# Summary

Every day you can read and hear about the impact of AI on companies of any industry and size. But are really all business at a stage where they can benefit from the wonders of AI? What about small companies that are not in the tech and dont have the budget to hire data scientists and machine learning engineers. For example, like small retailers of fast moving consumer goods; FMCG in short. that might only have a few stores in a city. They are experts in their field, but lack the personal or infrastructure to have their own AI initiatives. How can they benefit from AI to for example optimize their planning and supply chain?

Today on the show I am talking to Eric Weisz, co-founder of Circly, an AI startup that has build a self-service platform for SME's to help them with demand forecasting. Making it possible for none data scientists with little historical data to use their platform and benefit from accurate predictions.

On the show we talk about the challenges of building such a one-fits all platform that has to provide value to all kind of different customers without intensive manual configuration and tuning. We talk about how to verify and maintain data quality, and how approaches from federated machine learning can be used to ensure the effective use of prediction model. So that based on the available data and its characteristics models are selected the are efficient to run as a platform provider, reducing costs, while providing highly accurate predictions for customers.

## TOC

00:00:00 Beginning

00:02:42 Guest Introduction

00:05:98 Circly: Demand Prediction for SME's

00:08:05 Demand prediction as an SaaS offering

00:14:37 Ensuring and maintaining data quality

00:26:09 Prediction model selection based on data and efficiency

00:35:04 Federated Machine Learning & Weight sharing

00:39:58 Feature selection and context enrichment

## Sponsors

- Quantics: Supply Chain Planning for the new normal - the never normal - https://quantics.io/

- Belichberg GmbH: We do digital transformations as your innovation partner - https://belichberg.com/

## References

Eric Weisz - https://www.linkedin.com/in/ericrweisz-circly-ai-retail/

Circly - https://www.circly.at/

Today on the show I am talking to Michael Trimmel, head of AI at HalloSofia about his journey as an entrepreneur, building AI Startups.

This episode will be most valuable to people that interested in creating an AI startup or at the beginning of this journey.

Michael will tell his personal startup story, describing his troubles and learnings on the way. Its particular important to him to highlighting that one can get into AI without having a traditional computer science background.

We will be talking on how to get started as an Entrepreneur, what makes a good founding team, how to build a support network, how to build first prototypes, how to benefit from accelerator program and what funding options there are in Austria.

I hope this interview will provide you with useful information and tips to get you started on your own journey.

# TOC

00:00:00 Beginning

00:02:31 Guest Introduction

00:07:17 Co-Founder of Cortecs GmbH

00:11:48 Head of AI at HelloSofia

00:20:22 What you need to build a startup

00:23:48 The founding team

00:31:03 The Business Network

00:37:36 Incubators & Accelerators

00:43:56 Funding

00:49:18 Navigating the AI Hype

# Sponsors

Quantics: Supply Chain Planning for the new normal - the never normal - https://quantics.io/

Belichberg GmbH: We do digital transformations as your innovation partner - https://belichberg.com/

# References

Michael Trimml - https://www.linkedin.com/in/michael-trimmel-91aa3a152/

Hallo Sofia - https://www.hallosophia.com/

Startup House - https://www.startuphouse.at/

Austrian Startups - https://austrianstartups.com/

Austrian Startups - ELP - https://austrianstartups.com/elp/

Hummel Accelerator - https://hummelnest.net/

INITS - https://www.inits.at/

FFG - https://www.ffg.at/

# Summary

In our bodies, the Immune system is detecting foreign pathogens or cancer cells, called antigens, with the help of antibody proteins that detect and physically attach to the surface of those cells.

Unfortunately our immune system is not perfect and does not detect all antigens, meaning that the immune system does not have all antigens it would need to detect all cancer cells for example.

Modern cancer therapies like CAR T-cells therapy therefor introduces additional antibody proteins into the system. This is still not enough to beat cancer, because cancer is a very diverse decease with a high variation of mutations between patients, and the antibodies used in CAR T-cell therapy are developed to be for a cancer type or patient group, but not for individual patience.

