# Summary Today on the show my guest is Mykola Belich found of Belichberg a software development company developing multiple products and services in the data and ML space. On the show Mykola will talk about their work in the area of video surveillance for photovoltaic plants in isolated rural areas.= On the show he will share some of the biggest challenges that they had to overcome to apply computer vision on low power edge devices in deployment scenarios where maintenance is only possible under high costs or not at all. # References Mykola & Belichberg - https://www.linkedin.com/feed/update/urn:li:activity:6997863170191073280/ Intels OpenVino - https://www.intel.com/content/www/us/en/developer/tools/openvino-toolkit/overview.html

# Intro Self driving cars have been a highly debated topic for years now, but today on the show we are not going to focus on the state of those autonomous vehicles, driver assisting systems or what so ever, but instead we are going to focus on the testing aspect of autonomous operating agents.  Today on the show I have the pleasure to talk to Florian Thaler, researcher at the Virtual Vehicle Research GmbH in Graz, focusing on testing reinforcement learning agents for safety critical systems. Florian will tell us about his experience building a testing infrastructure for a vehicle development platform that applies the training paradigm of reinforcement learning to achieve its goals. He will share with us some of the important concepts and practises of testing RL and Machine Learning systems in general. # References * Florian Thaler: https://www.linkedin.com/in/florian-thaler-78744b214/ * Virtual Vehicle Research Center: https://www.v2c2.at/ * Spider Platform: https://www.youtube.com/watch?v=N7JgCbBiPtk * Spider Platform https://www.youtube.com/watch?v=5LDrxTxPDB8 * https://arxiv.org/pdf/1906.10742.pdf : Testing Machine learning systems

# Summary Today on the Austrian AI Podcast, we join the hype and talk about big language models and program synthesis. I am sure you have heard about OpenAI's code pilot, or AWS Codewhisper, or even about Google's internal projects to use big language models to help developers write better code, faster. Program synthesis models are all over the news and today I have the pleasure to talk to Klaudius Kalcher cofounder of Magic.dev an AI startup that builds Software, that builds Software. In the episode we talk about how to use big language models for program synthesis. About what is out there already, open challenges and what is the state of the art. Klaudius shares with us how he thinks about the program synthesis and how at Magic.dev they are building a software development support system in the form of an AI companion. # References [https://www.linkedin.com/in/klaudiuskalcher/](https://www.linkedin.com/in/klaudiuskalcher/) [https://magic.dev/](https://magic.dev/waitlist) [https://arxiv.org/pdf/2207.11280.pdf](https://arxiv.org/pdf/2207.11280.pdf) - PanGu-Coder [https://github.com/features/copilot](https://github.com/features/copilot) [https://aws.amazon.com/blogs/aws/now-in-preview-amazon-codewhisperer-ml-powered-coding-companion/](https://aws.amazon.com/blogs/aws/now-in-preview-amazon-codewhisperer-ml-powered-coding-companion/) [https://ai.googleblog.com/2022/07/ml-enhanced-code-completion-improves.html](https://ai.googleblog.com/2022/07/ml-enhanced-code-completion-improves.html)

# Summary Today on the show we will be talking about agent based modelling that is used to simulate macro economical systems, like the British labour market. For this I am talking to Maria del Rio Chanona currently doing her PhD at the Complexity Science Hub Vienna. Maria will not only tell us about ways to use agent based modelling to simulate the effect of policy changes or external events like the pandamic on complex systems like the labour market, but during our deep dive, she will talk about her most recent work that focuses on using NLP to tab new sources of fine grain data on an individual level, like social media that can be used feed the next generation of agent based models. # References [https://www.csh.ac.at/researcher/maria-del_rio-chanona/](https://www.csh.ac.at/researcher/maria-del_rio-chanona/) [https://mariadelriochanona.info/](https://mariadelriochanona.info/) Forecasting the propagation of pandemic shocks with a dynamic input-output model [https://www.sciencedirect.com/science/article/pii/S0165188922002317](https://www.sciencedirect.com/science/article/pii/S0165188922002317) Occupational mobility and automation: a data-driven network model [https://royalsocietypublishing.org/doi/full/10.1098/rsif.2020.0898](https://royalsocietypublishing.org/doi/full/10.1098/rsif.2020.0898) Mental health concerns prelude the Great Resignation: Evidence from Social Media [https://arxiv.org/abs/2208.07926](https://arxiv.org/abs/2208.07926)

# Episode Most AI practitioner's, including myself, think of long running algorithms as something that is caused by big data or poor implementation and that can be solved best by more compute, but today on the show we will be discussing hard problems and their runtime complexity with Kirill Simonov from the algorithm and complexity group at the technical university Vienna. Kirill is talking about this research in algorithm complexity and gives us a taste of how to solve hard problems with for example, approximation algorithms, that exchanging the accuracy or correctness of results for lower runtimes, or parameterized complexity algorithms that reduce runtime by limiting the solution space. # References Kirill Simonov: https://www.ac.tuwien.ac.at/people/ksimonov/ Thesis: https://bora.uib.no/bora-xmlui/bitstream/handle/11250/2735169/archive.pdf?sequence=1&isAllowed=y Lecture on Fixed-Parameter Algorithms: https://www.youtube.com/watch?v=4q-jmGrmxKs

