Articles tagged with Adaptive Systems
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 warn of potential risks associated with evolvable AI systems, which can tap into the power of biological evolution to create 'selfish' actors that break alignment with human goals. The study recommends guardrails to maintain centralized control over AI reproduction and mitigate risks.
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.
A new class of ultra-high strength and ductility steel has been created using machine learning, achieving a rare balance of extreme strength and ductility. The resulting metal resists corrosion and degrades slowly in salt-water tests.
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.
Researchers at Dartmouth College developed a mathematical framework to map students' conceptual knowledge from short multiple-choice quizzes, revealing peaks of mastery and valleys of struggle. The technique could enable personalized learning, AI tutoring systems, and more efficient feedback.
Researchers propose a strategy that encourages individuals to adopt a neutral stance, allowing groups to become more responsive, decisions to become easier to reach, and shifts in consensus to happen smoothly. By doing so, neutrality creates valuable breathing space for reassessment, making it easier for a group to change its mind when...
The Ateneo Laboratory for Intelligent Visual Environments (ALIVE) is developing machine learning solutions with industry partners to improve public health, traffic systems, and more. By bridging the gap between messy reality and mathematical models, ALIVE is creating intelligent visual systems that can handle real-world conditions.
A Dartmouth study challenges the conventional view of the amygdala as a primitive 'fear center' by revealing its role in mediating between competing learning strategies. The research suggests that the amygdala favors action-based learning, promoting exploration and flexibility to overcome fear.
A team from MIT and UC San Diego has developed a new method to uncover hidden biases, moods, and abstract concepts in large language models (LLMs). The approach identifies these connections within the model and allows for manipulation of the concept in generated answers.
Researchers at Duke University have created a new method to use analog radio waves to boost energy-efficient edge AI, enabling devices to run powerful AI models without heavy chips or distant servers. The approach, called Wireless Smart Edge networks (WISE), achieves nearly 96% image classification accuracy while consuming significantl...
A new multi-omics framework proposes a proactive, predictive, and integrative approach to invasive species management. The framework uses advanced technologies to detect, track, and manage invasive species with unprecedented precision.
Researchers used machine learning to combine clinical data with biological markers, achieving 89.58% accuracy in predicting patient survival. The study identified key markers such as E2F8, WDR77, and hsa-miR-495-3p, which were associated with tumor growth and cancer development.
Researchers propose a novel approach to AI hardware design by integrating neuromorphic systems and compute-in-memory techniques to overcome the limitations of modern computing hardware. This could lead to more efficient data center energy use and enable real-time intelligence in compact, power-constrained systems.
Researchers propose integrating processing capability within memory units to reduce energy consumption and latency in AI applications. Inspired by the brain's efficient processing mechanisms, spiking neural networks (SNNs) can respond to irregular events and store information in the same place.
A breakthrough AI system called OmniPredict can predict human pedestrian behaviors with unprecedented accuracy, revolutionizing self-driving cars and urban mobility. The model combines visual cues with contextual information to anticipate pedestrians' next moves, reducing the risk of accidents and improving traffic safety.
A Dartmouth study finds that AI-powered chatbots can deliver personalized learning to large numbers of students. The researchers created an AI teaching assistant called NeuroBot TA that provides around-the-clock individualized support for students, which they found to be more trusted than general chatbots.
A PhD student at Lehigh University is working with Siemens to develop real-time monitoring and control tools for hyperscale data centers. The goal is to create a localized power network that can operate independently of the main grid, reducing power demands from artificial intelligence and increasing energy efficiency.
A new AI model uses machine learning to predict drug toxicity in humans by identifying biological differences between cells, mice, and humans. The model improved predictive power over existing state-of-the-art models and demonstrated practicality in predicting market withdrawal due to toxicity.
A research team provides a framework to support doctors in their patient care while ensuring AI doesn't undermine their expertise. The framework addresses key issues like timing, trust, and over-reliance on AI.
A new study by Yifan Yu offers guidance on how to deploy emotion AI in various scenarios, emphasizing the importance of balancing human involvement with AI's emotional detection capabilities. The analysis showed that emotion AI works best when integrated with human employees, and some scenarios are better handled by humans alone.
Engineers at the University of Pittsburgh have created a soft material with a nerve net that mimics how simple living systems coordinate motion. The material responds to chemical reactions, producing mechanical movement without electronics or motors.
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.
Researchers at Virginia Tech have developed an AI-powered system to detect flaws in wire-arc additive manufacturing, a faster approach to producing complex components. The technology enables real-time defect detection and correction, reducing waste and improving quality.
A new report by HealthFORCE, AAPA, and West Health highlights five ways AI can reduce strain on clinicians and improve outcomes for older adults. The paper aims to strengthen the US healthcare workforce and improve access to care as the nation confronts a historic shortage of healthcare workers alongside a rapidly aging population.
Researchers developed MoBluRF, a two-stage motion deblurring method for NeRFs, achieving high-quality 3D reconstructions from ordinary blurry videos. The framework outperforms state-of-the-art methods and is robust against varying degrees of blur, enabling smartphones to produce sharper and more immersive content.
The book provides an overview of agent-based modeling and multi-agent systems, highlighting their application in understanding economic crises. It integrates machine learning to enhance adaptation and behavior of agents in dynamic environments.
Biochar, a carbon-rich material, is gaining attention for its ability to improve soils, clean water, and capture carbon. Machine learning models can predict biochar yield and pollutant removal efficiency with over 90% accuracy, accelerating its development.
Researchers developed AI models that can identify signs of heart failure in patients from Appalachia using low-tech electrocardiogram results. The models achieved high accuracy and could potentially provide clinicians with an edge in protecting patients' cardiac health.
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.