By Milena Perraudin, data innovation alliance

On the 13th of November, the data innovation alliance organized yet another impactful Expert Day at the Technopark Zurich. This edition of the Expert Day seamlessly intertwined with the annual General Assembly, presenting an enticing program to learn, exchange and get inspired. Welcoming over 30 participants, the event enfolded plenty of opportunities for engagement within the three Expert Groups: ‘Digital Health’, ‘Smart Services’ and ‘Data Sharing and Visualization’. 

The event started with the Expert Group Meetings, featuring three concurrent sessions led by subject-specific experts, followed by engaging discussions among the participants. Transitioning into the plenary session, Oliver Dürr from the University of Zürich and the University of Fribourg delivered a thought-provoking keynote on how we can contribute to a future worth wanting. In the following General Assembly, Andrea Dunbar was elected as a new board member. Closing the event in customary fashion, participants enjoyed an aperitif, a delightful opportunity to unwind and cultivate connections. 

In this blog post, we take you through the highlights of the day.

Expert Group Workshop Data-Driven Healthcare by Digital Health

The Expert Group Digital Health delved into the transformative intersection of advanced data analytics and healthcare. The rise of sophisticated data analytics has heralded a new era for healthcare organizations, offering more profound insights into patient management and enhancing the efficiency of clinical decisions. By fusing data-centric clinical study with comprehensive care, we are spearheading a healthcare transformation that prioritizes the use of data to convert medical observations into practical insights. Nonetheless, the journey is marked with challenges, particularly concerning data interoperability. Given the diversity of data sources in healthcare—ranging from clinical information systems to patient surveys, diagnostic tools, and biobanks—interoperability extends beyond system-to-system communication to include all parties involved in healthcare delivery. Essentially, data must be interpretable by both the technology and the human elements within the healthcare continuum.

During today’s seminar, we delved into the subject of data interoperability through the lens of both healthcare providers and industry experts. Sebastiano Caprara, who leads the Digital Medicine Unit at Balgrist University Hospital, shared their strategies for overcoming interoperability hurdles in consolidating clinical data for research purposes. They have established a series of repositories where data is not only anonymized but also structured for easy access by researchers, bypassing the need to navigate through disparate clinical databases. Caprara outlined their progressive steps towards utilizing data-led clinical research to advance integrated care, starting from the raw clinical data to exploiting extensive data stores to identify trends, forecast health outcomes, and refine clinical practices.

Complementing Caprara’s broader view on interoperability, Aron Horvath from Unit8 presented a focused example of a data-oriented solution for diagnosing abnormalities in electrocardiograms (ECG). They have introduced an open-source framework that utilizes ECG time-series data to pinpoint health anomalies, thereby enabling healthcare practitioners to make well-informed decisions that enhance patient care. While this tool is in its nascent stages, it is constantly evolving and improving, benefitting from the contributions of its active community of expert users, owing to its open-source status.

Expert Group Workshop Social Value Creation by Smart Services

The Expert Group Smart Services met for a workshop on social value creation by Smart Services. Social values are an important factor of a sustainable business strategy. One common way to understand sustainability is the triple bottom line concept. It states that firms should commit to measuring their social and environmental impact, in addition to their financial profit. The triple bottom line can be broken down into “three Ps”: profit, people, and the planet. Firms can use these categories to conceptualize their environmental responsibility and determine any negative social impacts to which they might be contributing. The workshop began with an introduction to the topic by the organizers, followed by a presentation by the master student project of Robin Stieger. Focusing on “people”, the presentation was centered around the terms health, safety, knowledge and education, skills, and employment. The group discussed how social value is created on an individual level, but also on a community level. 

Among various workplace health topics, also the importance of avoiding jobs in dangerous or hazardous environments and giving access to work to people was discussed. Additionally, the positive and potentially negative impacts on individual and collective competences of doing work assisted by smart services was discussed. Among others, enterprise social networks got special attention in the workshop. For instance, organizations form successful strategic partnerships with nonprofit organizations that share a common purpose-driven goal.

Overall, the workshop was a great success, and the group came up with many innovative ideas for promoting social value creation by smart services.

