AI2FUTURE2022 – 2 days, 40+ moving & inspiring discussion and presentations
Petar Veličković
Petar is a Staff Research Scientist at DeepMind, Affiliated Lecturer at the University of Cambridge, and an Associate of Clare Hall, Cambridge. He holds a PhD in Computer Science from the University of Cambridge (Trinity College).
His research concerns geometric deep learning—devising neural network architectures that respect the invariances and symmetries in data (a topic I’ve co-written a proto-book about). For his contributions, he is recognised as an ELLIS Scholar in the Geometric Deep Learning Program. He particularly focus on graph representation learning and its applications in algorithmic reasoning (featured in VentureBeat). He iscthe first author of Graph Attention Networks—a popular convolutional layer for graphs—and Deep Graph Infomax—a popular self-supervised learning pipeline for graphs (featured in ZDNet). My research has been used in substantially improving travel-time predictions in Google Maps and guiding intuition of mathematicians towards new top-tier theorems and conjectures.
Idea
Amazing things that happen with Human-AI synergy
For the past few years, I have been working on a challenging project: teaching machines to assist humans with proving difficult theorems and conjecturing new approaches to long-standing open problems. Alongside our pure mathematician collaborators from the Universities of Oxford and Sydney, we have demonstrated that analyzing and interpreting the outputs of (graph) neural networks offers a concrete way of empowering human intuition. This allowed us to derive novel top-tier mathematical results in areas as diverse as representation theory and knot theory.
The significance of these results has been recognised by the journal Nature, where our work featured on the cover page. Naturally, being on a project of this scale gets one thinking: what other kinds of amazing things can one do when AI and human domain experts synergistically interact? During this talk, I will offer my personal perspective on these findings, the key details of our modelling work, and also positioning them in the “bigger picture” context of synergistic Human-AI efforts.