machine learning algorithm to classify osteosarcoma at diagnosis based on gene expression modules. This approach enables personalized treatment strategies for osteosarcoma patients.
Researchers seek to develop algorithms providing meaningful explanations for AI decision-making, enabling higher human trust and adoption in fields like science. The project focuses on symbolic reasoning and estimating explanation accuracy, addressing the need for transparent AI systems.
The NERVE Center has developed test methods and metrics for various robots, identifying limitations to improve systems. The center's success grew its research capabilities through partnerships with NIST and the U.S. Army.
Researchers develop CRISPR-Cas systems associated with transposons to rewrite large chunks of DNA in organisms like E. coli. This expands the CRISPR toolbox for flexible genome editing and has significant implications for therapeutics, biotechnology, and agriculture.
Two billion people globally rely on mountain water for drinking and irrigation, which is under threat due to global heating. Researchers propose integrated water strategies that include scientists working directly with communities to drive climate adaptation and boost water security.
Researchers at North Carolina State University developed a CRISPR-based system that uses engineered bacteriophages to deliver genetic payloads to specific bacteria, even in complex environments. This technology enables precise single-letter changes to the genome without double-strand DNA breakage.
Researchers propose a broader definition of learning that includes behavioral adaptation to environmental features, enabling collaboration across fields and promoting new research. This 'mechanism-free' approach highlights the importance of system-level responses to environment in various domains.
The University of California, San Diego is part of the National Institutes of Health's Bridge to Artificial Intelligence program, aiming to create comprehensive AI-ready datasets. The program will support researchers in developing interpretable and trustworthy AI technologies to improve human health.
A new University of Illinois project aims to improve undergraduate students' ability to estimate their knowledge using artificial intelligence methods. The researchers will utilize machine learning to anticipate student performance and provide personalized feedback to enhance studying strategies.
A new study introduces a novel epigenetic predictor, PCBrainAge, that captures aging heterogeneity across multiple brain regions. The tool demonstrates stronger associations with AD dementia and pathologic AD compared to existing age predictors.
NC State researchers discovered a new way to make the difficult-to-characterize gut bacterium Bifidobacterium more responsive to antibiotics. They also found tiny changes in different strains that reflect large differences in their characteristics, highlighting the need for individualized CRISPR-based genome engineering approaches.
A team of researchers led by Danilo Vasconcellos Vargas has developed a new method called 'Raw Zero-Shot' to evaluate the robustness of artificial neural networks in image recognition. The study found that Capsule Networks produced the densest clusters, indicating improved transferability and potential solutions for improving AI robust...
Homa Alemzadeh's research aims to develop safety monitoring tools for robotic surgery, enabling better training for surgeons and increasing the availability of less-invasive procedures. Her work has the potential to improve patient outcomes in underserved rural communities.
Researchers conducted wave-optics simulations to study the impact of turbulence on light beams, finding that branch point density grows non-linearly with grid resolution. The study's results could lead to more accurate modeling and improved performance in Adaptive Optics systems.
Researchers aim to develop an audio health library to enhance communication during triadic visits. The study will analyze primary care visits and conduct user-design sessions to refine the system.
Researchers developed a machine learning model that provides good predictions for human speech recognition in noisy environments, benefiting hearing-impaired listeners. The model outperformed expectations and showed strong correlations with measured data.
A team of scientists developed a soft haptic sensor that can accurately estimate contact points and forces using computer vision and deep neural networks. The sensor is sensitive enough to detect even tiny forces and detailed object shapes.
A team of scientists developed an AI-based model to predict personal thermal comfort based on spatial parameters, achieving exceptional accuracy. The study highlights the importance of incorporating architectural features in models to reduce energy consumption.