Today on the austrian AI podcast I am talking to Moritz Schäfer who is working on applying Diffusion Models to predict protein structures that support the development of patient specific, and therefore cancer mutation specific antibodies. This type of precision medicine would enable a higher specificity of cancer Therapie and will hopefully improve Treatment outcome.

Existing DL systems like Alpha Fold and alike fall short in predicting the structure of antibody binding sites, primarily due to lack of training data. So there room for improvement, and Moritz work is focused on applying Diffusion Models (so models like DALL-E or Stable Diffusion), which are most well known for their success in generating images, to problem of protein prediction. Diffusion models are generative models that generate samples from their training distribution based on an iterative process of several hundred steps. Where one starts, in case of image generation from pure noise, and in each step replaces noise with something that is closer to the training data distribution.

In Moritz work, they apply classifier guided Diffusion models to generate 3d antibody protein structures.

This means that in the iterative process of a diffusion model where in each step small adjustments are performed, a classifier nudges the changes towards increasing the affinity of the predicted protein to the specific antigen.

# TOC

00:00:00 Beginning

00:03:23 Guest Introduction

00:06:37 The AI Institute at the UniWien

00:07:57 Protein Structure Prediction

00:10:57 Protein Antibodies in Caner Therapy

00:16:17 How precision medicine is applied in cancer Therapy

00:22:17 Lack of training data for antibody protein design

00:30:44 How Diffusion models can be applied in protein design

00:46:06 Classifier based Diffusion Models

00:51:18 Future in prediction medicine

# Sponsors

Quantics: Supply Chain Planning for the new normal - the never normal - https://quantics.io/

Belichberg GmbH: We do digital transformations as your innovation partner - https://belichberg.com/

# References

Moritz Schaefer - https://www.linkedin.com/in/moritzschaefer/

Unser Institut - [https://www.meduniwien.ac.at/ai/de/contact.php](https://www.meduniwien.ac.at/ai/de/contact.php)

Lab website - [https://www.bocklab.org/](https://www.bocklab.org/)

LLM bio paper: [https://www.biorxiv.org/content/10.1101/2023.06.16.545235v1](https://www.biorxiv.org/content/10.1101/2023.06.16.545235v1)

Diffusion Models - https://arxiv.org/pdf/2105.05233.pdf

Diffusion Models (Computerphile) - https://www.youtube.com/watch?v=1CIpzeNxIhU

# Summary

Today on the show I am talking to Martin Huber Co-Founder and CEO of AMRAX.

We will talk about their product Metaroom; an AI application that is build on-top of consumer smartphones and makes it possible to create a digital twins of buildings for indoor user cases, like interior and light design.

We will focus less on algorithms and Machine Learning Methods, but on the impact that sensors and hardware platform have on the AI applications that can be build on top of them.

Martin will explain how Apple's LiDAR Sensors, available in their pro devices, in combination with the Apple Roomplan API are a unique and powerful platform to build AI applications, but at the same time forces one to focus on vertical integration and solutions. We will discuss how as an AI startup in this space one has to be super focused to be successful.

# TOC

00:00:00 Beginning

00:02:57 Guest Introduction

00:05:08 Building hardware vs. writing software

00:07:12 AMRAX & Metaroom by AMRAX

00:13:51 3D Reconstruction with and without LiDAR

00:24:45 Data processing on device & in the cloud

00:30:55 Strategic positioning as a startup

00:37:11 Digital twin for smart home use cases

00:43:47 Future LiDAR sensors and their impact

# Sponsors

Quantics: Supply Chain Planning for the new normal - the never normal - https://quantics.io/

Belichberg GmbH: We do digital transformations as your innovation partner - https://belichberg.com/

# References

Martin Huber - https://www.linkedin.com/in/martin-huber-b940a084/

Lidar - https://amrax.ai/news/power-of-lidar

Metaroom - https://www.linkedin.com/showcase/metaroom-by-amrax/

Apples Roomplan - https://developer.apple.com/augmented-reality/roomplan/

Sony LiDAR Sensors - https://www.sony-semicon.com/en/news/2023/2023030601.html

# Summary

Personalized Medicine has the goal to improve the efficiency of medical treatments that is particularly import for deceases like cancer.

