Statistical modeling lies at the heart of data science. Well-crafted statistical models allow data scientists to draw conclusions about the world from the limited information present in their data. In ...

When AI models fail to meet expectations, the first instinct may be to blame the algorithm. But the real culprit is often the data—specifically, how it’s labeled. Better data annotation—more accurate, ...

A new kind of large language model, developed by researchers at the Allen Institute for AI (Ai2), makes it possible to control how training data is used even after a model has been built.

Data modeling best practices help define a formal process that gives structure and direction to an organization’s data. Read more about data modeling now. Data modeling, at its core, is the process of ...

As more organizations embrace big data and analytics to gain insight from extremely large datasets, the tools and systems used to manage data have grown, changed, and mul­tiplied. Instead of just ...

Statistical modelling of zero-inflated count data addresses datasets in which the frequency of zero outcomes exceeds that predicted by standard count distributions. Such phenomena arise across ...

EY's Alexy Thomas says connected, trustworthy data—not AI models alone—will determine India's long-term AI innovation and ...

Data operationalization, complemented by the pragmatic deployment of AI use cases with said data, is, at its core, a move ...

LFM2.5-230M proves that while 3-billion-parameter models like VibeThinker are solving advanced calculus, a ...