Expert Group Workshop Spatial Computing for Business Opportunities by Data Sharing and Visualization

 

Keynote Humanity, AI and Innovation: What Future Do We want?

In Oliver Dürr’s keynote on ‘Humanity, AI, and Innovation: What Future Do We Want?’ the overarching question posed was, ‘How can we all contribute to a future worth wanting?’. The key takeaways emphasized adopting a holistic-integral perspective in addressing this question. Furthermore, he advocated for the design of technologies, processes, businesses and institutions in alignment with values and aims that benefit society sustainably. He further highlighted that ethics cannot be added at the end of an innovation process but must be ingrained from the beginning. Dürr recommended starting with ‘Value Based Engineering’ and the ISO/IEC and IEEE 24748-7000 standard.

General Assembly (GA)

Thilo Stadelmann (ZHAW Center of AI) made the decision to step down from the data innovation alliance board after eight years. As a foundational member from day one, Thilo Stadelmann played a pivotal role in the alliance’s success, and we express our sincere gratitude for his exceptional contributions.

In the spirit of new beginnings, we extend a warm welcome to Andrea Dunbar (CSEM & EPFL), unanimously elected as the new board member – congratulations! Additional applause goes to the existing board members Matthias Brändle (La Mobilière), Hans Peter Gränicher (D ONE), Christoph Heitz (ZHAW IDP), Anne Herrmann (FHNW Institute for Market Supply & Consumer Decision-Making) and Matthias Werner (Trumpf Schweiz AG), who were re-elected for another two years. 

The GA featured the exciting announcement of the new Innovation Booster program: Artificial Intelligence, set to commence in the upcoming year and run for four years. This initiative, powered by Innosuisse and managed by the data innovation alliance, aims to drive innovation through the socially and economically viable application of AI. Stay tuned for more details.

In addition to celebrating these milestones, the GA saw unanimous approval for the financial report for 2022, the budget for 2024, and the discharge of the board and auditors. The assembly concluded with the announcement of the 11th IEEE Swiss Conference on Data Science (SDS2024), scheduled for May 30-31, 2024, at The Circle, Zurich Airport. Save the date and find more information here.

By Jürg Meierhofer, ZHAW

The Smart Service Summit was a one-day conference held in Zürich on 27 October 2023. The event brought together experts and leaders from various fields such as smart services, digital transformation, and product management. The aim of the summit was to explore how smart services can help build resilience in a changing world.

The summit was a success in terms of attendance, engagement, and feedback. The participants expressed their satisfaction with the quality and relevance of the content, the diversity and expertise of the speakers, and the networking and learning opportunities. The organizers received positive comments and suggestions for improvement from the attendees.

The main outcomes and takeaways of the summit were:

• Smart services are a key driver of innovation and resilience in a changing world, especially in the context of the COVID-19 pandemic and the digital transformation.
• Smart services require a holistic and customer-centric approach that involves co-creation, collaboration, and integration of different stakeholders and technologies.
• Smart services pose significant challenges and risks, such as data security, privacy, ethics, and regulation, that need to be addressed and managed effectively.
• Smart services offer tremendous potential and value for various industries and domains, such as manufacturing, healthcare, education, and mobility.

The organizers of the summit thanked the speakers, sponsors, partners, and participants for their contribution and support. They also announced that the next edition of the Smart Service Summit will take place in 2024 and invited everyone to stay tuned for more information.

By Jürg Meierhofer, ZHAW

On October 25 and 26, 2023, we conducted the Smart Data Forum, a two-day event co-organized by Easyfairs and the databooster, at the Messe Zürich. The theme of the forum was “Smart Services and Resilience”, and it featured presentations and discussions from experts and practitioners in the field of smart service systems.

The forum aimed to explore how smart services can help businesses and society cope with the challenges of volatility, uncertainty, complexity, and ambiguity (VUCA) in the current and future environment. The speakers shared their insights and experiences on how to design smart services that are resilient, sustainable, adaptable, and innovative.