The basic premise of personalized medicine is that by understanding what distinguishes different patients from one another, we can design patience specific treatment plans that are more effective then giving the same treatment to everyone.

These patience differences can be studied from different perspectives, like a patience gender, their age, life style or more modern state of the art modalities, like a patience genome and epigenetic information.

Today on the show I am talking to Daria Romanovskaia an PhD Candidate in the Bocklab at the Research Center for Molecular Medicine in Vienna.

Daria describes how she applied machine learning methods on genetic and epigenetic data to identify patterns in gene regulation that can be used to create bio-makers that enable a targeted approach to decease treatment.

We will discuss how representation learning is used to perform dimensionality reduction and how clustering in combination with biological understanding of deceases can be used to identify relevant up and down regulated genes.

# TOC

00:00:00 Beginning

00:00:27 Episode Introduction

00:01:47 Guest Introduction

00:03:57 What is precision medicine?

00:06:35 Short introduction into genetics

00:13:25 High dimensional data and how to work around it

00:17:16 Variational Autoencoders

00:24:19 Topic Modelling

00:32:19 Analyzing embedding clusters

00:35:56 Combining data analysis and domain expertise

00:39:32 The human cell atlas

# Sponsors

Quantics: Supply Chain Planning for the new normal - the never normal - https://quantics.io/

Belichberg GmbH: We do digital transformations as your innovation partner - https://belichberg.com/

# References

https://www.linkedin.com/in/daria-romanovskaia-b37b2a8a/ - Daria Romanovskaia

https://www.bocklab.org/ - Bock research lab at CeMM

https://www.humancellatlas.org/ - Human Cell Atlas

https://cemm.at/research/groups/christoph-bock-group

# Summary

Did you ever had the experience that you where training a network, investing a lot of time in finding the right hyper parameters and testing different initializations to push that validation accuracy over certain threshold? Only to then find out when putting the model into production, that it significantly underperforms?

If you did, then you experienced one common problem with deep neural networks. The performance gap between in and out-of distribution generalization.

Today on the show PhD Rahim Entezari is giving us a wonderful tour through his PhD journey investigating ways to understand and improve generalization performance of deep neural networks.

Rahim will explain how one can improve generalization by different methods in data or in parameter space.

We will discuss how using different forms of sparsity, or the efficient creation of deep ensemble networks by permutation of network configurations can improve generalization from a parameter space perspective.

Or, from a data perspective where we discuss how data quality and data diversity effects the generalization performance of modern deep neural networks.

I hope you enjoy this interview, full of interesting concepts and ideas from deep learning theory.

# TOC

00:00:00 Introduction

00:02:18 Background Knowledge

00:06:56 Guest Introduction

00:12:35 Generalization from a Data or Parameter Perspective

00:16:21 In and out of distribution Generalization

00:20:30 Structured and Unstructured Sparsity

00:29:55 Generalization in Parameter space

00:46:56 Generalization in Data space

# Sponsors

Quantics: Supply Chain Planning for the new normal - the never normal - https://quantics.io/

Belichberg GmbH: We do digital transformations as your innovation partner - https://belichberg.com/

# References

Rahim Entezari - https://www.linkedin.com/in/rahimentezari/

# Episode

Today I am trying a new podcast format. This episode is a collection of short interviews taken live at the DSC DACH 2023 conference that has taken place in Vienna from the 20 to 21 of April.

The DSC DACH conference is a technology conference had a diverse set of talks and tracks, from industrial applications of AI to the next generation of deep learning methods. The motto of this years iteration was "Save the world through data". An ambitious goal, but I think some of the speakers had interesting ideas and I had a good time listening to their talks.

The goal of this new episode format my dear listener, is to give you a chance to get a glimpse of the type of talks and content that is presented at the event. So you know what you missed, can look into the things that interest you further or even join next years even.

I am curious what you think about this episode format, as I wan to repeat in the future to provide a type of live coverage of AI events, in addition to the normal guest interviews. Drop me an email or write me on LinkedIn if you have a strong opinion on the topic.

