Étiquette : Databooster

Expert Group Meeting – Machine Learning Clinic

The Expert Group will meet in Zurich for the first time in 2022 to discuss Challenges in applied computer vision!

Location: PwC, Birchstrasse 160, 8050 Zurich

More details are soon to follow.

Participation is limited to 14 people. Please register using the form below.

ONLINE – Expert Group Meeting – Smart Maintenance

We are pleased to invite you to open 2022 with a meeting of the “Smart Maintenance Expert Group”. The meeting will take place online on 20 January 2022 at 14:30.

Niels Uitterdijk from Amplo GmbH will talk about
Successes and pitfalls on the road to a generic, operational machine learning platform for service engineers

Industrial machine manufacturers have heavily invested in IoT infrastructures in the last decade, yet tools that extract actionable insights remain illusive. To drastically reduce the maintenance costs and data analysis efforts, Amplo developed a smart maintenance platform which gives service engineers themselves access to state of the art operational machine learning systems that are deployable without any code or machine learning expertise. Currently, the platform provides models that diagnose failures, analyse production quality and monitor machine performance. 

The benefits are clear. Tritium avoids hours of data analysis and enjoys automated root cause analysis, allowing them to instantly send out repair orders. Solarmanager is able to notify thousands of homeowners when their PV panels are underperforming. TB Safety now services their filters only when necessary, instead of inspecting them every year.

Program

14:30 – 14:45 An introduction round
14:45 – 15:30 Successes and pitfalls on the road to a generic, operational machine learning platform for service engineers
15:30 – 16:30 Networking on “Wonder”

Registration

Please register to the meeting using the form below. For questions you are welcome to contact us directly through lilach.gorenhuber@zhaw.ch or palm@zhaw.ch

Looking forward to meeting you,
Maik, Thomas, Manuel and Lilach

No Time To Die

The organization of the 11th meeting of the Spatial Data Analytics Expert Group included some unexpected twists. After postponing the original meeting in September, we also had to switch to an online format at short notice on the new date. Although the excitement couldn’t quite compete with a real agent movie, we were at least pleased that we could finally welcome a large number of participants.

The real excitement came from the announced contents. Dr. Joachim Steinwendner from FFHS had offered to host the meeting and had prepared a program with the topic GIS and Health. The two announced talks were titled after Bond movies.They addressed the interface between GIS and Health, once from the pharmacological point of view of and once from the perspective of geoinformatics.

PD Dr. Stefan Weiler focused on the first view. In his talk “On Her Majesty’s Secret Service” he presented the role of geodata in medicine with numerous illustrations (e.g. the Corona dashboards). Joachim Steinwendner then changed the perspective in his talk “The World Is Not Enough”. He asked the audience to imagine a GIS in which the coordinate system did not map the world, but rather the human body.

The meeting ended in an informal exchange under wonder.me. Plans were made for future collaborations or at least for the next visit to the cinema.

Webinar – Databooster Workshops on Usability Testing – H2020 DomOS Expert Review

Usability Test for evaluating a product or service by testing it with representative users

This workshop on user experience brings together researchers and service leaders to share knowledge and support projects with data science tools.

The goal of the workshop is to share knowledge on user experience principle, tools and applications. You will get an overview of the European consortium domOS project – A building local communication network provides access to sensors data and smart devices / appliances to applications either hosted in the gateway or in the cloud.

See the FLYER for more information.

For registration please use the form below.

Databooster – to support SMEs

The NTN Innovation Booster is an Innosuisse-founded initiative which brings together the most important players from science/research, society and industry. Its main mission is to develop innovation ideas carried out by interdisciplinary teams.

The NTN Innovation Booster is composed of 18 programs, out of them 6 will start in 2022. Each NTN Innovation Booster program targets a specific domain. One of these programs is NTN Databooster which supports innovation around data-based value creation. Databooster focuses on the design of data-based services for the industrial and service sectors. Its goal is to combine new methods and technologies in the field of data science with new business models and service concepts.

Nowadays, companies collect vast amounts of data to extract information/knowledge. This information is usefull to optimise their process, develop new services and/or products.

The main mission of the NTN Databooster is to support SMEs to:

identify their business/technology challenges in a competitive innovation environment
shape their innovation idea and get the opportunity to build their first prototype
have access to funding instruments for their research and development programmes.

  • identify their business/technology challenges in a competitive innovation environment,
  • shape their innovation idea and get the opportunity to build their first prototype,
  • have access to funding instruments for their research and development programmes.

> Register now

According to the current COVID guidelines, wearing masks is mandatory. Only holders of a valid COVID certificate (vaccinated, cured or tested negative) will be allowed to participate in the event.
We ask you to provide your COVID certificate and your identity card on site.

