WeAreDevelopers Congress Vienna
Sneak Peek of the Agenda

28.11.-29.11.19

Days
2

Developers
2,000+

Speakers
60+

Workshops
25+

Solution Architect, Nvidia Corporation

Siddha Ganju

Siddha Ganju, who Forbes featured in their 30 under 30 list, is a Self-Driving Architect at Nvidia. Previously...

Siddha Ganju, who Forbes featured in their 30 under 30 list, is a Self-Driving Architect at Nvidia. Previously at Deep Vision, she developed deep learning models for resource constraint edge devices. A graduate from Carnegie Mellon University, her prior work ranges from Visual Question Answering to Generative Adversarial Networks to gathering insights from CERN's petabyte-scale data and has been published at top-tier conferences including CVPR and NeurIPS. Serving as an AI domain expert, she has also been guiding teams at NASA as well as featured as a jury member in several international tech competitions.

Deep Learning for Mobile devices

Theme: AI
Over the last few years, convolutional neural networks (CNN) have risen in popularity, especially in the area of computer vision. Many mobile applications running on smartphones and wearable devices would potentially benefit from the new opportunities enabled by deep learning techniques. However, CNNs are by nature computationally and memory intensive, making them challenging to deploy on a mobile device. We explain how to practically bring the power of convolutional neural networks and deep learning to memory and power-constrained devices like smartphones. We’ll illustrate the value of these concepts with real-time demos as well as case studies from Google, Microsoft, Facebook and more. You will walk away with various strategies to circumvent obstacles and build mobile-friendly shallow CNN architectures that significantly reduce memory footprint.

Data Science Team Lead, eBay

Or Levi

The Fake News Arms Race: How AI Can Create – and Detect – Fakes

Theme: AI
Recent advancements in AI have made powerful content-manipulation tools - that can create realistic images and videos - accessible to the public. Deep-Fakes have already undermined trust in democracy, incited violence and damaged the reputation of brands and individuals. This has motivated the research of technologies that can catch the fakes, such as AdVerif.ai. In this session, Or Levi - Founder of AdVerif.ai, will take us through the emerging Fake News arms race, between “Bad AI” for generating fakes and “Good AI” for detecting them. Learn how AI can help fight Fake News with a spectrum of tools, ranging from Machine Vision for detecting manipulated images to Natural Language Processing for identifying psycho-linguistic features, and data pipelines for Deep Learning at the scale of billions of items.

Developer Advocate, Cisco DevNet

Flo Pachinger

Florian (or Flo) is a Developer Advocate at Cisco DevNet focusing on IoT and Network Programmability. With a s...

Florian (or Flo) is a Developer Advocate at Cisco DevNet focusing on IoT and Network Programmability. With a software and networking background he is working since a couple of years on many IoT projects mostly in Germany. DevNet leads the Developer Community within and outside of Cisco and covers information for APIs, DevOps, applications, automation and infrastructure management.

From CLI to APIs: You can easily talk to your network now

Theme: IoT
How you manage your IoT network infrastructure is changing from classic CLI towards a more API-fun way! This session will tell what is nowadays possible with tools like RESTCONF, NETCONF, gRPC and YANG data models to monitor, configure and manage gateways and other network components. It will also cover on how you can leverage these technologies with real-life use-cases, e.g. Connected Machines in IIoT. To make it more fun, there will be a demo about how you can talk to or manage your devices via a chatbot.

Tech Speaker, Mozilla Foundation

Tanay Pant

Tanay Pant is an Indian author, hacker, developer and tech enthusiast. He is best known for his work on 'Build...

Tanay Pant is an Indian author, hacker, developer and tech enthusiast. He is best known for his work on 'Building a Virtual Assistant for Raspberry Pi' published by Apress and 'Learning Firefox OS Application Development' published by Packt. He is also an official representative of Mozilla. He has been listed in the about:credits of the Firefox web browser for his contributions to the different open source projects of the Mozilla Foundation. He also writes for a number of websites like SitePoint and Tuts+ where he shares tips and tricks about web development.

