Artificial intelligence: basics, applications and future prospects

Artificial intelligence

• • Definition and basic concepts.
  • Historical development and milestones.
  • Difference between weak and strong AI.

Classification of generative AI

• • Definition and examples (e.g. GPT-4, DALL-E; current version in each case).
  • Possible applications and challenges.
  • Differences to other forms of AI.

Basic principles

• • Role of data, algorithms and models.
  • Importance of big data and data processing.
  • Training, test and synthetic data.

Machine learning & deep learning

• • Basics of machine learning.
  • Supervised, unsupervised and reinforcing learning.
  • Important algorithms and their applications.
  • Structure and function of neural networks.
  • Introduction to Deep Learning.

Computer Vision

• • Basics of computer vision.
  • Techniques and applications (e.g. object recognition, image classification).
  • Practical examples and tools (e.g. OpenCV).

AI for data analysis

• • How AI can support decision-making in companies.
  • Business Understanding and Data Understanding.
  • Evaluations and forecasts (predictive analytics) with AI.

Applications and challenges

• • Everyday applications and invisible AI.
  • Ecological and sociological effects.
  • The ESG criteria and their relationship with AI.
  • Ethical issues and data privacy.
  • Discussion of opportunities and risks.

Regulation and ethics

• • Introduction to the EU AI Act.
  • Classifications and regulations.
  • Effects on the development and use of AI.

Raising awareness for the handling of data

• • Importance of data quality.
  • data privacy and data security.
  • Responsible handling of data.

Industries and future prospects

• • Advantages and challenges in various industries.
  • Discussion of future developments and potential.
  • AI in the (future) world of work: areas of application and requirements for professional fields.