You have to see Visual AI to understand it.
Live demonstrations, concrete examples, and just enough technical background to assess the possibilities and limitations realistically.
This is no longer only about individual image or video generators. Increasingly, it is about how multiple AI systems, software and human decisions become functioning workflows.
Live, not just polished demos.
Andreas Ebrahim shows current systems in action.
Images are generated and altered. Videos are created. Synthetic reality becomes visible. Different AI systems are combined.
And we show just as openly where AI hallucinates, breaks physics, becomes inconsistent or produces convincing nonsense.
No prepared magic show.
Real systems, with their strengths and their limits.
Questions are welcome. Interruptions are explicitly encouraged.
- What image and video AI can actually do today
- How synthetic images and videos are created
- How individual AI tools become functioning workflows
- Why specialized AI roles often work better than one universal bot
- AI as an ideation partner and the expansion of creative search spaces
- Deepfakes and synthetic reality
- Why AI can be astonishingly good — and astonishingly wrong
- Where current systems still fail technically
- Why plausible AI output is not automatically truth
- Which tasks are better handled by software, AI or humans
- Why human gates, context and responsibility still matter
- What decision-makers can practically take away from this
For whom?
Companies, executives, associations, public authorities, organizations and teams that want a realistic understanding of generative AI.
No technical background is required.
The goal is not to turn the audience into AI specialists.
The goal is for them to leave with a much better understanding of:
What is already possible today.
Where the limits are.
And what practical consequences follow from that.
Andreas Ebrahim
Andreas Ebrahim has many years of experience in international software, internet and technology projects.
For several years, he has worked intensively and hands-on with generative AI, especially Visual AI, synthetic identities and AI-native production systems.
The focus is practical transfer rather than academic research:
What does this technology mean for people and organizations that do not develop it themselves — but need to deal with its possibilities and consequences?