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/
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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%
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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/
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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
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1:15:14
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
Österreich