Open Sourcing SVHN Preprocessing Code
Releasing a set of tools for converting the Street View House Numbers (SVHN) dataset into images with additional preprocessing options such as grayscaling.
New approaches to Deep Networks – Capsules (Hinton), HTM (Numenta), Sparsey (Neurithmic Systems) and RCN (Vicarious)
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)
New Ideas in Reinforcement Learning
Today’s blog post is about Reinforcement Learning (RL), a concept that is very relevant to Artificial General Intelligence. The goal of RL is to create an agent that can learn to behave optimally in an environment by observing the consequences – rewards – of its own actions. By incorporating deep… Read More »New Ideas in Reinforcement Learning
Reading list – October 2017
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
Literature Review: ‘A Distributional Perspective on Reinforcement Learning’
This article assesses the research paper, ‘A Distributional Perspective on Reinforcement Learning’ by the authors, Marc G. Bellemare, Will Dabney and Remi Munos, published in the proceedings of the 34th International Conference on Machine Learning (ICML) in 2017. Bellemare et al.’s paper will be assessed on several criteria. Firstly, content… Read More »Literature Review: ‘A Distributional Perspective on Reinforcement Learning’
Introducing Abdel
Hello everyone! I am Abdelrahman Ahmed (or Abdel for short) and I joined the Project AGI team as a Research Assistant a few months ago. I thought it would be a good time to tell you more about myself and what I am working on at Project AGI. Who am… Read More »Introducing Abdel
Attention in Artificial Intelligence systems
One of the features of our brain is its modularity. It is characterised by distinct but interacting subsystems that underlie key functions such as memory, language, perceptions, etc. Understanding the complex interplay between these modules requires decomposing them into a set of components. Indeed, this modular approach to modeling the… Read More »Attention in Artificial Intelligence systems
Continuous Learning
The standard machine learning approach is to learn to accomplish a specific task with an associated dataset. A model is trained using the dataset and is only able to perform that one task. This is in stark contrast to animals which continue to learn throughout life and accumulate and… Read More »Continuous Learning
Reading list – August 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
AGI Conference 2017
We attended the 2017 10th Conference on Artificial General Intelligence, which was located in our hometown of Melbourne, Australia! Excitingly, the IJCAI 2017 conference is also in Melbourne this week and ICML 2017 was in Sydney this year. In particular, the “Architectures for Generality and Autonomy” workshop may be of interest… Read More »AGI Conference 2017