AI-powered transcription service Otter.ai is today announcing a complete overhaul of its platform, offering workers new intelligently generated in-meeting action items, alongside a centralized environment for all meeting transcriptions and notes. Building on the launch of its Otter Assistant in August 2021, the new update aims to streamline communication further by continuing to use conversational AI to improve both the in-meeting and post-meeting experience. The update consists of a new-look home feed, which now acts as a centralized hub for all meeting and post-meeting actions. Users can connect their Google or Microsoft Outlook calendars to Otter to keep track of upcoming meetings, use it to directly join meetings or schedule their Otter Assistant to join. Shared conversations, highlights and comments, and tagged action items will also all be accessible from the new home feed.

Mage, an archaic term for a magician or someone who makes magic, is now also the name of a Silicon Valley startup that's demonstrating some magic of its own. The Santa Clara, California-based company today released to general availability its prize low-code tool for product developers to build AI ranking models. Year-old Mage has been in private beta for the last 12 months working closely with early paying customers to make its tool user-friendly, intuitive, and simple to use, the company said. After working with hundreds of product developers at Airbnb, CEO and cofounder Tommy Dang saw that those developers knew how AI could be used to improve their product, but that they also had to rely on data science resources to help implement their ideas. Data scientists do not come inexpensively anywhere in the world.

Pinterest has updated the look of its app for iOS and Android with a renewed focus on personalized recommendations and a more efficient use of space. The most obvious change when opening the app after the update is the reduced space around pins. Pinterest is cramming the home feed with as much content as it can while still keeping things aesthetically pleasing. The next-most obvious change is the carousel of personal recommendations at the top of the home feed. You can scroll through and tap on one of the topics of interest to open up a dedicated feed.

Our engineers are tasked with showing the right idea to the right user at the right time across home feed, search, Related Pins and more. Engineers use shared Pin features and user attributes to make more than 10B recommendations every day. Because multiple teams use the same data pipelines and frameworks, it's important that models can be used consistently in both a development environment and in production. Before, teams created separate processes for developing machine learning (ML) models. As these models became more complex, and teams increasingly had similar needs for a model development workflow, we needed a common language to express, evaluate and deploy models across multiple teams.

The home feed, a collection of Pins from the people, boards and interests followed, as well as recommendations including Picked for You, is the most heavily user-engaged part of the service, and contributes a large fraction of total repins. The more people Pin, the better Pinterest can get for each person, which puts us in a unique position to serve up inspiration as a discovery engine on an ongoing basis. The home feed is a key way to discover new content, which is valuable to the Pinner, but poses a challenging question. Given the ever increasing number of Pins from various sources, how can we surface the most personalized and relevant Pins? Pinnability is the collective name of the machine learning models we developed to help Pinners find the best content in their home feed.