It may be true that "AGI components of the system act by reacting to various events", however the same cannot be said for natural intelligence. Our brains use a much more computationally efficient approach. We construct a prediction of what is to occur. As long as our prediction is not violated, there is no need to react. Confirming a prediction is computationally less expensive than being purely data driven. Our cognitive load is light until something occurs that was not predicted. Then we correct our prediction. Besides being computationally efficient, this approach does not suffer from the brittleness of reflexive responses.
This allows me to drive at 70 mph on the highway while chatting with a passenger...until I see red brake lights ahead. Then my conversation pauses as my attention and my entire cognitive faculty is redirected toward the road ahead.
Computers, light switches, and monosynaptic reflexes are all event/data driven. They are entirely reflexive...like the outdated model of behavioral psychology. Intelligent animals, on the other hand, operate within a continuous cycle of sensing, predicting, and acting within an environment or umwelt.
It is absolutely true that human intelligence is not a matter of exclusively reacting to external events, and many previous chapters in our series discuss related issues, including forecasting, planning, and others.
This chapter considers the technique of programming event processing, which, along with external events, also includes events within the intelligent system itself.
It may be true that "AGI components of the system act by reacting to various events", however the same cannot be said for natural intelligence. Our brains use a much more computationally efficient approach. We construct a prediction of what is to occur. As long as our prediction is not violated, there is no need to react. Confirming a prediction is computationally less expensive than being purely data driven. Our cognitive load is light until something occurs that was not predicted. Then we correct our prediction. Besides being computationally efficient, this approach does not suffer from the brittleness of reflexive responses.
This allows me to drive at 70 mph on the highway while chatting with a passenger...until I see red brake lights ahead. Then my conversation pauses as my attention and my entire cognitive faculty is redirected toward the road ahead.
Computers, light switches, and monosynaptic reflexes are all event/data driven. They are entirely reflexive...like the outdated model of behavioral psychology. Intelligent animals, on the other hand, operate within a continuous cycle of sensing, predicting, and acting within an environment or umwelt.
It is absolutely true that human intelligence is not a matter of exclusively reacting to external events, and many previous chapters in our series discuss related issues, including forecasting, planning, and others.
This chapter considers the technique of programming event processing, which, along with external events, also includes events within the intelligent system itself.
Great write-up. We also recently published an article on how to bridge Backend and Data Engineering teams using Event Driven Architecture - https://packagemain.tech/p/bridging-backend-and-data-engineering