We are very happy to announce that we’ve been awarded...
Read MoreCerenaut
Understanding animal intelligence
Improving machine intelligence
FEATURED RESEARCH
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 from neuroscience and psychology - and contribute insights back to these fields.
NEWS
- Jul 2021: We presented 2 papers at IJCNN (RSM and Consolidation with AHA)
- Jun 2021: Kicked off the Working Memory Hackathon with WBAI
- May 2021: CFP for the Working Memory Hackathon (with WBAI) is out!
- May 2021: 2 papers accepted into IJCNN, on RSM and using AHA for memory Consolidation - see https://cerenaut.ai/publications
- Feb 2021: We're excited to welcome 2 new Masters students from Monash Uni. Amir Mayan working on the Left and Right Brain project (inspired by Elkhonon Goldberg), and Mohit Gupta working on improving episodic memory with disentangled representations.
RECENT ARTICLES
Continual Few-Shot Learning with Hippocampal Replay
In continual learning, the neural network learns from a stream...
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Video Prediction using Recurrent Sparse Memory
We recently presented 2 papers at the International Joint Conference...
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One-shot learning for the long term: consolidation with an artificial hippocampal algorithm
We were really excited to present a couple of papers...
Read MoreFeatured Articles
Towards Biologically Inspired Executive Control
Executive Control is core to what most people recognise as...
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Research Roadmap: 2020-2021
We’ve just undertaken a review and refresh of our research...
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Sparse Unsupervised Capsules Generalize Better
We’ve just uploaded a spin-off research paper to arXiv titled...
Read MoreHow to build a General Intelligence: What we think we already know
Authors: D Rawlinson and G Kowadlo This is the first...
Read MoreAbout Us
Cerenaut (formerly ProjectAGI) is an independent research group that undertakes fundamental research at the intersection of AI, Neuroscience and Psychology. We’re based in Australia.
Our name reflects humanity’s journey towards higher cognition and intelligence: cere = of the brain, naut = journey
We co-supervise student research projects and collaborate with researchers on topics of shared interest. If you’d like collaborate, get in touch!