{'fraction_non_alphanumeric': 0.024232633279483037, 'fraction_numerical': 0.0, 'mean_word_length': 5.103448275862069, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Modeling Concept System

Understanding thinking and realizing human-level artificial intelligence have long been regarded as one of the ultimate dreams. However, most of the studies dedicated to this goal are in a direction that could never achieve it. This direction is that trying to explain intelligent behavior by human-summarized codes of conduct. Expert systems, machine learning, and almost all theories that interpret psychological phenomena are products of this idea. Since the code itself is one-sided, this method is limited from its beginning. To truly understand the thinking, we must embrace a new way: treat all codes of conduct, such as rationality or survivability, as phenomena rather than a goal of intelligence. These phenomena will emerge based on some basic settings when the intelligent machine interchange information with the outer world. In this paper, we will model the data structure of thinking, namely, the concept system, which is the first part of modeling thinking based on the idea above. To demonstrate the power of this model, we discussed how cognitive phenomena can emerge based on this model. These phenomena include recognition, reasoning, causality, semantic concepts, and the concept of concepts.