  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. 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.
