It’s such a joy to be able to test an idea, go straight to the idea without wrestling with the tools. We recently developed an experimental setup which, so far, looks like it will do just that. I’m excited about it and hope it can help you too, so here it is. We’ll go through the why we created another framework, and how each module in the experiment setup works.
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
Eager Execution is an imperative, object oriented and more Pythonic way of using TensorFlow. It is a flexible machine learning platform for research and experimentation where operations are immediately evaluated and return concrete values, instead of constructing a computational graph that is executed later.
The dataset is an integral part of an ML engineer’s toolkit. We recently compiled useful information about a range of these well known datasets. It’s all in one place, and hopefully useful to others as well.
ML Today Today’s Machine Learning has demonstrated unprecedented performance in what seems like every application thrown at it. Almost all the success has been based on advanced memory systems that can learn to recognise an input based on a large number of training examples. This is the equivalent to memory… Read More »The case for Episodic Memory in Machine Learning
2018 is a fresh new year and an exciting milestone for Project AGI. Dave and I have been discussing, dreaming, playing around with and striving towards general purpose AI for over 6 years. It started with musings on the algorithmic underpinnings of consciousness and the nature of intelligence. We quickly… Read More »2018 a Milestone for Project AGI
There are plenty of established machine learning frameworks out there, and new frameworks are popping up frequently to address specific niches. We were interested in examining if one of these frameworks fits in our workflow. I surveyed the most popular frameworks, and aim to provide a helpful comparative analysis.
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
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