Ubuntu is set to replace the 30-year-old computer menu system with a “Head-Up Display” that allows users to simply type or speak menu commands.

Instead of hunting through drop-down menus to find application commands, Ubuntu’s Head-Up Display lets users type what they want to do into a search box. The system suggests possible commands as the user begins typing – entering “Rad” would bring up the Radial blur command in the GIMP art package, for example. HUD also uses fuzzy matching and learns from past searches to ensure the correct commands are offered to users.

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I am a partner at Union Square Ventures. My wife founded and runs DailyLit. Together we have three wonderful children. Enjoy reading! Albert.

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Why Deep Dive?
One of our central problems here at NYTimes.com is surfacing content. With hundreds of articles, blog posts, media features and apps published every day it is simply impossible for readers to see them all. This is a good problem to have, but a problem none the less. So how do we help readers find everything they would want to read given that they only have the time to scan a small fraction of what we have to offer? We do so in many ways:

Our homepage/section front/subsection breakdown of the site itself goes a long way in allowing our editorial voice to guide visitors to a combination of what is important and what they want to read. The Recommendation Engine allows us to leverage the power of distributed computing to reference each user’s personal browsing history, then leverage connection via our semantic tags. Facebook and Twitter allow us to provide a social angle showing what people in networks are sharing. Elements such as Most E-Mailed take another approach allowing people to follow what is most popular.

What is Deep Dive?
Deep Dive stands among these, but differentiates itself in one key way: it allows users to discover something then focus their attention deeper based on that piece of content. Each of the methods above take a global viewpoint. For example the Recommendation Engine looks at all of the articles a reader has viewed (over a 30-day time period), Most E-Mailed shows popularity across the site. With Deep Dive, a visitor is able to leverage topical tagging (and eventually semantic, editorial, social and other) connections between content stemming from the piece of content that has piqued their interest.

The first iteration of Deep Dive is basically a glorified “show me more like this” engine customized for the news reading experience. It allows a user to dive into a root article and see related articles based on date and topics. It is presented in a custom viewing experience that allows for quick scanning of related articles to allow readers to quickly get a deep contextual understanding of the greater scope of the story. At launch, this includes primarily articles and blog posts, but we intend to expand the viewer to support video, multimedia and other content types as well.

Looking Ahead
Beyond a root article, users can create any combination of topics and search terms they would like to customize a personally compelling dive. In the future we look to take advantage of more semantic and descriptive data to increase the ways in which users can refine their dives.

Bringing the concept into the temporal dimension, Deep Dive will allow readers to follow these story arcs. By saving a deep dive, users are basically telling our system what slice of the news they want to follow. As new articles are published that fit the criteria, alerts will tell readers that there is something happening surfacing the article for them. For custom dives, this system can be used to keep and eye out for rare occurrences or simply keep up to date on a specialized interest.

Thinking socially, dives could easily be shared across existing social media or internal NYTimes social tools. Influence for dives can be measured by how many people follow them and navigate through them, which can lead into personal reputation for the people crafting the dives.

Ultimately, we look forward to continually improving Deep Dive as NYTimes content metadata and social mechanisms mature. We would love to hear any ideas or uses that you have for this concept.

Thanks,
David, Brandon and Priya