Diversity: Fully Exploit the Potential of Data Science

Just around 25% of the participants of the 8th Swiss Conference on Data Science are female. This aligns with the report from the World Economic Forum that claims that women make up only an estimated 26% of workers in data and AI roles globally. But why? And how can we change that?

We talked to Christian Hopp from BFH, who has addressed academic careers, gender, and diversity in his research, and Teresa Kubacka, who works as a freelance data scientist at Litix and is a member of a community “Women in Machine Learning and Data Science Zurich”

Why do you think diversity is important in Data Science?

Christian Hopp: Very generally, why would anyone think that diversity should not be important in Data Science? Aiming for example at gender balance or the equal representation of minorities in Data Science means fully exploiting the talent pool and fairly distributing opportunities.

Software, algorithm, or more broadly technology development in general, is first and foremost an interactive process, where various actors are involved and where communication and collaboration help to combine different knowledge bases. Hence, to fully exploit the potential of Data Science, it is paramount to involve individuals from all sorts of backgrounds (gender, ethnicity, age, socio-economic background, etc.). Diversity broadens the search horizon, it may help to develop insight and products/service offerings that are more responsible, more ethically, and socially acceptable. When the process itself is more inclusive, so will the final solutions developed be. This may range from making AI applications less racist or including more female-centered views when developing algorithms. If we fail to pay attention to diversity, many of the unconscious biases that have been uncovered may find their way into algorithm development. In the end, we may end up with severely biased algorithms and less trusted by potential user groups.  Diversity in Data Science can endorse these relevant values and viewpoints already during the technology development process. Values like diversity and inclusiveness of the development teams/companies need to be front and center to ensure responsible and inclusive technology and innovation development. 

Teresa Kubacka: Data science needs diversity because we live in diverse societies.  Although we tend to think that “data is the new oil” and “data speaks for itself”, data is not part of the natural world as the oil or the force of gravity, but it is people who actively create data. People decide which data is important enough to collect and what to leave out. People decide which research question is worthwhile the effort and which projects to invest in. This is why if our goal is to create data-driven products that make sense for all the members of society, we have to include a diverse representation of society in the process of creating those products and defining what is important. We have many examples of data projects that backfired spectacularly because they have been designed and developed by a homogeneous group of people (for example a health monitoring app that doesn’t have a functionality to track menstruation). Luckily, we also have many examples of projects where inclusive data science projects led to more empowerment or have driven positive change. This holds for all kinds of diversity, not only gender diversity. Last but not least – can we afford to lose talented data scientists only because they don’t have “the right appearance (gender, skin color, etc.) for the job”? 

What’s keeping women out of Data Science?

Christian Hopp: To be honest. I sincerely do not fully know, but I wish to understand. We have done prior research in STEM fields, focussing on female academic careers. We found that gender stereotyping attributes lower field-specific ability to women. In sum, women aspiring for an academic STEM career with leadership responsibility are confronted with “double” incongruity: First, they are experts in domains that are clearly male-dominated, subjecting them to severe biases stemming from the perception of their abilities. Second, even an aspiration for leadership was still seen as atypical for women. It could be that careers in data science are fraught with problems because women have to fulfill expectations in very male-dominated environments. 

Also, interactions with colleagues and superiors played a similarly important role in academic careers in STEM fields. Oftentimes role models are important to pursue a certain career path. Especially early career stages are sensitive periods in which influential imprints may be left. A lack of prominent role models might keep women out of data science as a career choice. But to answer this more fully, we would need more empirical evidence as to how individual aspects of gender imbalance interact and co-evolve with systemic ones. Formal and informal rules, proximate social structures, organizational culture, professional networks, couple perspectives, prevailing stereotypes, and individual motives may all interact here.  

Teresa Kubacka: In my experience, it’s not the lack of interest. I meet plenty of women who are fascinated by data science and are highly competent to become good data scientists. Many obstacles that they face are the same for women in tech in general. Here I’ll focus on the ones most characteristic for data science. 

One group of obstacles is related to a stereotypical perception of who can be a good data scientist: it is a person with a formal degree in an area historically predominantly given to men (computer science, mathematics, etc.), so women are statistically more likely to be perceived as not having the “right” qualifications. This happens also because a data scientist is often perceived to be a better software engineer and there is low awareness of a big variety of different flavors of data scientist roles among recruiters as well as applicants – some roles are more product-oriented, there are many teams where a data scientist needs to be a good communicator and structured, analytical thinker in the first place, some data science roles have a strong UX and product design component. 