Some of the topics that were covered in the forum included:

• How to use data and analytics to support decision making and service innovation in smart service systems
• How to leverage digital platforms and ecosystems to create value and network effects in smart service systems
• How to apply design thinking and agile methods to develop user-centric and co-creative smart services
• How to balance resilience and efficiency in smart service systems
• How to measure and improve the performance and impact of smart services

The forum was moderated by Jürg Meierhofer, senior lecturer and director for studies (CAS Smart Service Engineering, MAS Industry 4.0) at the ZHAW Zurich University of Applied Sciences. He also provided some useful frameworks and tools for understanding and designing smart services.

The speakers were Reto Zuest, Mirko Maurer, Daniel Politze, Nikolas Schaal, Nikolaus Obwegeser, and Jean Paul Potthoff. They represented different sectors and domains, such as manufacturing, IT management,  transport, or research and development. They presented some interesting case studies and best practices from their own organizations or projects.

We learned a lot from the forum and I enjoyed networking with other participants who shared a common interest in smart service systems. The forum was a valuable opportunity to exchange knowledge and ideas on how to create smart services that can enhance resilience and sustainability in business and society.

By Jürg Meierhofer, ZHAW

On September 29, 2023, the Expert Group Smart Services held its first presence meeting since 2019 to discuss strategic topics for the Expert Groups in the upcoming years. There was broad agreement that service value creation using generative artificial intelligence has significant future potential and that there is a considerable need for research in this area. From the perspective of service value creation, embedding artificial intelligence in service concepts along the value chain and customer lifecycle represents a promising topic. Interestingly, the multidisciplinary perspectives of the Expert Group turned out to be relevant for successful projects, such as consumer and work psychology aspects, data analytics and data management, as well as business modelling.

The Expert Group’s strategic core team meeting was followed by a very inspiring presentation by Dr. Sebastian Domaschke from our member company eraneos (https://www.eraneos.com/). The presentation showed by 3 impressive case studies how data could be used in practice to create business value. Many thanks to Sebastian!

By Reik Leiterer and Gundula Heinatz, data innovation alliance

In addition to several input talks, the workshops also included ideation sessions in which challenges were identified and possible solutions discussed in the context of the databooster program (powered by Innosuisse). As usual, the event was concluded with an aperitif used for a lively exchange between the experts – and all participants agree: Together we move faster.

Of course, this is not a trivial process and thus Alves presented the interdisciplinary challenges they faced: in the areas of food production, land use, policy/regulation, food logistics, energy systems, consumer behaviour, and many others.

The challenge team (see photo) dealt with topics such as the adoption of new supply chain technology by SMEs, what knowledge and skills are needed for sustained performance, or possibilities on how food supply chain performance can simultaneously meet ESG requirements and profitability targets.

As AI continues transforming all application sectors, discussions about its ethical implications and regulatory requirements are gaining a lot of traction. The workshop Machine Learning and Responsible AI took up precisely this current development. Organized by Ricardo Chavarriaga (ZHAW), Philipp Schmid (CSEM), Andrea Dunbar (CSEM) and the Databooster Focus Topic on Responsible AI, the current state of discussions on ethical implications and regulation of AI systems was presented, and the participants learnt about state-of-the-art techniques for explainability and interpretability. Next to enjoying the inspiring impulse talks about “Ethics, Operationalizing Responsible AI and regulatory compliance» and «Advancements in Interpretable AI for Healthcare: Paving the Way Towards More Reliable Computational Models”, the participants got the opportunity to discuss their specific challenges, meet potential partners and identify ideas to follow. Moreover, representatives of the Databooster program were present to help participants in receiving further support for developing innovative approaches and further research to address challenges in responsibly applying AI in real-world cases.

Florian Scheidegger (IBM Research) and Raphael von Thiessen about the KI Sandbox offered by the Canton of Zurich) and Michele Bolla about the GeoML use case developed by ERNI, showed how complex and diverse the topic of opendata is in Switzerland. In the subsequent Idea session, it became clear that there is still a lot of potential for development, especially in the linking of open data, the standardization and harmonization of meta-information, and the transformation of ideas into profitable business models.

