Cerenaut Research

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


About Us

Cerenaut (formerly ProjectAGI) undertakes fundamental research in artificial intelligence & machine learning. We’re based in Melbourne, Australia. 

We collaborate with academic institutions, companies and individuals, on topics of shared interest. If you’d like to work with us, get in touch! We co-supervise student research projects and engage with the research community here in Australia.