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.
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
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
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
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
We recently published 2 new ML/neuroscience research projects as part of the Request for Research (RFRs) projects, with WBAI. They’re fascinating topics that have arisen through the relationship with our advisor Elkhonon Goldberg from the Luria Neuroscience Institute.
Over the last few years, there have been several breakthroughs and exciting new research directions in Reinforcement Learning, Hippocampus Inspired Architectures, Attention and Few-Shot Learning. There has been a move towards multi-component, heterogeneous, stateful architectures, many guided by ideas from cognitive sciences. Google DeepMind and Google Brain are leading the… Read More »Exciting New Directions in ML/AI
The dataset is an integral part of an ML engineer’s toolkit. We recently compiled useful information about a range of these well known datasets. It’s all in one place, and hopefully useful to others as well.
ML Today Today’s Machine Learning has demonstrated unprecedented performance in what seems like every application thrown at it. Almost all the success has been based on advanced memory systems that can learn to recognise an input based on a large number of training examples. This is the equivalent to memory… Read More »The case for Episodic Memory in Machine Learning
2018 is a fresh new year and an exciting milestone for Project AGI. Dave and I have been discussing, dreaming, playing around with and striving towards general purpose AI for over 6 years. It started with musings on the algorithmic underpinnings of consciousness and the nature of intelligence. We quickly… Read More »2018 a Milestone for Project AGI