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 discussions 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 & FZI | Karlsruhe, Germany

 

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

 

Main Host Katrin-Cécile Ziegler

Katrin-Cécile Ziegler 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.


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  • Day 1

    October 01, 2019 at ZKM

  • Day 2

    October 02, 2019 at DIZ

  • AI as Chance for Sustainability and Fairness
    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

  • AI technology has led to major improvements in human-like perception of machines. Natural language processing and image recognition are reliably used in commercial applications today and only the beginning of possibilities and methods businesses can apply.

    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 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

  • What modes of transport will we use in the future?
    AI has the prospect to play a major role and help solve current mobility problems. Check out the latest developments presented in this session.
    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

  • Artificial Intelligence – Hype or Reality
    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?

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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