Folgen
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## 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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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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Fehlende Folgen?
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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
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## Summary
Today on the show I am talking to Proofreads CTO Alexandre Paris. Alex explains in great detail how they analyze digital books drafts to identify mistakes and instances within the document that dont follow guidelines and preferences of the user.
Alex is explaining how they fine-tune LLMs like, Mistrals 7B to achieve efficient resource usage and provide customizations and serve multiple uses cases with a single base model and multiple lora adapters.
We talk about the challenges and capabilities of fine-tuning, how to do it, when to apply it and when for example prompt engineering of an foundation model is the better choice.
I think this episode is very interesting for listeners that are using LLMs in a specific domain. It shows how fine-tuning a base model on selected high quality corpus can be used to build solutions outperform generic offerings by OpenAI or Google.
## 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
00:02:46 Guest Introduction
00:06:12 Proofcheck Intro
00:11:43 Document Processing Pipeline
00:26:46 Customization Options
00:29:49 LLM fine-tuning
00:42:08 Prompt-engineering vs. fine-tuning
### References
https://www.proofcheck.io/
Alexandre Paris - https://www.linkedin.com/in/alexandre-paris-92446b22/
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Today I am talking to Philip Winter, researcher at the Medical Imaging group of the VRVis, a research center for virtual realities and visualizations.
Philip will explain the benefits and challenges in continual learning and will present his recent paper "PARMESAN: Parameter-Free Memory Search and Transduction for Dense Prediction Tasks". Where he and his colleagues have developed a system that uses a frozen hierarchical feature extractor to build a memory database out of the labeled training data. During inference the system identified training examples similar to the test data and prediction is performed through a combination of parameter-free correspondence matching and message passing based on the closes training datapoints.
I hope you enjoy this episode and will find it useful.
## 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
00:03:04 Guest Introduction
00:06:50 What is continual learning?
00:15:38 Catastrophic forgetting
00:27:36 Paper: Parmesan
00:40:14 Composing Ensembles
00:46:12 How to build memory over time
00:55:37 Limitations of Parmesan
### References
Philip Winter - https://www.linkedin.com/in/philip-m-winter-msc-b15679129/
VRVIS - https://www.vrvis.at/
PARMESAN: Parameter-Free Memory Search and Transduction for Dense Prediction Tasks - https://arxiv.org/abs/2403.11743
Continual Learning Survey: https://arxiv.org/pdf/1909.08383
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## Summary
AI is currently dominated by Deep Learning and Large Language Models, but there is other very interesting research that has the potential to have great impact on our lives in the future; one of them being Quantum Machine Learning (QML)
Today on the podcast, I am talking to Christa Zoufal, Quantum Machine Learning Researcher at IBM.
Christa will explain how Quantum Computing (QC) and Quantum Machine Learning relates to classical computing (CC).
We will discuss Qbit's, the fundamental information storage and processing unit of quantum computers that in comparison with bits in a classical computers, not only store a single state of zero or one, but a superposition of the two.
Christa will explain how Quantum algorithms are created by building quantum circuits that operate on those qbits. We will talk about the challenges when operating on those physical qbits that need to be kept at very low temperatures in order to operate without noise perturbing their states, and why one combines multiple physical qbits to a single logical qbit in order to perform error correction.
How current quantum circuits are limited in the number of logical qbits they can contain and the number of operations; in the sense of the size of an algorithm, they can perform.
Why these properties make QC is the wrong choice for BigData applications, but QM could in the future be used for applications where very little data is available and quantum algorithms exist that outperform their known classical counterparts.
## 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
00:03:21 Guest Introduction
00:07:08 Quantum Computer (QC) & Quantum Machine Learning (QML)
00:14:50 Difference between classical computing and QC
00:24:59 What kind of programs one can run on a QC
00:38:43 Examples of Quantum Machine Learning
00:49:25 Current applications of QC
## References
Christa Zoufal: https://www.linkedin.com/in/christa-zoufal/
IBM Quantum Learning: https://learning.quantum.ibm.com/course/basics-of-quantum-information
Quantum machine learning course: https://github.com/Qiskit/textbook/tree/main/notebooks/quantum-machine-learning
Scott Aaronson @ Lex Podcast https://www.youtube.com/watch?v=nK9pzRevsHQ
State of the Art Bottlenecks: https://arxiv.org/abs/2312.09121
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Hello and welcome back to the AAIP
This is the second part of my interview with Eldar Kurtic and his research on how to optimiz inference of deep neural networks.
