The SAFEXPLAIN project, formed by an inter-disciplinary consortium of six partners coordinated by the Barcelona ...

More information: Zhenghao Yin et al, Experimental quantum-enhanced kernel-based machine learning on a photonic processor, Nature Photonics (2025). DOI: 10.1038/s41566-025-01682-5 ...

Inside the Session What: Predicting the Future using Azure Machine Learning When: Aug. 7, 2025, 9:30 a.m. - 10:45 a.m. Who: Eric D. Boyd, Founder and CEO of responsiveX Why: Learn the fundamentals of ...

20+ Machine Learning Methods in Groundbreaking Periodic Table From MIT, Google, Microsoft Your email has been sent A new “periodic table for machine learning” is reshaping how researchers ...

As machine learning (ML) models become increasingly advanced, humans are tasked with understanding the steps an algorithm takes to arrive at its result. In the healthcare industry, this means ...

Synergy of Graph Data Management and Machine Learning in Explainability and Query Answering. Abstract: Graph data, e.g., social and biological networks, financial transactions, knowledge graphs, and ...

Deep learning neural networks, exemplified by models like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) and Generative Adversarial Networks (GANs), have achieved ...

More information: Yun-Fei Shi et al, Machine Learning for Chemistry: Basics and Applications, Engineering (2023). DOI: 10.1016/j.eng.2023.04.013 ...

Similarly in AI, when we transition to a phase where the model’s accuracy beats the human baseline, and we reach that high degree of accuracy, explainability will become less relevant.

Machine learning has a wide range of applications in the finance, healthcare, marketing and transportation industries. It is used to analyze and process large amounts of data, make predictions ...