Monica Lam, professor of computer science and director of the Open Virtual Assistant Lab at Stanford, is building a new entrant to the virtual assistant market, one that differs significantly from existing assistants in three ways.  First, it is based on new, cutting-edge voice recognition technology developed in Lam’s lab that is more flexible and adaptable than the technology used by Google and Amazon. Second, Lam’s technology is privacy-preserving, keeping consumer data safely and securely out of the hands of private actors.  Third, Lam’s technology sits atop a fully open network, allowing any vendor to develop apps for the platform.  This is in stark contrast with the closed networks operated by Apple and Google. Funds from this grant will allow Lam and her team to continue to develop their technology, making improvements to accuracy and speed, the detection and handling of errors, and the handling of unexpected inputs.  In addition, grant funds will enable Lam to pilot the technology’s first real world application for consumers. 

Virtual assistants like Amazon’s Alexa are quickly becoming the gateway to all other digital products and services. The convenience and power of these assistants has led more than 50 million American households to adopt a virtual assistant over the past two years, an astonishing pace. Yet the marketplace for virtual assistants is dominated by just two firms, with Amazon and Google controlling 95% of the market. Because virtual assistants usefully connect to other digitally enabled devices and services, and because they need to constantly listen for voice prompts from their owners, they are poised to collect unprecedented amounts of personal information about consumers, from listening in on all the Internet of Things devices in our houses, to our communications on social media, from email to Facebook, and from our search history and purchasing records to our finances and health. In addition, unlike browser-enabled searches that return a full page of search results, queries of a virtual assistant yield only one answer, giving them a unique ability to shape (and manipulate) what we encounter and what we know via the World Wide Web. Since virtual assistants are powerful intermediaries between consumers and the wider world, it would benefit all consumers if the market for these assistants was robust, giving consumers many options to choose from.       This grant funds a project by Monica Lam, professor of computer science and director of the Open Virtual Assistant Lab at Stanford University, to build and pilot the first prototype of an open source, privacy preserving virtual assistant. The project, if successful, promises to expand the options available to consumers and offer the ease and convenience of a first-class virtual assistant without the sacrifice of personal privacy or transparency.

Funds from this grant support the production and dissemination of a special series by Annual Reviews’s online publication Knowable Magazine that aims to provide fresh, science-based, and publicly accessible perspectives issues related to the COVID-19 pandemic. Called Reset: The Science of Crisis and Recovery, the 9-month series will feature articles, essays, profiles, interviews, infographics, video, and comics exploring the scholarly work that informs the best response to the coronavirus pandemic. Content will include reporting and expert commentary in the digital publication Knowable Magazine and republished content in diverse media outlets. In addition, the team at Knowable will launch a series of online events featuring renowned scholars from an array of fields discussing timely topics around COVID-19 and providing reliable, evidence-based information and guidance. Content will be informed by Annual Review’s roster of 51 journals and 1,000 scholars, scientists, and journalists. Additional grant funds will support efforts to reach new audiences, including potential content distribution partnerships with Yahoo! News, local radio stations, the Smithsonian, the Aspen Institute, and the Huffington Post, among others.

With help from a prior grant from the Alfred P. Sloan Foundation, the Wikimedia Foundation in 2017 launched an ambitious project to add structured metadata to files in the Wikimedia Commons, Wikimedia’s repository of more than 65 million photos, videos and other media files. The project allowed users to add machine readable information to each file, including information on the file creator, the copyright status of the file, and the object, event, or subject depicted. This structured metadata makes files in the Commons much more easily discoverable, searchable, and shareable, and since 2017, metadata has been added to more than 11 million files in the Commons.     Funds from this grant support an expansion of this project and will help Wikimedia expand its use of structured metadata to the entire universe of Wikimedia sites, including Wikipedia and Wikidata. The potential benefits of this project are significant. For example, with content metadata, machine prompts could suggest appropriate images to add to a page being edited or could identify data that appears on the Spanish-language version of a Wikipedia article, but is missing from that page in Vietnamese. Grant funds will support the development of a set of structured data standards to apply across Wikimedia products, the creation of editing tools and interfaces to help users implement those standards, and outreach and public engagement efforts to engage the global Wikimedia community in the process. Over the three-year grant period, the project aims to add structured metadata to 5 million Wikipedia articles.