Leveraging Machine Data for Business Success Description

Theme: IoT
The rise in the use of sensors and IoT devices in factories and production floors has transformed the way how operations are conducted. Their impact on the efficiency and cost savings have been so significant that it has been dubbed as Industrialisation 4.0. Upgrading your existing data workflow incorrectly can defeat the purpose of real-time actionable guidance and can have huge cost implications. In this talk, you will learn how predictive maintenance is the holy grail of Industry 4.0 and how you can use CrateDB with an open-source stack to handle, enrich and derive valuable insights from the data. We will top this off with a real-world case study of a smart factory that made six-figure savings in 2018 through efficiency improvements. Join me and we shall put your machine data to work!

Cyber Threat Intelligence Director & Journalist, SecurityDiscovery.com

Bob Diachenko

Bob Diachenko is a Cyber Threat Intelligence Director and journalist at SecurityDiscovery.com consultancy.In...

Bob Diachenko is a Cyber Threat Intelligence Director and journalist at SecurityDiscovery.com consultancy.
In the past, Bob has worked in a company that had a big data breach. Now his goal is to help to protect data on the Internet by identifying data leaks and following responsible disclosure policies.
Bob is on the mission to make the cyber world safer by educating businesses and communities worldwide. Many of Bob's discoveries have been covered in major news and technology media, earning himself a reputation as one of the reputable data security analytics.

Data Breaches As A Necessary Evil

Theme: AI
Bob Diachenko is a Cyber Threat Intelligence Director and journalist at SecurityDiscovery.com consultancy. To discover data breaches, leakages, and vulnerabilities on the Internet, he uses the Shodan search engine (and similar - like BinaryEdge, Zoomeye) and simple dorks. No special software or active scanning, just 'bare hands' and some luck. If he can find your data, then anybody in the world can do it. Not only legitimate companies or businesses forgot to properly configure their datasets. For the past two years, Bob spotted at least three cases when malicious actors or criminals have inadvertently put stolen assets on the public. Focusing on these real-time cases and breaches in unsecured databases managed by criminals, Bob uncovers the key checklist that keeps your data safe and how to protect your personal data from being stolen.

Data Scientist, Jaggaer

Emir Ombasic

Born 1985 in Bosnia. Emir has a University degree in Mathematics and Information technologies and has spent th...

Born 1985 in Bosnia. Emir has a University degree in Mathematics and Information technologies and has spent the last 13 years of his career in software development.
Currently, he works as a Data Scientist at Jaggaer Vienna office, where he deals with large volumes of Procurement data and can apply his tool-set of Statistics, Discrete Mathematics and Linear Algebra. Over the years, his projects were ranging from Procurement SAP integrations via PHP, over Performance optimizations up until Order Delivery Time predictions, Digital Assistants and Spend Forecast consulting.
His primary focus is developing Python / Tensorflow / Keras AI Services and integrating them with existing solutions with the goal to increase user efficiency, reducing repetitive tasks and decreasing risks. Over the past years, he was able to integrate multiple solutions of this kind and also acted as AI / ML evangelist in the Procurement Business.

Automatic Spend Classification - An end to end solution

Theme: AI
Using the most recent fast Neural Network prototyping capabilities, it is made much easier for Data Scientists to try out different approaches in resolving real-life challenges for various businesses.
In this speech, Emir will focus on a working solution that he developed using Tensorflow / Keras and which resolves one of the challenges that large companies face: spend management and analytics of unclassified articles.
The solution bridges articles to a product category schema (so-called: [email protected]), processing them using NLP techniques and matching them via hierarchical model structure. This can be done on-demand or via batch processing.
Once companies are able to match the articles to categories, it is much easier to check the spend per category and spot increasing procurement costs. This is especially true for large corporations where the orders are made in large volumes and range from papers, printers up to laboratory equipment and specialized parts.

