Austrian Artificial Intelligence Podcast

Verfügbare Folgen

5 von 68

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.### Referencesbeatshaper.ai - Beatshaper an Copilot for Musics Producershttps://openai.com/index/musenet/ - OpenAI MuseNetPlease create a funny looking comic image, showing a panda with glasses that is very busy creating music on a computer.
--------
52:20
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/
--------
1:04:50
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%
--------
59:00
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/
--------
50:34
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
--------
1:15:14