Project AGI

Towards general-purpose machine learning


Algorithms for learning using only local and immediate credit assignment

High-order partially-observable sequences

Recurrent Sparse Memory (RSM) is an unsupervised method for simultaneously learning spatial and sequential structure in partially-observable high Markov order data streams

Fast (One-shot) episodic learning

Our Artificial Hippocampus Algorithm (AHA) can learn to generalize classes from a single instance, while still discriminating between instances of the same class - all without any provided labels

Research Strategy

We aim to discover new learning rules, architectures and representations by using computational neuroscience discoveries to guide our research


  • Oct 15 2019: Dave is presenting at the Melbourne MLAI meetup
  • Sep 2019: Episodic memory paper preprint available
  • July 2019: We welcome Bar Gal On as Commercial Director
  • June 2019: PhD student Shuai Sun begins APR internship with us to develop a clinical report feedback system in conjunction with Melbourne Health
  • May 2019: Recurrent Sparse Memory paper preprint available
  • Feb 2019: Satya Borgohain starts MSc project on self-organizing neural architectures co-supervised by us and Faculty of IT at Monash University


Project AGI is a privately funded research group based in Melbourne, Australia. We collaborate with academic institutions, companies and individuals on shared research interests. If you’d like to work with us, get in touch!