Austrian Artificial Intelligence Podcast

Manuel Pasieka

Guest Interviews, discussing the possibilities and potential of AI in Austria. Question or Suggestions, write to austrianaipodcast@pm.me

  1. 68. Markus Tretzmüller - Cortecs - Europäische LLM Infrastruktur Unabhängigkeit

    23 ИЮЛ.

    68. Markus Tretzmüller - Cortecs - Europäische LLM Infrastruktur Unabhängigkeit

    Seit Anfang des Jahres gibt es in Europa einen starkes politisches Verlangen, sich von den USA unabhängig zu machen. Dies betrifft nicht nur die aktuelle militärische Abhängigkeit, sondern auch die Abhängigkeit von US tech Unternehmen. Besonders interessant für den AAIP ist natürlich die starke abhängigkeit Europas von Amerikanischen und Chinesischen KI Modellen und der Computer Infrastruktur um diese Modelle zu nützen. Heute auf dem Podcast spreche ich mit Markus Tretzmüller, der Mitbegründer von Cortecs. Einem Österreichischen Unternehmen das es sich zum Ziel gesetzt hat, mittels eines Sky Computing Ansatzes, eine Routing Lösung zu entwickeln die es Europäischen Unternehmen ermöglicht lokale Cloud Anbieter für KI Anwendungen zu nützen. Diese ermöglicht es KI Lösungen zu entwickeln, die im Europäischen Rechtsraum operieren ohne auf die Vorteile von Hyperscalern wie Kosteneffizienz und Ausfallsicherheit verzichten zu müssen. Im Interview erzählt Markus warum es nicht reicht auf Europäische Neiderlassungen von US Unternehmen zu setzen um Unabhängigkeit und Datensicherheit zu gewährleisten, und welche Vorteile eine routing Lösung wie Cortecs bringen kann. Viel spass und spannendes zuhören. ## Referenzen - Cortecs: https://cortecs.ai/ - Building Your Sovereign AI Future - Sky computing: https://sigops.org/s/conferences/hotos/2021/papers/hotos21-s02-stoica.pdf - RouteLLM: https://arxiv.org/abs/2406.18665 - FrugalGPT - https://arxiv.org/abs/2305.05176

    43 мин.
  2. 67. Mathias Neumayer and Dima Rubanov - Lora a child friendly AI

    24 МАЯ

    67. Mathias Neumayer and Dima Rubanov - Lora a child friendly AI

    ## Summary Large Language Models have many strengths and the frontier of what is possible and what they can be used for, is pushed back on the daily bases. One area in which current LLM's need to improve is how they communicate with children. Todays guests, Mathias Neumayer and Dima Rubanov are here to do exactly that, with their newest product LORA - a child friendly AI. Through they existing product Oscar stories, they identified issues with age appropriate language and gender bias in current LLMS's. With Lora, they are building their own AI friendly solution by fine tuning state of the art LLMs with expert curated data that ensures Lora is generating the appropriate language for children of a specific age. On the show they will describe how they are building Lora and what they plan to do with it. ### References - https://oscarstories.com/ - GenBit Score: [https://www.microsoft.com/en-us/research/wp-content/uploads/2021/10/MSJAR_Genbit_Final_Version-616fd3a073758.pdf](https://www.microsoft.com/en-us/research/wp-content/uploads/2021/10/MSJAR_Genbit_Final_Version-616fd3a073758.pdf) - Counterfactual Reasoning for Bias Evaluation: [https://arxiv.org/abs/2302.08204](https://arxiv.org/abs/2302.08204)

    53 мин.
  3. 66. Taylor Peer - Beat Shaper - A music producers AI Copilot

    18 АПР.

    66. Taylor Peer - Beat Shaper - A music producers AI Copilot

    Today on the show I have the pleasure to talk to returning guest, Taylor Peer one of the co-founders of the startup, behind Beat Shaper. Taylor will explain how they are following an Bottom-up approach to create electronic music, giving producers, fine grained control to create individual music instruments and beat patterns. For this, Beat Shaper is combining Variational Auto-encoders and Transformers. The VAE is used to create high dimensional embeddings that represent the users preferences that are used to guide the autoregressive generation process of the Transformer. The token sequence generated with the transformer is a custom developed symbolic music notation that can be decoded into individual instruments. We discuss in detail the system architecture and training process. Taylor is explaining in depth how they build such a system, and how they have been creating their own synthetic training dataset that contains music in symbolic notation that enables the fine grained control over the generated music. I hope you like this episode, and find it useful. ### References beatshaper.ai - Beatshaper an Copilot for Musics Producers https://openai.com/index/musenet/ - OpenAI MuseNet Please create a funny looking comic image, showing a panda with glasses that is very busy creating music on a computer.

