This website describes the research that I am doing on a new measure of intelligence.
More information about the theoretical background and algorithm is available in the following publications:
The Agent Maze Experiments show how my algorithm can successfully measure the fluid and crystallized intelligence of an agent as it explores different environments. The Machine Learning Experiments show how my algorithm can successfully measure the fluid and crystallized intelligence of a deep network that learns to classify images of handwritten digits from the MNIST data set.
The TypeScript source code can be downloaded here.
These experiments show how P can be calculated for an embodied agent that learns to to predict the consequences of its actions in different environments.
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Maze
Explore current maze:
Polynomial window: +/- 5
Approximate max crystallized intelligence: 0
Enable learning
Re-calculate intelligence every changes.
These experiments show how fluid and crystallized P can be calculated for a deep learning algorithm.
Use the controls to load and train the network on the time series datasets and view the changing intelligence of the network over time.
The machine learning is done using TensorFlow.js.
Data sets
Train data: 70% Validation data: 15% Test data: 15%
Time window:
Number of models:
LSTM units per model:
Number of epochs:
Batch size:
Number of training cycles
Training:
Polynomial window: +/- 5
Match within: %