In the first part of the interview, we focused on sparsity and how high unstructured sparsity can be achieved without loosing model accuracy on CPU's and in part on GPU's.
In this second part of the interview, we are going to focus on quantization. Quantization tries to reduce model size by finding ways to represent the model in numeric representations with less precision while retaining model performance. This means that a model that for example has been trained in a standard 32bit floating point representation is during post training quantization converted to a representation that is only using 8 bits. Reducing the model size to one forth.
We will discuss how current quantization method can be applied to quantize model weights down to 4 bits while retaining most of the models performance and why doing so with the models activation is much more tricky.
Eldar will explain how current GPU architectures, create two different type of bottlenecks. Memory bound and compute bound scenarios. Where in the case of memory bound situations, the model size causes most of the inference time to be spend in transferring model weights. Exactly in these situations, quantization has its biggest impact and reducing the models size can accelerate inference.
Enjoy.
## AAIP Community
Join our discord server and ask guest directly or discuss related topics with the community.
https://discord.gg/5Pj446VKNU
### References
Eldar Kurtic: https://www.linkedin.com/in/eldar-kurti%C4%87-77963b160/
Neural Magic: https://neuralmagic.com/
IST Austria Alistarh Group: https://ist.ac.at/en/research/alistarh-group/
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Hello and welcome back to the AAIP
If you are an active Machine Learning engineer or are simply interested in Large Language models, I am sure you have seen the discussions around quantized models and all kind of new frameworks that have appeared recently and achieve astonishing inference performance of LLM's on consumer devices.
If you are curious how modern Large Language Models with their billions of parameters can run on a simple laptop or even an embedded device, than this episode is for you.
Today I am talking to Eldar Kurtic, researcher in the Alistarh group at the IST in lower Austrian and senior research engineer at the American startup Neural Magic.
Eldar's research focuses on optimizing Inference of Deep Neural Networks. On the show he is going to explain in depth show sparsity and quantization works, and how they can be applied to accelerate inference of big models, like LLM's on devices with limited resources.
Because of the length of the interview, I decided to split it into two parts.
This one, the first part, is going to focus on sparsity to reduce model size and enable faster inference by reducing the amount of memory and compute that is needed to store and run models.
The second part is going to focus on quantization as a mean to find representations of models with lower numeric precision that require less memory to store and process, while retaining accuracy.
In this first part about sparsity, Eldar will explain fundamental concepts like structured and unstructured sparsity. How and why they work and how currently we can achieve performant inference of unstructured sparsity only on CPU's and far less on GPU's.
We will discuss how to achieve crazy numbers of up to 95% unstructured sparsity while retaining the accuracy of models, but why it is difficult to leverage this under quoutes, reduction in model size, to actually accelerate model inference.
Enjoy.
## AAIP Community
Join our discord server and ask guest directly or discuss related topics with the community.
https://discord.gg/5Pj446VKNU
### References
Eldar Kurtic: https://www.linkedin.com/in/eldar-kurti%C4%87-77963b160/
Neural Magic: https://neuralmagic.com/
IST Austria Alistarh Group: https://ist.ac.at/en/research/alistarh-group/
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## Summary
Today on the show I am talking to Veronika Vishnevskaia. Solution Architect at ONTEC where she specialises in building RAG based Question-Answering systems.
Veronika will provide a deep dive into all relevant steps to build a Question-Answering system. Starting from data extraction and transformation, followed by text embedding, chunking and hybrid retrieval to strategies and last but not least methods to mitigate hallucinations of LLMs during the answer creation.
## 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
00:03:33 Guest Introduction
00:08:51 Building Q/A Systems for businesses
00:16:27 RAG: Data extraction & pre-processing
00:26:08 RAG: Chunking & Embedding
00:36:13 RAG: Information Retrieval
00:48:59 Hallucinations
01:02:21 Future RAG systems
## Sponsors
- Quantics: Supply Chain Planning for the new normal - the never normal - https://quantics.io/
- Belichberg GmbH: Software that Saves the Planet: The Future of Energy Begins Here - https://belichberg.com/
### References
Veronika Vishnevskaia - https://www.linkedin.com/in/veronika-vishnevskaia/
Ontec - www.ontec.at
Review Hallucination Mitigation Techniques: https://arxiv.org/pdf/2401.01313.pdf
Aleph-Alpha: https://aleph-alpha.com/de/technologie/
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# Summary
Today on the show I am talking to Manuel Reinsperger, Cybersecurity Expert and Penetration Tester. Manuel will provide us an introduction into the topic of Machine Learning Security with an emphasis on Chatbot and Large Language Model security.
