ProjectAGI

Some interesting finds: Acyclic hierarchical modelling and sequence unfolding

This week we have a couple of interesting links to share. From our experiments with generative hierarchical models, we claimed that the model produced by feed-forward processing should not have loops. Now we have discovered a paper by Bengio et al titled “Towards biologically plausible deep learning” [1] that supports this… Read More »Some interesting finds: Acyclic hierarchical modelling and sequence unfolding

How to build a General Intelligence: An interpretation of the biology

Figure 1: Our interpretation of the Thalamocortical system as 3 interacting sub-systems (objective, subjective and executive). The structure of the diagram indicates the dominant direction of information flow in each system. The objective system is primarily concerned with feed-forward data flow, for the purpose of building a representation of the… Read More »How to build a General Intelligence: An interpretation of the biology

How to build a General Intelligence: Circuits and Pathways

Figure 1: Our headline image is from the Cognitive Consilience: An atlas of key pathways cross-referenced to supporting literature articles. The complexity and variety of routing within the brain can be appreciated with this beautiful illustration. Note in particular the specialisation of cortical cells and the way this affects their… Read More »How to build a General Intelligence: Circuits and Pathways

Digital Reconstruction of Neocortical Microcircuitry (resource)

  We have found a fantastic resource, part of the IBM Blue Brain Project, that clearly and interactively maps out interactions between neocortical neurons. The data comes from their attempts to simulate a piece of cortex down to the level of biologically-realistic neurons. Interactive neocortex browser tool here: https://bbpnmc.epfl.ch/nmc-portal/web/guest/microcircuit The… Read More »Digital Reconstruction of Neocortical Microcircuitry (resource)

SDR-RL (Sparse, Distributed Representation with Reinforcement Learning)

Erik Laukien is back with a demo of Sparse, Distributed Representation with Reinforcement Learning. This topic is of intense interest to us, although the problem is quite a simple one. SDRs are a natural fit with Reinforcement Learning because bits jointly represent a state. If you associate each bit-pattern with… Read More »SDR-RL (Sparse, Distributed Representation with Reinforcement Learning)

Reading list – July 2015

This month’s reading list continues with a subtheme on recurrent neural networks, and in particular Long Short Term Memory (LSTM). First here’s an interesting report on a panel discussion about the future of Deep Learning at the International Conference on Machine Learning (ICML), 2015: http://deeplearning.net/2015/07/13/a-brief-summary-of-the-panel-discussion-at-dl-workshop-icml-2015/ Participants included Yoshua Bengio (University… Read More »Reading list – July 2015

Reading List – May 2015

John Lisman, “The Challenge of Understanding the Brain: Where We Stand in 2015“, Neuron, 2015 For many in ML and AI,  biological knowledge is focussed on cortex. This paper gives an excellent broad overview of current biological understanding of intelligence. Sebastian Billaudelle and Subutai Ahmad, “Porting HTM Models to the Heidelberg Neuromorphic… Read More »Reading List – May 2015