But thats enough for the intro and lets begin with the actual interviews.

I start with a short Interview with Aleksandar Djordjevic, on of the organizers of this conference who is going to tell us about its origin and what they want to achieve with the conference going forward.

Next I am speaking to William Amminger from Lumos Student Data Consulting who where presenting a project they are working on, and William was so kind to take the time, to answer a few questions about Lumos.

Next in line is Alexandra Ebert from Mostly.AI about open synthetic data and what role it plays in with respect to privacy and fairness.

Next is Stefan Papp about ways AI can help to battle climate change and the role Data Scientists and Tech Entrepreneurs play in finding ways to reduce a climate disaster.

Next I am talking to Danko Nikolic about a talk that he gave on the next generation of AI systems, that go beyond deep learning.

And last but not least, I am talking to Nicole Weinert about her work, in applying machine learning to empower education and therapeutic applications.

## TOC

00:00:00 Introduction

00:01:33 Aleksandar Djordjevic on DSC DACH 2023

00:17:11 William Amminger on Lumos Student Data Consulting

00:24:58 Alexandra Ebert on Data Privacy and Fairness

00:33:08 Stefan Papp  on Using Data Science to prevent a climate disaster

00:39:19 Danko Nikolic  on next generation AI Systems beyond eep learning

00:47:07 Nicole Weinert on ML for education and therapeutic applications

# Sponsors

Quantics: Supply Chain Planning for the new normal - the never normal - https://quantics.io/

Belichberg GmbH: We do digital transformations as your innovation partner - https://belichberg.com/

# References

DSCDACH 2023 - https://dscdach.com/index.html
Aleksandar Linc-Djordjevic - https://www.linkedin.com/in/aleksandar-linc-djordjevic/
William Amminger - https://www.linkedin.com/in/william-amminger-619a8517a/
Lumos Student Data Consulting - https://www.linkedin.com/company/lumos-student-data-consulting/
Alexandra Ebert - https://www.linkedin.com/in/alexandraebert/
Stefan Papp - https://www.linkedin.com/in/stefanpapp/
Danko Nikolic - https://www.linkedin.com/in/danko-nikolic/
Nicole Weinert - https://www.linkedin.com/in/nicole-weinert-644885114/

# Sumary

Today the Austrian AI Podcast is in Salzburg visiting the Salz21 an Innovation conference, that by the time this episode is out, has taken place from the 15 to the 16 of march 2023 in the city of Salzburg. This year there is a special interest in Artificial Intelligence, dedicating the "Stage of AI" to different presentations and discussion with a fast selection of speakers and topics.

I had the pleasure to meet several of the speakers and to record in total 3 interviews with the followings guests

- Wolfgang Trutschnig from the University of Salzburg on their AI bachelor and Master Program, as well on IDALab, an applied AI research lab that collaborates closely with companies in the region to enable funding of basic research projects and support local companies to stay ahead of their competitors.

- Gabrielle Bolek Fügl on a Salz21 panel discussion on Trustworthy AI and a workshop program that she has developed with Carina Zehetmaier that helps AI companies to develop products and services that are aligned with regulations and legal requirements.

- And last but not least, Mario Tuta from Stoic Analytics on what holds AI adoption back for small and medium companies in austria, and what is needed to overcome those hurdles.

## Sponsors

Quantics: Supply Chain Planning for the new normal - the never normal - https://quantics.io/

## References

https://www.linkedin.com/in/mariotuta/ - Mario Tuta

KMU Digitalisierungsstudie 2019 - Digitale Transformation von KMUs in Österreich by Arthur D. Little

https://stoic-analytics.com/

## TOC

00:00:00 Introduction

00:01:29 Beginning of the Interview

00:04:26 Career as a Data Science & Data Engineer Team Lead at Swarovski

00:12:17 First use cases and challenges on an internal cloud based data and compute platform

00:17:07 Salz21 Panel Discussion about challenges to adapt AI in SME's in Austria

00:24:05 How SME's identify AI services and solutions for their use cases

00:29:45 What is needed within a company to ease AI adoption

00:31:57 Buy or build?

