Background Research:
1. Professor Reinhard Heckel is a faculty member at the Technical University of Munich where he focuses his research on machine learning and its application in medical imaging. His work primarily revolves around large language models and AI.
2. Artificial Intelligence (AI) refers to the capacity of a computer system to mimic human intelligence processes, learn from data, and carry out tasks that would normally require human intervention.
3. Machine Learning (ML), a subset of AI, involves algorithms that can learn without being explicitly programmed by improving their functions over time with exposure to more data.
4. Large language models like ChatGPT are trained using mass amounts of text data collected from the internet which they then use to generate human-like text based on what they’ve learned.
5. Bias in AI refers to systematic errors introduced during model training process due to factors like inadequate representation in the training data or wrongful learning from skewed examples that cause an algorithm to favor one outcome over another for irrelevant reasons or assumptions.
6. Data privacy is increasingly important when it comes to using data for training such models considering vast amounts of personal information available online today.
FAQs:
1. Who is Prof Reinhard Heckel?
Prof Reinhard Heckel is a professor at the Technical University of Munich who specializes in machine learning with specific emphasis on large language models and medical imaging applications.
2. What does it mean by „training“ artificial intelligence?
Training artificial intelligence involves feeding a model large amounts of data so it can learn patterns, correlations or rules independent of explicit programming; optimising its functionality over time through iterative learning.
3.What are ‘large language models’ like ChatGPT mentioned here?
Large Language Models like ChatGPT are deep-learning-based systems trained on vast volumes of text which they use for generating new text that seems coordinatingly similar as per input received; essentially mimicking human-like conversation style discovered through training process.
4. What is bias in AI and how can it be avoided?
Bias in AI refers to systematic error introduced during model’s training that causes it to favor certain outcomes over others. This can be avoided by using balanced data for training, reviewing algorithmic decision-making regularly, and employing techniques specifically designed for reducing bias.
5. How do we ensure data protection in the context of AI?
Data protection can be ensured by following stringent privacy laws – anonymizing personal information wherever possible; using safe & encrypted transmission protocols and opt-out options for users not wishing their data to be used.
6. What are the applications of machine learning in medical imaging?
Machine learning has wide applications in medical imaging – accelerating image interpretation process, improving diagnostic accuracy, aiding personalized treatment strategies and predicting patient outcomes based on historical data.
Originamitteilung:
These days our data are collected everywhere in the internet and are also used to train large language models like ChatGPT. But how do we train artificial intelligence (AI), how do we avoid distortions – known as bias – in the models and how do we ensure that data are protected? Reinhard Heckel, a professor of machine learning at the Technical University of Munich (TUM), takes the time to answer these questions. Prof. Heckel conducts research on large language models and medical imaging applications.