The Data & AI Blog from Alexander Thamm GmbH

data.blog - Facts about Data Science & Artificial Intelligence

How reinforcement learning works

Transformer-XL, XLNet, XLM and CTRL

Smart Factory - how the right data strategy makes the networked production environment a success

Wu Dao 2.0 - What China's state-of-the-art model can do

Trust in Autonomous Driving

Explainable AI - Methods for explaining AI models

by Johannes Nagele | Feb 16, 2023

Explainable AI (XAI) is currently one of the most discussed topics in the field of AI. Developments such as the European Union's AI Act, which makes explainability a mandatory property of AI models in critical domains, have brought XAI into the focus of many companies developing or applying AI models. As this now applies to a large part of the economy, the demand for methods that can describe the decisions of models in a comprehensible way has increased immensely.

Why, AI? The basics of Explainable AI (XAI)

by Sirtan Zaefferer | Jan 26, 2023

So in theory, with XAI methods and the right know-how, even complex black-box models can be at least partially interpreted. But how does it look in reality? In many projects or use cases, interpretability is an important pillar of the overall success. Security aspects, lack of trust in the model results, possible future regulations as well as ethical concerns ensure the need for interpretable ML models.

data.basement - basic knowledge around the topics of data and AI