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 to readers.
We’re very excited to launch AGI Experimental Framework, AGIEF, our open source framework.
We first introduced it a while back, at the end of 2015 here, and it has certainly come a long way.
AGIEF was created to make running rigorous AI experiments convenient, reproducible and scalable. The goals are:
- Repeatability: ability to save/load, stop/start an experiment from any execution step, and know that it will execute deterministically
- Visualisation: ability to visualise all the data structures at any step
- Distributed operation for performance
The Github wiki and Readme describe the project in detail and how to get started.
The framework comprises 3 repositories.
agi – Java project comprising core algorithmic code and framework package to support compute nodes.
run-framework – Python scripts to run and interact with the compute nodes covering aspects such as generating input files, launching cloud infrastructure, running those experiments (locally or remotely), executing parameter sweeps and exporting and uploading the output artefacts.
experiment-definitions – contains the experiment definitions, the files required to run and repeat specific experiments.