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DSI Grant

Grant – Continual Few-Shot Learning

We are very happy to announce that we’ve been awarded an Artificial Intelligence for Decision Making Initiative grant from Defence Science Institute on Effective updating of deep learning models with limited new data. More specifically, the grant is for the Continual Few-Shot Learning project that we blogged about recently.


Continual Few-Shot Learning with Hippocampal Replay

In continual learning, the neural network learns from a stream of data, acquiring new knowledge incrementally. It’s not possible to assume an i.i.d. stationary dataset available in one batch. Catastrophic forgetting of previous knowledge is a well known challenge. A wide variety of approaches fall broadly into 3 categories [6]:… Read More »Continual Few-Shot Learning with Hippocampal Replay

One-shot learning for the long term: consolidation with an artificial hippocampal algorithm

We were really excited to present a couple of papers recently at IJCNN, the International Joint Conference on Neural Networks. One of those papers was an extension to the work that we previously published on AHA, Artificial Hippocampal Algorithm based on Complementary Learning Systems by O’Reilly and McClelland (see that… Read More »One-shot learning for the long term: consolidation with an artificial hippocampal algorithm


Towards Biologically Inspired Executive Control

Executive Control is core to what most people recognise as true intelligence. For example, the ability to attend to relevant cues and maintain task dependent information whilst ignoring distracting details and taking appropriate actions.  Working Memory (WM) is a core component of Executive Control. “Working memory is a short-term repository… Read More »Towards Biologically Inspired Executive Control By Ben Teoh

Research Roadmap: 2020-2021

We’ve just undertaken a review and refresh of our research roadmap! The topics and approach we will take in the coming year are all laid out in a new page: Research Roadmap Our primary topics for the coming year include: Continual Few-Shot Learning (CFSL) via our Episodic memory system Using… Read More »Research Roadmap: 2020-2021

5th WBAI Hackathon

The Whole Brain Architecture Initiative (WBAI) aims to foster research into architectural approaches to general intelligence. They have held a series of events – the Hackathons – that encourage and support the development of new models which provide working solutions to questions about the possible architecture of general intelligence in… Read More »5th WBAI Hackathon


AHA! an ‘Artificial Hippocampal Algorithm’ for Episodic Machine Learning

We’re very happy to report that we recently published a preprint on AHA, an ‘Artificial Hippocampal Algorithm’ for Episodic Machine Learning. It’s the culmination of a multi-year research project and is a starting point for the next wave of developments. This article describes the motivation for developing AHA and a… Read More »AHA! an ‘Artificial Hippocampal Algorithm’ for Episodic Machine Learning


Hippocampus and the Episodic confusion (for machine learning)

The standard definitions of Episodic and Semantic Memory hide some important subtleties that impact the development of Machine Learning algorithms for AI (particularly those building Episodic Learning capability). This article explores this issue and provides a context for upcoming articles work to take us a step closer to ‘animal-like’ machine learning