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Big Picture AI Workshop. Asilomar, CA March 24 - 26, 2004 Tom Dietterich Leslie Kaelbling Stuart Russell (organizers). Slides made by LPK based on notes taken by TGD. John Anderson Craig Boutilier Tom Dietterich Ian Horswill Michael Jordan Leslie Kaelbling Michael Kearns

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big picture ai workshop

Big Picture AI Workshop

Asilomar, CA

March 24 - 26, 2004

Tom DietterichLeslie Kaelbling Stuart Russell (organizers)

Slides made by LPK based on notes taken by TGD

participants
John Anderson

Craig Boutilier

Tom Dietterich

Ian Horswill

Michael Jordan

Leslie Kaelbling

Michael Kearns

Daphne Koller

John Laird

Jitendra Malik

David McAllester

Nils Nilsson

Fernando Pereira

Stuart Russell

Shankar Sastry

Michael Wellman

Participants
premise
Premise
  • 25 years ago, most AI researchers were motivated by goals of “human-level” AI
  • Since then
    • scope has narrowed to problems that are both more feasible and more practical
    • we have developed a large body of technical tools and sounder theoretical and experimental methodology
questions
Questions
  • Are we ready to go back and try to do human-level AI?
  • Do our new tools and techniques put us in a better position than we were in before?
  • Are there other “big AI” goals we should be aiming at?
  • What challenge problems would help us address these questions?
  • Note: these problems weren’t designed to be necessarily amenable to competitions or short-term evaluation, but to stretch us scientifically
challenge problem desiderata
Challenge-problem Desiderata
  • Graded series of problems
  • Whole problem should force a general solution
  • Integration of
    • perception
    • communication
    • action
  • Involvement of learning
  • Difference of opinion:
    • focus on human-level AI; “whole agents”
    • focus on super-human tools
four hlai problems
Four HLAI Problems
  • Real robot
  • Simulated robot
  • Trading agent
  • Office assistant
robot cook
Robot Cook
  • Make breakfast in any kitchen
  • Short order cook
    • perception and manipulation
    • cultural expectations about locations of things
    • time pressure and flexibility
simulated robots
Simulated Robots
  • Disaster relief
    • New environment you have not seen before
    • Exploration and mapping; putting out fires, dealing with other threats
    • Time-critical; competing goals; novel situations; tasked by controller, but not centrally controlled
    • Robocup rescue simulator?
automated trading corporation
Automated Trading Corporation
  • Exists on the web
  • Negotiates contracts
  • Insurance contract (no prior stake in order to evaluate)
  • How do you write contracts
    • insurance
    • guarantees of credit
office assistant
Office Assistant
  • Reads your office documents (mail, ppt, etc)
  • Listens to and watches your meetings
  • Carries out tasks for you:
    • purchasing
    • filing
    • travel planning
  • Takes initiative to remind you of things, reschedule meetings, etc.
  • System has to model human’s state of mind
big picture non human ai
Big-picture, Non-human AI
  • Leverage the way the world is changing around us; and help to change it
    • new sources of social interaction data
    • new sources of scientific data
    • interesting opportunities in these areas
    • how people interact with technology
cognitive google
Cognitive Google
  • Tell me about “shape matching in proteins”
  • Look for people who might have written down something interesting about that topic.
  • “find someone who had been to Barcelona of my age and my interest”
  • Community of Intelligent research assistants
high level perception
High-level Perception
  • Learn to describe complex scenes in natural language and answer questions about them
    • Example: observe someone assembling a piece of furniture. Then be able to show someone else how to do the same (or similar) assembly tasks.
    • Example: recognize when someone is taking out cash from an ATM. Generalize to all ATMs.
  • Watch TV:
    • Infer plans + goals from video
    • Write soap-opera summaries
    • Describe sports events
not easy
Not Easy!
  • All of these problems are very hard
  • Simple versions can be made
  • They will focus our long-term thinking
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