Moraliser, plagiarism engine, miracle machine: How well do we know AI?
The breakneck speed at which artificial intelligence is developing makes it difficult to predict where it is heading. Our researchers say: Instead of getting ever bigger and faster, we should now take the time to really understand AI.
AI knowledge: What is Botsplaining?
Botsplaining refers to the phenomenon when a chatbot provides information or advice in a patronising or overly explanatory way that can be perceived as preachy or unnecessary. An example:
AI knowledge: What is interpretability and explainability?
- Interpretability: The ability to understand the reasoning behind predictions and decisions made by an AI model.
- Explainability: The opportunity to present and provide reasons for the decisions of an AI system in a form understandable to humans.
- Why is this important? Interpretability and explainability allow users to understand why a system arrived at a certain decision. This is especially relevant in critical areas, such as medicine, finance and justice, where decisions may have far-reaching consequences. In addition, they can support us in identifying errors or biases in AI systems and to correct these.
AI knowledge: Typical AI mistakes
- Hallucinations: AI generates fictitious information, patterns or data, which are non-existent in the actual input. For example, an AI for image analysis adds anomalies, which do not exist in reality, to a CAT scan.
- Adversarial examples: specially crafted input, such as a slightly modified image, which is designed to look ‘normal’ to humans but causes a misclassification by an AI. For example, a picture of a panda is changed to a minimal extent, causing the AI to misidentify it as a gibbon.
- Bias: AI reproduces or propagates existing data biases. For example, a selection system which discriminates against applicants on the grounds of gender or origin.
- Overfitting/underfitting: AI is too attuned to the data on which it was trained, which leads to a bad performance on new data; or it has not learned the training data well enough to recognise even the simplest patterns.
AI knowledge: What is a neural network?
What is a neural network? The mathematician explains: Imagine it like a very complicated net of very small, interconnected light switches. Each switch (a ‘neuron’) can be switched on or off, based on the signals it receives from the other switches. The way how these switches are connected with each other and how they react to signals allows the network to learn and solve complex tasks, for example, to identify pictures or understand human language.
After completing his PhD at TU Wien, he conducted research at TU Graz, KAUST (Saudi Arabia) and ETH Zurich, where he became assistant professor in 2011 before moving to the University of Vienna in 2016. He is deputy head of the Data Science research network and head of the Mathematical Data Science research group at the Johann Radon Institute.
His research interests include teaching German in the context of digitality and generative artificial intelligence, and disinformation as an epistemological challenge.
Her research stations include the University of Oxford, the Massachusetts Institute of Technology and the University of Cambridge. Sliwa is a member of the Board of Directors of the new Cluster of Excellence Knowledge in Crisis at the Central European University, University of Vienna and University of Salzburg. She tweets @PASliwa.
- Read more about "Do we know what AI will know?"
- Website of Paulina Sliwa at the Institute of Philosophy
- Cluster of Excellence "Knowledge in Crisis"
- Website of Philipp Grohs at the Faculty of Mathematics
- Research Network Data Science at the University of Vienna
- Website of Matthias Leichtfried at the Institute of German Studies
- AI in studies and teaching
- Blog post: OK with AI?