This review explores the burgeoning intersection of large language models and traditional modeling & simulation techniques, offering a practical guide for researchers and practitioners.
New research reveals that even carefully curated datasets can subtly shift the behavior of powerful language models, creating unexpected and potentially harmful outcomes.
A new approach combines graph neural networks and manifold learning to visualize and interpret the complex behavior of Internet of Things devices.
A new language model, FiMI, is being developed to better understand and operate within the unique complexities of India’s financial landscape.
New research tackles the problem of ‘hallucinations’ in large language models to create more trustworthy AI systems for managing and responding to security incidents.
A new framework categorizes the rapidly evolving world of crypto-assets, connecting their technical underpinnings to market function and regulatory needs.
A new analysis reveals how platforms like Polymarket transform subjective beliefs into seemingly objective forecasts, creating an illusion of neutrality.
As AI systems gain agency, understanding and mitigating the evolving threat landscape becomes critical, particularly in safety-critical applications.
A new machine learning framework accurately forecasts galaxy bias by analyzing the interplay between dark matter halos and cosmic environments.
Researchers are applying concepts from the Renormalisation Group to build more reliable and interpretable deep learning models.