Interview mit Prof. Reinhard Heckel: „Daten sind die entscheidende Komponente für generative KI’’

Unsere Daten werden im Internet inzwischen überall gesammelt und auch zum Training von Large Language Models wie ChatGPT eingesetzt. Doch wie wird die Künstliche Intelligenz (KI) trainiert, wie wird sichergestellt, dass keine Verzerrungen, sogenannte Bias in den Modellen entstehen und wie wird dabei der Datenschutz eingehalten? Antworten auf diese Fragen gibt Reinhard Heckel, Professor für Maschinelles Lernen an der Technischen Universität München (TUM). Er forscht zu Large Language Models und bildgebenden Verfahren in der Medizin.

Background Research:

Prof. Reinhard Heckel is a renowned figure in the field of Artificial Intelligence (AI) and machine learning. Currently, he serves as a professor at Technische Universität München (TUM), one of Germany’s most prestigious universities known for its engineering and technical programs.

Large Language Models (LLMs) like ChatGPT are algorithms used in language-based AI systems to understand, generate and predict textual patterns in human language, making interactions with AI more natural and efficient.

Data bias is a significant concern when training these models because it can result in inaccurate outputs or prejudiced decision-making processes that favor one group over another. Ensuring data privacy during the training process is also crucial due to concerns about personal information being misused or handled irresponsibly.

Image processing techniques in medicine involve analyzing medical images using computer algorithms to detect disease markers. These technologies have been applied widely within clinical settings for better diagnosis and treatment plans.

FAQ:

1. What are Large Language Models like ChatGPT?

These are highly sophisticated algorithms used mostly by AI systems to understand, simulate, and forecast human language’s text patterns.

2. Who is Prof. Reinhard Heckel?

Prof. Reinhard Heckel serves as a professor at Technische Universität München where he researches about Machine Learning with special focus on LLMs and image processing techniques exploited within medicine.

3.What does Prof.Heckel’s work signify?

His work primarily focuses on the assurance of unbiasedness during model training as well as ensuring all-around data privacy during this process.

4.What do you mean by Bias in model training?

In simple terms, Bias refers to errors or inaccuracies that can creep into our datasets which may further influence an AI system causing discrimination between different groups based upon collected data points discussion errors favouring one group above others.

5.How does medical image processing help diagnose diseases?

It involves analyzing medical images using robust computer algorithms that enable us to detect disease markers in early stages thereby providing a more personalised and advanced level of treatment.

6.What’s the significance of data privacy in AI?

Given that AI systems are usually trained on massive sets of data, which includes personal information, protecting it from misuse or irresponsible handling becomes priority for researchers like Prof. Heckel.

Originamitteilung:

Unsere Daten werden im Internet inzwischen überall gesammelt und auch zum Training von Large Language Models wie ChatGPT eingesetzt. Doch wie wird die Künstliche Intelligenz (KI) trainiert, wie wird sichergestellt, dass keine Verzerrungen, sogenannte Bias in den Modellen entstehen und wie wird dabei der Datenschutz eingehalten? Antworten auf diese Fragen gibt Reinhard Heckel, Professor für Maschinelles Lernen an der Technischen Universität München (TUM). Er forscht zu Large Language Models und bildgebenden Verfahren in der Medizin.

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