We are going to discuss topics like AI Red Teaming that focuses on identifying and testing AI systems within an holistic approach for system security. Another major theme of the episode are different Attack Scenarios against Chatbots and Agent systems.
Manuel will explain to use, what Jailsbreak are and methods to exfiltrate information and cause harm through direct and indirect prompt injection.
Machine Learning security is a topic I am specially interested in and I hope you are going to enjoy this episode and find it useful.
## 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
00:02:05 Guest Introduction
00:05:16 What is ML Security and how does it differ from Cybersecurity?
00:25:56 Attacking chatbot systems
00:41:12 Attacking RAGs with Indirect prompt injection
00:54:43 Outlook on LLM security
## Sponsors
- Quantics: Supply Chain Planning for the new normal - the never normal - https://quantics.io/
- Belichberg GmbH: Software that Saves the Planet: The Future of Energy Begins Here - https://belichberg.com/
## References
Manuel Reinsperger - https://manuel.reinsperger.org/
Test your prompt hacking skills: https://gandalf.lakera.ai/
Hacking Bing Chat: https://betterprogramming.pub/the-dark-side-of-llms-we-need-to-rethinInjectGPT: k-large-language-models-now-6212aca0581a
AI-Attack Surface: https://danielmiessler.com/blog/the-ai-attack-surface-map-v1-0/
https://blog.luitjes.it/posts/injectgpt-most-polite-exploit-ever/
https://github.com/jiep/offensive-ai-compilation
AI Security Reference List: https://github.com/DeepSpaceHarbor/Awesome-AI-Security
Prompt Injection into GPT: https://kai-greshake.de/posts/puzzle-22745/
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## Summary
At the end of last year, the EU-AI Act was finalized and it spawned many discussions and a lot of doubts about the future of European AI companies.
Today on the show Peter Jeitschko, founder of JetHire an AI based recruiting platform that uses Large Language models to help recruiters find and work with candidates, talks about this perspective on the AI-Act.
We talk about the impact of the EU AI-Act on their platform, and how it falls into a high-risk use-case under the new regulation. Peter describes how the AI-Act forced them to create their company in the US and what he believes are the downsides of the EU regulation.
He describes his experience, that the EU regulations hinder innovation in Austria and Europe and how it increases legal costs and uncertainty, resulting in decision makers shying away in building and applying modern AI systems.
I think this episode is valuable for decision makers and founders of AI companies, that are affected by the upcoming AI Act and struggle to make sense of it.
## 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
00:03:09 Guest Introduction
00:04:45 A founders perspective on the AI Act
00:13:45 JetHire - A recruiting platform affected the the AI Act
00:19:58 Achieving regulatory goals with good engineering
00:35:22 The mismatch between regulations and real world applications
00:48:12 European regulations vs. global AI services
## Sponsors
- Quantics: Supply Chain Planning for the new normal - the never normal - https://quantics.io/
- Belichberg GmbH: Software that Saves the Planet: The Future of Energy Begins Here - https://belichberg.com/
## References
Peter Jeitschko - https://www.linkedin.com/in/pjeitschko/
Peter Jeitschko - https://peterjeitschko.com/
JetHire - https://jethire.ai/
https://www.holisticai.com/blog/requirements-for-high-risk-ai-applications-overview-of-regulations
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# Summary
For the last two years AI has been flooded with news about LLMs and their successes, but how many companies are actually making use of them in their products and services?
Today on the show I am talking to Markus Keiblinger, Managing partner of Texterous. A startup that focus on building custom LLM Solutions to help companies automate their business.
Markus will tell us about his experience when talking and working with companies building such LLM focused solutions.
Telling us about the expectations companies have on the capabilities of LLMs, as well on what companies need to have in order to be successfully implementing LLM projects.
We will discuss how Textorous has successfully focused on Retriever Augmented Generation (RAG) use cases.
