Articles tagged with Artificial Intelligence
A new AI-powered software, EmbryoNet, can automatically detect defects in animal embryos and link them to underlying signalling pathways. This technology has the potential to replace lengthy animal studies in drug research, speeding up conventional processes while reducing costs significantly.
Researchers propose several key features to optimize sparsity, massive parallelism, and hierarchical structure in neural representation for neuromorphic systems. The goal is to achieve energy efficiency and compactness while retaining information at high fidelity.
A new large language model, 'llm-jp-3-172b-instruct3', has been released with approximately 172 billion parameters, trained from scratch using 2.1 trillion tokens of data. This model surpasses GPT-3.5 performance on benchmarks such as 'llm-jp-eval' and 'llm-leaderboard'.
A new study found that AI-generated empathetic responses were preferred over those from humans and expert crisis responders. The researchers suggest that AI can supplement human empathy, but should not replace it entirely due to potential biases and ethical concerns.
Using machine learning and AI, researchers have discovered a way to quickly and economically find high-performance materials for perovskite solar cells. By training an AI model on 101 synthesized molecules, they were able to identify 48 new molecules with potential for above-average efficiency.
A new contrastive learning model has been developed to forecast cyclone rapid intensification (RI) with high accuracy, reducing false alarms by a factor of three compared to existing techniques. The model achieved an impressive 92.3% accuracy when tested on data from the Northwest Pacific between 2020 and 2021.
A new study from the University of Eastern Finland finds that digital platforms and artificial intelligence can boost B2B service companies' competitiveness by enhancing agility, flexibility, and resilience. The study also highlights the importance of organizational capabilities for managing strategic change in volatile markets.
Scientists at the University of Lausanne used AI to simulate the last Alpine glaciation, finding ice covers 35-50% thinner than previous models. The new approach enables unprecedented accuracy and resolution, making it possible to describe complex topography numerically.
The new model, based on a PV-RNN framework, achieves compositionality by combining language with vision, proprioception, working memory, and attention. It requires less computing power than large language models (LLMs) and makes mistakes similar to humans.
BioChatter bridges the gap between large language models and biomedical research by providing a transparent and adaptable framework for custom research tasks. The platform can integrate with knowledge graphs and bioinformatics tools, making it easier for researchers to analyze complex datasets.
A groundbreaking AI tool called Moscot maps single cells in spatial tissues, enabling the observation of millions of cells simultaneously. This allows researchers to analyze complex cellular processes within entire living organs and organisms, providing new insights into diseases like diabetes.
Establishing a permanent lunar presence will depend on the use of AI, robotics, and 3D printing to adaptively respond to challenges. The moon's natural resources, such as regolith, can also reduce the need for Earth-launched supplies.
The study proposes a 'school within a school' approach to enable small changes that can affect entire systems. Researchers highlight the need for transformational changes to better prepare students for the age of Artificial Intelligence.
A new study published in Environmental DNA revealed that insecticides and fungicides are the main factors affecting biodiversity in UK waters. The AI technology analyzed water and biofilm samples from 52 freshwater lakes, identifying key links between pollutants and biodiversity loss.
A recent study by Aalto University found that successful AI adoption depends on employees' emotional and cognitive trust. The research team analyzed a consulting company's attempt to implement an AI tool, which ultimately failed due to employees' discomfort with AI following their digital footprints.
A new study published in Journal of Management Studies reveals that employee perceptions of AI performance and feelings towards AI vary, affecting AI performance and adoption. Four different trust configurations were identified, leading to distinct behaviors and a vicious cycle of biased data inputs degrading AI performance.
A new interposer design uses optical connections to transfer data between chips, enabling high-performance computing and addressing the 'memory wall' limit on AI growth. The technology allows for faster communication, reconfigurable pathways, and real-time network control.
An English literature graduate has developed a new method for large language models to understand and analyze short text chunks, such as those on social media profiles. The method successfully grouped nearly 40,000 Twitter user biographies from accounts tweeting about US President Donald Trump into 10 categories.
Researchers developed ProteinReDiff, an AI-powered method to redesign proteins for improved ligand binding. The approach uses initial protein sequences and ligand SMILES strings, reducing reliance on detailed structural data.