Intro I am sure most of you are listening or looking at some weather forecast during the day and more often than we like to see, we read news about climate change causing new temperature records or glaciers melting at accelerating rates. Today we are not going to talk about climate change or weather forecast directly, but its underlying principle, Hydrology (which is study of water movement and distribution in a physical system). We will talk about strategies to build Hydrological models and more concretely we are looking at the intersection of Machine Learning and Hydrology. For this I am talking to Moritz Feigl. Co-founder and Chief Data Scientist at Baseflow.ai During his PhD, Moritz investigated how Hydrology can benefit from Machine Learning, and in the interview we are going to contrast and compare two main approaches in Hydrological modeling. On one side we look at process-based models that are build on a systematic understanding of the physical world and principles and on the other side, at data-driven models; like modern Deep Learning systems that are learning a input-output relationship based on observations alone. Moritz explains different ways how to combine those traditionally opposing approaches to get the best of both worlds, increasing the accuracy of predictions and enhancing our understanding of the underlying physical systems. References https://www.linkedin.com/in/moritz-feigl/ https://www.linkedin.com/company/baseflow-ai-solutions/ https://baseflow.ai/ https://abstracts.boku.ac.at/search_abstract.php?paID=3&paSID=19947&paSF=&paLIST=0&language_id=DE

# Summary Have you every had troubles paying your bills and got some nasty calls or letters about it? Debt collection is surely one area where I would not have thought to find AI, but today on the show, I am talking to Stephan Stricker Founder and CEO of Pair Finance and Maxime Kaniewicz, Data Science Team lead. On how they combine the insights and methods from targeted marketing with reinforcement learning, to nudge customers towards paying their bills. I think this episode is of great value to anyone who is thinking of building reinforcement learning systems for real business cases. We speak about many of the main challenges in reinforcement learning, like how to collect intermediate rewards that match the business objects without running into the alignment problem. Or how to evaluate and compare different agents and policies without loosing revenue and cause damage to the business. We discuss the necessity of historical training data and the continuous flow of new training data in order to improve and optimize the system. We hear about ways to overcome the cold start problem by helping the agent to expand into new environments by providing new actions in combination with new priors and experiences. I hope you will like this episode, and I can ensure you that there is a lot to learn. # References https://www.pairfinance.com/ https://www.linkedin.com/in/stephanstricker/- Stephan Stricker - Founder and CEO of Pair Finance https://www.linkedin.com/in/maxime-kaniewicz/- Maxime Kaniewicz - Data Science Team Lead

When we talk about data on this podcast, its mostly about training data and its properties that are relevant for the training machine learning models. But today we look at the bigger picture and the use of data in future data economies. How should the future use of data on a bigger scale look like? How can we make sure to build trustworthy and ethical data economy that follow our European Values? Today on the show I am talking to Nina Popanton from the Data Intelligence Initiative (DIO) about its role as an enable of data collaborations. We talk about the challenges and the opportunities they see for companies, academia and the public sector when sharing data for specific use cases. We discuss the motivation for stockholders to come together and share their data, and under which circumstances they are willing to do so. In addition we are taking a step back and have a look the greater picture and the socioeconomic responsibility of a data sharing economy, driven by the "European Strategy for Data" and European projects like Gaia-X. I know this episode diverges from our usual focus on AI and its methods, but I hope it will be an inspiration to you. Let's get started … # References Nina Popanton - https://www.linkedin.com/in/nina-popanton-4b1541179/ DIO - https://www.dataintelligence.at/ Gaia-X - https://www.gaia-x.eu/ GreenData Hub - https://www.greendatahub.at/

Today on the show I am talking to Adrian Schiegl, the head of data science at XUND; an Austrian AI Startup that develops systems to predict medical diagnoses based on patients self reported symptoms. During the interview Adrian is going to share some of the findings, challenges and solutions XUND has experienced and developed since its inception in 2018. For example, Xund's decision to move away from developing an mobile phone based self diagnosis system towards an Medical API that enables other vendors to integrate their automatic diagnoses system into their own products. In addition Adrian is telling us about recent research projects and future goals of the company, to move into hospitals and clinics in order to support the digitalization of a patients medical journey and ensure the most effective treatment possible. #References Adrian Schiegl : https://www.linkedin.com/in/adrian-schiegl/ Xund: https://xund.ai/ Bayesian Neural Networks : https://proceedings.neurips.cc/paper/2020/hash/322f62469c5e3c7dc3e58f5a4d1ea399-Abstract.html

This is the second part of my interview with Hamid Eghbal-zadeh, post-doc at the Johannes Kepler University at the Institute of Machine Learning. In the interview, we are talking about his research on a series of different aspects of representation learning with deep neural networks in order to make them more robust and improve their out-of-distribution behavior. In this second part, we are talking about disentangled representations and the benefit they bring to agents trained in contextualized reinforcement tasks, in order to operate in unseen contexts and environments. References: Personal Homepage: https://eghbalz.github.io/ Hamid on LinkedIn: https://www.linkedin.com/in/hamid-eghbal-zadeh-8642b3a8/ H. Eghbal-zadeh, Representation Learning and Inference from Signals and      Sequences, PhD Thesis, 2019. H. Eghbal-zadeh, F. Henkel, G.      Widmer, Context-Adaptive      Reinforcement Learning using Unsupervised Learning of Context Variables, In      Proceedings of Machine Learning Research, NeurIPS 2020 Workshop on      Pre-registration in Machine Learning, PMLR 148:236-254, 2021.