Researchers baked 3x inference speedups directly into LLM weights — without speculative decoding

Researchers from the University of Maryland, Lawrence Livermore, Columbia and TogetherAI have developed a training technique that triples LLM inference speed without auxiliary models or infrastructure ...
Open Heart

Enhanced cardiovascular disease risk prediction using integrated machine learning models: a study from the UK Biobank cohort

Objective Cardiovascular diseases (CVD) remain the leading cause of mortality globally, necessitating early risk ...
eLife

Boosting the signal: Expectation-driven gain modulation of preparatory spatial attention

The evidence is solid but not definitive, as the conclusions rely on the absence of changes in spatial breadth and would benefit from clearer statistical justification and a more cautious ...
eLife

A proteome-wide biochemical screen defines binding determinants of the core autophagy protein LC3B

Human MAP1LC3B (LC3B) binds proteins involved in autophagy and other cellular processes using a degenerate four-residue short linear motif known as the LC3-interacting region (LIR). Biochemical and ...
Psychology Today

Diagnosing Schizophrenia With Machine Learning

Machine learning can predict many things, but can it predict who will develop schizophrenia years before the average ...
Psychology Today

Debugging Overconfidence: Is AI Too Sure of Itself?

AI doesn’t just simulate human thinking and language—it mimics our cognitive biases too. Overconfidence is one of the most powerful and overlooked issues.