TechTalks from event: Learning Semantics Workshop

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Afternoon Session

  • From Machine Learning to Machine Reasoning Authors: Léon Bottou
    A plausible definition of “reasoning” could be “algebraically manipulating previously acquired knowledge in order to answer a new question”. This definition covers first-order logical inference or probabilistic inference. It also includes much simpler manipulations commonly used to build large learning systems. For instance, we can build an optical character recognition system by first training a character segmenter, an isolated character recognizer, and a language model, using appropriate labeled training sets. Adequately concatenating these modules and fine tuning the resulting system can be viewed as an algebraic operation in a space of models. The resulting model answers a new question, that is, converting the image of a text page into a computer readable text. This observation suggests a conceptual continuity between algebraically rich inference systems, such as logical or probabilistic inference, and simple manipulations, such as the mere concatenation of trainable learning systems. Therefore, instead of trying to bridge the gap between machine learning systems and sophisticated “all-purpose” inference mechanisms, we can instead algebraically enrich the set of manipulations applicable to training systems, and build reasoning capabilities from the ground up.
  • Towards More Human-like Machine Learning of Word Meanings Authors: Josh Tenenbaum
    How can we build machines that learn the meanings of words more like the way that human children do? I will talk about several challenges and how we are beginning to address them using sophisticated probabilistic models. Children can learn words from minimal data, often just one or a few positive examples (one-shot learning). Children learn to learn: they acquire powerful inductive biases for new word meanings in the course of learning their first words. Children can learn words for abstract concepts or types of concepts that have no little or no direct perceptual correlate. Children's language can be highly context-sensitive, with parameters of word meaning that must be computed anew for each context rather than simply stored. Children learn function words: words whose meanings are expressed purely in how they compose with the meanings of other words. Children learn whole systems of words together, in mutually constraining ways, such as color terms, number words, or spatial prepositions. Children learn word meanings that not only describe the world but can be used for reasoning, including causal and counterfactual reasoning. Bayesian learning defined over appropriately structured representations — hierarchical probabilistic models, generative process models, and compositional probabilistic languages — provides a basis for beginning to address these challenges.
  • Learning Semantics of Movement Authors: Timo Honkela et al
    In this presentation, we consider how to computationally model the interrelated processes of understanding natural language and perceiving and producing movement in multimodal real world contexts. Movement is the specific focus of this presentation for several reasons. For instance, it is a fundamental part of human activities that ground our understanding of the world. We are developing methods and technologies to automatically associate human movements detected by motion capture and in video sequences with their linguistic descriptions. When the association between human movement and their linguistic descriptions has been learned using pattern recognition and statistical machine learning methods, the system is also used to produce animations based on written instructions and for labeling motion capture and video sequences. We consider three different aspects: using video and motion tracking data, applying multi-task learning methods, and framing the problem within cognitive linguistics research.