AI is currently experiencing a new wave of hype. The number of AI-assisted applications is growing rapidly, making it increasingly important to understand the basics to evaluate its possibilities, limitations, and risks. This course covers fundamental AI terminology, as well as technologies like machine learning and networks. It also discusses AI's societal implications. The goal of this course is to equip you with the knowledge to actively engage with AI and be prepared for the inevitable changes it brings.

Ultimately, the aim is for this course to provide you and your team with a shared language to discuss AI topics and make informed decisions about implementing it within your organization.

✔️ Identify autonomy and adaptivity as key concepts of AI

✔️ Distinguish between realistic and unrealistic AI (science fiction vs. real life)

✔️ Explain why machine learning techniques are used

✔️ Distinguish between unsupervised and supervised machine learning scenarios

✔️ Explain the principles of three supervised classification methods: the nearest neighbor method, linear regression, and logistic regression

✔️ Explain what a neural network is and where they are being successfully used

✔️ Understand the technical methods that underpin neural networks

✔️ Understand the difficulty in predicting the future and be able to better evaluate the claims made about AI

✔️ Identify some of the major societal implications of AI including algorithmic bias, AI-generated content, privacy, and work

• Level: Beginner

• Certificate: Available by the facilitator upon completion of the learning program

• Length: Independent study 8h, kickoff and workshops 8.5h, experiment at work 4h

• Target audience: Teams in all kinds of organizations