Emir will demonstrate multiple approaches that he has taken and show the pros and cons of different model configurations ranging from single model to orchestrated multiple dependent models.

Research Collaborator, Massachusetts Institute of Technology (MIT)

Eric Steinberger

Eric Steinberger's research focuses on Deep Reinforcement Learning. Since his first research appointment at ag...

Eric Steinberger's research focuses on Deep Reinforcement Learning. Since his first research appointment at age 18, he worked with MIT, TU Wien, and the University of Luxembourg to apply Deep Learning to complex real-world problems. His most recent project at MIT investigates how micro-lending in Africa could be improved by carefully designing game-theoretic incentives while keeping methods practical. Eric always loved science, but he never appreciated overcomplicated papers and reports. Therefore, he started giving talks introducing the technical sides of Deep Learning to a wider audience. His second area of interest is climate science, where he is also part of a team creating educational content for children.

What we need to do to continue advancing Deep Learning

Theme: AI
Machine Learning algorithms beat top humans in complex games like Go, Poker, and Dota 2. But to do so, they need many times more gameplay experience than humans could ever get. AI speaks our languages. But to do so, they need to read gigantic amounts of text. And so on. Recent progress was possible because of a 300.000x increase in AI compute over 6 years. This can't continue; even Google hits a limit at some point. How can we continue AI research without increasing compute and data?

Ethical Hacker, Nethemba

Pavol Luptak

Pavol gained his BSc. at the FEI-STU in Bratislava and MSc in Computer Science at the Czech Technical Universi...

Pavol gained his BSc. at the FEI-STU in Bratislava and MSc in Computer Science at the Czech Technical University with a master thesis focused on ultra-secure systems. He holds many prestigious security certifications including CISSP and CEH, he is Slovak OWASP chapter leader, co-founder of Progressbar and SOIT organizations where he is responsible for IT security.
Pavol uses to have regular presentations at various worldwide security conferences (in the Netherlands, Luxembourg, Berlin, Warsaw, Krakow, Prague). In the past, he demonstrated vulnerabilities in the public transport SMS tickets in all major cities in Europe, together with his colleague Norbert Szetei he practically demonstrated vulnerabilities in Mifare Classic RFID cards. He has 14 years of experience in IT security, penetration testing and security auditing including social engineering and digital forensic analysis.
He is the co-author of the OWASP Testing Guide v3, has a deep knowledge of the OSSTMM, ISO17799/27001 and many years experience in seeking vulnerabilities. He has a knowledge of many programming languages (ASM, C, C++, XSLT, Perl, Java, PLSQL, Lisp, Prolog, scripting languages) and operating systems. He is also focused on VoIP and interesting IT security research.

Achieving financial freedom using cryptocurrencies (2019 edition)

Theme: Blockchain
Real digital privacy starts with protecting your financial transactions. Leaving no traces. Making impossible to see or intervene with your voluntary economic interactions. With the rise of anonymous cryptocurrencies, for the first time in our human history, we can do a global business and stay anonymous. Liberate yourself.

Senior Solution Architect, Alibaba Cloud

Oliver Arafat

Oliver Arafat works as a Senior Solution Architect at Alibaba Cloud where he helps customers of ...

Oliver Arafat works as a Senior Solution Architect at Alibaba Cloud where he helps customers of all sizes and industries reaching their full potential with cloud-based solutions. Prior to joining Alibaba Cloud, he held similar roles with Amazon Web Services and Microsoft.

The Future is already here – Open Sesame, Alibaba!

Theme: Cloud
Alibaba is the heart of a global digital economy that touches people’s lives every day. No matter if it is cashless supermarkets blurring the lines between online and offline experiences, world’s biggest e-shopping festival generating more than 25 billion USD revenue on one single day, or smart cities, and smart factories: It is not a concept, it is already a reality and much of this is powered by Alibaba Cloud, world’s fastest growing public cloud provider. Let’s take a glimpse into the Alibaba Cloud cave – Open Sesame!

