Paul G. Allen Family Foundation Awards $5.7 Million to Advance Artificial Intelligence Research


Paul G. Allen Family Foundation Awards $5.7 Million to Advance Artificial Intelligence Research


Top researchers receive grants to further advance early-stage research in the field of artificial intelligence

SEATTLE, Wash. – December 3, 2014 – The Paul G. Allen Family Foundation announced today the award of Allen Distinguished Investigator (ADI) grants to five groups of researchers working in one of three fundamental areas in the field of artificial intelligence (AI): machine reading; diagram interpretation and reasoning; and spatial and temporal reasoning. Since 2010, the Foundation’s ADI program has supported and empowered scientists who are positioned to take on the unconventional and unexpected. With this current grant, the Foundation is awarding a total of $5.7 million in funding over three years. To date, Mr. Allen has committed more than $79.1 million to artificial intelligence research.

“The Allen Distinguished Investigator program has become a platform for scientists and researchers to push the boundaries on the conventional and test the limits of how we think about our existence and the world as we know it,” said Dune Ives, senior director of Vulcan Philanthropy and co-manager of The Paul G. Allen Family Foundation. “We are only beginning to grasp how deep intelligence works. We hope these grants serve as a valuable catalyst for one day making artificial intelligence a reality.”

The current focus on artificial intelligence (AI) is part of the larger goal of the ADI program to advance areas of science that have great promise, but may face significant funding challenges or a need for innovation. The field of artificial intelligence promises to give machines the ability to think analytically, and accumulate and apply knowledge. Today’s researchers have only begun to scratch the surface for what is possible and with further advancement, the potential is enormous.

The three areas of focus for the artificial intelligence ADIs include:

  • Machine reading – While much current work has focused on entity linking and fact extraction, computers can construct richer semantic representations from language to better support automated reasoning, such as answering questions or performing a task.
  • Diagram interpretation and reasoning – Diagrams play a fundamental role in communicating knowledge, especially concepts that are difficult to express in words, yet for machines, they remain difficult to interpret or to understand deeper symbolic meaning.
  • Spatial and temporal reasoning – Common sense reasoning about time and space is fundamental to human intelligence – we are able to interpret meaning from configurations of objects, motion, spatial transformation, spatial actions, temporal relations and even qualitative language – but this remains a challenging area for AI.

This year’s focus areas complement the work of the Allen Institute for Artificial Intelligence (AI2). AI2 brings together top researchers and software engineers to explore today’s key questions in AI and create powerful new tools for intelligence augmentation.

“It is significant that the Foundation has included AI as a new category within their Allen Distinguished Investigators program,” said Oren Etzioni, CEO of the Allen Institute for Artificial Intelligence. “We are eager to collaborate with the worldwide scientific community, and are thrilled with the superb quality of this year’s investigators.”

AI2 believes that AI requires a collaborative effort. Early research is focused on machines that can gather knowledge via reading, and then understand and answer questions based on their own reasoning.

About the ADI Recipients

Support for early-stage research is essential to achieving world-changing breakthroughs, and the ADI program makes research at the cusp of knowledge possible. By supporting innovative and cutting-edge scientists, the Foundation is working to answer big, open questions of science that may be too risky or audacious for traditional sources of funding. The Foundation also looks to give these scientists enough funds to truly gain momentum in their respective fields. As part of this competitive grant program, ADIs advance the state of human knowledge, push the boundary of scientific inquiry and collectively move the needle. The new ADI recipients are:

  • Devi Parikh, Virginia Tech
    The vast majority of human interaction with the world is guided by common sense. We use common sense to understand objects in our visual world – such as birds flying and balls moving after being kicked. How do we impart this common sense to machines? Machines today cannot learn common sense directly from the visual world because they cannot accurately perform detailed visual recognition in images and video. In this project, Parikh proposes to simplify the visual world for machines by leveraging abstract scenes to teach machines common sense.
  • Maneesh Agrawala, University of California and
  • Jeffrey Heer, University of Washington
    For hundreds of years, humans have communicated through visualizations. While the world has changed, we continue to communicate complex ideas and tell stories through visuals. Today, charts and graphs are ubiquitous forms of graphics, appearing in scientific papers, textbooks, reports, news articles and webpages. While people can easily interpret data from charts and graphs, machines do not have the same ability. Agrawala and Heer will develop computational models for interpreting these visualizations and diagrams. Once machines are better able to "read" these diagrams, they can extract useful data and relationships to drive improved information applications.
  • Sebastian Riedel, University College London
    Machines have two ways to store knowledge and reason with it. The first is logic – using symbols and rules, and the second is vectors – sequences of real numbers. Both logic and vectors have benefits and limitations. Logic is very expressive, and a good tool to prove statements. Vectors are highly scalable. Riedel will investigate an approach where machines convert symbolic knowledge, read from text and other sources, into vector form, and then approximate the behavior of logic through algebraic operations. Ultimately, this approach will enable machines to pass high-school science exams or perform automatic fact checking.
  • Ali Farhadi, University of Washington and 
  • Hannaneh Hajishirzi, University of Washington
    Farhadi and Hajishirzi’s project seeks to teach computers to interpret diagrams the same way children are taught in school. Diagram understanding is an essential skill for children since textbooks and exam questions use diagrams to convey important information that is otherwise difficult to convey in text. Children gradually learn to interpret diagrams and extend their knowledge and reasoning skills as they proceed to higher grades. For computers, diagram interpretation is an essential element in automatically understanding textbooks and answering science questions. The cornerstone of this project is its Spoon Feed Learning framework (SPEL), which marries principles of child education and machine learning. SPEL gradually learns diagrammatic and relevant real-world knowledge from textbooks (starting from pre-school) and uses what it’s learned at each grade to learn and collect new knowledge in the next, more complex grade. SPEL takes advantage of coupling automatic visual identification, textual alignment, and reasoning across different levels of complexity.
  • Luke Zettlemoyer, University of Washington
    The vast majority of knowledge and information we as humans have accumulated is in text form. Computers currently are not able to figure out how to translate that data into action. Zettlemoyer is building a new class of semantic parsing algorithms for the extraction of scientific knowledge in STEM domains, such as biology and chemistry. This knowledge will support the design of next-generation, automated question-answering (QA) systems. Whereas existing QA systems, including IBM's Watson system for Jeopardy, have been very successful, they are typically limited to factual question answering. In contrast, Zettlemoyer work aims to, in the long term, enable a machine to automatically read any text book, extract all of the knowledge it contains, and then use this information to pass a college-level exam on the subject matter.