Over the past years, AI research of AI•DA members has consistently been in the national and international spotlight. In the past few years, many AI•DA members have been recognized for their excellence in research.

Scientific Awards. AI•DA members have earned numerous best paper awards of different kinds at top AI-related venues, e.g., at AIDE, CVPR, ECML, GCPR, ICCV, IEEE Big Data, VLDB, and Artificial Intelligence Journal. They also received numerous oustanding reviewer awards, e.g., at AAAI, NIPS, CVPR, ICCV, ECCV. Other grants and awards hold by AI•DA members: ERC grant, Lichtenberg Professorship award, Fraunhofer ATTRACT Fellowship, DFG Emmy Noether award as well as the European Association for Artifical Intelligence (EurAI, formally known as ECCAI) Dissertation Award for the best AI PhD thesis in Europe.

Editorial work. AI•DA members are editors-in-chief of the Data Mining and Knowledge Discover (DAMI) Journal and the Machine Learnng and AI section at Frontiers in Big Data. They are editorial members, often action and associate editors, of many of the leading AI(-related) journals such the Journal of Artificial Intelligence Research (JAIR), the AI Journal (AIJ), the Machine Learning Journal (MLJ), the Journal of Machine Learning Research (JMLR), IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) and the Data Mining and Knowledge Discovery Journal (DAMI).

Conferences. AI•DA members regularly serve on the scientific programme committee (PC), often at senior level, for several top AI(-realted) conference such as IJCAI, AAAI, NIPS, ICML, ICLR, KDD, ECML PKDD, ACL, ICCV, ECCV, CVPR, RSS, ICRA. They co-chaired the scientific programme committee of several of top AI(-related) venues such as the International Machine Learning Conference (ICML) 2010, the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) 2006 and 2013, the International Conference on Discovery Science (DS) 2013, the Annual German Conference on AI (KI) 2017, the International Conference on Uncertainty in AI (UAI) 2017, the Bianual Conference of the German Society for Corgnitive Science (KogWis) 2018, and the Annual Meeting of the Association for Computational Linguistic (ACL) 2018. They also co-organized the workshop tracks of the first Federate AI Conference (IJCAI/ECAI/ICML) 2018 as well as the European Conference on Computer Vision (ECCV) 2018, numerous international workshops, and Dagstuhl seminars on AI(-related) topics and provided several tutorials at top international AI(-related) venues.

Projects. AI•DA members have been involved in major projects of the European Commission (EU), the German Science Foundation (DFG), the Federal Ministry of Education and Research (BMBF), the Federal Office for Agriculture and Food (BLE), the German-Israeli Foundation for Scientific Research and Development (GIF) and many more such as a Google Faculty Research.

Teaching. And AI•DA members are regularly teaching classes on AI(-related) topics such as "Introduction to AI", "Statistical Relational AI", "Statistical Machine Learning", "Computer Vision", "Natural Language Processing", "Deep Learning: Architectures and Methods", "Deep Learning for NLP", "Deep Learning in Computer Vison", "Probabilistic Graphical Models", "Robot Learning", "Cognitive Modelling", "Introduction to Psychology", and "Data Management"

The website CSRankings.org publishes its rankings of Computer Science Schools based on the number of publications by faculty that have appeared in the most selective conferences in each area of Computer Science. The rankings are designed to identify institutions and faculty actively engaged in research across a number of areas of computer science. The AI•DA members were ranked #1 in Germany, #2 in Europe and among the top 25 institutions world-wide in the combined areas of Artificial Intelligence, Computer Vision, Machine Learning, Natural Language Processing, and Robotics.

The image is a screenshot of CSRankings.org taken on August 24th, 2018, using the combined areas of Artificial Intelligence, Computer Vision, Machine Learning, Natural Language Processing, and Robotics.

The image is a screenshot of CSRankings.org taken on July 25th, 2018, using the combined areas of Artificial Intelligence, Computer Vision, Machine Learning, Natural Language Processing, and Robotics.

Machine learning experts from around the world will gather again at the 36th International Conference on Machine Learning (ICML) to present the latest advances in machine learning understanding. The International Conference on Machine Learning is one of the most prestigious conferences for peer-reviewed research in Machine Learning, alongside NeurIPS, ICLR and others. And ICML is of the most relevant to Deep Learning (DL), although NeurIPS has a longer DL tradition and ICLR, being more focused, has a higher DL density.

Despite the strong industrial interest and massive contributions from companies like Google, Microsoft or Facebook, the 2019 International Conference on Machine Learning remains an academic conference. Summing up the relative contribution of academia and industry for all papers (i.e. number of industrial/academic affiliations divided by number of total affiliations per paper) [1], 77% of the contribution are from academia such as the TU Darmstadt. Researchers from the TU Darmstadt have co-authored six papers at ICML 2019, and the research will be presented in oral paper and poster sessions. The researchers from the TU Darmstadt are also organizing and participating in workshops throughout the conference. The low acceptance rate of 23% allows to keep highest quality of all accepted and peer-reviewed papers.

