Fundamental, which just closed a $225 million funding round, develops "large tabular models" (LTMs) for structured data like ...

Advances in artificial intelligence, particularly large language models (LLMs), have been driven by the "scaling law" paradigm: performance improves with more data, computation, and larger models.

Once a model is deployed, its internal structure is effectively frozen. Any real learning happens elsewhere: through retraining cycles, fine-tuning jobs or external memory systems layered on top. The ...

Although large language models (LLMs) have the potential to transform biomedical research, their ability to reason accurately across complex, data-rich domains remains unproven. To address this ...