Contact person: Sebastian Feger (sebastian.feger@um.ifi.lmu.de)

Internal Partners:

  1. LMU, Sebastian Feger, sebastian.feger@um.ifi.lmu.de
  2. GE, Jan Alpmann, alpmann@german-entrepreneurship.de

External Partners:

  1. European Laboratory for Learning and Intelligent Systems (ELLIS)
  2. ETH Zurich AI center

 

Connecting the right people with the right expertise is essential for innovation, but finding suitable collaborators can be challenging. In this micro project, we explored a novel approach that leverages publicly available performance data (e.g., Github profiles) to automatically match people and expertise for innovation. Building on such data, we developed a recommender system that suggests potential collaborators based on their demonstrated expertise.

The project builds on the previous two micro projects, which identified a set of functional questions to match people for innovation and implemented a prototype for creating user profiles based on those questions. In this third micro project, we developed a demonstrator that creates recommendations for collaboration based on the automated collection and analysis of GitHub data. In contrast to the previous micro projects, the approach focuses on demonstrated expertise, rather than relying on self-reported knowledge, which can be inaccurate or incomplete.

Results Summary

To develop the demonstrator, we used the GitHub API to retrieve information about users’ activities and built a recommender system that suggests potential collaborators based on their GitHub statistics. We also created a user interface for founders to find potential co-founders based on the expertise shown. This system offers a reliable and objective way of matching people with the right expertise, which can be especially valuable in high-tech environments where expertise is critical for success.

We evaluated the effectiveness and usability of the developed recommender system and user interface through a series of interviews and online studies. First, we interviewed domain experts to establish the requirements and important features of the model. Second, we interviewed potential founders to evaluate the general approach and gather feedback on the recommender system. Finally, we conducted an online study to evaluate the usability of the user interface. The study involved a group of participants using the interface to find potential collaborators and provide feedback on their experience. Overall, the evaluations provided valuable insights and feedback for improving the system’s effectiveness and usability. We plan to incorporate the feedback from the evaluations to further improve the system and enhance its ability to match people and expertise for innovation in the future. The user-centered design process benefited from close collaboration and interaction with experts working at external partners ELLIS and the ETH Zurich AI Center. The long-term goal is to help connect experts and entrepreneurs across physical boundaries, creating a vibrant and agile high-tech environment on a European scale.

Tangible Outcomes

  1. Functional prototype with real-world data from github: http://143.42.16.26:3000/ 

Contact person: Wolfgang Köhler (koehler@fortiss.org)

Internal Partners:

  1. fortiss GmbH, Dr. Wolfgang Köhler
  2. German Entrepreneurship GmbH, Andreas Keilhacker

 

SMEs are especially underrepresented among AI adopters in Europe, and there exists little to no formal exploration into understanding the challenges with AI adoption from an SME perspective.

Therefore, it is important to learn from existing and successful support structures and formats to replicate and adapt successful mechanisms into different types of organizations and businesses. The aim is to facilitate the adoption of AI and to increase the innovative strength, especially within the SME sector.

In this microproject we focus on the identification of existing support structures and formats for innovation and assess if they are suitable for an implementation in the respective organizations’ innovation strategy.To achieve the intended objective of the microproject, intensive research, and interviews with experts will be conducted. The outcome of the project is a white paper containing applicable mechanisms and formats for AI-innovation as well as an initial interview workshop with experts to test the applicability.​

Results Summary

The available/ published offerings for AI implementation strongly vary in quality and applicability.​

The chosen support offerings lower the obstacles to getting started with a successful AI implementation. All it takes is a little bit of courage and willingness to change to seize the opportunities that AI offers.