Software Engineer, PUBG

Jihwan Chun

Jihwan Chun is a software engineer at PUBG Corporation who is enthusiastic about cloud infrastructure, contain...

Jihwan Chun is a software engineer at PUBG Corporation who is enthusiastic about cloud infrastructure, container and Kubernetes. He has recently contributed to building a global-scale game server infrastructure for the video game called Playerunknown's Battlegrounds. These days his main interest is building a robust and scalable data platform for data-driven development in game industry.

Sphynx: PUBG's Data Platform Powered by Apache Spark on Kubernetes

Theme: AI
Kubernetes has achieved one of the dominating platform for container-based infrastructure. Many platforms are starting to support Kubernetes as first-class and Apache Spark, analytics engine for large-scale data processing, is one of them. From Spark 2.3, Spark can run on clusters managed by Kubernetes. PUBG Corporation, serving an online video game for 10s of millions of users, decided to migrate its on-demand data analytics platform using Spark on Kubernetes. At this talk, Jihwan Chun and Gyutak Kim will describe the challenges and solutions building a brand-new data platform project powered by Spark on Kubernetes. Sphynx, the project which will be discussed at the talk, is a platform for managing on-demand Spark clusters and connected Jupyter Notebooks as containerized applications on Kubernetes.

Lead Data Scientist, Ocean Protocol

Marcus Jones

Marcus Jones is the lead Data Scientist at Ocean Protocol, a blockchain decentralized marketplace for data, pi...

Marcus Jones is the lead Data Scientist at Ocean Protocol, a blockchain decentralized marketplace for data, pipelines, and models for the AI ecosystem. Marcus has been hacking in Python development and open source projects for over 10 years, starting at a national research agency in Vienna Austria, moving to international consulting, and most recently landing in Berlin at Ocean. Marcus is focused on the intersection of Artificial Intelligence and the new decentralized Web 3.0 ecosystem and how it will empower Data Engineers and Data Scientists.

Ocean Protocol - Empowering a new AI ecosystem an open source blockchain protocol

Theme: AI
The open-source and decentralized Ocean Protocol project has evolved from a proof of concept to deployed into production. The simplest case is unlocking data assets to empower artificial intelligence - more data is better! This talk will go beyond data and explore the future of decentralized AI. Ocean Protocol supports advanced access control and orchestration, built on our smart contracts and Service Execution Agreements (SEAs). These mechanisms allow more complex use cases, such as aggregate data assets (the whole can be greater than the sum of the parts!), and execution of a complete data science workflow. Ocean Protocol empowers data scientists with an open and cryptographically secure platform to build applications such as compute over private data, and federated learning to unlock the true potential of AI.

Blockchain Engineer, ING-DiBa AG

Cees van Wijk

Cees is Blockchain Engineer in ING’s Blockchain and Distributed Ledger Technology chapter. In that role he e...

Cees is Blockchain Engineer in ING’s Blockchain and Distributed Ledger Technology chapter. In that role he engineers real world Blockchain applications various blockchain platforms such as Corda, Ethereum and Corda. Besides that he worked on Blockchain research such as ING’s Zero Knowledge Range-Proof in Ethereum.

Real-world blockchain apps; challenges and solutions

Theme: Blockchain
With the first blockchain and Distributed Ledger applications going live this year Cees will explain the technical challenges ING blockchain faced and how they resolved them. This talk will cover code examples and live demo using Corda Distributed Ledger Technology (DLT) and Kotlin. Specifically, the audience will learn:
- How Corda works, how it’s both similar and different to Blockchain technology. and why we like it so much.
- How we do Continuous Deployment of smart contracts (that by definition are immutable and are operated by multiple organizations..)
- How keep confidential information secret in a distributed ledger (that needs to share information with many organizations in order to allow decentralized validation.
- How we meet our performance goals (using a technology that needs to reach consensus on a globally deployed network)
- How we reliably integrate Blockchain applications with our traditional IT systems.

