Neurosymbolic AI

One of the biggest problems with neural networks today is their utter lack of interpretability. Rules within hidden layers are essentially unknown because they depend on previous layers and computations, which makes the entire algorithm a black box. Combine this idea with some of the more modern neural net architectures that have hundreds of layers and billions of parameters and it becomes clear why interpretability is not possible.

Recent developments in the space have tried to tie neural networks together with symbolic logic, which is a representational way mathematicians think about logic and decisions.

Symbolic logic

One type of logic commonly used in neurosymbolic AI is first-order logic. First-order logic, also known as predicate logic, extends propositional logic by incorporating variables, quantifiers, and predicates to reason about objects and their relationships. It allows for the expression of complex statements and lets us make inferences based on logical rules.

For example, let’s consider the statement,“If Fido is a dog, it has four legs.” Let’s define our events:

• $A$ ...