Austrian Artificial Intelligence Podcast

As you surely know, OpenAI is not very open about how their systems works or how they build them. More importantly for most uses and business, OpenAI is agnostic about how users apply their services and how to make most out of the models multi-step "reasoning" capabilities . As a stark contrast to OpenAI, today I am talking to Marius Dinu, the CEO and co-founder of the austrian st

Today on the podcast I have to pleasure to talk to Jules Salzinger, Computer Vision Researcher at the Vision & Automation Center of the AIT, the Austrian Institute of Technology. Jules will share with us, his newest research on applying computer vision systems that analyze drone videos to perform remote plant phenotyping. This makes it possible to analyze plants growth, but as

## Summary Today on the show I am talking to Proofreads CTO Alexandre Paris. Alex explains in great detail how they analyze digital books drafts to identify mistakes and instances within the document that dont follow guidelines and preferences of the user. Alex is explaining how they fine-tune LLMs like, Mistrals 7B to achieve efficient resource usage and provide customizations an

Today I am talking to Philip Winter, researcher at the Medical Imaging group of the VRVis, a research center for virtual realities and visualizations. Philip will explain the benefits and challenges in continual learning and will present his recent paper "PARMESAN: Parameter-Free Memory Search and Transduction for Dense Prediction Tasks". Where he and his colleagues have

## Summary AI is currently dominated by Deep Learning and Large Language Models, but there is other very interesting research that has the potential to have great impact on our lives in the future; one of them being Quantum Machine Learning (QML) Today on the podcast, I am talking to Christa Zoufal, Quantum Machine Learning Researcher at IBM. Christa will explain how Quantum Comput

Hello and welcome back to the AAIP This is the second part of my interview with Eldar Kurtic and his research on how to optimiz inference of deep neural networks. In the first part of the interview, we focused on sparsity and how high unstructured sparsity can be achieved without loosing model accuracy on CPU's and in part on GPU's. In this second part of the interview,

Hello and welcome back to the AAIP If you are an active Machine Learning engineer or are simply interested in Large Language models, I am sure you have seen the discussions around quantized models and all kind of new frameworks that have appeared recently and achieve astonishing inference performance of LLM's on consumer devices. If you are curious how modern Large Language M

## Summary Today on the show I am talking to Veronika Vishnevskaia. Solution Architect at ONTEC where she specialises in building RAG based Question-Answering systems. Veronika will provide a deep dive into all relevant steps to build a Question-Answering system. Starting from data extraction and transformation, followed by text embedding, chunking and hybrid retrieval to strategi

# Summary Today on the show I am talking to Manuel Reinsperger, Cybersecurity Expert and Penetration Tester. Manuel will provide us an introduction into the topic of Machine Learning Security with an emphasis on Chatbot and Large Language Model security. We are going to discuss topics like AI Red Teaming that focuses on identifying and testing AI systems within an holistic approac

## Summary At the end of last year, the EU-AI Act was finalized and it spawned many discussions and a lot of doubts about the future of European AI companies. Today on the show Peter Jeitschko, founder of JetHire an AI based recruiting platform that uses Large Language models to help recruiters find and work with candidates, talks about this perspective on the AI-Act. We talk abo