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  2. Résumé parsing - Wikipedia

    en.wikipedia.org/wiki/Résumé_parsing

    With recent advancements in machine learning, the text mining and analysis processes, which ensure up to 95% accuracy in data processing, many AI technologies have sprung up to help the job seekers in the creation of application documents. These services focus on creating ATS-friendly resumes, execute resume check and screening, and help with ...

  3. Artificial intelligence in hiring - Wikipedia

    en.wikipedia.org/wiki/Artificial_intelligence_in...

    AI is commonly utilized in the recruitment process because it can help boost the number of qualified applicants for positions. Companies are able to use AI to target their marketing to applicants who are likely to be good fits for a position. This often involves the use of social media sites advertising tools, which rely on AI.

  4. LinkedIn - Wikipedia

    en.wikipedia.org/wiki/LinkedIn

    LinkedIn allows professionals to build exposure for their brand within the site itself and on the World Wide Web as a whole. With a tool that LinkedIn dubs a Profile Strength Meter, the site encourages users to offer enough information in their profile to optimize visibility by search engines. It can strengthen a user's LinkedIn presence if ...

  5. AOL Mail

    mail.aol.com

    Get AOL Mail for FREE! Manage your email like never before with travel, photo & document views. Personalize your inbox with themes & tabs. You've Got Mail!

  6. Generative artificial intelligence - Wikipedia

    en.wikipedia.org/wiki/Generative_artificial...

    Generative AI can also be trained on the motions of a robotic system to generate new trajectories for motion planning or navigation. For example, UniPi from Google Research uses prompts like "pick up blue bowl" or "wipe plate with yellow sponge" to control movements of a robot arm. [ 78 ]

  7. Automatic summarization - Wikipedia

    en.wikipedia.org/wiki/Automatic_summarization

    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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