Author

ProjectAGI

15 posts

Posts by ProjectAGI.

  1. Reading list – May 2016

    Digit classification error over time in our experiments. The image isn’t very helpful but it’s a hint as to why we’re excited :) Project AGI A few weeks ago we paused the “How to build a General Intelligence” series (part 1, part 2, part 3, part 4). We paused it because the next article in…

  2. Reading list: Assorted AGI links. March 2016

    A Minecraft API is now available to train your AGIs Our News We are working hard on experiments, and software to run experiments. So this week there is no normal blog post. Instead, here’s an eclectic mix of links we’ve noticed recently. First, AlphaGo continues to make headlines. Of interest to Project AGI is Yann…

  3. 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 claim. The paper looks for…

  4. 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 actual agent-world system. The executive…

  5. 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 interactions with other cells in…

  6. How to Build a General Intelligence: Reverse Engineering

    Figure 1: The physical architecture of general intelligence in the brain, namely the Thalamo-Cortical system. The system comprises a central hub (the Thalamus) surrounded by a surface (the Cortex, shown in blue). The Cortex is made up of a number of functionally-equivalent units called Columns. Each Column is a stack of cell layers. Columns share…

  7. 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 paper containing the original research…

  8. 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 a reward value, it is…

  9. 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 of Montreal), Neil Lawrence (University…

  10. 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 Computing Platform“, arXiv.org, 2015 Progress in…

  11. The Real Risks of AI

    By Punarjay Chakravarty In the past year or so, there has been a spate of high-profile pronouncements by respected scientists and engineers, cautioning the world about the potential ill-effects of AI. Stephen Hawking, one of the foremost experts on theoretical and astro physics told the BBC that “The development of full artificial intelligence could spell…

  12. Deep learning, a history in context

    DeepMind, based on a deep learning architecture made a big impact in the tech industry. Deep learning continues to gain traction in the machine learning community and in the mainstream, both in terms of public awareness of its capabilities as well as through commercialisation. This article by Simon Winder provides a very accessible introduction to…

  13. Recent HTM projects

    It’s exciting to see growing interest and participation in the AGI community. This is another brief post to share two examples. In this case, they both build on one approach to AGI, and that is HTM. FXAI – Explorations Into Computational Intelligence A blog that is pretty well described by the title. The author, Felix…

  14. Connectome browser – hierarchy viewer?

    Hierarchy Viewer? Ongoing research to map out the patterns of connectivity in the human brain continues to produce stunning visuals. Are we looking at the hierarchy as proposed in the Memory-Prediction framework? In any case, it’s fun to explore, which you can do here: http://www.humanconnectomeproject.org/data/relationship-viewer/

  15. Introduction

    by David Rawlinson and Gideon Kowadlo The Blog This blog will be written by several people. Other contributors are welcome – send us an email to introduce yourself! The content will be a series of short articles about a set of common architectures for artificial general intelligence (AGI). Specifically, we will look at the…

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