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Identifying interactions at scale for LLMs

ai, AI (Artificial Intelligence), articles, Artificial Intelligence, deep dive

By Landon Butler, Justin Singh Kang, Yigit Efe Erginbas, Abhineet Agarwal, Bin Yu, Kannan Ramchandran Understanding the behavior of complex machine learning systems, particularly Large Language Models (LLMs), is a critical challenge in modern artificial intelligence. Interpretability research aims to make the decision-making process more transparent to model builders and impacted humans, a step toward […]

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Information-driven design of imaging systems

ai, AI (Artificial Intelligence), articles, Artificial Intelligence, deep dive, NeurIPS

An encoder (optical system) maps objects to noiseless images, which noise corrupts into measurements. Our information estimator uses only these noisy measurements and a noise model to quantify how well measurements distinguish objects. By Henry Pinkard, Leyla Kabuli, Eric Markley, Tiffany Chien, Jiantao Jiao, Laura Waller Many imaging systems produce measurements that humans never see

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Information-driven design of imaging systems

ai, AI (Artificial Intelligence), articles, Artificial Intelligence, deep dive, NeurIPS

An encoder (optical system) maps objects to noiseless images, which noise corrupts into measurements. Our information estimator uses only these noisy measurements and a noise model to quantify how well measurements distinguish objects. By Henry Pinkard, Leyla Kabuli, Eric Markley, Tiffany Chien, Jiantao Jiao, Laura Waller Many imaging systems produce measurements that humans never see

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Signals for 2026

ai, AI (Artificial Intelligence), AI & ML, Artificial Intelligence, data, deep dive, infrastructure, operations, Security, Software Architecture, software development

We’re three years into a post-ChatGPT world, and AI remains the focal point of the tech industry. In 2025, several ongoing trends intensified: AI investment accelerated; enterprises integrated agents and workflow automation at a faster pace; and the toolscape for professionals seeking a career edge is now overwhelmingly expansive. But the jury’s still out on

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Learning from failure to tackle extremely hard problems

articles, Artificial Intelligence, deep dive

By Sangyun Lee and Giulia Fanti This blog post is based on the work BaNEL: Exploration Posteriors for Generative Modeling Using Only Negative Rewards. Tackling very hard problems The ultimate aim of machine learning research is to push machines beyond human limits in critical applications, including the next generation of theorem proving, algorithmic problem solving,

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