The Heidelberg Laureate Forum has a single purpose: To provide some of the brightest minds in mathematics and computer science with the space and time to make connections and find inspiration. The HLFF Spotlight series shines a light on some of the brilliant individuals attending the event.

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Albrecht Schmidt is the Scientific Co-Chair (Computer Science) of the Heidelberg Laureate Forum Foundation. He is also a Professor of Computer Science at LMU Münich, where he is the chair of the group on “Human-Centered Ubiquitous Media.” He is a member of the German Academy of Sciences (Leopoldina), a fellow of the Association for Computing Machinery (ACM), and a Member of the Committee for the ACM Prize in Computing, one of the prizes celebrated at the HLF.

 

As the new Scientific Chair for computer science, what do you see as your role in shaping the Forum's program and spirit – and what would you like to bring to it that's distinctly yours?

My first step is to understand what creates value for our participants. What makes laureates want to come? Why are students and researchers excited to join? I want to make sure that we have a strong scientific core that is exciting and sparks debate, insights, and new connections. I also want to foster a spirit of deep and honest discussion.

What I would like to bring that is distinctly mine is an optimistic perspective on the current transformation and a desire to bring together people who want to create a better future.

Your research asks how digital technology can extend human cognitive and perceptual abilities. Where do you think that question is headed most urgently in the age of generative AI?

To me, generative AI is an amazing tool. It has transformed how I work and how I do research. It is changing our practices and also how our team teaches and conducts research. At the same time, I see current generative AI as just a glimpse into a future of cognitive and perceptual augmentation. It feels a little like the World Wide Web in 1993, when I was an undergraduate: You could already see the enormous potential, but you could also see that it was not really there yet.

For generative AI to become truly useful for individuals and society, I think the key challenge is to make it personal and personally meaningful. I do not want augmented ideas that are generic and that I share with one billion other people. I want generative AI that supports my cognition and my perception in an individual way. I want a system that shares my experiences, that has seen and heard what I have seen and heard, and that can create responses that are personal and fitting for me and my context.

And I want this to be under my individual control, as a personal system, rather than something run and potentially manipulated by a company or an authority. This is also at the core of our AI-Twin project, which we are currently working on.

What first drew you to computer science, and specifically to human-computer interaction, as a field?

I like puzzles. Programming in BASIC on a Commodore C116 felt like an infinite puzzle in which, at the same time, I could set the puzzle and solve it myself.

Then came my fascination with automation. At school, a teacher brought Fischertechnik sets and we built simple automations for them in BASIC and Assembly. Beyond robotics, I was fascinated by the automation of cognitive tasks: Once you know how to calculate something, you can write a program and have it repeat the process again and again.

During my studies in Manchester, I became excited about neural computing and did my master's thesis on neural networks, which was not a particularly popular topic in 1995. At the same time, the World Wide Web was just exploding, and I moved more towards distributed systems for a PhD position.

At some point, I realized that the power of what you can do with computers was inaccessible to most people (and probably still is on a global scale). I started asking myself: What would the world be like if everyone could do with computers what I could do after six years of studying computer science? At that point, much of my research shifted towards human-computer interaction, and today towards cognitive augmentation.

You've held professorships across multiple different German universities before LMU – how has moving between those research cultures shaped how you think about the field?

I enjoy working with interesting people: people I can learn from and people who open up new perspectives for me. I also like trying out new things.

Working in different places and moving between universities was never really the plan. It was more a side effect of looking for places where I had good resources to pursue the research I was passionate about and environments that allowed me to explore new directions.

Many of the people I worked with at different places, mainly in Germany and the UK, have become good friends as well as great scientific colleagues. The students were also very different at the different institutions. Interestingly, moving away from a place often led to collaborations that continued for many years.

For me, this has widened my perspective on what relevant research questions are and what good research practices look like. It has also reinforced my view that collaboration, both within an institution and across institutions, is essential for meaningful innovation in my field.

In practical computer science, including human-computer interaction and artificial intelligence, innovation is a team effort. Having an environment with curious students, colleagues with different perspectives, and connections to applications is key to achieving breakthroughs.

My decisions to move to a new place, even at later stages of my career, have always been closely linked to the people I would join, what I could bring to the new place, and what I could learn there.

What conversation or connection are you most hoping for at this year's HLF?

I am curious about how research and teaching are changing at this point in time. What will remain from our established practices, and where will we see fundamental change?

I am particularly interested in how individuals are changing their practices in response to new technological opportunities, including easy remote collaboration and advanced AI models.

I am also interested in why people think we should do research in computer science and mathematics: What motivates them, and why do they pursue this direction? I am looking forward to learning how teaching, learning, and research are conducted in different places around the world, what the challenges are, and what people hope for.

From my experience at previous HLFs, I am especially excited to connect with people across disciplines, backgrounds, and places around the world. I want to learn what drives people and understand their hopes and worries for our shared world, well beyond the scientific questions.

What's one piece of advice you would give young researchers about choosing a research question worth spending a career on?

In practical computer science, research is inherently linked to technological advances. AI research has changed with the availability of computing and memory, and human-computer interaction has changed with the interfaces and computational capabilities available to us.

For me, one of the “tricks” is therefore not to choose one specific research question for an entire career, but to have a research theme or vision that is high-level enough to remain relevant over a long period of time. The concrete research questions can then emerge from that broader theme in the context of the technologies and the environment available at a particular point in time.

For me, the vision of enhancing human cognition and perception with digital technologies has worked for many years, despite massively changing technologies and very different research environments. And I am still excited about it. At the same time, this research theme goes back to ideas pioneered since the 1960s by people such as Vannevar Bush, J. C. R. Licklider, and Douglas Engelbart. It has served me well as a way of finding interesting questions and putting them into a larger context.

It is also essential for me to personally see the value of the research. I ask myself: Is it worthwhile to spend my time on this question? Will it have an impact beyond providing me with an interesting intellectual challenge?

There is one more aspect: In computer science, we usually move from working on a specific research question ourselves, for example during a master's thesis or PhD, to eventually leading a team. For me, a good research question should therefore also train the minds and develop the abilities of the people working on it. If tackling a research question does not advance our skills and abilities, I would not pursue it.

Given the current dynamics in many areas of computer science, it is also perfectly reasonable to take on questions that are interesting and challenging now and that have clear value if solved. As long as we gain new insights and develop our abilities along the way, this can be a very productive approach. But it requires being open and willing to move fluidly towards new opportunities.

What part of the program of the upcoming 13th HLF are you most excited for and why?

All of it! Looking at the many lectures, Spark Talks, and panels, I am really excited. But I would highlight two things:

The panel on AI and mathematics is a clear highlight for me. Almost every day, we see headlines about successful uses of AI to address longstanding questions. We have amazing experts on the panel who will discuss these developments with us and share their insights, expectations, and perspectives on where this may lead.

The other highlight is Friday, when the participants themselves will help decide the program. With the Un-Conference, we have a format that directly picks up the ideas and topics that participants consider particularly important. I am really curious to see what we will end up discussing there.