The workshop Build a Cloud IoT System Using a RaspberryPi offered by D-ONE (Heiko Krömer, Stepan Gaponiuk) gave an interactive hands-on experience with the Internet of Things (IoT). Participants were able to set up their own end-to-end cloud IoT-system using RaspberryPi microcontrollers and following a brief theoretical introduction to RaspberryPi, a sensor to controller connection was realized.

The format convinced once again with very good speakers and open-minded participants interested in exchange and cooperation. We are already looking forward to the next event and a lively and active participation.

Co-creation for solving tomorrow’s challenges today

Subsequently, various break-out groups were formed, and challenges and solutions with a focus on practical implementations were developed and discussed. The key challenges identified were: i) What is a structured approach to develop responsible AI?, ii) How to set up a risk assessment which is suited for addressing the risk-based approach required by the EU AI act?, iii) How can data scientist be connected with other stakeholders for making sure that the engineering of AI is fully connected with an integrated risk assessment?, and iv) How to measure the “degree of responsibility” of responsible AI?

For identified challenges for which no solution was found during the workshop, specific innovation support programs were presented offering possibilities to be able to continue the work in depth afterwards.

Under the GEOspatial tag, 2 workshops were held in cooperation with the GEOSummit. GEOSpatial Data Science – The Power of Knowing Where (by D ONE – M. Kliesch, A. Soleymani, P. Thomann) and GEOSpatial Business – Innovation & Business Cases (by Expert Group Spatial Data Analytics – S. Keller, R. Leiterer, Swiss Geoinformation Strategy SGS – C. Najar) and the Swiss Territorial Data Lab STDL – R. Rollier, R. Pott).

One of the underlying theses for this was that the technological and algorithmic development is driven primarily by academia and less by industry which stands in contrast to other applications of DL such as image recognition, speech recognition and gaming, which are driven by industry giants like Google, Meta, or Microsoft. In a co-creation setup, the following challenges were addressed, and solutions discussed based on the use-cases of wind turbines and aircraft engines: i) dealing with the lack of labeled historical faults, ii) effective combination of domain knowledge for fault isolation, iii) upscaling the Fault Detection and Isolation (FDI) algorithms to multi-component systems, and iv) quantifying uncertainty in fault detection problems.

The SDS2023 workshop day convinced with the awesome atmosphere, highly committed workshop organizers and open-minded participants interested in exchange and cooperation! Thanks a lot for this inspiring day!

(SDS Orga-Team, Images © Simone Frischknecht, data innovation alliance)

by Jochen Wulf and Jürg Meierhofer – (Data-Driven Service Engineering Group at ZHAW)

At this year’s IEEE Swiss Conference on Data Science there was a very informative workshop on Generative AI in Practice. The presentations and discussions in this workshop made clear that the generative AI technologies, and Large Language Models (LLMs) in particular, are very versatile and powerful. It also became apparent, however, that the business potential of LLMs largely remains unclear.

OpenAI’s AI chatbot ChatGPT has already gained over 100 million users within the first two months of its release. This makes this internet service the fastest growing of its kind. In comparison, the runner-up, Tiktok, took a full nine months to reach a similar number of users. The potential of the underlying technology, LLMs, is undisputedly recognized by business leaders. According to a study by Gartner among business executives¹, 45% of respondents have already intensified their investment in artificial intelligence (AI) as a result of the success of ChatGPT.

¹ https://www.gartner.com/en/newsroom/press-releases/2023-05-03-gartner-poll-finds-45-percent-of-executives-say-chatgpt-has-prompted-an-increase-in-ai-investment

LLMs are extremely large-scale artificial neural networks that are trained with terabytes of textual content to complete texts. LLMs can therefore generate new content and thus belong to the class of generative AI solutions. In contrast, discriminatory AI models can only establish assignments or classifications between different inputs and predefined outputs.

LLMs can be used for a variety of purposes, such as text summaries, sentiment analysis, or named entity recognition. Although there are already initial indications of the productivity gains that can be achieved through the use of LLMs, the role of this technology for the business models of industrial companies is still largely unclear.

