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The artificial neuron model used by Jeff Hawkins and Subutai Ahmad in their new paper (image reproduced from their paper, and cropped). Their neuron model is inspired by the pyramidal cells found in neocortex layers 2/3 and 5. It has been several years since Jeff Hawkins and Numenta published the Cortical Learning Algorithm (CLA) whitepaper.…
Authors: D Rawlinson and G Kowadlo This is the first of three articles detailing our latest thinking on general intelligence: A one-size-fits-all algorithm that, like people, is able to learn how to function effectively in almost any environment. This differs from most Artificial Intelligence (AI), which is designed by people for a specific purpose. This…
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 paper containing the original research…
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 a reward value, it is…
An interesting article by Gerard Rinkus comparing the qualities of sparse distributed representation and quantum computing. In effect, he argues that because distributed representations can simultaneously represent multiple states, you get the same effect as a quantum superposition. The article was originally titled “sparse distributed coding via quantum computing” but I think that gets the…
By Gideon Kowadlo and David Rawlinson Introduction We’ve been building and testing AGI algorithms for the last few years. As the systems become more complex, we have found it ever more difficult to run meaningful experiments. To summarise, the main challenges are: testing a version of the algorithm repeatedly and over some range of parameters…
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 of Montreal), Neil Lawrence (University…
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 Computing Platform“, arXiv.org, 2015 Progress in…
By David Rawlinson and Gideon Kowadlo The aim of this post is to take a short departure from more technical issues and deal with some of the philosophical questions about Artificial General Intelligence (AGI). The Elephant in the room This metaphor implies willful ignorance of something blindingly obvious. Something that’s difficult to ignore. It requires effort…
By Gideon Kowadlo and David Rawlinson In our last blog post, we discussed the repeating functional columnar structure of the neocortex, and the inconsistent terminology used to discuss it throughout the literature. As mentioned in that post, the function of the column is an important concept for understanding the function of the neocortex, and as a consequence,…