Deep Learning

We’re used to hearing that AI sometimes “hallucinates” — making funny or random mistakes. Hallucinations are unintended errors caused by the limits of statistical prediction. But the new research goes further: it shows that AI can knowingly choose to lie when deception helps it achieve a goal. The publication Can LLMs Lie? takes us into a world where AI acts more like a strategic agent, capable of manipulating information to maximize outcomes. ...

Clinical trial enrollment is a critical bottleneck in drug development: nearly 80% of trials fail to meet target enrollment, costing up to $8 million per day if delayed. In this work, we introduce a multimodal deep‐learning framework that not only predicts total participant count but also quantifies uncertainty around those predictions. Challenges in Enrollment Forecasting Traditional approaches fall into two camps: Deterministic models – e.g. tabular ML like XGBoost or LightGBM – which output a point estimate but ignore variability in recruitment rates. Stochastic models – e.g. Poisson or Poisson–Gamma processes – which simulate recruitment and give confidence intervals, but often struggle with high-dimensional, heterogeneous data. Model Architecture Inputs ...

Diagnosing mental disorders such as autism, depression, or schizophrenia goes beyond taking simple brain images. Resting-state fMRI (rs-fMRI) observes brain activity while at rest, revealing which regions activate simultaneously. This forms the basis for functional connectivity. Traditional studies have used graphs and neural networks, but they mostly focus on pairwise interactions — asking “do regions A and B co-activate?” But what about higher-order relationships, like among regions A, B, and C all at once? ...