Viewing 5 videos from 2014 in Distinguished Lecture Series See AI2’s full collection of videos on our YouTube channel.
    • November 4, 2014

      Raymond Mooney

      Traditional logical approaches to semantics and newer distributional or vector space approaches have complementary strengths and weaknesses.We have developed methods that integrate logical and distributional models by using a CCG-based parser to produce a detailed logical form for each sentence, and combining the result with soft inference rules derived from distributional semantics that connect the meanings of their component words and phrases. For recognizing textual entailment (RTE) we use Markov Logic Networks (MLNs) to combine these representations, and for Semantic Textual Similarity (STS) we use Probabilistic Soft Logic (PSL). We present experimental results on standard benchmark datasets for these problems and emphasize the advantages of combining logical structure of sentences with statistical knowledge mined from large corpora.

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    • July 25, 2014

      Pedro Domingos

      Building very large commonsense knowledge bases and reasoning with them is a long-standing dream of AI. Today that knowledge is available in text; all we have to do is extract it. Text, however, is extremely messy, noisy, ambiguous, incomplete, and variable. A formal representation of it needs to be both probabilistic and relational, either of which leads to intractable inference and therefore poor scalability. In the first part of this talk I will describe tractable Markov logic, a language that is restricted enough to be tractable yet expressive enough to represent much of the commonsense knowledge contained in text. Even then, transforming text into a formal representation of its meaning remains a difficult problem. There is no agreement on what the representation primitives should be, and labeled data in the form of sentence-meaning pairs for training a semantic parser is very hard to come by. In the second part of the talk I will propose a solution to both these problems, based on concepts from symmetry group theory. A symmetry of a sentence is a syntactic transformation that does not change its meaning. Learning a semantic parser for a language is discovering its symmetry group, and the meaning of a sentence is its orbit under the group (i.e., the set of all sentences it can be mapped to by composing symmetries). Preliminary experiments indicate that tractable Markov logic and symmetry-based semantic parsing can be powerful tools for scalably extracting knowledge from text.

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    • May 13, 2014

      Bart Selman

      In recent years, there has been tremendous progress in solving large-scale reasoning and optimization problems. Central to this progress has been the ability to automatically uncover hidden problem structure. Nevertheless, for the very hardest computational tasks, human ingenuity still appears indispensable. We show that automated reasoning strategies and human insights can effectively complement each other, leading to hybrid human-computer solution strategies that outperform other methods by orders of magnitude. We illustrate our approach with challenges in scientific discovery in the areas of finite mathematics and materials science.

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    • March 31, 2014

      Dan Roth

      Machine Learning and Inference methods have become ubiquitous and have had a broad impact on a range of scientific advances and technologies and on our ability to make sense of large amounts of data. Research in Natural Language Processing has both benefited from and contributed to advancements in these methods and provides an excellent example for some of the challenges we face moving forward. I will describe some of our research in developing learning and inference methods in pursue of natural language understanding. In particular, I will address what I view as some of the key challenges, including (i) learning models from natural interactions, without direct supervision, (ii) knowledge acquisition and the development of inference models capable of incorporating knowledge and reason, and (iii) scalability and adaptation—learning to accelerate inference during the life time of a learning system.

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    • January 23, 2014

      Gary Marcus

      For nearly half a century, artificial intelligence always seemed as if it just beyond reach, rarely more, and rarely less, than two decades away. Between Watson, Deep Blue, and Siri, there can be little doubt that progress in AI has been immense, yet "strong AI" in some ways still seems elusive. In this talk, I will give a cognitive scientist's perspective on AI. What have we learned, and what are we still struggling with? Is there anything that programmers of AI can still learn from studying the science of human cognition? Less

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