Austrian Artificial Intelligence Podcast

## Summary Today we have as a guest Alexander Zehetmaier, co-founder of SunRise AI Solutions. Alex will explain how SunRise AI is partnering with companies to navigate this challenging space, by providing their guidance, knowledge and network of experts to help companies apply AI successfully. Alex will talk in detail about one of their Partners, Mein Dienstplan that is developing an Graph Neural Network based Solution that is generating complex work time tables. Scheduling a Timetable for a large number of employees and shifts is not an easy task, specially if one has to satisfy hard constraints like labor laws, and soft constraints like employee preferences. Alex will explain in detail how they have developed a hybrid solution to use Graph Neural Network to create candidates that are validated and improved through heuristic based methods. ## AAIP Community Join our discord server and ask guest directly or discuss related topics with the community. https://discord.gg/5Pj446VKNU ## TOC 00:00:00 Beginning 00:02:23 Guest Introduction 00:04:19 SunRise AI Solutions 00:7:45 Mein Dienstplan 00:19:52 Building timetables with genAI 00:39:36 How SunRise AI can help startups ## References Alexander Zehetmaier: https://www.linkedin.com/in/alexanderzehetmaier/ SunRise AI Solutions: https://www.sunriseai.solutions/ MeinDienstplan: https://www.meindienstplan.at/

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 startup extensity.ai. Extensity.ai as a company follows an open core model, building an open source framework which is the foundation for AI Agent systems that perform multi-step reasoning and problem solving, while generating revenue by providing enterprise support and custom implementation's. Marius will explain how their Neuro-Symbolic AI Framework is combining the strengths of symbolic reasoning, like problem decomposition, explainability, correctness and efficiency with an LLM's understanding of natural language and their capability to operate on unstructured text following instructions. We will discuss how their framework can be used to build complex multi-step reasoning workflows and how the framework works like an orchestrator and reasoning engine that applies LLM's as semantic parsers that at different decision points decide what tools or sub-systems to apply and use next. As well how in their research, they focus on ways to measure the quality and correctness of individual workflow steps in order to optimize workflow end-to-end and build a reliable, explainable and efficient problem solving system. I hope you find this episode useful and interesting. ## AAIP Community Join our discord server and ask guest directly or discuss related topics with the community. https://discord.gg/5Pj446VKNU ## TOC 00:00:00 Beginning 00:03:31 Guest Introduction 00:08:32 Extensity.ai 00:17:38 Building a multi-step reasoning framework 00:26:05 Generic Problem Solver 00:48:41 How to ensure the quality of results? 01:04:47 Compare with OpenAI Strawberry ### References Marius Dinu - https://www.linkedin.com/in/mariusconstantindinu/ https://www.extensity.ai/ Extensity.ai - https://www.extensity.ai/ Extensity.ai YT - https://www.youtube.com/@extensityAI SymbolicAI Paper: https://arxiv.org/abs/2402.00854

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 well how certain plant decease spreads within a field. We will discuss how the diversity im biology and agriculture makes it challenging to build AI systems that generalize between plants, locations and time. Jules will explain how in their latest research, they focus on performing experiments that provide insights on how to build effective AI systems for agriculture and how to apply them. All of this with the goal to build scalable AI system and to make their application not only possible but efficient and useful. ## TOC 00:00:00 Beginning 00:03:02 Guest Introduction 00:15:04 Supporting Agriculture with AI 00:22:56 Scalable Plant Phenotyping 00:37:33 Paper: TriNet 00:70:10 Major findings ### References - Jules Salzinger: https://www.linkedin.com/in/jules-salzinger/ - VAC: https://www.ait.ac.at/en/about-the-ait/center/center-for-vision-automation-control - https://www.ait.ac.at/en/about-the-ait/center/center-for-vision-automation-control - AI in Agriculture: https://intellias.com/artificial-intelligence-in-agriculture/ - TriNet: Exploring More Affordable and Generalisable Remote Phenotyping with Explainable Deep Models: https://www.mdpi.com/2504-446X/8/8/407

## 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 and serve multiple uses cases with a single base model and multiple lora adapters. We talk about the challenges and capabilities of fine-tuning, how to do it, when to apply it and when for example prompt engineering of an foundation model is the better choice. I think this episode is very interesting for listeners that are using LLMs in a specific domain. It shows how fine-tuning a base model on selected high quality corpus can be used to build solutions outperform generic offerings by OpenAI or Google. ## AAIP Community Join our discord server and ask guest directly or discuss related topics with the community. https://discord.gg/5Pj446VKNU ## TOC 00:00:00 Beginning 00:02:46 Guest Introduction 00:06:12 Proofcheck Intro 00:11:43 Document Processing Pipeline 00:26:46 Customization Options 00:29:49 LLM fine-tuning 00:42:08 Prompt-engineering vs. fine-tuning ### References https://www.proofcheck.io/ Alexandre Paris - https://www.linkedin.com/in/alexandre-paris-92446b22/