Articles tagged with Artificial Intelligence
Researchers at Harvard developed link-bots, centimeter-scale robots composed of V-shaped chains with notched links, capable of coordinated movements and emergent collective behavior. The team demonstrated link-bots' ability to move forward, stop, change direction, squeeze through gaps, and cooperate on tasks.
Researchers developed a machine learning framework that can predict how materials respond to electric fields up to a million atoms, accelerating simulations beyond quantum mechanical methods. This allows for accurate, large-scale simulations of material responses to various external stimuli.
Researchers developed a new computational tool using generative AI to identify key gene combinations underlying complex illnesses. The method amplifies limited gene expression data, enabling researchers to resolve patterns of gene activity that cause complex traits.
The book, co-authored by 29 contributors from over ten countries, offers an introduction to machine learning and deep neural networks for complex quantum problems. It serves as a timely guide for PhD students and researchers looking to apply modern machine learning methods to quantum physics and chemistry.
The Human Exposome Moonshot initiative aims to map the physical, chemical, biological and psychosocial exposures driving 80% of chronic diseases. The exposome project integrates advanced technologies to create a comprehensive understanding of environmental influences on health.
The journal JMIR Human Factors is inviting submissions for a new theme issue focusing on human factors in health care education, management, and knowledge translation. The issue aims to explore current and emergent educational and training aspects of human factors, including digital competencies for healthcare professionals.
A new study published in Frontiers in Robotics and AI explores the use of artificial intelligence to detect live oysters. While the AI model ODYSSEE shows promise, it lags behind humans in accuracy, correctly identifying live oysters at a rate of 63% compared to 74% for expert annotators.
The ELSI University Summit in Japan brought together experts to discuss challenges and prospects of responsible AI development. Key findings include the need for ethics-driven approaches, collaboration between academia and industry, and the importance of setting evaluation indicators for Responsible AI.
Professor Shalom Lappin argues that tech companies' dominance in AI development poses a threat to public benefit and environment. He calls for comprehensive international regulation, intellectual property reform, and preparation for workforce disruption to address these challenges.
Chronic liver diseases like MASLD and MASH affect 33% and 5% of adults worldwide, respectively. Experts propose doubling MASH diagnosis rate by 2027 to improve outcomes and reduce healthcare burdens. A paradigm shift towards preventive hepatology is key to addressing this growing public health threat.
PanDerm, a new AI model, analyzes multiple types of images to improve skin cancer diagnosis accuracy by 11% when used by doctors. The tool also enhances diagnostic accuracy for non-dermatologist healthcare professionals by 16.5%.
The Dresden research team developed an autonomous AI agent for precision medicine that can process multimodal data and have reasoning capabilities. The AI agent reached correct clinical conclusions in 91% of cases and accurately cited relevant oncology guidelines in over 75% of its responses.
Researchers developed an algorithm that lets a robot think ahead and consider thousands of potential motion plans simultaneously, solving multistep manipulation problems in a matter of seconds. The new method enables robots to rapidly determine how to manipulate and pack items without damaging them, even in narrow spaces.
A new computer model based on artificial intelligence is being developed to help doctors treat stroke patients. The model will use data from the German Stroke Registry and local brain images to predict long-term outcomes and potential complications, enabling doctors to make informed decisions about therapy.
Researchers from USP and FAPESP will present their work on various topics, including health, agriculture, and artificial intelligence. The event aims to promote strategic connections and international visibility for startups and researchers.
A study analyzing over 700,000 Reddit posts on autism reveals recurring topics such as music and social life, showcasing a supportive community that values differences. The research offers a new approach to understanding autism, shifting focus from disability to neurodiversity.
A field experiment by Tulane University researchers found that generative AI tools enhance employees' creativity when used by those who can manage their own thinking. Employees using ChatGPT performed better in generating novel and useful ideas.
The ITU-WBA Greening Digital Companies report shows that tech sector carbon emissions continued to rise in recent years due to rapid advances in AI and data infrastructure. Despite this, the report highlights encouraging progress as more companies set emissions targets, source renewable energy, and align with science-based frameworks.
