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Artificial intelligence (AI)

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  • Deep Reinforcement Learning in Aktion: Ein 3D-Modell eines neuronalen Netzwerks mit leuchtenden Knoten, die durch pulsierende Linien verbunden sind, welche die Flüsse von Aktionen und Aktivierungen symbolisieren. Das neuronale Netzwerk interagiert mit einer simulierten Umgebung, dargestellt durch ein Schachbrett und Spielfiguren, die den Lernprozess und die Entscheidungsfindung des Agents in Echtzeit veranschaulichen.

Reinforcement Learning

2025-01-26T18:51:25+01:00Tags: , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |

Reinforcement learning is a branch of machine learning that trains agents to make optimal decisions by interacting with their environment. Reinforcement learning is used in autonomous vehicles, robotics, games such as AlphaGo and AlphaZero and the optimization of resources in energy systems.

  • Retrieval-Augmented Generation - RAG

Retrieval Augmented Generation (RAG) – using AI models effectively

2024-03-30T10:36:37+01:00Tags: , , , , , , , , , , , , |

What is Retrieval Augmented Generation (RAG)? Retrieval-Augmented Generation (RAG) is an advanced AI technique in AI language modeling based on the integration of external information sources to improve and augment answer generation. A RAG system combines the comprehensive knowledge capacities of a Large Language Model (LLM) with the ability to obtain specific information from an external knowledge repository. This AI method allows the model to generate answers based not only on its internally trained knowledge, but also on current, specific and extensive external data. Find out more now!

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