Many of us think of reading as building a mental database we can query later. But we forget most of what we read. A better analogy? Reading trains our internal large language models, reshaping how we ...

The domain of digital public health is rapidly evolving with the emergence of large language models (LLMs), which are poised to revolutionize disease ...

LLM answers vary widely. Here’s how to extract repeatable structural, conceptual, and entity patterns to inform optimization ...

Pinterest launched a next-generation CDC-based database ingestion framework using Kafka, Flink, Spark, and Iceberg. The system reduces data availability latency from 24+ hours to 15 minutes, processes ...

Researchers generated images from noise, using orders of magnitude less energy than current generative AI models require.

As EU mulls new regulations, a real-world study of a million PHEVs shows even when charged up, they still use 3x as much gas ...

Global electricity consumption for data centers is set to more than double by 2030. According to the International Energy Agency, consumption will hit around 945 TWh in just four years. That’s roughly ...

Large-language models (LLMs) have taken the world by storm, but they’re only one type of underlying AI model. An under-the-radar company, Fundamental, is set to bring a new type of enterprise AI model ...

The era of size inclusivity is seemingly over. Our critic traces the shift and hopes designers might learn from it. By Vanessa Friedman I know models have always been skinny, but it seems to me they ...

Jeremiah Fowler, a veteran security researcher, recently stumbled upon 149,404,754 unique logins and passwords, totaling about 96GB of raw data. There was no encryption… and it didn’t even have a ...

MongoDB Inc. is making its play for the hearts and minds of artificial intelligence developers and entrepreneurs with today’s announcement of a series of new capabilities designed to help developers ...

Organizations have a wealth of unstructured data that most AI models can’t yet read. Preparing and contextualizing this data is essential for moving from AI experiments to measurable results. In ...