Projektass. Dipl.-Ing. Dr.techn., TU Wien

Jelena Milosevic

Jelena is a PostDoc at the Institute of Telecommunications, TU Wien, where she works on detection of cyberatta...

Jelena is a PostDoc at the Institute of Telecommunications, TU Wien, where she works on detection of cyberattacks in communication networks and adversarial machine learning. She obtained her Ph.D. in 2017 from the Faculty of Informatics, University of Lugano, Switzerland, where her main focus was on machine learning for resource-constrained devices. Previously, she was an intern at Intel and IBM Labs, where she worked on deep learning for embedded systems and time-series analysis for anomaly detection.

Attacking Machine Learning Methods Used for Detection of Cyber Attacks

Theme: AI
The number of users of connected devices and complexity of communication networks is increasing. This rises the interest of attackers since there is more information to gain access to. At the same time, it makes network traffic analysis based on detection and prevention of cyber-attacks inherently complex.

In order to enable effective detection of cyber attacks, many machine learning methods are proposed. Main reasons are that they scale well with the increased amount of information, they are fast and, at the same time, provide opportunity to protect network users not just from known threats but also unknown variants of previous attacks (big advantage in comparison to previously used signature-based detectors). However, recent research on security of machine learning shows that there are various inherent properties of machine learning methods that allow attackers to bypass these methods once deployed in practice.

In this talk, I will first discuss machine learning methods suggested for detection of cyber attacks and discuss their detection performance. Then, I will show how to perform few recently proposed attacks on these machine learning methods and how such attacks can affect previously observed detection performance. Finally, I will outline current open problems in security of machine learning and detection of cyber attacks.

Student, Chemical Engineering

Alexandra Waldherr

Alexandra Waldherr is 17 years old and currently attend a higher technical college for chemical engineering....

Alexandra Waldherr is 17 years old and currently attend a higher technical college for chemical engineering.
Since age 12 she's passionate about science, especially physics and chemistry of tiny matter. Besides school, she challenges herself in science competitions and Python/Arduino projects at home.
In 2018 she finished the Viennese Physics' Olympiad best girl; this summer, she worked with the department for material chemistry of TU Vienna and did an internship at AF-Institute Vienna (AI and data management for the medical sector). She was invited to speak at the FifteenSeconds-Festival and she will give a talk on nanomaterials and biocomputing at Gainer-Festival 2019.

Say hello to future's hardware!

Theme: AI
Quantum computing is something everybody heard off, but nobody really knows about. Do we need it?
From lab data problems and AI, I stumbled into optimization and qubits. Interestingly, methods chemists use daily are tightly bound to quantum computing!
Her talk should give the audience a feeling for what quantum computers are, the chemistry and physics behind how they operate and how they are proposed to enhance AI. She will finish with a live-glimpse into IBM-Q (free portal to 'program' one of the world's first quantum computers).

Digital Transformation Strategist, Tricentis

Gerta Sheganaku

Gerta started her career early on during her studies of computer science at TU Vienna. As a project manager fo...

Gerta started her career early on during her studies of computer science at TU Vienna. As a project manager for process automation projects she developed the vision and roadmap for production data analytics and predictive maintenance solutions for industry customers. She initiated and led a cross-organizational R&D project funded by the Vienna Business Agency, focusing on quality analysis of manufacturing processes, and presented their work in the context of recent IT market trends at industry.tech15, the Austrian symposium on Industry 4.0.
Gerta currently works as a Digital Transformation Strategist at Tricentis, an Austrian unicorn startup with solutions for Test Automation, Test Management, and Robotic Process Automation. She helps her customers develop strategies from a people, process and tooling perspective to make testing a key enabler for their digital transformation journey.

