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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 begin as seedlings in which a smaller team, perhaps as small as a single lab, will lay the groundwork to define the scope of the problem to be addressed or to discover and shape the team needed to address that problem. Later, 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 launch with multiyear project timelines, engineering system builds and tests, and iterative benchmarking against natural intelligence results and AI application goals.

Launched Missions

  • The Language Mission broadly aims to understand the relationship between language and human intelligence. Scientific goals include understanding how humans and machine learning models interpret and generate language and determining the role of language in the acquisition, representation, and use of knowledge across various domains of cognition. Engineering goals include building AI systems that learn language from human-scale datasets and understand language with human-like robustness and flexibility.
  • 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.

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?

Past Missions

  • 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.