Vast amounts of valuable research data remain unused, trapped in labs or lost to time. Frontiers aims to change that with FAIR² Data Management, a groundbreaking AI-driven system that makes datasets reusable, verifiable, and citable. By uniting curation, compliance, peer review, and interactive visualization in one platform, FAIR² empowers scientists to share their work responsibly and gain recognition.
Two of the trickiest qualities to balance in the world of machine learning are fairness and accuracy. Algorithms optimized for accuracy may unintentionally perpetuate bias against specific groups, while those prioritizing fairness may compromise accuracy by misclassifying some data points.
Research has shown that large language models (LLMs) tend to overemphasize information at the beginning and end of a document or conversation, while neglecting the middle.
A team of computer scientists and AI researchers from FAIR at Meta, INRIA, Université Paris Saclay and Google, has developed a possible means for automating data curation for self-supervised pre-training of AI datasets.