It's not just about making AI smarter, but also about making sure people can trust it and understand how it works.

Artificial intelligence (AI) models—specifically, generative AI (GenAI) models—are becoming increasingly relevant for today’s businesses, yet many questions remain about how such models work and how ...

Machine learning interatomic potentials (MLIPs) enable more efficient molecular dynamics (MD) simulations with ab initio accuracy, which have been used in various domains of physical science. However, ...

The standard guidelines for building large language models (LLMs) optimize only for training costs and ignore inference costs. This poses a challenge for real-world applications that use ...

Purpose: Is used to train the machine learning model. Function: Think of it as the study material for the model. It provides examples and patterns for the model to learn from and build its internal ...

* The Matrix analogy: Are we training AI inside simulations? Whether you're a data scientist, CTO, or just curious about how AI models learn, this episode offers a deep dive into one of the most ...

Understand why test data management is a more complex and impactful challenge than test automation itself, and how to address ...

Once, the world’s richest men competed over yachts, jets and private islands. Now, the size-measuring contest of choice is clusters. Just 18 months ago, OpenAI trained GPT-4, its then state-of-the-art ...