Large language models (LLMs) have recently been enhanced through retrieval-augmented generation (RAG), which dynamically integrates external knowledge sources to improve response quality for open-domain questions and specialized tasks. However, RAG systems face several significant challenges that limit their effectiveness. The real-time retrieval process introduces latency in response generation, while document selection and ranking errors can… →
Time-series forecasting plays a crucial role in various domains, including finance, healthcare, and climate science. However, achieving accurate predictions remains a significant challenge. Traditional methods like ARIMA and exponential smoothing often struggle to generalize across domains or handle the complexities of high-dimensional data. Contemporary deep learning approaches, while promising, frequently require large labeled datasets and… →
Large language models (LLMs) like OpenAI’s GPT and Meta’s LLaMA have significantly advanced natural language understanding and text generation. However, these advancements come with substantial computational and storage requirements, making it challenging for organizations with limited resources to deploy and fine-tune such massive models. Issues like memory efficiency, inference speed, and accessibility remain significant hurdles.… →
Managing datasets effectively has become a pressing challenge as machine learning (ML) continues to grow in scale and complexity. As datasets expand, researchers and engineers often struggle with maintaining consistency, scalability, and interoperability. Without standardized workflows, errors and inefficiencies creep in, slowing progress and increasing costs. These challenges are particularly acute in large-scale ML projects,… →
CONCLUSION: The compliance of patients with intensive wearing face masks was high. The intensive wearing of face masks had a good intervention effect on the treatment of postinfectious cough, which could effectively reduce cough symptoms of patients, shorten the course of the disease, and reduce medical expenditure. →
Mathematical problem-solving has long been a benchmark for artificial intelligence (AI). Solving math problems accurately requires not only computational precision but also deep reasoning—an area where even advanced language models (LLMs) have traditionally faced challenges. Many existing models rely on what psychologists term “System 1 thinking,” which is fast but often prone to errors. This… →
Large Language Models (LLMs) are used to create questions based on given facts or context, but understanding how good these questions are can be difficult. The challenge is that questions made by LLMs often differ from those made by humans in terms of length, type, or how well they fit the context and can be… →
One of the major hurdles in AI-driven image modeling is the inability to account for the diversity in image content complexity effectively. The tokenization methods so far used are static compression ratios where all images are treated equally, and the complexities of images are not considered. Due to this reason, complex images get over-compressed and… →
CONCLUSIONS AND RELEVANCE: Combining healthy lifestyle management with guideline-based care for chronic low back pain led to small improvements in disability, weight, and quality of life compared with guideline-based care alone, without additional harm. Targeting lifestyle risks in the management of chronic low back pain may be considered safe and may offer small additional health… →
Adopting advanced AI technologies, including Multi-Agent Systems (MAS) powered by LLMs, presents significant challenges for organizations due to high technical complexity and implementation costs. No-Code platforms have emerged as a promising solution, enabling the development of AI systems without requiring programming expertise. These platforms lower barriers to AI adoption, allowing even non-technical users to leverage… →