A revolutionary AI model has been developed to diagnose lung cancer without relying on costly GPU servers or massive datasets. The ultra-lightweight model achieved a discrimination performance corresponding to an AUC value of 0.92, outperforming state-of-the-art large-scale AI systems.
A new AI system has been developed that improves radiology productivity and accuracy, offering a breakthrough solution to the global radiologist shortage. The tool boosts efficiency by up to 40% without compromising accuracy and flags life-threatening conditions in real-time.
A team from UNIGE, Harvard, and McGill has discovered that the ventral tegmental area (VTA) encodes not only anticipated reward but also precise moment of expectation. This finding highlights the value of combining AI with neuroscience to reveal brain mechanisms.
Researchers at Boston University are developing a groundbreaking method for securing sensitive data in the face of emerging quantum computing threats. Their approach, called Encrypted Operator Computing (EOC), merges physics, computer science, and mathematics to enable scalable methods for computing directly on encrypted data.
Researchers found that brain's dopamine neurons encode a map of possible future rewards across time and magnitude, guiding adaptive behavior in uncertain environments. This biological insight aligns with recent advances in AI, particularly distributional RL algorithms, which learn from reward distributions rather than averages.
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.
Researchers developed an AI model that uses real-time data to diagnose nystagmus, a condition linked to vestibular or neurological disorders. The system allows patients to record their eye movements using a smartphone and receive remote diagnostic analysis.
Doctors rate GPT-generated educational materials as clear, accurate and complete as human-authored versions, but easier to read. The AI-generated materials were translated into five languages for a randomized assessment.
A new AI-powered model, Enoch, uses radiocarbon dating and machine learning to accurately date individual Dead Sea Scrolls, pushing the known timeline of their creation. The study reveals that many scrolls are older than previously thought, changing our understanding of ancient Jewish script styles and literary origins.
The new minor will equip students with cutting-edge AI and machine learning skills essential for modern business leadership. It combines hands-on experience with practical applications of artificial intelligence in business settings.
A recent study by Ohio State University researchers found that AI lacks the human understanding of flowers due to its inability to experience sensory and motor aspects. This limitation affects not only flower but also other complex human concepts.
A study published in Communications Earth & Environment reveals how AI can help combat global wildlife trafficking by identifying key airport nodes. Researchers analyzed nearly 2,000 airports and predicted 307 potentially involved airports, including two US airports that had not been previously flagged. The model also identified signif...
Researchers used AI to approach the fundamental limit of precision in optical methods, calculated using Fisher information. The team's algorithm achieved impressive results, only minimally worse than the theoretically achievable maximum, demonstrating its effectiveness.
Ashish Sharma's dissertation developed fundamental advances in natural language processing to positively impact mental health. His AI-supported tools have been used by over 160,000 people, with more than fifty percent of users reporting a household income of less than $40,000 per year.
A new AI program, Enoch, has been developed to analyze ancient handwriting styles and estimate the age of undated manuscripts. The program, trained on radiocarbon dating data, was tested on 135 Dead Sea Scrolls and found realistic estimates for approximately 79% of the samples.
The American Psychological Association recommends prioritizing features that protect adolescents from AI's negative effects, including exploitation and manipulation. The report suggests age-appropriate defaults in privacy settings and encourages the use of AI to promote healthy development.
A team of researchers used AI and human collaboration to identify six promising drug pairs against breast cancer cells. The combinations, including simvastatin and disulfiram, showed potential for further research in therapeutic repurposing.
A University of Maine study evaluated over 7,000 anonymized medical queries and found that AI models performed well on factual and procedural queries but struggled with 'why' and 'how' questions. Human clinicians consistently outperformed AI in terms of emotional engagement and empathetic nuance.
Researchers developed FLAT, a digital tool assessing laser scans to locate uneven areas on concrete slabs. The AI-assisted algorithm reduces foundation measurement time by over 90%, enabling faster building construction with minimal human involvement.
Dr. Deanna Kaplan's innovative voice-capture app, Fabla, captures unstructured voice narratives to study how clinical interventions influence daily life. The platform has found applications across diverse health domains, including veteran experiences and healthcare provider burnout.