Another group of obstacles is more of a mundane nature, but can, unfortunately, be a real deal-breaker. One big issue is a lack of data science roles with 60% workload and a relatively small market for freelancers, which in Swiss reality means that women who have to share a large portion of family responsibilities cannot easily work as part-time data professionals. It is also not easy to make a transition into data science gradually on the job, without investing time after work into getting a certification (like a CAS or a Bootcamp). Working women with family responsibilities are particularly impacted by this because they cannot afford the time. 

The third group of obstacles comes from within the existing machine learning/data science community. Some things that used to be perceived as normal in male-dominated communities are perceived as hostile by many women. For example, until not long ago one of the most important AI conferences used to be called “NIPS” with a pre-conference event called “TITS”. Only after severe criticism, it has been hesitantly renamed to “NeurIPS” (link 1, link 2). As a woman wanting to enter the field, you start asking yourself: will I be taken seriously there if they picked an acronym for a conference after a female body part? 

The last thing that I think is also relevant is that on one side, requirements for data scientists written by some recruiters are unrealistic, but on the other side, many women don’t believe that they can apply and do the job if they fulfill only part of the requirements and learn the rest on the job. This is why it’s important to build up their confidence – for example by creating networks of female professionals who can exchange experiences, by creating inclusive environments that allow for free experimentation and learning by doing, and by engaging in different mentoring programs both as mentor and mentee. 

How can you support and push diversity forward?

Christian Hopp: Generally, I think it is important to increase awareness through communication. Organizations need to put the benefits of diversity front and center. Not only on the webpage and other communications but in the hearts and minds of people working in data science. We need to wholeheartedly embrace the notion that diversity leads to better, more inclusive, and more innovative outcomes. Second, and that being said from a middle-aged, white, male professor from a non-academic family background trying to educate the next generation of data scientists, we need to activate and communicate through role models. Career paths in data science need to become more visible for women, diverse, or minority individuals. Third, we need more mentoring for diverse, minority individuals in companies but also in academic training.

Teresa Kubacka: I can give some examples based on the activities in our community. We organize meetups and workshops aimed to support women and gender minorities in data science and machine learning. Our community members can talk about their data science projects and learn new skills in a friendly environment. We try to give an opportunity to speak to people who have different kinds of data science roles and life situations to present a variety of inspiring role models. We put a lot of emphasis on events where the participants can engage in informal coaching and at least once a year we try to organize a career event. We also support other communities for women in tech, for example by participating in the conference “WeTechTogether”, where more than 20 communities took part, and where WiMLDS, Litix, and Databooster organized a geodata workshop together. 

However, we can only do that much and there is still much more systemic change that needs to happen. There is no one single solution and every organization will have to find its own path. Some solutions come as an answer to the obstacles to diversity I described before. For example, the Swiss job market would need to open up for more part-time work in data science. Over the last few years, we have already seen an increase in 80% workload positions. So if you are a manager and have an open position for a data scientist, consider making it a 60-100% position or a job-sharing position. If you have an employee with a strong analytic skill that is inspired by data science, think if there is a way for this person to learn some data science skills within their current role. As a general rule, we are all biased and use stereotypes, so it’s always good to check your bias and privilege, and question your instinctive hiring choices because they may act against inclusiveness and diversity. If you design a product or start a project, put effort into assembling a diverse team. If you can, encourage people from historically non-privileged groups to participate in high-profile projects and give them credit and recognition for their effort. Once an organization embraces diversity as its value, and not as an option for interested participants, many of these changes will follow naturally as a consequence of this choice. 

The Art of Data Fusion

By Nicolas Lenz (Litix), Stefan Keller (OST) and Reik Leiterer (ExoLabs)

Geodata are used in various industries and academic fields and often have to meet specific requirements in order to be used, for example in terms of geometry, recording time point or semantics. But often different geodata sets have similar geometric properties but different semantics or are captured at different times – or vice versa. Accordingly, the added value arises when your data sources start to «talk to each other», connection points between the data are used or possible gaps can be filled. In this context, there is a multitude of technical terms, which are sometimes used differently depending on the subject area, sometimes are used synonymously and sometimes are used inappropriately in their terminology – so you will read about «append», «merge», «relate», «link», «connect», «join», «combine», or «fuse», just to mention a few.

In the last meeting of the Spatial Data Expert Group on the 4th of November, this topic was presented and discussed, and the challenges and potential of the concept were highlighted. This included a critical examination of the semantic classification as well as the presentation of various possible applications in research and industry. Our host was the UZH Space Hub at the University of Zurich, represented by Dr. Claudia Röösli.

