New study finds bigger datasets might not always be better for AI models

From ChatGPT to DALL-E, deep learning artificial intelligence (AI) algorithms are being applied to an ever-growing range of fields. A new study from University of Toronto Engineering researchers, published in Nature Communications, suggests that one of the fundamental assumptions of deep learning models—that they require enormous amounts of training data—may not be as solid as …

Study explores the scaling of deep learning models for chemistry research

Deep neural networks (DNNs) have proved to be highly promising tools for analyzing large amounts of data, which could speed up research in various scientific fields. For instance, over the past few years, some computer scientists have trained models based on these networks to analyze chemical data and identify promising chemicals for various applications.