A terminology comment: as part of your classification system. you are calling ‘supervised learning’ and ‘reinforcement learning’ two different AI/AGI ‘learning algorithm architectures’. This takes some time for me to get used to. It is more common in AI to say that SL and RL solve two different problems, are different types of AI.
The more common framing would be to say that an RL system is fundamentally an example of an an autonomous agent type AI, and an SL system is fundamentally an example of an input classifier or answer predictor type AI. Both types can in theory be built without any machine learning algorithm inside, in fact early AI research produced many such intelligent systems without any machine learning algorithm inside at all.
An example of a machine learning architecture, on the other hand, would be something like a deep neural net with backpropagation. This type of learning algorithm might be used to build both an SL system and an RL system.
In Barto’s work that you reference, he writes that
Both reinforcement learning and supervised learning are statistical processes in which a general function is
learned from samples.
I usually think of a ‘machine learning algorithm/architecture’ as being a particular method to learn a general function from samples. Where the samples come from, and how the learned function is then used, depends on other parts of the ‘AI architecture’, the non-ML-algorithm parts.
So where you write ‘Predicted architecture of AGI learning algorithm(s)‘, I would tend to write ‘predicted type of AGI system being used’.
I’m interested to see where you will take this.
A terminology comment: as part of your classification system. you are calling ‘supervised learning’ and ‘reinforcement learning’ two different AI/AGI ‘learning algorithm architectures’. This takes some time for me to get used to. It is more common in AI to say that SL and RL solve two different problems, are different types of AI.
The more common framing would be to say that an RL system is fundamentally an example of an an autonomous agent type AI, and an SL system is fundamentally an example of an input classifier or answer predictor type AI. Both types can in theory be built without any machine learning algorithm inside, in fact early AI research produced many such intelligent systems without any machine learning algorithm inside at all.
An example of a machine learning architecture, on the other hand, would be something like a deep neural net with backpropagation. This type of learning algorithm might be used to build both an SL system and an RL system.
In Barto’s work that you reference, he writes that
I usually think of a ‘machine learning algorithm/architecture’ as being a particular method to learn a general function from samples. Where the samples come from, and how the learned function is then used, depends on other parts of the ‘AI architecture’, the non-ML-algorithm parts.
So where you write ‘Predicted architecture of AGI learning algorithm(s)‘, I would tend to write ‘predicted type of AGI system being used’.