The Impact of LLMs on Business Models

In the following, we present the findings derived from own prototypes and a comprehensive analysis of more than 50 real-world use cases pertaining to the application of LLMs within various companies. A distinction can be made between four mechanisms of how business models are changed with LLMs:

• new customer benefits
• new sales and communication channels
• increased business process automation
• improved use of information resources

New Customer Benefits

LLMs play a crucial role in operating personal assistance systems. Instacart, a grocery delivery service, utilizes LLMs to address nutrition queries and offer personalized product recommendations.

Furthermore, LLMs serve as personal coaches, particularly valuable in the realm of learning. Khan Academy, an educational platform, employs LLMs to detect errors in programming tasks and generate helpful solution hints.

LLMs also possess the capability to independently generate content that is relevant to customers. Copy.ai, an online service, exemplifies this by creating blog posts, social media content, and website material based on bullet-style keywords and predefined language styles.

Additionally, LLMs facilitate voice-based interactions with machines. Mercedes, for instance, integrates LLMs into the infotainment systems of their premium vehicles to provide comprehensive answers to complex customer questions while driving.

New Sales and Communication Channels

LLM-based chatbots offer significant advantages in automating sales and customer service processes. In Switzerland, the insurer Helvetia has successfully implemented an LLM-based chatbot to handle inquiries regarding their product range.

Another notable example is Solana, a blockchain operator, leveraging ChatGPT in their customer service operations. By utilizing LLM-based chatbots, Solana effectively assists customers in resolving intricate service-related challenges, ensuring a seamless user experience.

Increased Business Process Automation

LLMs offer remarkable potential in enhancing automation within information-intensive business processes. The Radisson hotel chain has effectively employed LLMs to automate the handling of customer inquiries and cancellations, enabling swift and accurate responses. Additionally, LLMs generate helpful suggestions for emails and review responses, streamlining communication and enhancing customer satisfaction.

Another notable application is observed in Swiss Migros Bank, where LLMs play a pivotal role in partially automating mortgage application processing. By intelligently recognizing case-specific requirements and evaluating text-based customer documents, LLMs assist in expediting and improving the efficiency of the application review process.

Improved Use of Information Ressources

The fourth mechanism focuses on the enhanced exploitation of information resources. Morgan Stanley, a securities trading company, exemplifies this by leveraging LLMs to facilitate employee access to and evaluation of internal documents. Through the application of LLMs, Morgan Stanley streamlines the process of retrieving and analyzing crucial information, ensuring efficiency and informed decision-making within the organization.

Likewise, Zurich Insurance capitalizes on LLMs to automate contract evaluation and ascertain the validity of insurance claims. This strategic employment of LLMs empowers Zurich Insurance to effectively and efficiently assess the presence of a claim, ultimately leading to enhanced operational processes.

Current Challenges in the Commercial Application of LLMs

When evaluating the strategic importance and necessity for action concerning the commercial application of LLMs, companies are faced with three fundamental questions.

What are technical risks associated with the use of LLMs?

One significant challenge arises from the risk of LLMs generating false or inaccurate statements, commonly known as hallucinations. However, advancements in prompt engineering, which involve carefully formulating textual instructions, have already proven effective in mitigating this risk to a considerable extent. Additionally, the development of fact-checking methods is underway to ensure that the output generated by LLMs is rooted in accurate and verified information.

Another crucial technical concern revolves around the security of sensitive data shared during the prompting process. The potential exists for malicious actors to employ targeted prompts, referred to as Training Data Extraction Attacks, to extract training data from LLMs. Consequently, it is imperative to eliminate the possibility of shared data being utilized to train publicly accessible LLMs. Alternatively, dedicated LLMs can be utilized to safeguard the confidentiality of shared data.

What legal framework conditions need to be taken into account?

When utilizing LLMs, it is essential to adhere to relevant data protection regulations, particularly if personal data is being processed. This entails fulfilling information obligations and respecting individuals’ rights to information, similar to other AI applications.

