Looking back, I realize my previous article didn’t delve deeply into the potential impact of recent AI advancements. While I can’t predict the future with certainty, I’m ready to make informed predictions about how current AI developments may disrupt traditional systems. In a nutshell, I foresee a significant, imminent impact across various industries. While IT, Finance, and Marketing are often touted as the prime sectors for AI disruption, I believe Manufacturing also stands at the cusp of substantial transformation. With the power of AI, smaller enterprises might disrupt the markets currently dominated by big players, potentially leading us into a scenario reminiscent of the next Innovator’s Dilemma. Furthermore, I anticipate an urgent need for individuals to adapt to these changes, with the integration of new AI tools into educational curriculums being an ideal approach.

Ah, large language models (LLMs), GPT-4, and generative models in general… It seems like everybody’s talking about them these days. So, naturally, I felt the unbearable pressure of groupthink to also write about it. As someone who’s been in the NLP field for quite some time, I have seen the transformative power of these models firsthand. In this post, we’ll explore the potential of LLMs as enablers and force multipliers, transforming what individuals can achieve in various fields. Just imagine the possibilities!

In 2022 we exchanged the heating in our house including solar thermal energy as well. Since I like to know what is going on in the house I was interested to read out and store the information from the heating. However as it turns out the manufacturer doesn’t allow this unless you pay for a subscription to send the data to their servers and then access it from there. Without the subscription all that was possible was to use an app to read the current state of the system. This got me thinking whether I could reverse engineer the app communication to extract the data I need without a subscription.

This is part of a lecture I gave at KIT (Karlsruhe Institute of Technology) in the master course “neural networks”. It is to show what adversarial attacks are and what they mean for machine learning based classification.