>> I guess I can sum this up with, I wish people looked at AI more as a tool to help guide our intuition helping us solve problems we already have well defined knowledge (and data) of, and not as an means to an end itself.
Problem is, most machine learning algorithms cannot incoprorate background knowledge except by hard-coding inductive biases (as, e.g. the convolutional filters in convolutional neural nets). Unfortunately, this is a very limited way to incorporate existing knowledge.
This is actually why most machine learning work tries to learn concepts end-to-end, i.e. without any attempt to make use of previously learned or known concepts: because it doesn't have a choice.
Imagine trying to learn all of physics from scratch- no recourse to knowledge about mechanics, electomagnetism, any kind of dynamics, anything. That's how a machine learning algorithm would try to solve a physics problem. Or any other problem for which "we already have well defined knowledge (and data) of". We might as well be starting at around before the stone age.
Problem is, most machine learning algorithms cannot incoprorate background knowledge except by hard-coding inductive biases (as, e.g. the convolutional filters in convolutional neural nets). Unfortunately, this is a very limited way to incorporate existing knowledge.
This is actually why most machine learning work tries to learn concepts end-to-end, i.e. without any attempt to make use of previously learned or known concepts: because it doesn't have a choice.
Imagine trying to learn all of physics from scratch- no recourse to knowledge about mechanics, electomagnetism, any kind of dynamics, anything. That's how a machine learning algorithm would try to solve a physics problem. Or any other problem for which "we already have well defined knowledge (and data) of". We might as well be starting at around before the stone age.