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Efficient Multimodal Bayesian Learning for Broad Knowledge Acquisition

Learn how large-scale Bayesian learning from text, images, and video can enhance knowledge acquisition for AGI through probabilistic models and inference methods. Explore the potential of incorporating built-in and learned components to scale up and improve real-world data analysis.

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Efficient Multimodal Bayesian Learning for Broad Knowledge Acquisition

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  1. AGI Through Large-Scale, Multimodal Bayesian Learning Brian Milch MIT March 2, 2008

  2. Need for Broad Knowledge How can I get from Boston to New Haven without a car? Broad and deep world knowledge How many U.S. congress members have PhDs? About how cold is it in this picture? Image (c) ukdave.com

  3. Acquiring Such Knowledge • Proposal: Learn knowledge from large amountsof online text, images, video • Learn by Bayesian belief updating, maintaining probability distribution over: • Models of how world tends to work • Past, current, and future states of the world

  4. Variables in Probability Model Tendencies Language Use Appearances … … … World History Utterance Structure Scene Structure … … … … … … … Linguistic Data Video Data

  5. Data to Learn From • Text? • Lots available; broad coverage • No connection with sensory input • Experience of physical or virtual robot(s)? • Multimodal; get to actively manipulate world • Physical: hard to get broad experience • Virtual: may not generalize to physical world • Multimodal data on the Web • Broad coverage; linguistic and sensory • Disjointed; sometimes not factual; passive + − + − − + −

  6. Built-In Components • Why built-in components? • Children don’t learn from scratch • Why not exploit known algorithms, data structures (rendering, parse trees, …) • Modules for reasoning about: • Space, time, physical objects, shape • Language • Other agents

  7. Learned Components Dirichlet process prior allowing models to grow to explain data [Kemp et al., AAAI 2006] Tendencies Relational probabilistic models [Getoor & Taskar 2007] with initially unknownobject types, predicates, dependencies … World History … … Structures with initially unknown objects, relations and attributes (over time) … … … … …

  8. Algorithms • Probabilistic inference • Markov chain Monte Carlo [Gilks et al. 1996] • Variational methods [Jordan et al. 1999] • Belief propagation [Yedidia et al. 2001] • Hybrids with logical inference [Sang et al. 2005; Poon & Domingos 2006] • Parallelize interpretation of documents, images, videos • Still unclear how to scale up sufficiently

  9. Measures of Progress • Should be able to show steady improvement on real data sets (object recognition, coreference, entailment, …) • Serve as resource for shallower, hand-built systems (replacing Cyc, WordNet) • Spin off challenges for researchers in specialty areas

  10. Conclusions • Potential path to AGI: Bayesian learning on large amounts of multimodal data • Attractive features • Exploits well-understood principles • Learns broad, real-world knowledge • Connected to mainstream AI research

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