Graphical models provide a robust framework for representing the conditional independence structure between variables through networks, enabling nuanced insight into complex high-dimensional data.
Probabilistic graphical models are a powerful technique for handling uncertainty in machine learning. The course will cover how probability distributions can be represented in graphical models, how ...
We know that correlation does not imply causation, but careful analyses of correlations are often our only way to quantify cause and effect in domains ranging from healthcare to education. This ...
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AI inference costs dropped up to 10x on Nvidia's Blackwell — but hardware is only half the equation
New deployment data from four inference providers shows where the savings actually come from — and what teams should evaluate ...
The AI industry stands at an inflection point. While the previous era pursued larger models—GPT-3's 175 billion parameters to PaLM's 540 billion—focus has shifted toward efficiency and economic ...
Forbes contributors publish independent expert analyses and insights. I write about the economics of AI. When OpenAI’s ChatGPT first exploded onto the scene in late 2022, it sparked a global obsession ...
This post details the beginning of Bloomberg’s journey to build a machine learning inference platform. For those readers who are less familiar with the technical concepts involved in machine learning ...
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