A new study assesses the historical knowledge of AI chatbots like ChatGPT-4 and finds they struggle with nuanced, PhD-level inquiry. The models performed best on legal systems and social complexity but struggled with topics such as discrimination and social mobility.
The Global Conference on Gerophysics aims to bridge statistical mechanics, complex systems theory, and dynamical modeling with biological mechanisms of aging. The conference will bring together leading scientists to promote healthy longevity and reshape our understanding of the aging process.
The paper highlights the need for a critical re-evaluation of how we study digital technologies' impact on young people's mental health. Oxford researchers call for a collaborative evidence-based framework to regulate AI integration into online platforms, ensuring its safety and benefits for children and adolescents.
The workshop explored how to responsibly integrate AI into society, discussing key stakeholders, transparency, accountability, and human-AI collaboration. EU researchers emphasized the importance of addressing ethical considerations in AI design and governance.
The open-access book delves into the impact of AI and autonomous systems on human lives and ways of working, emphasizing their importance in prioritizing human well-being and creativity. Key learnings include the need for ethical design practices and considering AI as part of a larger system to enhance human experiences.
The KAIST team developed a next-generation neuromorphic semiconductor-based integrated system that can learn and correct errors on its own. This technology will revolutionize AI use in everyday devices, making them faster, more private and energy-efficient.
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.
Researchers found that wearable devices can identify, differentiate, and predict flares in IBD by analyzing circadian patterns of heart rate variability, heart rate, oxygenation, and daily activity. The study showed that these metrics changed up to seven weeks before flares developed, enabling continuous disease monitoring.
A new study has revealed the alarming extent to which glaciers have shrunk over the past 40 years in Svalbard, with an area loss of over 800 km² detected. The research found that more than half of the glaciers undergo seasonal cycles in glacier calving, highlighting the vulnerability of these ice masses to climate change.
The Abundant Intelligences program challenges the current AI trajectory, incorporating Indigenous knowledge systems to create an alternative approach. This initiative seeks to rethink how we conceive of AI, prioritizing human thriving, preserving Indigenous languages, and addressing environmental issues.
A new study by Mass General Brigham investigators found that Black people were less likely than non-Black people to undergo metabolic and bariatric surgery, even though both groups were equally likely to discuss it with health care providers. Men were also less likely to discuss and receive MBS than women.
A new automated job hazard analysis tool promises to significantly reduce workplace accidents and improve safety in the construction industry. The University of South Australia's research team has built a 'knowledge graph' to predict hazards, which can be analysed in real-time to identify potential risks and control measures.
The partnership establishes Ohio's first specialized degree programs and research experiences in quantum computing, cultivating scientific and entrepreneurial talent. Miami University and Cleveland Clinic will accelerate the leading-edge power of quantum computing to shape the future of healthcare and technology.
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.
A multicenter study shows trained health professionals with AI assistance can meet diagnostic standards for lung ultrasounds. This technology has the potential to increase access to lung ultrasound in areas without expert personnel.
The 'MarginCall' project aims to create a fully integrated system for real-time, highly accurate assessments of surgical margins during cancer surgeries, reducing the likelihood of cancerous cells being left behind. This approach promises to be faster and more cost-effective than existing techniques.
DNNs have an inbuilt 'Occam's razor,' favouring simpler solutions that fit training data. This bias helps them generalize well on simple patterns but may struggle with complex data, aligning with real-world data characteristics.
A new AI model will analyze combined data from calcium-scoring CT scans, clinical risk factors, and demographics to identify high-risk patients. The project aims to accurately predict cardiovascular events, including heart failure, and provide personalized treatment options.
The University of Texas at Austin appoints Charles 'Charley' Taylor to lead a new Center for Computational Medicine, developing advanced medical applications and modeling. This move strengthens collaborations between Dell Medical School and the Oden Institute, driving innovation in health and technology.
A research team at George Mason University is developing mixed reality and AI technologies to improve first responder training. They aim to reduce training-related risks and casualties by creating interactive scenarios that mimic real-world challenges.
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.