Personal Data Fusion
From data to context, to timely insights.
Finding without searching
The Internet gave us instant access to the world’s digital information. Smartphones brought that information into the palms of our hands. Online workspaces extended the information experience to our colleagues and collaborators. Real-time data streams have multiplied and multiplexed, first exhilarating us and then overwhelming us as information overload set in.
Enter Personal Data Fusion®, ModuleQ’s patented artificial intelligence technology that ends information overload. Personal Data Fusion connects the dots in our data, creating context so AI can relate to our circumstances and automatically filter incoming data streams to match our current needs.
With your personal context always in focus, ModuleQ’s People-Facing AI then applies additional AI techniques such as machine learning, natural language processing, and knowledge-based reasoning to source relevant insights from thousands of information sources. ModuleQ is a cloud-based “second brain,” working 24-by-7 on your behalf so that the information you need finds you, effortlessly, without you having to search for it across all the countless virtual corners of the Internet.
When your phone tells you to “leave now for your meeting because traffic is heavy,” that’s a simple form of data fusion: surfacing a timely insight by connecting the dots across location, calendar, and road conditions. For a fleeting moment, it seems like your phone is looking out for you, almost as if it cares about your success. Personal Data Fusion extends this paradigm across our data universe.
- Object assessment uses your work data such as email, appointments, customers, and documents to build a map of your collaboration landscape: people, organizations, industries, products, geographies, etc.
- Situation assessment analyzes your relationships with these things and how they are changing over time, so the AI keeps up with you and adapts automatically without any manual updates or configuration.
- Impact assessment predicts the information that will be most useful to you given the specifics of your situation, so the AI can surface crucial insights at just the right moments.
A Brief History of Information Discovery
The Web Wave
The first wave of the Internet revolution was the Web Wave, set in motion by Netscape and Yahoo! in 1994. The Web Wave made information available. Over the course of a decade, almost every important information source became available online. The Web Wave was fast, colossal, and devastating, sweeping away broad swathes of traditional information networks. Telephone directories, encyclopedias, and paper maps vanished with hardly a trace; many print newspapers and bricks-and-mortar bookstores followed.
Search engines such as Lycos, Excite, AltaVista, and Yahoo! revolutionized the way that we find information online by allowing us to search with keywords rather than by page numbers or titles. 1996 brought us AskJeeves, the first natural language search engine where human editors manually matched search queries. In 1998, Google launched with the revolutionary PageRank algorithm, a powerful tool for reducing information overload by elevating important information above the noise.
The Mobile Wave
The second wave of the Internet revolution was the Mobile Wave. Although NTT DoCoMo brought the mobile Internet to Japan in 1999, it was Apple and the iPhone that truly launched the Mobile Wave in 2007. By 2014, over 2 billion people had mobile broadband connections.
The Mobile Wave made information accessible anywhere and everywhere. Over a decade, mobile devices channeled the world’s information to our pockets, our palms, and our wrists. This gave us the ability to find answers literally at our fingertips. “The truly personal computer,” declared The Economist in February 2015, “is the defining technology of the age.”
Yet the mobile phone also brought a smaller screen and a proliferation of apps. Screens filled with seductive red badges became the norm, constantly calling our attention to dozens or even hundreds of different destinations. Our phones are “truly personal computers” in form factor only: they badger us all day, without really understanding anything about us.
The Collaboration Wave
As professionals, external information “out there” on the Internet is only part of the equation. Much of the information we need comes from our collaborators. Perhaps surprisingly, collaborative information changed little through Web Wave and the Mobile Wave. Email, already in broad use long before the rise of the World Wide Web, became accessible via the Web and then on mobile devices. But the changes were superficial.
In 2013, Slack launched a new chat app for work. The underlying idea was not new, but Slack’s streamlined, human-centered design resulted in rapid adoption. In 2016, Microsoft responded with Teams. The apps satisfied an intense desire, especially on the part of younger employees accustomed to texting, for a more casual, flexible, and immediate way of connecting with coworkers.
Then, in 2020, the Covid-19 pandemic kicked off a massive transition to Work From Home, and then increasingly Work From Anywhere. Teams, Slack, and Zoom were crucial to keeping people connected. Teams grew from 20 million users in 2019 to over 115 million users by the end of 2020. Almost overnight, collaborative online workspaces became the primary channel for knowledge workers to access the information they needed to do their jobs.
The Human-Centered AI Wave
Although Work From Anywhere is liberating and potentially productivity-enhancing, it has created a massive information discovery challenge. How can we maintain shared context, when we no longer occupy a shared physical environment? How do we keep information flowing, without hallway conversations and coffee chats? Working remotely weakens organizational fabric, freezing collaboration patterns into static siloes and impeding the flow of information.
Under these conditions, artificial intelligence can help us maintain shared context and surface the information we need to connect with each other and collaborate effectively. To be a trusted partner in our work, AI must respect our priorities and objectives. It needs to put our interests first and empathize with our circumstances. It must be human-centered.
The calls for Human-Centered AI have grown as the dangers of biased, manipulative, and exploitive AI algorithms have become clearer. In 2019, Stanford established the Institute for Human-Centered Artificial Intelligence. Shoshana Zuboff published The Age of Surveillance Capitalism, inveighing against “instrumentarian” AI that manipulates people at the expense of their own interests. In 2021, the backlash against social media made Human-Centered AI the focus of global attention.
Human-Centered AI requires new thinking about how people interact with AI, and how AI processes data about people and their environment. ModuleQ’s founders, Dr. David Brunner and Dr. Anupriya Ankolekar, have devoted their professional careers to developing these new approaches. Building on their work, ModuleQ’s Personal Data Fusion and People-Facing AI provide the core intelligence and the extensible platform for empathetic, explainable, ethical AI solutions in the enterprise.