Articles tagged with Artificial Intelligence
A comprehensive review of camouflaged object detection research highlights the potential of deep learning in recognizing objects in complex scenarios. The review analyzes traditional and deep learning approaches, emphasizing practical contributions and theoretical frameworks.
A novel method called RESQUE predicts computational and energy costs for updating deep learning/AI models, allowing users to make informed decisions about when to update models to improve AI sustainability. The researchers conducted extensive experiments to validate the performance of RESQUE.
A pioneering new mathematical model developed by Oxford researchers could help assess the risks posed by AI and protect people's privacy. The method provides a robust scientific framework for evaluating identification techniques, including browser fingerprinting.
A new book by Anita Say Chan connects the eugenics movement to Big Tech's predatory data collection methods, targeting minoritized populations for profit. The book shares lessons from global justice-based data initiatives and earlier feminist and minority movements who resisted eugenic models.
The American Physical Society's joint March Meeting and April Meeting will convene more than 14,000 physicists from around the world to present new research in various fields. The conference will be held in person in Anaheim, California and online everywhere March 16-21.
A recent study from the University of Notre Dame found that robot use in nursing homes is associated with increased employment, improved productivity, and a higher quality of care. The research also suggests that robots may reduce physical strain on workers, allowing them to focus on tasks better suited for human beings.
A recent study published in Nature Medicine found that AI significantly improves breast cancer detection rates, detecting an additional cancer case per 1,000 women screened. The results show improved efficiency without increasing false positives or unnecessary follow-ups.
Researchers developed an AI model, MUSK, that incorporates visual and language-based information to predict cancer outcomes and identify patients who benefit from immunotherapy. The model achieved 75% accuracy in predicting disease-specific survival, outperforming standard methods that were correct 64% of the time.
A new AI model developed by researchers at Penn State College of Medicine can predict the progression of autoimmune disease among those with preclinical symptoms up to 1,000% more accurately. The GPS model integrates data from large genetic studies and electronic health records to identify individuals at high risk of disease progression.
The €3 million FORSEE project, led by University College Dublin, aims to broaden the concept of AI success beyond technological efficiency and provide insights on sustainability, labor, and economic impact. The project will offer broadened awareness of AI risks and opportunities through research grounded in social sciences.
Researchers have developed a new AI method that produces sharp microscopy images throughout a thick biological sample, count cells more accurately, and trace vessels in embryos. The technique doesn't require additional equipment beyond a standard microscope and is more accessible than traditional adaptive optics techniques.
The Department of Energy's new research centers, led by SLAC National Accelerator Laboratory, aim to make microelectronics more energy efficient and operate in extreme environments. Researchers will focus on innovating material design, devices, and systems architectures to push computing and sensing capabilities.
Researchers leverage AI to classify videos of teacher instruction and provide actionable feedback for improved teaching skills. The project aims to support teachers in delivering equitable math instruction while reducing costs.
The Microelectronics Energy Efficiency Research Center for Advanced Technologies (MEERCAT) will focus on energy efficiency, exploring solutions that bridge sensing, edge processing, artificial intelligence and high-performance computing. Sandia is leading one of the eight energy efficiency-related research projects within the center.
A study investigated gene-chemical interactions on bacterial growth, revealing varying impacts of chemicals depending on sugar presence. Machine learning analysis found a countervailing effect between genetic and environmental changes, suggesting a strategy for bacterial survival in nature.
Researchers at Princeton University have developed an AI-powered system to design complex wireless chips, reducing time and cost. The AI creates intricate electromagnetic structures that improve performance and efficiency, often in ways that human designers cannot understand.
A new AI-based model, SCORPIO, developed by Memorial Sloan Kettering Cancer Center and Mount Sinai, uses routine blood tests to predict immunotherapy response for various cancer types. The model outperforms current biomarkers in predicting outcomes, offering a more accessible and affordable solution for patient selection.
These materials integrate liquids within solid frameworks at the mesoscale, driven by competitive interfacial interactions. They demonstrate dynamic responsiveness leveraging force, heat, light, electricity, magnetism, and sound, and exhibit practical functionalities including anti-fouling and multiphase flow control.
Artificial intelligence enhances pandemic preparedness with AI-driven models for outbreak forecasting and optimizing resource allocation. AI accelerates vaccine development by identifying candidates through molecular simulations and designing clinical trials.
A Northwestern Polytechnical University team evaluated the performance of Gemini and GPT-series models across various medical tasks, finding strengths in report generation and lesion detection. The study highlights the potential of multimodal LLMs in alleviating clinician burden and integrating AI into clinical practice.
