In this case study, we will demonstrate the advantages of keeping a personal journal and diary using an AI-based text analysis app. Advanced text analysis capacities combined with GPT AI functionality can dramatically improve the ability to self-reflect and enhance a regular introspection practice.
The most common use of AI models today is to generate text, however, these models have a huge potential to be used for introspection and the psychology of self. In this article, we will show how you can harness the power of data science to analyze your own thinking and then use AI to generate insights that can help you enhance your ideas and get a deeper understanding of yourself. This approach can be used both for professional and personal purposes: from developing your discourse on a certain topic to the psychology of self.
Network graphs offer an efficient way to remember something that is connected in nature. Much more so than traditional bullet lists and notes.
Mental disorders and various self-imposed psychological obstacles often arise because of the lack of choice. Gregory Bateson wrote about the double-bind origin of schizophrenic behavior where “crazy” reaction is simply a way of transcending a limiting situation, when the person sees no other choice. Various psychotherapeutic practices focus on expanding the range of possibilities for the patient, offering more choices than is currently available. Text network visualization can be a useful tool to aid this process. In this case study we are going to demonstrate how it works.
Tarot is a deck of cards used since the 15th century to play various games as well as for divination purposes. We at Nodus Labs studied the structure of various Tarot decks, treating the cards as the nodes and relations between them as edges, building a graph of relations between the cards that are invariant across various Tarot decks. We discovered that the structure of the resulting graph has a very specific community structure, which makes Tarot a very efficient tool for telling narratives. We are currently working on practical implementations of this study.
