AI Profiles: an Interview with Kristian Kersting

AI MATTERS, VOLUME 4, ISSUE 3 4(3) 2018

AI Proﬁles: An Interview with Kristian Kersting Marion Neumann (Washington University in St. Louis; [email protected]) DOI: 10.1145/3284751.3284758

Introduction on the PC (often at senior level) for several top conference and co-chaired the PC of ECML This column is the sixth in our series proﬁling PKDD 2013 and UAI 2017. He is the Special- senior AI researchers. This month we inter- ity Editor in Chief for Machine Learning and AI view Kristian Kersting, Professor in Computer of Frontiers in Big Data, and is/was an action Science and Deputy Director of the Centre for editor of TPAMI, JAIR, AIJ, DAMI, and MLJ. Cognitive Science at the Technical University of Darmstadt, Germany. Getting to Know Kristian Kersting When and how did you become interested in AI? As a student, I was attending an AI course of Bernhard Nebel at the University of Freiburg. This was the ﬁrst time I dived deep into AI. However, my interest in AI was probably trig- gered earlier. Around the age of 16, I think, I was reading about AI in some popular science magazines. I did not get all the details, but I was fascinated.

What professional achievement are you most proud of? Figure 1: Kristian Kersting We were collaborating with biologists on un- derstanding better how plants react to (a)biotic Biography stress using machine learning to analyze hy- perspectral images. We got quite encourag- After receiving his Ph.D. from the University of ing results. The first submission to a jour- Freiburg in 2006, he was with the MIT, Fraun- nal, however, got rejected. As you can imag- hofer IAIS, the University of Bonn, and the TU ine, I was disappointed. One of the biologists Dortmund University. His main research in- from our team looked at me and said ”Kristian, terests are statistical relational artificial intel- do not worry, your research helped us a lot.” ligence (AI), probabilistic deep programming, This made me proud. But also the joint work and machine learning. Kristian has published with Martin Mladenov on compressing linear over 170 peer-reviewed technical papers and and quadratic programs using fractional auto- co-authored a book on statistical relational AI. morpishms. This provides optimization flags He received the European Association for Arti- for ML and AI compilers. Turning them on ficial Intelligence (EurAI, formerly ECCAI) Dis- makes the compilers attempt to reduce the sertation Award 2006 for the best AI disser- solver costs, making ML and AI automatically tation in Europe and two best-paper awards faster. (ECML 2006, AIIDE 2015). He gave several tutorials at top AI conferences, co-chaired several international workshops, and cofounded What would you have chosen as your the international workshop series on Statisti- career if you hadn’t gone into CS? cal Relational AI (StarAI). He regularly serves Physics, I guess, but back then I did not see Copyright c 2018 by the author(s). any other option than Computer Science.

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What do you wish you had known as a What is the most interesting project you Ph.D. student or early researcher? are currently involved with? That “sleep is for post-docs,” as Michael Deep learning has made striking advances Littman once said. in enabling computers to perform tasks like recognizing faces or objects, but it does not show the general, flexible intelligence that lets Artificial Intelligence = Machine Learning. people solve problems without being specially What’s wrong with this equation? trained to do so. Thus, it is time to boost its Machine Learning (ML) and Artificial Intelli- IQ. Currently, we are working on deep learn- gence (AI) are indeed similar, but not quite the ing approaches based on sum-product net- same. AI is about problem solving, reason- works and other arithmetic circuits that ex- ing, and learning in general. To keep it sim- plicitly quantify uncertainty. Together with ple, if you can write a very clever program that colleagues—also from the Centre of Cognitive shows intelligent behavior, it can be AI. But un- Science—we combining the resulting proba- less the program is automatically learned from bilistic deep learning with probabilistic (logical) data, it is not ML. The easiest way to think of programming languages. If successful, this their relationship is to visualize them as con- would be a big step forward in programming centric circles with AI first and ML sitting inside languages, machine learning and AI. (with deep learning fitting inside both), since ML also requires to write programs, namely, AI is grown up - it’s time to make use of it of the learning process. The crucial point is for good. Which real-world problem would that they share the idea of using computation you like to see solved by AI in the future? as the language for intelligent behavior. Due to climate change, population growth and food security concerns the world has to seek As you experienced AI research and more innovative approaches to protecting and education in the US and in Europe, what improving crop yield. AI should play a major are the biggest differences between the role here. Next to feeding a hungry world, two systems and what can we learn from AI should aim to help eradicate disease and each other? poverty. If you present a new idea, US people will usu- We currently observe many promising and ally respond with “Sounds great, let’s do it!”, exciting advances in using AI in while the typical German reply is “This won’t education, going beyond automating work because ...”. Here, AI is no exception. Piazza answering, how should we make It is much more critically received in Germany use of AI to teach AI? than in the US. However, this also provides research opportunities such as transparent, fair AI can be seen as an expanding and evolv- and explainable AI. Generally, over the past ing network of ideas, scholars, papers, codes three decades, academia and industry have and showcases. Can machines read this been converging philosophically and physi- data? We should establish the “AI Genome”, cally much more in the US than in Germany. a dataset, a knowledge base, an ongoing ef- This facilitate the transfer of AI knowledge via fort to learn and reason about AI problems, well-trained, constantly learning AI experts, concepts, algorithms, and experiments. This who can then continuously create new ideas would not only help to curate and personal- within the company/university and keep pace ize the learning experience but also to meet with the AI development. To foster AI research the challenges of reproducible AI research. It and education, the department structure and would make AI truly accessible for all. tenure-track system common in the US is ben- eficial. On the other hand, Germany is offering What is your favorite AI-related movie or access to free higher education to all students, book and why? regardless of their origin. AI has no borders. We have to take it from the ivory towers and “Ex Machina” because the Turing test is shap- make it accessible for all. ing its plot. It makes me think about current

20 AI MATTERS, VOLUME 4, ISSUE 3 4(3) 2018 real-life systems that give the impression that they pass the test. However, I think AI is hard than many people think.

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