Conference AI for SMEs 2023
Look forward to our presentations and learn about funding and how to implement AI yourself. Network with experts and AI-interested people!
October 26, 2023 IHK Munich & Upper Bavaria. Max-Joseph-Straße 2, 80333 Munich
Organizers
- Wolfgang Köhler (fortiss)
Event Contact
Programme
| Time | Speaker | Description |
|---|---|---|
| 13:00 | Armin Barbalata | Joint welcome of the organizers |
| 13:10 | Prof. Dr. Alexander Pretschner | Introduction to AI |
| 13:40 | Nicole Höss | Examples from medium sized companies |
| 14:05 | Johanna Fahrnhammer | Make or Buy? Two examples of collaboration with start-ups |
| 15:15 | Dr. Martin Häusl | Generative AI: An introduction |
| 15:30 | Johanna Fahrnhammer | Workshop "Experience AI Introduction" |
| 16:30 | Closure & Networking |
Background
Artificial intelligence (AI) is one of the most important cornerstones of digitalization as a key technology. Due to the emergence of generative AI solutions such as ChatGPT, Google Bard and DALL-E, the topic has noticeably broadened its potential. The possible applications in a wide variety of industries and business areas are diverse. The benefits for companies should not be neglected, however, as AI can be used to save resources, increase efficiencies and develop entirely new business models.
To make AI more tangible for businesses, the annual conference “AI for SMEs” will take place on October 26, 2023. Join us and receive numerous impulses through expert presentations and best practices. Look forward to learning how other companies have implemented AI, what funding opportunities are available, how you can implement AI yourself, and more. Also, take the chance to network with experts and other AI-interested people in our networking area!
Prof. Dr. Alexander Pretschner, scientific director and speaker of fortiss GmbH, asks the crucial question during his talk "When is AI good (enough)?" How "well" does AI work, and when does it work well enough? We prefer to use machine learning, currently the most successful form of AI, for problems that we cannot describe precisely ("Detect pedestrians!" or "Give useful answers to arbitrary questions!"). The question of the quality of corresponding systems is a very difficult one from the outset. He argues that it is often useful and sometimes not – and what that means for the application of Machine Learning in practice.