These numbers show that the TU Darmstadt succeeds in its mission of being a leading AI university not only in Europe but also in the world. Actually, the TU Darmstadt is the only University from Germany among the Top 50 contributing academic institutions at ICML 2019 (https://i.redd.it/37hxhsmfzix21.png"):

The image is due to [1] and shows the top contributing acadmic institutions of ICML 2019.

[1] Andreas Doerr, Research Scientist at the Bosch Center for Artificial Intelligence (BCAI), , https://www.reddit.com/r/MachineLearning/comments/bn82zen_icml_2019_accepted_paper_stats/ accessed May 22nd, 2019

According to an article in the 8/2017 issue of the Manager Magazin, AI•DA is part of the Brain Valley, an AI hotspot of research, industry and startups in the southwest of Germany:

The image is a link from the full article, which can be found in the 8/2017 issue of the Manage Magazin (retrieved on August 4th, 2018).

Big Data is no fad. The world is growing at an exponential rate, and so is the size of data collected across the globe. Data is becoming more meaningful and contextually relevant, breaks new ground for machine learning (ML), in particular for deep learning (DL), and artificial intelligence (AI), and even moves them from research labs to production [1]. The problem has shifted from collecting massive amounts of data to understanding it - turning it into knowledge, conclusions, and actions. Multiple research disciplines, from cognitive sciences to biology, finance, physics, and the social sciences, as well as many companies believe that data-driven and "intelligent" solutions are necessary in order to solve many of their key problems. However, are AI, ML and DL really the same things, as suggested by many recent news, blogs and media? For example, when AlphaGo [2] defeated South Korean Master Lee Se-dol in the board game Go in 2016, the terms AI, ML, and DL were used in the media to describe how AlphaGo won. AI and ML are very much related.

According to, e.g., Stanford's John McCarthy [3], one of the founders of the field, AI is "the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable." This is fairly generic and includes all kinds of tasks such as abstractly reasoning and generalizing about the world, solving puzzles, planning how to achieve goals, moving around in the world, recognizing objects and sounds, speaking, translating, performing social or business transactions, creative work (e.g. making art or poetry), and controlling robots. And, the behavior of a machine is not just the outcome of the program, it is also affected by its "body" and the enviroment it is physically embedded in. To keep it simple, however, if you can write a very clever program that has, say, human-like behavior, it can be AI. But unless it is automatically learned from data, it is not ML: ML is the science that is "concerned with the question of how to construct computer programs that automatically improve with experience", following, e.g., CMU's Tom Mitchell [4].

So, AI and ML are both about constructing intelligent computer programs, and deep learning, being an instance of machine learning, is no exception. Deep learning [5, 6], which has achieved remarkable gains in many domains spanning object recognition, speech recognition, and control, can be viewed as constructing computer programs, namely programming layers of abstraction in a differentiable way using reusable structures such as convolution, pooling, auto encoders, variational inference networks, and so on. That is, we replace the complexity of writing algorithms that cover every eventuality with the complexity of finding the right general outline of the algorithms - in the form of e.g. a deep neural network - and processing data. By virtue of the generality of neural networks - they are general function approximators - training them is data hungry and coming up with the right architecture and data is notoriously difficult. Benchmark training sets for object recognition store hundreds or thousands of examples per class. Learning to play video games may require hundreds of hours of training experience and/or very expensive compute power. In contrast, writing an algorithm that covers every eventuality of a task we want to solve - say, computing the shortest way to get to an airport - is a lot of manual work but we know what the algorithm does by design and we can study and understand more easily the complexity of the problem it solves. Especially when a machine has to interact with a human, this seems to be valuable.

This illustrates that ML and AI are indeed very similar, but not quite the same. The easiest way to think of their relationship is to visualize them as concentric circles with AI first and ML sitting inside (with DL fitting inside both), since ML also requires to write algorithms that cover every eventuality, namely, of the learning process. The crucial point is that they share the idea of using computation as the language for intelligent behaviour. What kind of computation and how to program it? That remains an open question. Computation neither rules out search, logical, probabilistic and constraint programming techniques nor (deep) (un)supervised and reinforcement learning methods, among others, as computational models do contain all of them. Reconsider AlphaGo. AlphaGo and its successor AlphaGo Zero [7] both combine deep learning and tree search - ML and AI. Or consider the "Allen AI Science Challenge" [8]. The task is to comprehend a paragraph that states a science problem at the middle school level and then answer a multiple-choice question. The winning models all employed ML but did not pass yet the test at the level of a competent middle schooler. All winners argued it was clear that applying a deeper, semantic level of reasoning with scientific knowledge to the question and answers, i.e., AI would be the key to achieving true intelligence. That is, we have to cover knowledge, reasoning, and learning, whether just programmed or learning-based programmed.

