GenAI refers to AI models that generate content such as text, images or code and are trained on large amounts of data, such as GPT models.

• General use

  General purpose AI chatbots cover knowledge in a broad sense. You can ask anything from food recipes to something very specific.
• Specialised use

  AI chatbots that are specialised for a specific purpose, such as reference tools or teaching mentors.
• Open source vs. closed source

  Open source is AI chatbots and language models that are made available for you to work with.
  
  Closed source are licensed AI chatbots and language models.

• Cloud services vs. local servers

  The majority of AI chatbots are located in the cloud. This means that language models and AI chatbots are located in large data centres around the world and can be accessed from there. The language models and AI chatbots can also be downloaded or developed on your own servers. SDU has several servers in the basement for this purpose.

Image recognition is often used in creative and general contexts, for example to analyse, classify or generate content based on images as object recognition or style transfer.

Models such as Convolutional Neural Networks (CNNs) or vision transformers trained on large datasets fall into this category. Results often require validation because they can be prone to error or bias.

Potentials

• Administrative tasks, such as automating document preparation, email replies, reporting, summarising meeting notes and generating standard contracts.

• Research support, such as generating hypotheses, literature summaries or draft articles. It can also help formulate complex ideas quickly.

• Education, such as creating personalised learning materials, exercises or explanations and can act as a virtual tutor for students.

Challenges

There is a risk of inaccurate or fabricated answers (hallucinations).

Diffusion models are a type of generative AI that uses stochastic processes to gradually transform noise into structured data, such as images or sound.

The model learns to reconstruct data by reversing a diffusion process where information has been gradually added as noise. This approach has proven particularly effective in image generation and is used in models such as DALL-E 2 and Stable Diffusion.

Potentials

• Education, e.g. creating illustrative images for course materials or interactive learning environments.

• Research support,  e.g. visualising complex data or simulations - for example in medical imaging or materials research.

• Creative production, e.g. generating realistic images, artworks or visual concepts based on text descriptions - used in design, advertising and entertainment.

Risks

• High computational requirements and risk of generating unethical or copyrighted content.


• Requires responsible use and control mechanisms.

Edge solutions are AI models - often small and optimised language models - that run locally on devices such as smartphones or IoT devices. This provides low response time, better data protection and the ability to use the solution offline.

Potentials

• Administrative tasks, enables local processing of sensitive data, e.g. in the healthcare or financial sector - without dependence on the cloud, increasing security.

• Research support, supports field research by running analyses on portable devices, e.g. in environmental monitoring or data collection in remote areas.

• Education, provides access to AI tools in classrooms without internet, such as translation, quizzes or learning apps on tablets.

Risks

Limited computing power and modelling capacity compared to cloud-based solutions.