AIxIA

Artificial Intelligence meets Intelligence Artificielle

Be part of the first French-German conference on the application of artificial intelligence!
Enjoy concret use cases and panel discussion about the application areas of artificial intelligence from Germany, France and the world!

Conference Day | October 1st
Location: ZKM – Zentrum für Kunst und Medien Karlsruhe | Karlsruhe, Germany

Workshop Day | October 2nd
Location: CyberForum e.V. | Karlsruhe, Germany

Join us and explore how to tackle the application of AI for your business!

Michel Paulin

Michel Paulin has spent most of his career in the IT, telecom and internet sectors.
He was Chief Executive Officer of Neuf Cegetel, for which he made the initial public offering, Méditel (now Orange Morocco) and SFR.
His appointment is part of a new phase in the development of OVH.
Michel PAULIN will oversee the implementation of the „Smart Cloud“ strategic plan, aimed at consolidating OVH’s position as an alternative leader in the cloud sector.

Read more.


 

Prof. Dr. Michael Feindt

Founder and Chief Scientist of Blue Yonder
Prof. Feindt’s main experience and most successful research work is data driven software development, understanding and learning effects through data analysis, usage, further development and discovery of new multivariate statistical algorithms and respective software (today labelled “predictive analytics” or “machine learning’’) in big data environments.

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Antoine Blondeau

Antoine Blondeau is co-founder and Managing Partner of Alpha Intelligence Capital, a venture capital firm investing in deep algorithmic science-based Artificial Intelligence companies, globally. A seasoned entrepreneur and investor, Antoine has 25 years of experience in the technology industry, having held senior leadership positions at Good Technology, Salesforce.com, and Sybase.

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SPEAKERS









 

Main Host

Katrin-Cécile Ziegler

Katrin-Cécile is a digital economist, speaker and journalist. She has been involved with the digital transformation for 15 years, has served as a leader in various sectors for many years, and is a multiple media award winner.
From 2009-2017 Katrin-Cécile worked as Editor-in-chief and Newsanchor of a southern German TV station.
As a freelance host, she is specialized in topics all around digitalization and is especially fascinated by the limitless possibilities of artificial intelligence and IoT.


 

Moderation Session I & II

Dr.-Ing. Matthias Richter

After his studies of computer science with specialization in computer vision, Dr.-Ing. Matthias Richter received his doctorate in computer graphics and media art at the KIT on the Application of Mechanical Learning Methods in the Industrial Checking. Since 2019 Matthias Richter is working as Machine Learning Engineer at inovex, where he helped customers with the conception, implementation and scaling of learning systems for productive use.

Matthias Richter is co-founder and organizer of karlsruhe.ai.
karlsruhe.ai organizes regular lectures on the subject of machine learning and Artificial Intelligence in the Karlsruhe area and has meanwhile developed more than 1400 members.


 

Moderation Session I & II

Dr.-Ing. Julius Pfrommer

Dr.-Ing. Julius Pfrommer has worked as an industrial engineer in Karlsruhe and Grenoble and received his doctorate in computer science at the KIT. Since 2018, Julius Pfrommer has been in charge of a research group at the Fraunhofer IOSB in Karlsruhe. His field of interest is the Application of machine learning to optimize physical processes with Applications ranging from manufacturing processes to the control of Combustion engines.

Julius Pfrommer is co-initiator and organizer of karlsruhe.ai.


Expand All +
  • Day 1

    October 01, 2019 at ZKM

  • Day 2

    October 02, 2019 at DIZ

  • Michel Paulin will oversee the implementation of the "Smart Cloud" strategic plan, aimed at consolidating OVH’s position as an alternative leader in the cloud sector and will explain why European champions are the key to strengthen our AI ecosystem. He is now the CEO of OHV and was also CEO of Neuf Cegetel, for which he made the initial public offering, Méditel and SFR.

  • Knowledge is always better than no knowledge. Pure gut-feeling decisions that often are faulted by cognitive biases. Provided that enough and relevant input data is available, a good decision target defined and correct scientific methods are applied, AI can provide solid knowledge. This is not possible for all sorts of decisions, but typically for mass decisions, since then typically one has lots of independent observations to learn from. I will give some examples of how good and sustainable decisions are taken by AI, e.g. to reduce food waste without reducing product availability in grocery stores.

    Recently there has been a discussion in the AI community about biases in data selection that lead to discrimination also in AI decisions, e.g. between genders or races. This has triggered research on discrimination-free algorithms. Such algorithms have the potential to lead to fair decisions even when the historic data were not fair.