Testing smarter, not harder with AI – a realistic overview of what is possible today

Theme: AI
To meet the quality needs and challenges of a fast-paced future driven by ever shorter release cycles and trends like IoT and robotics, our testing approaches need to match up.
Continuous Testing is currently bridging the gap between development and operations, but we are fast approaching a time when Continuous Testing will be unable to keep pace with shrinking delivery cycle times, increasing technical complexity, and accelerating rates of change. AI, imitating intelligent human behavior for machine learning and predictive analytics, can help us to overcome these challenges.
In this talk, we will highlight application areas in which AI can help across different stages of the software quality lifecycle, from test design, redundancy prevention, and defect detection, up to finding and steering the right controls and providing a resilient test automation.
We will discuss the areas in which we are already using basic forms of AI and what is still to come in the field of software quality assurance. We need to continue the testing evolution in order to achieve the efficiency needed for testing of robotics, IoT, and similar trends. We will give a realistic perspective of what can and cannot be achieved in the near future and will discuss how we can make use of advanced algorithms like automated test portfolio optimization, self-adjusting risk assessment, automated defect diagnosis, or smart environment provisioning, which may still lack the self-learning component to be classified as AI, but can help bridge the gap between the current state of continuous testing and a future state of AI-based testing.

Doctoral student, Graz University of Technology (TU Graz)

Anna Saranti

Anna Saranti is employed at the Medical University Graz in the FWF Project P-32554 'A reference model of expla...

Anna Saranti is employed at the Medical University Graz in the FWF Project P-32554 'A reference model of explainable AI for the medicaldomain', and a doctoral student at TU Graz, supervised by Prof. Dr. Andreas Holzinger, where she also completed her Masters’ in Computer Science with distinction with a thesis on 'Applying Probabilistic Graphical Models and Deep Reinforcement Learning in a Learning-Aware Application'.Anna is currently employed in the FWF Project P-32554 'A reference model of explainable Artificial Intelligence for the Medical Domain” and has been working as tutor for machine learning at both the TU Graz and TU Vienna. Anna has experience as a software developer and data scientist in several industrial companies, most notable of them being 'raicoon' - the first autonomous operations center for renewable energy.

Kandinsky Patterns for Visual Concept Learning

Theme: AI
Starting with the necessity and relevance of Explainable AI methods, we will introduce the notion of causability and its additional value over explainability.
Kandinsky figures and Kandinsky patterns are mathematically describable, simple self-contained hence controllable test data sets for the development, validation and training of explainability in artificial intelligence. With the help of Kandinsky patterns, we can develop explanations and IQ-Tests for AI in the medical domain. We will display an already worked out case, where a neural network implemented in TensorFlow has learned a visual concept. Real-time interaction with a prepared Colab notebook and explanation of the learned internal representations will be shown during the presentation.

Edge Computing / 5G Networks Expert with strong ties in the Blockchain and IoT Community

Josef Hammer

Josef is a highly results-driven software engineer with 27 years of experience in software development, curren...

Josef is a highly results-driven software engineer with 27 years of experience in software development, currently specializing in edge computing respectively 5G networks. He got to lead projects at AVL, and research and design software platforms at CERN. Now he has one of the most advanced 5G playgrounds at his fingertips at the Lakeside Labs as a senior researcher at the University of Klagenfurt. But he strongly believes that developments need to be pragmatic and satisfying also from a business standpoint. That's why he leaped at the opportunity to enjoy extensive advanced training at Stanford and Harvard University as well as the Copenhagen Business School.

Are you on the Edge? Or still in the Cloud?

Theme: IoT
This talk is about Edge Computing. As with the transition from mainframes to desktop computers, in the upcoming years a lot of processing will move from the cloud to the edge of the network, i.e. closer to the user. This will particularly affect areas with high data volume (IoT, AI) and low latency requirements (IoT).

In this session, I will give a short introduction to this exciting new area and its benefits and use cases. Furthermore, I'll show which frameworks and tools developers can use right now (Amazon IoT Greengrass, Microsoft Azure IoT Edge, ...) and where we might be headed. Finally, I'll address the upcoming 5G networks where edge computing will be a first-class citizen.

