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
Reproduced left to right from [8,10,1] Within a 5 day span in October, 4 papers came out that take a significantly different approach to AI hierarchical networks. They are all inspired by biological principles to varying degrees. It’s exciting to see different ways of thinking. Particularly at a time… Read More »New approaches to Deep Networks – Capsules (Hinton), HTM (Numenta), Sparsey (Neurithmic Systems) and RCN (Vicarious)
This month’s reading list has two parts: a non-Reinforcement Learning list, and a Reinforcement Learning list. Since our next blog post will be on Reinforcement Learning, readers might like to refer to our RL reading list separately. Non-Reinforcement Learning reading list A Framework for searching for General Artificial Intelligence Authors:… Read More »Reading list – October 2017
1. Neuroscience-inspired Artificial Intelligence Authors: Demis Hassabis, Dharshan Kumaran, Christopher Summerfield, and Matthew Botvinick Type: Review article in Neuron Publication date: 19 July 2017 This paper outlined the contribution of neuroscience to the most recent advances in AI and argued that the study of neural computation in humans and other… Read More »Reading list – August 2017
Our brain is a source of great inspiration for the development of Artificial General Intelligence. In fact, one of the common views is that any effort in developing human-level AI is almost destined to fail without an intimate understanding of how the brain works. However, we do not understand our… Read More »Unsupervised Learning with Spike-Timing Dependent Plasticity
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… Read More »Reading list – May 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… Read More »Reading list: Assorted AGI links. March 2016
What’s AlphaGo? AlphaGo is a system that can play Go at least as well as the best humans. Go was widely cited as the hardest (and only remaining) game at which humans could beat machines, so this is a big deal. AlphaGo has just defeated a top-ranked human expert. AlphaGo… Read More »What’s after AlphaGo?
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
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… Read More »New HTM paper – “Why Neurons Have Thousands of Synapses, A Theory of Sequence Memory in Neocortex”