Representation of individual tree characteristics based on multi-temporal airborne 3D-LiDAR data, in situ measurements, and multi-spectral satellite data. Fuses data – or not?

So, what is Data Fusion – with a strong focus on spatial data? For some, it means more a list of different data sets, with a narrative relating one data set to the next. For others, it means visualizing different data sources on the same graph to spot trends, dynamics, or relations. In the spatial domain, the basic concept of data fusion is often the extraction of the best-fit geometry data as well as the most suitable semantic data and acquisition times from existing datasets.

The keynote was given by Dr. André Bruggmann, Co-CEO, Data Scientist and Geospatial Solutions Expert at Crosswind GmbH. Under the motto “Unlock the Where.”, he presented how data fusion techniques help customers gain new insights, from (spatial) visualizations and web applications to facilitate strategic business decisions (e.g., selection of optimal point of sale locations). In addition, he presented a project where data fusion techniques are applied to make detailed and future-oriented statements about the assertiveness of e-mobility and identify relevant trends for the automotive industry.

Dr. André Bruggmann from Crosswind – “Unlock the Where”

These inputs led to an exciting discussion between the experts present – not only on the technical implementations presented, but also regarding the potential for optimisation and possible future cooperation. This is exactly how the initiators of the event had envisioned it – an open and inspiring exchange in line with the basic idea of open innovation.

Are you also interested in spatial data and its applications? Then come to the next expert group meeting on 15th of December on the topic of GIS and Health, hosted by Dr. Joachim Steinwendner from FFHS.

Smart Service Innovation for Adapting to the Pandemic Situation – Successful Smart Services Summit 2021

This image has an empty alt attribute; its file name is Summit21-1.jpg

By Jürg Meierhofer

On October 22, the expert group Smart Services welcomed worldwide top experts to the fourth Smart Services Summit. The focus was on how Smart Services allow firms to adapt in the COVID-19 pandemic. Examples of remote and collaborative working have created new forms of co-delivery where customers are integrated into the service processes. Such a change requires a mindset change for more traditional firms as the service model migrates from ‘do it for you’ to ‘do it yourself’ or some mix of ‘do it together’. Considering service science, the switch makes perfect sense as it means that the full set of resources within the ecosystem are now being used rather than only a part. Services can be delivered faster and at lower costs with the support of new technologies and when working with the customer in a co-delivery mode. The changes are leading to new value propositions and business models today and will lead to an evolution in Smart Services in the future. The changes themselves must be understood, and we may need to consider new or different implementation and delivery models for Smart Services. These new working approaches may also requite use to re-evaluate both training and education.

Across the papers and presentations, it became apparent that digital service innovation has substantially changed and accelerated since the start of the pandemic. Customer needs and service processes have undergone dramatic disruption, which is still ongoing. A common thread throughout all the papers was the concept of the ecosystem thinking, which was discussed from a wide field of perspectives and in a comprehensive way. In line with the concept of the Service-Dominant Logic, the needs of the different actors in the ecosystem need to be identified and integrated into the design of the services and the integration of the various resources in the ecosystem. The ecosystem perspective not only integrates the different human actors, but also technological, digital resources.

Innovation through intensive collaboration allows to switch different perspectives and innovation approaches. This results in seamless value propositions and solutions for the beneficiary actors, which is a necessary prerequisite for economic value creation. Well-designed service experiences based on a consequentially customer-centric view and approach are thus at the basis of value creation.

This transition to digital service innovation in ecosystems requires not only fundamental changes of the technological platforms. In particular, collaboration across actors, organizations, and industry requires a new level of trust, culture, skills, marketing approaches and innovation frameworks.

Many thanks to all those who spoke at, and attended, the Smart Service Summit. A big thinks to IBM, data innovation alliance, ZHAW Zurich University of Applied Sciences and Lucerne University of Applied Sciences and Arts for supporting the event.

Expert Group Meeting – Machine Learning Clinic

The meeting will be physical only!
Location: Bern, https://suedland.ch/angebot/forum/

Rare events – a real pain in Machine Learning. How to detect, classify or predict an event you have rarely seen before?

Let’s learn and discuss with experts from natural hazards: earthquake, lightning, flooding and stock market crash.

A great lineup of top experts:

  • Prof. Dr. Stefan Wiemer, Head of Swiss Seismological Service «Extreme events forecasting in seismology»
  • Dr. Thomas Krabichler, OST «Rare Events in Financial Modelling»
  • Dr. Ralf-Peter Mundani, FHGR «Predicting Natural Hazards such as Floods with Parallel Numerical Simulation»
  • Pad Pedram, CSEM «A data-driven approach for lightning nowcasting with deep learning»

followed by a real Apéro

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