    52 мин.
  4. 65. Daniel Kondor - CSH - The long term impact of AI on society

    11 ФЕВР.

    65. Daniel Kondor - CSH - The long term impact of AI on society

    Guest in this episode is the Computational Social Scientist Daniel Kondor, Postdoc at the Complexity Science Hub in Vienna. Daniel is talking about research methods that make it possible to study the impact of various factors like technological development on societies; and in particular their rise or fall, over long periods of time. He explain how modern tools from computational social science, like agent based modelling can be used to study past and future social groups. We talk about his most recent publication that takes a complex systems perspective on the risk AI poses for society and provided suggestions on how to manage such risks through public discourse and involvement of affected competency groups. ## References - Waring TM, Wood ZT, Szathmáry E. 2023 Characteristic processes of human evolution caused the Anthropocene and may obstruct its global solutions. Phil. Trans. R. Soc. B 379: 20220259. https://doi.org/10.1098/rstb.2022.0259 - Kondor D, Hafez V, Shankar S, Wazir R, Karimi F. 2024 Complex systems perspective in assessing risks in artificial intelligence. Phil. Trans. R. Soc. A 382: 20240109. https://doi.org/10.1098/rsta.2024.0109 - https://seshat-db.com/

    1 ч. 5 мин.
  5. 64. Solo - Manuel Pasieka on the hottest LLM topics of 2024

    31.12.2024

    64. Solo - Manuel Pasieka on the hottest LLM topics of 2024

    With the last episode in 2024, I dare to release an solo episode, summarizing my christmas research on the topics of - Small Language models - Agentic Systems - Advanced Reasoning / Test time compute paradigm I hope you find it interesting and useful! All the best for 2025! ## AAIP Community Join our discord server and ask guest directly or discuss related topics with the community. https://discord.gg/5Pj446VKNU ## TOC 00:00:05 Intro 00:01:52 Part 1 - Small Language Models 00:20:16 Part 2 - Agentic Systems 00:36:16 Part 3 - Advanced Reasoning 00:58:08 Outro ## References - Testing Qwen2.5 - https://huggingface.co/spaces/Qwen/Qwen2.5 - Qwen2.5 Technical report - https://arxiv.org/pdf/2412.15115 - Agents: https://www.superannotate.com/blog/llm-agents - Scaling Test-time compute: https://arxiv.org/html/2408.03314v1 - Test time compute: https://huggingface.co/spaces/HuggingFaceH4/blogpost-scaling-test-time-compute - O3 achieving 88% on ARC-AGI https://arcprize.org/blog/oai-o3-pub-breakthrough - https://arxiv.org/html/2409.01374v1 - Human performance on ARC-AGI 76%

    59 мин.
  6. 63. Alexander Zehetmaier - Sunrise AI Solutions - Bringing AI to companies of every size

    19.11.2024

    63. Alexander Zehetmaier - Sunrise AI Solutions - Bringing AI to companies of every size

    ## 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/

    51 мин.
  7. 62. Marius-Constantin Dinu - extensity.ai - Building reliable and explainable AI Agent Systems

    29.10.2024

    62. Marius-Constantin Dinu - extensity.ai - Building reliable and explainable AI Agent Systems

    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

    1 ч. 15 мин.
  8. 61. Jules Salzinger - AIT - Building explainable and generalizable AI Systems for Agriculture

    03.10.2024

    61. Jules Salzinger - AIT - Building explainable and generalizable AI Systems for Agriculture

    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

    1 ч. 26 мин.
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Guest Interviews, discussing the possibilities and potential of AI in Austria. Question or Suggestions, write to austrianaipodcast@pm.me

Информация

  • Автор
    Manuel Pasieka
  • Годы выхода
    2021 - 2025
  • Выпуски
    70
  • Ограничения
    Без ненормативной лексики
  • Авторские права
    © Manuel Pasieka
  • Сайт подкаста
    Austrian Artificial Intelligence Podcast