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Microsoft's Copilot AI is stealing one of Midjourney's best features

PCWorld

It's been a year or two since Midjourney absolutely overturned what we thought of conventional AI art. And now Microsoft Copilot is taking one of its ideas and making it its own. Microsoft said Wednesday that it's adding a rewrite feature to its Copilot prompts. You'll also be able to write shareable prompts that you can provide other members of your team, and a new Catch Up feature will recommend next steps to jumpstart your day. Copilot's rewrite feature could be a powerful addition.


Microsoft Word Rewrite can now offer sentence-level writing suggestions - MSPoweruser

#artificialintelligence

During Build 2019 last year, Microsoft first announced the Rewrite feature in Microsoft Word. Rewrite feature allowed users to quickly find alternative expressions for phrases in their writing. Recently, Microsoft updated this Rewrite feature to make it even more useful. In addition to phrasal suggestions, Rewrite feature now offers sentence-level writing suggestions. This feature was made possible by Microsoft's cutting-edge, neural-network, machine learning models, which are trained on millions of sentences.


Micro-Browsing Models for Search Snippets

Islam, Muhammad Asiful, Srikant, Ramakrishnan, Basu, Sugato

arXiv.org Machine Learning

Abstract--Click-through rate (CTR) is a key signal of relevance for search engine results, both organic and sponsored. CTR of a result has two core components: (a) the probability of examination of a result by a user, and (b) the perceived relevance of the result given that it has been examined by the user . There has been considerable work on user browsing models, to model and analyze both the examination and the relevance components of CTR. In this paper, we propose a novel formulation: a micro-browsing model for how users read result snippets. The snippet text of a result often plays a critical role in the perceived relevance of the result. We study how particular words within a line of snippet can influence user behavior . We validate this new micro-browsing user model by considering the problem of predicting which snippet will yield higher CTR, and show that classification accuracy is dramatically higher with our micro-browsing user model. The key insight in this paper is that varying relatively few words within a snippet, and even their location within a snippet, can have a significant influence on the clickthrough of a snippet. Web search engines have become an essential tool for navigating the vast amounts of information on the internet.