On modelling emergence of logical thinking


Beyond Curve Fitting: Causation, Counterfactuals, and Imagination-based AI. AAAI Spring Symposium (2019) .


Recent progress in machine learning techniques have revived interest in building artificial general intelligence using these particular tools. There has been a tremendous success in applying them for narrow intellectual tasks such as pattern recognition, natural language processing and playing Go. The latter application vastly outperforms the strongest human player in recent years. However, these tasks are formalized by people in such ways that it has become ”easy” for automated recipes to find better solutions than humans do. In the sense of John Searle’s Chinese Room Argument, the computer playing Go does not actually understand anything from the game (Linhares and Chada 2013). Thinking like a human mind requires to go beyond the curve fitting paradigm of current systems. There is a fundamental limit to what they can achieve currently as only very specific problem formalization can increase their performances in particular tasks. In this paper, we argue than one of the most important aspects of the human mind is its capacity for logical thinking, which gives
rise to many intellectual expressions that differentiate us from animal brains. We propose to model the emergence of logical thinking based on Piaget’s theory of cognitive development.

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