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- it-sa insights
- Knowledge Forum D
- Industry 4.0 / IoT / Edge Computing
- Mobile Security
- Network Security / Patch Management
- SIEM / Threat Analytics / SOC
Hacking AI - How to Turn Machine Learning to be Evil by Data Poisoning
Those who use AI in industrial automation and find that their systems do not deliver should check their models as soon as possible.
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- it-sa insights
- Knowledge Forum D
Those who use AI in industrial automation and find that their systems do not deliver should check their models as soon as possible.
Language: German
Questions and Answers: No
Action description
AI is beautiful, but it makes a lot of work. Loosely based on Karl Valentin ("Art is beautiful,..."), this statement applies today to all application areas of AI and its most widespread element, machine learning (ML). Anyone who uses AI/ML in industrial automation and is surprised that their systems do not deliver what was agreed upon should have the setting or training of their models checked as soon as possible. Either by the supplier or by an independent service provider. Such algorithms are considered sophisticated, demanding, very complex and very hermetic. But they are vulnerable and error-prone just like any other code. Rule of thumb: The more complex and therefore more extensive, the more prone to failure. Currently, the nimbus of AI as unapproachable and infallible is disappearing. And anyone who, as a provider of corresponding products or platforms, has been able to give the impression that the work product of their data scientists is artificial intelligence, must now rethink. They must admit that there are many, sometimes embarrassing, gaps lurking inside their algorithms. Users can and should therefore scrutinize AI methods and systems for work and result security. In the lecture, you will learn what you need to watch out for. read more
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This action is part of the event it-sa Expo 2022
