Neuro-Symbolic AI

I want to go through the Wikipedia series on Machine Learning and Data mining. Data mining is the process of extracting and discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.

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Neuro-symbolic AI is a type of artificial intelligence that integrates neural and symbolic AI architectures to address the weaknesses of each, providing a robust AI capable of reasoning, learning, and cognitive modeling. As argued by Leslie Valiant and others, the effective construction of rich computational cognitive models demands the combination of symbolic reasoning and efficient machine learning.

In Thinking Fast and Slow, cognition is described as encompassing two components:

  1. System 1 is fast, reflexive, intuitive, and unconscious. System 1 is pattern recognition.
  2. System 2 is slower, step by step, and explicit. System2 handled planning, deduction, and deliberative thinking.

In this view, deep learning best handles the first kind of cognition while symbolic reasoning best handles the second kind. Both are needed for a robust, reliable AI that can learn, reason, and interact with humans to accept advice and answer questions.

Approaches

  • Symbolic Neural symbolic is the current approach of many neural networks in natural language processing, where words or sub word tokens are the ultimate input and output of large language models.
  • Symbolic[Neural] is exemplified by AlphaGo, where symbolic techniques are used to invoke neural techniques. In this case, the symbolic approach is Monte Carlo tree search and the neural techniques learn how to evaluate game positions
  • Neural | Symbolic uses a neural architecture to interpret perceptual data as symbols or and relationships that are reasoned about symbolically.
  • Neural: Symbolic -> Neural relies on symbolic reasoning to generate or label training data that is subsequently learned by a deep learning model
  • Neural_{Symbolic}uses a neural net that is generated from symbolic rules
  • Neural[Symbolic] allows a neural model to directly call a symbolic reasoning engine. An example would be ChatGPT using a plugin to query Wolfram Alpha.

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