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CS6700 Advanced AI Bart Selman

CS6700 Advanced AI Bart Selman. Admin. Project oriented course Projects --- research style or implementation style with experimental component. 1 or 2 students 70% grade Readings: Class presentation and discussion 20%

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CS6700 Advanced AI Bart Selman

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  1. CS6700 Advanced AIBart Selman

  2. Admin • Project oriented course • Projects --- research style or implementation • style with experimental component. • 1 or 2 students • 70% grade • Readings: Class presentation and discussion 20% • (group of 2 or 3 is assigned 1 or 2 papers) • Participation --- 10% • Weekly lecture on Wedn. at 3:35pm

  3. Projects Very open-ended Go for BIG ideas! (No such thing as “failure.”) E.g. Can you evolve a language from scratch? Can you learn from “reading”? (E.g. game play from text on discussion boards) Mechanical Turk --- Human-computing hybrids Mine info from twitter feeds Boost game play via “mimicry”

  4. Topics list • AI --- general intro • Knowledge-based approaches • --- inference • --- problem hardness • --- connections to statistical physics • Statistical and probabilistic inference • --- connections to graphical models / Bayes nets • --- Markov Logic • Multi-agent systems • --- combinatorial auctions • --- multi-agent reasoning • Planning • But, also, to get you thinking about future challenges / • directions for AI.

  5. In Class Discussion Papers Examples topics: AI: Science or Engineering? IBM’s Watson (world-level Jeopardy) Dr. Fill (world-level NYT crossword solver) Iamus (world-level classical music composition) Deep Learning (automatic structure discovery)

  6. Knowledge Intensive AI Knowledge-Data-Inference Triangle NLU Common Sense Computer Vision 20+ yr GAP! Google’s Knowl. Graph Iamus Watson Google Transl. Machine Learning Verification Dr. Fill Siri Robbin’s Conj. 4-color thm. Deep Blue Google Search (IR) Inference/Search Intensive Data Intensive Semantic Web

  7. Knowledge or Data? Last 5 yrs: New direction. Combine a few general principles / rules (i.e. knowledge) with training (ML) on a large expert data set to tune hundreds of model parameters. Obtain world-expert performance using inference. Examples: --- IBM’s Watson / Jeopardy --- Dr. Fill / NYT crosswords --- Iamus/ Classical music composition Performance: Top 50 or better in the world! Is this the key to human expert intelligence? Discussion / readings topic.

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