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Concept mapping and mind mapping software is used to create diagrams of relationships between concepts, ideas, or other pieces of information. It has been suggested that the mind mapping technique can improve learning and study efficiency up to 15% over conventional note-taking. [1]
Xmind is a mind mapping and brainstorming software, It is developed by Xmind Ltd., a company registered in Hong Kong, [1] and first released in 2007. [2] [1] The application can be used to visualize ideas, clarify thoughts, manage complex information, and promote team collaboration.
ChatGPT is a generative artificial intelligence chatbot [2] [3] developed by OpenAI and launched in 2022. It is currently based on the GPT-4o large language model (LLM). ChatGPT can generate human-like conversational responses and enables users to refine and steer a conversation towards a desired length, format, style, level of detail, and language. [4]
MindManager also has features that allow budget calculations and formulas, Gantt chart views of project timelines, and guided brainstorming. Documents can be attached to mind map topics and viewed within the MindManager application. Links, images, and notes can also be added to mind map topics and viewed and searched in a side panel.
Concept- and mind-mapping software for Linux (1 C, 5 P) Pages in category "Mind-mapping software" The following 25 pages are in this category, out of 25 total.
Artificial Intelligence (AI) has blown up in popularity as one of the most exciting and misunderstood technologies of recent times. This year saw the advent of ChatGPT, an AI Chat Bot that can ...
TheBrain, formerly branded PersonalBrain, is a mind mapping and personal knowledge base software application from TheBrain Technologies. It uses a dynamic graphical interface that maps hierarchical and network relationships. It includes the ability to add links to Web pages and files as well as notes and events using a built-in calendar.
Abstractive summarization methods generate new text that did not exist in the original text. [12] This has been applied mainly for text. Abstractive methods build an internal semantic representation of the original content (often called a language model), and then use this representation to create a summary that is closer to what a human might express.
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