The outcome of the project is a white paper containing applicable mechanisms and formats for AI-innovation as well as an initial interview workshop with experts to test the applicability.​

Based on the results, for example workshops can be held in the future to apply and further develop the identified mechanisms to the specific challenges of organizations in practice. ​

 

Tangible Outcomes

  1. Report of applicable mechanisms and formats for AI-innovation Report of the initial workshop:  https://www.humane-ai.eu/wp-content/uploads/2023/10/Report_WP7_D.2_Revised.pdf
  2. White Paper – Methods for AI implementation: Download PDF

Contact person: Jan Alpmann (alpmann@german-entrepreneurship.de

Internal Partners:

  1. German Entrepreneurship GmbH, Jan Alpmann
  2. Ludwig-Maximilians-Universität München (LMU), Albrecht Schmidt
  3. Volkswagen AG, Gülce Cesur  

 

A central challenge is to bring the people together to create innovations. Traditionally, this works only with a high density of experts, entrepreneurs, and customers, e.g. silicon valley. In distributed settings on a European scale, this does not work, despite the existence of matching platforms.

We take a different approach that is inspired by matching people in online dating. Individuals and companies often don’t know what they can offer or what they need. Hence, we suggest a more holistic approach. Based on questions asking about skills, interest, values, approaches, existing collaborations and digital artifacts (code, images, algorithms) we envision an intelligent matching platform. Through a workshop, we understand the right questions, what artifacts are telling, what good indicators for potential collaborations are, identifying AI techniques for making the matches and for identifying the system architecture of the platform.

Results Summary

The main results of the micro project include a detailed questionnaire based on qualitative interviews with different matching experts. The questionnaire is tailored to different user groups and represents a key building block for the development of future matching platforms. In addition, we conducted a live matching event entitled “AI Idea Prize: Show, Pitch and Match“ that brought together students in the field of AI and business experts. This event further informed our concept of a future matching platform and helped to finalize the questionnaire.

Tangible Outcomes

  1. Video presentation summarizing the project

Contact person: Florian Müller , LMU ( florian.mueller@tu-darmstadt.de

Internal Partners:

  1. Ludwig-Maximilians-Universität München (LMU), Florian Müller  

External Partners:

  1. Ecole Nationale de l’Aviation Civile (ENAC), Anke Brock  

 

Pilots frequently need to react to unforeseen in-flight events. Taking adequate decisions in such situations requires considering all available information and demands strong situational awareness. Modern on-board computers and technologies like GPS radically improved the pilots’ abilities to take appropriate actions and lowered their required workload in recent years. Yet, current technologies used in aviation cockpits generally still fail to adequately map and represent 3D airspace. In response, we aim to create an AI aviation assistant that considers all relevant aircraft operation data, focuses on providing tangible action recommendations, and on visualizing them for efficient and effective interpretation in 3D space. In particular, we note that extended reality (XR) applications provide an opportunity to augment pilots’ perception through live 3D visualizations of key flight information, including airspace structure, traffic information, airport highlighting, and traffic patterns. While XR applications have been tested in aviation in the past, applications are mostly limited to military aviation and latest commercial aircrafts. This ignores the majority of pilots in general aviation, in particular, where such support could drastically increase situational awareness and lower the workload of pilots. General aviation is characterized as the non-commercial branch of aviation, often relating to single-engine and single-pilot operations. To develop applications usable across aviation domains, we planned to create a Unity project for XR glasses. Based on this, we planned to, in the first step, systematically and iteratively explore suitable AI-based support on pilot feedback in a virtual reality study in a flight simulator. Based on our findings, we refine the Unity application and investigate opportunities to conduct a real test flight with our external partner ENAC, the French National School of Civil Aviation, who own a plane. Such a test flight would most likely use latest Augmented Reality headsets like the HoloLense 2. Considering the immense safety requirements for such a real test flight, this part of the project is considered optional at this stage and depends on the findings from the previous virtual reality evaluation. The system development particularly focuses on the use of XR techniques to create more effective AI-supported traffic advisories and visualizations. With this, we want to advance the coordination and collaboration of AI with human partners, establishing a common ground as a basis for multimodal interaction with AI (WP3 motivated). Further, the MP relates closely to “Innovation projects (WP6and7 motivated)”, calling for solutions that address “real-world challenges and opportunities in various domains such as (…)transportation […]”.

Results Summary

We explored AI and Mixed Reality for pilot support. One of the results includes an early mixed reality prototype for a popular consumer-grade flight simulator that allows to intuitively perceive actual 3D information that current 2D tools cannot present satisfactorily. Based on this mockup, we conducted a very early exploration into AI support strategies that would allow, for example, to convert air traffic control instructions to flight path renderings.