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 ...
The Ising model, when used as a technique, refers to a computational and analytical framework for studying systems of binary variables with pairwise interactions, typically on a lattice or graph, via ...
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 ...
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 ...
The centralized mega-cluster narrative is seductive – but physics, community resistance, and enterprise pragmatism are conspiring to scatter AI compute across a distributed lattice of specialized ...
Model inversion and membership inference attacks create unique risks to organizations that are allowing artificial intelligences to be trained using their data. Companies may wish to begin to evaluate ...
ByteDance’s Doubao Large Model team yesterday introduced UltraMem, a new architecture designed to address the high memory access issues found during inference in Mixture of Experts (MoE) models.
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