Moreover, along the lines of Berkeley's Michael Jordan [9], while the building block have begun to emerge, the principles for putting these blocks together have not yet emerged. However, statistical relational learning [10], probabilistic programming [11], and learning-based programming [12] may provide good starting points for developing Systems AI - the computational and mathematical modeling of complex AI systems - and in turn an engineering discpline for AI and ML.

[1] Jordan, M. I. and Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science 349, 255–260
[2] Silver, D., Huang, A., Maddison, C., Guez, A., Sifre, L., van den Driessche, G., et al. (2016). Mastering the game of Go with deep neural networks and tree search. Nature 529, 484–489
[3] McCarthy, J. (2007). What is artificial intelligence? Tech. rep., Stanford University, http://jmc.stanford.edu/artificial-intelligence/what-is-ai/index.html. Accessed June 2, 2018
[4] Mitchell, T. M. (1997). Machine learning. McGraw Hill series in computer science (McGraw-Hill)
[5] LeCun, Y., Bengio, Y., and Hinton, G. (2015). Deep learning. Nature 521, 436–444
[6] Goodfellow, I. J., Bengio, Y., and Courville, A. C. (2016). Deep Learning. Adaptive Computation and Machine Learning (MIT Press)
[7] Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., et al. (2017). Mastering the game of Go without human knowledge. Nature 550, 354–359
[8] Schoenick, C., Clark, P., Tafjord, O., Turney, P. D., and Etzioni, O. (2017). Moving beyond the Turing Test with the Allen AI Science Challenge. Commun. ACM 60, 60–64
[9] Jordan, M. (2018). Artificial Intelligence — The Revolution Hasn’t Happened Yet. https://medium.com/@mijordan3/artificial-intelligence-the-revolution-hasnt-happened-yet-5e1d5812e1e7, Accessed July 8, 2018.
[10] De Raedt, L., Kersting, K., Natarajan, S., Poole, D. (2016). Statistical Relational Artificial Intelligence: Logic, Probability, and Computation. Synthesis Lectures on Artificial Intelligence and Machine Learning, Morgan & Claypool Publishers, ISBN: 9781627058414.
[11] Pfeffer, A. (2016). Practical Probabilistic Programming. Manning Publications Co. ISBN: 9781617292339
[12] Kordjamshidi, P., Roth, D., Kersting, K. (2018). Systems AI: A Declarative Learning Based Programming Perspective. In Proceedings of the 27th International Joint Conference on Artificial Intelligence and the 23rd European Conference on Artificial Intelligence (IJCAI-ECAI).

The Federal Government of Germany aims to boost the research in the field of Artificial Intelligence (AI). For instance, 100 new professorships are said to be established. However, the white paper of the government does not answer what an AI professorship is at all. In order to give colleagues, politicians, and citizens an idea, we present a view (in German only) that is often followed when appointing professors for AI at German and international universities. We hope that it will help to establish a guideline with internationally accepted measures and thus make the public debate more informed.

Die deutsche Bundesregierung will die Forschung in der Künstlichen Intelligenz in Deutschland deutlich mehr fördern als bisher. Es sollen z.B. 100 neue Professuren entstehen. Allerdings beantwortet das Strategiepapier nicht, was eine Professur für Künstliche Intelligenz überhaupt ist. Um Kollegen, Politikern und Bürgern eine Idee zu geben, stellen wir eine Einordnung vor, wie sie in Berufungsverfahren an deutschen und internationalen Universitäten üblich ist. Wir hoffen, dass eine solche Einordnung einen Beitrag zu einem Leitfaden mit Messpunkten liefert und so die öffentliche Debatte aufgeklärter gestaltet.

Was ist eine KI Professur? (Feb. 2019, PDF)

Was ist eine KI Professur? (Feb. 2019, Fachbeitrag bei der Gesellschaft für Informatik e.V. (GI))

Journals. The top journals for core AI research are the AI Journal (AIJ) and the Journal of Artificial Intelligence Research (JAIR). There are also specialized journals that focus on specific areas of AI research. For machine learning, e.g., the top journals are the Journal of Machine Learning Research (JMLR), the IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), the Machine Learning Journal (MLJ), and the Data Mining and Knowledge Discovery Journal (DAMI), among others. All of them have acceptance rates below 30%.

Conferences. The main conferences for core AI research are the International Joint Conference on AI (IJCAI), the Conference for the Advancement of AI (AAAI), the European Conference on AI (ECAI). Again there are conferences focussing on specific AI topis. For machine learning, they are, e.g., the Annual Conference on Neural Information Processing Systems (NIPS), the International Machine Learning Conference (ICML), the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), the ACM conference on Knowledge Discovery and Data Mining (KDD), the SIAM Conference on Data Mining (SDM), and the IEEE International Conference on Data Mining (ICDM). These conferences are also very selective - in general less than 30% of the articles submitted are accepted - and their proceedings play a role which is as important as international journals.