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To understand intelligence, the Quest fosters and funds research Missions.  Each Mission brings together a team of scientists and engineers to pose and answer foundational questions of natural intelligence where current AI falls short, to build engineered systems as scientific hypotheses to advance these studies, and to execute tests of those systems to ensure that scientific progress is iteratively guided by natural intelligence results and real-world AI engineering challenges.  The current Quest missions are listed below, along with the scientific and engineering foundations they each seek.

Missions progress, from seedlings, through incubation, to launched missions.  As part of a seedling, a smaller team, perhaps as small as a single lab, will lay the groundwork to either more fully define the scope of the problem to be addressed, or discover and shape the team needed to address that problem.  During incubation, a larger team forms and begins to work towards a common understanding of both the problem and the solution space.  Successfully incubated missions will launch with multiyear project timeline, engineering system builds and tests, and iterative benchmarking against natural intelligence results and AI application goals.

Launched Missions

  • This research mission broadly aims to understand how children grasp new concepts from few examples, how children build upon layers of concepts to reach an understanding of the world and have the flexibility to solve an unbounded range of problems. Can we build AI that starts like a baby and learns like a child?
  • This research mission broadly addresses how we perceive the world around us and integrate this information to plan and complete tasks. Scientific goals include research into how perception, planning, and action interface, how we learn efficiently from small data sets and the creation of behavioral benchmark tasks.
  • This research mission looks at electrochemical synapses as building blocks to emulate and advance learning models. What can we learn from biological synapses to build better, more energy-efficient engineered hardware?  Science research goals include modeling the circuits that both underlie complex learned behaviors and begin to emulate and advance state of the art learning rules. 

Incubating Missions

  • This mission approaches how we can optimize human-AI group decision making. How can we create super-intelligent groups that operate effectively in dynamic environments? How can highly polarized groups make decision the whole group will implement?