Artificial Intelligence (AI):
AI is a field of computer engineering that aims to provide machines with capabilities that would otherwise require human thinking.
Machine Learning (ML):
Machine learning is an approach where computers learn from data and can improve themselves without explicit programming.
Natural Language Processing (NLP):
NLP is the technology that enables computers to understand and interpret human language.
Neural Networks:
Neural networks are computer models that are designed after the human brain to recognize patterns in data.
Deep Learning:
Deep Learning is an advanced method of machine learning that uses particularly deep layered neural networks to identify complex patterns in large sets of data.
Generative Pre-trained Transformer (GPT):
GPT is an advanced AI model that specializes in generating human-like texts by being pre-trained on a comprehensive set of language data.
Training and Fine-Tuning:
During training, an AI model learns from general data, and during fine-tuning, it adjusts to specific tasks or specific data to deliver better results.
Ethik und Bias in KI:
Ethik betrachtet die moralischen Aspekte und Auswirkungen der KI, während Bias die unbeabsichtigten Vorurteile beschreibt, die in KI-Systemen auftreten können.
Prompt Engineering:
Prompt Engineering ist der Prozess des Entwerfens und Optimierens von Anweisungen (Prompts), um von einem KI- System spezifische und präzise Antworten oder Ergebnisse zu erhalten. Es ist entscheidend beim Einsatz von Modellen wie GPT, um die gewünschten Outputs zu erzielen.
Assistive Technology:
Assistive technology encompasses systems and tools that have been developed to support people with various types of disabilities. In AI, this often refers to systems that use speech recognition, text-to-speech, and other interactive functions to improve accessibility.
Transfer Learning:
Transfer Learning is an approach in machine learning where a model developed for one task is adapted and reused for another, but related task. This accelerates the learning process and improves the model's efficiency on new tasks.
Data Mining:
Data Mining is the process of analyzing large datasets to discover patterns and relationships. It is a key element in many AI applications as it helps to extract insights and information from large volumes of data.
Reinforcement Learning:
In reinforcement learning, a model learns through rewards and punishments. It is often used in situations where an agent needs to learn how to make optimal decisions by interacting with its environment.
Algorithmic Fairness:
Algorithmic fairness refers to the efforts to ensure that AI systems deliver results that are free from discrimination or unfair biases against certain groups or individuals.
AI Governance:
AI Governance refers to the rules, policies, and practices that determine how AI systems are developed, deployed, and monitored responsibly and ethically.