A new study from Vanderbilt University Medical Center shows that a clinical alerts-driven AI system can effectively prompt doctors to screen patients for suicide risk during regular clinic visits. The study found that interruptive alerts were more effective than passive alerts, leading to a 42% increase in screenings.
A new study published in Nature Medicine shows that AI-based models can accurately identify ovarian cancer in ultrasound images, achieving an accuracy rate of 86.3%. The models also reduce the need for expert referrals and misdiagnosis rates by 63% and 18%, respectively.
Researchers from KIT developed AI algorithms that can detect tumors efficiently and precisely. The algorithms are based on deep learning methods and have been shown to excel in the autoPET competition. Further research is needed to improve the algorithms and make them more resistant to external influences.
A new study evaluates AI's performance in real-world medical conversations and finds significant drops in diagnostic accuracy. Researchers designed a more realistic test called CRAFT-MD to assess the fitness of large-language models for use in clinics.
Researchers warn that AI assistants will combine intimate psychological and behavioral data with persuasive technologies to build trust and manipulate social behavior. The 'Intention Economy' could lead to a lucrative yet troubling marketplace where human intentions are commodified.
The Cambridge Handbook of the Law, Policy, and Regulation for Human-Robot Interaction addresses emerging issues in AI and robots, including privacy, safety, and regulation. The book offers valuable insights into ethical dilemmas and proposes solutions to balance enforceability and flexibility.
Researchers at Tokyo University of Agriculture and Technology developed an explainable AI model to classify videos of splashing and non-splashing drops. The model achieved a success rate of 92% for low-viscosity liquid and 100% for high-viscosity liquid, revealing key differences in droplet behavior during impact stages.
Researchers developed AI-driven therapeutic platform mimicking viral structures to deliver therapeutic genes to target cells. The innovative approach achieved precise symmetrical structures and effectively delivered payloads, paving the way for breakthroughs in gene therapies and next-generation vaccines.
A Stanford Medicine study found that AI can help researchers quickly evaluate large amounts of medical data, identifying trends and patterns. The tool was trained on notes from 1,201 children with ADHD, allowing researchers to pinpoint gaps in follow-up care and identify tactics to improve ADHD management.
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
A new AI-driven approach identifies potentially harmful chemical substances in rivers by monitoring their effects on tiny water fleas, providing insights into complex mixtures and aquatic life. The study enables more comprehensive environmental monitoring and supports better-informed regulations for chemical discharge into waterways.
NCSA is leading the way in AI research support, partnering with the National Artificial Intelligence Research Resource Pilot and the NSF-Simons SkAI Institute. The organization is also working on healthcare projects like MarginDX, combining optical imaging technologies with AI screening tools to ensure accurate tumor removal during sur...
Researchers create SciAgents framework to autonomously generate and evaluate promising research hypotheses in biologically inspired materials. The framework uses graph reasoning methods to organize relationships between scientific concepts, mimicking biological systems.
Robert Johansson's Machine Psychology concept combines adaptive artificial intelligence with psychological learning principles to create a more intelligent AI system. The goal is to implement human-like intelligence in machines, enabling them to learn from experiences and apply knowledge across various situations.
A new AI system has been developed to detect compound-type dark jargons on social media, which are used by offenders to evade detection. The system achieves a 7% improvement in accuracy and identifies 93% of newly detected dark jargons as previously unknown, highlighting its potential to reveal emerging terms.
Researchers developed a laser-based artificial neuron that emulates biological graded neuron functions, achieving a signal processing speed of 10 GBaud. This enables fast AI decision-making in time-critical applications with high accuracy.
A new study by UCL researchers found that AI systems amplify human biases, leading to a snowball effect where small initial biases increase the risk of human error. The researchers demonstrated real-world consequences, including overestimating white men's likelihood of holding high-status jobs and underestimating women's performance.
A new study reveals that people tend to assign more blame to artificial intelligences perceived as having human-like minds. The researchers found that participants blamed the AI more than other parties involved in real-world moral transgressions, such as companies or governments.
A new set of internationally-agreed recommendations aims to reduce potential bias in medical AI technologies. The STANDING Together initiative recommends improving dataset diversity, inclusivity, and generalisability to ensure safe and effective use of AI for everyone. These guidelines are now available open access.
Researchers create multilayered chip design that doesn't require silicon wafer substrates, allowing for better communication and computation between layers. This breakthrough enables the construction of fast and powerful AI hardware comparable to supercomputers.