Passionate Full Stack Developer & Clean Code Evangelist, METRONOM

Björn Wendland

With over five years of professional experience, Björn joined METRONOM last year and subsequently established...

With over five years of professional experience, Björn joined METRONOM last year and subsequently established Kotlin as programming language and became the company's Kotlin Ambassador.
True to METRONOM's mission to “set the pace in food and technology”, he challenges solution, architecture and implementation every day.
Björn distinguishes himself by his sound advice and solid decision making. Colleagues describe him as biased in a trustful way.
In his free time, Björn enjoys competing (or mostly goofing off) with friends during a gaming or a bouldering session.

How we almost delivered 100 tons of Stracciatella Mousse

Theme: IoT
It's not a bug, it's a feature!
How often do we read or hear this saying?
At what point in time are you confident to say that it's a feature?
Is it when your service has a very good test-coverage, a user-focused design and never acts out of line because it utilizes fancy front-end testing tools and canary-releases?
Even with seemingly perfect coverage along all levels of the test pyramid, we wonder why bugs appear.
This talk is about how we almost delivered 100 tons of Stracciatella Mousse. On time!
This was not a bug in production. A store employee confirmed that specific order.
Nevertheless this behaviour sounds strange enough to investigate.
That's why we stepped back. Took a deep breath and had a look at our metrics.
Can we actually detect anomalies before someone drowns in a pool of tasty Stracciatella Mousse. Join the talk to find out!

Head of AI, craftworks GmbH

Simon Stiebellehner

Self-Supervised Learning - Towards Autonomously Learning Machines

Theme: AI
Supervised machine learning is powerful, but has severe limitations. For example, supervised learning requires extensive labeling of data sets, which is expensive. Also, learned representations are tailored to a very specific task, which often goes at the expense of model robustness. Overcoming these weaknesses, self-supervised learning has gained significant attention in recent past. Self-supervised learning is inspired by the way infants learn to understand the world around them. Even in absence of a "teacher" (i.e. labels), infants quickly grasp new concepts and apply them to other domains. They do so by autonomously creating learning tasks (e.g. grabbing a toy) from sensory input of the world around them. Self-supervised learning mimics this behavior. It is a concept that is centered around automatically creating labeled data sets (learning tasks) from the input. For example, we could automatically and repeatedly remove parts of facial images and train a model to estimate the missing parts (labels) based on the surroundings. We can then use the learnt understanding of the structure of faces for other purely supervised learning tasks. This typically results in better prediction quality and improved model robustness as well as reduced training time and lower number of required labels.

Head of Digital Government & Innovation, Austrian Federal Computing Centre

Matthias Lichtenthaler

Matthias Lichtenthaler (PMP) serves as the Head of Digital Government & Innovation of the Federal Computin...

Matthias Lichtenthaler (PMP) serves as the Head of Digital Government & Innovation of the Federal Computing Center of Austria. He leads the key activities to execute a holistic Digital Transformation approach for the public sector in Austria. He coordinates various Initiatives as part of the Digital Roadmap for Austria – including Public-Private Partnership Solutions bridging between Public and Private Sector.
During many years of project management in the area of Project, Document & Data Management he developed extensive experience in Capital Project Management, Content Analytics and Process Automation. Currently, he works with a certain focus on adapting latest technology trends like AI and Blockchain for the public sector. Altogether he can reflect upon 18 years of experience within IT Management. Previously he led the Digital Business at Accenture in Austria.

Real Data vs. Anonymization - Prerequisits for AI and Machine Learning

Theme: AI
Quality assurance for anonymization-software is a critical factor for a sustainable use of Artificial Intelligence. Questions about the comparativeness of efforts to anonymize data and overdoing anonymization have to be raised for adequate use of anonymization-software. BRZ experts will discuss these questions and further present anonymization principals and technical validation methodologies.

Name