Large language models (LLMs) can understand and generate human-like text across various applications. However, despite their success, LLMs often need help in mathematical reasoning, especially when solving complex problems requiring logical, step-by-step thinking. This research field is evolving rapidly as AI researchers explore new methods to enhance LLMs’ capabilities in handling advanced reasoning tasks, particularly… →
Large Language Models (LLMs) have gained significant attention in AI research due to their impressive capabilities. However, their limitation lies with long-term planning and complex problem-solving. While explicit search methods like Monte Carlo Tree Search (MCTS) have been employed to enhance decision-making in various AI systems, including chess engines and game-playing algorithms, they present challenges… →
CONCLUSION: The breast cancer digital tool had no statistically significant impact on patient activation, HRQoL, and health status over time compared to standard care alone in women with early-stage breast cancer. Future research should focus on identifying and addressing barriers to digital tool engagement to improve efficacy. Clinical trial information The study was registered at… →
CONCLUSIONS: PBR is feasible and safe as a point-of-care test for LC1 pelvis fracture instability. →
CONCLUSION: This substudy analysis supplements previous findings that revefenacin provides sustained bronchodilation over 24 hours. Assessing additional complementary COPD clinical trial endpoints can help clinicians make treatment decisions. →
The dynamics of protein structures are crucial for understanding their functions and developing targeted drug treatments, particularly for cryptic binding sites. However, existing methods for generating conformational ensembles are plagued by inefficiencies or lack of generalizability to work beyond the systems they were trained on. Molecular dynamics (MD) simulations, the current standard for exploring protein… →
Artificial intelligence (AI) and machine learning (ML) revolve around building models capable of learning from data to perform tasks like language processing, image recognition, and making predictions. A significant aspect of AI research focuses on neural networks, particularly transformers. These models use attention mechanisms to process data sequences more effectively. By allowing the model to… →
Artificial intelligence is advancing rapidly, but enterprises face many obstacles when trying to leverage AI effectively. Organizations require models that are adaptable, secure, and capable of understanding domain-specific contexts while also maintaining compliance and privacy standards. Traditional AI models often struggle with delivering such tailored performance, requiring businesses to make a trade-off between customization and… →
Model Predictive Control (MPC), or receding horizon control, aims to maximize an objective function over a planning horizon by leveraging a dynamics model and a planner to select actions. The flexibility of MPC allows it to adapt to novel reward functions at test time, unlike policy learning methods that focus on a fixed reward. Diffusion… →