Generative AI – From Big Picture, to Idea, to Implementation

Recently, we have seen a shift in AI that wasn’t very obvious. Generative Artificial Intelligence (GAI) – the part of AI that can generate all kinds of data – started to yield acceptable results, getting better and better. As GAI models get better, questions arise e.g. what will be possible with GAI models? Or, how to utilize data generation for your own projects?

In this course, we answer these and more questions as best as possible.

There are 3 angles that we take: 

Tech angle: we see what GAI models exist and how they are implemented. We will focus on only relevant parts of the code and not on administrative code that won’t be accurate a year from now (it’s one google away). Further, there will be an excursion: from computation graphs, to neural networks, to deep neural networks, to convolutional neural networks (the basis for image and video generation).

The architecture list is down below.

Application angle: we get to know many GAI application fields, where we then ideate what further projects could emerge from that. Ultimately, we point to good starting points and how to get GAI models implemented effectively.

The application list is down below

Ethical angle/ Ethical AI: we discuss the concerns of GAI models and what companies and governments do to prevent further harm.