RAGs is a mechanism that makes it possible to provide information to an LLM in a controlled menner, so the LLM can answer questions or follow instructions making use of that information. This enables companies to make use of their data to solve problems with LLMs, without having to train or even fine-tune models. On the show, Markus will tell us of one of these RAG projects and we will contrast building a RAG system based on Service Provider offerings like OpenAI or self hosted open source alternatives.
Last but not least, we talk about new use cases emerging with multi-modal Models, and the long term perspective that exists for custom LLM Solutions Providers like them in focusing on building integrated solutions.
## 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
00:06:40 Challenges of applying AI in medical applications
00:17:56 Homogeneous Ensemble Methods
00:25:50 Combining base model predictions
00:40:14 Composing Ensembles
00:52:24 Explainability of Ensemble Methods
## Sponsors
- Quantics: Supply Chain Planning for the new normal - the never normal - https://quantics.io/
- Belichberg GmbH: Software that Saves the Planet: The Future of Energy Begins Here - https://belichberg.com/
### References
- Markus Keiblinger: https://www.linkedin.com/in/markus-keiblinger
- Texterous: https://texterous.com
- Book: Conversations Plato Never Captured - but an AI did: https://www.amazon.de/Conversations-Plato-Never-Captured-but/dp/B0BPVS9H9R/
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## Summary
Hello and welcome back to the Austrian Artificial Intelligence Podcast in 2024.
With this episode we start into the third year of the podcast. I am very happy to see that the number of listeners has been growing steadily since the beginning and I want to thank you dear listeners for coming back to the podcast and sharing it with your friends.
Gabriel is a Bioinformatician at the Austrian Institute of Technology and is going to explain his work on ensemble methods and their application in the medical domain.
For those not familiar with the term, an Ensemble is a combination of individual base models that are combined with the goal to outperform each individual model.
So the basic idea is, that one combines multiple models that each have their strength and weaknesses into a single ensemble that in the best case has all the strengths without the weaknesses.
We have seen one type of ensemble methods in the past. These where homogeneous ensemble methods like federated learning, where one trains the same algorithm multiple times by multiple parties or different subsets of the data, for performance reasons or in order to combine model weights without sharing the training data.
Today, Gabriel will talk about heterogeneous ensembles that are a combination of different models types and their usage in medical applications. He will explain how one can use them to increase the robustness and the accuracy of predictions. We will discuss how to select and create compositions of models, as well how to combine the different predictions of the individual base models in smart ways that improve their accuracy over simply methods like averaging over majority voting.
## 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
00:06:40 Challenges of applying AI in medical applications
00:17:56 Homogeneous Ensemble Methods
00:25:50 Combining base model predictions
00:40:14 Composing Ensembles
00:45:57 Explainability of Ensemble Methods
## Sponsors
- Quantics: Supply Chain Planning for the new normal - the never normal - https://quantics.io/
- Belichberg GmbH: Software that Saves the Planet: The Future of Energy Begins Here - https://belichberg.com/
## References
Gabriel Alexander Vignolle - https://www.linkedin.com/in/gabriel-alexander-vignolle-385b141b6/
Publications - https://publications.ait.ac.at/en/persons/gabriel.vignolle
Molecular Diagnostics - https://molecular-diagnostics.ait.ac.at/
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# Summary
I am sure that most of you are familiar with the training paradigm of supervised and unsupervised learning. Where in the case of supervised learning one has a label for each training datapoint and in the unsupervised situation there are no labels.
Although there can be exceptions, everyone is well advise to perform supervised training when ever possible. But where to get those labels for your training data if traditional labeling strategies, like manual annotations are not possible?
Well often you might not have perfect labels for your data, but you have some idea what those labels might be.
And this, my dear listener is exactly the are of weak supervision.
Today on the show I am talking to Andreas Stephan who is doing is PhD in Natural Language Processing at the University of Vienna in the Digital Text Sciences group led by Professor Benjamin Roth.
Andreas will explain about his recent research in the area of weak supervision as well how Large Language Models can be used as weak supervision sources for image classification tasks.
# TOC
00:00:00 Beginning
00:01:38 Weak supervision a short introduction (by me)
00:04:17 Guest Introduction
00:08:48 What is weak supervision?
