Google is popular Notebook Service, which can create podcast audio type recordings according to information from your flow, recently introduced interactive mental cards as a way to organize and explore information visually in the user’s notebook. This feature can automatically generate a ramification scheme for main subjects and sub-themes from the documents you download. See the example below for an example.
In functionality, each mental card begins with a central concept (often the title of notebook or the global theme) and branches into key ideas discovered through your notes – similar to octopus tentacles. The system visually sums up your source materials. It highlights the relationships between ideas in a hierarchical knot structure.
The company is deploying functionality, a process that started on March 19.
AI / NLP treatment of notes and generation of nodes
Under the hood, Notebooklm hits the Grand Language Models (LLM) by Google Gemini, which analyzes the text to identify important concepts, subjects and how they relate. Essentially, it makes an extraction of outline or concept: the model could be invited to “summarize the subjects and the key sub-themes present in these notes”. Each node of the mental card represents a concept taken from the content, the subnudes decomposing more detailed sub-themes. For example, if a student’s notebook concerns the decline of the ecosystem of coral reefs, AI could generate high -level nodes such as “oceanic acidification”, “increase in sea temperatures”, “pollution”, etc., each with other branches, An example Google offers on its website.
To guarantee precision, the AI of notebooklm focuses on information based on user sources. He can meet several documents in the notebook to find common themes. This can also reveal how different sources intersect and capture that they agree or conflict.
Each node is linked to the content that inspired it. In other words, notebooklm keeps a trace of the source or section of the text supports a given node. This allows him to provide a context when you interact with the knot. Once the AI has built data from the mental card (subjects, sub-themes, relationships), the presentation layer makes it as an interactive diagram.
There is also an option to download the mental card as a sharing image. In addition, the nodes are interactive: the overview can highlight the node or show on which source it is based and by clicking on additional actions.
Expansion of nodes and questions of questions and answers
One of the new aspects of notebooks’ mind cards is the way they are linked to the AC AI AI capabilities. The mental card is not isolated; It is linked to the Chat Notebooklm interface. Click on a node can ask a question at AI or recover relevant information on this subject. In practice, when a user selects a node (let’s say “surfishing” on the coral reef card), Notebooklm will generate a Immediate response in the cat panelExplaining that subtopic with details from sources. It is indeed a contextual question and answer session: the system knows which concept you clicked, so that it can withdraw a prepared summary or dynamically question the LLM for an explanation, limited to the relevant source material.
Comparison with other knowledge mapping tools
Knowledge mapping tools are widely available. For example, there are several free options in Python such as Pydot, Pymindmap and Networkx with Graphviz. In addition, several Python libraries work with established mental mapping formats. The examples include PYM, Freeplane2MD and MD2MM. But the LM notebook is more comparable to other pre -existing tools with an integrated user interface. While Notion is an all-in-one workspace that supports notes and writing assisted by the AI, it has no view of an integrated mental card. And in terms of IA integration, the concept AI focuses on the generation of content in the notes but does not automatically visualize relationships. NoteBooklm is built around the cartography led by AI.
Meanwhile, the rating application Obsidian Understands a global network graph showing relations between notes. This approach differs from hierarchical cards of notebooklm of the content of the documents. Obsidian connections are created manually via links, while Notebooklm automatically generates nodes and relationships. In addition, the view of the Obidian graphic provides an overview of your entire safe, while Notebooklm offers a targeted view for a particular set of sources. The canvas of the obsidian allows manual creation of visual cards, but lack of summary of the AI.
There is also MindmeisterA dedicated mind mapping tool where users manually add nodes with in -depth formatting options. It offers suggestions fed by AI for new nodes but does not automatically generate whole cards from documents such as the notebooklm.