Welcome to XAIES,
the expert platform for Explainable Artificial Intelligence solutions and systems. We use well-established models like the versions of the LIME (Local Interpretable Model-Agnostic Explanations) algorithm, activations maps, deep Taylor decompositions, etc. and new models based on computational topology and spline-type spaces interpretability. XAIES is aiming to enable the next generation expert systems based on statistical learning models of today. The expert systems that can learn with data will be introduced as use-cases featured as XAI competitions.
General artificial intelligence similar to the human mind is a longstanding dream of humanity.
The first AI systems that were explainable and have been successfully implemented in practice were the Experts Systems. Think about MYCIN, deployed after a 6 years effort from the Stanford University researchers that was able to treat meningitis better than medical doctors. Nowadays, machine learning-based AI systems and especially deep learning are statistically developed algorithms with amazing accuracy and learning capabilities. But they lack the interpretability and the reasoning of an expert system. Therefore, we consider that at the future core of modern XAI as explainable AI-systems it will be a significant place for the next generation machine-learning based expert systems.