00:16:02 Paper: SepLL: Separating Latent Class Labels from Weak Supervision Noise
00:26:28 Benefits of priors to guide model training
00:29:38 Data quality & Data Quantity in training foundation models
00:36:10 Using LLM's for weak supervision
00:46:51 Future of weak supervision research
# 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
- Andreas Stephan - https://andst.github.io/
- Stephan et al. "SepLL: Separating Latent Class Labels from Weak Supervision Noise" (2022) - https://arxiv.org/pdf/2210.13898.pdf
- Gunasekar et al. "Textbooks are all you need" (2023) - https://arxiv.org/abs/2306.11644
- Introduction into weak supervision: https://dawn.cs.stanford.edu/2017/07/16/weak-supervision/
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## Summary
I am sure my dear listener, you have heard about genAI that is driven by gigantic foundations models like GPT4 or Stable Diffusion. Generating Texts, Images and even Videos. But what about the 3D Space? What about 3D models that are used as digital twins for the metaverse, for digital cities that are required for self-driving cars to navigate safely in an metropolitan area?
What does 3D genAI look like today?
Well to give us an insight into what is possible today and what they are building for tomorrow, I have Cristian Duguet on the show. Co-Founder of Nuvo. A company that is focused on building the 3D Foundation models of the future.
On the show Cristian will explain to use some of the limitations of current 3D reconstructions from 2D images or sensors. Like issues with shiny surfaces or transparent materials, but as well, how we are currently lacking a good 3D representation for future foundation models.
We will discuss how one at the moment has to choose between two conflicting representations. 3D point clouds and 3D meshes. Where point clouds are easy to create from real world object with sensors like the newest IPhone, but are required in high density and therefore compute in order to generate photo realistic renderings.
Where 3D meshes on the other hand, made up of polygons in 3D space, need far less points and provide a very easy way to modify and change 3D objects, but are difficult to generate out of sensor data.
Cristian explains what is needed to build future foundation models and how they will be able to combine the benefits of point clouds and 3D meshes, as well how Nuvo are working towards building those next generation 3D foundation models.
---
## TOC
00:00:00 Beginning
00:02:27 Guest Introduction
00:06:40 Nuvo 3D
00:17:56 What motivates the demand in 3D models
00:25:50 GenAI for 3D present and future
00:40:14 How Nuvo wants to build 3D foundation models
00:52:24 How will future foundation models look like
## 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
Cristian Duguet - https://www.linkedin.com/in/cristianduguet/
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## Summary
I have a awful memory, but its good enough most of the time, so I can remember where I left my coffee mug or when I am searching for it, where I have looked before. Imagine a person that has no recollection of what happened in their past. They might be running between room A and room B trying to find their coffee mug for ever, not realising they put it in the dishwasher.
What this person is lacking, is an episodic memory. A recollection of their, personal, previous experiences. Without them, they can only rely on what they observe and think about the world at the present moment.
Today on the Austrian Artificial Intelligence Podcast, I am talking to Fabian Paischer, PhD Student at the JKU in Linz and the ELISA PhD Program. Fabian is going to explain his research, developing an episodic memory system for reinforcement learning agents.
We will discuss his Semantic HELM paper in which they have been using pre-trained CLIP and LLM models to build an agents biography that serves the agent as an episodic memory.
How pre-trained foundation models help to build representations that generalize Reinforcement learning systems and help to understand and navigate in new environments.
This agent biography serves as a great help for the agent to solve specific memory related tasks, but in addition provides ways to interpret an agents behavior and thinking process.
I hope you enjoy this very interesting episode about current Reinforcement learning research.
## TOC
00:00:00 Beginning
00:02:08 Guest Introduction
00:07:15 Natural Language and Abstraction
00:10:37 European Ellis PhD Program
00:13:14 Episodic Memory in Reinforcement Learning
00:18:35 Symbolic State representation & Episodic Memory
00:27:04 Pre-trained Models for scene presentation
00:36:25 Semantic Helm Paper & Agent Interpretability
00:45:47 Improvements and Future research
## 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
Fabian Paischer: https://www.jku.at/en/institute-for-machine-learning/about-us/team/fabian-paischer-msc/
Ellis PhD Program: https://ellis.eu/
SHELM Paper: https://arxiv.org/abs/2306.09312
HELM Paper: https://arxiv.org/abs/2205.12258
CLIP Explained: https://akgeni.medium.com/understanding-openai-clip-its-applications-452bd214e226
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# 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/
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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/
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# 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
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# 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
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