DeepMind Has Reconciled Existing Neural Network Limitations To Outperform Neuro-Symbolic Models

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Neural networks have achieved success in various perceptual tasks. However, it is stated that they are ineffective in solving problems requiring higher-level reasoning. Recent experiments with two recently released video question-answering datasets (CLEVRER and CATER) show that neural networks cannot adequately reason about the Spatio-temporal and compositional structure of visual scenes.

On the other hand, Neuro-symbolic models that combine algorithms with symbolic reasoning techniques to predict, explain, and consider counterfactual possibilities are assumed to be much more suitable than neural networks. It leverages several independently-learned modules such as:

  • A neural network ‘perceptual’ front-end to detect objects
  • A dynamics module to infer objects’ behavior over time
  • A symbolic statistical semantic parser that represents the questions
  • A hand-coded symbolic executor interprets inputs and predicts answers



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