Epistemic AI breaks entirely with the current state of artificial intelligence and with the most exciting ongoing efforts, such as continual learning (making the learning process a life-long endeavour), multi-task learning (aiming to distil knowledge from multiple tasks to solve a different problem) or meta-learning (learning to learn). As these are all still firmly rooted in AI’s conventional principles, they fail to recognise the foundational issue that the discipline has with the representation of uncertain knowledge.
Our proposal goes beyond ‘human-centric’ AI, the push to make artificial constructs more trustable by human beings and more capable of understanding humans, since it strives to model the uncertainty stemming not just from human behaviour, but from all sources of uncertainty present in complex environments.
Epistemic AI’s overall objective is to create a new paradigm for a next-generation artificial intelligence providing worst-case guarantees on its predictions, thanks to a proper modelling of real-world uncertainties. Firstly, a new mathematical framework for optimisation under epistemic uncertainty will be formulated, superseding existing probabilistic approaches. The new optimisation framework will lay the premises for the creation of new ‘epistemic’ learning paradigms. In Epistemic AI we will focus, in particular, on some of the most important areas of machine learning: unsupervised learning, supervised learning and reinforcement learning.
Last but not least, the goal of the project is to foster an ecosystem of academic, research, industry and societal partners throughout Europe able to drive and sustain the EU’s leadership ambition in the search for a next-generation AI.