00:38:23 How to grow AI adoptions within a company

00:42:46 The impact of Generative AI on SME's

00:47:08 How much will generative AI replace jobs or boost the productivity of people?

# Sumary

Today the Austrian AI Podcast is in Salzburg visiting the Salz21 an Innovation conference, that by the time this episode is out, has taken place from the 15 to the 16 of march 2023 in the city of Salzburg. This year there is a special interest in Artificial Intelligence, dedicating the "Stage of AI" to different presentations and discussion with a fast selection of speakers and topics.

I had the pleasure to meet several of the speakers and to record in total 3 interviews with the followings guests

- Wolfgang Trutschnig from the University of Salzburg on their AI bachelor and Master Program, as well on IDALab, an applied AI research lab that collaborates closely with companies in the region to enable funding of basic research projects and support local companies to stay ahead of their competitors.

- Gabrielle Bolek Fügl on a Salz21 panel discussion on Trustworthy AI and a workshop program that she has developed with Carina Zehetmaier that helps AI companies to develop products and services that are aligned with regulations and legal requirements.

- And last but not least, Mario Tuta from Stoic Analytics on what holds AI adoption back for small and medium companies in austria, and what is needed to overcome those hurdles.

## Sponsors

Quantics: Supply Chain Planning for the new normal - the never normal - https://quantics.io/

## References

https://www.linkedin.com/in/gabriele-bolek-f%C3%BCgl/ - Gabriele Bolek-Fügl

https://www.womeninai.at/ - Women in AI Austria

## TOC

00:00:00 Introduction

00:01:29 Beginning of the Interview

00:03:44 Salz21 Pan el Discussion about Trustworthy AI Systems

00:07:42 How to integrate an AI System into a company

00:10:27 Workshop on building compliant AI Systems

00:14:44 Legal requirements for AI compliance

00:19:35 What companies benefit from an AI compliance workshop

00:24:55 Future Trustworthy AI Guidelines and Certifications

00:30:18 How to check and monitor AI Systems within a company

00:32:23 Biggest challenges when aligning AI systems with compliance requirements

00:43:10 Impact of AI on society and the labour market

# Summary

Today the Austrian AI Podcast is in Salzburg visiting the Salz21 an Innovation conference, that by the time this episode is out, has taken place from the 15 to the 16 of march 2023 in the city of Salzburg. This year there is a special interest in Artificial Intelligence, dedicating the "Stage of AI" to different presentations and discussion with a fast selection of speakers and topics.

I had the pleasure to meet several of the speakers and to record in total 3 interviews with the followings guests

- Wolfgang Trutschnig from the University of Salzburg on their AI bachelor and Master Program, as well on IDALab, an applied AI research lab that collaborates closely with companies in the region to enable funding of basic research projects and support local companies to stay ahead of their competitors.

- Gabrielle Bolek on a Salz21 panel discussion on Trustworthy AI and a workshop program that she has developed with Carina Zehetmaier that helps AI companies to develop products and services that are aligned with regulations and legal requirements.

- And last but not least, Mario Tuta from Stoic Analytics on what holds AI adoption back for small and medium companies in austria, and what is needed to overcome those hurdles.

## Sponsors

Quantics: Supply Chain Planning for the new normal - the never normal - https://quantics.io/

## References

Wolfgang Trutschnig - https://www.linkedin.com/in/wolfgang-trutschnig-a0527281/

IDALab - https://www.plus.ac.at/aihi/der-fachbereich/ida-lab/projekte/

Dr. Ulrike Ruprecht (Lab-Manager), email:  [email protected]

## TOC

00:00:00 Introduction

00:01:28 Beginning of the Interview

00:04:03 AI Stage Panel Discussion about Dave an AI Service Agent

00:11:43 Uni Salzburg Master in Data Science and Bachelor in AI

00:18:00 IDALab

00:20:13 Projects and Collaborations at IDALab

00:24:13 The role of Basic AI Research in the near future