Within Hessian.AI, we are aiming at a new generation of AI, which we call Reasonable Artificial Intelligence (RAI). This new form of AI will learn in more reasonable ways, with models being trained in a decentralized fashion, continuously improving over time, building an abstract knowledge of the world, and with an innate ability to reason, interact, and adapt to their surroundings. This idea of Reasonable Artificial Intelligence has been proposed as a Cluster of Excellence (EXC 3057).
RAI is structured in four research labs, each tackling an important aspect of RAI: (1) The research lab „Systemic AI“ works on software and systems methods that enable modular, collaborative, decentralized, interactive, and incremental training of RAIs and support their efficient integration into existing large-scale software systems; (2) „Observational AI“ rethinks contextual learning and brings together different AI regimes to inject common-sense knowledge; (3) „Active AI“ focuses on continual and adaptive lifelong learning with active exploration; (4) Finally, „Challenging AI“ develops challenges and benchmarks to monitor and evaluate RAI’s development. The PIs from different disciplines will work together as multidisciplinary teams, crossing boundaries, fostering diversity, and creating a new learning-centered computing paradigm that will change the way we develop and use AI.
„Systemic AI“ (SAI) aims to develop systemic foundations of RAIs to increase the efficacy and quality of building and embedding RAIs into large-scale software systems. We will raise the level of abstraction akin to how high-level programming historically shifted development efforts away from low-level issues and broadened the class of developers that can build sophisticated software systems. To this end, we will embrace and generalize differentiable programming to multi-paradigm neuro-symbolic programming and map high-level programming abstractions to hybrid AI architectures composed of many AI modules. Crucially, we aim for neuro-symbolic programming foundations that innately support collaborative, decentralized, interactive, and incremental learning; this will not only improve the utility of data with privacy and ownership regulations, but also enable efficient use of computational resources and support the development of AIs that can be easily modified even after deployment. We will also go beyond just programming and embedding of RAIs and rethink and redesign both core building blocks of software stacks (such as databases) and user interfaces so that they can actively adapt their functionality to RAI software and enable RAI to learn from interactions with the outside world.
Professor Dr. Mira Mezini is one of the 3 speakers for project RAI, and one of the 21 principal investigators.