How do you make data accountable? With an AI model.
Michael Welsch is a mechanical engineer. His approach makes data accountable. A neural network learns to decompose and reconstruct a company's data lake, which it can only do if it learns to model the inner relationships step by step. The AI software is then capitalized on the company's balance sheet as a proxy for the actual data lake. Welsch presented his idea to some auditors and large accounting firms. They were also puzzled by the approach.
The podcast is growing and we want to keep growing. That's why our German-language podcast is now available in English. We are happy about new listeners.
We thank our new partner [Siemens](https://new.siemens.com/global/en/products/automation/topic-areas/artificial-intelligence-in-industry.html)
Our guest: https://www.linkedin.com/in/michael-welsch-/
Questions? robert@aipod.de or peter@aipod.de
Om Podcasten
The Industrial AI Podcast reports weekly on the latest developments in AI and machine learning for the engineering, robotics, automotive, process and automation industries. The podcast features industrial users, scientists, vendors and startups in the field of Industrial AI and machine learning. The podcast is hosted by Peter Seeberg, Industrial AI consultant and Robert Weber, tech journalist.Their mission: Demystify Industrial AI and machine learning, inspire industrial users.
The hosts:
Peter Seeberg is an Industrial AI and machine learning expert for the manufacturing industry. He worked over 25 years in IT (Intel) and 10 years in Automation. He co-initiated the Industrial Data Intelligence Startup (Softing) where he was responsible for managing machine learning projects in industrial environments. Today he advises companies when it comes to Industrial AI and machine learning. Together with Robert Weber, journalist for industrial topics, he discusses AI and ML applications, standards, and education topics, make or buy decisions as well as regulation for AI in manufacturing.