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Categories: Geoscience: Geomagnetic Storms, Mathematics: Modeling
Published Research finds improving AI large language models helps better align with human brain activity
(via sciencedaily.com) Original source With generative artificial intelligence (GenAI) transforming the social interaction landscape in recent years, large language models (LLMs), which use deep-learning algorithms to train GenAI platforms to process language, have been put in the spotlight. A recent study found that LLMs perform more like the human brain when being trained in more similar ways as humans process language, which has brought important insights to brain studies and the development of AI models.
Published The origin of the sun's magnetic field could lie close to its surface
(via sciencedaily.com) Original source Surprise findings suggest sunspots and solar flares could be generated by a magnetic field within the Sun's outermost layers. If confirmed, the findings could help scientists better predict space weather.
Published How AI helps programming a quantum computer
(via sciencedaily.com) Original source Researchers have unveiled a novel method to prepare quantum operations on a given quantum computer, using a machine learning generative model to find the appropriate sequence of quantum gates to execute a quantum operation. The study marks a significant step forward in unleashing the full extent of quantum computing.
Published Large language models can't effectively recognize users' motivation, but can support behavior change for those ready to act
(via sciencedaily.com) Original source Large language model-based chatbots can't effectively recognize users' motivation when they are hesitant about making healthy behavior changes, but they can support those who are committed to take action, say researchers.
Published Scientists use generative AI to answer complex questions in physics
(via sciencedaily.com) Original source Researchers used generative AI to develop a physics-informed technique to classify phase transitions in materials or physical systems that is much more efficient than existing machine-learning approaches.
Published Researchers wrestle with accuracy of AI technology used to create new drug candidates
(via sciencedaily.com) Original source Researchers have determined that a protein prediction technology can yield accurate results in the hunt to efficiently find the best possible drug candidates for many conditions.
Published Wavefunction matching for solving quantum many-body problems
(via sciencedaily.com) Original source Strongly interacting systems play an important role in quantum physics and quantum chemistry. Stochastic methods such as Monte Carlo simulations are a proven method for investigating such systems. However, these methods reach their limits when so-called sign oscillations occur. This problem has now been solved using the new method of wavefunction matching.
Published Century of statistical ecology reviewed
(via sciencedaily.com) Original source A special review examines highly-cited papers in statistical ecology. The review, which covers a century of research, details how models and concepts have evolved alongside increasing computational power.
Published New work extends the thermodynamic theory of computation
(via sciencedaily.com) Original source Physicists and computer scientists have recently expanded the modern theory of the thermodynamics of computation. By combining approaches from statistical physics and computer science, the researchers introduce mathematical equations that reveal the minimum and maximum predicted energy cost of computational processes that depend on randomness, which is a powerful tool in modern computers.
Published New machine learning algorithm promises advances in computing
(via sciencedaily.com) Original source Systems controlled by next-generation computing algorithms could give rise to better and more efficient machine learning products, a new study suggests.
Published AI advancements make the leap into 3D pathology possible
(via sciencedaily.com) Original source Researchers present Tripath: new, deep learning models that can use 3D pathology datasets to make clinical outcome predictions. The research team imaged curated prostate cancer specimens, using two 3D high-resolution imaging techniques. The models were then trained to predict prostate cancer recurrence risk on volumetric human tissue biopsies. By comprehensively capturing 3D morphologies from the entire tissue volume, Tripath performed better than pathologists and outperformed deep learning models that rely on 2D morphology and thin tissue slices.
Published An AI leap into chemical synthesis
(via sciencedaily.com) Original source Scientists introduce a large language model-based AI system that revolutionizes chemistry by integrating 18 advanced tools for tasks like organic synthesis and drug discovery.
Published Emergency department packed to the gills? Someday, AI may help
(via sciencedaily.com) Original source Emergency departments nationwide are overcrowded and overtaxed, but a new study suggests artificial intelligence (AI) could one day help prioritize which patients need treatment most urgently.
Published Researchers use foundation models to discover new cancer imaging biomarkers
(via sciencedaily.com) Original source Researchers have harnessed the technology behind foundation models, which power tools like ChatGPT, to discover new cancer imaging biomarkers that could transform how patterns are identified from radiological images. Improved identification of such patterns can greatly impact the early detection and treatment of cancer.
Published Why getting in touch with our 'gerbil brain' could help machines listen better
(via sciencedaily.com) Original source Researchers have debunked a 75-year-old theory about how humans determine where sounds are coming from, and it could unlock the secret to creating a next generation of more adaptable and efficient hearing devices ranging from hearing aids to smartphones.
Published Toxic chemicals can be detected with new AI method
(via sciencedaily.com) Original source Researchers have developed an AI method that improves the identification of toxic chemicals -- based solely on knowledge of the molecular structure. The method can contribute to better control and understanding of the ever-growing number of chemicals used in society, and can also help reduce the amount of animal tests.
Published New computer algorithm supercharges climate models and could lead to better predictions of future climate change
(via sciencedaily.com) Original source A study describes a new computer algorithm which can be applied to Earth System Models to drastically reduce the time needed to prepare these in order to make accurate predictions of future climate change. During tests on models used in IPCC simulations, the algorithm was on average 10 times faster at spinning up the model than currently-used approaches, reducing the time taken to achieve equilibrium from many months to under a week.
Published Improved AI process could better predict water supplies
(via sciencedaily.com) Original source A new computer model uses a better artificial intelligence process to measure snow and water availability more accurately across vast distances in the West, information that could someday be used to better predict water availability for farmers and others. The researchers predict water availability from areas in the West where snow amounts aren't being physically measured.
Published From disorder to order: Flocking birds and 'spinning' particles
(via sciencedaily.com) Original source Researchers have demonstrated that ferromagnetism, an ordered state of atoms, can be induced by increasing particle motility and that repulsive forces between atoms are sufficient to maintain it. The discovery not only extends the concept of active matter to quantum systems but also contributes to the development of novel technologies that rely on the magnetic properties of particles, such as magnetic memory and quantum computing.
Published AI deciphers new gene regulatory code in plants and makes accurate predictions for newly sequenced genomes
(via sciencedaily.com) Original source Elucidating the relationship between the sequences of non-coding regulatory elements and their target genes is key to understanding gene regulation and its variation between plant species and ecotypes. Now, an international research team developed deep learning models that link gene sequence data with mRNA copy number for several plant species and predicted the regulatory effect of gene sequence variation.