  • Aligning human capital supply with market demand
    The intersection of demand and supply in consultancy services consists of semantic information in the form of texts, unstructured data, usually in the form of résumés, profiles and job descriptions.
    The specific financial and business risks of IT consultancies equate to the challenges of a multi-level inventory; either having too large of an inventory with costly non-productive resources (i.e. consultants on the bench – as a consequence of diminishing or changing demand) or lost business resulting from demand that is not met with adequate resources.
    We build a solution that consumes and converts semantic information into a corporate and market analysis.
    The design and development of such a system needs to solve a sequence of specific challenges with a variety of AI approaches:
    1. Text extraction and text mining
    2. Classification of important concepts via machine learning models
    3. Transferring human knowledge and expertise into an analytical system using knowledge graphs (ontology), classifications and data curation.
    AI & Technologies

  • Digital Products for new Assets
    Applied AI and the advantages of cloud platform ecosystem shown at following use cases:
    - AI-based calibration of a grinding machine with direct connection of the EDI hive Framework to the control system (-> IoT-Platform)
    - AI- and cloud-based testing demonstrated on automated load profile generation for testing of battery cells for electric vehicles
    - Advantages of cloud platform ecosystem demonstrated on EDI hive with Siemens Mindsphere platform
    AI & Technologies

  • Making sense out of sensor data – How Data Science was used to optimize energy consumption of transalpine oil pipeline

    Why are Data Science projects in an industrial context still rare? I made the experience that, especially in complex industry facilities, understanding the data and identifying a concrete use case requires a lot of domain expertise. Building up this domain expertise might be the hardest part for a data scientist. BUT: without clear data understanding there won’t be a precise problem definition and sooner or later the project will fail! In this talk I will explain the key success factors of a concrete Data Science project with TALGroup, which is the operator of the transalpine oil pipeline transporting about 45 millions of tons of crude oil every year across the alps. Based on the experiences made in that project I will focus on two questions:

    1. How did we overcome this “hardest part” from raw sensor data and to a concrete Data science problem definition?
    2. How did Machine Learning algorithms help in order to identify root causes for efficiency losses while pumping raw oil?
    AI & Technologies

  • Why natural language is the next step in the AI evolution
    In 2010 ImageNet finally ended the AI winter and gave machines the sense of sight. Within the following years dramatic improvements in tasks such as image classification and object detection lead to innovations like face ID and autonomous driving. Recently, similar developments happened in the field of natural language. Using Attention mechanism and transformers tasks such as question answering and text summarization reached new benchmarks.
    This talk will not only explain those, but point out how Transfer Learning and open source models such as Google Bert will open the field to new innovations in AI.
    AI & Technologies

  • Artificial Intelligence at the Digital Workplace – Physiolytics for Flow-aware Notifications

    Digital technologies enabling the delivery of information in the form of real-time notifications are the foundation of today’s digital workplace. Beside all its potential, digital technologies also lead to interruptions at work. Thus, designing human-centric intelligent digital notifications is becoming an important challenge.
    Flow, a state in which individual employees are completely absorbed and highly concentrated when performing a task was first investigated by psychologist Mihaly Csikszentmihalyi and it has been shown that flow states can in turn lead to a higher level of well-being, satisfaction or performance of the employee. To date, however, researchers have relied primarily on self-reported scales when measuring flow. Recent work in the disciplines of Psychology, Computer Science and Information Systems has focused on the unambiguous identification of flow states using physiological indicators. Physiolytics leverages physiological data and applies machine learning techniques in order to recognize affective-cognitive human states. This endavor also opens up new possibilities for the objective and continuous measurement of flow states in real time.
    We introduce an exemplary use case which uses physiological data about a possible flow state of the employee to design human-centric intelligent notifications. Thus, one can decide whether a notification should be delivered immediately (case 1 - employee not in flow) or delayed (case 2 - employee in flow). As a result, the flow state of the employee would not be disturbed and could be maintained. Being able to recognize flow automatically opens up many more use cases. However, beside solving the technological challenges further attention must be also paid to ethical and data protection issues.
    AI & Business

  • How AI and Voice-driven Tech Can Change Healthcare
    Healthcare is experiencing a digital transformation journey that is volatile, yet rife with impactful uses cases for industry-disrupting technologies. Voice technology has been one of those to gain ground in healthcare tech in the recent years. It has the potential to streamline this globally costly system, while changing the way how physicians provide and patients receive health care. I will present the landscape of applications at the intersection of voice and healthcare, as some of the most promising and market-ready AI systems today. Yet, many questions still arise e.g. What are the key challenges? Are the players of this traditional and conventional field ready to change their mindset?

