SymbolicAI: A neuro-symbolic perspective on LLMs

This is the voodoo that excites me.

Examples I found interesting:

Semantic map lambdas

  S = Symbol(['apple', 'banana', 'cherry', 'cat', 'dog'])
  print(S.map('convert all fruits to vegetables'))
  # => ['carrot', 'broccoli', 'spinach', 'cat', 'dog']

comparison parameterized by context

  # Contextual greeting comparison
  greeting = Symbol('Hello, good morning!')
  similar_greeting = 'Hi there, good day!'

  # Compare with specific greeting context
  result = greeting.equals(similar_greeting, context='greeting context')
  print(result) # => True

  # Compare with different contexts for nuanced evaluation
  formal_greeting = Symbol('Good morning, sir.')
  casual_greeting = 'Hey, what\'s up?'

  # Context-aware politeness comparison
  politeness_comparison = formal_greeting.equals(casual_greeting, context='politeness level')
  print(politeness_comparison) # => False

bitwise ops

  # Semantic logical conjunction - combining facts and rules
  horn_rule = Symbol('The horn only sounds on Sundays.', semantic=True)
  observation = Symbol('I hear the horn.')
  conclusion = horn_rule & observation # => Logical inference

`interpret()` seems powerful.

OP, what inspired you to make this? Where are you applying it? What has been your favorite use case so far?

Why is carrot the vegetablefication of apple?

I think it's interpreting the command as "replace each fruit with a vegetable", and it might intuit "make the resulting vegetables unique from one another" but otherwise it's not trying to find the "most similar" vegetable to every fruit or anything like that.