there is overfitting in Actionism (decision-making control)

there's interaction with environment in symbolism (logicism)

there's interaction with environment in connectionism (bionicism)

there's interaction with environment in Actionism (decision-making control)

combinatorial explosion in symbolism (logicism)

combinatorial explosion in connectionism (bionicism)

combinatorial explosion in Actionism (decision-making control)

computational complexity in symbolism (logicism)

computational complexity in connectionism (bionicism)

computational complexity in Actionism (decision-making control)

AI is a field of ML. It uses statistical methods to give the
computer the ability to ”learn” from data, without being
explicitly programmed

DL (including Machine learning (ML)) is a study of learning
algorithms

choose the right one

It is a machine learning type that learns from data that has not
been labeled

It is goal-oriented learning that is based on interaction with the
environment.

Many real practical problems fall into this machine learning
category where you have little labeled data, and the rest of the
data is unlabeled

Input data (also known as training examples) comes with a
label, and the goal of learning is to predict the label for new,
unforeseen examples

Reinforcement learns through

Labeling data is an inexpensive or time-consuming process.
Besides, it mandates having domain experts to label data
accurately.