The book is "finally" making its revolution

Thanks to artificial intelligence, the book is transformed into a network of interconnected, adaptive, and navigable knowledge, going beyond the traditional linear format. By Xavier Comtesse and Pascal Eichenberger

An article available in AGEFI: https://agefi.com/actualites/opinions/le-livre-fait-enfin-sa-revolution

Artificial intelligence ( AI) is taking hold of the ancient book, not to converse with it like ChatGPT , but to fundamentally reorganize it. Indeed, AI is capable of extracting the cognitive value of any text in the form of a "knowledge graph." In doing so, it illuminates the book's core content. Goodbye tables of contents, hello knowledge graphs (a new graphical representation of knowledge).

Thus, the book has just undergone its most profound transformation since Gutenberg. From now on: We no longer turn pages, we immerse ourselves in its content. And we speak of the AI-Book.

The end of the chapters

In this new digital book, chapters, page numbering, tables of contents, and siloed knowledge organization are a thing of the past. Everything becomes fluid and interconnected.

For example, in a traditional textbook, if you don't understand a concept in chapter 3, you're stuck in chapter 4. But in the age of cognitive links , the book becomes a constellation of micro-concepts, "knowledge capsules," so to speak. If a student struggles with a concept, the AI doesn't just repeat the same sentence; it activates a cognitive link, a related concept. The textbook adapts to the reader's mental landscape. The classic textbook has a top-down structure. Knowledge flows from the author to the student.

Nowadays, the book no longer serves to store knowledge (the Internet already does that), it serves to connect it. It no longer describes "here is everything you need to know", it specifies "how these ideas are linked together and how to navigate among them", it maps meaning.

We are moving from a pedagogy of stock (accumulation of knowledge) to a pedagogy of flow (mastery of links).

Game-changing graphs

Unlike a traditional database, this is simply a PDF that the AI will read. To clarify, the key concept of a knowledge graph relies on triplets of the type: "Subject — Predicate — Object". For example: "mRNA vaccine — uses — Lipid nanoparticles" which are repeated almost indefinitely.

1. In the scientific field.

Research is often hampered by data silos. A knowledge graph makes it possible to link publications, chemical structures, clinical trial results, and genes. The advantage: We can discover correlations invisible to the naked eye (very useful fordrug repositioning ).

2. In the entrepreneurial environment.

Enterprise documentation (Wikis, Notion, SharePoint) is often an impenetrable maze. The advantage: a graph allows you to map technical dependencies, employee expertise, and project histories.

3. In pedagogy.

We are moving away from linear learning in favor of adaptive learning. The advantage: if a student does not understand concept B, the graph then shows that they lack the prerequisites for concept A. We can visualize the "path of knowledge".

A thread of Ariadne

Previously, in siloed books or relational databases, one had to constantly search for information. Now, in a system of iterative links, one no longer searches, one navigates. One follows a logical thread that leads one from concept to concept. One flies from one piece of knowledge to another.

Note: Discover a concrete example of an AI-powered book: https://app.manufacturethinking.ch

By Xavier Comtesse and Pascal Eichenberger