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NotebookLM (Google NotebookLM) is a research and note-taking online tool developed by Google Labs that uses artificial intelligence (AI), specifically Google Gemini, to assist users in interacting with their documents. It can generate summaries, explanations, and answers based on content uploaded by users.
Otter.ai was founded as AISense in 2016 by Sam Liang and Yun Fu, two computer science engineers with a long history of working with artificial intelligence. [ 2 ] [ 3 ] In January 2018, the company announced a partnership with Zoom Video Communications to transcribe video meetings post-conference. [ 4 ]
Taking accurate, concise notes in meetings is no small feat. It’s hard to capture everything that’s valuable—people talk fast, it’s sometimes hard to tell what’s important in the moment ...
Notion AI uses artificial intelligence, which enables prediction of some results, text generation and solving arithmetic. It will also work with other datasets in response to an input by the user of the AI. This AI can automate some tasks and assist the user to make a paragraph or outline from a line of text.
When students get a question wrong, they’re now taken through a brief set of additional questions—powered by generative AI—meant to lead them to the correct answer without telling it to them ...
Notes stored as XML; can attach any filetype Microsoft OneNote: Notebooks, notebook sections, section groups, tags (could be applied to content blocks) Yes Yes Yes [Notes 8] Yes Yes [Notes 9] Yes Yes Yes Yes Yes No Yes Imports: Evernote XML. [4] Exports: OneNote binary format. [5] MyInfo: Notebooks, sections, notes, tree, tags, custom ...
Obsidian is a personal knowledge base and note-taking software application that operates on Markdown files. [3] [4] [5] It allows users to make internal links for notes and then to visualize the connections as a graph. [6] [7] It is designed to help users organize and structure their thoughts and knowledge in a flexible, non-linear way. [8]
AI evals are “not cheap,” notes Epoch’s Besiroglu, who says that costs can quickly stack up to the order of between $1,000 and $10,000 per model, particularly if you run the eval for longer ...
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