    Use Case: Zana – an intelligent voice assistant for remote health monitoring
    In the evolving landscape of patient-provider communication, our team is building Zana – an intelligent voice-powered assistant for monitoring of health anytime, anywhere. I will show how Zana is built, the ‘Zana Brain’ relying on our proprietary Natural Language Understanding techniques, and the real-file problems Zana is intended to solve.
    AI & Business

  • Answering a Multi-Million Dollar Question: Predicting the Next Super Food with AI Fueled Social Data Intelligence
    In the agile market environment of the food industry, it is a central task to quickly predict reliable consumer trends and then take advantage of the knowledge advantage. Together with Danone we faced this challenge. Our AI-supported systems analyzed the voices of consumers in social media worldwide. This is how we identified the Trend Ingredients 2020. Find out what challenges we faced and what solutions we found.
    AI & Business

  • AI assistants conquer the B2B world
    AI can provide a competitive advantage in most industries. The possible applications penetrate any business areas. From “intelligent” chatbots to predictive analytics, AI is on the advance. Hence, the impact of AI on Businesses will be transformative. What will future professions look like and how can we leverage the most of humans and AI working together?
    AI & Business

  • Machine Learning for Service Robots - Scientific, Safety and Ethical Challenges in Real-Life Environments
    With increasing domestic applications of smart machines and robots, researchers and developers have to face new challenges and design requirements. Among the most discussed and disputed topics are the reliability and safety of Machine Learning and Artificial Intelligence algorithms in Human Robot Interaction. In this domain especially, ethical aspects are gaining attention, and raise more questions than answers about possible social consequences of robots replacing and interacting tightly with humans. In project SINA we develop a robot assistant for people in need of care taking these concerns into account.
    AI & ethics

  • With great power comes great responsibility. AI technology has challenged the status quo of humans as decision maker. What is the best (European) way to tackle those future-oriented decisions that need to be made for a “humane” symbiosis of AI and Humans?

    AI & ethics

  • Building Trustworthy AI Applications
    With the rise of AI, there has been an increased awareness about fundamental principles concerning trustworthy AI technology, including AI ethics, fairness and expandability. In this talk, I will provide a perspective on building trustworthy AI applications from the perspective of enterprise applications, including the need to explain AI decisions to business users, ensuring fair and non-biased decisions, and guiding ethical principles.
    AI & ethics

  • Amongst the many cybersecurity challenges, authentication is a key one, because it may be the weakest link, as it also relates to human behavior. Once the user has opened the door, how can you be assured that it is the same user who visits the rooms of your house ? One time authentication is far from being enough and secure : you need to check, on a regular basis, that it is still the same user that you expect behind the screen. How can you do this without hurting the user experience (you can’t request the user to re-authenticate every minute). It takes AI and behavior analysis to do this in a way that is totally transparent to the user. Systancia is developing “continuous authentication” by applying AI to this very specific use case, and the first outcomes of our experiences (based on data from our own employees) show positive results for a viable solution in business real life. We see value for such a technology in the Privilege Access Management (PAM) use cases : users such as system administrators who have a privileged access to IT resources and applications. Whether these privileged users work in the office, at home, while in mobility or from a third party service provider, you must be guaranteed that someone has not taken their seat as they access to your most critical IT assets.
    AI & ethics

  • AI plays an important role in the context of Advanced Driver-Assistance Systems and Autonomous Driving (ADAS / AD). Among other important challenges, we need to teach the virtual driver to see and understand what happens around the vehicle - the perception problem . Informed by the perception system, the planning system determines the car’s next steps such as accelerate, slow down, go straight, make a turn, … . Once the virtual driver is trained to behave like a human driver - or better - another major challenge comes into play: How do we decide whether the virtual driver gets a driver’s license? - the validation problem . In his talk, Lars will demonstrate use cases in the context of self-driving cars and how they are being addressed.

    In an industrial context, virtual twins are major actors for emulating a physical system in simulation-based engineering. This type of twin is based on numerical models with the objective of designing, testing and optimizing complex systems. However, they are not meant to assimilate nor process incoming data. Therefore, a virtual twin is not compatible with real-time responses to data-driven parameters or sensors. With the advances in computing power and machine learning, a new generation of twins, called digital twins, has emerged. This second type of twin is purely data-driven and make it possible real-time decision making for prediction or maintenance. Yet, the downside is the amount and quality of the needed data for creating a complete model. Besides, in an industrial environment, collecting data can be expensive or even impossible. Hence, we propose a new paradigm using advantages from both virtual twins and machine learning techniques, the hybrid twins. This new type of twin embrace the physical-based models from the virtual twin and the data-driven models from machine learning approach. The first one intents to represent as accurate as possible the system, while the second one models the inevitable and persistent bias between this physical-based representation and the physical measures. During this talk will be presented this three different approaches for complex industrial systems modelization and some new data-driven machine learning algorithms in the context of hybrid twins.

