Describing Basic Concepts of Artificial Intelligence

When discussing Artificial Intelligence (künstliche intelligenz), you often refer to systems or programs that can solve problems intelligently. An algorithmus is a step-by-step method for processing information (daten). These daten can be structured into datasets, called strukturierte datensätze. AI systems, such as those using maschinenlernen, are lernfähig, meaning they can improve from previous experience. A modell is a computational representation that is created from data so that it can make predictions or decisions. The performance of the model can be evaluated through a specific prozess, in which the system prozessieren (processes) inputs. In the field of robotik, AI can be used to automate and control physical machines. Another field is simulation, where smart (smart) systems mimic real-life behaviors.

Example sentences:

• Künstliche Intelligenz verwendet Algorithmen, um große Datenmengen zu verarbeiten.
• Im maschinellen Lernen wird aus strukturierten Datensätzen ein Modell entwickelt.
• Die Performance eines Modells hängt von der Qualität der Daten ab.
• Robotik nutzt künstliche Intelligenz zur Steuerung der Prozesse.
• Simulationen helfen beim Testen von neuen Algorithmen.

Describing Machine Learning and Data-Driven Decision Making

Machine learning (maschinenlernen) allows a system to learn from examples, called exemplare, and to identify important characteristics known as merkmale (features). During training, a model is given a trainingsset, which includes many examples with their features. The process of modellanpassung (model adjustment) and modellentwicklung (model development) involves refining the model structure to improve predictions. Validierung is done using data not included in training, the validierungssatz, to check how well the model generalizes. Systems can be modular, allowing for flexible improvements.

Example sentences:

• Der Algorithmus lernt aus den Merkmalen im Trainingsset.
• Validierung ist notwendig, um das Modell zu testen.
• Die Modellentwicklung passiert schrittweise und kann modular sein.
• Ein gutes Modell merkt relevante Muster in den Daten.
• Der Validierungssatz enthält neue Exemplare zur Überprüfung.

Describing Neural Networks, Deep Learning and Their Applications

Neural networks (neuronale netzwerke) and deeplearning are inspired by the human brain. Nodes called dendriten receive sensory data (sensorische daten) which are processed through verbindungen and synapsen. Structures like konvolutionales neuronales netzwerk are especially good at processing images. The anpassungsfähigkeit of such networks makes them suitable for various tasks like conversation (chatbot), emotion interpretation (einfühlsam, empathisch, gefühlen), or even robotics (robotertechnologie). These systems get better with consistent feedback and gain a kind of artificial gedächtnis (memory). When comparing results, systems should get genauer (more accurate) and handle information with more geduldiger (patience).

Example sentences:

• DeepLearning nutzt sensorische Daten zur Modellanpassung.
• Ein Chatbot kann empathisch auf Gefühle reagieren.
• Konnekivität zwischen Neuronen ermöglicht schnelle Reaktionen.
• Feedback hilft beim Anpassen der Synapsen im Netzwerk.
• Mit jedem Training werden die Resultate genauer und relevanter.