Spatial Data was a workshop topic at the #wetechtogether conference which took place on October 2. The data innovation alliance, its member Litix and WiMLDS Zurich sponsored and organized two workshops entitled “Jump into Geodata”. The participants learned how to access Swiss geoservices with Python and how to use them for the presentation and analysis of geodata. We would like to thank the participants for their attendance and the lively discussions in the workshops. We hope that this will lead to new innovative geospatial projects in the future!
Innovative ideas around geodata are indeed welcome, since Spatial Data Analytics is one of four focus topics in the Databooster initiative. Project ideas related to geospatial data have increased chances of receiving Databooster support!
Being part of the #wetechtogether conference was a great experience for data innovation alliance and Litix. We support the main goal of the conference to empower people to bring more diversity into tech. We are already looking forward to the 2022 edition!
The NTN Innovation Booster – Databooster powered by Innosuisse is a community with an open innovation culture, in which key players around data-based value creation work together to develop innovation ideas and explore new business areas and approaches. The Databooster aims to bring together actors of all segments to ideate and develop radical solutions.
In doing so, they give Swiss companies a decisive competitive advantage and create important added value for the Swiss economy and society.
We would very much like to draw your attention to the following event and invite you to participate in the idea submission.
This event is an opportunity for every one of you: who has a cool project idea and is in need of a partner and/or even a feedback on the idea and also persons who don’t actually share an idea there but are interested in listening to them. This is meant for researchers, startups, SMEs as well as corporations. There are ideas shared and there might be just one that strikes you as the thing to do!
We invite submissions of ideas related to all aspects of data-based value creation. We especially want to solicit ideas in these focus topics, namely: Industry 4.0, Language-based Human-Machine Interaction, Smart Services and Spatial Data Analytics.
Out of all well-defined presented ideas a jury will award the best 3 innovation teams for funding by 10-20 kCHF to further shape or test your idea.
The Databooster Matchmaking event will take place on the December 2, 2021, 13:00 – 17:00 at Fernfachhochschule Schweiz in Zurich (Gleisarena at Zollstrasse 17).
How to participate?
1. Join us if you want to pitch your idea. You get 5 minutes time to present/pitch your case. After all pitches are done, you get 10 minutes slot for 1-on-1 discussions with persons from the audience who are interested in your idea. You could be talking for 10 mins or for the next hour! And if you are interested in any other pitch, you can also go and speak with them!
2. Join us if you want to hear about all these cool pitches, these ideas. Learn what is going on in the minds of these young companies, researchers and professionals. There is definitely an idea that would appeal to you.
Deadlines:
Those wanting to pitch: send your name and innovation idea (200 words, related category) ASAP. We will not accept entries after Nov 16, 2021.
Those not wanting to pitch, but still participate: register latest by Nov 16, 2021.
Number of participants is limited to a maximum of 40 participants.
Save the date and register to present and follow all idea presentations, matchmake with interested parties for project development, research, and learn about specific funding schemes.
10X-Service Design Lab An accelerator for business development
Automotive industry is going through a digital transformation and striving to expand its business models into new mobility solutions. Their core product-driven business is expanding to service-orientated business models which requires a new set of expertise and capabilities. In his talk, Linus examines the possibilities to use service design in business development for human-centered accelerated decision-making by application of digital co-creation.
Come and join us at TECHNOPARK® Zürich for the next AI Use-Case talk.
AI experts will share insightful use cases. Learn about the technical challenges they faced and their solutions. After the keynotes, we will venture into an in-depth technical discussion.
Keynote 1:
Marc Tesch, CEO from LearnBI will speak about Industry 4.0 and how Predictive Maintenance minimizes expensive downtimes, saves costs and increases product quality.
Keynote “Produktivsetzung von komplexen Predictive Maintenance am Fallbeispiel “Sortieranlage der Schweizer Post”
Language: German
Keynote 2:
Prof. Dr. Kevin Schawinski, CEO & Co-Founder of Modulos AG will talk about Automated Machine Learning.
Keynote “Automated Machine Learning: from cows to galaxies”
Language: English
Keynote 3:
Dr. Mortiz Platscher, Senior Machine Learning Engineer of Acodis AG will talk about vast amounts of technical documents such as manuals, drawings and building plans.
Keynote “Structuring Information in Technical Documents – Challenges and Solutions”
Language: English
On-site spaces are limited. Book your space by contacting the organizer directly at nadine.furrer@aspaara.com.
The Expert Group will come together in September at the following location: Trivadis AG, room Zuse, Sägereistrasse 29, 8152 Glattbrugg
The topics of discussion are as follows:
Methods of Statistical Disclosure Control applied on Microdata
Simon Würsten, SBB
Simon will talk about how he applied methods of anonymization on microdata, his experiences and possible complications regarding big data.
Big Data and AI Technologies on Microsoft Azure Cloud
Gerald Reif, IPT
Modern cloud providers enable powerful AI and Big Data technologies, platforms and tools. We will have a look at the underlying concepts and specific implementations and services on Microsoft’s Azure Cloud.
Reproducible Data Science
Luca Furrer, Trivadis
Luca will discuss the different aspects of a reproducible data science process and explain why he thinks the question of reproducibility should be considered for every AI project. Furthermore, he will present a set of open source tools which can be useful achieve reproducibility.
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