    By Antoine Couret, Geo4Cast

    By ADAC
    AI Industry Solutions: Mobility

  • Data-driven process understanding and optimization for robotic manufacturing tasks
    Application of industrial robots to manufacturing tasks is currently limited by the complexity of the programming task and the lack of insight in the actual process and the physics behind it. With complex, flexible and fast processes in small areas, it becomes increasingly difficult to understand and evaluate the robot behavior. Combined with high work piece and process variance, many automation projects remain unrealized due to economic infeasibility.
    Data visualization, statistical analysis and machine learning have the potential to fundamentally change the way robot programmers approach their job. Such techniques are likely to drastically widen the application area of industrial robots. ArtiMinds is developing novel software solution to facilitate this development.
    AI industry solutions: production

  • Production
    Digitalization of production systems has enabled the massive gathering of data. AI can make meaningful use of this data. From predicting malfunctions of machines and finding root causes to new methods in quality assurance, there is great potential. Close human-robot collaboration is on the advance. AI can reinforce the collaboration by “humanizing” the robot behaviour. To what extent is this already reality in production?

    Use Case:
    AI-assited Product Design & Prediction of congestion in internal logistic systems


    The availability of vast amounts of data promises new insights into areas which are up to now largely dominated by human experience and intuition. AI has the potential to improve productivity, transparency and product development in the manufacturing industry. Due to the many possibilities offered by AI and the uncertainty that nevertheless exists about success for each of these possibilities, decision makers have a hard time to decide where and how to apply AI.

    In this session two applications of AI in the manufacturing industry are presented: One is looking at AI-assisted product design, while the other presents an artificial neural network trained to predict congestion in internal logistic systems.
    AI industry solutions: production

  • AI in retail – a purely analytical challenge? The real problems retailers are facing
    The online shop dm.de offers a thoroughly personalized space called ‚Meine Produkte‘. Customers are provided with AI-based personal shopping recommendations based on Smart Data. During the speech we enter the engine room and look at and discuss the building blocks that were necessary for providing personalized shopping recommendations. We have a look at algorithms, CI/CD, Tooling and the overall data and service architecture – the dmTECH Data Science Pyramid of Needs.
    AI industry solutions: retail

  • HOW AI EMPOWERS RETAILERS – Reinventing Campaign Targeting, Delivering Relevant Messages & Driving Significant Business Impact.

    The hype around artificial intelligence as a helpful tool for retail marketing teams has been around for a couple of years. Although the first investments have been made, most of the technologies have failed to deliver, so far. Most projects start with a lot of data work and end up with rule based machine learning logics that deliver low business impact. In this keynote, Tinyclues is going to present a way to use Deep Learning in order to solve a real business problem. Starting with a Retailer’s painpoint: Campaign Targeting, Yannik Kottusch will outline ways to deploy impactful algorithms to boost relevance and campaign revenue.
    AI industry solutions: retail

  • Of Merchants, Markets and Machines: How AI will revolutionize retail

    Voice Commerce, Personalization, Predictive Basket, Adaptive Pricing, ...
    Artificial intelligence brings completely new possibilities to eCommerce. Can stationary retailers also benefit from AI? How does the customer change? And do we think far enough ahead, when the world turns faster and faster? A small expedition into the realm of a new species which is currently undergoing a rapid evolution - and which will dramatically change retail within the next years.
    AI industry solutions: retail

  • Amidst all the noise about Artificial Intelligence, what is real, how do we define machine intelligence, why is it transformative and how does one measure impact? Where in the world do we see meaningful implementations of machine intelligence?

  • and with Antoine Couret, Hub France IA

  • We will start the workshop with an inspirational talk about the current state of AI. This will focus on possibilities of AI in the field of computer vision like object detection and classification in images or videos. It will also show how AI can be used to generate visual content. We have a look on possible applications of AI in general and in computer vision. Together we will brainstorm and discuss possible applications of the workshop participants. Afterwards we will dig into an example use case to see how state of the art image classification can be applied easily with only a few lines of code, which the participants can follow.

  • How to Boost Business in EU - IoT Platform with AI Learn more about Digital Business models, their integration into IoT infrastructure, and strategic possibilities of including AI technology to boost your digital business

WANT TO BE A PART OF IT?

Would you like to support AIxIA as a sponsor or partner and promote Artifcial Intelligence this way?
Then contact our project managers and help us create a conference of superlatives with us!

Christin Eckerle
Innovation & Digital Ecosystems

Mail to:
dehubAI@diz-bw.de


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