This thread An interesting benchmark for intelligent agents got me thinking about what it would be like to have a suite of environments that could be used as a benchmark to gauge Artificial General Intelligence.
Such thoughts made me asked this question on Quora:
Now, I’d like to ask my question to The Nupic/HTM community. We know that the brain has one ubiquitous, repeating structure. Let’s call this structure the ‘smallest unit of intelligence’ or SUI (I’m talking about a cortical column).
We know the SUI must do many things, such as learn sequences, predict, reason by analogy and many other tasks and features pertaining to General Intelligence.
Why can’t we, use Machine Learning techniques and Deep Learning AI algorithms to determine what the structure of that shape Smallest Unit of Intelligence must be?
What we would need is a suite of environments (sensorimotor) of various types, all semantically encoded. That way you drop an agent with a particular brain structure into each one and see how well it learns the structure of its environment, the deep learning supervisor tries to improve the brain with a different SUI structure each iteration.
Would anybody be interested in working on this project? What do you think of the idea?