Artificial intelligence in everyday life: Word suggestions when chatting
Artificial intelligence (AI) have long been involved as Alexa and Siri or in autonomous driving, and AI also supports us in our daily chatting with friends: We get words suggested to us and thus not only save valuable time, but also make fewer mistakes when writing. But how does the cell phone know what I want to write next? How can such word suggestions be generated in such a way that they are most likely to deliver the word the user wants? How well do such word suggestions work? In the end, can such predictive models be used to imitate the user's language so well that no one notices that the text is generated by an AI insted of a real human being?
In this workshop, students will create their own predicitve model, improve it, and test it out. In the process, the practical relevance of mathematics in everyday life becomes apparent and the students experience a completely new action-oriented computer-based approach to mathematics.
Duration: from 5 hours
Contents: Stochastics (concept of probability), nth-order Markov chains (transition graphs and tables), weighted average, logarithm function, optimization, basic machine learning strategies
Previous knowledge: relative und absolute frequency, concept of probability
Target group: mathematics courses or computer science courses in 10th grade and above
Created by: Stephanie Hofmann
Registration: Dates can be arranged individually via this form.
Image source: https://unsplash.com/photos/ik_AuIWeBBM
Material
The interactive learning material can be accessed via the online platform workshops.cammp.online. How to create an account on the platform and use the material is explained in this video. In addition, accompanying material is provided for teachers on the online platform, which can be accessed via a password that can be requested by e-mail.
List of publications and talks to this module
- Hofmann, S. & Frank, M. (in Druck): Maschinelles Lernen im Schulunterricht am Beispiel einer problemorientierten Lerneinheit zur Wortvorhersage, GDM, Frankfurt.
- Hofmann, S. & Frank, M. (2022). Teaching data science in school: Digital learning material on predictive text systems. In G. Bolondi & J. Hodgen (Eds.), ''Proceedings of the Twelfth Congress of the European Society for Research in Mathematics Education''.