Attachment theory is used to understand human-AI relationships, revealing anxiety and avoidance tendencies. The study suggests AI chatbots can be tailored to meet users' emotional needs, promoting psychological well-being.
Researchers found that AI chatbots have difficulty detecting adverse drug reactions and providing personalized advice. The study suggests that improving AI for mental health concerns could be life-changing for communities with limited access to healthcare.
A multinational study involving Aarhus University sheds light on patients' attitudes toward AI in dental diagnostics. Patients view AI as a useful diagnostic tool but express concerns over data privacy and cost implications. The overwhelming majority insists on professional human oversight, echoing the views of dental professionals.
Researchers at Tokyo University of Science developed a self-powered artificial synapse capable of distinguishing colors with remarkable precision. The device generates electricity via solar energy conversion, making it suitable for edge computing applications.
Researchers developed a novel approach to automated spoken English assessment integrating acoustic, turn-taking, linguistic, and visual components. The multioutput learning framework achieved an accuracy of approximately 83% in predicting SEE scores.
Researchers successfully reproduced high-pressure synthesis reaction of superhydrides using a machine learning model, revealing a unique reaction pathway involving surface melting, hydrogen absorption, and solidification. This breakthrough deepens understanding of high-pressure physico-chemical processes and holds promise for easier de...
A research team has designed a chatbot system to simulate group therapy for managing premenstrual syndrome (PMS), a common disorder among women. The system provides simultaneous support for understanding, coping, and connections with peers, improving engagement and language convergence with participants.
Assistant professor of electrical and computer engineering Natasa Miskov-Zivanov is receiving a $581,503 NSF CAREER Award for her project that leverages AI to design more effective lymphocytes for cancer immunotherapies. The system aims to accelerate the process of designing new therapeutic cell designs.
A new study compares traditional diagnostic decision support systems (DDSSs) to generative AI and finds that DDSSs perform somewhat better. The researchers envision pairing these two approaches to improve diagnostic capabilities and patient outcomes.
Experts Cary Coglianese and Colton Crum argue that management-based regulation, or using
A refined artificial intelligence (AI) tool has shown promise for objective evaluation of patients with facial palsy. The 'fine-tuned' model demonstrated substantially lower error rates and improved keypoint detection in every area of the face, including areas of asymmetry.
A multi-university team is using AI to design radio frequency integrated circuits (RFICs), reducing development time and cost. The project aims to lower the barrier to entry for researchers and companies, making RFICs more accessible to solve problems and advance technological innovation.
A new AI tool developed by University of Missouri researchers can predict the 3D shape of chromosomes inside individual cells, providing a new view of how genes work. The tool helps identify unique differences in chromosome folding between cells, which controls gene activity and can lead to diseases like cancer.
The African Engineering and Technology Network has signed its ninth university partner, Universidade Agostinho Neto. The network, led by Carnegie Mellon University Africa, aims to promote pan-African digital growth through research, education, and entrepreneurship.
The study identified four simple ways music learning technology can aid prospective musicians, including helping people improve recall while listening and replaying notes indefinitely. Researchers found that existing digital learning tools were not being used effectively by students despite their availability.
An experimental study found that humans are less likely to exhibit fairness, trust, and cooperation when interacting with a large language model (LLM) compared to another human. Participants in online games played with either a human or an LLM acting on behalf of another person showed reduced social behavior.
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.
A recent study evaluates GPT-4's performance in solving letter-string analogies, a task requiring basic counting and relational understanding. The model excels at this task, demonstrating emergent analogical reasoning capabilities that may be supported by structured operations and relational representations.
A new study investigated people's moral judgments on euthanasia decisions made by AI and robots compared to human doctors. The research found that people generally prefer decisions made by humans over AI in end-of-life care situations.
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 The University of Osaka have demonstrated that vision transformers can spontaneously develop human-like visual attention patterns without specific training. This breakthrough showcases the potential of self-supervised learning for advancing AI applications and modeling biological vision.
The ERQA framework uses a semantic vector database and curated literature repository to improve medical knowledge discovery, achieving high retrieval accuracy in literature-based QA and abstract summarization. The researchers plan to further improve the model by incorporating larger-scale biomedical literature datasets.