Additionally, companies need to consider the evolving legal landscape, such as the European Union’s AI Act. The current draft of the AI Act specifies certain requirements for LLMs, including the prevention of generating illegal or discriminatory content and the disclosure of copyrighted content used during training. However, a comparison of different LLMs reveals that most models do not fully comply with these requirements, particularly regarding copyright compliance. Therefore, it is crucial for companies to carefully assess and ensure compliance relevant legal obligations when making long-term technology decisions involving LLMs.

In which areas should companies invest in LLMs?

Providers of standard software and Internet services are already investing heavily in LLMs. This includes areas such as sales management, customer service, marketing and knowledge management. Non-software companies will likely source such software rather than build it themselves.

More interesting for non-software companies are application areas in which LLMs have a direct impact on their value propositions or business-critical business processes. For example, robotics manufacturer Boston Dynamics uses LLMs to enable voice-based interaction between users and machines. Ivaldi, a distributed production specialist, uses LLMs to help maintenance teams troubleshoot. Rolls-Royce uses artificial intelligence to harness unstructured data and optimize supply chain management.

These illustrations highlight the substantial innovation potential of LLMs, extending beyond software companies to various other industries. Particularly noteworthy is the possibility for non-software companies to harness this potential by reimagining user interactions or unlocking significant optimization opportunities.

By Jürg Meierhofer, ZHAW

With the CAS Smart Service Engineering of ZHAW School of Engineering we had the chance to spend two days this week at the Mobiliar Forum Thun (many thanks Fabrizio Laneve) . Moderated by Ina Goller in very focused and agile way, we further developed and refined the mutual value creation in the ecosystems of our service innovation cases.

It turned out that ecosystem design is a) extremely important for value creation, b) not trivial, c) creates new resources by successfully integrating existing resources. Our ambition was to quantify the value stream in the ecosystems. It turned out that while even the quantification of economic value is very demanding, but often possible, the quantification of social and emotional value streams is a widely open field which requires further research. We are looking forward to this challengin yet extremely rewarding work.

CAS Smart Service Engineering: https://www.zhaw.ch/de/engineering/weiterbildung/detail/kurs/cas-smart-service-engineering-data-product-design/

Mobiliar Forum Thun: https://www.mobiliar.ch/die-mobiliar/nachhaltigkeit-engagement/das-gesellschaftsengagement-der-mobiliar/unternehmen-und-arbeit/innovationsplattform-mobiliar-forum-mit-innovation-fuer-die-zukunft

The practical conference on digital ecosystems in Winterthur just before this workshop (http://www.zhaw.ch/i40konferenz)  was the ideal kick off to this workshop, in which we all became co-creative ecosystem designers ourselves.

By Manuela Hürlimann, ZHAW and Thomas Zaugg, Roche

On May 10th, 2023, the “Natural Language Processing in Action” Expert Group of the data innovation alliance and SwissNLP organised a meeting in Zurich with three exciting presentations on speech processing.

Oscar Koller, a principal applied scientist at Microsoft, presented on the use of end-to-end neural systems for automatic speech recognition in Swiss German. He discussed how the current industry paradigm of hybrid ASR is being replaced with end-to-end models, such as those that have been winning recent benchmarks. Oscar shared the results of his team’s comparison of different neural network architectures, and highlighted the advantages of using transducers for improved real-time performance in their work.

Claudio Paonessa, a researcher at FHNW, discussed how recent advances in speech-to-text, text-to-speech, and translation for Swiss German can be combined with a large language model to create a voice-based conversational assistant. He shared a demo of the model in action, showcasing its ability to give apt replies. However, he also acknowledged that processing time still needs to be reduced to give a real-time feeling, and suggested reducing model size as one possible solution.

Dr. Edith Birrer, a senior researcher at iHomeLab, HSLU, presented results from her team’s work on using speech processing in the context of home care. Together with international project partners, they ran interviews and workshops to identify potential use cases for home care workers. While they had originally planned to focus on care documentation, their results showed that most care workers found supporting services – such as a to-do list that can be ticked off verbally – to be more useful. They implemented three use cases and tested them in a lab with carers, showing a high level of enthusiasm among users, but emphasizing the need to address data privacy concerns before such technologies can become widely used.

After the presentations, attendees enjoyed an apéro and continued discussing the topics at hand.