Active learning framework streamlines scientific data collection for discovery
ALF: An Active Learning Framework for Scientific Discovery
Machine Learning
Summary
Collecting good scientific data can be slow and costly because each new piece often needs expensive experiments or simulations. The authors created ALF, a complete tool to help scientists and engineers pick the most useful data to collect next. ALF works both with existing datasets for testing ideas and in real experimental settings for gathering brand new data. This can save time and money by focusing resources on the most important experiments. The tool is open-source and easy to use through a simple programming interface.
What this means in practice
- •For machine learning engineers: Integrate a modular active learning tool to optimize costly data labeling and experiment selection in scientific workflows.
- •For laboratory automation teams: Use a unified framework to drive experiment planning by iteratively selecting the most informative samples to test next.
Authors
Shikha Surana, Alex Hawkins-Hooker, Olivia Gallup, Christoph Brunken, Jules Tilly, Paul Duckworth
Abstract
Machine learning for scientific discovery is almost systematically data bound. Producing relevant high quality data, under budget constraints, is amongst the most promising ways to advance the field. Active learning (AL) offers promise wherever labelling requires expensive experiment, measurement, or simulation. Most existing tools cover only part of the data acquisition loop, and typically focus on either offline benchmarking or online deployment, but not both. We present ALF, a modular AL Framework that runs the full data acquisition loop via five modular components. One clear API for both settings: offline, against an existing dataset for controlled and reproducible experimentation; and online, against an oracle for acquiring new candidates in real-world deployments. ALF is open-source and available at https://github.com/instadeepai/alf.