This workshop will delve into the implications of artificial intelligence on applied research in the technological sector, focusing on technology readiness levels 4-7.
Objectives
We argue that AI methods such as machine learning, deep learning, natural language processing, and reinforcement learning are not only transforming applications but also fundamentally altering the way scientists conduct their work.
Examples of implications
- Automation: AI streamlines repetitive tasks like data collection, analysis, and processing, thereby saving time and reducing errors
- Speed: AI processes large volumes of data rapidly and with high accuracy, enhancing research efficiency
- Insights: AI uncovers patterns and correlations that might not be immediately apparent to human researchers, leading to deeper and more nuanced scientific insights
- Everyday operations: AI may help in translating texts, improving the readability of texts, provide guidelines when coding, …
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Topics for discussion
- Impact on Scientists: What does AI integration mean for the daily work of scientists? How do scientists need to be educated?
- Facilitating innovation: (how) can new ideas emerge? How much will innovation processes be accelerated?
- Which of the functions of science (such as explainability, experimentation, predictions, testability) are particularly suitable for AI support?
- Best Practices: Do we need guidelines to ensure the responsible and ethical use of AI tools in scientific research, thereby protecting scientific integrity?
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The panelists
- Dr. Nikolaus Krall, founder of Allcyte, EVP Precision Medicine Exscientia
- Klara Neumayr, PhD student, department of chemistry and physics of materials, Paris-Lodron-University of Salzburg
- Dr. Elizabeth Churchill, director of user experience at google
