Recently, using artificial intelligence (AI) in drug discovery has received much attention since it significantly shortens the time and cost of developing new drugs. Deep learning (DL) …
Artificial intelligence (AI) is currently being utilized in a wide range of sophisticated applications, but the outcomes of many AI models are challenging to comprehend and trust …
M Mitchell, DC Krakauer - Proceedings of the National …, 2023 - National Acad Sciences
We survey a current, heated debate in the artificial intelligence (AI) research community on whether large pretrained language models can be said to understand language—and the …
AI is undergoing a paradigm shift with the rise of models (eg, BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks. We …
The field of explainable artificial intelligence (XAI) aims to bring transparency to today's powerful but opaque deep learning models. While local XAI methods explain individual …
Alloy modelling has a history of machine-learning-like approaches, preceding the tide of data-science-inspired work. The dawn of computational databases has made the integration …
Interpretability in machine learning (ML) is crucial for high stakes decisions and troubleshooting. In this work, we provide fundamental principles for interpretable ML, and …
Feature attributions based on the Shapley value are popular for explaining machine learning models. However, their estimation is complex from both theoretical and …
Abstract Explainable Artificial Intelligence (XAI) has experienced a significant growth over the last few years. This is due to the widespread application of machine learning, particularly …