Forbes

The Pros And Cons Of Using Synthetic Data For Training AI

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 ...
Nature

Online test-time adaptation for better generalization of interatomic potentials to out-of-distribution data

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, ...
Nature

Guiding questions to avoid data leakage in biological machine learning applications

Machine learning methods for extracting patterns from high-dimensional data are very important in the biological sciences. However, in certain cases, real-world applications cannot confirm the ...
VentureBeat

Train-to-Test scaling explained: How to optimize your end-to-end AI compute budget for inference

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 ...
inc42

What Is Training Data? Here’s All You Need to Know

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 ...
Forbes

AI’s New Training Data: Your Old Work Slacks And Emails

Defunct startups are being liquidated for their Slack archives, Jira tickets, and email threads—operational exhaust that AI labs now treat as premium training data. When Shanna Johnson was winding ...