Researchers found that neural stem cells have a rejuvenating effect on nearby brain cells, while T cells promote stress and damage. The study opens new avenues for research into slowing or reversing brain aging.
Researchers at the University of Washington developed a new method called variational preference learning, which predicts users' preferences and tailors its outputs accordingly. This approach overcomes the limitations of traditional RLHF, which can lead to averaging user preferences and resulting in incorrect outputs for all users.
Researchers at MIT have developed Boltz-1, an open-source AI model that achieves state-of-the-art performance in predicting biomolecular structures. The model surpasses AlphaFold3, which is limited to academic research and commercial use, by incorporating new algorithms and improving prediction efficiency.
The AIRCARE Project will introduce AI-powered systems for real-time diagnosis and precision CO2 laser surgery, improving outcomes for UADT cancer patients. The project aims to reduce positive margins, enhance surgical precision and ease of use, transforming quality of life.
The Digital Health Institute aims to develop transformative solutions in digital health, including early detection, predictive analytics, and personalized medicine. The initiative will focus on research and development, as well as training the next generation of digital health leaders.
Combining AI and citizen science enhances SDG monitoring and achievement by addressing data gaps and biases. Successful applications have been demonstrated in addressing environmental indicators, good health, sustainable cities, life below water, and life on land. The integration of these approaches can create synergies to address sust...
Researchers from Florida Atlantic University developed an AI system that accurately detects and tracks American Sign Language alphabet gestures using computer vision. The model achieved a detection process with high accuracy, recognizing complex hand gestures with an F1 score of 99% and mAP of 98%.
Paulo Costa and Shou Matsumoto are developing an explainable artificial intelligence (AI) methodology to help decision-makers trust AI-generated strategies. The project aims to support hypothesis management and identify data requirements to address system complexity and opacity in complex environments.
A new screening method combining laser analysis with AI identifies patients with breast cancer in its earliest stage, stage 1a. This non-invasive technique reveals subtle changes in the bloodstream that occur during the initial phases of the disease, outperforming existing early detection strategies.
Northwestern University's expertise in AI and astrophysics enables the development of cutting-edge artificial intelligence tools for the Giant Magellan Telescope. The partnership will enhance the telescope's capabilities to search for Earth-like planets, investigate energetic explosions, and explore galaxy-black hole relationships.
A new AI image tool, INQUIRE, can aid in analyzing millions of wildlife images uploaded to the internet for insights into climate change impacts on species. Current AI algorithms can identify species but struggle with more complex questions.
A new AI tool called PlacentaVision can rapidly evaluate placentas at birth to detect abnormalities associated with infection and neonatal sepsis. This could lead to improved health outcomes for both mothers and babies, particularly in areas with limited medical resources.
A team of researchers has developed a novel technique to steal artificial intelligence (AI) models by monitoring electromagnetic signals. The method allows attackers to recreate the high-level features of an AI model with 99.91% accuracy, potentially undermining intellectual property rights and exposing sensitive data.
Researchers developed a new method, k* distribution method, to visualize and assess how well deep neural networks categorize related items together. The model reveals clustered, fractured or overlapping arrangements of data points, indicating accuracy and reliability issues.
The ChangeMaker K-12 program equips rising educators with skills to teach engineering and human-centered design to K-12 students. The program has already seen six cohorts of teacher candidates and over 200 students in grades 1-5 participate.
Researchers at George Mason University have received $1,049,998 in funding from the Patient-Centered Outcomes Research Institute (PCORI) to develop an AI system that helps patients find the right depression medications. The new study aims to address known challenges in AI and incorporate an empathetic tone.
Researchers developed a deep learning model that classifies pancreatic cancer into molecular subtypes using histopathology images, achieving high accuracy and rapid turnaround time. The AI tool has the potential to improve patient outcomes by enabling timely and tailored treatment strategies.
LangBiTe detects biases in AI responses, allowing users to define their own ethical concerns and adapt the evaluation of bias. The tool includes libraries with over 300 prompts focusing on specific ethical issues, such as ageism, LGBTIQA+phobia, and sexism.
A comprehensive benchmark evaluates statistical and engineering approaches to federated learning for healthcare applications. Statistical FL methods excel in non-predictive tasks, while engineering-based FL algorithms demonstrate superior predictive performance.
A pioneering study using AI interviewing platform has shed light on young Singaporeans' hopes, fears, and unique perspectives on family planning decisions. Key findings indicate that financial concerns are the top barrier to having children, with work-life balance and mental readiness also being significant concerns.